Targeting neuromodulation to improve neuropsychiatric function

Abstract

Systems and methods for targeted neuromodulation are provided. A first image representing a structure of the brain is acquired from the first imaging system, and a second image representing connectivity of the brain is acquired from the first imaging system or the second imaging system. A first utility value associated with directly adjusting tissue within the region of interest is determined for each of a plurality of voxels within the region of interest from the first image. A second utility value associated with indirectly adjusting tissue outside the region of interest by adjusting tissue within the region of interest is determined for each of the plurality of voxels from the second image. A total utility value for each of the plurality of voxels is determined from the first utility value and the second utility value, and an optimal location is determined from the total utility value.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 400,960, filed on August 25, 2022, entitled "Focused Neuromodulation to Improve Neuropsychiatric Function", U.S. Patent Application No. 18 / 310,465, filed on May 1, 2023, entitled "A Screening, Monitoring, and Treatment Framework for Focused Ultrasound", and U.S. Patent Application No. 18 / 227,270, filed on July 27, 2023, entitled "An Analytical Framework for Assessing Human Health". The entire contents of these applications are incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to the field of medical systems, and more particularly, to the targeting of neuromodulation therapies for neuropsychiatric function. Background Art

[0004] Tractography is performed using data from diffusion magnetic resonance imaging (MRI). Free water diffusion is called "isotropic" diffusion. If water diffuses in a medium with a barrier, the diffusion will be non-uniform, which is called "anisotropic" diffusion. In this case, the relative mobility of molecules from the origin has a shape different from a sphere. This shape is typically modeled as an ellipsoid, and this technique is called diffusion tensor imaging. The barrier can be many substances: cell membranes, axons, myelin, etc.; but in white matter, the main barrier is the myelin sheath of axons. Axon bundles provide an obstacle to perpendicular diffusion and a path for parallel diffusion along the fiber direction. Summary of the Invention

[0005] According to one aspect of the present invention, there is provided a method for targeting neuromodulation in a patient's brain for improving, diagnosing, and managing one of neuropsychiatric functions. A first image representing the structure of the brain is obtained from a first imaging system, and a second image representing the connectivity of the brain is obtained from one of the first imaging system and a second imaging system. For each of a plurality of voxels within a region of interest, a first utility value associated with directly modulating the tissue within the region of interest is determined from the first image. For each of the plurality of voxels, a second utility value associated with indirectly modulating the tissue outside the region of interest by modulating the tissue within the region of interest is determined from the second image. The total utility value for each of the plurality of voxels is determined based at least on the first utility value and the second utility value. The optimal location for neuromodulation is determined based on the total utility value for each of the plurality of voxels.

[0006] According to another aspect of the present invention, a system is provided. An imaging interface receives a first image representing the structure of a brain from a first imaging system and receives a second image representing the connectivity of the brain from one of the first imaging system and a second imaging system. A targeting component determines, for each of a plurality of voxels within a region of interest from the first image, a first utility value associated with directly modulating tissue within the region of interest, and determines, for each of the plurality of voxels from the second image, a second utility value associated with indirectly modulating tissue outside the region of interest by modulating tissue within the region of interest. The targeting component then determines a total utility value for each of the plurality of voxels based at least on the first utility value and the second utility value, and determines an optimal location for neuromodulation based on the total utility value for each of the plurality of voxels. A neuromodulation system delivers neuromodulation to the optimal location.

[0007] According to yet another aspect of the present invention, a method for improving a patient's neuropsychiatric function is provided. An impact volume having a center point within a target region is selected, the target region including the nucleus accumbens and the ventral internal capsule, the center point being between 7 millimeters (mm) and 12 millimeters from the midline laterally on the right and left sides of the brain, between 1 millimeter and 6 millimeters in front of the anterior commissure (AC), and between 2 millimeters above and 2 millimeters below the AC. Neuromodulation is delivered to the selected impact volume. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The foregoing features of the present invention and other features will become understandable to those skilled in the art of the field to which the present invention pertains after reading the following description with reference to the accompanying drawings, wherein:

[0009] Figure 1 A system for targeting neuromodulation to treat or diagnose the neuropsychiatric function of a specific region of a patient's brain is shown;

[0010] Figure 2 A system for evaluating the impact of neuromodulation on one or more patients is shown;

[0011] Figure 3 A system for determining the risk or diagnostic validation of neuropsychiatric function based on imaging of a patient's brain, such as selecting who to treat, how often to treat, facilitating the use of other treatment decisions, is shown;

[0012] Figure 4 A first method for targeting neuromodulation in a patient's brain to treat or diagnose or manage neuropsychiatric function is shown;

[0013] Figure 5 A second method for targeting neuromodulation in a patient's brain to treat or diagnose or manage neuropsychiatric function is shown;

[0014] Figure 6Disclosed is a method for determining the risk of neuropsychiatric function based on the imaging of a patient's brain;

[0015] Figure 7 is a schematic block diagram of an exemplary system of hardware components capable of implementing Figures 1 to 6 the systems and methods disclosed in;

[0016] Figure 8 is a coronal view of a brain MRI scan slice identifying the nucleus accumbens and the internal capsule;

[0017] Figure 9 is an axial view of a brain MRI scan slice identifying the anterior commissure, posterior commissure, mid-commissure point, and midline of the brain;

[0018] Figure 10 is a schematic diagram of exemplary treatment sites and affected volumes for neuromodulation of addiction;

[0019] Figure 11 is a coronal view of a brain MRI scan slice schematically showing an exemplary treatment site;

[0020] Figure 12 is an illustration of an MRI image of a brain with implanted DBS electrodes; and

[0021] Figure 13 is a schematic block diagram of an exemplary system of hardware components capable of implementing Figures 1 to 12 the systems and methods disclosed in. Detailed Description

[0022] Various examples of the systems and methods described herein utilize a connectome of the brain registered to anatomical imaging to determine optimal locations and shapes for applying neuromodulation. Neuromodulation techniques generally involve sending energy to specific brain regions to alter neurons, axons, and overall neural, nervous system, nerve activity, and the targeting of neuromodulation has traditionally been based on anatomy. Unfortunately, this requires coarser adjustment settings and timely titration cycles to achieve the desired effect. The present disclosure relates to using information based on an individual's nervous system pathways to specifically localize regions targeting precise brain regions and modulate the corresponding pathways to optimize the desired effect. Additionally, the system can supplement this data with detection and prediction guided by integrated biometric parameters of functional state, symptoms and disease state, networks, and biobehavioral, cognitive feedback, virtual reality cues, and imaging to quantify functional state, symptoms and disease state, and observe through neuromodulation screening and other treatments. In one example, screening can be performed with micro-therapies with focused ultrasound and the patient is evaluated to see if the treatment is effective. If not, higher frequencies and doses can be used. Such screening can be used for disease diagnosis, determining appropriate treatments (e.g., drugs, neuromodulation, other treatment methods), and determining the need, frequency, and dose of neuromodulation treatment.

[0023] As used herein, "neuromodulation technique or modality" or "neuromodulation" is any suitable technique for applying local energy (or other neuromodulation modality) to the brain for modulating (e.g., activating, inhibiting, regulating, resetting, normalizing) neural activity and neural networks, or altering the permeability of the blood-brain barrier for applying treatment at a specific location. Non-limiting neuromodulation techniques or modalities include ultrasound, such as focused ultrasound; electrical stimulation, such as superficial stimulation (including percutaneous, transcutaneous, or subcutaneous stimulation) or cortical or deep brain stimulation; magnetic stimulation, including, for example, transcranial magnetic stimulation; optogenetic stimulation; or the application of electromagnetic radiation pulses. Other modalities of neuromodulation include the application of light, pressure, and heat / cold. As used herein, the term "modulate" refers to inhibiting, exciting, regulating, normalizing, resetting, or normalizing neural activity.

[0024] As used herein, a "prediction model" is a mathematical model or machine learning model that predicts the future state of a parameter or estimates the current state of a parameter that cannot be directly measured.

[0025] As used herein, a "voxel" is any defined volume within a region in an image or model, and the image or model is generated from an image representing the smallest analysis unit within the image or model.

[0026] "Neuropsychiatric function" includes, for example, the behavioral expression of brain function, such as cognition, initiation, motivation, emotional regulation, behavioral control, and the perception and / or understanding of the characteristics of emotional stimuli (including processing speed). Other examples of neuropsychiatric function include general intellectual function, basic attention, complex attention (working memory), executive function, memory (visual and verbal), language, visual construction function, and visuospatial construction. Thus, non-limiting examples of neuropsychological function include general intellectual function; attention, such as the ability of basic attention, monitoring and direct attention, and the flexible allocation of attention resources; working memory or divided attention, which refers to a memory system with a limited capacity in which information at the direct focus of attention can be temporarily stored and manipulated (e.g., the ability to hold two lines of thought simultaneously in a flexible manner); executive function, which includes, for example, planning, problem-solving skills, intentional and self-directed behavior, organizational skills, goal-directed behavior, the ability to generate multiple coping strategies, and the maintenance of a set of concepts (i.e., maintaining (or not losing) control or tracking of what one is doing); the ability to evaluate and modify behavior based on feedback; language and visual memory, or the ability to retain and store new information for future use; visuospatial skills, such as judging the direction of a line or discerning spatial relationships and patterns; visual construction skills, including two-dimensional construction skills (e.g., drawing or completing a puzzle) and three-dimensional construction skills (e.g., arranging blocks to match a design); language, such as confrontation naming (e.g., naming a specific word on demand, such as when a picture of an object is shown), word fluency, or generating a non-redundant list of words belonging to a specific category; motivation / drive / initiation in the interpersonal, cognitive, or behavioral domain; emotional regulation (e.g., the ability to control and direct emotions and feelings in an appropriate manner); and the interpretation of emotional stimuli (e.g., the ability to interpret emotional facial expressions, postures, body language, prosody, and contextual information to infer the emotional state of others or to help identify an appropriate emotional response). Impaired neuropsychiatric function refers to abnormal neuropsychiatric function compared to the normal healthy population.

