Devices, systems, and methods for neuromodulation

Abstract

Systems and methods for rendering a network in a brain of a subject are provided. The method includes receiving task-based functional magnetic resonance imaging (fMRI) data of a brain of a subject, receiving resting state fMRI data of the brain, localizing an effector-specific region of the brain and an inter-effector region of the brain based on the task-based fMRI data and the resting state fMRI data, and determining an effector-specific region of the brain and an inter-effector region of the brain based on the effector-specific region and the inter-effector region. And identifying a network in the brain based on the resting state fMRI data based on functional connections with the inter-effector regions, and then generating one or more maps of the network.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63 / 419,696, filed on October 26, 2022, entitled “DEVICES, SYSTEMS, AND METHODSRELATED TO A MIND-BODY INTERFACE (MBI) CIRCUIT FOR NEUROMODULATION,” the contents of which are incorporated herein in their entirety.

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] This invention was made with government support under Grants MH096773, MH124567, MH121276, and MH122066 awarded by the National Institutes of Health. The government has certain rights in this invention. Technical Field

[0005] The present disclosure generally relates to devices, systems and methods for neuromodulation in neurological and psychiatric disorders and brain damage from any source. Background Art

[0006] Patient-specific precision and accuracy are critical for successful neuromodulation, both for established (e.g., deep brain stimulation (DBS) of the ventral intermedia (VIM) nucleus of the thalamus) and investigational (e.g., DBS of the centromedial (CM) nucleus) clinical targets. The current standard of care for FDA-cleared neuromodulation—whether DBS, focused on US (VIM, GPi, STN) for movement disorders or TMS (dlPFC) for depression—relies on very simple, one-size-fits-all clinical consensus coordinates for targeting. Several recent studies have suggested that advanced imaging-driven (e.g., DTI, functional connectivity) patient-specific targeting may improve outcomes while increasing procedural efficiency. Additionally, a better neurophysiological understanding of the targeted structures and their interconnections in individual patients is critical for selecting the most appropriate multiple candidate targets for intervention, for assessing and / or predicting treatment response, for identifying the relatively optimal target within a circuit (for a given patient), and for identifying and validating novel targets anywhere in the brain. The current poor understanding of the neuropsychology behind successful neuromodulation of brain dysfunction impedes the ability to match patients to the most appropriate treatment and slows patients' progression toward potentially life-changing neuromodulation. Summary of the invention

[0007]

[0013] In various aspects of the present disclosure, devices, systems, and methods are provided that relate to the identification and characterization of targets for clinical treatment of a subject's brain.

[0008] In one aspect, a system for mapping a network in a subject's brain is provided. The system includes at least one processor in communication with at least one memory device. At least one processor is programmed to receive task-based functional magnetic resonance imaging (fMRI) data of the subject's brain, receive resting state fMRI data of the brain, locate effector-specific regions of the brain and inter-effector regions of the brain based on the task-based fMRI data and the resting state fMRI data, identify a network in the brain based on functional connectivity with the inter-effector regions based on the resting state fMRI data, and generate one or more graphs of the network.

[0009] In another aspect, a method for identifying a treatment is provided. The method includes receiving task-based fMRI data of a subject's brain, receiving resting-state fMRI data of the subject's brain, locating effector-specific regions of the brain and inter-effector regions of the brain based on the task-based fMRI data and the resting-state fMRI data, identifying a network based on functional connectivity with the inter-effector regions based on the resting-state fMRI data, generating one or more graphs of the network, and selecting a treatment based on the one or more graphs of the network.

[0010] The present teachings include devices and systems for a mind-body interface (MBI). In some aspects, an MBI identifies interconnected brain regions. In some aspects, an MBI device includes an MBI interface system, an MRI system, and at least one computing system. In some aspects, an MBI system identifies interconnected brain regions. In some aspects, an MBI system includes an MRI system and at least one computing device.

[0011] The present teaching includes a method for using a mind-body interface identification system to identify a group of interconnected brain regions to improve the treatment of neuropsychiatric symptoms, disorders or brain damage in a patient-specific manner. In some aspects, the mind-body interface includes a mind-body interface system, an MR system and at least one computing device. In some aspects, the treatment can be an invasive or non-invasive neuromodulation or ablation technique. In another aspect, the functional state and the response to the treatment can be evaluated by the MBI system. In some aspects, the precise functional mapping (PFM) functional connectivity method applied to BOLD data from all brain states (e.g., rest, task, watching a movie, sleeping, sedation) can be used to identify interconnected brain regions. In another aspect, the BOLD data can be further annotated using structural metrics (e.g., cortical thickness) and DTI. In another aspect, the difference in relative functional connectivity at baseline can be used to triage patients to the most appropriate therapy. In aspects, changes in functional connectivity in response to various treatments including neuromodulation can be used to evaluate and further refine treatment parameters. In another aspect, MBI enables the actual realization of behaviors as expressed by movement and physiological changes, and therefore allows superior, more behavioral interventions compared to more brain regions associated with more abstract functions (e.g., dorsolateral PFC). In some embodiments, interconnected brain regions can be well defined in the cortex, basal ganglia, thalamus, brainstem, and cerebellum. In some embodiments, the method enables the system to search for the most effective, most reliable, and safest nodes therein, for any given condition, symptom, or injury type. In some embodiments, the central median nucleus of the thalamus can be identified. In some embodiments, the patient has generalized epilepsy, chronic pain, Tourette's disease, or Parkinson's disease.

[0012] Other objects and features will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Those skilled in the art will appreciate that the drawings described below are for illustration purposes only and are not intended to limit the scope of the present teachings in any way.

[0014] Figure 1A is a block diagram schematically illustrating a computing device according to an aspect of the present disclosure.

