Methods and devices for photobiomodulation

Abstract

Systems and methods are described for treatment of neurological conditions in which transcranial illumination using infrared and / or near-infrared wavelengths of light are delivered into the brain of a patient using a portable head wearable device. Systems and methods are also described to deliver light to patient tissues for photobiomodulation therapy to treat different neurological conditions with different illumination protocols.

Description

132049-00217 METHODS AND DEVICES FOR PHOTOBIOMODULATION CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to International Application No. PCT / US2023 / 082207 filed December 1, 2023, U.S. Provisional Application No. 63 / 656,537 filed June 5, 2024, U.S. Divisional Application No. 18 / 662,858 filed May 13, 2024, U.S. Provisional Application No. 63 / 641,889 filed May 2, 2024, and U.S. Provisional Application No. 63 / 568,409 filed March 21, 2024, and also to U.S. Provisional Application No. 63 / 429,815 filed December 2, 2022 and U.S. Provisional Application No. 63 / 541,995, filed October 2, 2023. This application is also a continuation in part of PCT / US2023 / 036721 filed November 2, 2023, and also a continuation-in-part of US Patent Application 17 / 949,997, filed on September 21, 2022, which is a continuation-in-part of International Application No. PCT / US2022 / 020770 filed on March 17, 2022, the entire contents of each of the above-mentioned applications being incorporated herein by reference. FIELD OF THE INVENTION

[0002] The presently disclosed subject matter relates generally to methods and devices for transcranial illumination for the therapeutic treatment of neurological conditions. Preferred embodiments can include wearable devices that communicate with mobile devices such as web enabled phones and tablets to facilitate system operation and patient data analysis. This can optionally include cross-modal brain stimulation, diagnostic modalities and, more particularly, provide methods and devices for treating children suffering from autism that can optionally utilize simultaneous audio and light stimulation. BACKGROUND

[0003] Research indicates that in treating many neurological and psychiatric conditions, a strong combinatory effect of two separate types of treatments exists. For example, in the treatment of depression and anxiety, a combination of both medications and cognitive behavioral therapy (or dialectic behavioral therapy) produces stronger effects than either one of those modalities independently. 1 ME151319109v.1132049-00217

[0004] Furthermore, music therapy and videos games have been used to treat epilepsy patients. Some of the results indicate that listening to specific musical content in combination with pharmacological treatment reduced both the frequencies of epileptic discharges and frequencies of seizures. Similarly, combining video games with pharmacological treatment has also been shown to modulate the brain neuroplasticity and improve age-related neuronal deficits and enhanced cognitive functions in older adults. Therefore, adding two types of different treatments together has been shown to improve the outcome of the overall treatment of neurological and psychiatric conditions across various domains. Overall, when treating psychiatric or neurological disorder, combinatory effects of brain stimulation through various channels is likely to be stronger than unimodal stimulation.

[0005] For children diagnosed with Autism Spectrum Disorder (“ASD”), one of the most common challenges they face is learning language. Studies show that children with ASD struggle with acquiring syntax. As a result, they cannot parse sentences, understand speech, and / or acquire or produce new words. In particular, learning language by the age of five (being able to speak full sentences) is critical for future successful integration with neuro-typical community and independent functioning. In addition, language learning may only occur during the sensitive period (REFS), which ends between 5–7 years of age. If a child does not fully learn language during that period, subsequent learning is highly effortful and achieving fluency is unlikely. Furthermore, being able to comprehend and produce language reduces tantrums and improves behavior in individuals with ASD. Therefore, delays in speech development is one of the most critical symptoms that needs to be alleviated.

[0006] Another critical symptom that needs to be alleviated in children with ASD is anxiety. General anxiety is frequently quite debilitating in ASD children and it affects, among other things, children’s ability to learn and ability to integrate socially. Children with ASD are frequently prescribed medication to reduce their anxiety, but these medications often have unintended side effects and may be not effective.

[0007] In the United States, there are over 1.5 MM children currently diagnosed with ASD, and approximately 80,000 new children are diagnosed with ASD annually. Across the world, approximately 1.5 MM–2 MM new children are annually diagnosed with ASD. Autism services cost Americans approximately $250 billion a year, which includes both medical costs (outpatient care, home care, and drugs) and non-medical costs (special education services, residential services, etc.). In addition to outright costs, there are hidden ones, such as emotional 2 ME151319109v.1132049-00217 stress as well as the time required to figure out and coordinate care. Research indicates that lifelong care costs can be reduced by almost two thirds with proper early intervention. Further research indicates that ASD is often correlated with mitochondrial dysfunction. Mitochondria in brain cells of autistic individuals does not produce enough adenosine triphosphate (“ATP”). The result of mitochondria dysfunction may be especially pronounced in the brain, since it uses 20% of all the energy generated by the human body, which may lead to neuro-developmental disorders, such as ASD. Encouraging research has shown that infrared and red light may activate a child’s mitochondria, and therefore increase ATP production. The metabolic pathways impacting neurological function have been studied in significant detail. See, for example, Naviaux, “Metabolic features and regulation of the healing cycle-A new model for chronic disease pathogenesis and treatment”, Mitochondrion. 2019 May;46:278-297. doi: 10.1016 / j.mito.2018.08.001. Epub 2018 Aug 9. Note also, Mason et al. “Nitric Oxide inhibition of respiration involves both competitive (heme) and noncompetitive (copper) binding to cytochrome c oxidase”, PNAS, vol.103, no.3, January 17, 2006, the entire contents of the above two publications being incorporated herein by reference.

[0008] Transcranial photobiomodulation (“tPBM”) of the brain with near infrared and red light has been shown to be beneficial for treating various psychiatric and neurological conditions such as anxiety, stroke and traumatic brain injury. Remarkably, autism spectrum disorder may potentially be treated therapeutically with tPBM as several scientists have recently linked the disorder to mitochondria disbalance and tPBM can potentially affect mitochondria by causing it to produce more ATP. Patients treated with tPBM will absorb near infrared light, which can potentially reduce inflammation, increase oxygen flow to the brain and increase production of ATP. However, devices and methods are needed that will enable additional treatment options for various neurological conditions.

[0009] One problem with language acquisition is that many children with ASD cannot focus on the language enough to extract syntactic features of words, to parse sentences, and / or to attend to syntactic and semantic clues of speech. Therefore, their word learning may be delayed.

[0010] The problem with anxiety is that ASD children frequently get very stressed and do not know how to calm themselves before a particular learning or social situation. As a result, they are unable to participate in regular activities (such as playdates or classes). 3 ME151319109v.1132049-00217

[0011] Accordingly, there is a need for improved methods and devices providing treatment of neurological disorders and to specifically provide therapies for the treatment of children. SUMMARY

[0012] Preferred embodiments provide devices and methods in which a head wearable device is configured to be worn by a subject that is operated to deliver illuminating wavelengths of light with sufficient energy that are absorbed by a region of brain tissue during a therapeutic period. Transcranial delivery of illuminating light can be performed with a plurality of light emitting devices mounted to the head wearable device that can also preferably include control and processing circuitry.

[0013] Systems and methods described herein can comprise a plurality of therapeutic protocols for different neurological conditions including autism, attention deficit disorders, conditions causing seizures and fetal syndrome caused by exposure to medications or toxic substances prior to birth. Sensors can be utilized prior to, during and after photobiomodulation therapy to measure physiological status of the patient to monitor and diagnose transient conditions that can be used to guide treatment. The sensor data for each patient can be logged and stored in a database for each patient along with the treatment protocol selected and personalized for treatment sessions. A battery powered head worn device can be used for light delivery and sensor positioning relative to the cranium of the patient.

[0014] Therefore, providing brain stimulation with one or more of, or combinations of (i) infrared, near infrared and red light to improve operational states of the brain such as by ATP production in the brain, for example, and (ii) provide additional specific linguistic input(s) to learn syntax will improve language acquisition in ASD children. Therefore, providing brain stimulation with a combination of (i) near infrared and red light to reduce anxiety and (ii) specific meditations written for ASD children will reduce anxiety. Reduced anxiety leads to both improved language learning and better social integration. Providing an audio language program specifically designed for ASD children, may focus the attention of the child on the language, provide the child with the information about linguistic markers, and improve the child’s ability to communicate. This is likely to reduce lifelong care costs for affected individuals. 4 ME151319109v.1132049-00217

[0015] Preferred embodiments can use a plurality of laser diodes or light emitting diodes (LEDs) configured to emit sufficient power through the cranium of a patient to provide a therapeutic dose during a therapeutic period. This plurality of light emitting devices can be mounted to circuit boards situated on a head wearable device. For the treatment of children the spacing between light emitters in each array mounted to the head wearable device can be selected to improve penetration depth through the cranium. As the cranium of a child increases in thickness with age, the parameters of light used to penetrate the cranium will change as a function of age. As attenuation of the illuminating light will increase with age, the frequency of light, power density and spot size of each light emitter can be selectively adjusted as a function of age. The system can automatically set the illumination conditions as a function of age of the patient. The thickness of the cranium of an individual patient can also be quantitatively measured by x-ray scan and entered into the system to set the desired illumination parameters needed to deliver the required power density to the selected region of the brain. The density of the cranium can also change as a function of age and can be quantitatively measured by x-ray bone densitometer to generate further data that can be used to control and adjust the level of radiance applied to different regions of the cranium.

[0016] Aspects of the disclosed technology include methods and devices for cross-modal stimulation brain stimulation, which may be used to treat ASD children. Consistent with the disclosed embodiments, the systems and methods of their use may include a wearable device (e.g., a bandana) that includes one or more processors, transceivers, microphones, headphones, LED lights (diodes), or power sources (e.g., batteries). One exemplary method may include positioning the wearable device on the head of a patient (an ASD child). The method may further include transmitting, by the wearable device (e.g., the LED lights), a pre-defined amount of light (e.g., red or near infrared light). The method may also include simultaneously outputting, by the headphones of the wearable device or other device that can be heard or seen by the patient, a linguistic input to the patient, for example. The linguistic input may include transparent syntactic structures that facilitate, for example, learning how to parse sentences. Also, the method may include outputting specific meditations written for ASD children, that may help ease anxiety, and thus allowing ASD children to better learn language and more easily integrate socially. In some examples, the method may further include receiving a response to the linguistic input from the patient, that the one or more processors may analyze to determine the accuracy of the response and / or to generate any follow-up linguistic inputs. Further, in some 5 ME151319109v.1132049-00217 examples, the frequency and / or type of light outputted by the wearable device may be adjusted based on the response received from the patient. Also, in some examples, the wearable device may be paired to a user device (e.g., via Bluetooth®) that determines and sends the linguistic input(s) to the wearable device or other devices including one or more transducer devices, such as speakers, or display devices that can generate auditory or visual signals / images that can be heard and / or seen by the patient.

[0017] The battery powered headset can preferably be configured with an onboard power control device that automatically controls optical power output of the device during a therapeutic session. Therapeutic sessions can have preset operating conditions for each patient, or a class of patients, as described herein whereby a power distribution circuit board can independently control current levels through each of a plurality of light sources at a selected frequency and duty cycle. Preferred embodiments provide closed loop control of each light source such that the emitted light signal remains within 10% of a nominal value, and preferably within 5% of the selected nominal value. Safety features can be implemented in preferred embodiments in which a sensor can be used to monitor a selected operating condition of the head mounted device. For example, if a patient alters the position of or removes the headset form his or her head during a session, the sensor can transmit a signal to the system control which can, depending upon the received signal, switch off the power to the headset and / or record the time of the signal reception, and optionally send a signal to a remote device by wireless or wired connection communicating the change in state of the device. In another example, if light being emitted by one of the light sources exceeds a threshold value, or if an operating temperature of a component in the optoelectrical system exceeds a threshold temperature, this will trigger a shutoff of the light sources and cause a signal to be sent to an external device communicating the change in operating condition and record time and cause of the change. As the length of a therapeutic session may vary from patient to patient, the operating conditions can be selected based on a plurality of preset operating parameters stored in a device memory. A therapeutic session may last for at least 5 minutes for treatment of certain conditions, whereas the session may last at least 10 minutes for a further condition, and may last 15 or more minutes for a further distinct condition. Power levels may vary for each of these different treatment modules and different light sources may be controlled differently during one or more sessions. 6 ME151319109v.1132049-00217

[0018] The head wearable device can comprise rigid, semi-rigid or flexible substrates on which the light emitters and circuit elements are attached. The flexible substrates can include woven fabrics or polymer fibers, molded plastics or machine printed components assembled into a band that extends around the head of the patient. Circuit boards on which electrical and optical components are mounted and interconnected can be standard rigid form or they can be flexible so as to accommodate bending around the curvature of the patient’s head. As children and adults have heads in a range of different sizes, it is advantageous to have a conformable material that can adjust to different sizes. More rigid head wearable devices can use foam material to provide a conformable material in contact with the patient’s head. The head wearable device can be used in conjunction with diagnostic devices and systems that can be used to select the parameters for the therapeutic use of light as described herein. A computing device such as a tablet or laptop computer can be used to control diagnostic and therapeutic operations of the head worn device and other devices used in conjunction with a therapeutic session. Such computing devices can store and manage patient data and generate electronic health or medical records for storage and further use. The computing device can be programmed with software modules such as a patient data entry module, a system operating module that can include diagnostic and therapeutic submodules, and an electronic medical records module. The system can include a networked server to enable communication with remote devices, web / internet operations and remote monitoring and control by secure communication links.

[0019] The computing device can include connections to electroencephalogram (EEG) electrodes to monitor brain activity before, during or after therapeutic sessions to generate diagnostic data for the patient. The EEG electrodes can be integrated with the head wearable device and be connected either directly to a signal processor thereon that can be connected to the LED controller, or alternatively, can communicate by wired or wireless connection to the external computing device such as a touchscreen operated tablet display device. Light sensors that are optically coupled to the head of the patient can be used to monitor light delivery into the cranium of the patient and / or can measure light returning from the regions of the brain that receive the illuminating light. An array of near infrared sensors can be mounted on the LED panels or circuit boards, for example, that can detect reflected light or other light signals returning from the tissue that can be used to diagnose a condition of the tissue. Diagnostic data generated by the system sensors can be used to monitor the patient during a therapeutic period 7 ME151319109v.1132049-00217 and can optionally be used to control operating parameters of the system during the therapy session such as by increasing or decreasing the intensity of the light delivered through the cranium or adjusting the time period or areas of the brain being illuminated during the therapy session.

[0020] For the treatment of children having an autism spectrum disorder, they often are not responsive to instructions, may exhibit behaviors such as self-injury or attempt to injure others, and may exhibit movements that are not conducive to standard therapeutic treatment. Specifically, it can be necessary with many patients that a device placed on the head must be light in weight and be untethered such as by a wired connection during treatment. Consequently, it is important to have a battery powered device that does not have a wired connection during a therapeutic period or session. Any communication that occurs between the head mounted device and an external device used to control and / or monitor the device during a therapeutic period is preferably performed by wireless connection. Thus, an external computing device such as a mobile communication device such as a mobile phone or a tablet display device can communicate wirelessly with the head mounted device. Such devices can include one or more processors configured to stream data to and from the head mounted device. Such devices are connectable to private or public communications networks to facilitate communication with parents and teachers, for example, that are involved with a child’s treatment, medical history and education plan.

[0021] Machine learning tools can be employed to process data generated by the devices and methods described herein. Methods such as principal component analysis (PCA), support vector machines (SVM), convolutional and / or recurrent neural networks, clustering and other numeric and quantitative methods can be employed to characterize therapeutic outcomes and generate operational parameters for different classes of patients that exhibit different behavioral and / or medical conditions that can be effectively treated by photobiomodulation therapy. Neurologic conditions can impact sleep patterns and learning capacity of children and such computational methods can be used to improve therapeutic treatment.

[0022] Further features of the disclosed design, and the advantages offered thereby, are explained in greater detail hereinafter with reference to specific embodiments illustrated in the accompanying drawings, wherein like elements are indicated be like reference designators. 8 ME151319109v.1132049-00217 BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, are incorporated into and constitute a portion of this disclosure, illustrate various implementations and aspects of the disclosed technology, and, together with the description, serve to explain the principles of the disclosed technology. In the drawings:

[0024] FIG. 1 is an example head wearable device, in accordance with some examples of the present disclosure.

[0025] FIGs. 2A-2C show rear, side and front views of a patient with the head wearable device of FIG. 1.

[0026] FIG. 3 illustrates use of a portable phone or tablet device connected to the head wearable device.

[0027] FIG.4 schematically illustrates the operating elements of the head wearable device and control features.

[0028] FIG. 5 schematically illustrates the components of a head wearable device in accordance with preferred embodiments.

[0029] FIG. 6 illustrates a screen shot of a testing procedure used with preferred embodiments of the invention.

[0030] FIG 7 is a process flow diagram in accordance with preferred methods of operating the head wearable device and control system.

[0031] FIG. 8 is a process flow diagram illustrating the use of EEG measurements in conjunction with transcranial illumination of a patient.

[0032] FIG.9 illustrates a table with exemplary parameters having variable ranges between upper and lower thresholds used for transcranial illumination of a patient in accordance with preferred embodiments.

[0033] FIG.10A illustrates a process flow diagram for selecting and optimizing parameters over multiple therapeutic sessions including manual and automated selection tracks.

[0034] FIG. 10B illustrates an exemplary photobiomodulation device in accordance with embodiments described herein. 9 ME151319109v.1132049-00217

[0035] FIGs. 10C and 10D illustrate side and perspective views, respectively, of a device for partial insertion within an oral cavity in accordance with certain embodiments described herein.

[0036] FIGs. 10E and 10F illustrate side and perspective views, respectively, of a device for partial insertion within an oral cavity in accordance with certain embodiments described herein.

[0037] FIGs. 10G and 10H illustrate cross-section and end views, respectively, of an exemplary device for partial insertion according to certain embodiments.

[0038] FIG. 11 illustrates a process flow diagram for administering a therapeutic session to a patient in accordance with various embodiments described herein.

[0039] FIG. 12A illustrates a further view of a head worn device having circuit housing elements accessible to a user that is communicably connected to a first tablet device used by the patient during a therapy session and a second tablet used by an operator to monitor, control and / or program the system for diagnostic and therapeutic use as described generally herein.

[0040] FIG. 12B illustrates a top view of a head mounted photobiomodulation system including an electroencephalographic (EEG) electrode system with wireless transmission of data to an external processing system.

[0041] FIG. 12C shows a rear view of the system of FIG. 12B with light emitting and / or sensor arrays mounted on a rear band configured for positioning to transmit and / or receive signals through the cranium via selected transmission paths such as, for example, the lambdoid suture and / or the squamosal suture.

[0042] FIG.12D illustrates an enlarged side cross-sectional view of a spring mounted light emitting array, a sensor array, or a combination thereof in accordance with embodiments described herein.

[0043] FIG. 12E illustrates a side view of a head wearable device in accordance with some embodiments described herein.

[0044] FIG. 12F illustrates a rear view of the head wearable device of FIG. 12E.

[0045] FIG. 12G illustrates an alternative embodiment of the head wearable device with different placement of the head strap in accordance with some embodiments described herein. 10 ME151319109v.1132049-00217

[0046] FIG. 12H illustrates a head wearable device according to various embodiments described herein.

[0047] FIG. 12I illustrates placement of an LED module in a core of a headband in accordance with some embodiments described herein.

[0048] FIG. 12J illustrates placement of the LED module in a completed headband in accordance with some embodiments described herein.

[0049] FIG. 12K schematically illustrates the electrical connections between elements of the head wearable device in accordance with several embodiments described herein.

[0050] FIGs. 12L and 12M illustrate top and bottom views, respectively, of a power printed circuit board in accordance with some embodiments described herein.

[0051] FIGs. 12N and 12O illustrate top and bottom views, respectively, of an occipital power distribution printed circuit board in accordance with some embodiments described herein.

[0052] FIGs. 12P and 12Q illustrate top and bottom views, respectively, of a frontal power distribution printed circuit board in accordance with some embodiments described herein.

[0053] FIGs. 12R and 12S illustrate top and bottom views, respectively, of an LED printed circuit board in accordance with some embodiments described herein.

[0054] FIG. 13A illustrates a further view of a head worn device having circuit housing elements accessible to a user that is communicably connected to a first tablet device used by the patient during a therapy session and a second tablet used by an operator to monitor, control and / or program the system for diagnostic and therapeutic use as described generally herein.

[0055] FIG. 13B illustrates a rear view of a head mounted photobiomodulation system including an electroencephalographic (EEG) electrode system with wireless transmission of data to an external processing system.

[0056] FIG. 13C shows an exploded view of the system of FIG. 13B with light emitting and / or sensor arrays mounted on a rear band configured for positioning to transmit and / or receive signals through the cranium via selected transmission paths such as, for example, the lambdoid suture and / or the squamosal suture.

[0057] FIG. 13D shows a rear exploded view of the occipital mount and its housing. 11 ME151319109v.1132049-00217

[0058] FIG. 13E shows a front exploded view of the system of FIG. 13D. illustrates a side view of a head wearable device in accordance with some embodiments described herein.

[0059] FIG. 13F illustrates a portion of the occipital mount that rests against the patient’s cranium.

[0060] FIG. 13G illustrates a head wearable device according to various embodiments described herein.

[0061] FIG. 13H illustrates placement of LED modules throughout a headband in accordance with some embodiments described herein.

[0062] FIG. 13I illustrates placement of racetracks throughout a headband to allow positioning of LED modules.

[0063] FIG. 13J illustrates an LED in a racetrack and translating.

[0064] FIG. 13K illustrates the layers of a headband.

[0065] FIG. 13L shows a headband adjusting in length.

[0066] FIG. 13M illustrates a side view of a LED module.

[0067] FIG. 13N shows an interior view of a LED module housing.

[0068] FIG. 13O illustrates an exploded view of a LED module with a LED panel.

[0069] FIG. 13P illustrates a side view of an alternative embodiment of a LED module.

[0070] FIG. 13Q illustrates a front view of the LED module of FIG. 13P.

[0071] FIG. 13R illustrates an internal view of the LED module of FIG. 13P.

[0072] FIG. 13S illustrates LED modules mounted to the occipital mount.

[0073] FIG. 13T illustrates exemplary LED circuit board details for a first side of the circuit board on which the LED is mounted.

[0074] FIG. 13U illustrates exemplary LED circuit board details for a second side of the circuit board on which the microcontroller is mounted.

[0075] FIG. 13V illustrates patient ergonomics and various head strap sizes in various embodiments taught herein 12 ME151319109v.1132049-00217

[0076] FIG 13W illustrates an exemplary control panel in various embodiments taught herein.

[0077] FIG 13X illustrates exemplary connector pod locations in various embodiments taught herein.

[0078] FIG. 13Y illustrates exemplary wiring channels in various embodiment taught herein.

[0079] FIG. 13Z illustrates exemplary rear LED PCB wiring in various embodiments taught herein.

[0080] FIG. 13AA illustrates exemplary capacitive touch sensor locations.

[0081] FIG. 13AB illustrates exemplary add-on LED locations in a housing in various embodiments taught herein.

[0082] FIGs. 13AC-13AL illustrate an exemplary front view, rear view, left side view, right side view, top view, bottom view, a larger head size perspective view, a smaller head size perspective view, a mid-size perspective view, and a rear panel of therapeutic devices described herein.

[0083] FIG.14 illustrates a process sequence that can be implemented with the therapeutic devices described herein.

[0084] FIG.15 illustrates a circuit for operating photobiomodulation devices of the present description.

[0085] FIG. 16 shows resting power as a function of frequency bands for patients with different diagnoses.

[0086] FIG. 17A is a block diagram representing the user assessment, personalized treatment selection, and performance feedback process.

[0087] FIG. 17B is an illustration of regions of the brain that can be selected for treatment in various embodiments taught herein.

[0088] FIG. 18A illustrates a method for therapeutic photobiomodulation for treatment of diseases or disorders in accordance with some embodiments described herein.

[0089] FIG. 18B illustrates a method of treating and monitoring epileptic patients using photobiomodulation therapy. 13 ME151319109v.1132049-00217

[0090] FIGs. 18C and 18D illustrate a communication protocol a system using the communication protocol for performing photobiomodulation therapy as described herein.

[0091] FIG. 19A illustrates a graphical user interface including user prompts to resolve patient status requirements accordance with various embodiments taught herein.

[0092] FIGs. 19B-19Zg illustrate further graphical user interface images of the software program utilized to interact with physicians, therapists, and caregivers managing setup and utilization of the system as described in preferred embodiments herein.

[0093] FIG.20 illustrates a flowchart for a method for therapeutic photobiomodulation for treatment of diseases or disorders in accordance with various embodiments taught herein.

[0094] FIGs.21A and 21B illustrate two dimensional depictions of EEG data for a subject before and after a PBM therapy session, respectively, showing a decrease in the power of Delta waves.

[0095] FIGs. 21C and 21D show tabulated results for the power Delta and Theta waves over scaled time as a result of a clinical study.

[0096] FIG. 21E, depicts the change in EEG Delta signal versus the change in CARS for the clinical study.

[0097] FIG. 21F depicts the change in EEG Theta signal versus the change in CARS for the clinical study.

[0098] FIG.21G depicts the change in EEG for the Delta signal versus the Theta signal for the clinical study.

[0099] FIG, 22A illustrates a process sequence for treating abnormal epileptiform activity and / or seizures using photobiomodulation therapy.

[0100] FIG. 22B illustrates neuromodulation treatment sub-modules for an external computing device to control different treatment protocols for tethered and untethered operation of the headworn device.

[0101] FIG.23 is a block diagram representing the user assessment, personalized treatment selection, and performance feedback process for different operating modules of the system.

[0102] FIG. 24 is a block diagram for the reference population treatment effectiveness cluster analysis for personalized intervention clusters using machine learning. 14 ME151319109v.1132049-00217

[0103] FIG. 25 is a block diagram for the machine learning model used in the machine learning module (MLM) to create personalized treatment clusters based on reference population data.

[0104] FIG.26 is an illustration of an exemplary system that may be used to implement the functions and processes of certain embodiments of the present invention.

[0105] FIGs.27A and 27B illustrate delta wave EEG intensities measured for experimental (“active”) and control (“placebo”) groups, respectively, in a clinical trial utilizing photobiomodulation devices and techniques as described herein.

[0106] FIGs. 27C, 27D and 27E show an overlay of active and placebo delta wave EEG results after seven sessions of data collection, a cumulative composite score between the active and placebo groups, and a CARS scale test results for active and placebo control groups (before and after), respectively.

[0107] FIGs. 28A and 28B illustrate benefit index and side effect index scores, respectively, for experimental and control group subjects in a clinical trial utilizing photobiomodulation devices and techniques as described herein.

[0108] FIG. 29A illustrates tabulated frequency bands used in wavelet transform processing of EEG data for the diagnosis of autism spectrum disorder in children.

[0109] FIG. 29B illustrates an exemplary process flow diagram using the measurement of EEG data in the diagnosis of a neurological disorder, and the selection of one or more treatment protocols to treat a patient. DETAILED DESCRIPTION

[0110] Some implementations of the disclosed technology will be described more fully with reference to the accompanying drawings. This disclosed technology can be embodied in many different forms, however, and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods. Such other components not described herein can include, but are not limited to, for example, components developed after development of the disclosed technology. 15 ME151319109v.1132049-00217

[0111] It is also to be understood that the mention of one or more method steps does not imply that the methods steps must be performed in a particular order or preclude the presence of additional method steps or intervening method steps between the steps expressly identified.

[0112] Reference will now be made in detail to exemplary embodiments of the disclosed technology, examples of which are illustrated in the accompanying drawings and disclosed herein. Wherever convenient, the same references numbers will be used throughout the drawings to refer to the same or like parts.

[0113] FIG. 1 shows an example wearable device 50 that may implement certain methods for cross-modal brain stimulation. As shown in FIG. 1, in some implementations the wearable device 50 may include one or more processors, transceivers, microphones, headphones 52, LED lights 54, and / or batteries, amongst other things. The wearable device 50 may be paired with a user device (e.g., smartphone, smartwatch), which may provide instructions that may determine a frequency of transmitted light, the type of light (e.g., red light or infrared light), the meditations, and / or the linguistic inputs. Figs. 2A-2C depict rear side and front views of the head wearable device 50 positioned on the head of a patient with rear circuit board 56, side illumination panels 56, and front illumination panel 62 to provide transcranial illumination, and also earphones 52 to provide audio programming to the patient. The system can store audio files or video files that can be heard or seen by the user in conjunction with the therapeutic session for a patient.

[0114] FIG. 3 is an illustration of a system 100 for brain stimulation in accordance with various embodiments described herein. The system 100 includes a photobiomodulation device 110 in communication with a remote computing device 150. In exemplary embodiments, the computing device 150 includes a visual display device 152 that can display a graphical user interface (GUI) 160. The GUI 160 includes an information display area 162 and user-actuatable controls 164. Optionally, the computing device 150 is also in communication with an external EEG system 120’. Optionally, the computing device 150 is also in communication with an external light sensor array 122’. An operating user can operate the computing device 150 to control operation of the photobiomodulation device 110 including activation of the functions of the photobiomodulation device 110 and mono- or bi- directional data transfer between the computing device 150 and the photobiomodulation device 110. Further details concerning devices and methods for performing photobiomodulation to diagnose and treat neurological disorders can be found in U.S. Patent 16 ME151319109v.1132049-00217 Application No. 17 / 105,313, filed on November 25, 2020, now U.S. Patent No. 11,986,667 which is a continuation-in-part of International Application No. PCT / US2020 / 055782 filed on October 15, 2020, and is also a continuation-in-part of U.S. Patent Application No. 17 / 949,997, filed September 21, 2022, which is a continuation-in-part of International Application No. PCT / US2022 / 020770, filed March 17, 2022, which claims priority to U.S. Provisional Application No. 63 / 303,384, filed January 26, 2022, and to U.S. Provisional Application No. 63 / 272,823, filed October 28, 2021, and to U.S. Provisional Application No. 63 / 250,703, filed September 30, 2021, and to U.S. Provisional Application No. 63 / 162,484, filed March 17, 2021, the entire contents of each of these applications being incorporated herein by reference.

[0115] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools, stored data and / or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. Upon activation of the program control mode, the GUI 160 displays program control information in the information display area 162. Likewise, activation of other modes using user-actuatable controls 164 can cause the GUI 160 to display relevant mode information in the information display area 162. The system can be programmed to perform therapeutic sessions with variable lengths of between 5 and 30 minutes, for example. The patient’s use of language during the session can be recorded by microphone on the head wearable device or used separately and an analysis of language used during the session or stored for later analysis.

[0116] In the program control mode, the GUI 160 can display program controls including one or more presets 165. Activation of the preset by the operating user configures the photobiomodulation device 110 to use specific pre-set variables appropriate to light therapy for a particular class of patients or to a specific patient. For example, a specific preset 165 can correspond to a class of patient having a particular age or particular condition. In various embodiments, the pre-set variables that are configured through the preset 165 can include illumination patterns (e.g., spatial patterns, temporal patterns, or both spatial and temporal patterns), illumination wavelengths / frequencies, or illumination power levels.

