Prognosis

Abstract

1. A method for predicting a prognosis of a patient suffering from a psychiatric disorder who is undergoing neurofeedback (NF) treatment, the method comprising: measuring EEG signals from the patient's brain before, during, and / or after at least one treatment session of the NF treatment; generating an indicator of activity of at least one specific brain network based on the measured EEG signals; processing the indicator of activity, e.g., before, during, and / or after the at least one treatment session, in a temporal relationship with the at least one treatment session, to predict a prognosis of the patient's condition at the end of and / or during the NF treatment.

Description

[Technical Field]

[0001] Related Applications This application claims the benefit of priority under 35 U.S.C. §119(e) from U.S. Provisional Patent Application No. 63 / 433,025, filed December 16, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] FIELD AND BACKGROUND OF THE INVENTION The present invention, in some embodiments thereof, relates to predicting the prognosis of a patient who will respond to a treatment, and more particularly, but not exclusively, to predicting the prognosis of a patient who will respond to a NF treatment. Summary of the Invention

[0003] Some examples of some embodiments of the present invention are listed below (embodiments of the present invention may include features from more than one example and / or fewer than all features of the examples).

[0004] Example 1. A method for predicting the prognosis of a patient suffering from a psychiatric disorder who is undergoing neurofeedback (NF) treatment, comprising: measuring EEG signals from the patient's brain before, during, and / or after at least one treatment session of the NF treatment; generating an indication of activity of at least one specific brain network based on said measured EEG signals; processing indicators of such activity; predicting a prognosis of the patient's condition at the end of and / or during the NF treatment based on the processed indicators of activity in temporal relation to the at least one treatment session.

[0005] Example 2. The method of Example 1, wherein predicting the prognosis includes predicting an improvement in the patient's condition at the end of at least one session of the NF treatment.

[0006] Example 3. The method of any one of the preceding examples, wherein predicting the prognosis includes predicting an improvement in the patient's condition for at least one week after completion of the NF treatment.

[0007] Example 4. The method of any one of the preceding examples, wherein predicting the prognosis includes predicting improvement in one or more clinical assessment tests used to measure the clinical status of a patient suffering from the psychiatric disorder.

[0008] Example 5. The method of Example 4, wherein predicting the improvement includes predicting the patient's ability to achieve at least one of a target score on the one or more clinical assessment tests, a target score range on the one or more clinical assessment tests.

[0009] Example 6. The method of Example 4 or 5, wherein predicting said improvement comprises predicting said patient's ability to reach a target reduction in score on said one or more clinical assessment tests compared to a reference score or baseline score when said one or more clinical assessment tests are administered.

[0010] Example 7. The method of any one of Examples 4-6, wherein the psychiatric disorder comprises a stress disorder, and wherein predicting improvement comprises predicting the subject's ability to achieve a reduction of at least 3 points on the Clinician-Administered Post-Traumatic Stress Disorder (PTSD) Scale for DSM-5 (CAPS-5) compared to a reference or baseline score on the CAPS-5, and / or a reduction of at least 3 points on the PTSD Checklist for DSM-5 (PCL-5) compared to a reference or baseline score on the PCL-5.

[0011] Example 8. The method of Example 7, wherein the stress disorder comprises post-traumatic stress disorder (PTSD).

[0012] Example 9. The method of Example 7 or 8, wherein the NF treatment comprises training the patient to self-regulate limbic network activity or amygdala activity, and providing a feedback signal to the patient during the training with information about the activity or changes therein based on the measured EEG signal.

[0013] Example 10. The method of any one of Examples 4-6, wherein the psychiatric disorder comprises depression or major depressive disorder (MDD), and wherein predicting improvement comprises predicting the subject's ability to achieve a reduction in the Hamilton Depression Rating Scale (HDRS) of at least 3 points compared to a reference or baseline score for the HDRS, and / or a reduction in the Snaith-Hamilton Pleasure Scale (SHAPS) of at least 4 points compared to a reference or baseline score for the SHAPS, and / or a reduction in the Montgomery-Asberg Depression Rating Scale (MADRS) of at least 6 points compared to a reference or baseline score for the MADRS.

[0014] Example 11. The method of any one of Examples 4-6, wherein the psychiatric disorder comprises anhedonia and wherein predicting improvement comprises predicting the subject's ability to achieve at least a 5-point reduction on the Snaith-Hamilton Pleasure Scale (SHAPS) compared to a reference or baseline score on the SHAPS.

[0015] Example 12. The method of any one of the preceding examples, wherein said predicting includes predicting said patient's success in reducing their score on the CAPS-5 assessment questionnaire by at least 12 points or at least 6 points relative to their baseline score after one or more treatment sessions, or one or more weeks, of said NF treatment.

[0016] Example 13. The method of any one of Examples 1-11, wherein said predicting comprises predicting said patient's success in reducing their score on the CAPS-5 assessment questionnaire by at least 12 points or at least 6 points relative to their baseline score after 15 treatment sessions of said NF treatment or after 8 weeks of said NF treatment.

[0017] Example 14. The method of any one of the preceding examples, wherein said predicting includes predicting said patient's success in reducing their CAPS-5 assessment questionnaire score by at least 12 points or at least 6 points relative to their baseline score at least one week after completing said NF treatment.

[0018] Example 15. The method of any one of the preceding examples, wherein said predicting includes predicting said patient's success in reducing their CAPS-5 assessment questionnaire score by at least 12 points or at least 6 points relative to their baseline score three weeks after completing said NF treatment.

[0019] Example 16. The method of any one of the preceding examples, wherein said predicting includes predicting the patient's success in increasing their score on a cognitive reappraisal scale by at least one point relative to a baseline or reference score after one or more treatment sessions, or one or more weeks, of said NF treatment.

[0020] Example 17. The method of any one of Examples 10-16, wherein the NF treatment comprises training the patient to self-regulate activity of a mesolimbic network or activity of a reward brain network associated with the ventral striatum, and providing a feedback signal to the patient during the training with information about the activity or changes therein based on the measured EEG signals.

[0021] Example 18. The method of any one of the preceding examples, wherein said predicting comprises calculating a prognosis score indicative of said prognosis of said patient's condition based on said processed indicators of activity.

[0022] Example 19. The method of Example 18, comprising automatically modifying at least one parameter of said NF treatment based on said calculated prognostic score.

[0023] Example 20. The method of Example 18, comprising delivering an index with instructions for modifying at least one parameter of said NF treatment based on said calculated prognostic score.

[0024] Example 21. The method of Example 19 or 20, wherein the at least one parameter of the NF treatment includes at least one of a number of treatment sessions, a duration of each treatment session or at least one treatment session, an interval between treatment sessions, and feedback delivered to the patient.

[0025] Example 22. The method of any one of Examples 18-21, comprising delivering an indication for modifying at least one additional treatment provided to the patient.

[0026] Example 23. The method of Example 22, wherein the at least one additional treatment comprises at least one of a pharmaceutical treatment and a psychological treatment.

[0027] Example 24. The method of Example 20, wherein if the calculated prognostic score indicates that the subject will not be able to reach a target improvement, the delivered indication includes instructions to stop the NF treatment or to add at least one NF treatment session to the NF treatment.

[0028] Example 25. The method of Example 24, wherein if the calculated prognostic score indicates that the subject will not be able to reach a target improvement, the delivered indication includes instructions to initiate at least one additional treatment for the NF treatment or to modify the at least one additional treatment.

[0029] Example 26. The method of Example 25, wherein if the calculated prognostic score indicates that the subject will not be able to reach the target improvement, the delivered indication includes instructions for initiating or modifying at least one of pharmaceutical, psychological, and / or psychotherapeutic treatment.

[0030] Example 27. The method of Example 20, wherein if the calculated prognostic score indicates the subject's ability to reach a target improvement, the delivered indication includes instructions to proceed with NF treatment or to shorten the duration of NF treatment or at least one treatment session.

[0031] Example 28. The method of Example 27, wherein if the calculated prognostic score indicates the subject's ability to reach a target improvement, the delivered indication includes instructions to stop or modify at least one of pharmaceutical, psychological, and / or psychotherapeutic treatment.

[0032] Example 29. The method of Example 28, wherein if the calculated prognostic score indicates the subject's ability to reach target improvement, the delivered indication includes instructions for reducing the dosage of at least one drug administered to the subject during the pharmaceutical treatment.

[0033] Example 30. The method of any one of the preceding examples, comprising repeating said measuring and said generating during a selected period before, during, and / or after said at least one treatment session, wherein said processing comprises identifying changes in said generated indicator of activity during said selected period, and wherein said predicting comprises predicting said prognosis based on the identified changes.

[0034] Example 31. The method of Example 30, wherein said processing includes extracting one or more parameters of the generated indicator of said activity, and said predicting includes predicting said prognosis based on values ​​of said one or more extracted parameters.

[0035] Example 32. The method of Example 31, wherein said processing includes using values ​​of said one or more extracted parameters in a predictive model, said predictive model including weights for said one or more extracted parameters, and said predicting includes predicting said prognosis based on an output of said predictive model.

[0036] Example 33. The method of Example 32, wherein the one or more extracted parameters include at least two parameters of the generated signal of the indicator, and wherein the predictive model is used to calculate a relationship between values ​​of the at least two parameters, and wherein predicting includes predicting the prognosis based on the calculated relationship.

[0037] Example 34. The method of Example 32, wherein the predictive model is used to calculate a relationship between one or more of the extracted parameters and a reference value, and wherein predicting includes predicting the prognosis based on the calculated relationship.

[0038] Example 35. The method of any one of the preceding examples, wherein the method is performed by a device or system.

[0039] Example 36. A system for calculating a prognostic score, comprising: a memory storing a signal indicative of activity or a change therein of at least one specific brain network in a subject suffering from at least one symptom of a psychiatric disorder, the signal being measured before, during, and / or after neurofeedback (NF) treatment delivered to the subject, and at least one prognosis prediction software; A control circuit, the control circuit comprising: processing the stored signals indicative of the activity; a control circuit configured to calculate a prognosis score based on the processing results and using the at least one prognosis software, the prognosis score indicating a prognosis of the subject's condition at the end of the NF treatment and / or during the NF treatment.

[0040] Example 37. The system of Example 36, wherein the control circuitry is configured to process the stored signals indicative of the activity by extracting values ​​of predetermined parameters from the stored signals indicative of the activity, and wherein the calculating includes calculating the prognostic score by using the extracted values ​​as input information for the at least one prognostic software.

[0041] Example 38. The system of Example 37, wherein the control circuitry is configured to process the stored signal indicative of the activity by dividing the signal indicative of the activity into epochs in a range of 1 millisecond to 120 seconds, and wherein the extracting includes extracting the value of the parameter of the signal indicative of the activity independently for each epoch of the epochs.

[0042] Example 39. The system of any one of Examples 36-38, wherein the signal indicative of activity stored in the memory is derived from an EEG signal measured from the subject's brain during the NF treatment.

[0043] Example 40. The system of any one of Examples 36-39, wherein the prognostic score predicts the subject's ability to achieve a target score on a clinical assessment test during, at the end of, and / or after the NF treatment, and the target score or an indicator thereof is stored in the memory.

[0044] Example 41. The system of any one of Examples 36-39, wherein the prognostic score predicts the subject's ability to achieve a target change in clinical assessment test score relative to a baseline value during, at the end of, and / or after the NF treatment, and at least one of the target change and / or the baseline value is stored in the memory.

[0045] Example 42. The system of Example 40 or 41, wherein the clinical assessment test comprises at least one of the following: the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) rating scale, the Patient Health Questionnaire-9 (PHQ-9) test, the Hamilton Depression Rating Scale test, the Snaith-Hamilton Pleasure Scale for Patients with Disabilities (SHAPS) test, the Montgomery-Asberg Depression Rating Scale (MADRS) test, the Patient Health Questionnaire (PHQ) test, the Clinical Global Impression (CGI) test, an Attention Deficit Hyperactivity Disorder (ADHD) related test, the Adult ADHD Subject Symptom Rating Scale (AISRS) test, the Adult ADHD Self-Report Scale (ASRS) test, the Test of Attention Variables (TOVA) test, or any derivative thereof.

[0046] Example 43. The system of any one of Examples 36-42, wherein the control circuitry is configured to generate a prognostic indicator of the calculated prognostic score.

[0047] Example 44. The system of Example 43, wherein the prognostic indicator includes at least one suggestion for modifying at least one parameter of the NF treatment based on the calculated prognostic score, and the at least one suggestion is stored in the memory.

[0048] Example 45. The system of Example 44, wherein the at least one parameter includes at least one of a number of treatment sessions, a duration of at least one treatment session, and / or a time interval between two treatment sessions.

[0049] Example 46. The system of any one of Examples 43-45, wherein the prognostic indicator includes at least one suggestion for administering at least one additional treatment to the subject or modifying at least one parameter of the at least one additional treatment, and wherein the at least one suggestion is stored in the memory.

[0050] Example 47. The system of Example 46, wherein the at least one suggestion stored in the memory includes at least one of a suggestion to start or stop pharmaceutical treatment and / or to change the dosage and / or administration regime of at least one drug administered to the subject.