[0015] Figure 1B is a flow chart illustrating a method of identifying a treatment;

[0016] Figure 1C is a flow chart illustrating a method of generating a map, evaluating connections, and / or selecting or evaluating a treatment according to some aspects of the present disclosure;

[0017] Figure 2 is a block diagram schematically illustrating a system according to an aspect of the present disclosure;

[0018] Figure 3 is a block diagram schematically illustrating a remote or user computing device according to an aspect of the present disclosure;

[0019] Figure 4 is a block diagram schematically illustrating a server system according to an aspect of the present disclosure;

[0020] Figure 5A Depicted are resting-state functional connectivity (RSFC) seeds along a continuous line of cortical locations in the left precentral gyrus in a single example participant;

[0021] Figure 5B Depicts how, in a single example participant, Figure 5A RSFC seeded with lines of consecutive cortical locations in the left precentral gyrus are shown;

[0022] Figure 6 Depicted are RSFC seeded from a line of consecutive cortical locations in the left precentral gyrus for the example group of highly sampled participants;

[0023] Figure 7 Depicted are RSFCs seeded from a line of consecutive cortical locations in the left precentral gyrus used for within-participant replication;

[0024] Figure 8 Depicted are RSFCs seeded from a line of consecutive cortical locations in the left precentral gyrus for group-averaged data;

[0025] Fig. 9 Delineated discrete functional networks using a whole-brain, data-driven hierarchical approach applied to resting-state fMRI data, which defined the spatial extent of the networks;

[0026] Fig.10 Delineated discrete functional networks using a whole-brain, data-driven hierarchical approach applied to resting-state fMRI data for additional participants;

[0027] Fig.11 Depicted are examples of how the pattern of inter-effector connectivity becomes more distinct from surrounding effector-specific motor regions as the connectivity threshold increases from 80 to 97 percent;

[0028] Fig.12 A depicts a functional connectivity map seeded along a continuous dotted line in the precentral gyrus from fMRI data averaged from 262 human newborns, all scanned shortly after birth.

[0029] Fig.12 B depicts a functional connectivity map seeded along a continuous dotted line in the precentral gyrus from fMRI data from a newborn scanned at 13 days after birth.

[0030] Fig.12 C depicts a functional connectivity map seeded from a continuous line of dots along the precentral gyrus in fMRI data from 11-month-old infants;

[0031] Fig.12 D depicts a functional connectivity map seeded along a continuous dotted line in the precentral gyrus from fMRI data from 9-year-old children;

[0032] Fig.12 E depicts a functional connectivity map seeded from a continuous dotted line along the precentral gyrus from fMRI data from adult participant P01;

[0033] Fig.12 F depicts a functional connectivity map seeded along a continuous line of dots in the precentral gyrus from fMRI data from an adolescent who underwent extensive cortical reorganization after severe bilateral perinatal stroke;

[0034] Fig.13 Depicted are brain regions with the strongest functional connectivity with the left middle intereffector area in the cortex, striatum, thalamus, and cerebellum in an exemplary participant;

[0035] Fig.14 The brain regions with the strongest functional connectivity with the left middle intereffector area in the cortex, striatum, thalamus, and cerebellum in other participants were depicted;

[0036] Fig.15 Depicted are brain regions that are functionally more strongly connected to the inter-effector regions than to any foot / hand / mouth regions from adult participant P01;

[0037] Fig.16 A depicts the brain region with the strongest functional connectivity to the intermediate inter-effector region in the medial cortex;

[0038] Fig.16 B depicts the brain regions with the strongest functional connectivity to the intermediate inter-effector regions in the striatum;

[0039] Fig.16 C depicts the brain region with the strongest functional connectivity to the intermediate inter-effector region in the thalamus;

[0040] Fig.16 D depicts the brain regions with the strongest functional connectivity to intermediate inter-effector regions in the cerebellum;

[0041] Fig.17 depicts inter-network relationships visualized in network space using spring embedding graphs;

[0042] Fig.18AInter-effector and effector-specific functional connectivity was depicted for all participants;

[0043] Fig.18B depicts inter-network relationships visualized in network space using a spring embedding graph for all actors;

[0044] Fig.19 Inter-effector and effector-specific regions tested for systematic differences in the temporal order of their subslow fMRI signals are depicted;

[0045] Fig. 20 Inter-effector areas related to cortical thickness are depicted;

[0046] Fig.21 Cerebellar connectivity and task activation were depicted;

[0047] Fig.22A The functional connectivity strength between the M1 region and the individual-specific cingulate-opercular network was depicted;

[0048] Fig. 22B The functional connectivity strength between the M1 region and the intermediate insula is depicted;

[0049] Fig. 22C The strength of functional connectivity with the vermis of lobule VIIIa of the cerebellum is depicted;

[0050] Fig.22D The functional connectivity strength between the M1 region and the dorsal posterior putamen is depicted;

[0051] Fig.22E Depicted are the strength of functional connectivity between the M1 region and the thalamic nuclei: the ventral intermediate nucleus;

[0052] Fig.22F The strength of functional connectivity between the M1 region and the parafascicular nucleus is depicted;

[0053] Figure 22G The strength of functional connectivity between the M1 region and the ventral lateral anterior nucleus is depicted;

[0054] Fig.22H The functional connectivity strength between the M1 region and the adjacent postcentral gyrus is depicted;

[0055] Fig.22I Cortical thickness in the M1 region is depicted;

[0056] Fig.22J Fractional anisotropy within 2 mm below the cortex in the M1 region is depicted;

[0057] Figure 22K Relative myelin density in the M1 region is depicted;

[0058] Fig.23 Task fMRI activations during the motor task battery test are depicted, including movements of the toes, ankles, knees, hips, abdomen, shoulders, elbows, hands, eyebrows, eyelids, tongue, and swallowing;

[0059] Fig.24 The activation intensity for each movement calculated along the dorsoventral axis within M1 is depicted;

[0060] Fig.25 fMRI activations of the somatomotor hand and Brodmann areas 1–4 are depicted for all participants;

[0061] Fig.26 fMRI activation of the inter-effector network and Brodmann areas 1–4 for all participants is depicted;

[0062] Fig. 27 Inter-effector regions co-activated during abdominal contractions are depicted;

[0063] Fig.28 Inter-effector area activity during locomotion is depicted;

[0064] Fig.29 depicts event-related task fMRI data during a motor planning task with separate planning and execution phases for movements of the hands and feet;

[0065] Fig.30 Activation curves with unimodal and bimodal fits are depicted;

[0066] Fig.31 An activation with a bimodal curve is depicted;

[0067] Fig.32 The tasks used for each participant are depicted;

[0068] Fig.33 Coactivation with CON is depicted;

[0069] Fig.34 A depicts a functional connectivity map seeded from points in the precentral gyrus in macaque monkeys;

[0070] Fig.34 B depicts a schematic representation of proposed similar regions in macaques and humans;

[0071] Fig.35 Penfield’s classic gnome depicting a sequential diagram of the body in the primary motor cortex;

[0072] Fig.36 Depicted is an integration-segregation model of primary motor cortical organization, with effector-specific regions represented by concentric rings in which proximal body parts surround relatively more isolable distal body parts;

[0073] Fig.37 depicts motion stimuli mapped onto the cortex; and

[0074] Fig.38 An overview of the motor control and coordination tasks is depicted. DETAILED DESCRIPTION

[0075] The present disclosure is based, at least in part, on the discovery that the mind-body interfaces described herein can greatly improve the ability to treat a variety of neuropsychiatric symptoms, disorders, and brain injuries.