[0117] In some embodiments, the photobiomodulation device 110 can transmit and / or receive data from the computing device 150. For example, the photobiomodulation device 17 ME151319109v.1132049-00217 110 can transmit data to log information about a therapy session for a patient. Such data can include, for example, illumination patterns, total length of time, time spent in different phases of a therapy program, electroencephalogram (EEG) readings, and power levels used. The data can be transmitted and logged before, during, and after a therapy session. Similar data can also be received at the computing device 150 from the external EEG system 120’ or the external light sensor array 122’ in embodiments that utilize these components. In the stored data manipulation and / or visualization mode, the operating user can review the data logged from these sources and received at the computing device 150. In some embodiments, the data can include information regarding activities used in conjunction with the therapy session (i.e., information related to tasks presented to the patient during the therapy session such as task identity and scoring). For example, activity data can be input by an operating user on the assessment mode screen as described in greater detail below.

[0118] In the audio system mode, the user can control audio information to be delivered to the patient through speakers 116 of the photobiomodulation device 110. Audio information can include instructions to the patient in some embodiments. In other embodiments, audio information can include audio programming for different therapeutic applications.

[0119] In the assessment mode, a user can input or review data related to patient assessment such as task identity and scoring. For example, FIG. 6 illustrates a particular assessment test displayed in the information display area 162 of the GUI 160. This assessment test, the Weekly Child Test, includes rating scales representing scoring on a variety of individual metrics geared to an overall assessment of the severity of autism in the child.

[0120] As described in greater detail below, the computing device 150 and photobiomodulation device 110 can communicate through a variety of methods. In some embodiments, a direct (i.e., wired) connection 117 can be established between the computing device 150 and the photobiomodulation device 110. In some embodiments, the computing device 150 and the photobiomodulation device 110 can communicate directly with one another through a wireless connection 118. In still further embodiments, the computing device 150 and the photobiomodulation device 110 can communication through a communications network 505.

[0121] In various embodiments, one or more portions of the communications network 505 can be an ad hoc network, a mesh network, an intranet, an extranet, a virtual private 18 ME151319109v.1132049-00217 network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi network, a WiMAX network, an Internet-of-Things (IoT) network established using Bluetooth® or any other protocol, any other type of network, or a combination of two or more such networks.

[0122] In exemplary embodiments, the system 100 is configured to treat autistic patients and, in particular, juvenile autistic patients. As such, it is desirable in many embodiments to create a wireless connection between the photobiomodulation device 110 and the computing device 150 as a juvenile patient is less likely to sit still for the length of a therapy session. Wireless connection and use of a battery to power the photobiomodulation device 110 enables uninterrupted transcranial illumination for the entire length of a single therapy session and, further, enables the juvenile patient to move and engage in activities that may, or may not, be associated with the therapy.

[0123] FIG. 4 shows block diagrams of a remote computing device 150 and photobiomodulation device 110 suitable for use with exemplary embodiments of the present disclosure. The remote computing device 150 may be, but is not limited to, a smartphone, laptop, tablet, desktop computer, server, or network appliance. The remote computing device 150 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for implementing exemplary embodiments. The non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more magnetic storage disks, one or more optical disks, one or more flash drives, one or more solid state disks), and the like. For example, memory 156 included in the remote computing device 150 may store computer-readable and computer-executable instructions or software for implementing exemplary operations of the remote computing device 150. The remote computing device 150 also includes configurable and / or programmable processor 155 and associated core(s) 404, and optionally, one or more additional configurable and / or programmable processor(s) 402’ and associated core(s) 404’ (for example, in the case of computer systems having multiple processors / cores), for executing computer-readable and computer-executable instructions or software stored in the memory 156 and other programs for implementing exemplary embodiments of the present disclosure. Processor 155 and 19 ME151319109v.1132049-00217 processor(s) 402’ may each be a single core processor or multiple core (404 and 404’) processor. Either or both of processor 155 and processor(s) 402’ may be configured to execute one or more of the instructions described in connection with remote computing device 150.

[0124] Virtualization may be employed in the remote computing device 150 so that infrastructure and resources in the remote computing device 150 may be shared dynamically. A virtual machine 412 may be provided to handle a process running on multiple processors so that the process appears to be using only one computing resource rather than multiple computing resources. Multiple virtual machines may also be used with one processor.

[0125] Memory 156 may include a computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, and the like. Memory 156 may include other types of memory as well, or combinations thereof.

[0126] A user may interact with the remote computing device 150 through a visual display device 152, such as a computer monitor, which may display one or more graphical user interfaces 160. In exemplary embodiments, the visual display device includes a multi- point touch interface 420 (e.g., touchscreen) that can receive tactile input from an operating user. The operating user may interact with the remote computing device 150 using the multi- point touch interface 420 or a pointing device 418.

[0127] The remote computing device 150 may also interact with one or more computer storage devices or databases 401, such as a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and / or software that implement exemplary embodiments of the present disclosure (e.g., applications). For example, exemplary storage device 401 can include modules to execute aspects of the GUI 160 or control presets, audio programs, activity data, or assessment data. The database(s) 401 may be updated manually or automatically at any suitable time to add, delete, and / or update one or more data items in the databases. The remote computing device 150 can send data to or receive data from the database 401 including, for example, patient data, program data, or computer-executable instructions.

[0128] The remote computing device 150 can include a communications interface 154 configured to interface via one or more network devices with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a 20 ME151319109v.1132049-00217 variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, 802.11, T1, T3, 56kb, X.25), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections (for example, WiFi or Bluetooth®), controller area network (CAN), or some combination of any or all of the above. In exemplary embodiments, the remote computing device 150 can include one or more antennas to facilitate wireless communication (e.g., via the network interface) between the remote computing device 150 and a network and / or between the remote computing device 150 and the photobiomodulation device 100. The communications interface 154 may include a built- in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the remote computing device 150 to any type of network capable of communication and performing the operations described herein.

[0129] The remote computing device 150 may run operating system 410, such as versions of the Microsoft® Windows® operating systems, different releases of the Unix and Linux operating systems, versions of the MacOS® for Macintosh computers, embedded operating systems, real-time operating systems, open source operating systems, proprietary operating systems, or other operating system capable of running on the remote computing device 150 and performing the operations described herein. In exemplary embodiments, the operating system 410 may be run in native mode or emulated mode. In an exemplary embodiment, the operating system 410 may be run on one or more cloud machine instances.

[0130] The photobiomodulation device 110 can include a processor board 111, one or more light emitter panels 115a-115e, one or more speakers 116, and one or more batteries 118. The photobiomodulation device 110 can optionally include a light sensor array 122 and an EEG sensor system 120. Although five light emitter panels 115a-115e are described with respect to this disclosure, one or ordinary skill in the art would appreciate that a greater or fewer number of panels may be used. In an exemplary embodiment, the light emitter panels 115a-115e hare flexible. In an exemplary embodiment, the light emitter panels 115a-115e are positioned at the front, top, back, and both sides of the user’s head. In embodiments wherein the photobiomodulation device 110 does not have a full cap over the user’s head (i.e., a headband-style device), the top panel may be omitted.

[0131] FIG. 5 illustrates a schematic layout of the photobiomodulation device 110 of the present invention. The processor board 111 is, for example, a printed circuit board including 21 ME151319109v.1132049-00217 components to control functions of the photobiomodulation device 110. The processor board 111 can include a central processing unit 112 and a power management module 114 in some embodiments.

[0132] The power management module 114 can monitor and control use of particular light emitter panels 115a-115e during a therapy session. In some embodiments, the power management module 114 can take action to control or provide feedback to a patient user related to whether light emitter panels 115a-115e are not used, or are only partially used, during a particular therapy session. By mitigating use of certain panels during a session, longer operation can be achieved. Moreover, different classes of patient (e.g., patients of different ages) can have different cranial thicknesses. As a result, different transmission power (and penetration) may be necessary as a function of patient age. The power management module 114 can control power output to light emitter panels to provide a therapeutically beneficial dose of illumination while still extending battery life.

[0133] Using the wearable device 50, certain methods of the present disclosure may perform photobiomodulation (stimulating brain with light) and linguistic training simultaneously to treat children with ASD. The wearable device 50 may include several near infrared and / or red lights to stimulate the language area of the brain. These methods associated with the wearable device 50 may include determining an area of the head to position the wearable device 50 (e.g., the temporal lobe, the prefrontal cortex, and / or the occipital lobe) to output the infrared and / or red lights. The light absorbed by the brain tissue may increase the production of ATP, which may provide the neurons more energy to communicate with each other and provide increased brain connectedness. The wearable device 50 may simultaneously receive linguistic inputs from an application of a user device that is transmitted to the user via the headphones of the wearable device 50. The linguistic inputs may help facilitate language learning. Therefore, by providing these combined mechanisms (photobiomodulation and linguistic input), for example, to children diagnosed with ASD, may significantly improve lifelong outcomes. Further, the wearable device 50 may output meditations that may help reduce anxiety of the patient user (such as an ASD child), which may allow the user to better learn language and integrate socially.

[0134] Autism spectrum disorders are associated with brain inflammation, in particular, inflammation characterized by activation of brain macrophages (microglial activation) (see, e.g., Rodriguez et al., Neuron Glia Biol 2011, 7(204):205-213; Suzuki et al., JAMA Psychiatry 22 ME151319109v.1132049-00217 2013, 70(1):49-58; and Takano, Dev Neurosci 2015, 37:195-202, the entire contents of each of which are hereby incorporated herein by reference). Brain inflammation can be identified by the presence of delta waves (high voltage slow waves) during wakefulness that can be detected using electroencephalography (EEG). In healthy individuals, delta waves in EEG are detected during the period of restorative sleep, but not during wakefulness. Wakeful delta waves in EEG are associated with pathological conditions that are, in turn, associated with brain injury and inflammation characterized by activation of brain macrophages (microglial activation). Such pathological conditions include, among others, classical mitochondrial diseases like Alpers syndrome, traumatic brain injury and autism spectrum disorders (ASD). The presence of wakeful delta waves in an individual with a pathological condition described above indicates that healing activities normally confined to sleep were not sufficient for inhibiting brain inflammation.

[0135] Evidence also indicates that wakeful delta wave power is a reliable marker of brain inflammation and microglial activation. For example, symptomatic improvement in traumatic brain injury and genetic forms of mitochondrial brain disease is accompanied by a decrease in wakeful delta wave power. The presence of wakeful delta waves in an individual with ASD is indicative of brain inflammation.

[0136] Activation of microglia during brain inflammation results in synthesis and release of nitric oxide (NO) in the inflamed brain tissues. NO inhibits oxidative phosphorylation in the mitochondria by binding to the iron and copper atoms present in the mitochondrial electron transport chain complex IV cytochrome oxidase and inhibiting its activity (see, e.g., Mason et al., PNAS 2006103(3):708-713, the entire contents of which are hereby incorporated herein by reference). Decreased levels of oxidative phosphorylation result in the increased levels of dissolved oxygen in the cell which, in turn, results in the increased levels of reactive oxygen species (ROS) and mitochondrial damage and fragmentation.

[0137] Near infrared light penetrates biological tissues, including bone structures such as the cranium. It can act to displace NO bound to the complex IV cytochrome oxidase, thereby reversing the effect of elevated NO levels and reversing the inhibition of oxidative phosphorylation. Restoration of mitochondrial oxygen consumption has the effect of stimulating healing of the inflamed tissues. Thus, without being bound by a specific pathway when other physiologic pathways, medications or therapeutic agents may alter the circumstances impacting treatment of a particular patient, it is believed that illuminating brain 23 ME151319109v.1132049-00217 tissue of a subject with near infrared light can reverse inhibition of oxidative phosphorylation mediated by NO, stimulate oxidative phosphorylation and facilitate healing of inflamed brain tissues, thereby reducing brain inflammation. It is also believed, without being bound by a specific pathway as noted above, that the reduced inflammation of brain tissue resulting from illumination as described in the present application with red, near infrared light and / or infrared portions of the electromagnetic spectrum can cause a decrease in the wakeful delta waves, for example. Indeed, as indicated by the results of the clinical trial described herein, photobiomodulation therapy resulted in a statistically significant decrease in the delta waves in the treatment group as compared to a control group, which, in turn, was associated with a statistically significant reduction in autism symptoms.

[0138] Methods for providing cross-modal brain stimulation may include determining the light frequency, location of the LED lights (e.g., areas of the brain needing increased ATP, areas of the brain most likely to respond to light treatments, and / or areas of the brain associated with language (e.g., auditory cortex, Broca area, Wernike area)), whether ATP production increased, and the overall effect of the treatments. Accordingly, based on the determined overall effect on the brain, the wearable device may be dynamically adjusted on a user-specific basis.

[0139] The wearable device 50 may be specifically tailored for children with ASD, such that it improves language skills, alleviates anxiety, and / or reduces tantrums. Further, the wearable device 50 may be used on a daily basis, in the convenience of the family’s home, without a need for a specially trained therapist. Moreover, the wearable device 50 may be non- invasive, may not require a prescription, and / or may lack side effects.

[0140] Methods for using the devices of the present disclosure may further include determining the location(s) of the light emitting diodes that may be used to stimulate specific brain areas responsible for language, comprehension, energy production, and / or for self- regulation (e.g., reducing anxiety). The methods may also include determining total power, power density, pulsing, and / or frequency. The total power may be 400–600 mW (0.4–0.6W) with 100–150 mW per each of four panels. The power for each panel may be selectively stepped down to the 50-100 mW range, or increased to the 150-200 mW range depending on the age or condition of the patient. Each of these ranges may be further incremented in 10 mW steps during a treatment session or between sessions. The spot size of the light generated by each LED or laser can optionally be controlled by adjusting the spacing between the light 24 ME151319109v.1132049-00217 emission aperture of the LED or by using a movable lens for one or more LEDs on each circuit board that can be moved between adjustable positions by a MEMS actuator, for example.

[0141] Further, the wearable device 50 may be comprised of a comfortable material for prospective patients. For example, the wearable device may be comprised of plastic, fabric (e.g., cotton, polyether, rayon, etc.), and / or the like. Because ASD patients in particular are especially sensitive, the aforementioned materials may be integral in allowing ASD patients to wear it for a sufficient amount of time without being irritable. Of course, the wearable device 50 may need to be both safe and comfortable. The electric components (e.g., processors, microphones, headphones, etc.) may be sewn into the wearable device 50 and may be difficult to reach by children, for example. A cloth or fabric covering can contain the head worn frame and optoelectronic components to the extent possible without interfering with the optical coupling of the LED to the cranium. Further, the weight of the wearable device 100 may be light enough to allow it to be worn comfortably. Moreover, the wearable device 100 may require a power source (e.g., one or more replaceable batteries) that allows it to be portable.

[0142] Regarding the linguistic inputs, a patient user device (e.g., a smartphone or tablet) may include an application that 1) performs language acquisition: (e.g., develops and records a vast number of short vignettes specifically designed to make syntactic structure transparent and teaches how to parse sentences); 2) involves a system of specifically designed mediations to alleviate anxiety; and 3) involves a system of musical rewards to keep users (children) interested and engaged.

[0143] The application may disambiguate syntactic structure of a language. Present research suggests that word learning spurt occurs after the children learn basic syntax (and it occurs at the syntactic-lexicon interface). Furthermore, without syntax children may not move beyond speaking 10–15 words, which may be used for simple labeling, but not to express their needs, wants and feelings. This means that there may be no ability for proper communication without learning syntax first. In addition, syntax may be necessary to parse the acoustic wave or sound that children hear into sentences and words. Syntax may also be necessary for specific word-learning strategies (e.g., syntactic bootstrapping).

[0144] Syntactic bootstrapping is a mechanism which children use to infer meanings of verbs from the syntactic clues. For example, when a child hears “Michael eats soup” this child infers that “eats” is a transitive verb. A classic example used by a famous psycholinguist professor Leila Gleitman is the made-up verb “Derk”. By putting this verb in several syntactic 25 ME151319109v.1132049-00217 contexts, the meaning of the verb becomes transparent “Derk! Derk up! Derk here! Derk at me! Derk what you did!” Dr. Gleitman argued that children infer the meanings of verbs from hearing them in different syntactic contexts. In addition, Dr. Pinker argued that children also use semantic bootstrapping (contextual clues) to infer meanings of the words. Therefore, there are several mechanisms (most likely innate) available to a typical child while learning language. Overall, there is scientific consensus that typical children learn language by specifically focusing on syntactic and semantic clues of speech.

[0145] However, studies suggest that children who are on the autism spectrum cannot always extract syntactic structure and semantic contexts from the imperfect linguistic input they receive. Usual linguistic input is too messy, incomplete and confusing for them. People frequently speak in fragments of sentences, switch between topics, use incorrect words or use words in incorrect forms. Human speech may be too messy to allow for simple learning based on this type of speech alone. Neurotypical children can still extract syntactic structure from this messy input by being predisposed to pay attention to specific syntactic cues (e.g., to look for nouns and verbs in the string of speech). When children grasp syntactic structure of a language, they learn to parse sentences, and therefore, acquire more words. Several studies corroborated this hypothesis that massive word learning happens at this syntax-lexicon interface, including studies with children on the spectrum.

[0146] Many children suffering from ASD seem to be unable to move beyond simple labeling, are unable to speak in full sentences, and therefore are unable to communicate effectively. There are many reasons for this difficulty, one of them is that those children do not usually pay enough attention to speech and communication, and therefore they do not pay enough attention to syntactic clues and are not able to parse individual sentences. However, without grasping syntactic structure of the language, word learning beyond simple labeling becomes impossible, specifically, acquisition of verbs may become impossible. Timely acquisition of verbs (not just nouns to label objects around them) may be critical for ASD children, as research shows that the best predictor of future integration with the neuro-typical community (and normal functioning) is speaking full sentences by 5 years of age. Therefore, specifically, the problem is that children with ASD are not focused on the language enough to extract syntactic features of words, to parse sentences and to attend to syntactic and semantic clues of speech. Therefore, their word learning is delayed. 26 ME151319109v.1132049-00217

[0147] Accordingly, the aforementioned application may calibrate the imperfect linguistic input for ASD children, thus, making syntactic structure as transparent as possible. For example, the child will hear a noun: “dog”, then she will hear “1 dog, 2 dogs, 3 dogs, 4 dogs, 5 dogs”. then she will hear “my dog is brown”, “ my brown dog is cute”, “ my brown dog is small”, “I have a small, cute, brown dog”, “my dog barks,” “dogs bark” “dogs chase cats”, “dogs eat meat”, “I have a dog,” and so on. By putting the same word in different syntactic contexts over and over again we will flood the child with the information about linguistic markers (syntactic roles in the sentences, countable, noun, animate / inanimate and so on).

[0148] Therefore, the application may “wake up” (activate) language learning and make a child pay attention to the syntactic cues of the linguistic input. Further, the application may be refined by observing the behavior of the users and recording their improvements. A method for treatment 450 is described in connection with the process flow diagram of FIG. 13 wherein preset or manually entered parameters 452 can be entered by touch actuation on the tablet touchscreen so that the system controller can actuate the illumination sequence. These parameters are stored 454 in memory. The software for the system then executes stored instructions based on the selected parameters to provide transcranial illumination 456 for the therapeutic period. The system can utilize optional audio or video files 458 in conjunction with the therapy session. The system than communicates the recorded data 460 for the therapeutic session for storage in the electronic medical record of the patient. The data can be used for further analysis such as by application of a machine learning program to provide training data.

[0149] The following describes an example of a battery powered system as previously described herein where one or two 9 volt batteries are inserted into battery holders in the side and rear views showing the LED case design shown in the figures.

[0150] If the LED is uncased, a small tube can be used to ensure that it remains centered and held securely in place. This tube can fit through a hole in the foam band for proper location and is 3 / 8” outside diameter. The PCB serves as a backing on the foam and allows clearance for the connecting cable. The same type of construction can be applied to the electronics mounted area, the battery, and the speakers. Sensors used to measure characteristics of the patient during use, such as EEG electrodes, photodetectors and / or temperature sensors can be mounted to the circuit boards carrying the LEDs or laser diodes as described generally herein. Detected electrical signals from the sensors can be routed to the 27 ME151319109v.1132049-00217 controller board and stored in local memory and can also be transmitted via wireless transmission to the external tablet device so that a user or clinician can monitor the therapeutic session and control changes to the operating parameters of the system during use.

[0151] The electronics can comprise three or more separate PCB configurations with the LED PCB having (6) variations for the associated positions on the head. There can be two LED PCB boards on each side (front and rear) with at least one illuminating the temporal lobe on each side and at least one board centered for illuminating the frontal lobe. One or two boards can conform to one or both of the parietal lobe and the occipital lobe.

[0152] The system is fitted on the head of a patient and radiates energy via IR LEDs at 40Hz into the patient’s head, for example. The IR LEDs are split into six boards with each containing one IR LED. The LED utilized for preferred embodiments can be the SST-05-IR- B40-K850.

[0153] The LED boards can illuminate during the on-time of the 40Hz signal. The duty cycle of the 40Hz signal will be equal to the power setting. For example, a power setting of 25% will require a 25% duty cycle for the 40Hz.

[0154] One or more 9V batteries can be the system’s source of power. A buck converter reduces the 9V from the battery to 2.5V for the LEDs. One or more batteries of different voltages can be employed particularly where different batteries can be used for the light emitters and powering the circuitry.

[0155] In this section, note the calculation of the LED’s absolute maximum optical flux output assuming that they are the only components powered by a single 9V battery.

[0156] Table 1 shows the current limits of important components. These current limits cannot be violated without the risk of permanent damage to the component.28 ME151319109v.1132049-00217

[0157] Conservation of energy dictates that the current sourced by the buck converter will not be the same as the current sourced by the battery. Equation 1 calculates the current drawnfrom thebattery (IBATT).where VLED_PWR is the LED supply voltage (2.5 V), ILED_PWR is the buck converter output current, η is the efficiency (minimum of 0.85), and VBATT is the battery voltage.

[0158] The efficiency of the buck converter changes over the output current range. The minimum efficiency is 0.85 at the maximum current of 2.5A.

[0159] Note that the battery voltage is inversely proportional to the battery draw. For a fixed load, the battery will draw more current as the battery discharges. Therefore, a minimum battery voltage must be specified and observed by the system microcontroller to avoid exceeding the battery’s maximum discharge current. Table 2 demonstrates how battery current draw increases as the battery discharges. Each battery draw value is calculated withEquation 1 with the followingvalues: η=0.85, VLED_PWR=2.5V, ILED_PWR=2.5A, and the batteryvoltage for VBATT.

[0160] Use Equation 2 to calculate the absolute minimum battery voltage, VB_AM. Use the same values as before, but let IB_MAX=1. Battery current draw reaches 1.0A when the battery voltage discharges to 7.35V, therefore the LEDs must be turned off to avoid exceeding the L522 battery maximum discharge specification of 1.0A. The buck converter supplying 2.5A at 2.5V with a battery voltage below 7.35V risks permanent damage to the battery.29 ME151319109v.1132049-00217

[0161] The absolute minimum battery voltage also affects battery life. FIG. 25 illustrates a discharge curve for the (Energizer L522) battery and it demonstrates that a lower absolute minimum battery voltage prolongs battery life. With a 7.35V absolute minimum battery voltage, the LEDs can be safely powered for approximately 24 minutes (if the battery was drawing 500mA instead of 1.0A). Thus, a lower absolute minimum battery voltage is beneficial.

[0162] The 2.5A sourced by the buck converter must be shared amongst six boards (LED). Thus 2.5A / 6 = 416mA from the buck converter per LED.

[0163] The duty cycle of the 40Hz will attenuate the optical flux. Equation 3 shows how to calculate the average flux for a single pulsed LED. Ee_pulse is the optical flux during the pulse and D40HZis the 40Hz duty cycle. The range of D40HZis a number between 0 and 1 inclusive.

[0164] As an example, Table 3 lists the optical flux for each power setting.

[0165] The output optical flux decreases with temperature and must be de-rated accordingly. Sources of heat to take into account are the LEDs’ self-heating and the heat from 30 ME151319109v.1132049-00217 the patient’s head. For the purposes of this analysis, assume the patient’s head is at body temperature, 37°C.

[0166] Table 4 above lists two thermal coefficients. The thermal resistance of the LED can be understood as for every watt consumed by the LED, its temperature will rise by 9.2°C. The third graph below shows normalized V-I characteristics of the LED relative to 350mA at 2V (at 350mA, forward voltage ranges between 1.2V and 2.0V, but here we continue to use worst-case value of 2.0V).

[0167] At 416mA (the maximum current available per LED), the forward voltage is approximately 2V + 0.04V = 2.04V. Using Equation 4, the temperature rise due to self- heating is 7.8°C at a 100% 40Hz duty cycle.9.2Equation 4. Temperature Rise due to Self-Heating

[0168] The LED can rise to a temperature of TLED = 37°C + 7.8°C = 44.8°C.Using thetemperature coefficient of radiant power from Table 4 and Equation 5, the change in radiant power due to temperature is -5.94%. Therefore, de-rating the worst-case optical flux of 292mW derived above by 5.94% yields approximately 275mW. %6 ℃ 7Equation 5. Change in Output Flux due to Temperature.

[0169] Note that the system also provides a temperature coefficient for forward voltage. Forward voltage decreases with temperature rise. For a worst-case analysis, the decrease in forward voltage due to temperature can be ignored.

[0170] An optical flux of 275mW is the minimum absolute maximum that can be achieved if the buck converter and the battery are pushed to their limits assuming that the battery is only supplying power to the LEDs. 31 ME151319109v.1132049-00217

[0171] Since the battery may also be powering the digital logic which includes the microcontroller, the Bluetooth module or other wireless connection, etc. the LEDs cannot draw the 1.0A maximum from the battery.

[0172] The steps below are an effort to summarize the approach described above. 1. Start by selecting the target current for a single LED, If. 2. The current sourced by the buck converter will be ILED_PWR = 6 × If. If ILED_PWR exceeds 2.5A, you must decrease If. 3.Use Equation 2 to calculate the minimum safe battery voltage to ensure desired battery life and safe operating conditions. For efficiency, either use the worst- case value of 0.85or select the closest efficiency for your value of ILED_PWRfrom Table 5.4. Use graph to approximate the optical flux output at If. a. Note: Graph is normalized to optical flux of 265mW at 350mA. 5. Use graph to approximate the forward voltage at If. a. Note: Graph is normalized to 2.0V forward voltage at 350mA. 6. Calculate the self-heating temperature rise, T∆_LED, using Equation 4. Use D40Hz=1 for 100% 40Hz duty cycle as the worst-case temperature rise. 7. De-rate the optical flux for a T∆_LED rise over ambient temperature. Use 37°C for ambienttemperature. This de-rated optical flux is the maximum flux output for a single LED. 32 ME151319109v.1132049-00217

[0173] Table 6 gives examples of target LED current and the resulting system specification. Allow a 100mA margin on the battery draw for supply logic. Values calculated in Table 6 assume worst-case efficiency of 0.85.

[0174] The maximum target LED current is 339mA resulting in a temperature adjusted flux output of 223mW. Table 7 demonstrates how the 40Hz duty cycle attenuates the LED output flux.

[0175] EEG can be used to augment the use of TPBM to reduce symptoms of autism, for example, and this procedure is described in further detail below. 33 ME151319109v.1132049-00217

[0176] The head wearable device reduces symptoms of autism by applying tPBM to stabilize functional brain connectivity, while using EEG data as a measure of the efficacy of tPBM and as a guide for continuous applications. The head wearable device can include EEG electrodes situated on one or more of the light emitter printed circuit boards as described herein. Between one and six EEG electrodes can be mounted on one or more of the light emitter panels so that they are interleaved between the light emitters or surround them so as to detect brain wave signals occurring during illumination.

[0177] Autism (ASD) is a life-long disorder characterized by repetitive behaviors and deficiencies in verbal and non-verbal communication. Resent research identified early bio- markers of autism, including abnormalities in EEG of ASD infants, toddlers and children as compared to typical children. For example, children diagnosed with ASD present with significantly more epileptiforms (even, when they do not develop seizures), some researchers report as many as 30% of ASD children present with epileptiforms (e.g., Spence and Schneider, Pediatric Research 65, 599-606 (2009). A recent longitudinal study (from 3 to 36 months) detected abnormal developmental trajectory in delta and gamma frequencies, which allow distinguishing children with ASD diagnosis from others (Gabard-Durnam et al 2019). Short-range hyper-connectivity is also reported in ASD children. For example, Orekhova et al (2014). showed that alpha range hyper-connectivity in the frontal area at 14 months (and that it correlates with repetitive behaviors at 3 years old). Wang et al (2013), has indicated that individuals with ASD present with abnormal distribution of various brain waves. Specifically, the researchers argued that individuals with ASD show an excess power displayed in low-frequency (delta, theta) and high-frequency (beta, gamma) bands as shown in FIG. 16, and reduced relative and absolute power in middle-range (alpha) frequencies across many brain regions including the frontal, occipital, parietal , and temporal cortex. This pattern indicates a U-shaped profile of electrophysiological power alterations in ASD in which the extremities of the power spectrum are abnormally increased, while power in the middle frequencies is reduced.

[0178] Duffy & Als (2019) argued, based on EEG data, that ASD is not a spectrum but rather a “cluster” disorder (as they identified two separate clusters of ASD population) and Bosl et al Scientific Reports 8, 6828 (2018) used non-linear analyses of infant EEG data to predict autism for babies as young as 3 months. Further details concerning the application of computational methods of Bosl can be found in US Patent publication 2013 / 0178731 filed on 34 ME151319109v.1132049-00217 March 25, 2013 with application number 13 / 816,645, from PCT / US2011 / 047561 filed on August 12, 2011, the entire contents of which is incorporated herein by reference. This application describes the application of machine learning and computational techniques including the use of training data stored over time for numerous patients and conditions that can be used to train the a machine learning system for use with the methods and devices described herein. A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism. An array of 32 or 64 EEG channels can be used with electrodes distributed around the cranium of the child. Overall, the consensus is that ASD is a functional disconnectivity disorder, which has electrophysiological markers, which can be detected through an EEG system. Dickinson et al (2017) showed that at a group level, peak alpha frequency was decreased in ASD compared to TD children.

[0179] Transcranial photobiomodulation as described herein is used to treat many neurological conditions (TBI, Alzheimer, Depression, Anxiety), and is uniquely beneficial to autism, as it increases functional connectivity AND affects brain oscillations (Zombordi, et al, 2019; Wang et al 2018). Specifically, Zomorrodi et al Scientific Reports 9(1) 6309 (2019) showed that applying tPBM (LED-based device) to Default Mode Network increases a power of alpha, beta and gamma, while reduces the power of delta and theta (at resting state). Wang et al (2018) also showed significant increases in alpha and beta bands. Finally, Pruitt et all (2019) showed that tPBM increases cerebral metabolism of human brain (increasing ATP production).