[0027] Impaired neuropsychiatric function can also include, for example, abnormal anxiety, stress, fear, mood, depression, obsessive-compulsive disorder, and compulsion. In various patients exhibiting impaired neuropsychiatric function, including patients with neuropsychiatric disorders, mental disorders, neurodevelopmental disorders, or neurodegenerative disorders, the impaired neuropsychiatric function can be improved. Non-limiting examples of such disorders include anxiety disorders, addictions, mood disorders, obsessive-compulsive disorder (OCD), depression, post-traumatic stress disorder, bipolar disorder, autism, autism spectrum disorder, dyslexia, attention deficit disorder, acquired brain injury, stroke, schizophrenia, and other forms of dementia, as well as Parkinson's disease. With respect to addictions, an addiction includes an addiction to addictive behaviors, addictive chemicals, or combinations thereof. Non-limiting examples of addictions to addictive behaviors include gambling, food (resulting in or associated with, for example, obesity or eating disorders), sex, shopping, exercise and physical activity, video games, media / internet use, pathological work, compulsive criminal behavior, and combinations thereof. Non-limiting examples of addictions to addictive substances include addictions to nicotine; alcohol; marijuana; painkillers, such as opioids; heroin; benzodiazepines; stimulants, such as amphetamines (including methamphetamine and dextroamphetamine) and methylphenidate; inhalants, such as gasoline, household cleaning products, and aerosols; sedatives / hypnotics, such as barbiturates, zolpidem tartrate, and eszopiclone; and combinations thereof. These are merely examples of situations in which neuropsychological function may be impaired and thus in need of improvement. Although the present disclosure has been primarily described in the context of addictions, these methods and systems can be used in other situations in which a patient exhibits impaired neuropsychiatric function, such as Alzheimer's disease and related dementias, as well as cognitive disorders, including but not limited to mild cognitive impairment (MCI), Lewy body dementia, frontotemporal dementia, vascular dementia, Parkinson's dementia, chronic traumatic encephalopathy, Huntington's disease, multiple system atrophy, and the like.

[0028] "Registration" of two or more images includes any process of assigning relative positions between pixels or multi-pixel features on two or more images. For example, the assignment can be represented by an explicit transformation model between two or more images or by a feature matching technique that identifies common structural features between two images.

[0029] As used herein, "intensity distribution" represents the spatial variation in the intensity of the local energy provided to the brain. The intensity distribution can include variations between the two sides of a region that is provided with local energy or more complex spatial variations of the energy.

[0030] Figure 1A system 100 for targeted neuromodulation to treat or diagnose impaired neuropsychiatric function in a specific region of a patient's brain is shown. The system 100 includes a processor 102 and a non - transitory computer - readable medium 110 that stores executable instructions for targeted neuromodulation to treat or diagnose impaired neuropsychiatric function. The executable instructions include an imaging interface 112 that receives a first image (representing the structure of the brain) and a second image (representing the connectivity of the brain) of the patient from one or more associated imaging systems (not shown). In one embodiment, the first image is a T1 magnetic resonance imaging (MRI) image, and the second image is a diffusion tensor imaging (DTI) image generated using an MRI imager. The imaging interface 112 can include appropriate software components for communicating over a network with an imaging system (not shown) or a repository (not shown) storing the images via a network interface (not shown) or a bus connection.

[0031] In some embodiments, the imaging interface 112 can segment the first image into multiple sub - regions of the brain. The identified sub - regions can include, for example, the frontal pole, the temporal pole, the superior frontal region, the medial orbitofrontal region, the caudal anterior cingulate region, the rostral anterior cingulate region, the olfactory cortex region, the parahippocampal gyrus region, the perirhinal gyrus region, the lingual gyrus region, the cuneus region, the isthmus region, the precuneus region, the paracentral lobule region, and the fusiform gyrus region. For example, the first image is registered to a standard atlas to provide the segmented parts. In another embodiment, the imaging interface 112 can include a convolutional neural network trained on multiple annotated image samples and can be used to provide the segmented image. An example of such a system can be found in "3D Whole - Brain Segmentation Using Spatially Localized Atlas Network Blocks" by authors Huo et al. (available at https: / / doi.org / 10.48550 / arxiv.1903.12152), the entire content of which is incorporated herein by reference. The imaging interface 112 can also register the second image with the first image, thereby knowing the positions of the nodes in the connectome within the brain.

[0032] Each of the first image and the second image can be provided to a targeting component 116 that selects a location and an intensity distribution for neuromodulation. The targeting component 116 generates a connectome of the brain representing the neural connections within the brain from the second image. An area of interest can be defined within the first image, for example, based on the segmentation of the first image, and the location and intensity distribution of neuromodulation within the area of interest can be selected according to the generated connectome. It should be understood that the connectome can be determined as a passive connectome or an active connectome, where the passive connectome represents the physical connectivity between parts of the brain and the active connectome represents the activity induced in parts of the brain in response to energy provided at a specific location.

[0033] The targeting component 116 can divide a defined region of interest into a plurality of voxels and can assign a utility value to each voxel based on the position of each voxel within the region of interest and the connections of each voxel to other regions of the brain in the connectome to form a utility map. It should be understood that the term "utility value" is used herein broadly to encompass any method for assigning values associated with neuromodulation to tissue regions and is expressly intended to encompass both a cost method in which positive values are assigned to regions where energy application is not desired and a utility method in which positive values are assigned to regions where energy application is desired. In one example, the utility value of each voxel can be determined as a function of at least a first utility value determined from a first image and a second utility value determined from a second image.

[0034] For example, the first utility value can be determined based on the type of tissue represented by each voxel. In one embodiment, the region of interest from the first image can be registered to a standardized histological-based atlas of the region of interest, the histological-based atlas containing values for the respective regions associated with the region of interest. In one example, the values in the histological-based atlas can be assigned based on the expected concentration of excitatory or inhibitory neurons in the population at each location, e.g., as determined by analyzing histological samples representing the region of interest in an individual population. In one example, information from the second image can be used to adjust the value of the first utility value. For example, depending on the particular disorder being treated, higher values can be assigned to regions in a hemisphere or region with low connectivity, indicating an increased likelihood of overall brain connectivity, or lower values can be assigned, indicating a reduced secondary effect of neuromodulation. In the example of addiction, the region of interest can be all or a part of the nucleus accumbens, and the ventral internal capsule and the neurons of interest can include, for example, GABAergic neurons, such as including medium spiny projection neurons (MNSs). For brain injury and stroke, these values can be determined based on proximity or connectivity to the damaged brain tissue. For neurodevelopmental disorders, tissue within the nucleus accumbens with a high concentration of excitatory or inhibitory neurons and networks can be assigned greater values to reduce anxiety and craving. For epilepsy and pain, the region of interest can be within the epileptic focus, the surrounding cortical and subcortical brain tissue, the afferent and efferent fibers of the damaged region, and the thalamus, hippocampus, temporal lobe, insula, corpus callosum.

[0035] For a stroke, the region of interest can be located, for example, within one or more of the stroke area, the surrounding cortical and subcortical brain tissue, the afferent and efferent fibers of the damaged area, and the thalamus, nucleus accumbens, motor cortex, sensory cortex, visual cortex, or language cortex. For a stroke, the region of interest can be located, for example, within one or more of the brain injury area, the surrounding cortical and subcortical brain tissue, the afferent and efferent fibers of the damaged area, and the thalamus, nucleus accumbens, motor cortex, sensory cortex, visual cortex, or language cortex. For Parkinson's disease, the region of interest can be located, for example, within one or more of the globus pallidus, subthalamic nucleus, thalamus, pulvinar, thalamic afferents and efferents. For a neurodevelopmental disorder, the region of interest can be located, for example, within one or more of the thalamus and related nuclei, sensory cortex, visual cortex, frontal lobe, and afferents and efferents related to sensation, vision, and the frontal lobe. For a neurodevelopmental disorder, the region of interest can be located, for example, within one or more of the hippocampus, nucleus basalis of meynert, nucleus accumbens, and internal capsule, sensory cortex, parietal lobe, frontal lobe, cingulate cortex, and temporal lobe. For each of these disorders, a greater value can be assigned to excitatory or inhibitory neurons and networks with a high concentration, depending on the specific disorder. Similarly, in the case where a patient exhibits any other damaged neuropsychiatric function, an appropriate region of interest can be located and treated as specified herein.

[0036] A second utility value can be determined from a second image and represents the indirect modulation of neurons connected to a given voxel. Various regions of the brain (such as the frontal lobe) are generally available for indirect modulation, while modulation of other regions (such as the amygdala) can result in undesirable side effects, such as anxiety. Using the connectivity data from the second image, a set of positions connected to each voxel can be determined, as well as the connection strength for each of these positions. The second utility value for each voxel can be determined as a weighted linear combination of the values of these positions, weighted by weights derived from the connection strength of each position.