[0076] We have discovered and characterized a previously unrecognized distributed brain system for integrating abstract behavioral planning with the movement and autonomic functions of the body. As described herein, this may be referred to as a mind-body interface (MBI) or a somatic cognitive action network (SCAN). The systems and methods provided herein are configured to identify neuromodulation targets, facilitate advanced imaging-based methods for identifying novel and established targets, support trial patients for the most appropriate treatment, and provide a basis for evaluating therapeutic responses via patient-specific imaging.

[0077] The nodes of the MBI or (SCAN) are the supplementary motor area (SMA), the intermediate insula, the newly described inter-effector area in the primary motor cortex (M1), the dorsal putamen, the VMI and CM nuclei of the thalamus, the red nucleus, the STN, the substantia nigra, and the dorsal motor nucleus of the vagus nerve. Advanced precise functional mapping (PFM) functional connectivity (FC) analysis can be used to identify and characterize the entire MBI, as well as each of these individual nodes and their functional subdivisions.

[0078] MBI represents the third brain system, which is also important for initiating and controlling movement in addition to the eye movement circuit and the classical circuit that controls highly dexterous effectors (feet / toes, hands / fingers and mouth / tongue). In addition to overall systemic motor control, MBI also carries downstream information about action planning and behavioral goals, and physiologically prepares for upcoming activities via direct control of the autonomic nervous system (e.g., adrenal medulla). Since arousal is a prerequisite for successful action, MBI also carries vital arousal / vigilance information. In addition, MBI receives and processes important feedback for controlling real-world actions (such as posture, pain, and visceral sensation). Existing neuromodulation has targeted different nodes of MBI, such as VIM for essential tremor and Parkinson's disease (PD) and CM for generalized epilepsy, Tourette's disease, pain, Parkinson's disease, and consciousness disorders. The vagus nerve stimulation process used for generalized epilepsy and being studied for depression may also regulate the activity in MBI via the dorsal motor nucleus of the vagus nerve and CM.

[0079] Given the capabilities of the MBI, modulation of its activity could be used to treat apathy, avolition, and akinetic mutism attributed to a variety of illnesses or injuries, in addition to tremor (global motor control), generalized epilepsy (arousal), chronic pain, and impaired consciousness (arousal). Given that the MBI also controls autonomic effects secondary to abstract thought and planning, it could also be a target for panic and anxiety disorders.

[0080] One aspect of the present disclosure provides devices, systems, and methods related to mind-body interfaces.

[0081] Techniques are described for identifying a set of interconnected brain regions, known as mind-body interfaces (MBIs), in a patient-specific manner using advanced brain MRI methods. A method for assessing their functional status and response to any type of intervention, including the use of invasive and non-invasive neuromodulation, and ablation techniques are also described, which can greatly improve the ability to treat a variety of neuropsychiatric symptoms, disorders, and brain injuries. MBI nodes can be identified using a novel precise functional mapping (PFM) functional connectivity method applied to BOLD data from all brain states (e.g., resting, task, watching a movie, sleeping, sedation). They can be further annotated using structural metrics (e.g., cortical thickness) and DTI. Differences in relative functional connectivity at baseline can be used to triage patients to the most appropriate treatment, while changes in functional connectivity in response to various treatments (including neuromodulation) can be used to assess and further refine treatment parameters. MBI enables the actual implementation of behavior as expressed by movement and physiological changes, and therefore allows superior, more behaviorally targeted interventions compared to targeting more brain regions associated with more abstract functions (e.g., dorsolateral PFC). In addition, specific nodes of the MBI system are clearly defined in the cortex, basal ganglia, thalamus, brainstem, and cerebellum, making them more precise targets. An understanding of the functionality of the MBI enables the system to search for the most effective, reliable, and safest nodes within it for any given condition, symptom, and type of injury. For example, methods for identifying the MBI enable precise and accurate identification of the CM (central median nucleus) of the thalamus in an individual. This is of great clinical importance because the CM is being studied as a potential target for systemic epilepsy, chronic pain, Tourette's, and Parkinson's disease, but there are currently no accepted consensus clinical coordinates for identifying the CM, and there are no established, standard methods available for identifying the CM from structural MRI data.

[0082] Computing Systems and Devices

[0083] In various aspects, the disclosed MBI methods may be implemented using a computing system or computing device.

[0084] Figure 1A100 is a component configuration of a computing device 102, which includes a database 110 and other related computing components. In some aspects, computing device 102 is similar to computing device 302 (eg, Figure 2 ). User 104 can access components of computing device 102. In some aspects, database 110 is similar to database 308 (eg, Figure 2 shown).

[0085] In one aspect, database 110 includes MBI data 112 and MRI data 118. MBI data 112 may include data for operating an MBI system using MRI data. Non-limiting examples of MBI data 112 include various MRI data, any parameters for controlling the operation of an MBI device, and any parameters defining equations or other algorithms for implementing an MBI system as disclosed herein. MRI data 118 may include data for executing a method for implementing an MBI system or device as disclosed herein. Non-limiting examples of MRI data 118 include measurements of background noise or MRI signals, any parameters defining equations and other algorithms for implementing equations and other algorithms for converting background noise and MRI signals into differential MRI signals as disclosed herein, and / or any parameters defining equations and other algorithms for implementing the MBI method described herein.

[0086] The computing device 102 also includes a plurality of components that perform specific tasks. In an exemplary aspect, the computing device 102 includes a data storage device 130, an MBI component 140, an MRI component 150, and a communication component 160. The MBI component 140 is configured to implement the MBI method as described herein. The MRI component 150 is configured to implement the MBI method according to the MRI data disclosed herein. The data storage device 130 is configured to store data received or generated by the computing device 102, such as any data stored in the database 110 or any output of a process implemented by any component of the computing device 102.

[0087] The communication component 160 is configured to enable the computing device 102 to communicate with other devices (eg, Figure 2 The user computing device 330 shown in FIG. 1 is capable of communicating with the user via a network such as ( Figure 2 ) network 350) or multiple network connections using a predefined network protocol (such as TCP / IP (Transmission Control Protocol / Internet Protocol)) for communication.

[0088] Functional magnetic resonance imaging (fMRI) has been used to study and diagnose cognitive functions and neural connections in the brain. During resting-state fMRI or resting-state MRI, the subject is at rest. While the MRI data is being acquired, the subject lies in the MR scanner awake or sedated for a period of time. During task-based fMRI, the subject performs a task while the MRI data is being acquired.

[0089] Figure 1B A method 200 of identifying a treatment is depicted. The method 200 includes receiving 202 task-based fMRI data of a subject's brain, receiving 204 resting-state fMRI data of the subject's brain, locating 206 effector-specific regions of the brain and inter-effector regions of the brain based on the task-based fMRI data and the resting-state fMRI data, identifying 208 a mind-body interface (MBI) network based on functional connectivity with the inter-effector regions based on the resting-state fMRI data, generating 210 one or more maps of the MBI network, and selecting 212 a treatment based on the one or more maps of the MBI network.