[0180] Thus, preferred embodiments use a system that correlates continuously collected EEG data with observable symptoms (as reported by the parents) and use EEG to guide application of LED based tPBM. The symptoms provided by parents can provide ranked data can be used to formulate the parameters for a therapy session.

[0181] LED based tPBM can be applied to Default Mode Network (avoiding central midline areas) as well as Occipital lobe, and Broca area (left parietal lobe) as well as Wernike area (left temporal lobe).

[0182] Stimulating DMN (and simultaneous stimulation of frontal lobe with occipital lobe) increases long-range coherence. Stimulating language producing areas (e.g., Broca and Wernike areas with DMN) has been shown to facilitate language production in aphasic stroke patients (Naeser, 2014). 35 ME151319109v.1132049-00217

[0183] The device performs EEG measurements in combination with photobiomodulation therapy:

[0184] 1. Analyze initial EEG data for epileptiforms, long-range coherence and hemispheric dominance.

[0185] 2. Correlate EEG data with observed symptoms.

[0186] 3. Based on the observed symptoms and the EEG data, the head wearable device can apply tPBM. For example, for children with severe repetitive behaviors and strong delta and theta power in the prefrontal cortex, the device stimulates prefrontal cortex to increase power within alpha and beta frequency band (and decrease power of delta and theta bands). For children who struggle with language, the device can stimulate DMN and Broca and Wernike areas. For children with various and severe symptoms, the device can stimulate all identified targeted areas (DMN, Broca, Wernike, occipital lobes).

[0187] 4. The device can adjust power gradually and increasing it until the minimal change in brain oscillation is detected. This thresholding avoids applying too much power to a developing brain. The device operates at the lowest power that achieves the desired oscillation.

[0188] 5. As the symptoms improve and the measured EEG signal stabilizes, the power level of the device can be gradually reduced. This system can be automated to control each therapy session.

[0189] 6. Machine learning algorithms analyze EEG data and behavioral data, and the power alterations provided by the algorithm in the form of guidance to parents (and therapists), as well as indicate further improvements in the therapy being given to the patient.

[0190] 7. As the symptoms sufficiently improve (expected improvement is within 8 weeks based on Leisman et al 2018), the device controls a break from tPBM and collect only EEG and behavioral symptoms to monitor for possible regression.

[0191] 8. If any regress is detected, the device can instruct that tPBM is gradually resumed.

[0192] The device can apply tPBM to DMN, occipital lobe as well as to Broca and Wernike areas. The device collects EEG signals from prefrontal cortex, occipital cortex and temporal cortex (left and right to monitor hemispheric dominance observed in ASD children). 36 ME151319109v.1132049-00217 The platform connected to the device can conduct initial assessment of behavioral symptoms (to be correlated with EEG data) as well as ongoing collection of symptoms (allowing for continuous correlations with EEG). Therefore the platform will continuously measure the efficacy of tPBM and personalization can be developed. Initially, a baseline is established by two separate measures. First, functional brain connectivity and brain oscillations baselines can be established in targeted brain areas (e.g., F1 & F2, T3 & T4, O1 & O2) prior to using treatment. Second, baseline demographic information (age, gender, race, etc), most concerning symptoms, and medical history (e.g., known genetic mutations, mitochondrial dysfunctions, gastroenterological symptoms, asthma, epilepsy, medications taken on regular basis) of each child is collected from parents. After the initial low-dosage treatment is administered several times (>3), a child’s brain oscillations can be measured in order to establish the trend for reduction of delta brain waves , which is needed in order to detect the treatment’s effect on the brain’s electrophysiological activity and penetration of light through the skull. Separately, data can be collected from parents about the child's behavioral symptoms, including language, responsiveness, aggression, self-injurious behavior, irritability, and sleep disturbances. An AI algorithm, described in further detail below, processes collected data to determine the combination of effectiveness (as marked by behavioral symptoms and EEG data) as well as tolerability (as marked by behavior, reported by parents) to compute an optimal dosage (which include total power, time administered and frequency of pulsing). For example, the device used in some embodiments uses 40HZ pulsing, which usually increases focus. However, for hyper-active children 10HZ pulsing or continuous wave administration can be used and can be effective. To further improve personalization features, the system can be programmed to adjust for skin color based on the timing and strength of dosage. Darker skin pigments absorb light more than lighter skin, therefore fewer photons are likely to reach the brain. In the clinical study, children with darker skin showed improvement later than children with lighter skin, thereby indicating a need to adjust dosage based on skin absorption. Therefore, they might need longer usage of the device at a given dosage to detect improvements. The control software for the device can be programmed for such personal characteristics as race and ethnicity.

[0193] The process flow diagram in FIG. 8 illustrates the method 500 of performing transcranial illumination in combination with the use of one or more sensors to measure characteristics of the brain to monitor the treatment and detect changes in tissue that indicate 37 ME151319109v.1132049-00217 a response during one or more sessions. Preferred embodiments can utilize an EEG sensor array with the head wearable device to measure brain electric field conditions where manual or preset parameters are selected 502 for a therapeutic session. The system performs transcranial illumination 504 and data is recorded such as EEG sensor data. Depending on the measured data and condition of the patient, the system can automatically adjust operating parameters or they can be manually adjusted 506 by the clinician. The data can be communicated 508 to the computing device such as the control tablet device and stored in the electronic medical record of the patient. This can be transmitted by communication networks to a hospital or clinic server for storage and further analysis as described herein. Shown in FIG. 9 is a table 900a with exemplary values for illumination conditions that can be employed by the system. These parameters typically fall within a range of values that the system can use that extend between a minimum threshold and a maximum threshold. These thresholds can be age dependent as the thickness and density of the cranium of a child increase with age as described in Smith et al, “Automated Measurement of Cranial Bone Thickness and Density from Clinical Computed Tomography,” IEEE conference proceedings Eng Med Biol Soc. 2012: 4462-4465 (EMBC 2012), the entire contents of which is incorporated herein by reference. Thus, an age dependent quantitative rating can be associated with each patient that is used to define the illumination parameters used for that patient. Note that different lobes of a child may increase in thickness and / or density at different rates over time. Thus, the power density to be delivered to a child at age 4 will be less than that used for a 5 or 6 year old, for example.

[0194] Thus, an operating module of the software can be programmed to retrieve fields of data or data files from a patient data entry module that can include patient information and other initial observations of parents or clinicians regarding a child’s age, condition, medical history including medications that may impact a further diagnostic or therapeutic program. FIG. 10A illustrates a process flow diagram for a method 600 of selecting and optimizing parameters over multiple therapeutic sessions including manual and automated selection tracks. Initially, patient data related to a child or adult patient (such as age or condition) can be entered by a user into a memory of a computing device (step 602). For example, data can be entered by a user through the GUI 160 of the remote computing device 150 (such as a tablet computing device) and stored in the memory 156 as described previously in relation to FIG. 4. The method 600 can then follow one of two tracks. In one embodiment, the user can 38 ME151319109v.1132049-00217 manually select illumination or therapy session parameters for a first therapeutic dose level or dose level sequence based upon the patient data (step 604). For example, the user can manually select parameters from menu or other displays on the GUI 160 of the remote computing device 150. Then, the illumination and / or therapy session parameters (which may include user-selected parameters and other parameters whether automatically determined or set by default) can be displayed on the computer display (step 606). For example, the parameters can be displayed on the visual display device 152. The device can also be programmed to operate a linguistic and / or visual message therapy module that communicates auditory and / or visual messages to the patient during a therapy session.

[0195] In an alternative embodiment, the parameters can be set algorithmically or automatedly. The processor of the computing device can process the patient data (including, for example, age and condition data) to determine the first therapeutic dose level or dose level sequence (step 620). For example, the processor 155 of the remote computing device 150 can analyze and process the patient data. Then, the automatically selected illumination and therapy session parameters (as well as other session parameters) can be displayed on the display associated with the computing device (step 622). Optionally, the set of automatically selected parameters can be augmented in this step with additional manual parameters such as an audio or video file used as part of the therapeutic session.

[0196] Whether the parameters are determined automatically or manually, the head wearable device can then be positioned on the head of patient (e.g., a child or adult) and the therapy session can be actuated based on the session parameters (step 608). Data related to the patient or device during the session can be monitored and recorded. Then, the patient data (e.g., age or condition data) can be adjusted to optimize session parameters for future (i.e., second, third, or more) therapeutic sessions (step 610).

[0197] FIG.10B shows an example photobiomodulation device 1100 that is configured for delivery of a therapeutic dose of light into the oral cavity as described in US Patent No. 11,986,667, the entire contents of which is incorporated herein by reference. The oral cavity photobiomodulation treatment device can be controlled by an external computing device as described herein to control the operating parameters of the treatment device. As shown in FIG. 10B, in some implementations the photobiomodulation device 1100 may include LED light emitters 1102, one or more circuit boards including circuitry 1104 powered by one or more batteries, and / or internal wires 1106 connected to light emitters 1102, among other things 39 ME151319109v.1132049-00217 including sensors or other components as described herein.. The photobiomodulation device circuity 1104 can further include one or more processors, a transceiver, and / or a controller. The photobiomodulation device 1100 may be paired with a user device (e.g., smartphone, smartwatch, or tablet device as described herein), which may provide instructions that may determine a frequency of transmitted light and / or the type of light (e.g., red light or infrared light) pattern or intensity distribution.

[0198] Using the photobiomodulation device 1100, certain methods of the present disclosure may perform photobiomodulation (stimulating brain with light). In some examples, photobiomodulation may be performed simultaneously with linguistic training to treat, for example, children with ASD as described previously herein. Preferred photobiomodulation devices and methods may include several near infrared and / or red light emitters to stimulate certain blood vessels within the cranium or that flow directly into the brain. Methods associated with the photobiomodulation device 1100 may include determining a position the LED lights 1102 to output the infrared and / or red lights. The light absorbed by the blood vessels may increase the production of ATP, which may provide the neurons more energy to communicate with each other and provide increased brain connectedness. In some examples, the photobiomodulation device 1100 can determine an amount of change in ATP. Further, the photobiomodulation device 1100 may continue to output light until a desired amount of ATP change is reached. Therefore, in some examples, the photobiomodulation device 1100 may further include one or more sensors that can determine the amount of ATP of cells within a predetermined distance of the device. Also, using a controller, the frequency of transmitted light and / or the type of light emitted by the photobiomodulation device 1100 can be manually or automatically adjusted.

[0199] Methods for providing photobiomodulation may include determining the light frequency, location of the LED lights (e.g., blood vessels needing increased ATP), whether ATP production increased, and the overall effect of the treatments. Accordingly, based on the determined overall effect on the brain, the photobiomodulation device 1100 may be dynamically adjusted on a user-specific basis.

[0200] The photobiomodulation device 1100 may be specifically tailored for children and / or older adults, such that it alleviates certain ailments (e.g., ASD, Alzheimer’s Disease). Further, the photobiomodulation device 1100 may be used on a daily basis, in the convenience of the family’s home, without a need for a specially trained therapist. Moreover, the 40 ME151319109v.1132049-00217 photobiomodulation device 1100 can be non-invasive, not require a prescription, and lack side effects.

[0201] Methods of the present disclosure may further include determining the location(s) of the light diodes that may be used to stimulate specific brain areas responsible for language, comprehension, energy production, and / or for self-regulation (e.g., reducing anxiety). Therefore, application of light therapy by the photobiomodulation device 1100 may result in improved sleep, improved language, and / or improved general cognition. The methods may also include determining total power, power density, pulsing, and / or frequency. The total power may be 400–600 mW (0.4–0.6 W) with 100–150 mW per each of four panels.

[0202] Further, the photobiomodulation device 1100 may be comprised of a comfortable material for prospective patients. For example, the photobiomodulation device 1100 may be comprised of plastic, latex, silicone, rubber, and / or the like. Because ASD patients in particular are especially sensitive, the aforementioned shape and materials may be integral in allowing ASD patients to wear it for a sufficient amount of time without being irritable. Of course, the photobiomodulation device 1100 is preferably both safe and comfortable. The electric components (e.g., processors, wires, transceivers, etc.) may be included within the interior of the photobiomodulation device 1100 and may be difficult to reach by children, for example. Further, the weight of the photobiomodulation device 1100 may be light enough to allow it to be held in the mouth comfortably. Moreover, the photobiomodulation device 1100 may require a power source (e.g., batteries) that allows it to be portable. The emitter section 1102 can include one or more sensors as described herein to measure a fluid analyte, such as glucose or lactose, in a saliva sample that can be captured by a small port into a cell within the device. A motion sensor or piezoelectric sensor can be included to measure mechanical movements of the device.

[0203] As mentioned above, the photobiomodulation device 1100 can be paired to a user device, such that a user can adjust the position of the LED light emitters 1102 as well as the frequency and / or type of light. Further, the photobiomodulation device 1100 may be dynamically adjustable based on the determined ATP levels of the cells near photobiomodulation device 1100 before and after application of the light treatment. For example, the photobiomodulation device 1100 can be configured to treat a predetermined amount of ATP that the cells near the photobiomodulation device 1100 can have. Then, the photobiomodulation device 1100 may determine an amount of the ATP cells before application 41 ME151319109v.1132049-00217 of the light treatment and during the application of the light treatment. Based on the determined ATP levels, the photobiomodulation device 1100 may continue to apply light treatment until the predetermined amount of ATP is reached.

[0204] Shown in the side and perspective views of FIGs. 10C and 10D, respectively, is a device 1200 for partial insertion within the oral cavity. In this example, a pacifier such as used with infant children can be used for PBM therapy. The child can grasp the elements 1212 that serve as a mouthguard so as to limit the insertion portion or distal region of the device to a predetermined length. This defines the portion of tissue in the mouth, such as on the tongue 1206, that is illuminated by LED 1204, which is at a fixed position within the insertion portion 1202 so as to illuminate tissue region 1206. A wire 1208 can extend from circuit housing 1210 at the proximal portion of the device to connect to the LED emitter 1204. The distal section 1202 is shaped to improve contact with tissue region 1206 when placed in the patient’s mouth.

[0205] FIGS. 10E and 10F show side and perspective views of a further embodiment wherein the LED emitters are mounted to a circuit board in the circuit housing adjacent to a tube or optical fiber coupling 1242 that extends into the distal section to optically couple the LED emitted light onto the region 1206. The material of the distal section can optionally include reflector elements or surfaces to improve coupling onto tissue 1206 that can include one or more blood vessels to be illuminated. The insertion portion can be encapsulated in a white diffuse coating or layer that more efficiently couples light onto a clear portion 1402 (see FIG. 10G) of the surface configured to transmit light onto the tissue 1206.

[0206] Shown in FIGs. 10G and 10H are cross-sectional and end views respectively showing a circuit board 1406 within the circuit housing 1408 wherein the LED is mounted on a distally facing portion of the circuit board 1406. A frame 1412 situates connections within the housing 1408 to buttons or actuators 1440, 1446 that enable the user to control operation of the device including on / off operation and control of operating parameters. Indicator lights 1442, 1444 can indicate the operating status of the device. A cable 1422 can also be connected to the device to provide battery recharging, communication and control functions. The insertion portion 1404 can be attached to housing 1408 around reflector surfaces around the LED emitter that emits light onto the output surface 1402 that contacts the tissue surface. The treatment device as described herein is effective to improve the strength of muscles of the mouth of a child. As described by Cubas, et al, “Photobiomodulation in aspects of muscle function – study review”, Journal of Pre-Clinical and Clinical Research, Vol. 17, No. 1, pages 42 ME151319109v.1132049-00217 32-36 (2023), the entire contents of which is incorporated herein by reference, photobiomodulation can be applied to muscle groups to enhance strength and endurance using the illumination wavelengths and the optical flux described herein. Thus, the use of photobiomodulation to illuminate the oral cavity and the facial muscle groups in and around the mouth of newborn and infant children can enhance feeding capacity, for example.

[0207] FIG. 11 illustrates a process flow diagram for a method 700 for administering a therapeutic session to a patient in accordance with various embodiments described herein. As an optional first step, patient data can be input by a user to a computing device and stored in data fields in a patient data entry module resident in the computing device or a server device (step 702). Relevant patient data entered in this step can include patient age, weight, physical or mental condition, medication history or regimen, and a data map of cranial thickness or density as a function of location on the patient’s cranium. For example, the patient data entry module can reside in the memory 156 of the remote computing device 150, and patient data can be entered using the GUI 160 such as by using a keyboard, mouse, or multi-point touch interface 420. This step may be considered optional as the patient data for a particular patient may already be resident in patient data entry module (e.g., the data may have been entered during previous sessions and need not be re-entered). The patient data is then retrieved from the data fields in the patient data entry module using the wearable device operating module (step 704). The wearable device operating module can determine a power level as a function of time for each illumination LED 115a-115e in the array of the photobiomodulation device 110 based on the patient data to achieve the minimum therapeutic effect during the therapeutic session. Once the power levels are determined, the therapeutic session can be administered to the patient (step 706).

[0208] After concluding the therapeutic session, output data can be exported in a format compatible with standard medical records using a medical records module (step 708). Output data can include the illumination time and / or power for each individual illumination LED, a data distribution of which regions of the brain were illuminated, the cumulative power delivered, or annotations from a user conducting the session such as a medical professional. The data can be time-course data including time stamps that record when observations or other data events occurred within the therapeutic session.

[0209] Shown in FIG. 12A is a further implementation in which the head wearable device 800 has light emitting devices 810 at spaced locations around the head of the patient 43 ME151319109v.1132049-00217 connected by a cable 812 to a circuit housing having a first portion with an on / off switch 802 and a second portion with one or more control buttons or actuators 804 to manually select operating modes of the device as described herein. Headphone speakers and / or microphones 814 can be mounted to the head worn device 800 or speakers / microphones can alternatively be within a first tablet 820 that can be used by the patient during a therapy session. The first tablet or mobile phone 820 can be connected by wire or cable 806 to device 800 and can emit sounds or auditory signals for improving linguistic skills of the patient as described herein. The display on the first tablet can also be used to display images or video to the patient during the therapy session. A second tablet or mobile phone 840 can also communicate with the head worn device 800 and / or the first tablet by a cable or wireless connection 808. Tablet 840 can be used by an operating user to control operation of one or both of the head worn device 800 and first tablet 820, before, during or after a therapy session. For example, if an EEG sensor is used during a therapy session, this can serve to monitor the procedure or calibrate the power level to be used on a particular patient to establish the minimum level therapeutic dose, and optionally to also set a maximum dose for each period of illumination during the session, and further optionally to select which regions of the brain of the patient are to be illuminated during a session. The first tablet may be programmed only to provide the auditory and / or visual components to the patient, whereas the second tablet can be programmed solely for use by the operator or clinician to manage the therapy provided to one or more patients in separate sessions. The tablet used to manage patient data can also be connected by wired or wireless connection directly to an external EEG processing station 156 that receives wireless transmission of digitized EEG signals from the headset.

[0210] Shown in FIG. 12B is a top view of a headset 850 that incorporates an EEG electrode array including EEG electrodes 855, 856 located at different locations around the head of the patient. As described in further detail below such an EEG sensor array can be integrated with a light emitter array positioned around the head of the patient at different separate locations, or partially or entirely collocated with the EEG electrodes. The separation between light emitters and EEG electrodes can be adjusted depending on the treatment protocol for different neurological disorders as described herein. Light emitters and / or electrodes can be mounted on bands 854 that extend towards an upper housing or top portion 852 which can have a crown shaped bottom surface that can conform to the top of a user’s head to help stabilize the housing 852 which preferably has a low profile shape with a light 44 ME151319109v.1132049-00217 weight. The bands 854 can extend to a circumferential portion of the headset 880 extending around the user’s head such as depicted in FIG. 12A and other figures shown and described herein. The bands 854 or tubes containing the necessary wiring for EEG electrodes and / or light emitters can be situated on all sides of the user’s head so as to enable placement of light emitters and / or EEG electrodes as required for a specific application. Between 8-64 or more EEG electrodes can be mounted on the headset along with the same or a different number of light emitters as described herein. The tubes or bands 854 can also extend from the rear electronics module 882 for embodiments in which there is no housing 852 on the top of the patient’s head such as depicted in connection with FIGs. 2A-3 and 9-12, in which case the EEG circuitry can be integrated into module 882. The tubes or bands can include connectors 857 at one end so that they can be easily removed and replaced. Such a system can thereby incorporate disposable components thereby allowing the electronics module to be reused with other patients without loss of sterile conditions. In a further embodiment, the housing can be configured to include circuitry for detecting EEG signals wherein wiring from the EEG electrodes 855, 856 is amplified with amplifiers 858 for each channel followed by analog to digital converter 860 for each channel, processing of the digital signals with processor 862 that can multiplex the signals for transmission by wireless transmitter 864 and antenna 866 that communicates with an external transceiver as described herein. A power source such as a battery 869 and an impedence excitation source 865 can also be located in the housing 852. The circuitry and one or more power sources in housing 852 can also be located in a second circuit housing 882 situated on the back of the patient’s head. The housing 882 can also include circuitry, power and control operations for the light emitter system as previously described herein. A further embodiment can employ a battery situated in the rear housing to power both the circuitry in the top housing 852 and the rear housing 882. In a further embodiment, the circuitry in both housings can utilized a single processing unit to manage digital signals for both digital circuits. Such a control processor can be configured to control the light emitters, and the sensed EEG digital signals for external transmission. The transmitter 864 and antenna 866 can thereby operate as a transceiver to manage receipt of control signals to control operation of the light emitters and also control transmission of digitized EEG data. The EEG and light emitter control and processing functions can be performed by one or more processors. For applications requiring a larger number of EEG electrodes and / or light emitters one or more control and processing functions can be performed by a field programmable gate array (FPGA) or by an application specific 45 ME151319109v.1132049-00217 integrated circuit (ASIC) configured to process the larger number of channels at faster speeds and at lower power levels. Such a configuration can further reduce the size and weight to accommodate use by pediatric patients. If a larger number of light emitters and / or EEG sensors is required, the electronic components required to operate the integrated system can also be mounted on a single flexible circuit board extending from the rear housing to the top housing within a single flexible sleeve. Note that the bands 854 can comprise flexible or semi-rigid plastic components. The bands can comprise tubes or prongs in which the wiring for the EEG electrodes or light emitters can extend. The light emitters can comprise LEDs or laser diodes, for example, that can be mounted in the ends of the tubes that are oriented to direct light through the cranium. A lens can be attached to the distal exit aperture of the light emitter to define the focal region of tissue within the cranium. Where an array of light emitters is used, the distal lenses can provide overlapping illumination volumes of tissue. The tubes can comprise polyethylene or polypropylene materials that can be sterilized or replaced after a single use. As seen in FIG. 12D, the EEG electrodes and / or light emitters 874, 876, 878 can each be spring loaded with springs 872 to cause the contact surfaces to press against the scalp. Thus, the tissue contact surfaces 877 can move relative to the housing along axis 875. Arrays of two or more EEG electrodes, light sensors and / or light emitters can be housed 870 at well-defined separation distances to provide repeatable measurements. Each housing 870 can be situated on a band at selected locations on the head so as to precisely locate the sensors and light emitters as described herein to transmit and receive signals through the cranium and also through the lambdoid suture and / or the squamosal suture. In pediatric patients these small lines between cranial plates have less density and are thus more transmissive of red and infrared light signals for photobiomodulation as described herein. X-ray images of these sutures lines can be obtained for each patient in which it is desirable to direct illuminating light through one or more suture locations. This enables precise positioning of the light emitters relative to the suture lines. In such applications, the headset must be properly secured to the head to align the light emitters to the suture lines for the therapeutic period.

[0211] FIG. 12E illustrates a side view of a head wearable device 5000 in accordance with some embodiments described herein. The head wearable device 5000 includes a head mounting frame or headband 5006 connected to an occipital mount 5002. The frame or headband 5006 can comprise a length of material that surrounds the head of the user wherein 46 ME151319109v.1132049-00217 light sources are mounted to direct light inward through the cranium. The head mounted material has an inner surface and an outer surface on which circuit components can be mounted. The headband 5006 can include a size adjustment mechanism 5008. Alternatively, the optoelectronic circuit components described herein can be mounted on a flexible head wearable fabric with sufficient elasticity to be worn on different head sizes, but exert sufficient tension to cause the light emitting surface of the LEDs to come into contact with the scalp as previously described. Such features are described previously in the present application and can be adapted for this preferred circuit design. Note that this system can also include communication, user interface, audio and visual systems previously described generally in the present application. LED modules 5010 are mounted at locations on the headband 5006 and the occipital mount 5002 to illuminate different portions of a user’s cranium with therapeutic light. The head wearable device 5000 can include a head strap or band 5004 that connects a frontal portion of the headband 5006 to the occipital mount 5002 at the back of the user’s head.

[0212] The head wearable device 5000 is formed at least partially of a soft material with airy open spaces in some embodiments. In some embodiments, a surface of the headband 5006 is formed of a non-porous material to improve sterilizability and cleanability. In some embodiments, the material can include one or more of low-density polyethylene (LDPE), silicone, or ethylene-vinyl acetate (EVA) closed-cell foam. In some embodiments, the headband 5006 can include light, pastel, or bright colors that appeal to children for pediatric therapy applications.

[0213] The size adjustment mechanism 5008 can enable adjustment of the headband 5004 for comfort and / or to improve contact or coupling between the user’s scalp and the LED modules 5010. The size adjustment mechanism 5008 can include a band or strap that tightens against a patient’s skull or tightens below the occipital bone of the skull. In some embodiments, the adjustment mechanism can include a fastener such as a hook-and-loop fastener. For example, the headband 5006 can include two separated straps that fasten with the hook-and-loop fasteners or a single strap that passes through a retaining ring and doubles back upon itself so that hooks on the end of the strap can attach to a separate portion of the strap that includes loops. In some embodiments, the adjustment mechanism can include a snap closure wherein snaps or pegs in one portion of the headband 5006 connected with a variety of snaps or holes at different positions along the headband 5006. Similarly, the head 47 ME151319109v.1132049-00217 strap 5004 can include a size adjustment mechanism to enable sizing adjustments of the head strap 5004 to improve comfort for a user with a given head size. The same size adjustment mechanisms 5008 described herein for the headband 5006 can be employed in the size adjustment mechanism for the head strap 5004.

[0214] FIG. 12F illustrates a rear view of the head wearable device 5000 illustrating the occipital mount 5002. The occipital mount 5002 can include one or more LED modules 5010. The LED modules 5010 can be spaced in a pattern in some embodiments such as a cross pattern or a polygonal patterns such as square-shaped or diamond-shaped. In some embodiments, the LED modules 5010 can be positionable at different positions on the headband 5006 or occipital mount 5002. For example, headband 5006 or occipital mount 5002 can include multiple receptacles at different locations so that the LED modules 5010 can be moved to different receptacles as needed. In some embodiments, the headband 5006 or occipital mount 5002 can include racetracks or slots 5016 that allow one or more of the LED modules 5010 to translate or slide in one or more directions to improve positioning of the LED modules 5010.

[0215] In some embodiments, the occipital mount 5002 can include an electronics housing 5014 to accommodate a battery or other electronics or power sources to power elements of the head mounted device 5000 such as the LED modules 5010.

[0216] FIG. 12G illustrates an alternative embodiment of the head wearable device 5000’ with different placement of the head strap 5004’ in accordance with some embodiments described herein. The head wearable device 5000’ is substantially identical to the head wearable device 5000 described above except that the head strap 5004’ extends from one lateral side of the headband 5006 to the other lateral side of the headband 5006. This differs from head strap 5004 as shown in Figs. 12E-F that extends from the occipital mount 5002 forward to a portion of the headband 5006 adjacent to the patient’s forehead. In some embodiments, the head strap 5004, 5004’ is omitted from the head wearable device 5000, 5000’ entirely.

[0217] FIG. 12H illustrates the head wearable device 5000 according to various embodiments described herein. This figure illustrates a patient-contacting surface of the occipital mount 5002 to show arrangement of LED modules 5010 and mounting of the occipital power distribution printed circuit board (PCB) 5040. In this embodiment, the occipital power distribution PCB 5040 controls power distribution to a group of five LED 48 ME151319109v.1132049-00217 modules 5010 arranged as a group in the occipital region of the patient’s brain. A separate frontal power distribution PCB 5050 is positioned at the forehead region of the headband 5006 and powers a group of five LED modules 5010. Each group of LED modules 5010 is connected to a respective PCB 5050 by connection wires 5012. In some embodiments, the connection wires 5012 pass directly from the respective PCB 5040, 5050 to the corresponding LED module 5010 as opposed to passing serially through multiple LED modules 5010. In this arrangement, direct powering and addressing of each LED module 5010 by the PCB 5040, 5050 is possible as there is no daisy chaining. This enables consistent power delivery to all LEDs on the head mounted device 5000.

[0218] FIGs. 12I and 12J illustrate placement of an LED module in a multi-material headband 5006 in accordance with some embodiments described herein. The headband 5006 can include a relatively stiffer core 5030 surrounded by a relatively softer foam liner 5032 in some embodiments. The core 5030 can retain an LED 5036 of an LED module 5010 in a stable position within an opening 5016 (such as an oval or racetrack opening) due to friction fitting between the LED 5036 and the core 5030. The foam liner 5032 can surround the stiffer core 5030 to provide a comfortable surface against the patient’s head. As shown in the perspective and end views of FIG. 12I, the LED module 5010 can include the LED 5036 and LED PCB 5035, which is described in greater detail below. The LED 5036 projects from the LED PCB 5035 and extends through the core 5030. As shown in FIG. 12J, a front surface of the LED 5036 can be flush with the surface of the foam layer 5032 that contacts the patient so that the LED is placed as close as possible to the patient’s scalp without projecting outward to form a painful pressure point.

[0219] FIG. 12K schematically illustrates the electrical connections between elements of the head wearable device in accordance with several embodiments described herein. The electrical system of the head wearable device 5000 includes a battery 5060, the power PCB 5062, the occipital power distribution PCB 5040 (sometimes abbreviated as the occipital PCB), and the frontal power distribution PCB 5050 (sometimes abbreviated as the frontal PCB). The power PCB 5062 is connected to the occipital PCB 5040 via a cable 5052. The cable 5052 can carry signals for multiple operations or functionalities simultaneously. In some cases, the cable 5052 can carry signals at different voltages. In various embodiments, the cable 5052 can transfer power to the LED PCBs 5035 at the appropriate voltage VLED, can carry voltage for logic circuits such as TTL at 5 V or 3.3V, or carry voltage for inter- 49 ME151319109v.1132049-00217 integrated circuit (I2C) bus or Pulse-Width Modulation (PWM) Limit applications. Similar cables 5052 with similar functionalities connect the occipital PCB 5040 to the frontal PCB 5050; connect the occipital PCB 5040 to LED PCBs 5036; and connect the frontal PCB 5050 to LED PCBs 5036.