[0037] The influence volume associated with neuromodulation has an associated intensity distribution around a reference point (e.g., a center point), which represents the amount of energy provided to the region for a given location of the reference point. In an example, a value normalized by the maximum intensity can be assigned to each voxel, and this value can be used to weight the contribution of the voxel to the total cost associated with the location and intensity distribution. In other embodiments, it can be assumed that the intensity of the entire influence volume is substantially uniform, and thus no weighting is required. In addition to the utility value assigned to each voxel, each voxel can have one or more values representing the utility of applying the influence volume centered on that voxel, e.g., as the sum or weighted sum of all voxels within the influence volume. It should be understood that the influence volume itself can be changed by adding additional foci or selectively activating electrodes in focused ultrasound, or by changing the direction and intensity of the fields generated by the electrodes in deep brain stimulation to adjust the intensity distribution or shape of the influence volume. An optimization process (e.g., gradient descent) can be used to search the region of interest to find the best or near-optimal center point location and shape of the influence volume, and the resulting values can be provided to the treatment planning system 118 for generating a treatment plan for the patient. In one example, the treatment planning system 118 can be constrained to select an influence volume having a center point between 7 millimeters (mm) and 12 millimeters from the midline laterally on the right and left sides of the brain, between 1 millimeter and 6 millimeters in front of the anterior commissure (AC), and between 2 millimeters above and 2 millimeters below the AC.

[0038] In one example, the targeting component 116 can operate in conjunction with the neuromodulation system 120 to refine a map of the brain's connectome. Specifically, energy can be applied at various locations within the region of interest, and activity within the brain can be determined via a suitable functional imaging modality to determine whether a change in brain activity as expected from a second image is achieved following application of neuromodulation. Generally, these images can be acquired while the patient is performing a task or experiencing a cue related to their disorder. Additionally or alternatively, changes in connectivity within the brain following treatment can be used as a measure of brain connectivity and used to refine parameters within the targeting component. In one embodiment, activity within the brain in response to neuromodulation at a given location is recorded as the result of neuromodulation for that location. This can be used to modify the weight of each connection within the set of connections applied to each voxel, as well as to modify the value applied to the voxel itself for the patient. Additionally, the targeting component can generally be updated from this feedback to change parameters associated with targeting for other patients, such as values stored in a histological-based map of the region of interest, or the assignment of connection strengths using connectivity data from a second image. It should be understood that when energy is provided, multiple locations can be affected, and the detected activity can be attributed to each location based on the percentage of energy received at each voxel, e.g., represented as a voxel. Optionally, the results of multiple measurements can be compared, for example, by solving one or more n-dimensional linear systems, where n is the number of voxels within the region of interest. Thus, the activity connectivity associated with each location within the region of interest can be determined and used to adjust the values and connection weights associated with each of the multiple voxels.

[0039] Figure 2 A system 200 for evaluating the effects of neuromodulation on one or more patients is shown. System 200 includes a processor 202 and a non-transitory computer-readable medium 210 that stores executable instructions for targeted neuromodulation to treat and diagnose impaired neuropsychiatric function. The executable instructions include an imaging interface 212 that receives, for a patient, a first image representing the structure of the brain and a second image representing the connectivity of the brain. In one embodiment, the first image is a T1 magnetic resonance imaging (MRI) image and the second image is a diffusion tensor imaging (DTI) image generated using an MRI imager. The imaging interface 212 can also receive, for example, from the same or a different MRI imager, a functional image representing activity within the brain. The imaging interface 212 can include suitable software components for communicating over a network interface (not shown) or via a bus connection over a network with an imaging system (not shown) or a repository (not shown) storing images.

[0040] The first image is provided to the registration component 214, which divides the first image into multiple sub-regions of the brain. The identified sub-regions can include, for example, the frontal pole, the temporal pole, the superior frontal region, the medial orbitofrontal region, the caudal anterior cingulate region, the rostral anterior cingulate region, the olfactory cortex region, the parahippocampal gyrus region, the perirhinal gyrus region, the lingual gyrus region, the cuneus region, the isthmus region, the precuneus region, the paracentral lobule region, and the fusiform gyrus region. In one example, the registration component 214 registers the first image to a standard atlas to provide a segmented portion. In another embodiment, a convolutional neural network trained on multiple annotated image samples can be used to provide a segmented image. The registration component 214 can also register the second image with the first image, thereby knowing the positions of the nodes in the connectome of the brain. In addition to using the first image for registration, various biometric parameters can be extracted from the image, including parameters related to connectivity, networks, gray matter / white matter ratio, and volume.

[0041] Each of the segmented first image and the second image can be provided to the feedback component 216, which determines the treatment effectiveness of the patient based on patient data collected in response to a stimulus. The stimulus can be applied for neuromodulation in a given region, or the presentation of a cue related to the patient's disorder to the patient. An example of the presentation of a cue can be found in U.S. Patent Publication No. US2021 / 0162217, titled "Methods and systems of entering and monitoring addiction using cuereactive", filed on December 2, 2020, the entire content of which is incorporated herein by reference. During the presentation of a given stimulus, the electrical activity or other physiological activity of the brain can be recorded to determine whether the neural activity increases, decreases, or otherwise changes in response to the stimulus. The feedback collected can also include self-reports from the patient, observations by clinicians, measured electrical activity, measured biometric parameters (such as heart rate variability and blood pressure), and other relevant parameters.

[0042] In one embodiment, the identified activity can be used as feedback for determining the success of treating a patient by neuromodulation. This can be done during treatment or immediately after treatment (“acute feedback”), within a short time after treatment (e.g., several hours to several days) (“semi - acute feedback”), or over a longer time after treatment (e.g., several weeks to several months after treatment) (“chronic feedback”). Cues can be selected to measure the impact of the treatment on the disorder, and the cues can include cues related to addiction (e.g., pictures, scents, tastes, or sounds associated with addictive activities), activities that measure the patient's capabilities (e.g., memory and attention tasks), or other cues suitable for assessing the impact of a given disorder on the patient.

[0043] In some aspects, a measurement of the patient's baseline craving level for addictive behavior or addictive chemicals can be obtained. These baseline levels, which can be measured at various time points before or during treatment, can be measured when the patient is under, for example, the addiction care standards outlined by the American Society of Addiction Medicine (ASAM 2013). These baseline levels can be measured at intake, during medication - assisted and / or behavioral therapy. Such baseline craving levels can be measured in a clinical / laboratory setting. Additionally, during the course of treatment, these baseline levels can change or vary over time. For example, the patient can be asked to evaluate the craving for the substance or behavior for which he or she is seeking treatment on a 100 - point visual analog scale (VAS), where 100 represents the maximum craving and 0 represents no craving. After this baseline craving assessment, the method can include exposing the patient to cues associated with the addictive behavior or addictive chemicals and subsequently assessing the patient's final craving level to determine the change in craving during or after cue exposure. During or after exposure to the cues, the patient's final or subsequent craving level is measured in a timely manner such that the patient's final craving level is related to the patient's response to the cues. For example, the final craving level can be measured during exposure to the cues, within five minutes after exposure to the cues, within ten minutes after exposure to the cues, or at any measurable time period in between. More specifically, the method can include obtaining in a timely manner a measurement of the patient's final craving level for the addictive behavior or addictive chemicals during or after exposure to the cues and determining whether the patient's final craving level has increased, or whether the patient's final craving level has remained substantially the same or decreased compared to the baseline craving level. Then, the method can include providing or adjusting neuromodulation based on the comparison of the baseline craving level and the final craving level to improve the patient's addiction. For example, neuromodulation can be provided or adjusted when it is determined that the final craving level has increased to be higher than the patient's baseline craving level.

[0044] In some aspects, rather than measuring a patient's craving level, physiological, cognitive, psychosocial, or behavioral parameters related to the patient's addictive behavior or the patient's addictive chemical are measured. Particular methods can include obtaining a measurement of a baseline value of a patient's physiological, cognitive, psychosocial, or behavioral parameter. As described above, these baseline levels can change over time during the course of treatment. After this initial assessment, one method can include exposing the patient to a cue associated with the addictive behavior or addictive chemical, and subsequently assessing the value of an outcome parameter of the patient to determine a change in the parameter value during or after cue exposure. During or after exposure to the cue, the outcome or subsequent parameter value of the patient is measured in a timely manner such that the measured value of the patient's outcome parameter is related to the patient's response to the cue. More specifically, the method can include obtaining a measurement of the outcome value of the parameter in a timely manner during or after exposure to the cue to determine whether the outcome value of the parameter increases, or whether the outcome parameter value remains substantially the same or decreases compared to the baseline parameter value. The method can then include providing or adjusting neuromodulation to the patient based on a comparison of the outcome parameter value to the baseline parameter value. For example, neuromodulation can be provided or adjusted based on determining that the outcome parameter value has increased to be higher than the patient's baseline parameter value.