[0051] Example 48. The system of Example 46 or 47, wherein the at least one suggestion stored in the memory includes a suggestion to start, stop, or modify transcranial magnetic field (TMS) therapy delivered to the subject.

[0052] Example 49. The system of any one of Examples 46-48, wherein the at least one suggestion stored in the memory includes a suggestion to start, stop, or modify at least one of psychological treatment, cognitive behavioral therapy (CBT), and psychotherapy.

[0053] Example 50. A method for controlling a communication system including a communication circuit configured to communicate with at least one remote device; 50. The system of any one of Examples 43-49, wherein the control circuitry is configured to signal the communication circuitry to deliver the prognostic indicator to the remote device.

[0054] Example 51. The system of Example 50, wherein the at least one remote device includes an expert interface configured to generate and deliver a detectable indicator for the human, and the control circuitry is configured to send a signal to the expert interface via the communication circuitry to generate and deliver a detectable indicator for the human based on the prognostic indicator.

[0055] Example 52. The system of Example 51, wherein the expert interface is configured to receive input and deliver the input to the memory via the communication circuitry.

[0056] Example 53. The system of Example 52, wherein the input includes at least one of personal information about the subject, clinical data about the subject, clinical assessment data, pharmaceutical treatment-related data, data regarding the at least one symptom of the psychiatric disorder, data regarding the psychiatric disorder, data regarding the at least one clinical assessment test and / or results of the subject on at least one clinical assessment test.

[0057] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of this invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. Additionally, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0058] As will be appreciated by those skilled in the art, some embodiments of the present invention may be embodied as a system, a method, or a computer program product. Accordingly, some embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may be generally referred to herein as a "circuit," "module," or "system." Furthermore, some embodiments of the present invention may take the form of a computer program product embodied in one or more computer-readable mediums having computer-readable program code embodied thereon. Implementation of the methods and / or systems of some embodiments of the present invention may involve performing and / or completing selected tasks manually, automatically, or a combination thereof. Furthermore, depending on the actual instrumentation and apparatus of some embodiments of the methods and / or systems of the present invention, some selected tasks may be implemented by hardware, software, or firmware, and / or by a combination thereof, for example, using an operating system.

[0059] For example, hardware for performing selected tasks according to some embodiments of the present invention may be implemented as a chip or circuit. As software, selected tasks according to some embodiments of the present invention may be implemented as a plurality of software instructions executed by a computer using any suitable operating system. In exemplary embodiments of the present invention, one or more tasks according to some exemplary embodiments of the methods and / or systems described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes volatile memory for storing instructions and / or data, and / or non-volatile storage, e.g., a magnetic hard disk and / or removable media, for storing instructions and / or data. Optionally, a network connection is also provided. Optionally, a display and / or user input device, such as a keyboard or mouse, is also provided.

[0060] Any combination of one or more computer-readable media may be utilized in some embodiments of the present invention. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this specification, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0061] A computer-readable signal medium may include a propagated data signal having computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium but may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0062] The program code embodied on the computer-readable medium and / or data used thereby may be transmitted using any suitable medium, including, but not limited to, wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0063] Computer program code for carrying out operations for some embodiments of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program code may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., via the Internet using an Internet Service Provider).

[0064] Some embodiments of the present invention may be described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, create means for implementing the function / act specified in the block(s) of the flowchart illustrations and / or block diagrams.

[0065] These computer program instructions may also be stored on a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other device to function in a particular manner, such that the instructions stored on the computer-readable medium produce an article of manufacture that includes instructions that implement the function / act specified in the flowchart and / or block diagram block(s).

[0066] Computer program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be executed on the computer, other programmable apparatus, or other device to create a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide a process for implementing the function / act specified in the block(s) of the flowcharts and / or block diagrams.

[0067] Some of the methods described herein are generally designed to be used solely by a computer and may not be feasible or practical for purely manual execution by a human expert. A human expert wishing to manually perform a similar task, such as predicting a prognosis and / or calculating a predictive score, would be expected to use an entirely different method, e.g., utilizing specialized knowledge and / or the pattern recognition capabilities of the human brain, which would be much more efficient than manually performing the steps of the methods described herein. [Brief explanation of the drawings]

[0068] Some embodiments of the present invention are herein described, by way of example only, with reference to the accompanying drawings. Referring now in detail to the drawings in particular, it is emphasized that the details shown are by way of example and for illustrative purposes of explaining embodiments of the invention. In this regard, the description given in the drawings will make apparent to those skilled in the art how embodiments of the present invention may be practiced.

[0069] [Figure 1A] 1 is a general flowchart of a process for predicting a patient's improvement after treatment, e.g., neurofeedback treatment, according to some exemplary embodiments of the present invention. [Figure 1B] FIG. 1 illustrates a flow chart of NF treatment including improvement prediction points, according to some exemplary embodiments of the present invention. [Figure 1C] 1A-1C are graphs showing changes in signals indicative of activity of at least one brain network, as well as changes in improvement prediction scores derived from signals indicative of activity, during and after NF treatment, according to some exemplary embodiments of the present invention. [Figure 2] 1 is a detailed flowchart of a process for predicting a patient's improvement during and / or after NF treatment, according to some exemplary embodiments of the present invention. [Figure 3A] 1 is a block diagram of a system for predicting NF treatment delivery and patient improvement, optionally online during the delivery of a NF treatment session, according to some exemplary embodiments of the present invention. FIG. [Figure 3B] FIG. 1 is a block diagram of a system for delivering NF therapy in communication with a remote device used to predict patient improvement, optionally offline, according to some exemplary embodiments of the invention. [Figure 4] 3C is a detailed flowchart of a process for calculating a patient improvement prediction score performed by a system, such as one or both of the systems described in FIGS. 3A and 3B, according to some exemplary embodiments of the present invention. [Figure 5A] 1 is a flowchart of a process for processing brain region activity indices to calculate an improvement prediction score, according to some exemplary embodiments of the present invention. [Figure 5B] 1 is a flowchart of an NF treatment session, according to some exemplary embodiments of the present invention. [Figure 6] 1 is a flowchart describing a process for generating a predictive model, according to some exemplary embodiments of the present invention. [Figure 7] 10 is a heatmap showing an example of model accuracy scores. [Figure 8] Graph showing an example of change in predicted score in patients who did not show improvement. [Figure 9] Graph showing an example of change in predicted score in patients who showed improvement. [Figure 10] 1 is a table showing examples of change in predicted scores at each treatment session for three different patients. DETAILED DESCRIPTION OF THE INVENTION

[0070] In some embodiments, the present invention relates to predicting the improvement of a patient's condition in response to a treatment, and more particularly, but not exclusively, to predicting the improvement of a patient's condition in response to a NF treatment.

[0071] A broad aspect of some embodiments relates to predicting the prognosis of a subject suffering from at least one psychotic symptom, e.g., a subject receiving at least one treatment for treating the psychotic symptom. In some embodiments, the prognosis prediction includes predicting the subject's progress in a treatment process, e.g., NF treatment, or a combination treatment including NF treatment and at least one additional treatment, e.g., pharmaceutical treatment and / or psychological treatment. In some embodiments, the subject's progress is predicted based on signals, e.g., EEG signals, measured from the subject's brain, indicative of activity or changes in activity of at least one specific neural network, optionally associated with a specific brain region. In some embodiments, the measured EEG signals indicate activity of at least one specific brain region, e.g., a brain region associated with a specific brain network. Optionally, the EEG signals indicate activity of at least one specific brain region or multiple brain regions, e.g., multiple brain regions associated with at least one specific brain network. In some embodiments, the at least one specific brain network comprises a limbic brain network, and the one or more specific brain regions comprise at least one of the amygdala, thalamus, hypothalamus, and hippocampus. In some other embodiments, the at least one specific brain network comprises a reward brain network, and the one or more specific brain regions comprise at least one of the ventral striatum, ventral pallidum, anterior cingulate cortex, and orbitofrontal cortex. Alternatively or additionally, the at least one specific brain network comprises a response inhibition brain network, and the one or more specific brain regions comprise at least one of the right inferior frontal gyrus and right anterior insula.

[0072] In some embodiments, the signal is measured in time relation to the treatment, e.g., before, during, and / or after the treatment. In some embodiments, predicting the subject's progression includes predicting the subject's improvement after one or more treatment sessions of the treatment, predicting the subject's progress toward a desired, e.g., treatment target, goal, or lack of improvement or progress. Alternatively or additionally, predicting the subject's progression includes predicting progress relative to a previous state, progress relative to a previously measured value, progress relative to a reference state or value, and / or progress relative to a baseline state or value.

[0073] One aspect of some embodiments relates to predicting the prognosis, e.g., improvement or decline, of a subject receiving treatment, e.g., neurofeedback (NF) treatment, during treatment. In some embodiments, improvement is predicted based on signals measured from the subject's brain, e.g., EEG signals, which indicate the activity or change in activity of at least one brain network, e.g., a network associated with at least one specific brain region. As used herein, predicting a subject's improvement means predicting an improvement or lack of improvement in at least one of the subject's condition, the subject's mental state, symptoms of the mental state, the subject's ability to comply with treatment, and the subject's ability to reach the desired goal of treatment, e.g., the desired outcome of a clinical assessment of the subject's condition after treatment.

[0074] According to some embodiments, the subject is a patient who has been diagnosed with or is suspected of having, or is predicted to have, a mental disorder in the future, such as a stress disorder, depression, post-traumatic stress disorder (PTSD), major depressive disorder (MDD), bipolar disorder (BPD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), substance use disorder (SUD), or any other disorder affecting mental status, such as those described in The Diagnostic and Statistical Manual of Mental Disorders (DSM), e.g., the fifth edition of the DSM (DSM-V).

[0075] According to some exemplary embodiments, the prognosis, improvement or decline prediction is performed with an accuracy of at least 50%, e.g., at least 60%, at least 70%, at least 80%, at least 90%, or any intermediate, smaller or larger percentage value.

[0076] According to some embodiments, the overall treatment or at least one parameter of the treatment is modified based on the predicted improvement. In some embodiments, the at least one parameter includes at least one of the number of treatment sessions of the treatment, the interval between two consecutive treatment sessions, the length of the treatment session, the number and / or length of training blocks in each treatment session, and / or the behavior of stimuli delivered to the patient during the treatment (e.g., noise level, rate, threshold). In some embodiments, if the predicted improvement indicates no predicted improvement or low predicted improvement in one or more consecutive treatment sessions or in the overall treatment, the treatment is stopped. Alternatively, if the predicted improvement indicates no predicted improvement or low predicted improvement in one or more consecutive treatment sessions, one or more additional treatment sessions are added and / or modified. Alternatively or additionally, if the predicted improvement shows no predicted improvement or low predicted improvement in one or more consecutive treatment sessions, the treatment, e.g., NF treatment, is combined with an additional treatment, e.g., a drug treatment, and / or the adjunctive treatment (e.g., a pharmaceutical agent) is changed.

[0077] According to some embodiments, if the predicted improvement indicates subject improvement after one or more treatment sessions, treatment is shortened. Alternatively, if the predicted improvement indicates subject improvement after one or more treatment sessions, the baseline level of challenge or stimulus presented to the subject is increased in at least one additional treatment session.

[0078] According to some embodiments, the prognostic score is generated based on signals recorded from the subject's brain, e.g., EEG signals. In some embodiments, the EEG signals are processed, and the prognostic score is calculated using the processed signals and one or more prognostic prediction software. In some embodiments, the system for calculating the prognostic score generates an indicator, e.g., a human-detectable indicator, based on the prognostic score. Optionally, the indicator is generated by at least one of an expert, e.g., a physician, technician, or any person trained to monitor and / or control the NF treatment and / or prediction process, and / or an expert interface configured to generate and deliver the indicator to a remote device in communication with the system. Optionally, the remote device comprises the expert interface.

[0079] According to some embodiments, if the calculated prognostic score indicates that the subject is predicted to reach a desired goal after or during the NF treatment, an indicator is generated by the system. In some embodiments, the desired goal includes at least one of a target score on a clinical assessment test, a reference value or baseline score for the NF treatment, and / or a desired change in the clinical assessment test score compared to a desired clinical or behavioral outcome. In some embodiments, if the calculated prognostic score indicates that the subject is predicted to reach a desired goal after or during the NF treatment, the generated indicator includes instructions or suggestions for modifying at least one parameter of the NF treatment and / or modifying at least one parameter of an additional treatment delivered to the subject via the NF treatment. In some embodiments, if the calculated prognostic score indicates that the subject is predicted to reach the desired goal after or during NF treatment, the generated indication includes instructions to at least one of: shortening the NF treatment, shortening the NF treatment sessions, lengthening the interval between treatment sessions, ceasing delivery of the additional treatment, modifying the dosage of a drug administered to the subject in the additional treatment, modifying the frequency of drug therapy or other treatment methods such as transcranial magnetic stimulation (TMS).

[0080] According to some embodiments, if the calculated prognostic score indicates that the subject will not be able to reach the desired goal after or during the NF treatment, the generated indicator includes instructions to at least one of: stop the NF treatment, or add at least one additional NF treatment session to the NF treatment, start at least one additional treatment in addition to the NF treatment, start or modify at least one of pharmaceutical treatment, psychological treatment, cognitive behavioral therapy (CBT) treatment and / or psychotherapy treatment, and / or TMS treatment.