[0220] The power PCB 5062 can include a power gauge integrated circuit (IC) 5065, a charging circuit 5067, a voltage regulation module 5069, a USB interface 5066, an enable button 5064, and battery level and BlueTooth® low energy (BLE) connection indicators 5063. The power gauge IC 5065 can monitor the voltage level of the battery 5060 to determine the remaining energy (power) in the battery 5060. The battery level indicators 5068 can indicate visually to the user the level of remaining energy in the battery 5060 as measured by the power gauge IC 5065. The charging circuit 5067 enables wireless charging of the battery 5060 using inductive charging techniques such as those that conform to the Qi® wireless charging standard or other charging standards. The voltage regulation module 5069 can regulate the output voltage provided on the cable 5052 to the other components. The enable button 5064 can include a mechanical or electrical switch operable by the user to turn on or off the electrical systems of the head mounted device 5000. The USB interface 5066 enables wired charging of the battery 5060 and / or provides an interface for re- programming (e.g., flashing) or debugging components of the power PCB or connected PCBs. The USB interface 5066 can also be used for transfer of data such as usage statistics (e.g., recorded or sensed power levels, up-time or down-time, or error statuses).

[0221] The occipital PCB 5040 includes a processor such as a microcontroller unit (MCU) and BlueTooth® low energy (BLE) module 5044, non-volatile memory 5042, and a patient detection module 5046. The MCU / BLE module 5044 can operate as a central control circuit that controls power output to each individual LED PCB 5054. The MCU / BLE module 5044 enables communication with external devices using the BLE protocol. For example, an external device such as a computer, tablet, or smartphone operated by the user can wirelessly send instructions to the MCU / BLE control module 5044 to adjust individual LEDs to different power settings over time according to a therapeutic program. The memory 5042 can include one or more of logical address information or instructions to control the LED PCBs 5154. The patient detection module 5046 can detect whether or not the head mounted device 5000 is being worn by the patient. If the patient detection module 5046 having a contact sensor detects that the head mounted device 5000 is not being worn by a patient, it can send 50 ME151319109v.1132049-00217 signals to the controller 5044 or the power PCB 5062 to disable power to the LEDs to prevent light output. This automatic shutoff when the patient is not present can conserve power in the battery 5060 and provide safety by preventing illumination from being turned around when the light could accidentally enter a patient’s eyes, for example. This is especially important in pediatric applications where a child may inadvertently remove the head mounted device 5000 and could accidentally aim the light at their eyes if it were not automatically shut off. In some embodiments, the patient detection module 5046 can operate by employing optical sensors that detect whether LED light is being reflected by a very close object. Alternatively, the patient detection module 5046 can use an accelerometer or inertial sensor system to determine the orientation of the head mounted device 5000 and disable the device when the position is not consistent with placement on a head of a sitting or standing individual.

[0222] FIGs. 12L and 12M illustrate respective top and bottom views of the power PCB 5062 in accordance with some embodiments described herein. The USB interface port 5066 is located on a top side of the power PCB 5062. The power gauge IC 5065, charging circuit 5067, and enable button 5064 are located on a bottom side of the power PCB 5062. The power PCB 5062 can include a connector 5063 to connect the cable from the battery 5060. The power PCB 5062 also includes a connector 5061 to connect the cable 5052 to the occipital PCB 5040.

[0223] FIGs. 12N and 12O illustrate respective top and bottom views of the occipital PCB 5040 in accordance with some embodiments described herein. The occipital PCB 5040 can include a battery 5041 to power an on-board real-time clock (RTC) that clocks operations of the device, a motion sensor such as an accelerometer 5045 for headgear orientation detection, an electronically erasable programmable read-only memory (EEPROM) 5042 or other volatile and / or non-volatile memory, the control module 5044, and a BLE control module programming port 5043 on a top side of the PCB 5040. The one or more memory devices on the head mounted device can retain information such as any error messages recorded during a treatment session, The memory also stores the duration of each treatment session, the amount of light delivered to the patient during each session and can also record annotations to the record for each session such as symptoms, side effects or proposed improvements or notes regarding the session by an observer. Each recorded session can automatically transfer a treatment session record that can temporarily be stored in the memory until the next session. The BLE programming port 5043 enables debugging or 51 ME151319109v.1132049-00217 reprogramming of the control module 5044. The EEPROM memory 5042 can also be accessed and erased through the port 5043 or through signals sent from the power PCB 5062 through port 5066. The memory 5042 can include instructions for controlling LEDs in a particular program or pattern. The system can operate a first plurality of light sources according to a first pattern and control a second plurality of light sources (LEDs) according to a second pattern. Different light sources can emit at different optical wavelengths and at different duty cycles for example. The circuitry can include one or more current level sensors or temperature sensors to control operation of the device. This can include closed loop control of the light sources, for example, to maintain optical output from each light source within 5 -10 percent of the nominal output to treat a selected condition of the patient for the prescribed therapeutic period. It is also important to prevent operating temperatures of the device so as to prevent thermal injury to the patient. Thus, the head mounted device is configured to automatically shut off the light sources and / or power if such a condition is detected. The accelerometer 5045 can send signals to the patient detection module 5046 to help detect whether the system is in a ready state for being mounted on the patient’s head. The accelerometer or other sensor (pressure sensor, light sensor, etc as described herein) can also be configured to sense a change in orientation of the head mounted device relative to the users’ head and to transmit a signal to the control module to shut off the LEDs. The system can also operate a state machine that is regularly updated with operational data that can be automatically transmitted to an individual that is monitoring the therapeutic session. An alarm signal can also be sent to a remote user communicating that at least a portion of the system has been disrupted or changed so that remedial action can be taken to continue or stop the therapy session. The system clock can report the time elapsed, the time remaining or the time of disruption.

[0224] The occipital PCB 5040 includes individual connectors 5047 to connect cables 5052 to the LED PCBs 5035. The occipital PCB 5040 also includes a connector 5049 to connect cables 5052 to the frontal PCB 5046 and a connector 5046 to connect the cables 5052 to the power PCB 5062. Any of the connectors 5049, 5047, 5048 can include a flat-flex connector that allows low-profile and flexible connections to reduce the space taken by cables 5052 within the headband 5006 or occipital mount 5002.

[0225] FIGs. 12P and 12Q illustrate respective top and bottom views of the frontal PCB 5050 in accordance with some embodiments described herein. The frontal PCB 5050 includes 52 ME151319109v.1132049-00217 individual connectors 5056 to each LED PCB 5035 in the frontal or forehead region of the headband 5006. The frontal PCB 5050 also includes a connector 5054 to the cables 5052 from the occipital PCB 5040. The connector 5054 can be a flat-flex connector. Note that the use of two or more power control circuits mounted on the same or separate circuit boards can be used to control different sets of light sources. This can provide greater control over the operating conditions of the two or more groups of light sources to maintain operation with nominal operating conditions. Thus, a first plurality of light sources can be operated by a first power control circuit and a second plurality of light sources can be controlled by a second power control circuit. The use of a constant current control circuit improves the safe operation of the system. This system enables operation of the system at 500 milliamps and at a duty cycle of 35%, for example. It is desirable to operate the system at a currents above 400 milliamps and a duty cycle of less than 45% to improve safety, efficacy and durability of the system. This design is scalable, so that a third power control circuit can be used to control a third plurality of light sources, etc. Thus, a design of the system for older children or adult use can integrate more light sources to illuminate larger areas of the brain. In such a system 15-20 or more LEDs can be integrated for optimal control for a battery operated system, or for applications in which a power cable can be used to provide power to the head mounted system.

[0226] FIGs. 12R and 12S illustrate respective top and bottom views of the LED PCB 5035 in accordance with some embodiments described herein. The LED PCB 5035 can include a second controller 5031 with LED current feedback, an LED driving circuit 5038, the LED 5036, a temperature sensor, and an LED unique identification (ID) circuit 5033. The LED PCB 5035 can also include a connector 5037 to connect via cable 5052 with either the occipital PCB 5040 or the frontal PCB 5050. The LED driving circuit 5038 can be an LED constant current circuitry that maintains the proper current output to drive the LED 5035. The temperature sensor can sense the temperature and send signals to the microcontroller 5031. The microcontroller 5031 can halt power to the LED 5036 if an over-temperature or overheating condition is detected. The LED unique ID circuit 5033 can include a resistor bank that is set differently for each LED PCB 5035 in the system. The occipital PCB 5040 or frontal PCB 5050 can use the LED unique ID circuit 5033 to identify each connected LED PCB 5035 upon connection or at a subsequent time. The LED PCB 5035 can include a microcontroller reset switch 5032 that is not operator accessible but can be used during initial 53 ME151319109v.1132049-00217 setup or repair. The LED PCB 5035 also can include a controller programming port 5034 to enable debugging or reprogramming of the LED PCB 5035.

[0227] Photobiomodulation can be used to treat several ailments including Alzheimer’s disease, post-traumatic stress disorder (“PTSD”), cognitive enhancement, cognitive impairment from trauma and / or injury, depression, anxiety, mood disorders, Parkinson’s Disease, strokes, Global Ischema, and Autism Spectrum Disorder (“ASD”). In particular, these ailments can be treated with transcranial photobiomodulation, which involves targeted light energy to the brain. The devices associated with performing transcranial photobiomodulation are often applied over the head, such as in certain embodiments described herein. However many such devices, can be cumbersome and in particular, for especially sensitive patients (e.g., children with ASD), it can be difficult to comfortably apply the device for treatment over a meaningful duration of time without the patients attempting to shift or remove the device.

[0228] Aspects of the disclosed technology include devices for photobiomodulation, which can be used to treat various patients including ASD children and older adults. Consistent with the disclosed embodiments, the photobiomodulation device may be sized and shaped to fit inside the oral cavity of the human mouth. The photobiomodulation device may include one or more light emitting diode (LED) lights, which may be located in a center portion of the device. Further, the LED emitters may be positioned to point downwards or to other regions, such that light from the device affects blood vessels that flow within the body to regions of the brain. Preferred embodiments of can be used in conjunction with methods and devices that can illuminate blood vessels within the brain or that supply blood directly to the brain such as the internal carotid artery. Further, the LED light emitters may emit light at one or more wavelengths which can be red, infrared, and / or a combination of the two. The LED light emitters may have a material (e.g., latex, silicone, rubber, etc.) surrounding it that allows the light to penetrate tissue within the mouth yet is also difficult to chew. The surrounding material can comprise one or more lenses to couple the emitted light onto the tissue that contacts a surface of the device and wherein the tissue contains regions of vascular flow that is illuminated with the device. The photobiomodulation device may further include an extendable portion that protrudes outwards from the device in a longitudinal direction. In some examples, the extendable portion may include the LED light emitters. The photobiomodulation device may be shaped similarly or substantially similar to a pacifier, for example. Therefore, a wearer of the photobiomodulation device can bite down or suck on the extendable portion while it is 54 ME151319109v.1132049-00217 inside the mouth. The photobiomodulation device may further include one or more processors, transceivers, or power sources (e.g., batteries). Preferred embodiments can also include a cavity to collect a sample of fluid from within the mouth for further testing and analysis, such as a saliva sample. A surface of the device, or the cavity, can optionally include a sensor to measure further characteristics of the tissue and / or the sample. The sensor can be electronically connected to circuitry for readout of sensor data during use. The sensor can include a light sensor such as a photodetector to measure light from the tissue and / or sample. The device can be configured to communicate with an external portable communication device as previously described herein to store patient data in a memory, and to further process and communicate data as described in the present application.

[0229] In some examples, the frequency and / or type of light emitted by the photobiomodulation device may be adjustable. Therefore, the photobiomodulation device may further include a controller that allows the user to adjust the frequency, illumination pattern and / or intensity of light. Also, in some examples, the photobiomodulation device may be paired to a user device (e.g., via Bluetooth®) that can send instructions to adjust the operating parameters of light emitted. In some examples, the position of the LED light emitter may be adjustable, i.e., the LED light emitters can be moved or scanned in another direction (e.g., left, right, up, or down).

[0230] Some implementations of the disclosed technology will be described more fully with reference to the accompanying drawing. This disclosed technology can be embodied in many different forms, however, and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods. Such other components not described herein can include, but are not limited to, for example, components developed after development of the disclosed technology.

[0231] It is also to be understood that the mention of one or more method steps does not imply that the methods steps must be performed in a particular order or preclude the presence of additional method steps or intervening method steps between the steps expressly identified.

[0232] FIGs. 13A-13U illustrate an alternative embodiment of a head wearable device. FIG. 13A illustrates a side view of a head wearable device 5100 in accordance with some 55 ME151319109v.1132049-00217 embodiments described herein. The head wearable device 5100 includes a head mounting frame or headband 5106 connected to an occipital mount 5102. The frame or headband 5106 can comprise a length of material that surrounds the head of the user wherein light sources are mounted to direct light inward through the cranium. The head mounted material has an inner surface and an outer surface on which circuit components can be mounted. The headband 5106 can include a size adjustment mechanism 5108. Alternatively, the optoelectronic circuit components described herein can be mounted on a flexible head wearable fabric with sufficient elasticity to be worn on different head sizes, but exert sufficient tension to cause the light emitting surface of the LEDs to come into contact with the scalp as previously described. Such features are described previously in the present application and can be adapted for this preferred circuit design. Note that this system can also include communication, user interface, audio and visual systems previously described generally in the present application. LED modules 5110, 5110’ are mounted at locations on the headband 5106 and the occipital mount 5102 to illuminate different portions of a user’s cranium with therapeutic light. The head wearable device 5100 can include a head strap or band 5104 that connects a frontal portion of the headband 5106 to the occipital mount 5102 at the back of the user’s head.

[0233] The head wearable device 5100 is formed at least partially of a soft material with airy open spaces in some embodiments. In some embodiments, a surface of the headband 5106 is formed of a non-porous material to improve sterilizability and cleanability. In some embodiments, a surface of the headband 5106 is formed of a perforated material to improve heat dissipation and breathability. In some embodiments, the material can include one or more of low-density polyethylene (LDPE), silicone, or ethylene-vinyl acetate (EVA) closed- cell foam. In some embodiments the material can have cushion; for example, the material could be 2mm in thickness. In some embodiments, the headband 5006 can include light, pastel, or bright colors that appeal to children for pediatric therapy applications.

[0234] The size adjustment mechanism 5108’ can enable adjustment of the headband 5104 for comfort and / or to improve contact or coupling between the user’s scalp and the LED modules 5110, 5110’. The size adjustment mechanism 5108’ can include a band or strap that tightens against a patient’s skull or tightens below the occipital bone of the skull. In some embodiments, the adjustment mechanism 5108, 5108’ can include a fastener such as a hook- and-loop fastener. For example, the headband 5106 can include two separated straps that 56 ME151319109v.1132049-00217 fasten with the hook-and-loop fasteners or a single strap that passes through a retaining ring and doubles back upon itself so that hooks on the end of the strap can attach to a separate portion of the strap that includes loops. In some embodiments, the adjustment mechanism 5108 can include a snap closure wherein snaps or pegs in one portion of the headband 5106 connected with a variety of snaps or holes at different positions along the headband 5106. Similarly, the head strap 5104 can include a size adjustment mechanism 5108’ to enable sizing adjustments of the head strap 5104 to improve comfort for a user with a given head size. The same size adjustment mechanisms 5108 described herein for the headband 5106 can be employed in the size adjustment mechanism 5108’ for the head strap 5104.

[0235] FIG. 13B illustrates a rear view of the head wearable device 5100 illustrating the occipital mount 5102. The occipital mount 5102 can include an adjustment knob 5120 to enable size adjustments of the head band 5106 and / or the head strap 5104 as described above. The adjustment knob 5120 may be manually manipulated to adjust the size of the head band 5106 and head strap 5104. In some embodiments, the headband 5106 can be lengthened in the direction indicated by the arrow in the rotational direction indicated on the adjustment knob 5120. The occipital mount 5102 can include one or more LED modules. The LED modules can be spaced in a pattern in some embodiments such as a cross pattern or a polygonal patterns such as square-shaped or diamond-shaped. In some embodiments, the LED modules can be positionable at different positions on the headband 5106 or occipital mount 5102. For example, headband 5106 or occipital mount 5102 can include multiple receptacles at different locations so that the LED modules can be moved to different receptacles as needed. In some embodiments, the headband 5106 and the occipital mount 5102 can include receptacles at different locations so that the LED modules can be moved to different receptacles as needed. In some embodiments, the headband 5106 or occipital mount 5102 can include racetracks (not pictured) that allow the LED modules to translate or slide in one or more directions to improve positioning of the LED module. The LED modules, positioned on the headband 5106, can be enclosed within the material, such that, for example only the LED 5036 is exposed. In some embodiments the LED 5036 is flush with the material. In some embodiments, the LED 5036 is extends outside the material.

[0236] The occipital mount 5102 can also include circuitry, power and control operations for the light emitter system as previously described herein. In a further embodiment, the circuitry in the occipital mount 5102 can utilize a single processing unit to manage digital 57 ME151319109v.1132049-00217 signals for the digital circuits. Such a control processor can be configured to control the light emitters, and the sensed EEG digital signals for external transmission. The EEG and light emitter control and processing functions can be performed by one or more processors. In some embodiments, the occipital mount 5102 can include an electronics housing to accommodate a battery (not pictured) or other electronics or power sources to power elements of the head mounted device 5100 such as the LED modules 5110. In some embodiments, the occipital mount 5102 can include a membrane control panel 5144. The membrane control panel 5144 can include an on / off button 5182, a Bluetooth connectivity 5184 indicator, an IR Therapy indicator 5186, and / or a battery fuel gauge 5188.

[0237] FIGs. 13C-13F illustrates exploded views of the occipital mount 5102. FIGs. 1313E illustrate respective front and rear exploded views of the occipital mount 5102 in accordance with some embodiments described herein. The occipital mount 5102 is configured to communicate with an external computing device. In some embodiments this communication is achieved with a wireless connection. The communication can include user provided instructions of illumination parameters and an illumination period. The occipital mount 5102 can include a battery 5114 to power on the main circuit board 5178 and thereby the LED modules 5110, 5110’ wired to the main circuit board 5178. Some embodiments can includes a charging cord 5190 and a USB interface-charging port 5180 that enables a charging circuit to charge the battery 5114 which can be a 3.7 V lithium battery in this example. The circuitry can be mounted on a circuit board in which a processor, such as microcontroller, is connected to a battery gauge 5188, a Bluetooth connectivity 5184 indicator, an IR Therapy indicator 5186, power supply regulator, LED control field effect transistor and LED 5110. 5110’. The main circuit board is attached to a strap retainer 5126 and held in place with fasteners 5138. The strap retainer 5126 allows the adjustment mechanism 5108 to lock into place a maintain a certain circumference of the headband 5106. The strap retainer is secured to the proximal housing 5134, over the LED modules 5110’. In some embodiments, a majority of the occipital mount 5102 components are assembled to the proximal housing 5134. The LED modules 5110’ are attached to the proximal housing 5134. A fastener 5138 secures the center of the proximal housing to the strap retainer 5126. A cover 5146 is placed over the fastener 5138 to cushion the contact with the user’s head. A closed cell foam pad 5128 can attach to the rear of the proximal housing 5134 and cover the remaining fasteners. Located on the front of the occipital mount 5102 is the membrane 58 ME151319109v.1132049-00217 control panel 5144 and adjustment knob 5120. The adjustment knob 5120 can include a rigid knob 5122 with a pinion gear 5142 and a soft elastomer cover 5124 over the rigid knob 5122. Some embodiments can include a cam mechanism to couple to adjustment knob 5120 to the occipital mount 5102. Located on the underside of the occipital mount is the PS label 5148. FIG. 13F illustrates another exploded rear view of the occipital mount. Beneath the closed cell foam pad 5126 can be a molded pocket 5176 in which the momentary capacitive touch sensor 5174 is positioned in.

[0238] FIGS. 13G-I illustrates the head wearable device 5100 according to various embodiments described herein. This figure illustrates a patient-contacting surface of the occipital mount 5102 to show arrangement of LED modules or panels 5110. FIGs. 13H and 13I illustrates placement of an LED module in a multi-material headband 5106 in accordance with some embodiments described herein. In some embodiments, the head wearable device 5100 can include ten LED modules 5110 that can be parallel with respect to each other. The number of modules mounted on the head wearable device, or the number modules that are actually selected to illuminate different regions of the brain during a therapeutic period, can vary depending upon the age and the specific type of neurotherapy prescribed for each patient. Generally between 4 and 20 LED modules can be effective depending on the condition being treated. Older children and adults may require a larger number of modules delivering a larger dose during a treatment period, for example. The LED modules 5110, 5110’ are positioned throughout the head wearable device 5100 such that they illuminate the patient from a plurality of different angles. A second capacitive sensor 5168 can be located on the headband 5106 in addition to the momentary capacitive sensor 5174 located in the occipital mount 5102. In some embodiments, the headband 5106 can include a connector pod 5170. In some embodiments, wire management clips 5172 are included. The wire management clips 5172 may, for example, include channels to contain and organize wires 5192. As described above, in some embodiments, the headband 5106 or occipital mount 5102 can include tracks 5116. FIG. 13J illustrates the LED 5036 being adjusted within the tracks 5116 in the direction of the arrows. The tracks 5116 allow the LED modules 5110 to translate or slide in one or more directions to improve positioning of the LED module 5110. This is particularly important where the headband 5106 has been adjusted to fit the patient’s head. To facilitate effective therapy treatment, the LED modules 5110 must be positioned over certain areas of the patient’s brain. As the headband 5106 is adjusted to fit the patient’s 59 ME151319109v.1132049-00217 head, the LED modules 5110 may be positioned in unideal locations. The tracks 5116 allow the LED modules 5110 to be adjusted to improve positioning within the track. In an embodiment the track 5116 may support the LED in a left 5116a, central 5116b or right 5116c position. In one embodiment tracks 5116 can include three adjustment positions 5117, 5117’, 5117’’ which may correspond to LED positions for a large, medium or small patient..

[0239] In some embodiments, the head wearable device 5100 can include an occipital power distribution PCB. In this embodiment, the occipital power distribution PCB controls power distribution to a group of five LED modules 5110 arranged as a group in the occipital region of the patient’s brain. A separate frontal power distribution PCB can be positioned at the forehead region of the headband 5106 and powers a group of five LED modules 5110. Each group of LED modules 5110 is connected to a respective PCB by connection wires 5012. In some embodiments, the connection wires 5192 pass directly from the respective PCB to the corresponding LED module 5110 as opposed to passing serially through multiple LED modules 5110. In this arrangement, direct powering and addressing of each LED module 5110 by the PCB is possible as there is no daisy chaining. This enables consistent power delivery to all LEDs on the head mounted device 5110. Furthermore, two or more power control circuits mounted on the same or separate circuit boards can be used to control different sets of light sources. This can provide greater control over the operating conditions of the two or more groups of light sources, for example the LED modules 5110, 5110’, to maintain operation with nominal operating conditions. Thus, a first plurality of light sources can be operated by a first power control circuit and a second plurality of light sources can be controlled by a second power control circuit. The use of a constant current control circuit improves the safe operation of the system. The control circuit operates in response to programmed instructions. This operation enables the processor to execute a sequence of steps to activate certain LED modules 5110, 5110’ at a selected level of light to illuminate different regions of brain tissue of the patient. The selected level of light may be manually selected by the user with a user interface in communication with the wearable head device 5100.

[0240] FIG. 13K illustrates the layers of material of the headband or frame 5106 and the placement of an LED module in a multi-material headband 5106 in accordance with some embodiments described herein. The headband 5106 can include a loop panel that attaches to the fold panel 5195 that can comprise 2 mm thick neoprene, for example, wherein hook 60 ME151319109v.1132049-00217 panels, 5198, 5199 attach to portions of fold panel 5195,, and sew panel 5194 can comprise a stitched material 2 mm thick on the inside surface of the frame, in some embodiments. In this embodiment, the stiffer core are loop panels 5196, and 5167. A loop panel 5167 can include cutouts or openings in portions of the frame to accommodate the LED 5036 of the LED modules or LED circuit board panels 5110. A hook panel 5199 similarly includes cutouts or openings to accommodate the LED 5036 of the LED modules 5110. The fold panel 5195 also includes cutouts or openings 5116. The hook panel 5199 with cutouts adheres to the headband 5106. The loop panel 5167 with cutouts adheres to the section of the fold panel 5195 that includes cutouts or openings 5116. The hook panel 5198 adheres to another section of the fold panel 5195. The loop panel 5196 adheres to the inner facing side of the sew panel 5194. The surrounding or sew panel 5194 can encapsulate the multi- material elements.

[0241] The interior material can retain an LED 5036 of an LED module 5110 in a stable position within an opening 5116 (such as an oval or track opening) due to friction fitting between the LED 5036 and the core 5196, 5197. In some embodiments, the foam liner 5195 can surround the core 5196, 5197 to provide a comfortable surface against the patient’s head. The LED 5036 projects from the LED module 5110 and extends through the multi-material. A front surface of the LED 5036 can be flush with the surface of the foam layer 5195 that contacts the patient so that the LED emission surface is placed as close as possible to the patient’s scalp without projecting outward to form a painful pressure point. FIG. 13L illustrates the headband 5106 being adjusted. The headband 5106 can be lengthened or shortened with position adjustment of the LEDs being illustrated by the arrows. For example, the headband may be sized for a small pediatric head 1300a, a medium pediatric head 1300b, a medium adult head 1300c or a large adult head 1300d.

[0242] FIGs. 13M-O illustrates the LED modules 5110 attached to the headband 5106. FIG. 13M illustrates an interior side perspective of the LED module 5110. The LED module 5110 can include an LED bezel 5150, LED housing 5152, LED circuit board 5154, an IP membrane film 5156 that is a porous material such as a fabric or polymer barrier allowing air to pass through and heat to escape, and an aluminum cover 5158 having apertures for heat transmission away from the patients head. In some embodiments, the distance from the LED to the edge of the LED bezel or emission cone 5150 can be 3.62 mm. In some embodiments, the distance from the LED to the edge of the LED bezel or emission cone 5150 can be 4 mm. 61 ME151319109v.1132049-00217 In some embodiments, the distance to the edge of the LED bezel or emission cone 5150, including the diode, can be 6 mm. In some embodiments, the distance to the edge of the LED bezel 5150 or cone, through which light is directed onto the skin of the patient and through the portion of the cranium within the emission aperture of cone, including the diode, can be 6.4 mm. The cone can comprise an elastic polymer material to improve the comfort for the patient as well as efficient optical coupling. Thus, a range of the distance from the LED emitting surface to the outer surface of the emission cone is preferably between 3-7 mm. In some embodiments, the light emitted from the LED 5036 produces a 40 degree light cone in one example with the range can be from 25 degrees to 50 degrees for the cone of the emitted light. The circuit board 5154 can have a pad 5156 such as a fabric or polymer that transfers heat from the circuit board through the apertures of metal cover 5158. A film can be formed on the outside of cover 5158 in prevent ingress of fluids into the module or panel assembly (5152,5154, 5156, 5158) while also facilitating thermal transmission. FIG.13N illustrates the first side of the LED housing front panel 5152 which can comprise a molded polymer with a central opening for the LED to be mounted on for transmission of light through the opening 5160. The LED housing front panel 5152 can include heat dissipating castellations around the opening 5160.

[0243] The LED circuit board 5154 can include a controller 5151 on the bottom or second side of the circuit board (see FIGs. 13T and 13U) with LED current feedback, an LED driving circuit 5153 on the top or first side of the circuit board, the LED 5154 (shown in Fig. 13T), a temperature sensor 5135 (thermistor circuit), and an LED unique identification (ID) circuit includes the controller 5151 to enable recording of the LED emission data for each LED during a treatment period. The LED circuit board 5154 can also include a connector to connect via cable with either the occipital PCB or the frontal PCB. The LED driving circuit can be an LED constant current circuit 5153 that maintains the proper current output to drive the LED 5036. The temperature sensor 5135 can sense the temperature and send signals to the microcontroller. The microcontroller can halt power to the LED 5036 if an over- temperature or overheating condition is detected. The LED unique ID circuit can include a resistor bank that is set differently for each LED PCB in the system. The occipital PCB or frontal PCB can use the LED unique ID circuit to identify each connected LED PCB upon connection or at a subsequent time. The LED circuit board 5154 can include a microcontroller reset switch that is not operator accessible but can be used during initial setup 62 ME151319109v.1132049-00217 or repair. The LED circuit 5154 also can include a controller programming port to enable debugging or reprogramming of the LED circuit board 5154.

[0244] FIGs. 13P-R illustrate secondary LED modules 5510’ attached to the occipital mount 5102. The secondary LED modules 5510’ are in accordance with some of the embodiments described herein. FIG. 13P illustrates an interior side perspective of the LED module 5110’. In some embodiments, the distance from the LED to the edge of the LED bezel 5150’ can be 7 mm. In some embodiments, the distance to the edge of the LED bezel 5150, including the diode, can be 9.4 mm. This LED module 5110’ includes a longer LED bezel mount 5150’, a curved LED housing 5162, and membrane film 5156. This LED module also includes lugs 5164 for securing the LED module 5110’ in the occipital mount 5102. FIG. 13S illustrates these LED modules 5110’ wired directly to the main circuit board 5178 via wires 5192 according to some embodiments.

[0245] FIG. 13V illustrates patient ergonomics and various head strap sizes in various embodiments taught herein. For example, anthropometrics / ergonomics are shown via a front 1301, side 1302 and back 1303 views of a patient’s skull are depicted that correspond to those areas of the cranium wherein light is transmitted to illuminate those regions of the brain that are responsive to the therapeutic delivery off light as described herein for the treatment of neurological disorders, including autism as described herein. Also shown are views of a pediatric head strap for a 2 year old 1304 and 10 year old 1306a and 1306b as well as a view for an head strap suitable for an 18 year old patient 1308. As the circumferential size of the frame must be adjusted to enable a stable placement of the light sources relative to the specific areas of the brain for treatment, a two stage process for the adjustment of the position of the light sources relative to the cranium is advantageous. The first being the manual or motorized adjustment of the frame circumference. Thus, each individual patient can have the frame properly configured to match the patient’s head circumference. In one embodiment, a manually adjusted actuator, such as a rotating element on a housing positioned on the back of the child’s head is preferred. With this position, it is difficult for the child to see, reach or operate this feature, thereby preventing the unwanted interruption of treatment. The control panel can also be positioned in the housing at the back of the user’s head to minimize access and avoid disruption of treatment. 63 ME151319109v.1132049-00217

[0246] FIG 13W illustrates an exemplary control panel 1312 in various embodiments taught herein. The control panel includes indicator lights for power 1314, Bluetooth connectivity 1316, IR status 1318 and battery power level 1319.