[0045] The cues to which the patient is exposed can be visual cues, taste cues, auditory cues, tactile cues, olfactory cues, or a combination thereof. For example, in a natural, non-clinical environment, such as when the patient is at home, at work, or in other non-clinical settings, the patient can be exposed to cues via a smartphone, tablet, personal computer, or laptop. The patient can be exposed to cues via virtual reality, augmented reality, or mixed reality. The patient can be exposed to multiple cues during any assessment, and in the case of multi-substance use or behavior, the patient can be exposed to cues associated with different addictive substances or behaviors. In the case of chemical addiction, the cue can be, for example, an image of a drug, drug paraphernalia, or a drug user. The cue can be specific to the particular addictive behavior or addictive chemical for which the patient is seeking treatment, and can include multiple cues, including multiple different types of cues. For example, if the patient is addicted to alcohol, the cue can be the smell of alcohol, a visual image of a bar, or the sound of an open alcohol beverage container. For example, if the patient is addicted to heroin, the cue can be an image of heroin, a hypodermic needle, or a spoon and lighter. For example, if the patient is addicted to gaming, the cue can be a visual image of a casino or gaming chips. The above examples are merely exemplary and are intended to indicate that the cues can be addiction-specific and can stimulate different senses. These cues can also be similar to the characteristics of the patient, such as the patient's age, gender, race, preferred chemical, and route of use. In other words, the cues to which the patient is exposed can be personalized for the particular patient seeking treatment.

[0046] In terms of measuring a patient's physiological parameters, the physiological parameters can be the response of the patient's autonomic nervous system to a cue exposure, and multiple physiological parameters can be measured during any given assessment session. The physiological parameters can be measured by a wearable device (such as a ring, watch, or belt) or by a smartphone or tablet (e.g., in a natural non-clinical environment, such as when the patient is at home, at work, or in other non-clinical environments). Exemplary physiological parameters include heart rate, heart rate variability, sweat, saliva, blood pressure, pupil size, brain activity, skin electrical activity, body temperature, and blood oxygen saturation level. Table I provides a non-limiting example of physiological parameters that can be measured and exemplary tests for measuring the physiological parameters.

[0047] Table I

[0048]

[0049]

[0050] The physiological parameters can be measured using appropriate devices in a clinical environment or by wearable, implantable, or portable devices in a non-clinical environment. Some information can also be determined based on self-reporting by the user through an application in a mobile device or through interaction with an application on the mobile device. For example, a smartwatch, ring, or patch can be used to measure the user's heart rate, heart rate variability, body temperature, blood oxygen saturation, movement, and sleep. In a non-clinical environment, these values can also be subjected to circadian analysis to estimate variability. For example, a camera and dedicated software on a mobile device can be used to perform eye tracking.

[0051] Table II provides a non-limiting example of gamified and measurable cognitive parameters, as well as exemplary methods and tests / tasks for measuring such cognitive parameters. The cognitive parameters can be evaluated through a series of cognitive tests, such as measuring executive function, decision-making, working memory, attention, and fatigue.

[0052] Table II

[0053]

[0054]

[0055] These cognitive tests can be conducted in a clinical / laboratory setting or in a natural non-clinical setting, such as when the user is at home, at work, or in other non-clinical environments. Smart devices (such as smartphones, tablets, or smartwatches) can facilitate the measurement of these cognitive parameters in natural non-clinical settings. For example, the Erikson Flanker task, N-Back task, and Psychomotor Vigilance task can be obtained through applications on smartphones, tablets, or smartwatches. In one example, patients can be allowed to explore a virtual reality environment and collect items within the environment. Then, the patient is required to recalculate the location where each item was found within the virtual environment and the relationship of that location to the starting point, testing the patient's ability to recall the spatial relationships between virtual locations.

[0056] Table III provides non-limiting examples of parameters associated with a user's movement and activities, as well as exemplary tests, devices, and methods. For ease of reference, these parameters are referred to herein as "motor parameters," and these parameters can be measured. The use of portable monitoring, physiological sensing, and portable computing devices allows for the measurement of motor parameters. Using embedded accelerometers, GPS, and cameras, a user's movement can be captured and quantified to understand the impact on their health and the relationship to health-related parameters. Range of motion and gait analysis can be evaluated in a clinical setting using appropriate motion capture and camera equipment.

[0057] Table III

[0058]

[0059] Table IV provides non-limiting examples of parameters associated with a user's sensory acuity, as well as exemplary tests, devices, and methods. For ease of reference, these parameters are referred to herein as "sensory parameters," and these parameters can be measured.

[0060] Table IV

[0061]

[0062] Table V provides non-limiting examples of parameters associated with a user's sleep quantity, sleep stages, and sleep quality, as well as exemplary tests, devices, and methods. For ease of reference, these parameters are referred to herein as "sleep parameters," and these parameters can be measured.

[0063] Table V

[0064]

[0065] Table VI provides non-limiting examples of parameters extracted by locating biomarkers associated with a user, as well as exemplary tests, devices, and methods. For ease of reference, these parameters are referred to herein as "biomarker parameters," and these parameters can be measured. Biomarkers can also include imaging and physiological biomarkers associated with chronic health states and improvements or deteriorations in chronic health states.

[0066] Table VI

[0067]

[0068] Table VII provides non-limiting examples of psychosocial and behavioral parameters that can be measured, as well as exemplary tests, devices, and methods. For ease of reference, these parameters are referred to herein as "psychosocial parameters."

[0069] Table VII

[0070]

[0071] In addition to one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters, clinical data can also be part of a multidimensional feedback approach to evaluate the effectiveness of treatment. Such clinical data can include, for example, the user's clinical status, the user's medical history (including family history), employment information, and residential status. In particular, functional imaging can be used to evaluate the effect of neuromodulation on brain activity. For example, a decrease in activity in a region where inhibitory neurons are targeted or an increase in activity in a region where excitatory neurons are targeted can indicate that neuromodulation is successful. Similarly, connection information determined by tractography can be used to indicate changes in brain connectivity over time due to treatment. In particular, the quality, quantity, and density of connections shown by tractography, as well as changes in these values over time, can indicate the effectiveness of treatment and be used to guide changes in treatment frequency and parameters and to select patients to receive treatment. This is particularly important in addiction, where increased connectivity from the nucleus accumbens to the frontal lobe can indicate that treatment is successful, and in neurodevelopmental disorders, where increased brain connectivity in general can indicate a response to treatment. In addition, increased connectivity is a sign of increased neuroplasticity, which indicates improvement in traumatic brain injury and stroke.

[0072] The type of change in a patient's physiological parameter measurement or craving level during or after cue exposure can affect whether neuromodulation is provided or whether existing neuromodulation should be adjusted. For example, in terms of providing neuromodulation, if a patient's physiological parameter measurement or craving level increases during or after cue exposure compared to a baseline physiological parameter measurement or baseline craving level, the method can involve initiating neuromodulation. Conversely, if the physiological parameter measurement or craving level during or after cue exposure is substantially the same as the baseline physiological parameter measurement or baseline craving level, neuromodulation may not be applied. In the context of adjusting treatment in the context of neuromodulation, the method can involve adjusting the parameters or dose of neuromodulation, such as the duration, frequency, or intensity of neuromodulation. If a patient's physiological parameter measurement or craving level increases during or after cue exposure compared to a baseline physiological parameter measurement or baseline craving level, the method can involve adjusting the neuromodulation such that the neuromodulation is more effective. For example, if a patient previously underwent five minutes of focused ultrasound (FUS) during a treatment session, the patient can subsequently undergo twenty minutes of FUS during each treatment session, or if the patient undergoes FUS once every thirty days, the patient can subsequently undergo FUS once every two weeks. Conversely, for example, if the physiological parameter measurement or craving level after cue exposure is substantially the same as the baseline physiological parameter measurement or baseline craving level, the neuromodulation parameters may not need to be adjusted, and subsequent neuromodulation sessions can primarily be used as maintenance sessions, or the intensity, frequency, or duration of neuromodulation can be decreased. Optionally, if the physiological parameter measurement or craving level during or after cue exposure is substantially the same as the baseline physiological parameter measurement or baseline craving level, the patient can stop receiving any subsequent neuromodulation. The above scenarios are merely exemplary and are provided to illustrate that the presence and type of change in a patient's physiological parameter measurements and craving levels during and after cue exposure can affect whether treatment is provided or whether existing treatment should be adjusted or terminated.

[0073] In addition, the degree of measurement of physiological, cognitive, psychosocial, or behavioral parameters in a patient during or after cue exposure, and the degree of the patient's craving level during or after cue exposure can affect the parameters of initial or subsequent neuromodulation. For example, if the craving level of a particular patient seeking treatment is higher than the average craving level of the same patient population (patients with the same addiction) during or after cue exposure, the initial or subsequent treatment may be more aggressive (e.g., in the case of neuromodulation, the duration, frequency, or intensity of neuromodulation can be greater than the duration, frequency, or intensity provided to patients in the same patient population). Similarly, if a particular patient seeking treatment has physiological, cognitive, psychosocial, or behavioral parameter measurements that are higher than the average parameter measurements of the same patient population during or after cue exposure, the initial or subsequent treatment may be more aggressive. Conversely, if the craving level or parameter measurements of a particular patient during or after cue exposure are lower than the average craving level or parameter measurements of the same patient population, the initial or subsequent treatment may be less aggressive. In other words, during or after cue exposure, the severity or degree of the patient's final craving level or final physiological, cognitive, psychosocial, or behavioral parameter measurements (as well as baseline values and levels) can be related to the degree or aggressiveness of neuromodulation. The above scenarios are merely exemplary and are provided to illustrate that the degree of change in a patient's physiological parameter measurements and craving level during and after cue exposure can affect the parameters of initial and subsequent treatments.