[0081] According to some embodiments, NF treatment includes one or more NF sessions configured to teach a subject self-regulation, e.g., self-regulation of activity of at least one specific brain network. In some embodiments, NF treatment teaches a subject to self-regulate activity of at least one specific brain network, e.g., by instructing the subject to self-apply at least one cognitive strategy, e.g., a cognitive task, and provides the subject with instructions regarding activity or changes in activity of at least one specific brain network before, during, and / or after self-applying the at least one cognitive strategy. In some embodiments, the indicator provided to the subject is generated based on electrical signals, e.g., EEG signals, measured from the subject's brain, which are processed using a signature, e.g., an EEG fingerprint (EFP), to identify EEG signals or parameters thereof within the measured EEG signals that indicate activity or changes in activity of at least one specific brain network. In some embodiments, the measured EEG signals are processed using EFP, e.g., as described in WO2012 / 104853 A2, the entire contents of which are incorporated herein by reference.

[0082] According to some embodiments, the cognitive strategy, e.g., cognitive task, comprises a cognitive or mental exercise performed by the subject.

[0083] According to some embodiments, a subject's improvement is predicted solely based on signals measured from the subject's brain during treatment, e.g., before, during, and / or after at least one treatment session of treatment. Alternatively, a subject's potential improvement after NF treatment is predicted prior to initiating treatment, e.g., based on EEG signals measured from the subject prior to initiating NF treatment, and / or based on the subject's diagnosis and / or demographics.

[0084] According to some embodiments, prognosis, e.g., improvement of a subject, is predicted without the need to use any subjective tools, e.g., questionnaires, expert interviews of the subject, and / or expert observation of the subject.

[0085] According to some exemplary embodiments, prognosis, e.g., subject improvement, is predicted based on measurements of physiological parameters, e.g., EEG signals, blood pressure measurements, heart rate measurements, and / or any other measurements of physiological parameters, measured between treatment sessions. In some embodiments, the physiological parameters are measured in the subject's home. Optionally, an existing prediction of prognosis is updated based on the measured physiological parameters.

[0086] According to some embodiments, predicting the prognosis includes predicting the success of a trainee undergoing or to undergo NF training, also referred to herein as NF treatment, in reducing or increasing a condition, e.g., a clinical assessment questionnaire score, indicative of an improvement in the trainee's mental and / or cognitive condition.

[0087] In some embodiments, predicting the prognosis includes predicting the trainee's success in reducing their score on the CAPS-5 assessment questionnaire by at least 1 point, e.g., at least 12 points, or at least 6 points, compared to their baseline score after one or more training sessions, e.g., after a treatment session, or after one or more weeks of NF training.

[0088] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their score on the PTSD Checklist for DSM-5 (PCL-5) assessment questionnaire by at least 1 point, e.g., at least 3 points, compared to their baseline score after one or more training sessions, or one or more weeks after NF training. In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their score on the PTSD Checklist for DSM-5 (PCL-5) assessment questionnaire by at least 1 point after completing NF training, e.g., at least one week after completing NF training.

[0089] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their Hamilton Depression Rating Scale (HDRS) score by at least 1 point, e.g., at least 3 points, compared to their baseline score after one or more training sessions, or after one or more weeks of NF training.

[0090] In some embodiments, predicting the prognosis includes predicting the trainee's success in reducing their Hamilton Depression Rating Scale (HDRS) score by at least 1 point, e.g., at least 3 points, after completing NF training, e.g., at least one week after completing NF training.

[0091] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their Snaith-Hamilton Pleasure Scale (SHAPS) score by at least 1 point, e.g., at least 4 points, compared to their baseline score after one or more training sessions, or after one or more weeks of NF training.

[0092] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their Snaith-Hamilton Pleasure Scale (SHAPS) score by at least 1 point, e.g., at least 4 points, after completing NF training, e.g., at least one week after completing NF training.

[0093] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their Montgomery-Asberg Depression Rating Scale (MADRS) score by at least 1 point, e.g., at least 6 points, compared to their baseline score after one or more training sessions, or one or more weeks after NF training.

[0094] In some embodiments, predicting the prognosis includes predicting the trainee's success in reducing their Montgomery-Asberg Depression Rating Scale (MADRS) score by at least 1 point, e.g., at least 6 points, after completing NF training, e.g., at least one week after completing NF training.

[0095] In some embodiments, predicting the outcome includes predicting the trainee's success in reducing their Patient Health Questionnaire-9 (PHQ-9) test score by at least 1 point, e.g., at least 6 points, compared to their baseline score after one or more training sessions, or after one or more weeks of NF training.

[0096] In some embodiments, predicting the prognosis includes predicting the trainee's success in reducing their Patient Health Questionnaire-9 (PHQ-9) test score by at least 1 point, e.g., at least 6 points, after completing NF training, e.g., at least one week after completing NF training.

[0097] In some embodiments, predicting the outcome includes predicting the trainee's success in changing their score on the Emotion Regulation Questionnaire (ERQ) or a component thereof by at least 1 point. For example, in some embodiments, predicting the outcome includes predicting an increase of at least 1 point on a cognitive reappraisal scale, e.g., on the cognitive reappraisal facet of the ERQ, after one or more training sessions, or after one or more weeks of NF training, compared to a baseline or reference score.

[0098] In some embodiments, predicting the outcome includes predicting the trainee's success in increasing their score on a cognitive reappraisal measure, e.g., the cognitive reappraisal facet of the ERQ, by at least one point after completing NF training, e.g., at least one week after completing NF training.

[0099] A potential advantage of using signals measured from a subject's brain may be that the signals allow for having an objective tool to predict a subject's progress, e.g., improvement, during or after treatment, thereby allowing for more accurate and unbiased assessment and / or prediction.

[0100] As used throughout this application, the term "specific brain networks" is optionally interchangeable with the term "specific brain regions."

[0101] Before describing at least one embodiment of the present invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of components and / or methods set forth in the following description and / or illustrated in the drawings and / or examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

[0102] Exemplary General Patient Improvement Predictions According to some exemplary embodiments, a patient suffering from one or more symptoms of a psychiatric disorder, e.g., a psychiatric illness, is subjected to a prediction process configured to predict improvement in one or more of the symptoms after a specific period of time and / or after a specific treatment. Alternatively or additionally, the prediction process is configured to predict improvement in the patient's cognitive state after a specific period of time and / or after a specific treatment. Alternatively or additionally, the prediction process is configured to predict the patient's ability to achieve a target improvement, e.g., a target clinical improvement, after a specific treatment. In some embodiments, the target clinical improvement is a target clinical improvement measured by a cognitive scale used to measure improvement in a patient's state for a specific psychiatric disorder, e.g., a stress disorder, post-traumatic stress disorder scale, or depression scale.

[0103] Reference is now made to FIG. 1A, which illustrates an overall process for predicting patient improvement, according to some exemplary embodiments of the present invention.

[0104] According to some exemplary embodiments, signals from the brain, e.g., EEG signals, are measured in block 102. In some embodiments, the signals are electrical signals measured by one or more electrodes placed on the subject's head, e.g., by one or more electrodes placed on the subject's scalp. In some embodiments, the signals are measured by one or more electrodes placed on the subject's scalp, e.g., 2-6 electrodes, 3-10 electrodes, 3-22 electrodes, or any intermediate, fewer, or greater number of electrodes.

[0105] According to some exemplary embodiments, the signal is measured in temporal relation to a treatment session, for example, before, during, and / or after a treatment session, hi some embodiments, the treatment session is a treatment session for NF treatment, which may include one or more treatment sessions.

[0106] According to some exemplary embodiments, the signal is measured from the brain of a subject suffering from or diagnosed with a psychiatric disorder. In some embodiments, NF treatment is provided to the subject to reduce one or more symptoms associated with the psychiatric disorder and / or to prevent the occurrence of one or more symptoms. In some embodiments, the subject is a healthy individual who desires to build resilience to stress or improve at least one of wellness, concentration, and memory.

[0107] According to some exemplary embodiments, an indicator of activity or changes in activity of at least one specific brain network is generated by block 104. In some embodiments, the indicator is generated using the EEG signals measured in block 102. In some embodiments, the indicator is generated by processing the measured EEG signals having a predetermined signature, e.g., a predetermined electrical fingerprint (EFP), of the at least one specific brain network. Optionally or alternatively, the indicator is an indicator of activity or changes therein of a neural network, e.g., a neural network associated with at least one specific brain region, and the EFP is the EFP of the neural network.

[0108] According to some exemplary embodiments, at least one particular brain network is associated with activity in subcortical brain structures, such as limbic structures or brain regions, hi some embodiments, these brain regions include the amygdala and / or the limbic system.

[0109] According to some exemplary embodiments, patient improvement is predicted at block 106. In some embodiments, patient improvement during and / or after treatment is predicted at block 106. In some embodiments, patient improvement is predicted based on one or more parameters of the generated index. Additionally or optionally, patient improvement is predicted based on one or more parameters of at least one of the treatment, the patient's clinical condition, the patient's history, the patient's medical history, the patient's medication, and the patient's personal condition, e.g., age and / or gender.

[0110] According to some exemplary embodiments, predicting patient improvement in block 106 includes predicting that the patient will reach a desired goal, e.g., a desired clinical goal or a desired change relative to a reference or baseline, at the end of, after, and / or during treatment. In some embodiments, the desired goal includes a target score on at least one measurement scale that measures one or more symptoms of a disease or disorder, e.g., a psychiatric disorder. Alternatively or additionally, the desired goal includes a target score on at least one measurement scale that measures the patient's cognitive state. Alternatively or additionally, the desired goal includes a target score on at least one measurement scale that measures the patient's likelihood of developing one or more symptoms of a disease or disorder and / or that measures the patient's future cognitive state.

[0111] Exemplary prediction of treatment outcome According to some exemplary embodiments, prediction of the treatment outcome, e.g., improvement or lack thereof, of the patient's condition, is performed during the treatment, hi some embodiments, the prediction is performed during or after a treatment session of the treatment, e.g., during and / or after the first treatment session or any other treatment session.

[0112] Reference is now made to FIG. 1B, which illustrates prediction of treatment outcome, according to some exemplary embodiments of the present invention.

[0113] According to some exemplary embodiments, the treatment 120, e.g., the NF treatment, includes one or more separated treatment sessions, e.g., a first treatment session 122 and a second treatment session 124. In some embodiments, the treatment 120 includes multiple treatment sessions, e.g., up to 5 treatment sessions, up to 10 treatment sessions, up to 15 treatment sessions, up to 20 treatment sessions, up to 25 treatment sessions, up to 30 treatment sessions, or any intermediate, fewer, or greater number of treatment sessions. In some embodiments, the treatment sessions are separated by an interval 126, e.g., a fixed or variable period between successive treatment sessions.

[0114] According to some exemplary embodiments, treatment ends with a final treatment session 128, followed by a post-treatment period 130. In some embodiments, during each treatment session, e.g., an NF treatment session, the patient receives a feedback signal indicative of activity or changes in at least one specific brain network. In some embodiments, the specific brain network includes at least one of the limbic system, the mesolimbic system, a cognitive or executive control network, a positive valence system network, and a negative valence system network. In some embodiments, the feedback signal is generated based on signals, e.g., EEG signals, measured from the subject's brain during the treatment session. In some embodiments, the feedback signal is delivered to the patient while the patient is trained to self-regulate the activity of the at least one specific brain network, e.g., by performing at least one cognitive task.

[0115] According to some exemplary embodiments, the training sessions are conducted at the patient's clinic and / or home using a device configured to measure EEG signals and provide feedback signals to the patient. In some embodiments, during interval period 126, the patient optionally engages in daily activities without receiving any feedback signal regarding the activity of at least one specific brain network based on EEG measurements. Optionally, during interval period 126, the patient performs at least one cognitive task practiced during the training session without receiving any EEG-based feedback signal. In some embodiments, during a post-treatment period beginning when the last treatment session 128 ends, the patient optionally engages in daily activities without receiving any feedback signal regarding the activity of at least one specific brain network based on EEG measurements. Optionally, during post-treatment 130, the patient performs at least one cognitive and / or emotional task practiced during the training session without receiving any EEG-based feedback signal.

[0116] According to some exemplary embodiments, a prognosis, e.g., a treatment outcome or progress, is predicted during a first treatment session. Alternatively or additionally, the treatment outcome is predicted at the end of the first treatment session, e.g., at prediction point 132, based on EEG signals measured during or at the end of treatment session 122. Alternatively or additionally, the prognosis, e.g., a treatment outcome, is predicted at prediction points 134 and 140 during interval period 126 between two consecutive treatment sessions, based on EEG signals measured during interval period 126. In some embodiments, a prognosis is predicted during and / or at the end of each treatment session or several treatment sessions, e.g., at prediction points 136, 138, and 142.

[0117] According to some exemplary embodiments, the outcome of the treatment is predicted during and / or after at least one additional treatment session, eg, treatment session 124.