[0090] refer to Figure 1C , a method for generating a network diagram, evaluating regional connections between effectors, and / or providing treatment information or guidance is shown. The method can be performed by a computer system or by a computer system with input from a user (such as a clinician). For example, the computer system can receive input data 220. The input data can include fMRI data, which can be accessed from a magnetic resonance imaging (MRI) system or a computer memory. The fMRI data can include task-based fMRI data 222, which can be collected when the subject performs a motor task, such as a motor control task or a body mapping task. The fMRI data can also include resting state fMRI 226, such as eyes closed resting state, eyes open resting state, or eyes gaze resting state data.

[0091] The input data 220 may also include diffusion MRI data 230, such as diffusion weighted imaging data or diffusion tensor imaging data, and structural MRI data 236. The input data 220 may include data from a single time point or a series of time course data 228. For example, fMRI data may be collected before and after an intervention (e.g., treatment, clinical intervention, fixation intervention, drug administration, etc.) or before and after a clinical event (e.g., stroke). The imaging data may also be enhanced with subject data 224, such as clinical or demographic data. For example, the subject data 224 may include subject age, clinical history, diagnosis, treatment history, subject's hand dominance, etc.

[0092] Input data 220 may also include processing parameters. For example, a user may define or select seed locations 232 for processing 240, or may select or adjust a connection threshold 234 based on data, subject matter, desired application, and the like.

[0093] The method also includes processing the input data 220 in block 240, which may be performed by a computer system with or without user input. Processing 240 may be performed on individual subject data, data collected across multiple time points, or data grouped using population characteristics.

[0094] Processing 240 may include determining functional connectivity 248 using fMRI data (e.g., 226, 228). As a non-limiting example, connectivity may be estimated using a vertex / voxel-by-voxel functional connectivity matrix calculated from the fMRI data, and using the Fisher transform pairwise correlation of the time series of vertices / voxels in the brain. Other methods for determining functional connectivity may also be used. Determining functional connectivity 248 may also include identifying seed points. As a non-limiting example, a seed line may be defined below the precentral gyrus. The connections between each seed point may be analyzed using the Fisher transform correlation between the time course of each seed point and the time course of other voxels in the brain. In this manner, processing 240 may also include manual, semi-automatic, or automatic (e.g., using Freesurfer) segmentation 259 of MRI data. Segmentation 259 may include surface segmentation, surface delineation, and atlas registration. As a non-limiting example, segmentation may be performed using a T2-weighted image, and may be co-registered with other structural, functional, and diffusion MRI images.

[0095] Processing 240 may also include identifying brain regions, including localized effector regions 244 and localized inter-effector regions 246. Such regions may be identified using functional connectivity data. As a non-limiting example, a data-driven network detection algorithm may be used to identify network subdivisions. Other regions of interest analysis methods may also be used, for example, which may include the use of atlas data or machine learning algorithms. In some embodiments, inter-effector subnetworks may be manually or automatically grouped together into a network structure for further processing. The identified brain regions may be defined or visualized relative to known or predefined brain regions (e.g., Brodmann areas).

[0096] Processing 240 may also include extracting other measurements and parameters related to the connection. Such measurements can be compared between regions, such as between specific effector regions (e.g., feet, hands, mouth), or between effector regions and inter-effector regions. As an example, cortical thickness can be measured based on segmentation data that can be deformed and registered to functional data. In this way, the thickness of each region can be measured and compared. For example, a paired t-test across multiple subjects can be used to compare the thickness between the effector region and the inter-effector region. As another example, processing 240 may include measuring myelin density 250 within each identified region. Processing 240 may also include processing diffusion data 256, such as generating a fractional anisotropy map or a diffusion tensor image. As a non-limiting example, fractional anisotropy can be compared between identified regions (such as between the effector region and the inter-effector region).

[0097] The obtained data can be further processed 240 to compare outputs. For example, processing 240 can include comparing results (e.g., connections, effector regions and inter-effector region positions, regional thickness, myelin density, etc.) across different time points 254 (e.g., before and after intervention) for a given subject. The connections between regions can also be measured and compared 252. For example, the connection between the effector region and other brain regions (e.g., CON, adjacent postcentral gyrus, intermediate insula, cerebellum, putamen, thalamus, etc.) and the connection between the inter-effector region and other brain regions can be measured. For example, such functional connections can be compared between the effector region and the inter-effector region. The obtained data can also be grouped by patient population 258 and compared across various patient populations (e.g., age groups, patients with or without brain damage or stroke, etc.).

[0098] The method can provide several types of outputs 260 to generally characterize functional connectivity, brain regions, and treatment information. For example, the output 260 can include a connectivity map 262 that can provide a visual representation of the overall connectivity or local connectivity of effector regions and inter-effector regions. The output 260 can also include a network map 264 that provides visualization of inter-effector and effector regions. The output can also include coordinates for the locations of effector regions 268 and inter-effector regions 270, which can be visualized in conjunction with connectivity data, structural MRI images, diffusion images, etc. The output 260 can also include reports or visualizations or regional thickness and / or myelin density 266, such as or each identified region. The output 260 can also provide one or more population databases 278. For example, a map of brain connectivity and effector and inter-effector regional locations can be generated for a given patient population (e.g., infants, children, adults, PD patients, before and after treatment, etc.).

[0099] Output 260 may also include treatment information to guide treatment selection, goals, or evaluation. For example, output 260 may include treatment selection 272 based in part on other output data 260. Treatment selection 272 may include selection of drugs, invasive neuromodulation, non-invasive neuromodulation, or other interventions. For example, treatment selection 272 may include selection of deep brain stimulation (DBS) parameters. Treatment selection may be determined by a trained clinician under the guidance of the output data 260 provided by the method. In other configurations, treatment selection may be performed by an automated algorithm generated or trained based on population data, subject data, and outcome data. Graphs may also be used to identify local treatment targets 274 (e.g., DBS placement) for neuromodulation. Output 260 may also be used to evaluate treatment efficacy 276. For example, functional connectivity may be evaluated before and after treatment to evaluate changes in connectivity achieved by treatment.