[0074] In some aspects, the feedback is not a response to a cue, but a comparison of one or more combinations of a patient's physiological, cognitive, psychosocial, and behavioral parameters. For example, measurements of baseline values of one or more combinations of a patient's physiological, cognitive, psychosocial, and behavioral parameters can be obtained. Then, the patient can be exposed to neuromodulation (e.g., an initial focused ultrasound signal, an initial deep brain stimulation signal, or an initial transcranial magnetic stimulation signal) to a neural target site of the patient. During or after application of the initial focused ultrasound signal, the initial deep brain stimulation signal, or the initial transcranial magnetic stimulation signal, subsequent measurements of result values of one or more combinations of the patient's physiological, cognitive, psychosocial, and behavioral parameters can be obtained. The resulting values can be compared with the baseline values to determine whether the patient's addiction has improved. The neuromodulation can be adjusted when it is determined that the patient's addiction has not improved.

[0075] As described above, if it is determined that the neuromodulation is not successful, the neuromodulation can be provided to a different target location. It will be understood that the system can operate in conjunction with Figure 1 the system to guide the selection of a new target location for neuromodulation. In particular, a new target for neuromodulation can be selected, for example, by Figure 1 the targeting component 114 described above.

[0076] The presentation of stimuli can be repeated to accumulate functional imaging information associated with each of multiple stimuli, which can be accumulated and stored in a memory. In the case where neuromodulation is applied to the stimuli, the location of the applied neuromodulation can be recorded together with the functional imaging information. The accumulated imaging information and the connectome can also be provided to an expert system 218 that identifies the regions and nodes of the brain that respond to the stimuli and the correlations between the activities of the regions and nodes, herein referred to as co-activation locations. The identified co-activation locations can be informed by the connectome such that only regions and nodes connected within the connectome can be identified as co-activated. It should be understood that the functional imaging information can be accumulated in a single patient, a group of patients with a specific type of neuropsychiatric function, a group of patients with a general category of neuropsychiatric function, a group of patients without neuropsychiatric function, or two groups of patients with and without neuropsychiatric function. Depending on the patient or group of patients used and the stimuli used, the results can represent a patient-specific brain circuit patent, biomarkers for a general type of neuropsychiatric function or a specific type of neuropsychiatric function, or biomarkers associated with the response to neuromodulation or other treatments. The determined patterns can be used, for example, to guide the treatment of a specific patient or to identify biomarkers of neuropsychiatric function in new patients.

[0077] In one example, information collected in a group of patients with a general category of neuropsychiatric function or a specific disorder can be used to guide the selection of an initial location for applying neuromodulation. For example, if a particular location defined relative to one or more landmark structures within the brain has been consistently successful in a group of patients with the same or similar disorder, that location can be the default location for initial treatment by neuromodulation. As described above, feedback representing the patient's response to treatment can be collected and a new location can be selected for the patient if needed. The new location can be determined based on connectivity information and the information collected for a group of patients. In one embodiment, an analogical reasoning system can be used to locate past patients with similar characteristics to the current patient and can use the locations that were successful in the similar patients. The characteristics used to match the patient with similar patients can include demographic characteristics, medical history, measured biometric parameters such as blood pressure and heart rate variability, observations of the patient's response to cues, and the measured electrical activity in the brain during and after neuromodulation.

[0078] In another example, feedback from measurements of a patient (e.g., measured electrical activity and biometric parameters) can be used to automatically detect cues presented to the patient. In this embodiment, the neuromodulation and measurement of the electrical activity and biometric parameters can be provided by one or both of an implant device and a wearable device, and the periodic measurement of the electrical activity can be used to evaluate the patient. These measurement results can be provided to an expert system 218, which in this embodiment can be trained on data representing the patient's response to the presented cue to determine whether the patient has encountered a cue related to the patient's neuropsychiatric function. If it is determined that the patient is responding to a cue in the environment, the device providing neuromodulation can be activated to provide immediate treatment to the patient, thereby allowing neuromodulation to be automatically presented in response to environmental cues.

[0079] Figure 3 A system 300 for determining the risk of neuropsychiatric function impairment based on imaging of a patient's brain is shown. The system 300 includes a processor 302 and a non-transitory computer-readable medium 310 that stores executable instructions for determining the risk of neuropsychiatric function based on imaging of the patient's brain. The executable instructions include an imaging interface 312 that receives, for the patient, a first image representing the structure of the brain and a second image representing the connectivity of the brain. In one embodiment, the first image is a T1 magnetic resonance imaging (MRI) image and the second image is a diffusion tensor imaging (DTI) image generated using an MRI imager. The imaging interface 312 can also receive, for example, a functional image representing activity within the brain from the same or a different MRI imager. The imaging interface 312 can include suitable software components for communicating over a network interface (not shown) or over a bus connection on a network with an imaging system (not shown) or a repository (not shown) storing the images.

[0080] The first image is provided to a registration component 314 that segments the first image into a plurality of sub-regions of the brain. The identified sub-regions can include, for example, the frontal pole, the temporal pole, the superior frontal region, the medial orbitofrontal region, the caudal anterior cingulate region, the rostral anterior cingulate region, the olfactory cortex region, the parahippocampal gyrus region, the perirhinal gyrus region, the lingual gyrus region, the cuneus region, the isthmus region, the precuneus region, the paracentral lobule region, and the fusiform gyrus region. In one example, the registration component 314 registers the first image to a standard atlas to provide the segmented portion. In another embodiment, a convolutional neural network trained on a plurality of annotated image samples can be used to provide the segmented image. The registration component 314 registers the second image with the first image, thereby knowing the positions of the nodes in the patient's connectome.

[0081] The registered second image is provided to the machine learning model 316. The machine learning model can utilize one or more pattern recognition algorithms, for example, implemented as classification and regression models, each algorithm analyzing the provided connectome image to assign to the user a clinical parameter that represents one of the following: the likelihood that the patient has or will have a problem with a general class of neuropsychiatric functions during a specific time period, the likelihood that the patient has or will have a problem with a specific neuropsychiatric function during a specific time period, the likelihood that the patient will generally respond to a treatment for a neuropsychiatric function, or the likelihood that the patient will respond to a specific treatment for a neuropsychiatric function. It should be understood that the clinical parameter can be categorical or continuous. In the case of using multiple classification and regression models, the machine learning model can include an arbitration element that can be utilized to provide coherent results from the various algorithms. Based on the outputs of the various models, the arbitration element can simply select one category from the model with the highest confidence, select multiple categories from all models that meet a threshold confidence, select one category through a voting process among the models, or assign a numerical parameter based on the outputs of multiple models. Optionally, the arbitration element itself can be implemented as a classification model that receives the outputs of the other models as features and generates one or more output categories for the patient.

[0082] The machine learning model and any constituent models can be trained on training data representing various classes of interest. The training data can include, for example, registered connectome images or numerical and / or categorical features extracted from registered connectome images. For example, in a supervised learning model, a system can be trained using a set of examples with labels representing the desired outputs of the machine learning model. The training process of the machine learning model will vary depending on its implementation, but training generally involves statistically aggregating the training data into one or more parameters associated with the output categories. For rule-based models (such as decision trees), domain knowledge provided by one or more human experts, for example, can be used to replace or supplement the training data to select the rules for classifying the user using the extracted features. Any of a variety of techniques can be used for the model, including support vector machines, regression models, self-organizing maps, k-nearest neighbor classification or regression, fuzzy logic systems, data fusion processes, boosting and bagging methods, rule-based systems, or artificial neural networks.

[0083] For example, an SVM classifier can conceptually divide the boundaries in an N-dimensional feature space by using multiple functions called hyperplanes, where each of the N dimensions represents an associated feature of a feature vector. The boundaries delimit the ranges of feature values associated with each class. Thus, the output class and the associated confidence value of a specified input feature vector can be determined based on the position of the specified input feature vector in the feature space relative to the boundaries. In one embodiment, the SVM can be implemented by using a kernel method with a linear or non-linear kernel.

[0084] An ANN classifier includes a plurality of nodes having multiple interconnections. Values from the feature vector are provided to a plurality of input nodes. Each input node provides these input values to a layer of one or more intermediate nodes. A given intermediate node receives one or more output values from previous nodes. The received values are weighted according to a series of weights established during the training of the classifier. The intermediate node converts the values it receives into a single output according to a transfer function at the node. For example, the intermediate node can sum the received values and subject the sum to a binary step function. The final node layer provides confidence values for the output classes of the ANN, where each node has an associated value representing the confidence of one of the associated output classes of the classifier. Another example is to use an autoencoder to detect outliers in health-related parameters as an anomaly detector to identify when various parameters are outside the normal range of an individual.

[0085] Many ANN classifiers are fully connected and feedforward. However, a convolutional neural network includes convolutional layers, where nodes from a previous layer are only connected to a subset of the nodes in the convolutional layer. A recurrent neural network is a class of neural networks where the connections between nodes form a directed graph along a time series. Different from a feedforward network, a recurrent neural network can incorporate feedback from states caused by earlier inputs, such that the output of a recurrent neural network for a specified input can be a function not only of that input but also of one or more previous inputs. For example, a long short-term memory (LSTM) network is an improved version of a recurrent neural network, which makes it easier to preserve past data in memory.