[0118] According to some exemplary embodiments, predicting the outcome of a treatment includes predicting a patient's clinical improvement compared to a reference state after at least one treatment session, and / or at the end of treatment, and / or during a post-treatment period 130. In some embodiments, predicting clinical improvement includes predicting improvement in one or more symptoms of a disease or disorder, e.g., a psychiatric disorder. In some embodiments, predicting clinical improvement includes predicting achievement of a target change in a desired direction in the patient's clinical state, e.g., a score on a test measuring cognitive state.

[0119] Reference is now made to FIG. 1C, which illustrates a prediction of improvement in a subject undergoing NF treatment based on activity or changes therein of at least one specific brain network, according to some exemplary embodiments of the present invention.

[0120] According to some exemplary embodiments, a subject, e.g., a patient or subject suffering from at least one symptom of a psychiatric disorder, receives NF treatment 150. In some embodiments, NF treatment 150 is delivered to the subject over at least one NF treatment session, e.g., multiple treatment sessions beginning with session 1 and ending with session (n) 154, where (n) indicates the number of treatment sessions. In some embodiments, the number of treatment sessions is individualized for the subject and / or the particular clinical condition. Optionally, the number of planned treatment sessions is altered during the NF treatment, e.g., based on at least one of the subject's performance during the NF treatment, the subject's clinical condition, and / or a prediction of the subject's improvement during, at the end of, and / or after NF treatment 150.

[0121] According to some exemplary embodiments, during NF treatment, the patient practices self-regulation of activity of at least one specific brain network, e.g., a brain network associated with activity of the amygdala, a limbic brain region. In some embodiments, the at least one specific brain region is associated with a deep brain region, e.g., a subcortical brain region. In some embodiments, the subject practices self-regulation of activity of at least one specific brain network by performing a cognitive task, optionally while engaging in stimuli, e.g., challenges, selected to affect activation of the at least one specific brain network or to induce at least one symptom of a psychiatric disorder.

[0122] According to some exemplary embodiments, during an NF treatment session, a subject receives a feedback signal indicative of activity of, or changes in, at least aspects of a specific brain network based on EEG signals measured from the subject's brain, e.g., by one or more electrodes placed on the subject's head. In some embodiments, the one or more electrodes are non-invasive electrodes. In some embodiments, the feedback signal is delivered to the subject online, e.g., with a time delay of less than 60 seconds, less than 30 seconds, less than 15 seconds, less than 5 seconds, or any intermediate, lesser, or greater time delay, from the measurement of the EEG signals.

[0123] According to some exemplary embodiments, the feedback signal is derived from an activity index signal calculated from measured EEG signals indicative of activity or changes thereof in at least one specific brain network, e.g., activity signal 156. In some embodiments, activity signal 156 is delivered to the patient as the feedback signal. Alternatively, the activity signal is stored in a memory of the device and, optionally, used to predict patient improvement during and / or after NF treatment. Optionally, the prediction of patient improvement is based on an improvement prediction score 158 calculated from activity signal 156. In some embodiments, the improvement prediction score is cumulative, e.g., an improvement prediction score calculated at the end of an NF treatment session is based on activity signals calculated during the session and on improvement prediction scores calculated in one or more previous NF treatment sessions. Optionally, calculating a cumulative improvement prediction score increases the accuracy and / or stability of the prediction.

[0124] According to some exemplary embodiments, the improvement prediction score is calculated at the end of and / or during one or more treatment sessions. In some embodiments, there is a correlation between the activity signal 156 or a change therein and the improvement that predicts the score 158 or a change therein. In some embodiments, a change in the activity signal 156 precedes a change in the improvement prediction score. In some embodiments, the improvement prediction score 158 predicts an improvement in the patient's clinical condition, e.g., an improvement in the patient's mental state, during the NF treatment 150, at the end of the last NF treatment session, e.g., treatment session S(n), and / or after the NF treatment, in the post-treatment 160 period.

[0125] Exemplary predicted prognosis Reference is now made to FIG. 2, which illustrates a process for predicting prognosis, eg, improvement after NF treatment, according to some exemplary embodiments of the present invention.

[0126] According to some exemplary embodiments, predicting improvement after NF treatment includes predicting improvement after at least one or all planned treatment sessions of the NF treatment. In some embodiments, predicting improvement after NF treatment includes predicting improvement in a condition, e.g., a cognitive condition, of a subject receiving the NF treatment. Optionally, predicting improvement after NF treatment includes predicting a change in at least one scale measuring at least one parameter of the subject's condition after at least one treatment session or all treatment sessions of the planned NF treatment.

[0127] According to some exemplary embodiments, at least one treatment session of the planned NF treatment is delivered to a subject, e.g., a patient, in block 202. In some embodiments, during the treatment session, the patient is trained to control activation of at least one specific brain network, e.g., a brain network associated with the subcortical brain region of the limbic system, by applying cognitive tasks. Optionally, the cognitive tasks are selected to influence activation of the patient's at least one specific brain network. In some embodiments, during the treatment session, the patient receives online feedback signals indicative of activity levels of specific brain networks based on measured EEG signals, for example, as described in WO 2012 / 104853 A2, the entire contents of which are incorporated herein by reference. In some embodiments, the activity level of at least one particular brain network is determined using at least one signature, e.g., an electrical fingerprint (EFP) of a brain region associated with the particular brain network, e.g., the amygdala signature described in WO2012 / 104853 A2 or the ventral striatum signature described in WO2021 / 260697 A1.

[0128] According to some exemplary embodiments, EEG signals are measured in block 204. In some embodiments, the EEG signals are measured before, during, and / or after the treatment session. In some embodiments, the EEG signals are measured in block 204 after initiating the NF treatment session in block 203. In some embodiments, the EEG signals are measured by at least one electrode placed on the subject's head, e.g., placed on the subject's scalp. In some embodiments, the EEG signals are measured by multiple electrodes placed on the subject's head.

[0129] According to some exemplary embodiments, at least one indicator of specific brain network activity or changes therein is generated in block 206. In some embodiments, the indicator is generated based on the measured EEG signal without additional scan data, e.g., without simultaneously receiving signals from a functional magnetic resonance imaging (fMRI) device. In some embodiments, the indicator is generated and delivered to the patient online, e.g., with a delay of less than 30 seconds, e.g., less than 20 seconds, less than 10 seconds, less than 5 seconds, less than 3 seconds, less than 1 second, or any intermediate, shorter, or longer time delay, from the measurement of the EEG signal in block 204. Optionally, the indicator is delivered to the patient as a feedback signal that provides the patient with feedback regarding the activation of specific brain networks or changes therein. Additionally or optionally, the feedback signal is feedback regarding the patient's ability to affect the activation of at least one specific brain network, e.g., by applying at least one cognitive task, as described in block 202.

[0130] According to some exemplary embodiments, the index is generated in block 206 by processing the measured EEG signals using at least one EFP of a particular brain network. In some embodiments, processing the measured EEG signals using the EFP and / or other or additional calculations includes using the EFP to isolate a subset of EEG signals from the measured EEG signals recorded by selected electrodes having selected frequency bands measured at a particular time point. In some embodiments, the EFP includes information about selected electrodes, selected frequency bands, and selected measurement time points that indicate activity in the particular brain network. In some embodiments, the EFP is a function that allows for correlation between activation of a particular brain network at a particular time period measured by, for example, fMRI and a subset of measured EEG signals, optionally measured at a time delay from the activation measured by fMRI of the particular brain network.

[0131] According to some exemplary embodiments, an improvement prediction score is calculated at block 208. In some embodiments, the improvement prediction score is calculated based on the index generated in block 206. Alternatively or additionally, the improvement prediction score is calculated based on the EEG signal measured in block 204. In some embodiments, the improvement prediction score is calculated using one or more parameters, e.g., one or more features of the index generated in block 206. Alternatively or additionally, the improvement prediction score is generated based on at least one of one or more parameters of the NF treatment, the patient's clinical state, the patient's cognitive state, and the patient's medication.

[0132] According to some exemplary embodiments, the one or more parameters of the index include at least one of a measure of central tendency, variance, entropy of the NF cross-sections, or differences in these measures between NF cross-sections, or one or more other characteristics of the index. In some embodiments, the one or more parameters of the NF treatment include at least one of the number of treatment sessions, the length of the treatment sessions, the interval between successive treatment sessions, or any statistical manipulation thereof.

[0133] According to some exemplary embodiments, the improvement prediction score is calculated during at least one treatment session, at the end of a treatment session, and / or during two consecutive treatment sessions, optionally at a prediction point, for example, as shown in FIG. 1B.

[0134] According to some exemplary embodiments, the improvement prediction score is used to predict improvement in the patient's condition, e.g., cognitive and / or mental condition, after at least one NF treatment session in block 210. In some embodiments, the improvement prediction score is used to predict improvement in the patient's condition after the first treatment session and / or after at least one additional treatment session.

[0135] According to some exemplary embodiments, the improvement prediction score is used in block 212 to predict improvement in the patient's condition at the end of the NF treatment, for example, when completing the last treatment session of the planned NF treatment.

[0136] According to some exemplary embodiments, the improvement prediction score is used to predict improvement in the patient's condition in a period after the end of treatment, in block 214. In some embodiments, the improvement prediction score is used to predict improvement in the patient's condition in the post-treatment period 130 shown in FIG. 1B, e.g., during or at the end of the post-treatment period 130. In some embodiments, the post-treatment period lasts at least one week, e.g., at least one month, at least two months, at least three months, at least six months, or at least one year, after the end of the last treatment session.

[0137] According to some exemplary embodiments, if the predicted improvement is not the target improvement, e.g., the desired improvement, at least one parameter of the NF treatment is altered in block 216. In some embodiments, if the predicted improvement is less than the target improvement, e.g., if the predicted improvement is at least 80% less, e.g., at least 70% less, at least 60% less, at least 50% less, at least 40% less, at least 30% less, or any intermediate, smaller, or larger percentage value, from the target improvement, the NF treatment or NF treatment sessions are stopped. Alternatively, if the predicted improvement is less than the target improvement, e.g., if the predicted improvement is less than 5%, 10%, 15%, 20%, 25%, 30% less than the target improvement, the number and / or length of treatment sessions is increased. Optionally, if the predicted improvement is less than the target improvement, the NF treatment is stopped and / or an additional treatment, e.g., a pharmaceutical treatment, is added or altered. Optionally, if the predicted improvement is less than the target improvement, one or more additional treatment sessions, eg, booster treatment sessions, are added to the NF treatment.

[0138] According to some exemplary embodiments, if the predicted improvement is better than the target improvement, the NF treatment is stopped or shortened, e.g., the number of planned treatment sessions is reduced. Alternatively, if the predicted improvement is better than the target improvement, the duration of the treatment session is shortened. Alternatively, if the predicted improvement is better than the target improvement, the challenge presented to the patient during the NF treatment session, e.g., the level and / or complexity of the stimulation, is increased.

[0139] According to some exemplary embodiments, the NF therapy, e.g., at least one parameter of the NF therapy, is altered automatically, e.g., by a device that generates the prediction. Optionally, the device delivering the NF therapy automatically alters the NF therapy, optionally without sending an indication regarding the alteration to the patient and / or the professional. Alternatively or additionally, the professional alters the NF therapy, e.g., based on one or more indications generated by a device, e.g., a device that generates the prediction.

[0140] According to some exemplary embodiments, if the predicted improvement is not the target improvement, e.g., the desired improvement, at least one parameter of the overall treatment delivered to the patient is modified in block 218. In some embodiments, if the predicted improvement is not the target improvement, e.g., if the predicted improvement is less than the target improvement, the NF treatment is combined with psychological treatment and / or pharmaceutical treatment using at least one bioactive drug. Alternatively, if the predicted improvement is not the target improvement, e.g., if the predicted improvement is less than the target improvement, the NF treatment is replaced with psychological treatment and / or pharmaceutical treatment using at least one bioactive drug.

[0141] According to some exemplary embodiments, if the predicted improvement is greater than the target improvement, at least one additional treatment delivered to the patient in addition to the NF treatment, e.g., psychological treatment, cognitive behavioral treatment (CBT), psychotherapeutic treatment, transcranial magnetic stimulation (TMS), and / or pharmaceutical treatment, is stopped or modified, e.g., shortened. Alternatively, the at least one additional treatment is initiated based on the predicted improvement. In some embodiments, if the predicted improvement is greater than the target improvement, the additional pharmaceutical treatment delivered to the subject along with the NF treatment is modified, e.g., stopped, or at least the dosing of a biological agent, e.g., a drug, is modified, e.g., reduced.

[0142] According to some exemplary embodiments, the at least one additional treatment is modified, initiated or stopped based on the predicted prognosis, eg, predicted improvement.

[0143] Exemplary System Reference is now made to Figures 3A and 3B, which illustrate a system for delivery of NF therapy and prediction of patient improvement, according to some exemplary embodiments of the present invention.

[0144] According to some exemplary embodiments, the system 302 includes a control unit 304, a patient interface 306, and a supervisor interface 308. In some embodiments, the patient interface 306 and the supervisor interface are operatively coupled to a control circuit 310 of the control unit 304. In some embodiments, the control unit 304 further includes a memory 312 that stores at least one algorithm and / or lookup table for use in determining the activity level of at least one specific brain network. Alternatively or additionally, the memory 312 includes at least one EFP, e.g., an EFP model of at least one specific brain region, e.g., the EFP model described in WO 2021 / 260697 A1, which is incorporated herein by reference in its entirety. In other embodiments, the EFP model is stored within the control unit's memory 312.