[0247] FIG 13X illustrates exemplary connector pod locations 1320 in various embodiments taught herein.

[0248] FIG. 13Y illustrates exemplary wiring channels 1322 in various embodiments taught herein. These channels contain and organize wires connecting the individual light emitting panels and sensors that reside on the frame.

[0249] FIG. 13Z illustrates exemplary rear LED PCB wiring in various embodiments taught herein. As shown wiring 1324 may be used to connect the five pods 1326 directly to the main PCB located within the rear housing.

[0250] FIG. 13AA illustrates exemplary capacitive touch sensor locations. For example, exemplary locations may include a first location 1330 on head strap 1332 and a second location 1334 on rear head pad 1336.

[0251] FIG. 13AB illustrates exemplary add-on LED locations in a housing in various embodiments taught herein. For example, housing 1340 may include two add-on locations 1342.

[0252] FIG. 13AC shows a front view of the head worn system having adjustable top strap or band and a circumferential strap or band that provides a frame for mounting a plurality of light emitting panels that illuminate selected regions of the brain as described herein. A rear mounted circuit panel assembly is shown wherein the circumferential frame has ends coupled to an adjustment assembly within the circuit panel assembly that can enlarge or reduce to the circumference of the frame to adjust to different head sizes of adults and children. The frame and the circuit panels can be substantially enclosed within a foam and / or fabric cover which presents a child from contacting the wiring and light emitting panels mounted on the frame. FIG. 13AD shows a rear view of the head worn system that depicts the outer surfaces of the circuit panel assembly that has one or more control panels that enable a user or an individual monitoring therapy being provided to a child to observe the operating condition of the device or control certain operations of the device. A rotating circular element can be manually or remotely turned to adjust the size of the circumferential band to adjust to different head sizes. FIG. 13AE shows a left side view of the device having 64 ME151319109v.1132049-00217 an exposed portion of the band that can be actuated to move into the rear panel assembly to reduce the head size or to move out of the rear panel assembly to increase the head size. FIG. 13 AF shows a right side view which also has an exposed portion of the band to accommodate motion of the band into, or out of, the rear panel assembly. Note that the covering for the band or frame can be temporarily opened or detached from the band or frame so that the individual light emitting panels can be individually adjusted to properly position the panels relative to the regions of the brain to be illuminated. Thus, there are two distinct ways to adjust light panel positioning, the first using the rear panel adjustment element to properly configure the head size for each patient, and a separate light panel adjustment feature that adjusts the position of one or more of the light panels to illuminate the correct regions of the brain given the specific head size requirements. FIG. 13AG shows a top view of the head worn assembly. Note that the two opposing sides of the device can have different amounts of the band exposed, or the two sides can have the same amount of the band exposed. Thus, the system enables an asymmetric distribution of light emitting panels. In one operating configuration as described herein, one side can have one or more light emitting panels, and the opposite side can have no light emitting panels or fewer light emitting panels than the opposite side. Younger children have smaller head sizes and may require illumination from only one side during a therapy session. This can reduce the risk of over illumination conditions for younger patients. FIG. 13AH shows a bottom view of the head worm assembly. FIG. 13AI shows a perspective view in which the band has had both sides extended to enlarge the head size. FIG> 13 AJ has had both exposed portions of the band retracted into the rear panel assembly to provide the smaller head size option. FIG. 13AK illustrates a configuration in which there is no top band adjustment or wherein the top band is sized for the individual patient. FIG. 13AL illustrates a rear view of the rear panel assembly in which the rotary mechanism is not used to adjust frame size, but wherein a user can adjust size by manually adusting the position of one or both sides of the band relative to the rear panel assembly. A linear actuator or lever can also be used for adjustment on the rear panel assembly.

[0253] Shown in FIG. 14 is a process sequence 900 that can be implemented with a controller on the therapeutic device or in conjunction with an external controller as described herein. The user interface is configured to receive and store patient data 902. Certain data can be retrieved manually or automatically 904 so that parameters for a therapeutic session as 65 ME151319109v.1132049-00217 implemented 906 on the PBM device. The device is actuating to illuminate vascular tissue of the patient 908 to thereby modulate blood flow within the body including the brain of the patient. This can be implemented in combination with transcranial illumination of brain tissue in selected patients, which can include transcranial illumination of blood vessels in proximity to brain tissue that is also receiving light. A record of the therapeutic session is than communicated 910 for storage and further analysis.

[0254] Further methods of the invention can include photobiomodulation of lymphatic vessels to improve drainage to treat neurological conditions. See, for example, the publication by Semyachkina-Glushkovskaya et al., “Photobiomodulation of lymphatic drainage and clearance; perspective strategy for augmentation of meningeal lymphatic functions”, Biomedical Optics Express, Vol. 11, No. 2, February 2020, the entire contents of which is incorporated herein by reference. By using PBM to augment the rate of drainage of lymphatic fluid from the brain there are improvements in transport of components that adversely impact neurological condition of the patient. Improved drainage of the lymphatic system has been shown to improve the condition of autistic patients. See Antonucci et al., “Manual Lymphatic Drainage in Autism Treatment”, Madridge Journal of Immunology, Vol.3, Issue 1, December 2018, the entire contents of which is incorporated herein by reference. Thus, methods of treatment can include transcranial PBM of lymphatic channels in the brain. The LED array elements can be actuated to illuminate lymphatic channels at the energy densities described herein to perform therapeutic treatment of the patient. Imaging technologies including Optical Coherence Tomography (OCT) and ultrasound have been used to monitor lymphatic flow as well as blood flow and perfusion.

[0255] Methods for providing photobiomodulation may include determining the light frequency, location of the LED lights (e.g., blood vessels needing increased ATP), whether ATP production increased, and the overall effect of the treatments. Accordingly, based on the determined overall effect on the brain, the photobiomodulation device 1100 may be dynamically adjusted on a user-specific basis.

[0256] FIG. 15 illustrates an exemplary circuit 1220 for operating the PBM device. This embodiment can include a wireless charging element 1222 connected to a charging coil 1224 that enables charging circuit 1226 to charge the battery 1228 which can be a 3.7 V lithium battery in this example. The circuitry can be mounted on a circuit board in which a processor such as microcontroller 1240 is connected to a battery gauge 1232, power supply regulator 66 ME151319109v.1132049-00217 1230, LED control field effect transistor 1236 and LED 1234. In this example, an LED wavelength of 850 nm is shown but other wavelengths, or different wavelengths in the red and / or near infrared range can be used at different locations on the head mounted device as described herein. The device can include an on / off switch 1242 and an LED status indicator light 1244.

[0257] Fetal Alcohol Syndrome (FACS) results from a baby being exposed to alcohol during the neonatal stage of development. The fetal liver cannot metabolize alcohol (ethanol), so when alcohol enters the blood stream of the developing baby it interferes with the delivery of nutrition and oxygen to the developing organs. Therefore, it interferes with cell growth and proliferation. Specifically, ethanol in the developing baby’s blood stream can result in permanent and irreversible brain damage. Neurological and behavioral symptoms often reflect the affected brain areas. The most common affected brain areas are the prefrontal cortex, which results in difficulties with focus, decision making and social interactions; the hippocampus can also be affected, which results in difficulties with forming memories; the cerebellum, which results in difficulties controlling movements; and also the corpus callosum, which affects overall brain function and results in mental retardation.

[0258] Brain imaging studies have specifically identified these areas (frontal lobe, corpus callosum, hippocampus and cerebellum) as being most likely to be affected by FACS. Other imaging studies showed that FACS results in poor communication between various brain areas (i.e., poor brain connectivity). Children affected by FACS usually have smaller brains. In addition, children affected by FACS may develop physical characteristics like microcephaly, growth retardation, dislocated limbs, certain facial features (e.g., thinner upper lip) and cardiological problems. It should be noted that the physiological features of FACS may or may not be present and a percentage of FACS children are misdiagnosed as having ADHD (due to their difficulties with focus, organization, planning, decision making and memories). It should also be noted that Fetal Alcohol Syndrome disproportionally affects babies in the minority communities (specifically in the Black community). No treatment is currently available for FACS.

[0259] Other drugs can also result in prenatal disposition for the development of neurological abnormalities in children exhibited after birth. Prenatal exposure to opioids and other substances poses significant risks to neurodevelopment, affecting approximately 5% of pregnancies (Behnke 2013, Lester 2004). The economic burden associated with the cognitive 67 ME151319109v.1132049-00217 and developmental impairments in children born with neonatal abstinence syndrome (NAS) to mothers with substance use disorders is substantial.

[0260] The cost of care for this population can be viewed from both the educational cost and the medical cost of care. There is a significantly greater number of claims per year from age 1 to 8 for inpatient hospitalizations, outpatient encounters, and emergency department visits, while subsequently, adjusted mean annualized costs were nearly double for all healthcare services in children with neonatal abstinence syndrome and >4 times as high as for inpatient hospitalizations compared with children without NAS (Liu et al 2019). The direct medical costs, including neonatal intensive care and longer-term healthcare services, are estimated between $700K - $1.4MM (Kalotra 2002). From the educational perspective, approximately 20% of all children born with NAS subsequently require special needs education services (Fill et al 2018), and the cost of educating a student in special education is typically estimated to be about twice that of educating a student in general education (Griffith et al 2015). In general, children with NAS are 2 to 3 times more likely to fail to attain grade- level achievement and they have higher odds of failing to attain grade-level achievement at any measured time period (Morgan et al 2019). These medical and academic struggles are likely to translate to reduced earning potential and productivity in adulthood.

[0261] Current interventions for mitigating behavioral issues caused by FAS and prenatal opioid exposure, for example, include both non-pharmacologic and pharmacologic strategies. Non-pharmacologic interventions focus on sensory and environmental modifications such as tactile stimulation, positioning aids, and creating low-stimulation environments, which are designed to improve sensory-motor integration and behavioral regulation in affected infants (Yen 2022). Additionally, techniques like infant massage and hydrotherapy have been employed to support these outcomes. Pharmacologically, Medication for Opioid Use Disorder (MOUD) during pregnancy, using drugs such as buprenorphine or methadone, aims to stabilize opioid levels in the fetus. This stabilization reduces withdrawal symptoms and is believed to improve long-term developmental outcomes by normalizing brain connectivity related to socioemotional development (Liu 2022). Behavioral interventions also play a role, focusing on long-term support for emotional and behavioral regulation by addressing attention deficits and self-regulation issues within the child's biopsychosocial context (Jaeckel 2021, Nygaard 2016). 68 ME151319109v.1132049-00217

[0262] Despite these efforts, significant gaps remain in effectively addressing the core issues associated with prenatal opioid exposure. Current behavioral therapies often fail to adequately tackle hyperactivity, attention deficits, and self-regulation problems. Furthermore, MOUD can lead to side effects that may result in neurodevelopmental conditions in children. The availability of therapists is limited, and many families discontinue treatment due to insufficient progress, financial constraints, and the challenges of managing the effects of opioid exposure. Additionally, early intervention services are only available until the age of three, with inconsistent availability across states. Although brain stimulation technologies like transcranial magnetic stimulation (TMS) show promise, they are not yet available for young children. These gaps highlight the need for innovative solutions that can more effectively address the complex needs of children affected by prenatal opioid exposure.

[0263] The photobiomodulation devices described herein address motor, language, and neurocognitive delays in young children exposed to a drug or polysubstances, including opioids such as fentanyl, in utero. This solution features a data-driven system comprising a wearable headband that delivers near-infrared light therapy through the scalp and the cranium of the patient, integrated EEG sensors to assess the therapy's impact, and as described generally herein can optionally be configured to include a machine learning (AI) personalized software platform. The AI platform gathers and analyzes data from the head mounted sensors and parent / therapist assessments to evaluate the effectiveness and guide the use of transcranial photobiomodulation (tPBM) therapy. By utilizing computational machine learning methods, the system can predict each child's developmental trajectory and provide tailored treatment recommendations and automated control of headworn device operation.

[0264] The transcranial photobiomodulation (tPBM) therapy system with real-time EEG monitoring and AI directed personalization is configured for children with cognitive delays due to in utero drug exposure, addressing a critical gap in current therapeutic options. The integration of an AI-enabled software platform with the physical device creates a feedback loop that enhances treatment efficacy and personalization, potentially transforming lifelong outcomes for affected children. By offering a home-based solution, this technology not only provides improved therapeutic results but also facilitates ease of use for families, promoting treatment continuity and reducing caregiver burden. The system provides critical data on the safety and efficacy of tPBM in very young children, thereby reducing symptoms akin to ADHD and ASD in this population. 69 ME151319109v.1132049-00217

[0265] EEG-guided tPBM has applications beyond opiod-exposure consequences, including ADHD, cerebral palsy, epilepsy, and traumatic brain injury in children. It may also enhance cognitive functions in typically developing children. In adults, tPBM is being explored for neurological and psychiatric conditions such as Alzheimer's disease, Parkinson's disease, stroke recovery, depression, and anxiety disorders. Disclosed embodiments prioritize lightweight comfort for daily therapy and home-use flexibility, making it uniquely suitable for young children. Preliminary data indicates that EEG-guided tPBM can reduce autism symptoms (Fradkin 2024). The system provides real-time monitoring of tPBM delivery and its effects on the child’s brain, providing valuable insights into its therapeutic potential. This comprehensive data collection provides personalized therapeutic dosages tailored to individual needs. Children exposed in utero often face substantial neurodevelopmental delays, impacting cognitive, motor, and language skills. Studies indicate that prenatal opioid exposure is linked to lower cognitive scores and motor development issues as early as six months, with these challenges persisting into adolescence (Yeoh et al. 2019; Lee et al. 2023). These children frequently have smaller brain volumes, correlating with reduced intelligence and cognitive abilities (Yeoh et al. 2019, Balalian et al. 2023). Longitudinal studies show that these deficits can persist or worsen over time, with exposed boys showing stable deficits and girls experiencing increasing difficulties (Nygaard et al. 2015). In addition to cognitive delays, children exposed to opioids in utero often experience motor delays. Research has linked prenatal opioid exposure to lower motor scores and cognitive development issues, particularly noticeable from 6 months to 6 years of age (Yeoh et al. 2019; Fong et al. 2024). Methadone-exposed children are more likely to experience motor delays, with about one-third affected (Vassoler & Miller, 2021). Long-term effects include smaller brain volumes and structural abnormalities associated with cognitive and motor deficits (Yen & Davis, 2022).

[0266] Symptoms of in utero exposure to opioids can resemble features of Autism Spectrum Disorder (ASD), such as attention deficits and social interaction challenges (Fong, 2024; Dunn et al. 2023), especially in low-income populations (Azuine et al. 2019). Opioid- exposed children often exhibit increased emotional and behavioral difficulties, including aggression and conduct issues compared to non-exposed peers (Jaekel et al. 2021). They also show strong associations with attention problems akin to ADHD and struggle with emotional dysregulation and self-regulation issues. Prenatal opioid exposure is associated with specific 70 ME151319109v.1132049-00217 behavioral traits in children, primarily characterized by increased emotional and behavioral difficulties. These include, amongst others: a) externalizing behavior problems: Opioid- exposed children often exhibit higher levels of aggression and conduct issues compared to non-exposed peers (Jaekel et al 2021); b) Attention Deficits and ADHD Symptoms: There is a strong association with attention problems and symptoms akin to ADHD, which tend to persist or worsen over time (Jaekel et al 2021); c) Emotional Dysregulation: Children may struggle with regulating emotions, leading to increased emotional difficulties as they age (Jaekel et 2021); d) Self-Regulation Issues: Problems with self-regulation, including arousal, attention, affect, and action, are common, reflecting broader challenges in behavioral control. These traits suggest a complex interplay between prenatal exposure and postnatal environmental factors influencing long-term behavioral outcomes.

[0267] Prenatal opioid exposure significantly impacts brain structure and function in children. It often results in decreased brain volumes in regions like the thalamus, insular white matter, and brainstem (Greco et al. 2022). Functional connectivity studies reveal alterations in network dynamics affecting visual, subcortical, and default mode networks (Jiang et al. 2022), linked to cognitive and motor development issues such as lower IQ scores (Yeoh et al. 2019). Additionally, prenatal opioid exposure may disrupt neurotransmitter systems and reduce neurogenesis, contributing to long-term neurodevelopmental challenges (Jiang et al. 2022; Yeoh et al. 2019; Vishnubhotla et al. 2022).

[0268] Opioid-exposed infants exhibit altered functional connectivity, particularly affecting inter-network connections involving visual, subcortical, and default mode networks (Jiang et al. 2022; Greco et al. 2022). These changes are associated with reward-related frontal-sensory connectivity alterations and socioemotional development pathways (Liu et al. 2022). Increased connectivity between the amygdala and cortical regions like the medial prefrontal cortex has been observed in these infants (Radhakrishnan et al. 2020), indicating higher rates of potential visual and emotional problems.

[0269] In utero exposure also leads to significant neuroinflammation and developmental issues in children. Prenatal opioid exposure is associated with neonatal abstinence syndrome (NAS), characterized by withdrawal symptoms such as tremors and irritability (Yen & Davis, 2022). Long-term effects include reduced cognitive scores, lower birth weight, smaller head circumference, and altered brain structure (Yen & Davis, 2022). As they grow, these children exhibit motor impairments, inattention, hyperactivity, emotional dysregulation (Bunikowski 71 ME151319109v.1132049-00217 et al., 1998; Guo et al., 1994; Hickey et al., 1995; Ornoy et al., 1996), aggression, attention deficit issues, emotional dysregulation, and conduct problems (Jaekel et al., 2021). Co- exposure to substances like benzodiazepines and tobacco can exacerbate these effects (Ross et al., 2015), highlighting the need for further research into these outcomes.

[0270] None of the current treatment approaches for children exposed to polysubstances while in utero described above address the disconnectivity of the Default Mode Network (DMN). The DMN plays a significant role in neurodevelopmental disorders, influencing various cognitive and behavioral functions. In conditions such as autism spectrum disorder (ASD), the DMN shows atypical functional connectivity, particularly affecting social communication and introspection. The DMN generally refers to those regions of the brain that remain active during resting states when an individual is not consciously engaged in a specific activity. The DMN is generally considered to include the dorsal medial prefrontal cortex, the posterior cingulate cortex, precuneous, and angular gyrus. The precuneous region, for example, is associated with numerous cognitive functions including motor imagery, visuo spatial imagery and memory. Studies have found altered connectivity patterns within the DMN in individuals with ASD, which may contribute to difficulties in social interactions and self-referential processing (Yu et al, 2021) Additionally, the DMN is implicated in attention- deficit / hyperactivity disorder (ADHD), where atypical connectivity might be linked to distractibility and challenges in cognitive control (Choi et al 2021). These findings indicate that DMN dysfunctions are a common feature across multiple neurodevelopmental disorders, impacting core symptoms related to internal cognitive processes and social functioning. Therefore, by stimulating DMN in order to increase its functional connectivity this system can address core neurodevelopmental, cognitive and behavioral difficulties of children with in utero exposure to opioids and other substances that impact cognitive function.

[0271] Transcranial photobiomodulation (tPBM) can improve brain connectivity, which is beneficial for enhancing cognitive and neurological functions. Studies indicate that tPBM can modulate neuronal synchronization and connectivity, particularly enhancing EEG alpha and beta rhythms, which are crucial for cognitive processing (Wang et al, 2021, Shahadadian et al, 2022). tPBM has been shown to increase functional connectivity in brain networks, such as the frontal-parietal network, improving information processing speed and network efficiency (Dmochowski et al, 2020). Additionally, it enhances local information integration and complexity of brain networks, particularly in the frontal regions (Shahadidan et al 2022). 72 ME151319109v.1132049-00217

[0272] Transcranial photobiomodulation (tPBM) modulates the default mode network (DMN, affected by in utero exposure to substances), which is crucial for internally directed cognition and creativity. Studies indicate that tPBM can enhance connectivity within the DMN, thereby improving cognitive functions related to mind-wandering and divergent thinking. By targeting specific brain areas within the DMN, tPBM improves the network's activity, enhancing creative thinking and reducing anxiety, which is often negatively associated with creativity. These effects indicate that tPBM can be a useful tool for cognitive enhancement through modulation of the DMN (Shahadian et al, 2022; Urquhart et al, 2020; Peña et al, 2023).

[0273] Transcranial photobiomodulation (tPBM) affects the DMN by modulating neural oscillations and enhancing functional connectivity. Studies using near-infrared light at specific wavelengths and frequencies have shown that tPBM can increase the power of higher frequency brain oscillations, such as alpha, beta, and gamma, while reducing lower frequencies like delta and theta in the resting state (Urquhart et al 2020; Zomorrodi et al, 2019). This modulation of oscillatory activity can improve the integration and segregation of brain networks, as measured by inter-regional synchrony and graph theory metrics (Zomorrodi et al 2019). Additionally, tPBM has been demonstrated to target key DMN regions, thereby enhancing cognitive functions associated with this network (Peña et al 2023).

[0274] Transcranial photobiomodulation (tPBM) influences neural oscillations in the default mode network (DMN) by modulating the power of specific frequency bands. A study using tPBM with near-infrared light (810 nm) pulsed at 40 Hz demonstrated significant increases in the power of higher frequency oscillations, such as alpha, beta, and gamma waves, while reducing the power of slower frequencies like delta and theta during resting state (Zomorrodi et al, 2019). These changes suggest enhanced integration and segregation of brain networks, as evidenced by increased inter-regional synchrony and altered network properties assessed through graph theory metrics (Zomorrodi et al, 2019)

[0275] Transcranial photobiomodulation (tPBM) operates primarily through the following mechanisms: 1. Mitochondrial Activation: tPBM stimulates mitochondrial cytochrome c oxidase, enhancing ATP production, which boosts cellular metabolism and energy availability in neurons (Pruitt et al, 2021). 2. Neurovascular Modulation: It promotes increased cerebral blood flow and oxygen consumption, facilitating better nutrient delivery to 73 ME151319109v.1132049-00217 brain tissues (Lin et al 2024). 3. Neurotransmitter Release: tPBM can influence the release of neurotransmitters, which may enhance synaptic plasticity and improve communication between neurons (Hong et al 2024). 4. Anti-inflammatory Effects: The therapy activates anti- inflammatory pathways, reducing oxidative stress and promoting neuronal survival (Hong et al, 2024, Lin t al 2024). 5. Modulation of Neural Oscillations: tPBM alters neural oscillatory patterns, particularly increasing alpha and beta frequencies while decreasing delta and theta frequencies, which can enhance cognitive functions (Wang et al 2019, Chaudhary et al, 2023). Thus, tPBM can enhance the functional connectivity of the DMN, and improving cognitive functions associated with this network.

[0276] Consequently, tPBM can enhance motor function, as evidenced by significant improvements in grip strength, reaction time, and overall motor performance among athletes with a history of concussive injuries after an 8-week tPBM regimen. Improvements were noted in various cognitive metrics, including reaction time and information processing speed, alongside enhanced sleep quality and reduced psychiatric symptoms (Gaggi et al 2024). Furthermore, Fradkin et al (2024) showed that using tPBM with the present system for the treatment of autistic children as young as 24 months old, improves their symptoms of ASD and redistributes their brain oscillations (from slowest to fastest).

[0277] Cognitive and motor deficits in children who were exposed in utero to various substances - result from disbalances in the connectivity of the DMN, which makes their conditions similar to many other disorders (including ASD) that also correlate with DMN disbalances. Stimulating the DMN via tPBM can be safe and effective method for reducing neurodevelopmental delays caused by in-utero-exposure to various substances. Several issues however needs to be addressed to make this modality feasible. 1. Usability (the device needs to be comfortable to be tolerated by these neonates). 2. Safety: As neonates have their fontanellas open. therefore, the illumination pattern for targeted areas should exclude fontanelas but still include the cortical nodes of the DMN. 3. Additionally, treatment parameters (pulse, optical power, illumination time for each treatment session and the time between sessions etc) need to be modified to make it safe and effective for this specific population.

[0278] The devices and methods described herein demonstrate that safe treatment of autism symptoms in 2-7-year-old children for safe use in 6 month-3-year-old infants, certain design modifications are preferred to ensure that the device meets the anatomical and 74 ME151319109v.1132049-00217 physiological needs of this age group, and to integrate safety features to enhance device safety and prevent injury or misuse.

[0279] The device includes safety sensors, including ambient light detection one or more light sensors on the headworn device, EEG and heart rate variability (HRV) or heart rate sensors to monitor the environment and infant physiology during treatment, and automatically shut off the device when it is removed from the infant’s head. The heart rate sensor can comprise a green LED light source Pressure sensors on the circumferential strap that secures the device onto the patient can also be used to prevent excessive cranial pressure on the infant’s head.

[0280] Data analysis includes a quantitative analysis focusing on sensor accuracy and reliability, with thresholds set for activation (e.g., automatic shutoff if removed for >0.1 seconds, or after 10 seconds or 20 seconds to enable a caregiver to reposition the headworn device on the patient during treatment). Sensor outputs provide reference data for consistency across different protocols to ensure robust safety monitoring.

[0281] As there can be difficulty with EEG and HRV readings due to movement or condition of the patient, proper sensor placements relative to the cranium and / or signal stabilization techniques can be implemented for reliability and control of the headworn device during treatment sessions. Motion sensors such as MEMS accelerometers and / or gyroscopes can be mounted on the headworn device to track movement and disruption of treatment and can be used to automatically shut off the system. The motion sensors, and other sensors, can be used to flag, annotate or label portions of data being recorded as associated with movement, activity or condition of the patient before, during or after treatment.

[0282] The present system can provide optimization of the light dose through COMSOL finite element modeling for infant-specific treatment parameters. Due to thinner skulls, scalps, and potentially less hair coverage, infants can experience higher light transmission than older children and adults. Achieving an appropriate dose range for infants is essential to prevent tissue heating, overtreatment, and / or potential side effects such as headache, and ensuring that the device can progress safely through the prescribed treatment for each patient.

[0283] Using validated finite element modeling methods (FEM) of light transmission in tissue (COMSOL Multiphysics®. Version 6.2. COMSOL AB, Stockholm, Sweden), the system can simulate light propagation through the scalps and skulls of children over the age 75 ME151319109v.1132049-00217 range of significant cranium development from birth until the age of 5 years old, for example. 1-year-old infants can have cranial development characteristics, such as a specific range of thickness and density variation that can be mapped in relation to the cranial illumination zones selected for illumination to treat a specific condition as described herein. This can also be used to model sensor performance, such as EEG measurement calibration based measurements in infant and prenatal children to assist with optimal electrode placement to measure weak EEG signal that are diagnostically significant for various regions of the brain being illuminated for treatment. Monitoring brain activity is important specifically with respect to children at risk for experiencing seizures during treatment which can include prenatal infants who have been exposed to drugs or polysubstances prior to birth.

[0284] Using the models, the system operation can be simulated to estimate tissue temperature increases resulting from light absorption and thus define upper limits on illumination parameters for different regions of the brain undergoing therapeutic treatment as described herein. These methods of light transmission and light-absorption driven tissue temperatures in the cranial tissues of 2-7-year-olds and adults, can determine the maximum infant skin-applied light irradiance that delivers to the infant’s brain a dose equivalent to that found safe in feasibility studies in Major Depressive Disorders (MDD) and Autism Spectrum Disorder (ASD).\

[0285] Data analysis of light transmission and absorption driven calculations in infants can be verified using previously developed models as described herein. Dosing analysis can include comparison to existing dosimetry and dose-response data in MDD and ASD to set specific device parameters for infant use.

[0286] Multiple skull / scalp thickness and skin color models have been created to address this variability. Precise, infant-safe effective light dosage in 2-6-year-olds and older populations are used to personalize the treatment as computed tomography can be used to measure the cranial characteristics of individual patients. Acceptance criteria can include alignment with existing safe dosimetry data and less than + / - 10% deviation in dose accuracy.

[0287] The device implementations can be tested to comply with all applicable device safety standards. Testing by NRTL (at Nemko, Carlsbad, CA, for example) is conducted to verify the device’s safety and usability by a Nationally Recognized Testing Laboratory (NRTL) that confirms regulatory compliance, clinical acceptance, and commercialization readiness. The testing confirms adherence to applicable safety standards, ensuring the device’s integrity and 76 ME151319109v.1132049-00217 reliability in clinical settings. This testing can be necessary for FDA and other regulatory approvals: Electrical Safety (IEC 60601-1); Electromagnetic Compatibility (IEC 60601-1- 2); Biocompatibility (ISO 10993); Usability Engineering (IEC 62366); and Photobiological Safety (IEC 62471). The head mounted transcranial photobiomodulation (tPBM) device, tailored specifically for neonates. The system incorporates additional features including adjustable light dosage parameters, biometric monitoring sensors, and user interfaces designed for ease of use in neonatal care settings that are suitable for this sensitive patient population.

[0288] The device manages the dose-response relationship to ensure that the device is capable of delivering an effective therapeutic dose without causing overexposure. The biometric sensors integrated into the device to track real-time physiological responses. This includes monitoring heart rate, skin temperature, and other biomarkers that will inform the personalized dosing protocol during each treatment session. The user interface for healthcare providers, is configured for intuitive and easy to operate use in a neonatal intensive care unit (NICU) environment. The interface enables clinicians to monitor treatment progress, adjust settings, and review real-time feedback from the device’s sensors. This system ensures that the device can be used safely and effectively with integration into existing neonatal care protocols by a range of healthcare professionals, from neonatologists to neonatal nurses so that neonatal intensive care units (NICUs) or pediatric neurology departments can employ the system. This includes the aggregation of logged EEG data indicating improvements in neural activity related to cognition, sleep, and emotional regulation during treatment, to establish the efficacy of tPBM in this population.

[0289] tPBM (stimulation of the brain with near-infra red light) has been shown in animal and human studies (in vivo and in vitro) to increase blood oxygenation, cerebral blood flow, and mitochondrial ATP production. In addition, EEG and NIRS data has shown that tPBM improves brain connectivity. Therefore, blood brings more oxygen and nutrition to the brain. In addition, increased ATP production results in more neurogenesis and synaptogenesis. Furthermore, functional brain connectivity has been shown to improve after one session. tPBM has been shown to be beneficial for traumatic brain injury, depression, ischemic stroke, and Parkinson’s disorder. In addition, it has been shown to be effective for Down syndrome, autism and ADHD. Similarly, tPBM can be effective for the neurological symptoms of FACS and other pre-birth exposure to drugs impacting neurological development by increasing the 77 ME151319109v.1132049-00217 amount of oxygen and nutrients delivered to the brain, improving functional brain connectivity, and increasing neurogenesis and synaptogenesis. Specifically, the effect may be most pronounced in cortical structures (frontal lobes), which improves organization, focus, and decision making. The effect on memory and motor functions may be less pronounced since sub-cortical structures are implicated (e.g., hippocampus and cerebellum). However, due to neuroplasticity, the beneficial effect of tPBM may be most pronounced when treatment is administered to young children.