[0086] Rule-based classifiers apply a set of logical rules to extracted features to select an output class. Typically, the rules are applied in sequence, and the logical result of each step influences the analysis of subsequent steps. The specific rules and their sequence can be determined based on any or all of training data, simulated inferences from previous cases, or existing domain knowledge. An example of a rule-based classifier is the decision tree algorithm, where the values of features in a feature set are compared to corresponding thresholds in a hierarchical tree structure to select the class of a feature vector. A random forest classifier is a modification of the decision tree algorithm using the bootstrap aggregating or "bagging" method. In this method, multiple decision trees are trained on random samples of a training set, and the mean (e.g., mean, median, or mode) result across multiple decision trees is returned. For classification tasks, the result of each tree is categorical, so the modal result can be used. Regardless of the specific model employed, the clinical parameters generated by the machine learning model 316 can be provided to a user at the display 320 via a user interface, or stored on a non-transitory computer-readable medium 310, such as in an electronic medical record associated with the patient.

[0087] In view of the above structural and functional features, reference is made to Figures 4 to 9 for a better understanding of the example method. However, for purposes of simplifying the description, Figures 4 to 9 while shown and described as being executed serially, it should be understood and appreciated that the present example is not limited by the order shown, as some actions may occur in a different order, multiple times, and / or simultaneously in other examples. Additionally, not all of the described actions need to be performed to implement the method in accordance with the present invention.

[0088] Figure 4 A first method 400 for targeting neuromodulation in a patient's brain to treat or diagnose neuropsychiatric function is shown. At 402, a first image representing the structure of the brain is obtained from a first imaging system. At 404, a second image representing the connectivity of the brain is obtained from one of the first imaging system and a second imaging system. In one embodiment, the second image is one of a set of images representing the connectivity of the brain, each image being obtained by applying neuromodulation at a location within a region of interest associated with the image and measuring the activity level in at least one sub-region of a plurality of sub-regions of the brain in response to the applied neuromodulation. Additionally or alternatively, the second image can be obtained by diffusion tensor imaging.

[0089] At 406, the first image is segmented into multiple sub-regions of the brain to generate a segmented first image such that each of at least a subset of the multiple voxels including the first image is associated with one of the multiple sub-regions. It should be understood that for a given neuropsychiatric function, not all parts of the first image are of interest, and thus only those voxels representing the multiple sub-regions can be included in the segmented portion. At 408, based on the segmented first image and the second image, a location within the region of interest of the brain is selected as a target for neuromodulation.

[0090] Figure 5 A second method 500 for targeting neuromodulation in a patient's brain to treat or diagnose a neuropsychiatric function impaired by nerve damage is shown. At 502, a first image representing the structure of the brain is obtained from a first imaging system. At 504, a second image representing the connectivity of the brain is obtained from one of the first imaging system and a second imaging system. In one embodiment, the second image is one of a set of images representing the connectivity of the brain, each image being obtained by applying neuromodulation at a location within the region of interest associated with the image and measuring the activity level in at least one of the multiple sub-regions of the brain in response to the applied neuromodulation. Additionally or alternatively, the second image can be obtained by diffusion tensor imaging.

[0091] At 506, the first image is segmented into multiple sub-regions of the brain to generate a segmented first image such that each of at least a subset of the multiple voxels including the first image is associated with one of the multiple sub-regions. It should be understood that for a given neuropsychiatric function, not all parts of the first image are of interest, and thus only those voxels representing the multiple sub-regions can be included in the segmented portion. At 508, based on the segmented first image and the second image, a location within the region of interest of the brain is selected as a target for neuromodulation.

[0092] At 510, a cue associated with neuropsychiatric function is presented to the patient, and at 512, feedback from the patient in response to the cue is measured. The feedback can include a clinician's observations of the patient's appearance and behavior, the patient's self-report of symptoms of the disorder, electrical activity measured in the brain, and biometric parameters. At 514, the effectiveness of neuromodulation is determined based on the measured feedback. At 516, it is determined whether the effectiveness of neuromodulation meets a threshold. If the threshold is met (Y), then at 518, the location of neuromodulation is determined to be effective, and the method terminates. If the threshold is not met (N), then the selected location is determined to be ineffective, and the method returns to 508 to select a new location within the region of interest as the target for neuromodulation. It should be understood that steps 510, 512, 514, 516 can be performed during or immediately after treatment, within a short time after treatment (e.g., several hours to several days), or within a longer time after treatment (e.g., several weeks to several months after treatment).

[0093] Figure 6 A method 600 for determining the risk of impaired neuropsychiatric function based on imaging of a patient's brain is shown. At 602, a first image representing the structure of the brain is obtained from a first imaging system. At 604, a second image representing the connectivity of the brain is obtained from one of the first imaging system and a second imaging system. In one embodiment, the second image is one of a set of images representing the connectivity of the brain, each image being obtained by applying neuromodulation at a location within a region of interest associated with the image and measuring the activity level in at least one of a plurality of subregions of the brain in response to the applied neuromodulation. Additionally or alternatively, the second image can be obtained by diffusion tensor imaging.

[0094] At 606, the first image is segmented into a plurality of subregions of the brain to generate a segmented first image such that each of at least a subset of the plurality of voxels including the first image is associated with one of the plurality of subregions. It should be understood that for a given included neuropsychiatric function, not all parts of the first image are of interest, and thus only those voxels representing the plurality of subregions can be included in the segmented portion. At 608, the segmented first image and the second image are represented to a machine learning model trained on imaging data of a plurality of patients with known outcomes, and at 610, a clinical parameter representing the patient's risk of impaired neuropsychiatric function is generated from the representation of the segmented first image and the second image.

[0095] In one embodiment, a set of numerical features is extracted from the segmented first and second images and provided to a machine learning model. In another embodiment, the segmented first and second images are provided directly to the machine learning model. In yet another embodiment, the second image is registered with the first image to provide a registered connectome that represents the positions of nodes within the connectome relative to multiple subregions, and the registered connectome or a set of numerical features representing the registered connectome can be provided to the machine learning model. It should be understood that a clinical parameter can be any continuous or categorical parameter that represents the risk of a patient associated with impaired neuropsychiatric function, including the likelihood that the patient exhibits one or more of the impaired neuropsychiatric functions within an impaired neuropsychiatric function category that includes the impaired neuropsychiatric function, the likelihood that the patient has one or more of the impaired neuropsychiatric functions within an impaired neuropsychiatric function category that includes the impaired neuropsychiatric function, and the likelihood that the patient responds to treatment of the impaired neuropsychiatric function. The clinical parameter can then be stored in a non-transitory computer-readable medium or displayed to a user on an associated output device.

[0096] Figure 7 A method 700 for targeting neuromodulation in a patient's brain for improving, diagnosing, treating, and managing one of neuropsychiatric functions is shown. At 702, a first image representing the structure of the brain is acquired from a first imaging system. For example, the first imaging system can be a computed tomography (CT) system or a magnetic resonance imaging (MRI) system. At 704, a second image representing the connectivity of the brain is acquired from one of the first imaging system and a second imaging system. For example, the second imaging system can be an MRI system and the image can be generated by diffusion tensor imaging.

[0097] At 706, a first utility value associated with directly modulating the tissue within the region of interest is determined for each of a plurality of voxels within the region of interest from the first image. In one example, this is done by identifying the various tissue types in the first image and assigning values based on the tissue type. In another example, a portion of the first image representing the region of interest is registered to a histology-based atlas that has an assigned value for the first utility value for each of a plurality of positions within the histology-based atlas. For example, these values can be determined based on data captured in the population of interest (e.g., histology data) according to the expected concentration of inhibitory or excitatory neurons at a given position.

[0098] At 708, a second utility value associated with indirectly modulating tissue outside the region of interest by modulating tissue within the region of interest is determined for each of the plurality of voxels from the second image. In one example, for each voxel of the plurality of voxels, a set of locations outside the region of interest that are indirectly modulated when modulating the given voxel is determined from the second image. Each of the locations in the set has an associated indirect utility value, which generally represents the value of modulating that tissue. The corresponding strength of the connection between the voxel and each of the locations in the set can also be determined from the second image, where the strength between each of the locations in the set and the given voxel is represented as a connection weight. It should be understood that the weight can be determined as a linear or non-linear function of the connection strength. The second utility value for each voxel can be the sum of the products of the indirect utility values of its set of locations and the associated connection weights.

[0099] At 710, a total utility value for each of the plurality of voxels is determined based at least on the first utility value and the second utility value of the voxel. Each total utility value can be determined as a linear or non-linear function of the first utility value and the second utility value. In one example, the total utility value is the sum of the first utility value and the second utility value. At 712, an optimal location for neuromodulation is determined based on the total utility value for each of the plurality of voxels. For example, an appropriate optimization algorithm (such as gradient descent or simulated annealing) can be used to find the optimal location. It should be understood that neuromodulation will have an impact volume, the impact volume has a center point and a shape, and the optimal location and optimal shape of the impact volume can be determined. In one example, where the region of interest includes at least a portion of the nucleus accumbens and the ventral internal capsule, the optimization can be constrained such that the impact volume has a center that is between 7 millimeters (mm) and 12 millimeters laterally from the midline on the right and left sides of the brain, between 1 millimeter and 6 millimeters in front of the anterior commissure (AC), and between 2 millimeters above and 2 millimeters below the AC.