[0145] According to some exemplary embodiments, the control unit 304 includes an EEG recording unit 314 operatively coupled to the control circuitry 310. In some embodiments, the EEG recording unit 314 delivers signals from one or more electrodes placed on the subject's head to the control circuitry 310. In some embodiments, the control circuitry 310 is configured to process the received signals and analyze the processed signals to determine the activity level of at least one particular brain network and / or a relationship between the determined activity and at least one reference value or indicator thereof stored in the memory 312. In some embodiments, the control circuitry 310 determines the activity level or the relationship between the activity level and the at least one reference value or indicator thereof using at least one of an algorithm, a look-up table, and / or an EFP stored in the memory 312, for example, as described in WO2021 / 260697 A1 or WO2012 / 104853 A2, the entireties of which are incorporated herein by reference.

[0146] According to some exemplary embodiments, the EFP is specific to a brain network, e.g., a network associated with activity in deep brain regions, and / or subcortical brain regions, and / or limbic or mesolimbic brain regions, and / or the amygdala. In some embodiments, the control circuitry uses the EFP to isolate a subset of EEG signals indicative of activity of a particular brain network from the EEG signals received by the EEG recording unit. In some embodiments, the EFP includes information about the specific electrodes, specific frequency bands, and specific recording time points of the subset of EEG signals.

[0147] According to some exemplary embodiments, the EEG recording unit is operatively coupled to at least one electrode, e.g., a plurality of electrodes 316 and 318, disposed on the head 612 of the patient 322. In some embodiments, the plurality of electrodes includes 2, 3, 4, 5, 6, 7, 8, 9, 10, or any greater number of electrodes disposed on, and optionally attached to, the head 320 of the patient 322. Optionally, the plurality of electrodes is arranged in an array. In some embodiments, the electrodes are positioned at specific locations on the subject's head, e.g., at one or more of positions Fz, C3, C4, Cz, FCz, P3, Pz, and P4 of an extended 10-20 coordinate system. Alternatively, the electrodes are positioned at any position or combination of positions of an extended 10-20, 10-10, or any well-established coordinate system. In some embodiments, the control unit 304 is optionally connectable to at least one speaker or earphone 324 configured to deliver audio signals to the patient 322. In some embodiments, when the system is used to treat a stress disorder, e.g., PTSD, the electrodes are placed at locations FCz and Pz, or Fz, Cz, and Pz. In some embodiments, when the system is used to treat depression, e.g., MDD, the electrodes are placed at locations FCz, Cz, Pz, C3, C4, P3, and P4.

[0148] According to some exemplary embodiments, the control unit includes a communications circuit 326 configured to receive and / or deliver signals, e.g., wireless signals, to a remote device located outside the supervisor clinic, e.g., a remote computer, a remote server, or a remote cloud. In some embodiments, the remote device stores at least one algorithm, look-up table, and / or EFP. In some embodiments, the control unit 304 transmits electrical signals or processed electrical signals to the remote device via the communications circuit 326 and receives signals via the communications circuit 326 indicative of activity levels of particular brain networks and / or relationships between the activity levels of the brain networks and reference values ​​indicative of target activation levels of the brain networks.

[0149] Alternatively, the control circuitry 310 generates at least one signal indicative of the activity level of a particular brain network and / or a relationship between the activity level of the brain network and a reference value indicative of a target activation level of the brain network based on processing the received EEG signals using the EFP.

[0150] According to some exemplary embodiments, the control circuitry 310 sends signals to the patient interface 306 to deliver a feedback signal, for example, to display a visual signal on a screen, and / or to provide an audio signal, and / or to provide a tactile sensation, and / or to deliver an olfactory signal indicative of activity of at least one specific brain network based on the signals generated by the control circuitry. In some embodiments, the feedback signal changes continuously in response to the activation level of at least one specific brain network, or a change in the activation level relative to a baseline value. In some embodiments, the delivered feedback signal is updated every 1 second, 5 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, or any intermediate, smaller, or larger value based on signals received from electrodes, e.g., electrodes 316 and 318, and / or signals generated by the control circuitry 310 when processing EEG signals received using the EFP.

[0151] According to some exemplary embodiments, the patient interface 306 varies the feedback signal delivered to the patient, e.g., the visual interface presented to the patient, with a delay of about 1 second, 2 seconds, about 5 seconds, about 10 seconds, about 15 seconds, about 20 seconds, about 25 seconds, or any intermediate, smaller, or larger value relative to the timing at which the electrical signal is received from the electrodes 316 and 318. In some embodiments, the patient interface includes a display and / or a speaker.

[0152] According to some exemplary embodiments, the feedback signal optionally delivered via the patient interface is delivered as a two-dimensional (2D) visual signal or a three-dimensional (3D) visual signal. In some embodiments, the feedback signal is delivered using virtual reality, augmented reality, with or without audio, olfactory, and / or tactile signals.

[0153] According to some exemplary embodiments, the control unit 304 includes a prediction module 311 operably coupled to the control circuitry 310. In some embodiments, the prediction module 311 is configured to generate an improvement prediction score based on an index related to the activity of at least one particular brain region generated by the control circuitry 310. In some embodiments, the prediction module 311 generates the improvement prediction score based on one or more parameters, e.g., features, of the activity index.

[0154] According to some exemplary embodiments, the control circuitry 310 signals the supervisor interface to deliver visual and / or audio indicators to the treatment supervisor. In some embodiments, the visual and / or audio indicators present at least one of the patient's progress during treatment, brain network activity levels, changes in brain network activity levels, differences between baseline and brain network activity levels, the stage of the NF session, the operation of the system, and indicators or cues delivered to the patient. Additionally, the control circuitry signals the user interface to deliver visual and / or audio indicators to the treatment supervisor with information regarding the improvement prediction score and / or suggestions for modifying at least one parameter of the NF treatment and / or overall treatment based on the improvement prediction score.

[0155] According to some exemplary embodiments, the supervisor interface 308 includes a remote device, for example, a remote supervisor computer, coupled to the control unit 304 via a communications circuit 326 .

[0156] According to some exemplary embodiments, the control unit 304 transmits at least one of the data collected from the patient, the data sent to or displayed to the supervisor, and the improvement prediction score calculated by the prediction module 311 to a remote device, such as a remote database, cloud storage, or a remote computer, optionally to generate a database. In some embodiments, the database includes information collected from multiple systems and / or multiple patients.

[0157] According to some exemplary embodiments, for example, as shown in FIG. 3B , the control unit 305 does not include an integrated prediction module 311. In some embodiments, the prediction module is part of a remote device 311, e.g., a remote computer, a remote server, and / or remote cloud storage. In some embodiments, the remote device 311 is in communication with the control unit 305 via communications circuitry 326. In some embodiments, the activity indicators generated by the control circuitry 310 are transmitted to the remote device 311 via communications circuitry 326. In some embodiments, the remote device 311 calculates an improvement prediction score based on the activity indicators received from communications circuitry 326 and one or more algorithms, lookup tables, formulas, and / or software installed in memory of the remote device 311.

[0158] According to some exemplary embodiments, the remote device delivers an indication with information regarding the calculated improvement prediction score to the expert interface 308. Alternatively, the indication is delivered to the expert interface from the control unit 305 based on a signal received from the remote device 311. In some embodiments, the indication delivered to the expert includes one or more suggestions on how to modify the NF treatment and / or how to modify the overall treatment delivered to the patient based on the calculated improvement prediction score.

[0159] Exemplary Process for Predicting Patient Improvement According to some exemplary embodiments, the process for predicting a patient's improvement is performed by a device or system that delivers the NF treatment to the patient. Alternatively, the prediction process is performed by a separate and distinct device, e.g., a device in communication with the NF treatment device. In some embodiments, the separate device receives a signal from the NF device with information or indicators regarding the activity of at least one specific brain network. In some embodiments, the separate device calculates a prediction score using the received information or indicators and delivers the prediction score to the professional and / or optionally to the patient. Optionally, the separate device provides the professional with instructions and / or suggestions for how to modify the NF treatment and / or the patient's overall treatment.

[0160] Reference is now made to FIG. 4, which illustrates a process for predicting patient improvement performed by one or more devices, according to some exemplary embodiments of the present invention.

[0161] According to some exemplary embodiments, a signal, e.g., an electrical signal, is recorded from a patient's brain in block 402. In some embodiments, the signal is recorded by at least one electrode, e.g., electrodes 316 and 318 shown in Figures 3A and 3B. In some embodiments, the signal is recorded by at least one electrode, e.g., multiple electrodes, attached to the subject's head.

[0162] According to some exemplary embodiments, at least one stimulus is optionally delivered to the patient in block 404. In some embodiments, the stimulus is delivered in a time relationship with the signal recording, for example, before, during, and / or after recording the signals in block 402. In some embodiments, the stimulus includes at least one of a visual stimulus, an audio stimulus, a tactile stimulus, a sensory stimulus, and an olfactory stimulus. In some embodiments, the stimulus is selected to affect, e.g., increase or decrease activation, the activation of at least one specific brain network or a specific brain region, optionally associated with the brain network.

[0163] According to some exemplary embodiments, the stimulation is selected to affect the activation level of a particular brain network compared to the baseline activation of the particular brain network, e.g., the activation level of the particular brain network when no stimulation is delivered to the patient. In some embodiments, the stimulation and / or recorded signals are stored in memory 312 shown in Figures 3A and 3B.

[0164] According to some exemplary embodiments, EEG signals are measured from the recorded signals in block 410. In some embodiments, the EEG signals are measured by control circuitry 310 shown in FIGS. 3A and 3B , optionally using at least one of an algorithm, a formula, and a look-up table stored in memory 312. In some embodiments, the EEG signals are measured online from the recording of the signals in block 402, e.g., less than 30 seconds, less than 20 seconds, less than 10 seconds, or any intermediate, shorter, or longer period. Alternatively, the EEG signals are measured with a delay of at least 30 seconds, at least 1 minute, at least 5 minutes, or any intermediate, shorter, or longer period from the recording of the signals in block 402. In some embodiments, the EEG signals are measured after stopping signal recording.

[0165] According to some exemplary embodiments, an indicator of activity of, or changes in, at least one specific brain network is generated at block 412. In some embodiments, the indicator is generated by control circuitry 310. In some embodiments, the control circuitry is configured to apply at least one EFP of the specific brain network to the measured EEG signals to generate the activity indicator. In some embodiments, the EFP includes a model that includes information about at least one of a selected subset of one or more electrodes, a selected frequency band in the measured EEG signals, and a specific recording time point, and when applied to the measured EEG signals, provides an indicator of activity of the at least one specific brain network. Optionally, the model is used as a filter applied to the measured EEG signals to identify a subset of EEG signals within the measured EEG signals that are indicative of activity of, or changes in, the at least one specific brain network.

[0166] According to some exemplary embodiments, the index generated in block 412 changes over time according to changes in the measurements of the EEG signal in block 410 and / or according to changes in the signal recorded in block 402. In some embodiments, the index is generated as described above in WO2012104853 A2, which is incorporated herein by reference in its entirety.

[0167] According to some exemplary embodiments, the EFP, a model of the EFP, and / or the generated activity index are stored in memory 312 .

[0168] According to some exemplary embodiments, a feedback signal is optionally delivered to the patient at block 414. In some embodiments, the feedback signal includes the activity indicator generated at block 412. Alternatively, the feedback signal includes a visual and / or auditory interface that is modified according to the activity indicator generated at block 412 or changes in the activity indicator. In some embodiments, the feedback signal is generated by control circuitry 310 and delivered to the patient via patient interface 306 shown in FIGS. 3A and 3B. In some embodiments, the feedback signal is optionally delivered to the patient when EEG signals are measured as part of the NF treatment.

[0169] According to some exemplary embodiments, an improvement prediction score is calculated in block 416. In some embodiments, the improvement prediction score is calculated by the control unit 304, e.g., by the prediction module 311 shown in FIG. 3A. Alternatively, the improvement prediction score is calculated by a remote device in communication with the control unit, e.g., as shown in FIG. 3B. In some embodiments, the improvement prediction score is calculated by applying a prediction algorithm, e.g., a prediction model, to the activity index generated in block 412. Alternatively, the prediction algorithm is applied to the EEG signals measured in block 410.

[0170] According to some exemplary embodiments, the improvement prediction score is calculated prior to initiating NF treatment, e.g., during a baseline or screening procedure for NF treatment, based on signals recorded in block 402 prior to initiating NF treatment. Alternatively, the improvement prediction score is calculated during and / or after at least one treatment session of NF treatment.

[0171] According to some exemplary embodiments, an indicator regarding the patient's suitability for receiving NF treatment is optionally generated at block 418. In some embodiments, the indicator is generated based on EEG signals measured during a baseline and / or screening session of NF treatment prior to commencing the first treatment session.