[0100] Figure 2 Depicts a simplified block diagram of a system for implementing the computer-assisted method described herein. Figure 2 As shown, computing device 300 can be configured to implement at least a portion of the tasks associated with the disclosed method described herein. Computer system 300 may include computing device 302. In one aspect, computing device 302 is part of server system 304, which also includes database server 306. Computing device 302 communicates with database 308 via database server 306. Computing device 302 is communicatively coupled to user computing device 330 and mind-body interface (MBI) system 334 via network 350. Network 350 can be any network that allows local or wide area communication between devices. For example, network 350 can allow communication coupling to the Internet via at least one of many interfaces, including but not limited to at least one of the following networks, such as the Internet, local area network (LAN), wide area network (WAN), integrated services digital network (ISDN), dial-up connection, digital subscriber line (DSL), cellular phone connection and cable modem. User computing device 330 may be any device capable of accessing the Internet, including but not limited to a desktop computer, laptop computer, personal digital assistant (PDA), cellular telephone, smart phone, tablet computer, phablet, wearable electronic device, smart watch, or other web-based connectible or mobile device.

[0101] In other aspects, computing device 302 is configured to perform a plurality of tasks associated with the disclosed computer-assisted methods using an MBI loop. In some aspects, computing device 302 , user computing device 330 , and / or MBI system 334 may be operably connected via network 350 .

[0102] Figure 3Depicted is a configuration of a remote or user computing device 502, such as user computing device 330 (eg, Figure 2 502 may include a processor 505 for executing instructions. In some aspects, executable instructions may be stored in a memory area 510. Processor 505 may include one or more processing units (e.g., in a multi-core configuration). Memory area 510 may be any device that allows storage and retrieval of information such as executable instructions and / or other data. Memory area 510 may include one or more computer-readable media.

[0103] The computing device 502 may also include at least one media output component 515 for presenting information to the user 501. The media output component 515 may be any component capable of conveying information to the user 501. In some aspects, the media output component 515 may include an output adapter, such as a video adapter and / or an audio adapter. The output adapter may be operably coupled to the processor 505 and may be operably coupled to an output device, such as a display device (e.g., a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a cathode ray tube (CRT), or an "electronic ink" display) or an audio output device (e.g., a speaker or headphones). In some aspects, the media output component 515 may be configured to present an interactive user interface (e.g., a web browser or client application) to the user 501.

[0104] In some aspects, computing device 502 may include input device 520 for receiving input from user 501. Input device 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad or touch screen), a camera, a gyroscope, an accelerometer, a position detector, and / or an audio input device. A single component such as a touch screen may be used as both an output device of media output component 515 and input device 520.

[0105] The computing device 502 may also include a communication interface 525 that can be communicatively coupled to a remote device. The communication interface 525 may include, for example, a wired or wireless network adapter or a wireless data transceiver for a mobile phone network (e.g., Global System for Mobile Communications (GSM), 3G, 4G, or Bluetooth) or other mobile data network (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).

[0106] Stored in memory area 510 are, for example, computer-readable instructions for providing a user interface to user 501 via media output component 515 and optionally receiving and processing input from input device 520. The user interface may include, among other possibilities, a web browser and a client application. A web browser enables user 501 to display and interact with media and other information, typically embedded in a web page or website from a web server. Client applications allow user 501 to interact with a server application, such as one associated with a vendor or enterprise.

[0107] Figure 4 An example configuration of a server system 602 is shown. The server system 602 may include, but is not limited to, a database server 306 and a computing device 302 (both in Figure 2 In some aspects, server system 602 is similar to server system 304 (eg, Figure 2 ). Server system 602 may include a processor 605 for executing instructions. For example, the instructions may be stored in a memory area 625. Processor 605 may include one or more processing units (e.g., in a multi-core configuration).

[0108] Processor 605 may be operably coupled to communication interface 615 so that server system 602 may be able to communicate with a remote device, such as user computing device 330 (eg, Figure 2 ) or another server system 602. For example, the communication interface 615 can be connected via the network 350 (such as Figure 2 ) receives a request from a user computing device 330.

[0109] The processor 605 may also be operably coupled to a storage device 625. The storage device 625 may be any computer-operated hardware suitable for storing and / or retrieving data. In some aspects, the storage device 625 may be integrated into the server system 602. For example, the server system 602 may include one or more hard disk drives as the storage device 625. In other aspects, the storage device 625 may be external to the server system 602 and may be accessed by multiple server systems 602. For example, the storage device 625 may include multiple storage units, such as hard disks or solid-state disks in a redundant array of inexpensive disks (RAID) configuration. The storage device 625 may include a storage area network (SAN) and / or a network-attached storage (NAS) system.

[0110] In some aspects, the processor 605 can be operably coupled to a storage device 625 via a storage interface 620. The storage interface 620 can be any component capable of providing the processor 605 with access to the storage device 625. The storage interface 620 can include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides the processor 605 with access to the storage device 625.

[0111] Memory area 510 (eg Figure 3 ) and 610 may include, but are not limited to, random access memory (RAM), such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are examples only and are therefore not limited to the memory types that can be used for storage of computer programs.

[0112] The computer systems and computer-aided methods discussed herein may include additional, fewer or alternative actions and / or functions, including actions and / or functions discussed elsewhere herein. The computer system may include or be implemented via computer executable instructions stored on a non-transient computer-readable medium. The method may be implemented via one or more local or remote processors, transceivers, servers and / or sensors (such as processors, transceivers, servers and / or sensors installed on a vehicle or mobile device or associated with an intelligent infrastructure or a remote server), and / or via computer executable instructions stored on a non-transient computer-readable medium or medium.

[0113] The methods and algorithms of the present disclosure may be included in a controller or processor. In addition, the methods and algorithms of the present disclosure may be embodied as (one or more) computer-implemented methods for executing such (one or more) computer-implemented methods, and may also be embodied in the form of a tangible or non-transient computer-readable storage medium containing a computer program or other machine-executable instructions ("computer program" in this article), wherein, when the computer program is loaded into a computer or other processor ("computer" in this article) and / or executed by a computer, the computer becomes a device for practicing (one or more) methods. Storage media for containing such computer programs include, for example, floppy disks and floppy disks, compact discs (CD)-ROMs (whether writable or not), DVD digital disks, RAM and ROM memories, computer hard drives and backup drives, external hard drives, "thumb" drives, and any other storage media readable by a computer. The (one or more) methods may also be embodied in the form of a computer program, for example, whether stored in a storage medium or sent through a transmission medium (such as an electrical conductor, optical fiber or other optical conductor), or sent through electromagnetic radiation, wherein, when the computer program is loaded into a computer and / or executed by a computer, the computer becomes a device for practicing (one or more) methods. The method(s) may be implemented on a general purpose microprocessor or on a digital processor specifically configured to practice the process(es). When a general purpose microprocessor is employed, the computer program code configures the circuitry of the microprocessor to create a specific logic circuit arrangement. A computer-readable storage medium includes a medium readable by the computer itself or by another machine that reads computer instructions, for providing these instructions to the computer for controlling its operation. For example, such a machine may include a machine for reading the storage medium mentioned above.