[0100] In one embodiment, functional imaging (e.g., functional MRI or positron emission tomography (PET)) can be used to evaluate the effectiveness of neuromodulation and accordingly adjust the targeting for both current and future patients. For example, a third image representing brain activity can be obtained from a suitable functional imaging system at a first time; and neuromodulation can be applied at a later second time at the determined optimal location. A fourth image representing brain activity can be obtained from the functional imaging system at an even later third time, and the third and fourth images can be compared to determine the effectiveness of neuromodulation. Based on this comparison, values such as a first utility value of a voxel within an impact volume and a connection weight associated with the voxel within the impact volume can be adjusted based on the change in brain activity attributable to neuromodulation. In addition, parameters associated with targeting can be adjusted for future patients, such as a function for calculating connection weights based on connection strength determined by tractography and a histological atlas value for the first utility value.

[0101] In some aspects, reference Figure 8 and Figure 9 , provides a method of improving a patient's addiction to addictive behavior or addictive chemicals. Such a method can include delivering an FUS signal or a DBS signal to a treatment site including the nucleus accumbens 802, the ventral internal capsule 804, or both the nucleus accumbens 802 and the ventral internal capsule, the treatment site being 7 millimeters (mm) to 12 millimeters lateral to the midline 901, 1 millimeter to 6 millimeters anterior to the anterior commissure (AC) 902, and between 2 millimeters above the AC and 2 millimeters below the AC on the right and left sides of the brain. The midline is an imaginary line extending through the center of the brain. The AC 902 and the posterior commissure (PC) 904 are laterally oriented commissural white matter tracts connecting the two cerebral hemispheres along the midline. The mid-commissural point (MCP) 906 is the midpoint between the AC and the PC. The AC, PC, and MCP are reference points or landmarks used in imaging methods such as MRI, PET, and CT. Although the above method is described with respect to the AC, the treatment site can be determined with respect to the PC or the MCP. Reference Figures 10 to 12 , in the case of FUS, the treatment site 1002 is the center of the impact volume 1004 that directly receives the FUS signal. Figure 10 An impact volume of 5mm x 5mm x 7mm is shown, but the impact volume can vary depending on the brain region being treated (e.g., the nucleus accumbens). In the case of DBS, as Figure 12 shown, the treatment site 1202 is the region of the brain into which the centers of multiple electrical contacts of an electrical lead are inserted and directly receive electrical signals.

[0102] Regarding addiction, the neural target to which the stimulus is delivered can be a component of the patient's reward circuitry, such as, for example, the nucleus accumbens and the ventral internal capsule. The stimulus can be applied unilaterally or bilaterally to the neural target. Table VIII provides an exemplary list of neural targets, exemplary forms of neuromodulation, and exemplary neuromodulation parameters that can be applied to these neural targets as part of a patient's treatment.

[0103] Table VIII

[0104]

[0105] In certain aspects regarding FUS, the dose includes a system frequency of 0.1 MHz to 3 MHz; an intensity or power of 10 W to 200 W; pulse durations on / off of 10 milliseconds to 1000 milliseconds (on) and 10 milliseconds to 990 milliseconds (off); a total duration of 30 seconds to 30 minutes; and a number of ultrasound transducers / elements between 1 and 1024. It should be understood that various ultrasound systems will have different operating parameters. Table IX has parameters associated with ultrasound systems that can be used with the systems and methods herein.

[0106] Table IX

[0107]

[0108]

[0109] * Values for three transducers

[0110] Figure 13 is a schematic block diagram of an exemplary system of hardware components capable of implementing Figures 1 to 12 an example of the systems and methods disclosed in Figure 1 such as the system for targeted neuromodulation shown. System 1300 can include various systems and subsystems. System 1300 can be any one of a personal computer, laptop, workstation, computer system, device, application specific integrated circuit (ASIC), server, server blade center, or server farm.

[0111] System 1300 may include a system bus 1302, a processing unit 1304, a system memory 1306, memory devices 1308 and 1310, a communication interface 1312 (e.g., a network interface), a communication link 1314, a display 1316 (e.g., a video screen), and an input device 1318 (e.g., a keyboard and / or a mouse). The system bus 1302 may communicate with the processing unit 1304 and the system memory 1306. Other memory devices 1308 and 1310, such as hard disk drives, servers, stand-alone databases, or other non-volatile memories, may also communicate with the system bus 1302. The system bus 1302 interconnects the processing unit 1304, the memory devices 1306-1310, the communication interface 1312, the display 1316, and the input device 1318. In some examples, the system bus 1302 also interconnects another port (not shown), such as a Universal Serial Bus (USB) port.

[0112] System 1300 may be implemented in a computing cloud. In such a case, the features of System 1300 (e.g., the processing unit 1304, the communication interface 1312, and the memory devices 1308 and 1310) may represent a single instance of hardware or multiple instances of hardware, where an application executes on multiple instances (i.e., distributed) of hardware (e.g., a computer, a router, a memory, a processor, or a combination thereof). Optionally, System 1300 may be implemented on a single dedicated server.

[0113] The processing unit 1304 may be a computing device and may include an Application Specific Integrated Circuit (ASIC). The processing unit 1304 executes instruction sets to implement the operations of the examples disclosed herein. The processing unit may include processing cores.

[0114] The other memory devices 1306, 1308, and 1310 may store text or compiled forms of data, programs, instructions, database queries, and any other information that may be required to operate a computer. The memories 1306, 1308, and 1310 may be implemented as computer-readable media (integrated or removable), such as memory cards, disk drives, optical discs (CDs), or servers accessible via a network. In certain examples, the memories 1306, 1308, and 1310 may include text, images, video, and / or audio, portions of which may be available in a human-readable format.

[0115] Additionally or optionally, System 1300 may access an external data source or query source via the communication interface 1312, which may communicate with the system bus 1302 and the communication link 1314.

[0116] In operation, system 1300 can be used to implement one or more portions of a system in accordance with the present invention. According to some examples, computer-executable logic for implementing a quality assurance system resides in system memory 1306 and one or more of memory devices 1308, 1310. Processing unit 1304 executes one or more computer-executable instructions originating from system memory 1306 and memory devices 1308 and 1310. It should be understood that computer-readable media can include multiple computer-readable media, each operably connected to the processing unit.

[0117] In some aspects, a method is provided for improving impaired neuropsychiatric function in a patient. The method includes obtaining a parameter representing the connectivity of the patient's brain, such as measuring stress, anxiety, or craving. The parameter can be obtained by acquiring an image representing the connectivity of the patient's brain via an imaging system and extracting from the image a parameter representing the connectivity of the patient's brain. Physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof related to the patient's impaired neuropsychiatric function are also measured. A treatment can be provided or adjusted based on the parameter representing the connectivity of the patient's brain and the measurement of the patient's physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof. The measurement of physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof can be in response to the presentation of a cue related to a neuropsychiatric disorder exhibiting impaired neuropsychiatric disease function.

[0118] In some aspects, a method is provided for screening an individual with impaired neuropsychiatric function to determine whether the individual is a suitable candidate for treatment. The method includes obtaining a parameter representing the connectivity of the patient's brain. The parameter can be obtained by acquiring an image representing the connectivity of the patient's brain via an imaging system and extracting from the image a parameter representing the connectivity of the patient's brain. The method can also include: measuring physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof related to the patient's impaired neuropsychiatric function; and determining whether the individual is a suitable candidate for treatment based on the parameter representing the connectivity of the patient's brain and the measurement of the patient's physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof. The measurement of physiological, cognitive, psychosocial, behavioral parameters, or combinations thereof can be in response to the presentation of a cue related to a neuropsychiatric disorder exhibiting impaired neuropsychiatric disease function. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it should be understood that the embodiments can be practiced without these specific details. For example, circuits can be shown in block diagrams to avoid obscuring the embodiments with unnecessary details. In other instances, well-known circuits, processes, algorithms, structures, and techniques can be shown without unnecessary details to avoid obscuring the embodiments.

[0120] The implementation of the above technologies, modules, steps, and devices can be accomplished in various ways. For example, these technologies, blocks, steps, and devices can be implemented using hardware, software, or a combination thereof. For hardware implementation, the processing unit can be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the above functions, and / or combinations thereof. Additionally, it should be noted that embodiments can be described as processes shown in flowcharts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. Although a flowchart may describe operations as a sequential process, many operations can be performed in parallel or concurrently. Furthermore, the order of operations can be rearranged. A process terminates when its operations are completed, but it can have additional steps not included in the figure. A process can correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, its termination corresponds to the function returning to the calling function or the main function. Additionally, embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and / or any combination thereof. When implemented in software, firmware, middleware, scripting languages, and / or microcode, the program code or code segments for performing the necessary tasks can be stored in a machine-readable medium (such as a storage medium). A code segment or machine-executable instruction can represent any combination of a process, function, subroutine, program, routine, subroutine, module, software package, script, class, or instruction, data structure, and / or program statement. A code segment can be coupled to another code segment or hardware circuit by passing and / or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc. can be passed, forwarded, or transmitted in any suitable manner, including memory sharing, message passing, ticket passing, network transmission, etc.

[0121] For the implementation of firmware and / or software, these methods can be implemented using modules (e.g., processes, functions, etc.) that perform the functions described herein. Any machine-readable medium that tangibly embodies the instructions can be used to implement the methods described herein. For example, software code can be stored in a memory. The memory can be implemented within or external to the processor. As used herein, the term "memory" refers to any type of long-term, short-term, volatile, non-volatile, or other storage medium, and is not limited to any specific type of memory or the number of memories, nor to the type of medium storing the memory.