[0172] According to some exemplary embodiments, an indicator predicting improvement in the patient's condition at the end of NF treatment and / or during NF treatment is generated at block 420. In some embodiments, the indicator is generated by the control circuitry 310 or by the remote device 311. In some embodiments, the indicator is generated before initiating NF treatment. Alternatively or additionally, the indicator is generated during and / or at the end of at least one treatment session of NF treatment. Alternatively or additionally, the indicator is generated during the interval period between two consecutive treatment sessions. In some embodiments, the indicator includes information regarding the patient's ability to reach a desired target value on a scale measuring at least one symptom of the psychiatric disorder or the severity of the psychiatric disorder at the end of NF treatment, e.g., at the start of the last NF treatment session, during the last NF treatment session, and / or at the end of the last treatment session, e.g., the target reduction is a score on the scale.

[0173] According to some exemplary embodiments, the index includes information regarding the patient's ability to achieve at least a 3-point decrease, e.g., at least a 5-point decrease, at least a 6-point decrease, at least a 10-point decrease, at least a 12-point decrease, at least a 20-point decrease, or any intermediate, lesser, or greater decrease, on at least one clinical assessment test. In some embodiments, the clinical assessment test includes a Post-Traumatic Stress Disorder (PTSD) test, such as the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) and / or parameters or domains of the CAPS-5, the PTSD Checklist for DSM-5 (PCL-5), and the Patient Health Questionnaire-9 (PHQ-9).

[0174] Alternatively or additionally, clinical assessment tests include depression-related tests such as the Hamilton Depression Rating Scale or any derivatives, the Snaith-Hamilton Pleasure Scale (SHAPS) or any derivatives, the Montgomery-Asberg Depression Rating Scale (MADRS) or derivatives, the Patient Health Questionnaire (PHQ) or derivatives, and the Clinical Global Impression (CGI) or derivatives. Alternatively or additionally, clinical assessment tests include Attention Deficit Hyperactivity Disorder (ADHD)-related tests such as the Adult ADHD Investigator Symptom Rating Scale (AISRS) or derivatives, the Adult ADHD Self-Report Scale (ASRS) or derivatives, the Test of Variables of Attention (TOVA), the Patient Health Questionnaire (PHQ), and the CGI.

[0175] According to some exemplary embodiments, an indicator predicting improvement in the patient's condition after NF treatment is generated at block 422. In some embodiments, the indicator is an indicator predictive of patient improvement from the end of the last NF treatment session and / or from one or more NF sessions up to one week, up to one month, up to three months, up to six months, up to one year, or any intermediate, shorter, or longer period. In some embodiments, the indicator is generated by the control circuitry 310 or by the remote device 311. In some embodiments, the indicator is generated before initiating NF treatment. Alternatively or additionally, the indicator is generated during and / or at the end of at least one treatment session of NF treatment. Alternatively or additionally, the indicator is generated during the interval period between two consecutive treatment sessions.

[0176] According to some exemplary embodiments, the index comprises information regarding the patient's ability to reach a desired target value on a scale measuring at least one symptom of a psychiatric disorder or the severity of a psychiatric disorder after NF treatment. In some embodiments, the index comprises information regarding the patient's ability to achieve a reduction after NF treatment, e.g., from the end of the last NF treatment session or from one or more NF sessions, up to one week, up to one month, up to three months, up to six months, up to one year, or any intermediate, shorter, or longer period, e.g., the target reduction is a score on the scale.

[0177] According to some exemplary embodiments, the index includes information regarding the patient's ability to achieve at least a 2-point decrease, e.g., at least a 5-point decrease, at least a 6-point decrease, at least a 10-point decrease, at least a 12-point decrease, at least a 20-point decrease, or any intermediate, lesser, or greater decrease, in at least one clinical assessment test.

[0178] According to some exemplary embodiments, the indicators are delivered to the specialist at block 424. In some embodiments, the control circuitry, e.g., control circuitry 310, signals the specialist interface to deliver the indicators to the specialist using the specialist interface. Alternatively, for example, as shown in FIG. 3B, if the specialist interface is in communication with a remote device, the control circuitry 310 signals the remote device 311 using the communication circuitry 326 to deliver the indicators to the specialist via the specialist device 308.

[0179] According to some exemplary embodiments, suggestions or instructions on how to modify at least one parameter of the overall treatment provided to the patient are optionally delivered in block 426. In some embodiments, the suggestions or instructions are part of the indications delivered to the professional in block 424. In some embodiments, the suggestions or instructions include at least one of providing at least one treatment to the patient in addition to, or as an alternative to, an NF treatment, optionally in combination with an NF treatment, e.g., a social treatment, a psychological treatment, a psychiatric treatment, a pharmaceutical treatment.

[0180] According to some exemplary embodiments, suggestions or instructions on how to modify at least one parameter of the NF treatment provided to the patient are optionally delivered at block 428. In some embodiments, the suggestions or instructions are part of the indication delivered to the professional at block 424. In some embodiments, the suggestions or instructions include at least one of stopping the NF treatment, adding one or more additional treatment sessions, modifying the length of one or more treatment sessions, modifying the time interval between treatment sessions, or modifying the baseline difficulty or complexity of the stimulation delivered to the patient during a treatment session.

[0181] According to some exemplary embodiments, suggestions or instructions regarding how to modify at least one additional treatment provided to the subject, e.g., in addition to the NF treatment, are optionally delivered at block 430. In some embodiments, if the additional treatment includes a pharmaceutical treatment, the suggestions or instructions include stopping the pharmaceutical treatment, starting the pharmaceutical treatment, and / or modifying the dosage of at least one drug, e.g., a bioactive compound, administered in the pharmaceutical treatment. In some embodiments, if the additional treatment includes a psychological treatment, the suggestions or instructions include stopping or starting, or shortening or lengthening the psychological treatment or TMS.

[0182] According to some exemplary embodiments, the suggestions or instructions provided to the professional in blocks 426 and / or 428 are stored in a memory of the control unit, e.g., memory 312, or in a memory of the remote device 311. In some embodiments, the suggestions or instructions are selected and provided to the patient based on the calculated improvement prediction score. Additionally or optionally, the suggestions or instructions are based on at least one of the patient's clinical data, the patient's medical history, previous NF treatment sessions and / or successes or failures of previous NF treatments, and the patient's medications. In some embodiments, the suggestions or instructions are selected using at least one of an algorithm, a formula, and a look-up table, optionally associating the improvement prediction score with a predetermined set of instructions stored in memory 312 or the memory of the remote device 311.

[0183] According to some exemplary embodiments, the system automatically modifies at least one parameter of the therapy, e.g., NF therapy, provided to the patient, at block 432. Optionally, the system automatically modifies the NF therapy without providing an indication, e.g., a direction or suggestion, to the patient or professional. Alternatively or additionally, an indication that the at least one parameter has been modified is delivered to the patient and / or professional and / or stored in memory, e.g., memory 312 of the device.

[0184] Exemplary Prediction Score Generation According to some exemplary embodiments, a device, e.g., a control unit, computer, server, or cloud storage, and / or processing memory, calculates an improvement prediction score using one or more algorithms, formulas, lookup tables, and / or models stored in the memory of the device. In some embodiments, the prediction score is calculated using a model, e.g., a model selected for a particular patient, optionally based on at least one of clinical data, the subject's performance in NF treatment, medical history, and medication. Optionally, control circuitry of the device calculates the prediction score.

[0185] Reference is now made to FIG. 5A, which illustrates the calculation of an improved prediction score, according to some exemplary embodiments of the present invention.

[0186] According to some exemplary embodiments, brain network activity indicators are provided in block 502. In some embodiments, the activity indicators are stored in a memory of the device. In some embodiments, the activity indicators are signals indicative of activity of, or changes in, at least one specific brain network. In some embodiments, the activity indicators are generated, for example, as shown in blocks 104, 206, and / or block 412. In some embodiments, the activity indicators include EFP signals or changes therein. In some embodiments, the activity indicators are signals having a duration of at least 1 millisecond, e.g., at least 1 second, at least 1 minute, at least 2 minutes, at least 5 minutes, at least 10 minutes, at least 30 minutes, at least 40 minutes, or any intermediate, shorter, or longer period. In some embodiments, the signals are indicative of activity of, or changes therein, of, at least one brain network during a baseline portion of an NF treatment session and / or when stimuli are presented to the patient during an NF treatment session.

[0187] According to some exemplary embodiments, the provided indicators are divided into epochs at block 504, e.g., by the device's control circuitry and / or prediction module. In some embodiments, the indicator signals provided at block 502 are divided into one or more epochs, each of at least 1 second, e.g., at least 40 seconds, at least 1 minute, at least 90 seconds, at least 2 minutes, or any intermediate, shorter, or longer epoch time. In some embodiments, the epochs are the same length. Alternatively, the length of at least some of the epochs varies.

[0188] According to some exemplary embodiments, one or more predetermined features are extracted from the indicator signal at block 506. In some embodiments, the one or more predetermined features are extracted, e.g., feature values ​​are extracted separately for each epoch of the indicator signal. In some embodiments, the feature values ​​include at least one of absolute feature values ​​and relative feature values.

[0189] According to some exemplary embodiments, the extracted features are optionally combined with additional features to form a feature set at block 508. In some embodiments, the additional features include demographic data, and / or EEG data, and / or accelerometer measurements, eye tracking, cardiovascular measurements, etc.

[0190] According to some exemplary embodiments, at block 510, a predictive model, optionally a selected predictive model, is applied to the extracted feature values. In some embodiments, the model is applied to the extracted feature values, optionally in addition to the values ​​of additional integrated features, e.g., collected features. In some embodiments, the feature values ​​are used as inputs for the model. Optionally, the model is tuned to a particular subject or a particular group of subjects, e.g., a particular patient or patient group.

[0191] According to some exemplary embodiments, the predictive model includes one or more features, e.g., parameter weights. In some embodiments, the model is used to calculate a relationship between two or more extracted parameter values. Alternatively or additionally, the model is used to calculate a relationship between the value of at least one parameter and a reference value.

[0192] According to some exemplary embodiments, a predicted score is generated in block 512. In some embodiments, the predicted score is the output of a model applied in block 510 to the feature values. In some embodiments, the predicted score is generated based on a calculated relationship between at least two parameter values, or based on a calculated relationship between the value of at least one parameter and a reference value. Alternatively or additionally, the predicted score is generated based on different weights of one or more parameters.

[0193] According to some demonstrative embodiments, the actions depicted in blocks 504, 506, 508, 510, and 512 are performed by a device, for example, by a control circuit and / or a prediction module of the device, for example, by control circuit 310 and / or prediction module 311.

[0194] Exemplary NF Treatment Session According to some exemplary embodiments, NF treatment for treating PTSD includes multiple treatment sessions. In some embodiments, the treatment sessions are conducted for at least two consecutive weeks. In some embodiments, the treatment sessions are conducted for a total period of 2 to 20 weeks, e.g., 2 to 12 consecutive weeks. In some embodiments, the NF treatment includes at least five treatment sessions, e.g., 10, 12, and 15 treatment sessions conducted over a period of 5 to 12 weeks. In some embodiments, at least two treatment sessions are conducted weekly or sometime during the week.

[0195] Reference is now made to FIG. 5B, which illustrates a treatment session of NF therapy for treating a subject diagnosed with PTSD, according to some exemplary embodiments of the present invention.

[0196] According to some exemplary embodiments, one or more electrodes are placed on the patient's head at block 530. In some embodiments, the one or more electrodes include multiple electrodes placed at different locations on the subject's head, e.g., at locations C3, C4, Cz, FCz, P3, Pz, and P4 of a 10-20 coordinate system, on the subject's head, or any combination of these locations. In some embodiments, the electrodes are attached to the patient's head, e.g., scalp, using gel.

[0197] According to some exemplary embodiments, recording from the electrodes begins at block 532. In some embodiments, the electrodes are used to record EEG signals. In some embodiments, recording begins at block 532 before or after the electrodes are placed at block 530.

[0198] According to some exemplary embodiments, contact between the electrodes and the subject's head is determined in block 534. In some embodiments, contact is determined based on the recording started in block 532. In some embodiments, contact between the electrodes and the patient's head is presented to a supervisor or patient using, for example, an interface indicating electrodes with proper contact and electrodes that are not properly contacting the patient's head. In some embodiments, the system alerts the supervisor and / or user which electrodes are not properly contacting the patient's head during treatment and, optionally, what action to take to resolve the issue. In some embodiments, recording is started in block 532 after determining electrode contact in block 534.

[0199] According to some exemplary embodiments, the treatment session optionally includes an eyes-closed session at block 536. In some embodiments, during the eyes-closed session, signals are recorded from the patient while the patient is in rest mode, e.g., to calibrate the recording and / or EEG measurement system.

[0200] According to some exemplary embodiments, at block 538, the treatment session optionally includes a global baseline session. In some embodiments, during the global baseline session, EEG signals are measured and collected for a predetermined period of time necessary to determine activity or a biomarker of activity of at least one limbic brain network, e.g., using EFPs of the limbic brain network. In some embodiments, the predetermined period of time is up to 3 minutes, e.g., up to 2.5 minutes, up to 2 minutes, up to 1.5 minutes, or up to 1 minute.