[0290] In order to configure the headset, the individual light sources or arrays must be configured to illuminate selected regions of the brain that will address the condition of the patient to be treated. As described previously herein, regions of the brain, as shown in Fig. 17B, can be selected for treatment including the frontal lobes 3040, the parietal lobes 3042, the occipital lobes 3044, the cerebellum 3046 adjacent to the brain stem 3050, the temporal lobes 3049, and the hippocampus 3048. As noted previously, light sources such as LEDs or arrays thereof can be mounted to a headset so as to contact the tissue surface so as to illuminate selected areas. The cerebellum 3046, for example, can be illuminated by LEDs 3052, 3054 positioned adjacent to the occipital lobes and / or the cerebellum so as to treat selected areas. These LEDs can also be used the illuminate the brain stem 3050, or additional LEDs can be positioned to directly illuminate the brain stem without light passing through the cerebellum, for example. Note that in circumstances where therapy for treatment of the brain stem requires an adjustment to illuminate only specific areas of the brain stem, the LEDs may include a lens to focus the light on selected regions, or the illumination pattern and wavelength can be adjusted to precisely illuminate a specified sub region of the brain stem. Brain imaging methods can be used to map brain inflammation, for example, that can contribute to the disorder being treated and thereby used to select areas of the brain for treatment.

[0291] Neuroinflammation is a response that involves neurons, microglia and macroglia, which are cells that are present in the central nervous system (CNS) (Bradl and Hohlfeld, “Molecular Pathogenesis of Neuroinflammation”, J. Neuro Neurosurg Psychiatry; 74:1364- 1370 (2003); Carson et al., 2006a). Neuroinflammation has been reported to characterize many neurodegenerative diseases and neuropsychiatric conditions such as multiple sclerosis, narcolepsy, AD, Parkinson’s disease (PD), and ASD (Carson et al., 2006b; Frick et al., 2016). Autistic individuals often show signs of altered inflammatory responses and neuro-immune 78 ME151319109v.1132049-00217 system abnormalities throughout life, which implicates a potential role of inflammation in the etiology of ASD. This is further confirmed by increasing clinical and experimental evidence that links altered immune and inflammatory responses with the pathogenesis of ASD (Lucchina and Depino, 2014). Moreover, post mortem studies have supported this hypothesis, documenting substantial neuroinflammation in several brain regions of patients with ASD (Vargas et al., 2005).

[0292] During pregnancy, both environmental and genetic risk factors may affect inflammatory response of newborns, hence altering postnatal brain development (Adams- Chapman and Stoll, 2006). These genetic and environmental factors can directly elicit chronic neuroinflammation which in turn may modulate neuronal function and immune response via glia activation, or directly by affecting neuronal function (Depino, 2013) (See Figure 2). Valproic acid (VPA), as an environmental risk factor, elicited activation in different brain regions, with evidence of long-lasting glia activation in the hippocampus and the cerebellum (Lucchina and Depino, 2014). The hippocampus (Depino et al., 2011) and cerebellum (DeLorey et al., 2008; Martin et al., 2010) are two brain regions linked to autism- related behavior, namely, limited social interaction and repetitive behaviors. Additionally, several studies showed that altered social behavior in adult mice may be due to cerebellar inflammation as the cerebellum is considered to be involved in executive and cognitive functions (Shi et al., 2009; Koziol et al., 2014; Lucchina and Depino, 2014; Wang et al., 2014). Furthermore, this evidence suggested that astrocyte and microglia activation in the cortex and cerebellum increase expression of cytokines, including IL-6, TNF-α, MCP-1, TGF-β1, IFN-λ, interferon gamma, IL-8, and other associated genes involved with the immune response in different brain regions of autistic subjects (Vargas et al., 2005; Chez et al., 2007; Garbett et al., 2008; Li et al., 2009; Chez and Guido-Estrada, 2010). Alternatively, both these environmental and genetic factors can chronically alter immune response through increasing production of free radicals, which consequently activate glia cells, increasing the inflammatory response and then affecting neurons, thus mediating clinical symptoms of autism (Depino, 2013). These results suggest that reducing brain inflammation in the targeted brain areas (e.g., prefrontal cortex, cerebellum), can alleviate behavioral symptoms of ASD.

[0293] Near infrared light has an anti-inflammatory effect on distressed cells. When such light is used to illuminate oxidatively stressed cells or in animal models of disease, ROS levels are lowered. PBM is able to up-regulate anti-oxidant defenses and reduce oxidative 79 ME151319109v.1132049-00217 stress. It was shown that PBM can activate NF-kB in normal quiescent cells, however in activated inflammatory cells, inflammatory markers were decreased. One of the most reproducible effects of PBM is an overall reduction in inflammation, which is particularly important for disorders of the joints, traumatic injuries, lung disorders, and in the brain. PBM has been shown to reduce markers of M1 phenotype in activated macrophages. Many reports have shown reductions in reactive nitrogen species and prostaglandins in various animal models. PBM can reduce inflammation in the brain, abdominal fat, wounds, lungs, spinal cord.

[0294] Children and adolescents with autism often have an enlarged hippocampus. Individuals exhibiting autistic behavior have decreased amounts of brain tissue in parts of the cerebellum.

[0295] Default mode network (DMN) is under-connected in ASD (Ha et al, 2015). Stimulating nodes of DMN simultaneously increases functional connectivity. Stimulating Cerebellum reduces activation of microglial cells in that region (which is specifically affected by ASD).

[0296] The cluster-treatment mapper (CTM) takes the individual’s User Profile Model (UPM) vector and maps it into the clusters identified in the Embedded Cluster Predictor (ECP) to identify the optimal treatment options based on the Reference Population Module (RPM). It then feeds the identified cluster into the Personalized Treatment Module (PTM) for further processing.

[0297] The entire process for an individual is captured in the flowchart in FIG. 17A. User’s data is captured by the UPM, and an neuro-developmental assessment is performed 3006. The PTM leverages the Machine Learning Module (MLM) and the RPM to identify ideal treatments using the Neuromodulation Treatment Module (NMT) and Cognitive Programming Module (CPM) modules. Once the user engages in the treatment, the Sensor and Quanitative Feedback Module SQD module records data on the effect of the treatment, and the Performance Progress Module (PPM) assesses the effectiveness of the treatment, recording all the activities back into the UPM.

[0298] Preferred embodiments employ EEG measurements of the cerebellum to characterize improvements in patient function over the course of a series of treatment as described herein. As the cerebellum has been studied extensively in relation to ASD (see 80 ME151319109v.1132049-00217 D’Mello et al., “Cerebro-cerebellar circuits in autism spectral disorder”, Frontiers in Neuroscience; Nov. 2015, Vol. 9, Art. 408 and van der Heijden et al. “Abnormal Cerebellar Development in Autism Spectrum Disorders”, Dev Neurosci 2021; 43:181-190, the entire contents of each of these references being incorporated herein by reference), the measurement of changes in function by NMR and EEG demonstrate efficacy of the treatment methods as described herein.

[0299] Changes in skin color can be addressed in preferred methods described herein by classifying each patient by reference to a known scale of variance in skin pigmentation. See, for example, Everett et al., “Making Sense of Skin Color in Clinical Care”, Clin Nurs Res., 2012 November; 21(4); 495-516, which employed a spectrophotometer to establish a reference scale with white skin as reference color. The following example illustrates treatment protocols for a white reference color at a selected percentage of maximum light delivery that is used for a darker color of skin at an opposite end of the scale.

[0300] Example of changes in light delivery protocol for different skin colors: White Skinned Patient: Session 1: 2 minutes at 75% of total power of the device Session 2: 4 minutes at 75% of total power of the device Session 3: 6 minutes at the 75% of total power of the device Session 4: If hyperactivity is moderate to severe, stay at 6 minutes until session 16. If hyperactivity is minimal and mild then 8 minutes. Session 5: If hyperactivity is moderate to severe after session 4, then remain at 8 minutes until session 16. If mild hyperactivity, 10 minutes until session 16. Once full dosage is reached, 12 sessions of treatment are administered. Dark Skinned Patient: Session 1: 2 minutes at 100% of total power of the device Session 2: 4 minutes at 100% of total power of the device Session 3: 6 minutes at 100% of total power of the device Session 4: 8 minutes at 100% of total power of the device Session 5: 10 minutes at 100% of total power of the device Session 6: If strong hyperactivity, stay at 10. If hyperactivity is mild, raise to 12 and stay at 12 until session 18. 81 ME151319109v.1132049-00217 Once full dosage is reached, 12 sessions of treatment are administered.

[0301] Thus a scale of 1-10 can be employed, for example, in which each number on the scale corresponds to a percentage of 100% of the illuminating power that can be delivered by the device within an established safety limit for which the controller on the headset is programmed.

[0302] FIG. 18A illustrates a method 3600 for therapeutic photobiomodulation for treatment of diseases or disorders in accordance with some embodiments described herein. The method 3600 includes positioning a head mounted device 5000 on a patient’s head (step 3602). The head mounted device 5000 includes a plurality of light emitting devices 5036, a power distribution circuit board 5040, 5050, a memory 5042, and a battery 5060 providing power to the plurality of light emitting devices 5036. The memory 5042 includes instructions to control the emission of light by the plurality of light emitting devices 5036 during a therapeutic period. The method 3600 includes controlling a power output of each light emitting device 5036 in the plurality of light emitting devices using the power distribution circuit board 5040, 5050 according to the instructions in the memory 5042 (step 3604). The plurality of light emitting devices transmits illuminating light through a cranium of the patient at a near-infrared or infrared wavelength to deliver optical power to tissue within the cranium during the therapeutic period. The method 3600 includes a step of monitoring an operating condition of the head mounted device 5000 with a sensor (step 3604). The sensor can be a pressure, temperature, current, optical, or motion sensor in various embodiments. The sensor can measure acceleration or orientation in some embodiments similar to the accelerometer 5045 employed by the patient detection module 5046. Monitoring the operating condition can include detecting adverse events such as monitoring whether the head mounted device 5000 has been removed (advertently or inadvertently) from the patient’s head, monitoring temperature to determine if the device is overheating and / or causing the temperature of the tissue being illuminated to exceed a threshold, or monitoring optical power or current to determine whether the device is transmitting too great of an intensity of optical power. The method 3600 can also include transmitting a signal to the power distribution circuit board 5040, 5050 upon sensing a change in the operation condition of the head mounted device 5000 (step 3608). For example, a signal can be sent to the power distribution circuit board 5040, 5050 to stop power upon sensing a parameter that indicates an adverse operating condition. Conversely, a signal can be sent to 82 ME151319109v.1132049-00217 the power distribution circuit board 5040, 5050 that enables power distribution to the LEDs if the sensor detects that the operating condition is safe (i.e., no errors or warnings). The method 3600 also includes an optional step of storing a data record of the therapeutic period for the patient in the memory 5042 (step 3610). In such an embodiment, the memory 5042 can be non- volatile (e.g., EEPROM or solid-state storage) or the memory 5042 can be volatile memory such as any of the various forms of random access memory (RAM).

[0303] The treatment of additional neurological conditions can be addressed using further treatment methods as described herein. Note that many individuals exhibiting symptoms within the autism spectrum also experience seizures that can also be treated using the devices and methods described generally herein. Seizures are clinically manifested by periodically recurring uncontrolled movements that can include a temporary loss of consciousness or reduced awareness and are classified as epilectic. The abnormalities giving rise to seizures associated with autism spectrum disorders have been studied in connection with metabolic disorders. See, for example, Frye et al, Neuropathological Mechanisms of Seizures in Autism Spectrum Disorder, Frontiers in Neuroscience, 10: 192 (2016), which describes the disorders that give rise to both seizures and ASD. Mitochondrial disease has also been associated with ASD patients exhibiting treatment resistant epilepsy. See Frye, Metabolic and mitochondrial disorders associated with epilepsy in children with autism spectrum disorder, Epilepsy & Behavior, Vol. 47, pps 147-157 (June 2015).

[0304] Certain regions of the brain are known to be associated with seizures in ASD patients. As shown in Fig.18B, a process sequence 3800 is an illustrative example for treating a patient having seizures by delivering transcranial illumination to these regions including the cortex and hippocampus, for example. The left side of the temporal regions of the brain are associated with language development. This asymmetric characteristic is associated with delays in language development which occur in children exhibiting ASD. A subset of these children also experience seizures which can further delay language development. Thus, a user can select from a plurality of treatment protocols stored in a system for treating a patient in which the user can open a window listing protocols and select an authorized protocol for a particular patient. Thus, by actuating a user interface 3802 on a computing device, the user selects a therapeutic application for treatment of a patient having seizures, the computing device communicating with a head worn device for delivering light to the patient. 83 ME151319109v.1132049-00217

[0305] The treatment can include the step of first measuring the EEG of a patient 3804 including a Delta wave signal of an epileptic seizure to detect a region of the brain exhibiting an increase in the power of the measured Delta wave signal is a diagnostic indicator, for example. An increase in frequency of the epileptiform associated with seizures can also be an indicator.

[0306] Based on the EEG measurements, instructions 3806 from the computing device are transmitted to a controller on the head worn device to treat the patient with transcranial illuminating light from one or more light sources on the head worn device as described herein that emit a near-infrared wavelength of light wherein illumination parameters are selected based on an increase in the power of the measured Delta wave signal and / or a measured change in the frequency of the epileptiform at the detected region or location in the brain.

[0307] By monitoring 3808 further changes in the measured power of the Delta wave signal and / or the frequency of the epileptiform in response to the transcranial illumination during one or more treatment sessions, the effectiveness of the treatment can be assessed. The system stores the data in a memory and can be analyzed to determine if further treatment should be continued.

[0308] If further treatment is delivered to the patient, the prior illumination sequence can be repeated or the illumination parameters can be adjusted 3810 in subsequent treatment sessions with a further transcranial illumination period.

[0309] As the use of language by children being treated by the methods described, the monitoring of natural language development can be used to monitor the results of treatment. By recording the use of verbalized language in children before and after each treatment session, the recording language can be processed to classify language utilization by each child and monitor the effectiveness of the treatment protocols with the monitoring of changes in verbalization. The communication protocol described herein between the headset and the computing device paired with the headset enables the transmission of audio data and audio files to record utterances, words and sentences by the child wearing the headset having a microphone. The user can also actuate audio files remotely so that the child hears this audio stimulation selected to elicit a response. Machine learning tools can be trained and utilized to characterize utterances used over time during a series of treatment sessions and treatment protocols adapted to improve language utilization over time. 84 ME151319109v.1132049-00217

[0310] Communication between the photobiomodulation headgear and an external controlling computing device may take place using a number of wireless techniques. In a preferred embodiment, the communication takes place via the Bluetooth Low Energy (BLE) protocol between a BLE-equipped headgear and a nearby BLE-equipped computing device. In other embodiments, the communication may take place using other connectivity techniques such as, but not limited to, Bluetooth, WiFi, or Wireless Medical Telemetry Service.

[0311] The BLE protocol is a power-saving variant of the conventional Bluetooth standard. BLE is a wireless personal area network technology that operates in the 2.4GHz short-range radio frequency band using a transmission technique known as frequency- hopping spread spectrum (FHSS). There are a number of differences between BLE and classic Bluetooth. Among those differences, BLE uses less power using 0.01 to 0.5 watts rather than the 1 watt used by traditional Bluetooth and BLE operates at a (lower) maximum speed of 2 Mbps (and frequently less) rather than the maximum 3 Mbps attainable with classic Bluetooth. BLE has a nominal maximum range of under 100 meters. BLE provides 128 bit Advanced Encryption Standard (AES) in CCM mode. Another major difference with BLE is that data communication between BLE-enabled devices happens in short bursts after which the BLE device goes back to sleep until needed. In contrast, in classic Bluetooth the radio stays on (unless switched off) and is designed for continuous communication that may last for hours. While the use of classic Bluetooth or other communication methods by the headgear should be considered within the scope of the present invention, the power savings afforded by BLE make it a particularly apt choice for use by embodiments.

[0312] Figure 18C depicts an exemplary logical BLE protocol stack 1830 that includes a host layer and controller layer. The host and controller layers may be implemented separately on separate chips, but preferably are implemented in an integrated fashion on a single chip via a System on a Chip (SoC). An application layer (not pictured) holds GATT-based profiles and applications and sits on top of the host stack.

[0313] Starting from the bottom of the BLE protocol stack 1830, the controller layer consists of the physical layer 1831 and the the link layer 1832. The physical layer 1831 refers to the logical layer encompassing the physical Bluetooth radio used for transmitting and receiving data. The physical layer is also where the data is modulated / demodulated. 85 ME151319109v.1132049-00217

[0314] The link layer 1832 is an abstraction layer that provides the higher layers above with a way to interact with the physical layer / Bluetooth radio through the Host Controller Interface (HCI) 1835. Link layer 1832 manages the state of the radio as well as the BLE timing requirements.

[0315] HCI 1835 is used by the host layer to communicate with the controller layer. HCI is a standard protocol defined by the Bluetooth specification.

[0316] The host layer consists of Logical Link Control and Adaption Protocol (L2CAP) layer 1840, Security Manager (SM) layer 1841, Attribute Protocol (ATT) 1842 layer, Generic Attribute Profile (GATT) layer 1843 and Generic Access Profiles (GAP) layer1844, each of which are discussed further below.

[0317] L2CAP layer 1840 multiplexes protocols received from the upper layers. It takes data sent according to multiple protocols and places them in standard BLE packets that can be passed down to the lower controller layers.

[0318] Security Manager layer 1841 defines methods for key distribution and pairing as well as providing the functions needed for other layers of the stack to securely connect with another device in order to exchange data.

[0319] ATT layer 1842 defines how data (an attribute) is structured and exposed by a server to its client.

[0320] Generic Attribute Profile (GATT) layer 1843 is a service framework and it defines procedures for using ATT 1842. GATT layer 1843 defines how the data that is exposed is formatted by a BLE device and how that data can be accessed. A device can have two roles within GATT, that of a server that exposes data it controls and optionally some other aspects of its behavior that other devices may control, and that of a Client which is a BLE device that communicates with the Server to read the exposed data and / or to control the Server behavior. BLE devices can perform both roles at the same time.

[0321] In GATT layer 1843 related attributes (data) are gathered together by functionality and referred to as Services. For example, embodiments of the present invention may include a service called LED control. Characteristics are part of a service and represent information that the Server wants to expose to a client. For example, in one embodiment the LED Control Service may include a Characteristic entitled State of Charge indicating a remaining 86 ME151319109v.1132049-00217 battery level. These Characteristics can then be subject to various operations defined in BLE: Commands, Requests, Responses, Notifications, Indications and Confirmations.

[0322] GAP layer 1844 defines how the different BLE devices to interact with each other. GAP layer 1844 is responsible for directly interacting with the profiles and / or application as well as handling the initiation of security features, device discovery and connection-related services.

[0323] FIG. 18D depicts an exemplary environment suitable for practicing one or more embodiments of the present invention. Photobiomodulation headgear 1850 includes LEDs 1851 for performing photobiomdulation treatments of a patient 1857. In some embodiments, patient 1857 is a child. Photobiomodulation headgear 1850 includes BLE capability which may be implemented via BLE System on a Chip (BLE SoC) 1852. For example, the BLE 5.0 stack may be utilized, as implemented in the psoc 6 Peripheral Driver Library v3.1.5. The BLE stack may run on the ARM Cortex-M0+ core as part of psoc 6 CYBLE-416045 Soc(System on Chip). BLE SoC 1852 incorporates the controller, host and application into a single chipset. In other embodiments, BLE support may be provide through one or more chips that are not an SoC.

[0324] To operate securely while handling patient data, embodiments encrypt the data prior to transmission using a number of different techniques. In some embodiments, all of the patient and control data transmitted to and from the headgear is encrypted. In other embodiments only patient data is encrypted. In a further embodiment patient data and a subset of control data is encrypted. Although as noted above, BLE provides support for 128 bit AES encryption, in some embodiments, a separate encryption module 1853 may be provided via hardware or software for encrypting patient data and / or control data prior to transmission and / or storage using alternate techniques. Photobiomodulation headgear 1850 may also include a battery 1854 to supply power and memory 1855 in both volatile and non- volatile form for storing data on-board prior to communication.

[0325] Photobiomodulation headgear 1850 may be controlled via commands sent from a separate computing device 1860. For example, computing device 1860 may be a smartphone, tablet computing device, desktop or other computing device equipped with one or more processors and able to perform the functions described herein. Computing device 1860 may include a BLE application 1862 that interacts with BLE radio 1861 to communicate with BLE SoC 1852 on photobiomodulation headgear 1850. Through BLE application 1862, user 87 ME151319109v.1132049-00217 1869 can enter commands to control operations of Photobiomodulation headgear 1850 and read data relating to its performance. In some embodiments, BLE application 1862 may be used to receive sensor data from photobiomodulation headgear 1850 from a sensor performing an EEG on the patient during the photobiomodulation therapy. Computing device 1860 includes one or more processors 1865 with one or more cores. Additionally, computing device 1860 includes memory 1867, both volatile and non-volatile, for storing patient data 1863. Patient data 1863 may include previously stored data for patient 1857 as well as data received for that patient during a photobiomodulation therapy and afterwards. In some embodiments, computing device 1860 may include applications 1864 for analyzing patient data 1863.

[0326] As discussed above, BLE application 1862 may be used to send commands to read data and control the operation of photobiomodulation headgear 1850. For example, commands may relate to the intensity of the LED light projection, the frequency of the projection, the temperature of the LEDs, the duration of the LED projection, an interval between projection, etc. Further, the commands may be to read patient data resulting from the therapy, read data resulting from a concurrent EEG, read data from specific sensors on photobiomodulation headgear 1850 and / or read data from one or more additional optical data streams. In some embodiments, the photobiomodulation therapy may be instructed to synchronize with a clock provided by BLE SoC 1852.

[0327] Computing device 1860 may also include network interface 1868 enabling communication over network 1870 with remote computing device 1880. In some embodiments, network 1870 may be a local area network and remote computing device 1880 may be desktop or server located within the same healthcare facility where treatment is occurring. In other embodiments, network 1870 may the Internet or a cellular network and remote computing device 1880 may be a cloud server or other remotely located computing device not near the healthcare facility where the photobiomodulation therapy is taking place. Remote computing device 1880 includes one or more processors 1883 and memory 1884 of both volatile and non-volatile form along with network interface 1885 for communicating over network 1870 with computing device 1860. Remote computing device 1880 may also include patient data 1881 relating to patient 1857 from before, during and after the photobiomodulation therapy. Remote computing device 1880 may also include one or more applications 1882 for analyzing patient data 1881. Applications 1882 may include one or 88 ME151319109v.1132049-00217 more artificial intelligence models trained on patient data having similar health issues to patent 1857 that are used to analyze the results of the photobiomodulation therapy on patient 1857.

[0328] As previously discussed, in some embodiments, photobiomodulation headgear 1850 utilizes BLE as the protocol for communicating with BLE application 1862. The BLE interface on the headgear may be implemented as a collection of custom services and characteristics appended to a standard GATTS database server device acting as a BLE peripheral device ( a peripheral device is one that advertises its presence while also accepting connection from a central device. A central device is one that listens for and discovers advertising devices and is able to establish a connection with them). By extension, a BLE- enabled mobile app may be implemented as a BLE GATTS database client acting as a central BLE device.

[0329] Besides the addition of custom services and characteristics, other design choices may affect the advertising process (advertising packet structure and advertising interval), and BLE transport layer settings MTU size (maximum transmission unit), minimum connection interval and maximum connection interval).

[0330] In one embodiment, audio data may be sent between the photobiomoudlation headgear and an external computing device. In an embodiment, the audio may relate to sounds captured by a sensor on the headgear during therapy. In one embodiment, smaller audio files may be sent using BLE while larger audio files may be sent using classic Bluetooth or other communication protocols supported by both the headgear and the external computing device.

[0331] In some embodiments, the headgear includes the device name and the LED control service UUID in its advertising packet. By detecting the LED control service UUID in the mobile app, the mobile app can recognize an advertising BLE peripheral device as a headgear device. In one embodiment, the advertising interval is configured to 20 ms for a duration of 30 seconds at a time.

[0332] In one embodiment, the BLE link is set to operate with a preferred minimum connection interval of 30 ms and a maximum connection interval of 50 ms. In an embodiment, the MTU size (maximum transmission unit) is set to 23 bytes. 89 ME151319109v.1132049-00217

[0333] In an exemplary embodiment, the Characteristics for the GATTs Service : LED control may be may be defined as set forth in the below chart.

[0334] The “characteristic name” indicates the data being delivered within each respective frame of a data packet being transmitted. Thus, therapy status indicates the session is on or finished, the board status indicates which of the LEDs are emitting light, current draw status indicates how much current is being drawn in a time period and thus the optical flux being generated, the temperature status provides the temperature. 90 ME151319109v.1132049-0021791 ME151319109v.1132049-0021792 ME151319109v.1132049-00217

[0335] FIG. 19A illustrates a graphical user interface (GUI) 4100 including user prompts to resolve user status restrictions in accordance with various embodiments taught herein. The GUI 4100 can be deployed on an external device as described previously such as the remote computing device 150. The GUI 4100 includes static elements that provide information to the user and actuatable elements that can be actuated by a user to perform certain actions and to control an associated photobiomodulation unit. Actuation of the actuatable elements can occur when the user clicks the element with a mouse, inputs information with a keyboard, or gestures using one or more touches on a touchscreen. Static elements can include prescription information such as prescribed usage interval 4102 and therapeutic program duration 4104. The information displayed in these static elements can be retrieved from a database by the application running the GUI 4100 or can be pushed from a central server (e.g., from a physician’s office) to the external device and populated into the appropriate fields in the application.

[0336] FIGs. 19B-19Zg illustrate screens used to control setup. FIG. 19B indicates password setup 4300, FIG.19C indicates caregiver account creation, FIG.19 D indicates initial evaluation 4304. FIG. 19E indicates eye contact pattern and history 4306. FIG. 19 F indicates whether a caregiver or therapist 4308 is setting up an account. Fig. 19 G completes the user agreement 4310. FIG. 19 H indicates ID entry 4312. FIG 19 I indicates entry of verification code 4314. Fig.19J indicates login password 4318. FIG 19 L indicates authorization code 4320. FIG. 19M identifies the patient 4322. FIG. 19 N identifies behavior patterns 4324. Fig. 19O indicates condition and medications. FIGs, 19P-19Zg indicate user profile 4328, and sharing 4330, patient profile 4332, questionnaire 4334, updates 4336, 4338, 4340, 4342, 4344, 4346, prescriptions 4348, progress 4350, 4352 and the treatment device 4360

[0337] Actuatable elements can be dynamically displayed in the GUI 4100 when certain conditions are met or can have their appearance modified by the presence of certain conditions. For example, the session start 4110 element can appear on the GUI 4100 in an actuatable state or a deactivated state depending upon whether patient status requirements have been met. If patient status requirements have been met, the session start 4100 element is depicted in an actuatable state that can be actuated by the user to initiate a therapeutic session. If the application determines that patient status requirements have not been met, the session start 4110 element is depicted in a deactivated state that will not respond to user input. The deactivated state can be maintained until the patient status requirements are resolved. 93 ME151319109v.1132049-00217

[0338] In some embodiments, the use of the photobiomodulation device can be restricted through the application running on the external device until patient status requirements are met. For example, use of the photobiomodulation device can be restricted subject to payment by the patient or user. The application can determine whether the payment requirement of the patient or associated user or associated patient account has been met by, for example, contacting a financial institution or other third-party payment processor and identifying when or whether the most recent payment has been made whether by one-time payment or subscription. The GUI 4100 can prompt the user that the payment requirement has not been met using a payment element 4106 of the GUI 4100. Actuation of the payment element 4106 prompt can take a user to further information or explanation as to what steps should be taken to resolve this patient status requirement. In some embodiments, actuation of the payment element 4106 can take the user to a secure payment portal or front-end site for a financial institution to execute operations that cause a payment to be made using the external device, such as a tablet or mobile phone communication device. These operations can be incorporated into a downloadable software application that includes all operational modules, or any periodic updates thereto, to perform photobiomodulation therapy as generally described herein.

[0339] As a further example, use of the photobiomodulation device can be restricted subject to patient usage history. For certain patients it can be necessary to limit the time to sequential therapeutic sessions such as at least 12 hours between sessions, at least 24 hours between sessions or at least 48 hours between sessions. Thus a selected delay time can be prescribed so as to set the light delivery dose over a selected sequence of therapeutic sessions occurring over days, weeks or months. The device can thus employ a programmed calendar that will automatically prevent the device from operating during selected intervals between treatment. In some situations, the prescribed therapeutic regimen may include spaced therapeutic sessions such that the patient is not exposed to photobiomodulation therapy in between the spaced sessions. The application can determine patient usage history either from data stored on the external device (i.e., past sessions that the patient conducted using the external device) or from a database record such as from a doctor’s health records. The application can deny or restrict access to usage of the photobiomodulation device until the patient’s usage history meets requirements set out in the prescription (e.g., no more than 1, 2, 3, or any specified number of sessions within a set period of time). The GUI 4100 can prompt the user that the patient usage history has not been met using patient history element 4108. 94 ME151319109v.1132049-00217 Actuation of the patient history element 4108 prompt can take a user to a description of the limitations imposed by the prescription or a countdown clock until the next treatment session can be commenced in accordance with various embodiments.

[0340] Additional patient status requirements can include prescription status (e.g., whether a prescription has been issued by a physician, whether a prescription was received from the physician by the external device, whether a prescription has expired) or completion of prerequisites (e.g., usage of the device can be restricted if the user does not complete biographical information input, questionnaires, or surveys before initial treatment or since the most recent treatment session).

[0341] Although the static and actuatable elements are shown on GUI 4100 as visual elements, the skilled person would appreciate that other forms for the elements are possible to increase accessibility such as replacing and / or augmenting the visual static or actuatable elements with auditory prompts or prompts to receive speech from the user.

[0342] FIG. 20 illustrates a flowchart for a method 4200 for therapeutic photobiomodulation for treatment of diseases or disorders in accordance with various embodiments described herein. The method 4200 includes positioning a head mounted device 5000 on a patient’s head (step 4202). The head mounted device 5000 includes a plurality of light emitting devices 5036, a power distribution circuit board 5040, 5050, a memory 5042, and a battery 5060 providing power to the plurality of light emitting devices 5036. The memory 5042 includes instructions to control the emission of light by the plurality of light emitting devices 5036 during a therapeutic period. The method includes determining whether one or more patient status requirements are satisfied wherein the patient status requirements include, but are not limited to, verification of user payment, treatment history, prescription status, or completion of prerequisites (step 4204). The method includes instructing a user to take action to resolve patient status requirements using a prompt in an application running on an external device upon determining that the one or more patient status requirements are not satisfied (step 4206). The method 4200 includes controlling a power output of each light emitting device in the plurality of light emitting devices using the power distribution circuit board according to the instructions in the memory upon determining that the one or more patient status requirements are satisfied (step 4208). The plurality of light emitting devices transmit illuminating light through a cranium of the patient at a near-infrared or infrared wavelength to deliver optical power to tissue within the cranium during the therapeutic period. 95 ME151319109v.1132049-00217

[0343] According to the CDC, 1 in 36 children in the U.S. are diagnosed with ASD and 30% remain minimally verbal. The rate of ASD has increased with each CDC surveillance report. Despite decades of research, treatments for ASD remain very limited with most children left with life-long disability.