[0122] In addition, as disclosed herein, the term "storage medium" may refer to one or more memories for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, core memory, disk storage media, optical storage media, flash devices, and / or other machine-readable media for storing information. The terms "computer-readable medium" and "machine-readable medium" include, but are not limited to, portable or fixed storage devices, optical storage devices, wireless channels, and / or various other storage media capable of storing instructions and / or data. It should be understood that the "computer-readable medium" or "machine-readable medium" may include multiple media, each operably connected to the processing unit.

[0123] The foregoing are merely examples. Of course, it is not possible to describe every conceivable combination of components or methods, but one of ordinary skill in the art will recognize that many other combinations and permutations are possible. Accordingly, this disclosure is intended to cover all such changes, modifications, and variations that fall within the scope of this application, including the appended claims. As used herein, the term "comprising" means including but not limited to. The term "based on" means at least partially based on. Additionally, where the present disclosure or claims recite "a", "an", "first", or "another" element or the equivalent thereof, it should be construed to include one or more than one such element, neither requiring nor precluding two or more such elements.

Claims

1. A method for targeting neuromodulation in a patient's brain for improving, diagnosing, and managing one of neuropsychiatric functions, the method comprising: Obtain a first image representing the structure of the brain from a first imaging system; Obtain a second image representing the connectivity of the brain from one of the first imaging system and a second imaging system; Determine, for each of a plurality of voxels within a region of interest from the first image, a first utility value associated with directly modulating tissue within the region of interest; Determine, for each of the plurality of voxels from the second image, a second utility value associated with indirectly modulating tissue outside the region of interest by modulating tissue within the region of interest; Determine a total utility value for each of the plurality of voxels based on at least the first utility value and the second utility value; And Determine an optimal location for neuromodulation based on the total utility value for each of the plurality of voxels.

2. The method according to claim 1, wherein, Determining the second utility value for each of the plurality of voxels includes: For a given voxel of the plurality of voxels, determine from the second image a set of locations outside the region of interest that are indirectly modulated when modulating the given voxel, each location in the set of locations having an associated indirect utility value; Determine from the second image a corresponding strength of the connection between the given voxel and each location in the set of locations, the strength between each location in the set of locations and the given voxel being represented as a connection weight; and Determine the second utility value for the given voxel as the sum of the products of the indirect utility value and the connection weight for each location in the set of locations.

3. The method according to claim 2, further comprising: Obtain a third image representing the activity of the brain from one of the first imaging system, the second imaging system, and a third imaging system at a first time; Apply neuromodulation at the optimal location at a second time after the first time, the given location being within an influence volume associated with the optimal location; Obtain a fourth image representing the activity of the brain from one of the first imaging system, the second imaging system, and the third imaging system at a third time after the second time; And Modify the connection weight for each location in the set of locations of the given voxel based on the third image and the fourth image.

4. The method according to claim 1, wherein, Determining the first utility value for each of the plurality of voxels includes: registering a portion of the first image representing the region of interest with a histology-based atlas having an assigned value of the first utility value for each of a plurality of locations within the histology-based atlas.

5. The method according to claim 4, wherein, The assigned value within the histology-based atlas represents the expected concentration of excitatory neurons within the region of interest.

6. The method according to claim 4, wherein, The assigned value within the histology-based atlas represents the expected concentration of inhibitory neurons within the region of interest.

7. The method according to claim 4, further comprising: Obtain a third image representing the activity of the brain from one of the first imaging system, the second imaging system, and a third imaging system at a first time; Apply neuromodulation at the optimal location at a second time after the first time, the applied neuromodulation having an influence volume associated with the optimal location; Obtain a fourth image representing the activity of the brain from one of the first imaging system, the second imaging system, and the third imaging system at a third time after the second time; And Modify at least one of the assigned values of the first utility value at the plurality of positions within the histological-based atlas according to the third image and the fourth image.

8. The method according to claim 1, further comprising: Obtain a third image representing the activity of the brain from one of the first imaging system, the second imaging system, and the third imaging system at a first time; Apply neuromodulation at the optimal position at a second time after the first time, the applied neuromodulation having an influence volume associated with the optimal position; Obtain a fourth image representing the activity of the brain from one of the first imaging system, the second imaging system, and the third imaging system at a third time after the second time; And Modify the first utility value for at least one voxel within the influence volume according to the third image and the fourth image.

9. The method according to claim 1, wherein, Determining the optimal position of neuromodulation according to the total utility value of each of the plurality of voxels includes: determining the optimal position and the optimal shape of the influence volume associated with the optimal position of neuromodulation according to the total utility value of each of the plurality of voxels.

10. The method according to claim 1, further comprising: Apply neuromodulation at the optimal position; Measure feedback, the feedback including one of the patient's physiological parameters, cognitive parameters, psychosocial parameters, and behavioral parameters; Determine the effectiveness of the neuromodulation according to the measured feedback; And If the effectiveness of the neuromodulation fails to meet the threshold, select a new position within the region of interest from the total utility value of each of the plurality of voxels.

11. The method according to claim 1, further comprising: Apply neuromodulation at the optimal position having an influence volume; Measure feedback, the feedback including one of the patient's physiological parameters, cognitive parameters, psychosocial parameters, and behavioral parameters; Determine the effectiveness of the neuromodulation according to the measured feedback; And Modify the first utility value for at least one voxel within the influence volume according to the determined effectiveness of the neuromodulation.

12. The method according to claim 1, wherein The region of interest includes at least a part of the nucleus accumbens and the ventral internal capsule, and wherein determining the optimal position for neuromodulation from the total utility value of each of the plurality of voxels includes: determining the optimal position such that the influence volume associated with the optimal position has a center point that is between 7 millimeters (mm) and 12 millimeters laterally from the midline on the right and left sides of the brain, between 1 millimeter and 6 millimeters in front of the anterior commissure (AC), and between 2 millimeters above and 2 millimeters below the AC.

13. A system, comprising: An imaging interface that receives a first image representing the structure of the brain from a first imaging system and a second image representing the connectivity of the brain from one of the first imaging system and the second imaging system; A targeting component that determines, for each of a plurality of voxels within a region of interest from the first image, a first utility value associated with directly modulating tissue within the region of interest, determines, for each of the plurality of voxels from the second image, a second utility value associated with indirectly modulating tissue outside the region of interest by modulating tissue within the region of interest, determines a total utility value for each of the plurality of voxels based at least on the first utility value and the second utility value, and determines an optimal location for neuromodulation based on the total utility value for each of the plurality of voxels; And A neuromodulation system that delivers neuromodulation to the optimal location.

14. The system according to claim 13, wherein The neuromodulation system is a deep brain stimulation system, and the targeting component further determines at least one electrode to be activated based on the total utility value for each of the plurality of voxels.

15. The system according to claim 13, wherein The neuromodulation system is a focused neuromodulation system, and the targeting component further determines the number and direction of foci for excitation based on the total utility value for each of the plurality of voxels.

16. The system according to claim 13, further comprising a feedback component that measures one of the patient's physiological parameters, cognitive parameters, psychosocial parameters, and behavioral parameters, and determines the effectiveness of the neuromodulation based on the measured feedback. If the effectiveness of the neuromodulation does not meet a threshold, the targeting component selects a new position within the region of interest from the total utility values of each voxel of each of the plurality of voxels.

17. The system according to claim 13, further comprising a feedback component that measures one of the patient's physiological parameters, cognitive parameters, psychosocial parameters, and behavioral parameters, and determines the effectiveness of the neuromodulation based on the measured feedback. The targeting component modifies the first utility value of at least one voxel within the affected volume based on the determined effectiveness of the neuromodulation.

18. The system according to claim 13, wherein the targeting component selects target positions in the nucleus accumbens and the ventral internal capsule such that the affected volume associated with the optimal position has a center point that is between 7 millimeters (mm) and 12 millimeters from the midline laterally on the right and left sides of the brain, between 1 millimeter and 6 millimeters in front of the anterior commissure (AC), and between 2 millimeters above and 2 millimeters below the AC.

19. A method for improving the neuropsychiatric function of a patient, comprising: Select an influence volume having a center point within a target region that includes the nucleus accumbens and the ventral internal capsule, the center point being between 7 mm and 12 mm from the midline laterally on the right and left sides of the brain, between 1 mm and 6 mm in front of the anterior commissure AC, and between 2 mm above and 2 mm below the AC; And Deliver neuromodulation to the selected influence volume.

20. The method according to claim 19, wherein, Selecting the influence volume includes: Obtaining a first image representing the structure of the patient's brain from a first imaging system; Obtaining a second image representing the connectivity of the brain from one of the first imaging system and a second imaging system; Determining, for each of a plurality of voxels within a region of interest from the first image, a first utility value associated with directly modulating tissue within the region of interest; Determining, for each of the plurality of voxels from the second image, a second utility value associated with indirectly modulating tissue outside the region of interest by modulating tissue within the region of interest; Determining a total utility value for each of the plurality of voxels based at least on the first utility value and the second utility value; and Determining the influence volume based on the total utility value for each of the plurality of voxels.

21. The method according to claim 19, wherein, Delivering neuromodulation to the selected influence volume includes: delivering focused ultrasound to the selected influence volume.

22. The method according to claim 19, further comprising: Measuring feedback, the feedback including one of the patient's physiological parameters, cognitive parameters, psychosocial parameters, behavioral parameters; Determining the effectiveness of the neuromodulation based on the measured feedback; And If the effectiveness of the neuromodulation does not meet a threshold, selecting an influence volume having a center point within a target region.

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