[0201] Optionally, the local baseline is calculated before every NF cycle. In some embodiments, during the local baseline session, EEG signals are measured and collected for a predetermined period of time necessary to determine activity of at least one limbic brain network, for example, using EFPs of the limbic brain network. In some embodiments, the predetermined period of time is less than 2 minutes, e.g., less than 1.5 minutes, less than 1 minute, or any intermediate, smaller, or larger value.

[0202] According to some exemplary embodiments, the treatment session includes one or more training cycles at block 540. In some embodiments, the one or more training cycles include 1, 2, 3, 4, 5, 6, 7, or any greater number of NF training cycles. In some embodiments, the training cycles, e.g., NF training cycles, are consecutive training cycles. Optionally, the NF training cycles are performed consecutively at block 540. Optionally, the delay between two consecutive NF training cycles is less than 2 minutes, e.g., less than 60 seconds, less than 30 seconds, less than 10 seconds, less than 5 seconds, less than 1 second, or any intermediate, shorter, or longer period. Optionally, the training cycle includes at least one transfer cycle 542, in which feedback regarding the activation level of at least one limbic brain network was not delivered to the patient.

[0203] According to some exemplary embodiments, during an NF cycle, a patient performs a task, e.g., a cognitive task and / or an emotional task, while monitoring a patient interface, e.g., a visual, audio, tactile, and / or olfactory interface, that presents scenarios that change according to the determined activity level of at least one limbic brain network. In some embodiments, the cognitive task is selected to increase the activity level of at least one limbic brain network or its biomarkers. In some embodiments, an increase in activity of at least one limbic brain network or its biomarkers increases or decreases the amount of stimulus cues delivered to the patient as part of the interface. Additionally, human-detectable indicators, e.g., visual, audio, tactile, and / or olfactory indicators, delivered to the patient while monitoring the interface indicate whether a target activity level of the limbic brain network or its biomarkers has been crossed or whether the determined activity is closer to or in a desired direction toward the target activity level.

[0204] Exemplary EEG Processing The model used in block 510 of FIG. 5A was generated based on a large number of samples from many subjects who participated in NF treatment, eg, patients suffering from one or more PTSD symptoms.

[0205] Reference is now made to FIG. 6, which illustrates a process for generating a model, according to some exemplary embodiments of the present invention.

[0206] In some embodiments, an electrical fingerprint (EFP) neuromodulation signal being generated during NF treatment is provided in block 602. Optionally, an EEG signal is provided in block 602 instead of an EFP.

[0207] In some embodiments, the EFP or EEG signal is divided into multiple epochs at block 604. In some embodiments, when generating the model, the EFP or EEG signal is divided into multiple epochs, each having a duration between 1 second and 180 seconds, e.g., a duration between 30 seconds and 90 seconds, a duration between 50 seconds and 80 seconds, or any intermediate, shorter, or longer duration.

[0208] Additional data, such as demographic data, EEG data, clinical data, physiological measurements, medical history, laboratory data, accelerometer measurements, eye tracking, cardiovascular measurements, etc., is optionally provided in block 606 .

[0209] A set of features was extracted from the provided EFP or EEG signal and, optionally, from the provided additional data, at block 608. In some embodiments, a set of features is extracted separately for each epoch or a predetermined number of epochs of the provided EFP signal. The set of features includes First Peak to Peak score (this feature extraction provides the peak-to-peak difference between the first positive peak and the first negative peak of the EFP signal. If no positive peak can be identified before the negative peak, the difference between the first value of the EFP signal and the first negative peak is returned), Max negative slope (this feature extraction provides the largest negative linear slope coefficient that can be calculated within the limits of the EFP segment), NF slope (this feature extraction provides the slope coefficient of the linear regression line fitted to the neurofeedback EFP segment), KL divergence (this feature extraction provides the Kullback-Leibler divergence, which represents the difference between the local baseline and the neurofeedback probability distribution), JS divergence (this feature extraction provides the Jensen-Shannon divergence as an extended and symmetric version of the KL divergence), and Variance. difference (this feature extraction provides the Levene's test value for the variance difference between the local baseline and the neurofeedback segment), t-value (this feature extraction provides the Welch's t-test statistic for the difference between the local baseline and the mean of the neurofeedback segment), NF Kurtosis (this feature extraction provides the Fisher's kurtosis of the neurofeedback EFP distribution), NF Skewness (this feature extraction provides the sample skewness of the neurofeedback EFP distribution), AU LBL median (this feature extraction provides the neurofeedback segment EFP area under the local baseline median), AU LBL meanmean (this feature extraction provides the neurofeedback segment EFP area under the local baseline mean), Modified Z score (this feature extraction provides the difference between the local baseline and the neurofeedback median in terms of the local baseline median absolute deviation), Modified Z score with mean (this feature extraction provides the difference between the local baseline median and the neurofeedback segment mean in terms of the local baseline median absolute deviation), Z score with median (this feature extraction provides the difference between the local baseline and the neurofeedback segment median in terms of the local baseline interquartile range), Z score with mean median (this feature extraction provides the difference between the local baseline median and the neurofeedback segment mean in terms of the local baseline interquartile range), Z score (this feature extraction provides the difference between the local baseline and the neurofeedback segment mean in terms of the local baseline standard deviation), CL Effect Size size (this feature extraction provides the probability of superiority of the local baseline compared to the neurofeedback segment EFP), Effect size (this feature extraction returns the Cohen's d effect size, which represents the difference between the local baseline and the neurofeedback segment mean EFP, divided by the pooled standard deviation), NF entropy (this feature extraction provides the Shannon entropy of the neurofeedback segment EFP), LBL entropy (this feature extraction provides the Shannon entropy of the local baseline EFP), NF std (this feature extraction provides the neurofeedback segment standard deviation EFP), NF median (this feature extraction provides the neurofeedback segment median EFP), NF mean (NFmean (extraction of this feature provides the neurofeedback segment mean EFP), LBL std (extraction of this feature provides the local baseline standard deviation EFP), LBL median (extraction of this feature provides the local baseline median EFP), LBL mean (extraction of this feature provides the local baseline mean EFP), Part (number of NF segments corresponding to the LBL duration when NF and LBL are not equal in duration. If NF and LBL are of the same duration, there is only one part), Cycle (NF cycle number), and Session (NF session number), Pearson correlation coefficient (extraction of this feature provides a standardized measure of the linear correlation between the local baseline and neurofeedback segment EFP), Covariance coefficient (extraction of this feature provides an unstandardized measure of the joint variability of the local baseline and neurofeedback segment EFP), Granger causality causality (this feature extraction provides a p-value for a statistical hypothesis test that determines whether the local baseline EFP is useful for predicting the neurofeedback segment EFP); reversed granger causality (this feature extraction provides a p-value for a statistical hypothesis test that determines whether the neurofeedback segment EFP is useful for predicting the local baseline EFP); dynamic time warping (this feature extraction provides a metric of time-independent similarity between the local baseline and neurofeedback segment EFP); synchronization likelihood (this feature extraction provides a generalized synchronization metric that detects linear and nonlinear dependencies between the local baseline and neurofeedback segment EFP); NF spectral centroid (NF spectral centroid)The neurofeedback EFP signal may include one or more of: LBL spectral centroid (extraction of this feature provides an indication of the spectral center of mass in the neurofeedback EFP signal); LBL spectral centroid (extraction of this feature provides an indication of the spectral center of mass in the local baseline EFP signal); NF spectral flatness (extraction of this feature provides a measure of Wiener entropy indicative of the waveform noise of the neurofeedback EFP signal); and LBL spectral flatness (extraction of this feature provides a measure of Wiener entropy indicative of the waveform noise of the local baseline EFP signal).

[0210] The features were extracted into a tabular data format.

[0211] In block 612, a model is trained using, for example, a sequential attention-based deep neural network with a Transformer block tuned for tabular data, trained with a self-supervised approach that utilizes previously acquired unlabeled data (increasing the overall dataset size from approximately 12k to approximately 20k). Models are trained to predict treatment success across multiple thresholds (e.g., a 6-point / 12-point improvement in clinical assessment) and multiple time periods (e.g., 8 weeks after treatment initiation / 3 months after treatment). Multiple models are trained to cover a wide range of cross-validation combinations.

[0212] A model is selected in block 614, for example, based on its performance in predicting treatment success, optionally by examining the model at different clinical improvement thresholds and / or different post-treatment time periods.

[0213] Figure 7 shows an example of a heatmap depicting model accuracy scores.

[0214] Figure 8 shows the predicted scores (0: unsuccessful to 1: successful treatment) for the five patients who did not show clinical improvement after treatment.

[0215] Figure 9 shows the predicted scores (0: unsuccessful to 1: successful treatment) for the five patients who showed improvement after treatment.

[0216] Example usage of the model: For each prediction point (i.e., at the end of each session or during the session), all available data up to that point is fed into the model to provide more robust inference. For example, at the end of session number 2, data from sessions 1 and 2 are used for success prediction, or alternatively, only data from session 2 is used. Thus, at the end of session 3, data from sessions 1, 2, and 3 are used for inference, etc. Following this logic, multi-threshold / multi-period scoring techniques can be applied during training, and the overall prediction score can guide protocol decisions (see, e.g., FIG. 10).

[0217] 10 shows examples of multi-threshold and / or multi-time period success predictions for three patients. The multi-threshold success predictions relate to predicting success in reducing the CAPS-5 assessment questionnaire score by 12 points (12P) or 6 points (6P) relative to the baseline score. The multi-time period success predictions relate to predicting a patient's success in reducing the CAPS-5 after 8 weeks of NF treatment (indicating immediate success of NF treatment) or 3 months after completion of treatment (indicating an extended effect of NF treatment).

[0218] As shown in Figure 10, patient 1 (top panel) is an example of a patient who received a high predictive score during treatment that had significant clinical improvement and an increasing pattern as treatment continued.

[0219] A patient may have a clinical improvement immediately after treatment, e.g., after 8 weeks of treatment, but the predictive model predicts that the patient's score will decrease after completion of treatment, e.g., after 3 months of treatment. In this case, in some embodiments, a system using the predictive model provides suggestions for extending an existing treatment protocol, e.g., by adding additional treatment sessions or by modifying the protocol, e.g., to extend each or some treatment sessions.

[0220] Patient 2 (middle panel) is an example of a patient who had clinical improvement only immediately after treatment (i.e., 8 weeks), but whose scores decreased on clinical assessments at 3M. Thus, the model shows low scores during the 3M period, suggesting that this patient may have benefited from extended treatment or a modified protocol, or additional treatment sessions, e.g., a treatment booster.

[0221] Patient 3 (bottom panel) is an example of a patient who did not show clinical improvement after treatment, and in fact the patient's predicted scores were low throughout treatment. Model results would suggest that termination of treatment could be considered for this patient.

[0222] When used herein in reference to an amount or value, the term "about" means "within ±10% of."

[0223] The words "comprises," "comprising," "includes," "including," "has," "having," and their conjugations mean "including but not limited to."

[0224] The term "consisting of" means "including and limited to."

[0225] The term "consisting essentially of" means that a composition, method, or structure may include additional components, steps, and / or moieties, but only if the additional components, steps, and / or moieties do not materially alter the basic and novel characteristics of the claimed composition, method, or structure.

[0226] As used herein, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" can include a plurality of compounds, including mixtures thereof.

[0227] Throughout this application, embodiments of the invention may be presented with reference to a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values ​​within that range. For example, a description of a range such as "1 to 6" should be considered to have specifically disclosed subranges such as "1 to 3," "1 to 4," "1 to 5," "2 to 4," "2 to 6," "3 to 6," etc., as well as individual numbers within that range, e.g., 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0228] When numerical ranges are given herein (e.g., any set of numbers connected by "10-15," "10 to 15," or another such range designation), it is intended to include any number (fractional or integer) within the limits of the stated range, inclusive of the limits of the range, unless the context clearly dictates otherwise. The phrases "range / ranging / ranges between" a first stated number and a second stated number, and "from," "up to," "up to," or "through" a first stated number and a second stated number (or other such range terminology) are used interchangeably herein and are intended to include the first stated number and the second stated number and all fractional and integer numbers therebetween.

[0229] Unless otherwise indicated, the numerical values ​​used herein and any numerical ranges based thereon are approximations within the accuracy of reasonable measurement and rounding errors, as will be understood by one of ordinary skill in the art.

[0230] As used herein, the term "method" refers to methods, means, techniques, and procedures for accomplishing a given task, including, but not limited to, those methods, means, techniques, and procedures that are either known by or readily developed from known methods, means, techniques, and procedures by practitioners in the fields of chemistry, pharmacology, biology, biochemistry, and medicine.

[0231] As used herein, the term "treating" includes arresting, substantially inhibiting, slowing, or reversing the progression of a condition, e.g., a psychiatric disorder, substantially ameliorating one or more clinical or cosmetic symptoms of a condition, or substantially preventing the appearance of a clinical or cosmetic symptom of a condition.

[0232] It is understood that certain features of the invention that are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention that are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination, or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments should not be considered essential features of those embodiments, unless the embodiment is inoperable without those elements.

[0233] While the present invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the spirit and broad scope of the appended claims.