[0114] In some aspects, the computing device is configured to implement machine learning so that the computing device "learns" to analyze, organize and / or process data without explicit programming. Machine learning can be implemented by machine learning (ML) methods and algorithms. In one aspect, the machine learning (ML) module is configured to implement ML methods and algorithms. In some aspects, ML methods and algorithms are applied to data input and generate machine learning (ML) output. Data input may include but is not limited to images or frames of video, object characteristics and object classification. Data input may also include sensor data, image data, video data, telematics data, authentication data, authorization data, security data, mobile device data, geographic location information, transaction data, personal identification data, financial data, usage data, weather pattern data, "big data" set, and / or user preference data. ML outputs may include, but are not limited to: tracked shape outputs, classification of objects, classification (segmentation) of regions within medical images, classification of motion types, diagnosis based on the motion of an object, motion analysis of an object, and trained model parameters ML outputs may also include: speech recognition, image or video recognition, medical diagnosis, statistical or financial models, autonomous vehicle decision models, robot behavior modeling, fraud detection analysis, user recommendations and personalization, game AI, skill acquisition, targeted marketing, big data visualization, weather forecasts, and / or extracted information about a computer device, user, household, vehicle, or transaction party. In some aspects, data inputs may include certain ML outputs.

[0115] In some aspects, at least one of a variety of ML methods and algorithms may be applied, which may include, but are not limited to: genetic algorithms, linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines. In various aspects, the implemented ML methods and algorithms relate to at least one of a plurality of classifications of machine learning, such as supervised learning, unsupervised learning, adversarial learning, and reinforcement learning.

[0116] The method and algorithm of the present invention may be included in a controller or processor. In addition, the method and algorithm of the present invention may be embodied as a computer-implemented method for executing such a computer-implemented method (one or more), and may also be embodied in the form of a tangible or non-transient computer-readable storage medium containing a computer program or other machine-executable instructions ("computer program" in this article), wherein, when the computer program is loaded into a computer or other processor ("computer" in this article) and / or executed by a computer, the computer becomes a device for practicing the method (one or more). Storage media for containing such a computer program include, for example, floppy disks and floppy disks, compact discs (CD)-ROMs (whether writable or not), DVD digital disks, RAM and ROM memories, computer hard drives and backup drives, external hard drives, "thumb" drives, and any other storage media readable by a computer. The method (one or more) may also be embodied in the form of a computer program, for example, whether stored in a storage medium or sent through a transmission medium (such as an electrical conductor, optical fiber or other optical conductor), or sent through electromagnetic radiation, wherein, when the computer program is loaded into a computer and / or executed by a computer, the computer becomes a device for practicing the method (one or more). The method(s) may be implemented on a general purpose microprocessor or on a digital processor specifically configured to practice the process(es). When a general purpose microprocessor is employed, the computer program code configures the circuitry of the microprocessor to create a specific logic circuit arrangement. A computer-readable storage medium includes a medium readable by the computer itself or by another machine that reads computer instructions, for providing these instructions to the computer for controlling its operation. For example, such a machine may include a machine for reading the storage medium mentioned above.

[0117] The definitions and methods described herein are provided to better define the present disclosure and to guide those of ordinary skill in the art in the practice of the present disclosure. Unless otherwise noted, terms should be understood by those of ordinary skill in the relevant art according to conventional usage.

[0118] Treatment

[0119] Also provided is a method for treating, preventing or reversing the process of neuropsychiatric symptoms, disorders or brain damage in a subject in need of administration of a therapeutically effective amount of a neurotherapeutic so as to improve neuropsychiatric symptoms, disorders and brain damage.

[0120] Methods described herein are usually performed on subjects in need thereof. Subjects in need of the methods described herein can be subjects suffering from, diagnosed with, suspected of suffering from, or having a risk of suffering from neuropsychiatric symptoms, disorders, or brain damage. It is usually assessed whether treatment is needed by a medical history, physical examination, or diagnostic test consistent with the disease or condition in question. The diagnosis of various conditions treatable by the methods described herein is within the technical scope of the art. Subjects can be animal subjects, including mammals, such as horses, cattle, dogs, cats, sheep, pigs, mice, rats, monkeys, hamsters, guinea pigs, and humans or chickens. For example, subjects can be human subjects.

[0121] Generally, a safe and effective neurotherapeutic amount is an amount that will result in a desired therapeutic effect while minimizing undesirable side effects, for example, in a subject. In various embodiments, an effective amount of a neurotherapeutic as described herein can substantially inhibit, slow the progression of, or limit the development of a neuropsychiatric symptom, disorder, or brain damage.

[0122] According to the methods described herein, administration can be parenteral, pulmonary, oral, topical, intradermal, intramuscular, intraperitoneal, intravenous, intratumoral, intrathecal, intracranial, intracerebroventricular, subcutaneous, intranasal, epidural, ophthalmic, buccal, or rectal.

[0123] When used in the treatments described herein, a therapeutically effective amount of the neurotherapeutic may be used in pure form, or, where such a form exists, in the form of a pharmaceutically acceptable salt, and may be used with or without a pharmaceutically acceptable excipient. For example, the compounds of the present disclosure may be administered in sufficient amounts to ameliorate neuropsychiatric symptoms, disorders, and brain damage at a reasonable benefit / risk ratio applicable to any medical treatment.

[0124] The amount of the compositions described herein that can be combined with a pharmaceutically acceptable carrier to produce a single dosage form will vary depending on the subject or host being treated and the particular mode of administration. It will be understood by those skilled in the art that the unit content of the agent contained in the individual doses of each dosage form need not constitute a therapeutically effective amount in itself, since the necessary therapeutically effective amount can be achieved by administering multiple individual doses.

[0125] Toxicity and therapeutic efficacy of the compositions described herein can be determined by standard pharmaceutical procedures in cell cultures or experimental animals to determine the LD 50 (a dose lethal to 50% of the population) and ED 50 (the dose that is therapeutically effective in 50% of the population). The dose ratio between toxic and therapeutic effects is expressed as LD 50 / ED 50A therapeutic index of a ratio of , wherein a larger therapeutic index is generally considered optimal in the art.