[0344] There are many medical comorbidities associated with ASD that increase morbidity and mortality. For example, up to 35% of individuals with ASD may develop epilepsy. In addition, up to 80% of individuals with ASD have been shown to have frequent epileptic discharges which potentially interfere with brain function. Treating epilepsy and epileptiform activity on EEG records is complicated since these abnormalities tend to be refractory to treatment and because treatments are limited due to drug interaction with antiepileptic medication and because children with ASD are particularly sensitive to the adverse effect of antiepileptic medication. Thus, a safe, well-tolerated treatment for children with ASD and seizures and / or abnormal EEG is needed.

[0345] Studies have shown that transcranial photobiomodulation (tPBM) is safe and effective for reducing symptoms of ASD in young children. Seizures have not been reported as an adverse effect of tPBM, but the ability of tPBM to reduce seizure activity and the safety of tPBM in individuals with subclinical seizure activity or epilepsy has not been studied.

[0346] The present methods noninvasively deliver near infra-red (NIR) light through tPBM to the brain of children with ASD. The NIR light is delivered to specific brain areas with the head wearable device that enables tetherless use during treatment. This system is a non- invasive headband medical device specifically designed to safely and comfortably deliver tPBM to children with ASD. The device is a head worn assembly which can be adjusted to securely fit the head sizes of a 2 to 8 year old child, for example. It is designed to be comfortable for children who have sensory issues and has been used with children with ASD as described herein.

[0347] As described previously herein, he device contains independently driven NIR light emitting diodes (LEDs), a rechargeable battery, and a microcontroller with integrated Bluetooth connectivity, as well as safety and state monitoring circuits. The NIR LEDs emit 850nm light that is pulse-modulated at 40Hz, for example. A software package can be downloaded onto a mobile device such as internet enabled wireless phone that controls the device remotely via Bluetooth and allows the prescribing doctor to adjust the dosing parameters based on the individual characteristics of child. 96 ME151319109v.1132049-00217

[0348] The procedures described herein provide for a safe and efficacious use of tPBM in ASD children with abnormal EEG and / or clinical seizures. The results of this protocol provide for the safety of extending tPBM to the ASD population with epileptic brain activity and / or seizures, thereby removing seizures from the exclusion criteria of tPBM treatments. The procedure enables the efficacy of tPBM for reducing both ASD symptoms and stabilizing brain activity (e.g., reducing the number of epileptiform and / or active seizures). Most significantly tPBM is an effective treatment for drug refractory epilepsy and subclinical epileptiform discharges, two abnormalities that are difficult to treat.

[0349] The tPBM treatment is safe and effective for reducing symptoms in individuals with epileptiform EEGs with or without clinical seizures. The devices and methods described herein provide a safe tPBM treatment those with epileptiform activity in the brain, are efficacious in reducing core symptoms of ASD and for reducing the frequency of the epileptiform activity during wakefulness and sleep as well as the change in delta power (a biomarker for seizure vulnerability) that can be monitored by 24hr overnight video EEG, for example. The methodology can be applied to two classes or groups of individuals with ASD with electrographic epileptiform activity: those with clinical seizures and those without clinical seizures (i.e., with only subclinical epileptiform discharges on EEG).

[0350] The reduction in core ASD symptoms is measured by comparing several assessments before and after treatment. These assessments include the Childhood Autism Rating Scores (CARS), Social Responsiveness Scale (SRS), and the Clinical Global Impression Scale (CGI). For appropriately screened patients, treatment can result in a significant reduction in the frequency of electrographic epileptiform activity and the Delta power (a biomarker for seizure vulnerability).

[0351] The results of the treatment can be scored or ranked using the Childhood Autism Rating Scale (CARS2), the Social Responsiveness Scales (SRS), the Aberrant Behavior Checklist (ABC) and the Clinical Global Impressions (CGI-I) method. The safety protocol can include recording adverse effects monitored using a standardized adverse effects form (MDOTES) which is designed to elicit common side effects of neurologic and psychiatric treatments. At baseline the target symptoms are assessed and recorded so a change from baseline can be established in order to determine if new or worsening events occur. The protocol includes a review of on-going medical history and concomitant medication that is monitored at each treatment session. 97 ME151319109v.1132049-00217

[0352] During the tPBM session, any adverse effects are electronically recorded by automatically storing sensed events or by manual annotations recorded electronically in the medical record. If any adverse effects occur, the parents of children are contacted daily to make sure the adverse effect resolves. In preferred implementations, a 24-72 hour EEG before and after each course of treatment to determine if epileptiform activity has worsened or improved. In further implementations described herein, EEG monitoring can be integrated into each treatment session.

[0353] The depth of penetration of NIR wavelengths through the skull in humans showed that approximately 3% of NIR wavelengths penetrates through scalp and bone in human cadavers to reach surface brain cortex. Tedford showed that some NIR photons (808nm) from laser light applied at the skin (scalp) can be detected 4-5cm below the cortical surface in human cadavers. See Tedford CE, DeLapp S, Jacques S, Anders J. Quantitative analysis of transcranial and intraparenchymal light penetration in human cadaver brain tissue. Lasers Surg Med. Apr 2015;47(4):312-322 doi: 10.1002 / lsm.22343.

[0354] Both red and NIR wavelength photons are absorbed within the mitochondrial membrane by cytochrome C oxidase (COX) (complex IV of the electron transport chain), particularly in hypoxic and stressed cells. Through this mechanism, the light increases the production of adenosine triphosphate (ATP), the energy molecule of the cell, and increases local vasodilation by releasing nitric oxide from COX. In addition, PBM activates light sensitive ion channels that bring calcium into the cells, and activates transcription factors involved in anti-inflammatory and anti-oxidant signaling. See De Freitas L.F., Hamblin M.R. Proposed mechanisms of photobiomodulation or low-level light therapy. IEEE J. Sel. Top. Quantum Electron. 2016;22:7000417.

[0355] Positive results from application of tPBM have been observed in animal models of traumatic brain injury Neurodegenerative disease studies in small animals using tPBM have shown promising results in Alzheimer’s Disease (AD). See De Taboada L, Yu J, El-Amouri S, et al. Transcranial laser therapy attenuates amyloid-beta peptide neuropathology in amyloid- beta protein precursor transgenic mice. J Alzheimers Dis. 2011;23(3):521-535. tPBM intervention in humans has been reported to increase cerebral blood flow (rCBF) in normal individuals. In summary, tPBM is a mechanism for cell healing: Light photons penetrate tissue and act on the cytochrome c oxidase photoreceptors in the mitochondria of the cell. This is an enzyme that catalyzes the final step in ATP production. In a healthy cell, cytochrome c oxidase 98 ME151319109v.1132049-00217 combines oxygen with NADH (a crucial coenzyme in the making of ATP) to make the hydrogen ions that drive ATP production. Thus, there are several pathways by which light therapy can impact patients experiencing seizures or symptoms indicating such vulnerability.

[0356] When cells are damaged due to illness, stress, injury or aging, the mitochondria start making nitric oxide. This competes with oxygen and binds with cytochrome c oxidase, stopping the production of ATP. Damaged cells also produce oxidative stress which leads to inflammation and cell death. The light photons from tPBM break the bond between nitric oxide and cytochrome c oxidase, which results in nitric oxide (NO) is released into the bloodstream which increases circulation, oxygen is able to combine with NADH so that ATP can be produced, and oxidative stress is displaced.

[0357] Cells can work to regenerate tissue, fight infection, or perform whatever function they are programmed to do. This is how tPBM works to reduce inflammation and pain, increase circulation and restore normal cell function in general, and there are multiple FDA approved devices for wound healing, pain and inflammation. Since this mechanism works for all types of cells (e.g, including glial cells and neurons), this technology can potentially treat many conditions, including other neurological conditions. Furthermore, this technology is likely to be especially effective for ASD, as recent research has shown that ASD is associated with the inflammation of non-neuronal glial cells (astrocyte and oligodendrocyte). The present tPBM device, stimulates targeted brain areas including areas (e.g., occipital lobe and middle temporal gyrus) with high concentration of the inflamed glial cells. In addition, stimulating targeted brain areas simultaneously improves their functional brain connectivity (which is also crucial for ASD). As EEGs can identify specific brain areas associated with symptoms of seizures, the device can be tuned to deliver light to specific localized areas of the brain to reduce symptoms. Wang et al has studied specific localized epileptic diagnosis (the “epileptogenic zone” or EZ) for focal cortical dysplasia (FCD) in pediatric patients in which surgery was performed to alleviate epileptic seizures. Interictal epileptiform discharges and high frequency oscillations in the EEG signal were indicated to be viable biomarkers of in vivo epileptic tissue. See Wang et al, “Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in pediatric patients with epilepsy and focal cortical dysplasia type II: A retrospective study”, Frontiers in Neurology, August 5, 2022. Thus, localized light therapy treatment of specific regions of the brain can provide a non-invasive procedure to reduce symptoms. 99 ME151319109v.1132049-00217

[0358] In a study involving the methods and devices described herein, thirty 2-6 year old participants enrolled in the first clinical study (16 in active and 14 in sham-control condition). This was a randomized, double blind, placebo controlled study. Fradkin, Y. et al, Transcranial Photobiomodulation in Children Aged 2-6 Years: a Randomized Sham-Controlled Clinical Trial Assessing Safety, Efficacy and Impact on Autism Spectrum Disorder Symptoms and Brain Electrophysiology (April 26, 2024). Frontiers in Neurology 15:1221193; DOI 10.3389 / fneur.2024.1221193, the entire contents of this publication being incorporated herein by reference9.

[0359] The participants received tPBM treatment twice a week for up to 7 minutes. Participants in the sham condition were wearing the same device, which was not turned on. CARS 2 scores were collected before and after the end of the trial. In addition, EEG was collected during every session (twice a week). Participants in the active condition have improved significantly (as measured by the reduction of their CARS scores). Participants in the sham condition showed no such improvement. Furthermore, EEG data showed reduction of Delta waves over the course of the study for the participants in the treatment condition only. Lastly, the change in Delta waves correlated significantly with the change in CARS showing that the kids whose CARS scores reduced (reflecting their reduction of symptoms) also experienced reduction in their Delta waves. Lastly, we also found an increase in Theta waves in the children, whose CARS score decreased (inverse correlation between intensity of Theta and CARS scores). Recent research has shown that there is an insufficient intensity of Theta waves in the children diagnosed of ASD (e.g., Ortiz-Mantilla et al, 2019). This redistribution of brain waves from Delta to Theta is signaling the healing that is happening as a result of treatment with tPBM. Based on the results of the referenced study (See Fradkin Y, et al, (2024), a statistically significant reduction in CARS score after the course of the treatment was found. There was a statistically significant reduction in Delta waves and a statistically significant increase in Theta waves. FIG. 21A shows two dimensional graphical depiction of EEG data of a subject before a therapeutic session and FIG. 21B depicts EEG data subsequent to a therapeutic session indicating a demonstrable reduction in Delta wave power after the treatment. FIG. 21C shows a tabular summary of a mixed linear model of the power of Delta waves by scaled time. FIG. 21D shows a tabular summary of a mixed linear model of Theta waves by scaled time. This demonstrated a negative correlation between the power of Delta waves and the power of Theta waves as shown in the graphs of FIGs, 21E, 21F and 21G. FIG. 100 ME151319109v.1132049-00217 21E shows the EEG Delta measurement versus the associated change in the CARS assessment and FIG. 21F shows the EEG Theta measurement versus the associated CARS assessment. FIG. 21G demonstrates the negative correlation between Delta waves and Theta waves resulting from the therapy described in the Fradkin et al. publication. Furthermore, these findings corroborated results as we found significant difference in the mean change of CARS scores in the active and placebo groups after treatment with the methods described herein (Fradkin,, et al 2024). In addition, this study also showed that tPBM affects brain oscillations.

[0360] There are several reasons to believe that tPBM can improve epileptiform activity in individuals with ASD. First, major comorbid conditions associated with both ASD and epilepsy are metabolic disorders, particular mitochondrial disorders, and neuroinflammation. tPBM is believed to directly stimulate Complex IV of the mitochondrial electron transport chain to increase energy production in the mitochondrial as well as reduce neuroinflammation. Second, increased Delta waves have been linked to epileptic activity and tPBM has been shown to statistically significantly reduce Delta waves. Therefore, tPBM may be effective for both the treatment of ASD and for reducing epileptic brain activity. Mixed-model analysis, which controls for within subject variation, can be used for pre and post tPBM outcomes measurements. Sex, age, ASD severity as well as other confounders can be used as covariates as needed. Between subjects, variables include whether or not the participant has clinical seizures.

[0361] Assuming a medium effect size (f=0.25), an alpha of 0.05, two measurements, two groups, a correlation among repeated measures of 0.8 and a nonsphericity correct of 1, an N=30 will achieve a 99% power as determined by G*Power (ver 3.1.9.7)

[0362] 1. Autism Spectrum Disorder (diagnosed as Autistic Disorder on the ADOS-2 or the ADI-R).

[0363] 2. Between 4 and 16 years of age, at baseline.

[0364] 3. Autism severity of moderate or higher (≥4) under the 7-item clinical global impression-severity scale. Moderate level of autism severity (4) is defined by the diagnosis of ASD with one comorbidity such as epilepsy or abnormal EEG.

[0365] 4. Ability to maintain all ongoing complementary, dietary, traditional, and behavioral treatments constant for the study period. 101 ME151319109v.1132049-00217

[0366] 5. Unchanged complementary, dietary, traditional, and behavioral treatments for two months prior to study entry

[0367] 6. Ability to tolerate procedures.

[0368] 7. At least one 24hr EEG with data in EDF format that is accessible to investigators.

[0369] Exclusionary criteria:

[0370] 1. Significant self-abusive or violent behavior or evidence of suicidal ideation, plan or behavior

[0371] 2. Severely affected children as defined by CGI-Severity Standard Score = 7 (Extremely Ill)

[0372] 3. Severe prematurity (<34 weeks gestation) as determined by medical history

[0373] 4. Current uncontrolled gastroesophageal reflux disease sinceGERD can cause movements that appear like seizures

[0374] 5. Genetic syndromes

[0375] 6. Congenital brain malformations

[0376] 7. Any medical condition that the PI determines could jeopardize the safety of the study subject or compromise the integrity of the data.

[0377] 8. Failure to thrive or Body Mass Index < 5%ile or <5%ile for weight (male <11.2kg; female <10.8kg by CDC 2000 growth charts) at the time of the study.

[0378] 9. Concurrent treatment with drug that would significantly interact with treatment such as stimulants, anti-psychotics, and anti-histamines.

[0379] 10. Excessive hair that the caregivers are unwilling or unable to shave or braid.

[0380] 11. Inability to tolerate the required dosage of tPBM treatment due to sensory issues.

[0381] Following the child’s initial assessment, those that score less than 30 or above 45 on the CARS-2 are excluded, for example.

[0382] Treatment Plan: The treatment can take place two times a week for up to 60 minutes at a time. If the participant has had a fever above 100.4 degrees in the past 24 hours, their session will be rescheduled. The following titration protocol will be applied: 102 ME151319109v.1132049-00217

[0383] Session 1: 2 minutes (sub-clinical dosage)

[0384] Session 2: 4 minutes (sub-clinical dosage)

[0385] Session 3. If there are no reported side effects (i.e., hyperactivity; headaches), increase to 6 minutes.

[0386] Session 4: Increase to 8 minutes. If side effects are reported, stay at 6 minutes.

[0387] Session 5: Increase to 10 minutes. If side effects are reported, stay at 8 minutes (both clinical dosage).

[0388] Session 6 – 12: Treatment remains at 10 minutes as long as no side effects are reported.

[0389] The total time in the office will be about 40-60 minutes (including playing). Assessment Schedule

[0390] CARS-2, SRS, and CGI will be administered before and after treatment, as well as during a 1-month follow-up visit. Interviews with caregivers and their responses will be documented before, during, and after treatment including a 1-month follow-up. Treatment sessions will be video recorded, and data will be collected on child behavior by two 103 ME151319109v.1132049-00217 independent observers. More specifically, we will take data on the following behaviors: Negative vocalizations (screaming, cursing, yelling, grunting, “go away”, “stop”, etc.), Avoidant movements (physically resisting wearing the device; turning away [90 degree head turn]; movement away [at least 2 steps away from experimenter]; dropping to the floors; hands in a defensive position between participant and experimenter or covering face / head), Escape (any movement exhibited by the participant which results in removal of the device), Positive affect (laughing, smiling, clapping, and / or singing), and Approach (any movement toward the device helping put the device on, reaching for the device). CARS-2, SRS, CGI, and interviews are recorded in the electronic medical record for each patient. Behavioral observations will be conducted before during and after treatment. All treatment sessions will be video recorded for data collection purposes. This treatment can comprise up to 60-minute visits, twice a week, for 12 weeks.

[0391] Some prior participants have reported mild headaches after the first 1 or 2 treatments. Some prior participants have reported increased hyperactivity and agitation during the treatment. They may encounter minor frustration while becoming comfortable wearing the device. However, the procedures will not be any more frustrating than what the participant is likely to experience in daily life. Moreover, we will provide participants with preferred activities and toys to increase their comfort with wearing the device.

[0392] The treatment methodology is described in connection with the process sequence 3820 shown in FIG. 22A. The initial measurement involves recording 3822 one or more EEG sessions with the patient. The patient is ranked on a scale 3824 to classify that patient among the different treatment options base upon individual traits including behavioral attributes and EEG characteristics (e.g. sharp, spike, slow wave, localized, periodicity). Patients exhibiting exclusionary features are excluded 3826. An initial light therapy session is performed and an EEG measurement is recorded to confirm classification of the patient. Any adverse symptoms (e.g. headache) can result in reclassification 3820 to alter the treatment parameters for further treatment 3834. Otherwise, the initial classification will define the next treatment session 3832 having a specified length and illumination pattern. The treatment protocol can optionally include application of a machine learning protocol as described herein and as set forth in pending US Patent Application 17 / 949,997, filed on September 21, 2022, the entire contents of which is incorporated by reference. A selected computational module 3836 can be applied to the acquired data that performs an iterative 104 ME151319109v.1132049-00217 computational process trained based on data acquired over time based on the treatment of a statistically significant body of patients exhibiting clinical or sub clinical seizure characteristics. This can be used to further characterize the patient based on known correlations of EEG response and seizure activity 3838. The patient can then be ranked to further refine and personalize the ongoing treatment methodology 3840. The medical record of the patient is then updated.

[0393] The devices described herein provide a platform to personalize treatment based on the patient's individual parameters, through continuous data collection, analysis and resulting treatment recommendation. The methods provide for the construction of datasets to be used to train a machine learning module that is effective in further improving the personalized treatment for individual patients.

[0394] This implementation of the device includes a multiuser platform in combination with machine learning module. The multiuser platform includes validated questionnaires, patient's profile setup, current and all previous prescription details and appointment reminders, graphs of patients progress throughout one or more 10-week treatment cycles. This includes any adverse effects during the treatment cycles. It will show treatment compliance by providing monitoring information highlighting whether treatment happened according to prescription or (a) if there was an early termination or (b) a treatment was skipped due to an unforeseen event such as a patient's sickness or device malfunction. Eventually, it will also incorporate 3rdparty treatment modules – such as language learning programs, social skills programs, etc., thereby achieving a synergy of treatment and recommending an individual learning program to each patient based on their initial and ongoing evaluations.

[0395] This procedure generates a unified dataset from all previous and ongoing clinical data for a machine learning algorithm training, with data consolidation. Analysis is performed on data collected immediately and 1-3 months post-treatment to distinguish characteristics of treatment responders and non-responders, and to identify factors affecting long-term treatment efficacy, including demographics, EEG patterns, and symptomatology. Through weighted linear regression, key variables influencing treatment success will be determined, leading to the development of an equation for personalized dosing parameters based on individual patient characteristics, aiming to optimize treatment outcomes. The method correlates each contributing proposed variable (e.g., variability in skin color) with the final 105 ME151319109v.1132049-00217 CARS score. Success metric is Pearson’s R and crude P<.05. Because multiple comparisons can be done, the final p value can be adjusted with an appropriate correction (e.g., Bonferroni). After all individual correlations are computed, weighted regression is performed, identifying the relative weight of each contributing variable. A successful metric is statistically significant coefficients, with p value determined by Bonferroni correction.

[0396] Questionnaires for caregivers collect data indicating children’s initial condition, progress throughout the 10-week treatment in this example, and 1-month follow-up after the 10-week treatment cycle. Subsequently, review of the baseline caregiver's questionnaire, weekly intra-treatment caregiver's questionnaire and a post-treatment caregiver's questionnaires. These questionnaires are based on the Childhood Autism Rating Scale, Second Edition (CARS II) and the Social Responsiveness Scale, Second Edition. These four questionnaires with 100-150 children diagnosed with ASD are utilized in verifying usability, validity, and replicability.

[0397] The data is used to train the machine learning computational model that is based on the analysis, and uses systematized patient data, information from the collected questionnaires, and the recorded outcomes. The output of the model provides personalized parameters of the treatment (frequency, duration, pulsing, brain areas, power, etc.). The maximum possible dosage is incorporated into the computational so that it doesn’t cross the threshold in the dosing recommendation. The ongoing training of the model on the datasets produced by all the previous study participants use supervised learning based on the parameters for respondents / non-respondents in the recorded patient data. The previous study participants are clustered based on multiple parameters. For the additional and ongoing patients, the optimization of the dosing depends on the degree to which they are similar to the patients from the specific cluster (supervised learning algorithm). There is currently no publicly available data to be used in this training protocol.

[0398] Datasets accumulate with updated data and optimized further. A self-training algorithm, continuously intakes input information from the ongoing patients' treatment data, EEG characteristics, behavioral symptoms collected through the questionnaires, and prescriptions provided by clinicians. When training the model predicting treatment success (4.5 CARS improvement), existing data can be augmented, with 5-fold cross-validation (splitting it 80:20 into training and testing sets at random 5 times). The standard metrics used for evaluating binary -prediction are: 106 ME151319109v.1132049-00217 - precision: P(outcome is positive | prediction is positive) - recall (AKA sensitivity): P(prediction is positive | outcome is positive) - F1 (harmonic average of the above) - accuracy P(outcome = prediction) - Mattews correlation coefficient (same as Pearson's R, but reinvented for binary prediction) - Proficiency: I(prediction, outcome) / H(outcome) I=mutual information, H=entropy, i.e., -the fraction of information in the actual outcome captured by the prediction.

[0399] The model is monitored by comparing the predicted final CARS scores of the model with the CARS score administered by the clinician at the end of the 10-week tPBM treatment. Previous studies show that the average placebo effect is a change in 2 points on the CARS scale. Therefore, the model is successful if the ROOT_MEAN_SQUARE_ERROR is 2 or less. If it is higher than 2, the model has failed, and the model is either adjusted using the weighting coefficient of each individual parameter (i.e. a linear sum of weighted variables) and / or adjusted to include other parameters.

[0400] The initial formula can employ a curve fitting model, which can be a crude approximation for the adjusted algorithm, developed with a larger baseline of data points (ex. >1000 patients). This curve fitting formula uses the patient's parameters (e.g., skin color, severity of the symptoms, etc.) and parameters of the device (e.g., duration of the treatment and frequency). Due to the initial limited set of the data, this preferably includes no more than a total of ten parameters. The following subset, which appears to be more significant in previous studies includes: baseline severity of the treatment as measured by CARS score, skin color (because melanin absorbs light), age (because it correlates with the thickness of the skull), duration of the treatment, and frequency of the treatment. These parameters can be adjusted based on the outcomes. The output of the curve fitting formula is severity of the symptoms after the treatment as measured by CARS. Therefore, this addresses the insufficiency of the data by two means: by limiting the number of parameters, and by using an approximate initial curve fitting formula: CARS1 = CARS0 + CARS_SCALED * (1 - CARS_SCALED) * DELIVERED_ENERGY * SCALE 107 ME151319109v.1132049-00217 ● CARS_SCALED is the score from 0 to 1. ● 0 is assigned for every child whose CARS score is below 25 (i.e., a child who is not on the spectrum (CARS cutoff for autism is 30). ● 1 is assigned for every child whose CARS score is above 50, indicating extremely severe autism. This scaling helps avoid floor and ceiling effects. ● DELIVERED_ENERGY is the product of power, frequency, and duration of the treatment and some of the patient’s input parameters that will affect delivered energy. ● SCALE is the scaling coefficient to bring the numbers to the correct range.

[0401] The devices in use with different patients are collecting data from all the patients. With thousands of data points, more patient attributes are included (e.g., family structure and medical history) and train an ensemble model (e.g., Gradient Boosting Decision Trees) to predict treatment outcomes. To personalize treatment, the system can perform a parameter search (in the space of the device parameters holding the patient attributes constant) that improves or optimizes the predicted outcome for the given patient.

[0402] Shown in FIG. 22B is a detailed illustration of sub-modules of the neuromodulation treatment module 3008 that correspond to a selected plurality of treatment protocols to be used with corresponding neurological conditions as described herein. The first sub-module 3040 is configured to control light delivery sessions to patients that do not exhibit any seizure activity and have a normal range of EEG patterns consistent with behavior within the autistic spectrum. The second sub-module 3042 is configured to control light delivery sessions to patients who have seizures that are undergoing photobiomodulation therapy as described herein. The third sub-module 3044 is configured to control light delivery sessions to patients that are subclinical with respect to seizures, but that have abnormal EEG epileptiform activity that indicates a vulnerability to seizures. A fourth sub- module 3045 is configured to control light delivery for the treatment of fetal syndrome conditions as described herein in connection with infants exposed to drugs or other toxic agents prior to birth. The fifth sub-module 3047 is configured to control light delivery for the treatment of attention deficit disorder as described herein. The EEG data generated by these different modules are classified using different thresholds, filtering and signal processing protocols for use in properly classifying patients with respect to the diagnosis and treatment 108 ME151319109v.1132049-00217 of the different neurological conditions. Different machine learning programs can be applied to each of the different protocols to adjust treatment settings based on iterative computational processing of the recorded EEG data. This can be used to adjust the illumination parameters for each patient to personalize treatment.

[0403] The preferred embodiment of the present invention includes a neuro- biomodulation device, sensors and other measurement instruments, a software platform for personalized treatment recommendation and progress monitoring, and respective child, parent, and therapist interfaces described herein and as shown in FIGs. 23-26.

[0404] The User Profile Module (UPM) 3002 receives all data related to the child’s profile, including demographic data, neuro-developmental assessment data, health data, ongoing device data, treatment history, progress indicators, parental assessments, and behavioral data. The user profile is continuously updated with treatment and progress data and contains both baseline and longitudinal data.

[0405] The Reference Population Module (RPM) 3016 is the database containing all user profiles created within the UPM and is used as a calibration and testing sample for the Machine Learning Module 3018.

[0406] The neuro-developmental assessment module (NDA) 3006 uses the user profile together with questionnaire data to assess the baseline and continuous performance of the child along attachment, playing, communication and language, and other behavioral factors using a range of metrics and scores the child’s current state for each of the measures. As treatments are administered, the NDA scoring is updated and resulting recommendations modified. The NDA assessment together with the UPM data feed into the Personalized Treatment Module (PTM).

[0407] The Personalized Treatment Module (PTM) 3004 leverages the cluster-treatment mapping data from the Machine Learning Module 3018 to create personalized plans for the Neuromodulation Treatment Module (NMT) 3008 and the Cognitive Programming Module (CPM) 3010. This includes physical device treatment duration, intensity, and frequency as well as specific cognitive treatment activity portfolios to be administered to the child.

[0408] The Neuromodulation Treatment Module (NMT) 3008 leverages the personalized treatment recommendations of the PTM and provides them across the parent and therapist interfaces for administration. 109 ME151319109v.1132049-00217

[0409] The Cognitive Programming Module (CPM) 3010 leverages the personalized treatment recommendations from the PTM and provides cognitive activity and treatment content to the child via the child interface and / or the parent / therapist interfaces.

[0410] The Sensor and Quantitative Data Feedback Module (SQD) 3012 captures data from physical sensors and devices such as EEG, heart rate and pulse wearables, and other devices alongside with performance data of the child on the cognitive programming module (CPM) as well as parental and therapist feedback to measure the impact of the treatments on the NDA metrics of the child.

[0411] The Performance Progress Module (PPM) 3014 compares the individual data from the SQD 3012 with expected progress thresholds established for the selected cluster within the RPM 3016 and provides effectiveness scores for administered treatments.

[0412] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors.

[0413] The Feature Selection and Vectorization Module (FSV) 3020 takes individual data fields from the UPM and vectorizes them into n-dimensional numeric feature vectors that represent the initial UPM data. The FSV 3020 uses a modified tf / idf in the form of a boolean (feature) frequency-inverse boolean vector frequency (BF / IBVF) measure to convert different measurement variables into numeric data indices of a vector. This vectorization of variables can be expressed as:89^^89 = 89:;^ , =>? ∗ ^^89:;^ , =>?

[0414] Wherein bl represents the lthbin, dj represents the jthdocument, Freq(bl, dj) is thenumber of times bin l appears in document j, ∑^ 9@AB:;^, =>? represents the total number ofbins in document j, Ndoc is the number of users, and GHIJ.;^2is the number of uses with the bin bl. 110 ME151319109v.1132049-00217

[0415] The FSV 3020 then engages in dimensionality reduction of the vector into a lower-dimensional orthogonal subspace that captures as much of the variation of the UPM and RPM data sets as possible.

[0416] The Embedded Cluster Predictor (ECP) 3024 takes the reduced dimensionality vectors from the FSV 3020 and clusters them based on the K-means or LDA, bypassing BF / IBVF and dimensionality reduction) into bi-partite profile-treatment clusters using effectiveness scores from the PPM as a omni-distance measure.

[0417] The Deep Learning Module (DLM) 3022 takes data from the SQM and from the PPM for the reference population and adjusts the ECP 3024 clusters continuously with new feedback data from all users. The raw SQM data is augmented with gaussian noise to reduce inter-subject variability. DNN learns features automatically. Further details describing the use of machine learning computational systems and methods, and particularly with respect to the application of neural networks to EEG data and similar data sets is described in Moinnereau et al., “Classification of Auditory Stimuli from EEG with a regulated recurrent neural network reservoir”, arXiv:1804.10322v1 [eess.SP], published 27 April 2018, the entire contents of which is incorporated herein by reference. A regulated recurrent neural network (RNN) to characterize hearing of patients to auditory stimuli or speech which improved on the classification rates over other methods such as a naïve Bayes classifier or support vector machine (SVM). In this method, EEG signals recorded by methods previously described herein, are transformed into spike trains that are accumulated in a reservoir of connected neurons. For example, the process of encoding of signals into spike trains can assume that an analog signal is the result of filtering spike trans with a reconstruction finite impulse response (FIR) filter and uses the FIR to find spikes in the EEG signals according to the following two equations at every given time τ.