[0234] It is the applicant's intention that all publications, patents, and patent applications mentioned in this specification be incorporated herein by reference in their entirety, as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application should not be construed as an admission that such reference is available as prior art to the present invention. To the extent section headings are used, they should not necessarily be construed as limiting. In addition, any priority document of this application is incorporated herein by reference in its entirety.

Claims

1. 1. A method for predicting the prognosis of a patient suffering from a psychiatric disorder who is undergoing neurofeedback (NF) treatment, comprising: measuring EEG signals from the patient's brain before, during, and / or after at least one treatment session of the NF treatment; generating an index of activity of at least one specific brain network based on the measured EEG signals; processing the indicators of activity; predicting a prognosis of the patient's condition at the end of and / or during the NF treatment based on the processed indicators of activity in a temporal relationship with the at least one treatment session.

2. 10. The method of claim 1, wherein predicting the prognosis comprises predicting an improvement in the patient's condition at the end of at least one session of the NF treatment.

3. 10. The method of any one of the preceding claims, wherein predicting the prognosis comprises predicting an improvement in the patient's condition at least one week after completion of the NF treatment.

4. 10. The method of any one of the preceding claims, wherein predicting the prognosis comprises predicting improvement in one or more clinical assessment tests used to measure the clinical status of a patient suffering from the psychiatric disorder.

5. 5. The method of claim 4, wherein predicting improvement comprises predicting the patient's ability to achieve at least one of a target score on the one or more clinical assessment tests, a target score range on the one or more clinical assessment tests.

6. 6. The method of claim 4 or 5, wherein predicting improvement comprises predicting the patient's ability to reach a target reduction in score on the one or more clinical assessment tests compared to a reference score or baseline score when the one or more clinical assessment tests are administered.

7. 7. The method of any one of claims 4 to 6, wherein the psychiatric disorder comprises a stress disorder, and wherein predicting improvement comprises predicting the subject's ability to reach a reduction of at least 3 points on a Clinician-Administered Post-Traumatic Stress Disorder (PTSD) Scale for DSM-5 (CAPS-5) compared to a reference or baseline score on the CAPS-5, and / or a reduction of at least 3 points on a PTSD Checklist for DSM-5 (PCL-5) compared to a reference or baseline score on the PCL-5.

8. 8. The method of claim 7, wherein the stress disorder comprises post-traumatic stress disorder (PTSD).

9. 9. The method of claim 7 or 8, wherein the NF treatment comprises training the patient to self-regulate limbic network activity or amygdala activity, and providing a feedback signal to the patient during the training with information about the activity or changes therein based on the measured EEG signals.

10. 7. The method of any one of claims 4 to 6, wherein the psychiatric disorder comprises depression or major depressive disorder (MDD), and wherein predicting improvement comprises predicting the subject's ability to reach a reduction in the Hamilton Depression Rating Scale (HDRS) of at least 3 points compared to the HDRS reference or baseline score, and / or a reduction in the Snaith-Hamilton Pleasure Scale (SHAPS) of at least 4 points compared to the SHAPS reference or baseline score, and / or a reduction in the Montgomery-Asberg Depression Rating Scale (MADRS) of at least 6 points compared to the MADRS reference or baseline score.

11. 7. The method of any one of claims 4 to 6, wherein the psychiatric disorder comprises anhedonia, and wherein predicting improvement comprises predicting the subject's ability to reach at least a 5-point reduction on the Snaith-Hamilton Pleasure Scale (SHAPS) compared to the SHAPS reference or baseline score.

12. 10. The method of any one of the preceding claims, wherein said predicting comprises predicting said patient's success in reducing their CAPS-5 assessment questionnaire score by at least 12 points, or at least 6 points relative to their baseline score, after one or more treatment sessions, or one or more weeks, of said NF treatment.

13. 12. The method of any one of claims 1 to 11, wherein said predicting comprises predicting the patient's success in reducing a score on a CAPS-5 assessment questionnaire by at least 12 points or at least 6 points relative to a baseline score after 15 treatment sessions of said NF treatment or after 8 weeks of said NF treatment.

14. 10. The method of any one of the preceding claims, wherein said predicting comprises predicting said patient's success in reducing their CAPS-5 assessment questionnaire score by at least 12 points or at least 6 points relative to their baseline score at least one week after completing said NF treatment.

15. 15. The method of any one of claims 1-14, wherein said predicting comprises predicting the patient's success in reducing their CAPS-5 assessment questionnaire score by at least 12 points or at least 6 points relative to their baseline score three months after completing the NF treatment.

16. 10. The method of any one of the preceding claims, wherein said predicting comprises predicting the patient's success in increasing their score on a Cognitive Reappraisal Scale by at least one point relative to a baseline or reference score after one or more treatment sessions, or one or more weeks, of said NF treatment.

17. 17. The method of any one of claims 10 to 16, wherein the NF treatment comprises training the patient to self-regulate activity of a mesolimbic network or activity of a reward brain network associated with the ventral striatum, and providing a feedback signal to the patient during the training with information about the activity or changes therein based on the measured EEG signals.

18. 10. The method of any one of the preceding claims, wherein said predicting comprises calculating a prognosis score indicative of the prognosis of the patient's condition based on the processed indicators of activity.

19. 20. The method of claim 18, comprising automatically modifying at least one parameter of the NF treatment based on the calculated prognostic score.

20. 20. The method of claim 18, comprising delivering an index with instructions for modifying at least one parameter of the NF treatment based on the calculated prognostic score.

21. 21. The method of claim 19 or 20, wherein the at least one parameter of the NF treatment includes at least one of a number of treatment sessions, a duration of each treatment session or at least one treatment session, an interval between treatment sessions, and feedback delivered to the patient.

22. 22. The method of any one of claims 18 to 21, comprising delivering an indication to modify at least one additional treatment provided to the patient.

23. 23. The method of claim 22, wherein the at least one additional treatment comprises at least one of a pharmaceutical treatment and a psychological treatment.

24. 21. The method of claim 20, wherein if the calculated prognostic score indicates that the subject will not be able to reach a target improvement, the delivered indication includes instructions to stop the NF treatment or to add at least one NF treatment session to the NF treatment.

25. 25. The method of claim 24, wherein if the calculated prognostic score indicates that the subject will not be able to reach a target improvement, the delivered indication comprises instructions to initiate at least one additional treatment to the NF treatment or to modify the at least one additional treatment.

26. 26. The method of claim 25, wherein if the calculated prognostic score indicates that the subject will not be able to reach a target improvement, the delivered indication comprises instructions for initiating or modifying at least one of pharmaceutical, psychological, and / or psychotherapeutic treatment.

27. 21. The method of claim 20, wherein if the calculated prognostic score indicates the subject's ability to reach a target improvement, the delivered indication comprises instructions to proceed with the NF treatment or to shorten the duration of the NF treatment or at least one treatment session.

28. 28. The method of claim 27, wherein if the calculated prognostic score indicates the subject's ability to reach a target improvement, the delivered indication comprises instructions to stop or modify at least one of pharmaceutical, psychological, and / or psychotherapeutic treatment.

29. 29. The method of claim 28, wherein if the calculated prognostic score indicates the subject's ability to reach a goal improvement, the delivered indication comprises instructions to reduce the dosage of at least one drug administered to the subject during the pharmaceutical treatment.

30. 10. The method of any one of the preceding claims, comprising repeating said measuring and said generating during a selected period before, during and / or after said at least one therapy session, wherein said processing comprises identifying changes in said generated indicator of activity during said selected period, and wherein said predicting comprises predicting said prognosis based on said identified changes.

31. 31. The method of claim 30, wherein the processing comprises extracting one or more parameters of the generated indicator of activity, and the predicting comprises predicting the prognosis based on values ​​of the extracted one or more parameters.

32. 32. The method of claim 31 , wherein the processing comprises using values ​​of the one or more extracted parameters in a predictive model, the predictive model comprising weights for one or more of the extracted parameters, and wherein predicting comprises predicting the outcome based on an output of the predictive model.

33. 33. The method of claim 32, wherein the one or more extracted parameters include at least two parameters of the generated indicator signal, and the predictive model is used to calculate a relationship between values ​​of the at least two parameters, and wherein predicting comprises predicting the prognosis based on the calculated relationship.

34. 33. The method of claim 32, wherein the predictive model is used to calculate a relationship between one or more of the extracted parameters and a reference value, and wherein predicting comprises predicting the prognosis based on the calculated relationship.

35. 10. The method according to any one of the preceding claims, wherein the method is performed by a device or a system.

36. 1. A system for calculating a prognostic score, comprising: a memory storing signals indicative of activity or changes therein of at least one specific brain network in a subject suffering from at least one symptom of a psychiatric disorder, the signals being measured before, during, and / or after neurofeedback (NF) treatment delivered to the subject, and at least one prognosis prediction software; A control circuit, the control circuit comprising: processing the stored signals indicative of said activity; The system includes a control circuit configured to calculate a prognostic score based on the processing results and using the at least one prognostic software, the prognostic score indicating a prognosis of the subject's condition at the end of the NF treatment and / or during the NF treatment.

37. 37. The system of claim 36, wherein the control circuitry is configured to process the stored signals indicative of activity by extracting values ​​of predetermined parameters from the stored signals indicative of activity, and wherein the calculation comprises calculating the prognostic score by using the extracted values ​​as input information for the at least one prognostic software.

38. 38. The system of claim 37, wherein the control circuitry is configured to process the stored signal indicative of activity by dividing the signal indicative of activity into epochs in a range of 1 millisecond to 120 seconds, and wherein the extracting comprises extracting the value of the parameter of the signal indicative of activity independently for each epoch of the epochs.

39. 39. The system of any one of claims 36 to 38, wherein the activity-indicative signals stored in the memory are derived from EEG signals measured from the subject's brain during the NF treatment.

40. 40. The system of any one of claims 36 to 39, wherein the prognostic score predicts the subject's ability to achieve a target score on a clinical assessment test during, at the end of, and / or after the NF treatment, and the target score or an indicator thereof is stored in the memory.

41. 40. The system of any one of claims 36 to 39, wherein the prognostic score predicts the subject's ability to achieve a target change in clinical assessment test score relative to a baseline value during, at the end of, and / or after the NF treatment, and at least one of the target change and / or the baseline value is stored in the memory.

42. 42. The system of claim 40 or 41, wherein the clinical assessment test comprises at least one of the following: a Clinician Administered PTSD Scale for DSM-5 (CAPS-5) rating scale, a Patient Health Questionnaire-9 (PHQ-9) test, a Hamilton Depression Rating Scale test, a Snaith-Hamilton Pleasure Scale for Patients with Disorders (SHAPS) test, a Montgomery-Asberg Depression Rating Scale (MADRS) test, a Patient Health Questionnaire (PHQ) test, a Clinical Global Impression (CGI) test, an Attention Deficit Hyperactivity Disorder (ADHD) related test, an Adult ADHD Subject Symptom Rating Scale (AISRS) test, an Adult ADHD Self-Report Scale (ASRS) test, a Test of Variables of Attention (TOVA) test, or any derivative thereof.

43. The system of any one of claims 36 to 42, wherein the control circuitry is configured to generate a prognostic indicator of the calculated prognostic score.

44. 44. The system of claim 43, wherein the prognostic index includes at least one suggestion for modifying at least one parameter of the NF treatment based on the calculated prognostic score, and wherein the at least one suggestion is stored in the memory.

45. 45. The system of claim 44, wherein the at least one parameter comprises at least one of a number of treatment sessions, a duration of at least one treatment session, and / or a time interval between two treatment sessions.

46. 46. ​​The system of any one of claims 43 to 45, wherein the prognostic indicator comprises at least one suggestion to administer at least one additional treatment to the subject or to modify at least one parameter of the at least one additional treatment, and wherein the at least one suggestion is stored in the memory.

47. 47. The system of claim 46, wherein the at least one suggestion stored in the memory comprises at least one of a suggestion to start or stop pharmaceutical treatment and / or to modify the dosage and / or administration regime of at least one drug administered to the subject.

48. 48. The system of claim 46 or 47, wherein the at least one suggestion stored in the memory includes a suggestion to start, stop, or modify transcranial magnetic field (TMS) therapy delivered to the subject.

49. 49. The system of any one of claims 46 to 48, wherein the at least one suggestion stored in the memory comprises a suggestion to start, stop or modify at least one of psychological treatment, cognitive behavioral therapy (CBT), and psychotherapy.

50. a communication circuit configured to communicate with at least one remote device; 50. The system of any one of claims 43 to 49, wherein the control circuitry is configured to signal the communication circuitry to deliver the prognostic indicator to the remote device.

51. 51. The system of claim 50, wherein the at least one remote device includes a specialist interface configured to generate and deliver a human detectable indicator, and the control circuitry is configured to send a signal to the specialist interface via the communication circuitry to generate and deliver the human detectable indicator based on the prognostic indicator.

52. 52. The system of claim 51, wherein the expert interface is configured to receive input and deliver the input to the memory via the communications circuitry.

53. 53. The system of claim 52, wherein the input includes at least one of personal information about the subject, clinical data about the subject, clinical assessment data, pharmaceutical treatment-related data, data regarding the at least one symptom of the psychiatric disorder, data regarding the psychiatric disorder, data regarding the at least one clinical assessment test and / or results of the subject on at least one clinical assessment test.

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