[0126] The specific therapeutically effective dosage level for any particular subject will depend on a variety of factors, including the disorder being treated and the severity of the disorder; the activity of the specific compound employed; the specific composition employed; the age, weight, general health, sex, and diet of the subject; the time of administration; the route of administration; the rate of excretion of the composition employed; the duration of the treatment; drugs used in combination or concomitantly with the specific compound employed; and like factors well known in the medical arts (see, e.g., Koda-Kimble et al. (2004) Applied Therapeutics: The Clinical Use of Drugs, Lippincott Williams & Wilkins, ISBN 0781748453; Winter (2003) Basic Clinical Pharmacokinetics, 4th ed., Lippincott Williams & Wilkins, ISBN 0781741475; Sharqel (2004) Applied Biopharmaceuticals and Pharmacokinetics, McGraw-Hill / Appleton & Lange, ISBN 0071375503). For example, it is entirely within the technical scope of the art to start the dosage of the composition at a level lower than that required to achieve the desired therapeutic effect, and gradually increase the dosage until the desired effect is achieved. If desired, the effective daily dose can be divided into multiple doses for the purpose of administration. Therefore, a single dose composition can contain such an amount or multiples thereof to form a daily dose. However, it should be understood that the total daily dosage of the compounds and compositions disclosed herein will be determined by the attending physician within the scope of reasonable medical judgment.

[0127] In addition, each of the states, diseases, disorders and conditions described herein and other situations can benefit from the compositions and methods described herein. Typically, treating a state, disease, disorder or condition includes preventing, reversing or delaying the appearance of clinical symptoms in a mammal that may suffer from or be susceptible to the state, disease, disorder or condition, but has not yet experienced or shown its clinical or subclinical symptoms. Treatment may also include inhibiting a state, disease, disorder or condition, for example, preventing or reducing the development of a disease or at least one clinical or subclinical symptom thereof. In addition, treatment may include alleviating a disease, for example, causing at least one of a state, disease, disorder or condition or its clinical or subclinical symptoms to subside. The benefit to the subject to be treated may be statistically significant, or at least perceptible to the subject or physician.

[0128] Administration of the neurotherapy may occur as a single event, or over the time course of treatment. For example, the neurotherapy may be administered daily, weekly, biweekly, or monthly. For treatment of acute conditions, the time course of treatment is typically at least a few days. Certain conditions may extend treatment from a few days to a few weeks. For example, treatment may extend for a week, two weeks, or three weeks. For more chronic conditions, treatment may extend from a few weeks to a few months, or even a year or more.

[0129] Treatment according to the methods described herein can be performed prior to, concurrently with, or after conventional treatment modalities for neurological diseases.

[0130] The neurotherapy can be administered simultaneously or sequentially with another agent, such as an antibiotic, an anti-inflammatory drug, or another agent. For example, the neurotherapy can be administered simultaneously with another agent, such as an antibiotic or an anti-inflammatory drug. Simultaneous administration can occur by administering separate compositions, each composition comprising one or more neurotherapy, antibiotic, anti-inflammatory drug, or another agent. Simultaneous administration can occur by administering a composition comprising two or more neurotherapy, antibiotic, anti-inflammatory drug, or another agent. The neurotherapy can be administered sequentially with the antibiotic, anti-inflammatory drug, or another agent. For example, the neurotherapy can be administered before or after the administration of the antibiotic, anti-inflammatory drug, or another agent.

[0131] Drug administration

[0132] The agents and compositions described herein can be administered in various ways known in the art according to the methods described herein. The agents and compositions can be used as exogenous substances or endogenous substances for treatment. Exogenous agents are substances produced or manufactured outside the body and administered to the body. Endogenous agents refer to drugs produced or manufactured in the body by some type of device (biological or other) for delivery in the body or to other organs in the body.

[0133] As discussed above, administration can be parenteral, pulmonary, oral, topical, intradermal, intratumoral, intranasal, inhalation (e.g., in an aerosol), implant, intramuscular, intraperitoneal, intravenous, intrathecal, intracranial, intracerebroventricular, subcutaneous, intranasal, epidural, intrathecal, ophthalmic, transdermal, oral, and rectal.

[0134] Reagents described herein and compositions can be administered with the various methods well-known in the art.Administration can include, for example, oral administration, direct injection (for example, whole body or stereotactic), implantation is designed to secrete the cell of factor of interest, drug release biomaterial, polymer matrix, gel, permeable membrane, permeation system, multilayer coating, particulate, implantable matrix device, mini osmotic pump, implantable pump, injectable gel and hydrogel, liposome, micelle (for example, up to 30 μ 0), nanosphere (for example, less than 1 μ 0), microsphere (for example, 1-100 μ 0), reservoir device, above-mentioned any one combination, or other suitable delivery carriers, to provide the expected release profile of different proportions.Other methods that the controlled release of reagent or compositions is delivered will be known to the technician and within the scope of the present disclosure.

[0135] Delivery system can comprise for example perfusion pump, and it can be used for the mode administration reagent or composition similar to being used for insulin or chemotherapy delivery to specific organ or tumor.Usually, using such system, reagent or composition can be administered in combination with biodegradable, biocompatible polymer implant, and this implant releases reagent in the controlled time period at the selected position.The example of polymeric material comprises polyanhydride, polyorthoester, polyglycolic acid, polylactic acid, polyethylene vinyl acetate and copolymer and combination thereof.In addition, the release system of control can be placed near the treatment target, therefore only requires the small part of system dosage.

[0136] Reagent can be encapsulated in various carrier delivery systems and administered. The example of carrier delivery system includes microsphere, hydrogel, polymer implant, intelligent polymer carrier and liposome (generally referring to Uchegbu and Schatzlein (2006) " polymer in drug delivery " (Polymers in Drug Delivery), CRC, ISBN-10:0849325331). The system based on carrier for the delivery of molecule or biomolecule reagent can: provide intracellular delivery; adjust biomolecule / reagent release rate; increase the ratio of biomolecules arriving at its site of action; improve the transportation of drug to its site of action; allow co-location deposition with other reagents or excipients; improve the stability of reagent in vivo; extend the residence time of reagent at the site of action by reducing clearance; reduce the non-specific delivery of reagent to non-target tissue; reduce the stimulation caused by reagent; reduce the toxicity of the high initial dose due to reagent; change the immunogenicity of reagent; reduce dosage frequency, improve the taste of product; or improve the shelf life of product.

[0137] Screening

[0138] Methods for screening are also provided.

[0139] The subject method is applicable to the screening of various candidate molecules (e.g., potential therapeutic candidate molecules). Candidate substances for screening according to the methods described herein include, but are not limited to, parts of tissues or cells, nucleic acids, polypeptides, siRNAs, antisense molecules, aptamers, ribozymes, triple helical compounds, antibodies, and small (e.g., less than about 2000 mw, or less than about 1000 mw, or less than about 800 mw) organic or inorganic molecules, including, but not limited to, salts or metals.