[0418] where s represents the different EEG signals, h(k) is the reconstruction FIR filter, and M is the order of the filter. This can digitize the output to enable further processing of the data. The readout from the reservoir is classified over time using linear regression. The 111 ME151319109v.1132049-00217 results of the RNN were compared to a deep neural network (DNN) which can employ, in this example, three convolutional layers where EEG signals were input into the network. This enables EEG measurements to be used to measure the response of system users to auditory stimuli as described above where photobiomodulation is used to treat patients to improve language learning. Thus, changes in language comprehension can be quantified over time during treatment.

[0419] In a further example, as described in Chambon et.al., “A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series,” IEEE Neural Systems of Rehabilitation Eng. 26, 758-69 (2017) involving the use of a multivariate time series to classify sleep patterns, and further machine learning techniques to classify and score sleep patterns using a convolutional neural network in Chambon et. al., “A deep learning architecture to detect events in EEG signals during sleep”, arXiv:1807.05981v1 [eess.SP] 11 July 2018, in which the learning problem can be solved by a minimization problem to detect events from EEG signals measured during sleep. This can be expressed as event detector XYwhere an iterative computational process is used to detect events during sleep. As the patient is treated with photobiomodulation therapy, changes in the detected events can be quantified and stored over time in relation to the course of treatment. Note that x denotes input EEG signals while E(x) denotes a “true” or measured event; there are also “default” events used to train the neural network. An event label l can have a zero-value or a selected non-zero value such as 1.

[0420] The process detects spindles and K-complexes jointly or severally to detect and score events during sleep. Thus, the network identifies classes of sleep events using EEG sensor data that can be measured during the course of photobiomodulation therapy to characterize and quantify therapeutic outcomes of the treatment.

[0421] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic 112 ME151319109v.1132049-00217 plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established.

[0422] The Cluster-Treatment Mapper (CTM) 3026 takes the individual’s UPM vector and maps it into the clusters identified in the ECP to identify the optimal treatment options based on the RPM. It then feeds the identified cluster into the PTM for further processing.

[0423] The entire process for an individual is captured in the flowchart in FIG. 23. User’s data is captured by the UPM 3002, and an neuro-developmental assessment is performed 3006. The PTM 3004 leverages the MLM 3018 and the RPM 3016 to identify ideal treatments using the NMT 3008 and CPM 3010 modules. Once the user engages in the treatment, the SQD 3012 module records data on the effect of the treatment, and the PPM 3014 assesses the effectiveness of the treatment, recording all the activities back into the UPM 3002.

[0424] The reference population treatment cluster analysis process and the respective personalized treatment mapping process are shown in FIGs. 24 and 25 respectively. These constitute the integration of feedback into the system for learning and furthering the personalization prediction accuracy.

[0425] In FIG. 26, a preferred embodiment of the system 4000 is shown with its components, including the neuro-biomodulation device 4002 as described previously herein, physical and quantitative data collection systems 4012, interfaces for the user (child 4004 such as a tablet), parents (using a personal computer 4006 to access a website interface), and therapist interface 4008 and the software system 4010 for personalized treatment needs, assessment, recommendation, and progress monitoring. The neurobiomodulation device 4002 can include the head-mounted photobiomodulation device described previously and configured to be worn by a child during a therapeutic session.

[0426] In some embodiments, the photobiomodulation and / or neurobiomodulation devices and methods of use described herein can produce statistically significant improvements in autism symptoms and related indicators. A clinical trial was conducted with the objective of demonstrating that transcranial photobiomodulation (tPBM) is an effective treatment modality to improve language and communication skills in children with ASD. In recent pilot studies, tPBM has been shown to be an effective treatment for certain conditions such as stroke, traumatic brain injury (TBI), and depression (Ando et al., 2011; Cassano et 113 ME151319109v.1132049-00217 al., 2018; Naeser et al., 2020). Pilot studies have shown that tPBM can reduce symptoms of autism (Ceranouglu et al, 2019; Leisman et al 2018). The above referenced study hypothesized that children with ASD will demonstrate improvement in communication skills and language acquisition with experimental treatment.

[0427] The present clinical study examines the effect of tPBM modulation on symptoms of autism in children 2-6 years old. It is a randomized, placebo-controlled, double-blind study. Twenty-nine participants were enrolled and wore the tPBM device (such as the photobiomodulation device(s) of the present disclosure) for 6 minutes, and in which illuminating light delivering an energy in a range of 16-24 joules was administered during each session. Each participant completed 16 sessions during an 8-week course of the study. Data about children’s behavior was collected from parents through weekly interviews. Children's therapists are interviewed regarding any observed changes in child's behavior. Before and after treatment scores of Childhood Autism Rating Scales are compared for placebo and experimental conditions. EEG measurements from frontal, occipital and temporal areas or regions were collected before and after each treatment.

[0428] The results of the trial indicated a statistically significant reduction in autism symptoms as measured using the Childhood Autism Rating Scale (CARS). A CARS assessment was made for each participating child (n=21) before the beginning of the trial and after the trial by a blinded researcher. The CARS is defined such that higher scores indicate worse autism symptoms. As shown in the tables below, treatment using photobiomodulation in accordance with the systems and methods described herein produces a statistically significant improvement in children in the “experimental” group whereas children in the “placebo” group did not have a statistically significant improvement. In the preliminary CARS results Control: before: 40.37.5 after 39.87.3 Experiment : before: 45.35.7 after 35.4 4.7 T-test p-value: 1-sided: p=.001; 2-sided: p=.005.114 ME151319109v.1132049-00217

[0429] In the final clinical study results, the CARS scoring showed a statistically significant decrease for overall based on 16 active patients and a slight decrease in the placebo control group of 14 patients:

[0430] Clinical trials also indicate that systems and methods for photobiomodulation as described herein can produce measurable improvements in EEG data. EEG data was gathered before and after treatment from the frontal, occipital, and temporal areas in consenting subjects. (In some cases, it was not permissible or possible to collect EEG data from a child, for example, due to hair interference or sensory issues, and collected data was meaningless for analysis in some cases such as when a child is jumping around.) The data 115 ME151319109v.1132049-00217 was analyzed by the Pirogov Institute in Moscow. Changes in the EEG data show a decrease in delta waves and increase in alpha, beta, and theta in a few patients which is associated with better focus, implicit learning and faster language acquisition. Compared to placebo, active stimulation using photobiomodulation systems and methods as described herein presented suppression of the increase in the lower frequency bands (delta) and a further increase in power in the higher frequency bands (alpha, beta,). Normalization of alpha activity can represent normalization of DMN functions and is indicative of increased organization in the cortex including language areas. A summary of the trajectory of the alpha and beta is 13.2- 15.36-8.78-7.45-21.8-27.7 (increasing). A summary of the trajectory of the theta wave is 49.08-51.04-58.92-71.6-52.9-61.7 (increasing). Note that ASD is associated with lower theta wave values. Consequently, the measured increase in theta waves and the decrease in delta waves serves as a biomarker for improvement of cognitive function of ASD patients. A summary of the trajectory of the delta wave is 0-31.4-27.8-19.2-27.7-0 ( decreasing ). FIG. 27A illustrates the decreasing trend of the delta wave for the experimental group (“active”) including six individual subjects. Averaged data and a curve fit are also illustrated. FIG. 27B illustrates delta wave data for eleven subjects in the control group (“placebo”). After completion of the study, an overlay of the active and control groups of the EEG delta wave component is shown in FIG. 27C for data measured over seven sessions. The data indicate a statistically significant decrease in the active group undergoing photobiomodulation therapy. As can be seen, there does not appear to be a consistent trend among the control subjects with no visible decrease in delta wave intensity.

[0431] Progress of the subjects in the study was also analyzed through qualitative interviews. Specifically, a researcher conducted weekly interviews with parents regarding their observation of the children. In addition, most of the time when the parent came in the researcher collected notes as well. The table below shows averaged data for experimental and control groups for individual question categories and for aggregated “index” scores as described below:116 ME151319109v.1132049-00217

[0432] A 'total improvement score' was computed by combining the categories that are related to socialization (e.g. eye contact), language (new words), and responsiveness to create the total Benefit Index score. Fig. 27D shows an aggregate comparison of the active and control groups with the active group demonstrating a higher score. The placebo group showed improvement that can reflect a natural improvement or improvement due to a “placebo effect.” FIG. 27E shows an aggregate CARS graphical illustration of the before and after results for both active and control groups of the study with the active group showing a statistically significant decrease in comparison with the control group.

[0433] FIG. 28A illustrates values of the benefit index for individual subjects and the average for all subjects including error bars. The results show a statistically significant difference in the Benefit score between the Active and Placebo kids. Separately, the categories that relate to “over-excitement” such as hyperactivity, headaches, wakefulness, and others can be combined to create the Side Effect Index score. FIG. 28B illustrates values of the side effect index for individual subjects and the average for all subjects including error 117 ME151319109v.1132049-00217 bars. The results also show a non-statistically significant difference in Side Effects between Active and Placebo children.

[0434] A further analysis of clinical results can be based on a treatment protocol taking place 2 times a week for up to 60 minutes at a time. During this patient study, the participants first wear the mobile EEG device for approximately 15 minutes, and the EEG signals before treatment are collected. Then the participants wear the tPBM device for up to 15 minutes. Then the participants wear the EEG device again for approximately 15 more minutes. The child can be encouraged to play with toys and interact with the parent (or the experimenter). The children in the active and sham treatment group wear the same devices (the tPBM device won’t be turned on for children in the sham group). The total time in the office can be about 40-60 minutes (including playing). An example assessment schedule is shown in the table below Assessment Schedule

[0435] Several Standard tests (scales) can be used for pre-test and post-test, as well as weekly interviews. PRIMARY END POINT: 1. Childhood Autism Rating Scales, Second Edition (CARS2). a. Performed by a clinician at the beginning of the trial b. Performed by a clinician upon the completion of the trial 118 ME151319109v.1132049-00217 2. Secondary End POINTS: Social Responsiveness Scales (SRS) a. Performed by a clinician at the beginning of the trial b. Performed by a clinician upon the completion of the trial 3. Receptive-Expressive Language Scales, Third Edition (REELS-3) a. Performed by a clinician at the beginning of the trial b. Performed by a clinician upon the completion of the trial 4. EXPLORATORY END POINTS: EEG will be collected from each participant. a. It will be conducted pre-and post-treatment in each session. b. Mobile FDA-cleared medical grade EEG device. c. Performed by a research assistant who is conducting each session. 5. Parental interviews. a. Performed weekly by a research assistant 6. Therapist interviews a. Performed by a research assistant at the beginning of the trial. b. Performed by a research assistant at the midpoint of the trial. c. Performed by a research assistant upon the completion of the trial. Endpoints119 ME151319109v.1132049-00217120 ME151319109v.1132049-00217121 ME151319109v.1132049-00217122 ME151319109v.1132049-00217123 ME151319109v.1132049-00217Primary Endpoint: Childhood Autism Rating Scale (CARS2) Secondary Endpoints: ● Social Responsiveness Scales (SRS) ● Receptive-Expressive Language Scales, Third Edition (REELS-3) ● EEG Exploratory Endpoints: ● Synergy of tPBM treatment and ABA Therapy ● Parental interviews. ● Therapist interviews

[0436] Statistics :There are no universally accepted clinical outcome measures developed for measuring changes in core symptoms in ASD, based on interventions. A recently proposed clinical efficiency benchmark is a 4 - 4.5 decrease in CARS 2, based on a recent article by Jurek et. al. 2021, who conducted a panel of 5 experts including pediatric and adult psychiatrists who work with patients in Europe and India. Their proposal should be taken with caution, because they do not work with a diverse population, similar to the population in the United States. By using a reduction of 3 points in CARS, as it is a 10% reduction - based on 30 points being a cutoff score for ASD. Clinically, 10% reduction of CARS score of the mean moving from SEVERE to Moderate or from Moderate to Mild subgroup of the spectrum, which can mean more independent functioning (e.g., improved signaling of their needs, improved focus, responsiveness to language and therapy) and improved quality of life for children and their caregivers.

[0437] A sample size of 60 subjects, 30 in each arm is sufficient to detect a clinically important difference of 3 points between treatment groups in reducing symptoms of autism as measured by the CARSII assessment assuming that there is treatment standard of deviation of 124 ME151319109v.1132049-00217 8.02 using a two-tailed t-test of difference between means with 80% power and a 5% level of significance to reach in the primary end point. If this study reaches statistical and clinical significance with 60 participants, the plan is to continue recruiting up to 150 participants to measure the effectiveness in the secondary and exploratory end points.

[0438] Analysis Population: Data analysis will be conducted on the intent to treat (ITT) population which includes all subjects that were consented and randomized to either the presently described therapeutic device or the sham device. Additional analysis can be conducted on a per protocol population which is defined as all participants who have participated in the trial.

[0439] Effectiveness Analysis: The following analyses are conducted: 1. Primary analysis (based on 60 participants): a. Before and After treatment change in CARS scores in Active and Sham groups, b. Analysis of the number of subjects that achieved clinically meaningful difference, based on CARS 2. Statistical significance with CARS: based on power calculation based on CARS the following secondary analysis on the full data set at the end of the full study (with 150 participants). a. SRS and REELS: Before and After treatment changes in the scores in the Active and Sham groups . b. EEG data (Before and After treatment redistribution of the brainwaves in Active and Sham groups) 3. Exploratory end points: a. Correlation between the effectiveness of tPBM and Number of ABA hours received (using CARS). b. Analysis of Qualitative data such as parental interviews and therapists’ interviews.

[0440] The primary analysis of efficacy includes a comparison of change in mean CARSII scores between Baseline and 12 for the actively treated versus sham arm. The primary analysis will be performed on the ITT population. 125 ME151319109v.1132049-00217

[0441] A model for repeated measures fitted by a restricted maximum likelihood method will be used for the primary analysis. This model takes into account the presence of missing data and yields valid estimates under the assumption of data missing at random (MAR).

[0442] Fixed effects will further include treatment, visit, treatment by visit interaction, baseline CARSII score and baseline CARSII by visit interaction. A general (co)variance structure with unconstrained correlations and variances will be used to model the within- subject errors. If this analysis fails to converge, alternative variance-covariance structures will be considered. More specifically, the same mean model will be fitted with following variance-covariance structure (in this order): • An antedependence correlation structure • A heterogeneous Toeplitz correlation with unconstrained variances • A heterogeneous compound symmetry structure The Kenward-Roger approximation can be used to estimate denominator degrees of freedom.

[0443] The Secondary Analysis of Efficacy: In the first part of the secondary analysis of efficacy, a comparison of change in mean SRS and REEL scores is calculated between Baseline and Week 12 for the actively treated versus sham arm. The primary analysis will be performed on the ITT population. A model for repeated measures fitted by restricted maximum likelihood method will be used for the primary analysis. This model takes into account the presence of missing data and yields valid estimates under the assumption of data missing at random (MAR). Fixed effects can further include treatment, visit, treatment by visit interaction, baseline SRS and REEL score and baseline SRS and REEL scores by visit interaction. A general (co)variance structure with unconstrained correlations and variances can be used to model the within-subject errors.

[0444] In a second part of the secondary analysis of efficacy, linear regression analysis can be used to analyze the change in EEG waves distribution throughout the measurement. Pearson correlation of the distribution of brainwaves with time can be computed.

[0445] Exploratory Endpoint Analysis: CARS: Synergy of tPBM treatment and ABA therapy: The model will include ABA treatment used in the randomization as covariate (3 levels: 0 hours of ABA, <=10 hours of ABA and >10 hours of ABA.). This analysis will be conducted if there is a statistically significant correlation of the before and after treatment 126 ME151319109v.1132049-00217 change in CARS scores and the number of ABA hours each participant in receiving. Parental Interviews: Non-parametric statistics (e.g., Wilcoxon Signed Rank test). Therapist Interviews: Non-parametric statistics (e.g., Wilcoxon Signed Rank test).

[0446] Additional confounding variables for Post-Trial Analysis: ● Age ● Skin color ● Length, color and thickness of hair (we will encourage all participants to cut hair for the duration of the trial). ● Severity of condition ● Gender ● Other concomitant treatments ● # of languages spoken in the household ● Changes in amount of therapy during the trial ● Non-psychotropic medication

[0447] Further analytic methods can be employed to improve diagnostic and treatment modalities in the application of EEG measurements to diagnose and treat neurological disorders using photobiomodulation as generally described herein. The computational analysis of EEG data has been demonstrated using the wavelet transform to provide for the detection of seizure events for epileptic patients. See Faust et al, “Wavelet-based EEG processing for computer- aided seizure detection and epilepsy diagnosis,” Seizure 26 (2015) 56-64, which summarizes the development or the wavelet transform using a continuous time wavelet analysis (CWT) or a di...

Claims

132049-00217 CLAIMS What is claimed is:

1. A photobiomodulation neuro-therapy device comprising: a portable head mounted device that is sized to be positioned on a patient’s head, the portable head mounted device including a plurality of light emitting devices, a processor, a memory and a battery providing power to the portable head mounted device, each of the light emitting devices being operable in response to control signals to control transmission of transcranial illuminating light into the head of the patient having at least one of a red and a near infrared wavelength delivered during a therapeutic period wherein the processor executes instructions stored in the memory to control the emission of light by the light emitting devices during the therapeutic period, the processor being connected to each of the light emitting devices to control emission of the illuminating light; a computing device that is communicatively coupled to the portable head mounted device, the computing device having a processor programmed to control a light therapy session delivered to the patient wherein a neuromodulation treatment module includes a plurality of treatment parameters having a first delivery profile to control light delivery to a patient based on a first EEG data including abnormal epileptiform activity, a second delivery profile to control light delivery to a patient based on a second EEG data including only normal epileptiform activity, and a third delivery profile to control light delivery to a patient based on a third EEG data including seizure activity; and wherein each light emitting device is mounted to a panel that is attached to a frame positioned on the patient’s head, each panel including a circuit board having a driving circuit to actuate the light emitting device mounted to said panel.

2. The device of claim 1 wherein each wireless communication comprises a set of fields including one or more of a time stamp, a temperature, an LED board status, EEG data, and current draw.

3. The device of any one of claims 1-2 further comprising a communication circuit including a transceiver on the portable head mounted device to receive a wireless control signal to control an operation of the portable head mounted device. 132 ME151319109v.1132049-00217 4. The device of claim 1 wherein a control circuit comprises a wired parallel circuit connected to each light emitting device to independently control operation of each light emitting device.

5. The device of claim 4 wherein at least one LED is mounted, each LED circuit board being connected to a power control circuit mounted on the portable head mounted device. 6.The device of claim 1 wherein the battery is connected to a control circuit mounted on a main circuit board, the main circuit board being mounted within a housing attached to the frame. 7.The device of claim 1 wherein communication circuit communicates with the computing device by a cable, a wireless transmission or a combination thereof. 8.The device of any one of claims 1-7 wherein the computing device comprises a tablet display device having a touchscreen display that is operative in response to a plurality of touch gestures made by a user on the surface of the touchscreen display, the tablet display device including a processor programmed with one or more software modules to control operations of the tablet display device and the portable head mounted device.

9. The device of any one of claims 1-8 wherein the processor is configured to perform at least one of execute a machine learning operation to determine an operating parameter of the portable head mounted device, or communicate with an external computing device that performs a machine learning operation to determine an operating parameter of the portable head mounted device.

10. The device of any one of claims 9 wherein the machine learning operation comprises an iterative computational sequence executed on at least one of the processor or the external computing device. 133 ME151319109v.1132049-00217 11. The device of any one of claims 1-10 comprising a heart monitor that measures a heart rate of the patient.

12. The device of claim 11 wherein the heart monitor comprises a green LED for illuminating a blood vessel through the skin of the patient and detecting a pulsatile blood flow.

13. The device of any one of claims 1-10 further comprising an ambient light optical sensor.

14. The device of any one of claims 1-10 wherein each protocol comprises a plurality of preset parameters for the pulse rate, duty cycle, amplitude and duration of the LED illumination through the cranium of a treatment session.

15. The device of any one of claims 1-13 wherein the EEG data includes Delta wave epileptiform signals indicative of a seizure stored in the memory..

16. The device of any one of claims 1-15 further comprising a pressure sensor on the head mounted device to measure pressure on the cranium of the patient.

17. The device of any one of claims 2-16 wherein the transmitter transmits recorded EEG data during a treatment session.

18. The device of any one of claims 1-17 wherein the EEG electrode is mounted on the head mounted device.

19. The device of any one of claims 1-18 wherein fetal syndrome protocol is configured to treat a patient less than two years old.

20. The device of any one of claims 1-18 wherein the computing device has a plurality of software modules including a prescription module in which a treatment protocol is selected for operation of the head mounted device. 134 ME151319109v.1132049-00217 21. The device of any one of claims 1-20 wherein a seizure protocol program processes EEG data to measure Delta wave power and Theta wave power to diagnose a seizure condition.

22. The device of claim 21 wherein the Delta wave power and the Theta wave power have a negative correlation to diagnose a seizure condition.

23. The device of claim 21 or 22 wherein the seizure condition is ranked based on a level of epileptiform activity of the measured EEG data.

24. The device of any one of claims 21-23 wherein the measured EEG data record spikes during a treatment session, and optionally wherein the controller receives an instruction from an EEG signal processor to cease light delivery to the patient.

25. A photobiomodulation neuro-therapy device comprising: a portable head mounted device that is sized to be positioned on a patient’s head, the portable head mounted device including a light emitting device, a controller, a memory and a battery providing power to the portable head mounted device, the light emitting device being operable in response to the controller to control transmission of transcranial illuminating light into the patient having a near infrared wavelength to deliver an amount of power during a therapeutic period wherein the controller executes instructions stored in the memory to control the emission of light by the light emitting device during the therapeutic period; a head mounted electroencephalogram (EEG) sensor having one or more electrodes that measure electric field activity within a cranium of the patient, the EEG sensor being connected to at least one of the controller and an external computing device; and a communication circuit on the portable head mounted device, the communication circuit connected to the controller to communicate with the external computing device having a programmable processor that is operable to perform one or more of a plurality of different therapeutic treatment protocols to treat different classes of patients wherein each treatment protocol has different programmed 135 ME151319109v.1132049-00217 illumination parameters to treat the patient and wherein each treatment protocol includes corresponding stored EEG reference data.

26. The device of claim 25 wherein the treatment protocol for the patient is selected for at least one of an autism protocol, a seizure protocol, a fetal syndrome protocol and / or an attention deficit protocol during the therapeutic period.

27. The device of any one of claims 25 or 26 wherein the controller that controls a delivery of an auditory signal to the patient with headphones on the portable head mounted device and wherein the memory records the power of the transmitted light delivered to the patient during the therapeutic period.

28. The device of any one of claims 25-27 wherein the communication circuit further comprises a transceiver on the portable head mounted device to receive a wireless control signal from the external computing device that controls an operation of the portable head mounted device.

29. The device of any one of claims 25-28wherein the light emitting device further comprises a first light emitter that illuminates the patient at a first wavelength and a second light emitter that illuminates the patient at a second wavelength different than the first wavelength.

30. The device of any one of claims 25-29 wherein the light emitting device further comprises a plurality of panels that illuminate the patient from a plurality of different angles, each panel having one or more light emitting diodes (LEDs).

31. The device of any one of claims 25-30 further comprising a sensor that measures a physiologic response of the patient to the illuminating light or a condition of the head mounted device, and wherein the processor, in response to the measured physiological response from the sensor, controls an operation of the portable head mounted device.

32. The device of any one of claims 25-31 wherein the processor is configured to perform at least one of 136 ME151319109v.1132049-00217 execute a machine learning operation to determine an operating parameter of the portable head mounted device, or communicate with an external computing device that performs a machine learning operation to determine an operating parameter of the portable head mounted device.

33. The device of claim 32 wherein the machine learning operation comprises an iterative computational sequence executed on at least one of the processor or the external computing device.

34. The device of any one of claims 25-33 wherein the portable head mounted device has a size, shape and weight to be worn by a child, and wherein the processor is programmed to treat a neurological condition that comprises autism.

35. The device of any one of claims 25-34 further comprising an EEG sensor that measures EEG signals of the patient with a plurality of EEG electrodes attached to the head of the patient.

36. The device of claim 30 wherein each panel in the plurality of panels comprises an LED circuit board on which the at least one light emitting diode is mounted, each LED circuit board being connected to a controller circuit board mounted on the portable head mounted device.

37. The device of claim 25 wherein the battery is connected to the controller circuit board.

38. The device of any one of claims 25-37 wherein the communication circuit communicates with the external computing device by a cable, a wireless transmission or a combination thereof.

39. The device of claim 32 wherein the external computing device comprises a tablet display device having a touchscreen display that is operative in response to a plurality of touch gestures made by a user on the surface of the touchscreen display, the tablet 137 ME151319109v.1132049-00217 display device including a processor programmed with one or more software modules to control operations of the tablet display device and the portable head mounted device.

40. The device of any one of claims 25-39 further comprising a power management circuit connected to the battery that controls power distribution to the light emitting device.

41. The device of any one of claims 25-40 wherein the controller includes a power management module that controls power distribution to the light emitting device.

42. The device of claim 41 wherein the power management module comprises programmed instructions such that the processor executes a sequence of steps to illuminate different regions of brain tissue of the patient with selected levels of light.

43. The device of claim 42 wherein the selected levels of light comprise a plurality of presets such that a user can select at least one preset that includes a time duration, a total area of a cranium of the patient to be illuminated and a total amount of light to be delivered onto the total area of the cranium of the patient during a therapy session.

44. The device of claim 42 wherein the selected levels of light are manually selected by a user with a user interface.

45. The device of any one of claims 25-44 wherein the external computing device comprises a user interface that controls an operation of the portable head mounted device.

46. The device of claim 25 wherein at least one treatment protocol comprises a seizure treatment module that includes a program stored in a memory on the head mounted device that processes EEG data including an Alpha wave and a Theta wave components that are compared to stored reference values and exhibit a negative correlation to identify an epileptiform measurement of a seizure during a treatment session. 138 ME151319109v.1132049-00217 47. The device of claim 25 wherein the portable head mounted device is connected to the external computing device with a cable.

48. The device of claim 45 wherein the portable head mounted device communicates with the external computing device with a wireless connection wherein said communication includes illumination parameters and an illumination period.

49. The device of any one of claims 45-48 wherein the user interface comprises a graphical user interface operable on a display of the external computing device.

50. The device of claim 49 wherein the external computing device comprises a tablet display device.

51. The device of claim 50 wherein the tablet display device comprises a touchscreen display.

52. The device of claim 49 wherein the graphical user interface is operable on the touchscreen display that is responsive to a plurality of touch gestures whereby a user can control one or more operating parameters of the portable head mounted device.

53. The device of any one of claims 49-52 wherein the graphical user interface comprises a plurality of windows selectable by a user to perform a plurality of different data management and control functions of the device.

54. The device of any one of claims 52 and 53 wherein the touchscreen display is operative in response to a plurality of static or moving gestures, the static gestures including a plurality of icons displayable on the touchscreen display.

55. The device of claim 25 wherein the plurality of EEG electrodes include one or more EEG electrodes at one or more locations on the patient’s head, optionally including a prefrontal cortex, to measure Theta and Delta signals, and optionally one or more 139 ME151319109v.1132049-00217 electrodes on the occipital lobe and one or more electrodes on the temporal lobe to measure Alpha, Beta and Gamma signals.

56. The device of claim 25 wherein the EEG sensor generates EEG diagnostic data that is received by the processor to control a therapeutic transmission of light through a cranium of the patient.

57. The device of any one of claims 25-56 further comprising a light sensor positioned to measure light from the head of the patient in response to illuminating light from the portable head mounted device.

58. The device of claim 57 wherein the light sensor comprises a near infrared light sensor.

59. The device of claim 57 or claim 58 wherein the light sensor comprises an array of light sensors mounted on the portable head mounted device to measure light from at least one of the cranium or brain tissue of the patient, the light sensor array optionally generating diagnostic data that is processed to control a therapeutic transmission of light through the cranium.

60. The device of any one of claims 32 or 33 wherein the machine learning operation is executed by programmed instructions to process data that are received from the portable head mounted device and to adjust one or more operating parameters of the portable head mounted device.

61. The device of claim 60 wherein the machine learning operation processes a plurality of different patient data stored in the memory.

62. The device of claim 60 wherein the machine learning operation includes a neural network that generates a quantitative value to adjust an operating parameter of the portable head mounted device.

63. The device of any one of claims 25-62 wherein the portable head mounted device is programmed to emit light having a maximum radiance value and a minimum 140 ME151319109v.1132049-00217 threshold radiance value during the therapeutic period, a total radiance being selected to have a value within a range of selectable values based on an age and / or a cranium thickness of the patient.

64. The device of claim 25 wherein the controller receives EEG sensor data and, based on the EEG sensor data, selects a revised illumination radiance value and actuates the light emitting device of the portable head mounted device to direct light having the revised radiance value through cranial bone and into a region of a brain of the patient.

65. The device of claim 25 wherein the EEG sensor measures beta and theta EEG signals. 66 The device of claim 65 wherein the measured EEG signals are recorded and processed to determine a neurological condition of the patient.

67. The device of any one of claims 64-66 wherein the external computing device comprises a data processor configured to perform a diagnostic operation using measured EEG data wherein selected protocol reference data.

68. The device of any one of claims 25-67 wherein the external computing device is configured to perform wireless communications with the portable head mounted device during a therapeutic operation without a wired connection.

69. The device of claim 65 wherein the external computing device is configured to diagnose an autism spectrum disorder using at least one of the measured beta or theta EEG signals.

70. The device of claim 65 wherein the external computing device comprises a tablet display device having a touchscreen display having a graphical user interface having a plurality of selectable display screens that are responsive to touch actuated icons and gestures to operate the tablet display device and to control operation of the portable head mounted device. 141 ME151319109v.1132049-00217 71. The device of claim 32 wherein the machine learning operation comprises a neural network that optionally includes a recurrent neural network.

72. The device of claim 32 wherein the machine learning operation comprises a convolutional neural network to generate a frequency domain characteristic of measured EEG data.

73. The device of claim 32 wherein the machine learning operation comprises a minimization computation to minimize a metric.

74. The device of any one of claims 25-73 wherein the external computing device includes a processor configured to operate at least one of a data feedback module, a patient progress module, a vectorization module, or a cluster module. 142 ME151319109v.1

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