[0140] Candidate molecules encompass many chemical classes, for example organic molecules, such as small organic compounds having a molecular weight greater than 50 and less than about 2500 Daltons. Candidate molecules may include functional groups necessary for interaction with protein structures, particularly hydrogen bonding, and typically include at least an amine, carbonyl, hydroxyl or carboxyl group, and typically also include at least two functional chemical groups. Candidate molecules may include cyclic carbon or heterocyclic structures and / or aromatic or polyaromatic structures substituted with one or more of the above functional groups.

[0141] Candidate molecules can be compounds in a library database of compounds. Those skilled in the art are generally familiar with many databases of commercially available compounds for screening (see, for example, the ZINC database, UCSF, with 2.7 million compounds on 12 different molecular subsets; Irwin and Shoichet (2005) J Chem-Inf Model 45, 177-182). Those skilled in the art will also be familiar with various search engines to identify commercial sources or desired compounds and compound classes for further testing (see, for example, the ZINC database; eMoolecules.com; and electronic libraries of commercial compounds provided by suppliers, such as ChemBridge, Princeton BioMolecular, Ambienter SARL, Enamine, ASDI, LifeChemicals, etc.).

[0142] Candidate molecules for screening according to the methods described herein include lead-like compounds and drug-like compounds. Lead-like compounds are generally understood to have a relatively small scaffold-like structure (e.g., a molecular weight of about 150 to about 350 kD) with relatively small features (e.g., less than about 3 hydrogen donors and / or less than about 6 hydrogen acceptors; a hydrophobic characteristic xlogP of about -2 to about 4) (see, for example, Angewante (1999) Chemie Int. ed Engl. 24, 3943-3948). In contrast, drug-like compounds are generally understood to have a relatively large scaffold (e.g., a molecular weight of about 150 to about 500 kD) with relatively more features (e.g., less than about 10 hydrogen acceptors and / or less than about 8 rotatable bonds; a hydrophobic characteristic xlogP of less than about 5) (see, for example, Lipinski (2000) J. Pharm. Tox. Methods 44, 235-249). Initial screening can be performed with lead-like compounds.

[0143] When designing lead compounds based on spatial orientation data, it may be useful to understand that certain molecular structures are characterized as "drug-like". Such a characterization may be based on a set of empirically recognized qualities derived by comparing the similarities of known drugs within the pharmacopoeia. Although a drug is not required to meet all or even any of these characteristics, it is more likely for a drug candidate to meet clinical success if the candidate drug is drug-like.

[0144] Several of these "drug-like" properties have been summarized as Lipinski's four rules (often referred to as the "rule of five" due to the prevalence of the number 5 therein). While these rules are generally associated with oral absorption and used to predict the bioavailability of compounds during the lead optimization process, they can be used as effective guidelines for constructing lead molecules during rational drug design efforts, such as can be achieved using the methods of the present disclosure.

[0145] The four "rules of five" state that candidate drug-like compounds should have at least three of the following properties: (i) a weight of less than 500 Daltons; (ii) a logarithm of P less than 5; (iii) no more than 5 hydrogen bond donors (expressed as the sum of OH and NH groups); and (iv) no more than 10 hydrogen bond acceptors (the sum of N and O atoms). Furthermore, drug-like molecules typically have about About The span (width) between.

[0146] The definitions and methods described herein are provided to better define the present disclosure and to guide those of ordinary skill in the art in the practice of the present disclosure. Unless otherwise noted, terms should be understood by those of ordinary skill in the relevant art according to conventional usage.

[0147] In some embodiments, the numbers representing the amount of ingredients, characteristics such as molecular weight, reaction conditions, etc., used to describe and claim certain embodiments of the present disclosure should be understood to be modified by the term "about" in some cases. In some embodiments, the term "about" is used to indicate that the value includes the standard deviation of the mean value of the device or method used to determine the value. In some embodiments, the numerical parameters set forth in the written description and the accompanying claims are approximate values ​​that can vary depending on the desired characteristics sought to be obtained by the specific embodiment. In some embodiments, the numerical parameters should be interpreted according to the number of reported significant figures and by applying ordinary rounding techniques. Although the numerical ranges and parameters of the wide range of some embodiments of the present disclosure are approximate values, the numerical values ​​set forth in the specific examples are reported as accurately as possible. The numerical values ​​presented in some embodiments of the present disclosure may contain certain errors, which are necessarily caused by the standard deviations found in their corresponding test measurements. The description of the range of values ​​herein is intended only to be used as a shorthand method for individually citing each individual value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually quoted herein. The expression of discrete values ​​is understood to include the range between each value.

[0148] In some embodiments, unless otherwise specifically stated, the terms "a" and "an" and "the" and similar references used in the context of describing particular embodiments (particularly in the context of certain claims below) may be interpreted to cover both the singular and the plural. In some embodiments, the term "or" as used herein, including the claims, is used to mean "and / or" unless explicitly indicated to refer to only alternatives or the alternatives are mutually exclusive.

[0149] The terms "comprise," "have," and "include" are open-ended linking verbs. Any form or tense of one or more of these verbs, such as "include," "comprises," "has," "possess," "includes," and "includes," are also open-ended. For example, any method that "comprises," "has," or "includes" one or more steps is not limited to having only these one or more steps, and may also cover other unlisted steps. Similarly, any composition or device that "comprises," "has," or "includes" one or more features is not limited to having only these one or more features, and may cover other unlisted features.

[0150] Unless otherwise specified herein or clearly contradicted by context, all methods described herein may be performed in any suitable order. The use of any and all examples or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended only to better illustrate the present disclosure and does not impose limitations on the scope of the present disclosure otherwise claimed. The language in the specification should not be interpreted as indicating any non-claim elements that are essential to the practice of the present disclosure.

[0151] The grouping of the alternative elements or embodiments of the present disclosure disclosed herein should not be construed as limiting.Each group member can be quoted and claimed separately, or can be quoted and claimed in any combination with other members of the group or other elements found in this article.For convenience or patentability reasons, one or more members of a group can be included in the group or deleted from the group.When any such inclusion or deletion occurs, this specification is deemed to include the group as modified in this article, so as to meet the written description of all Markush groups used in the appended claims.

[0152] All publications, patents, patent applications, and other references cited in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication, patent, or other reference was specifically and individually indicated to be incorporated herein by reference in its entirety for all purposes. Citations cited herein should not be construed as an admission that this is prior art to the present disclosure.

[0153] Having described the present disclosure in detail, it will be apparent that modifications, variations and equivalent embodiments are possible without departing from the scope of the present disclosure as defined in the accompanying claims. Furthermore, it should be understood that all examples in the present disclosure are provided as non-limiting examples.

[0154] Example

[0155] The following non-limiting examples are provided to further illustrate the present disclosure. It will be appreciated by those skilled in the art that the techniques disclosed in the following examples represent methods discovered by the inventors in the practice of the present disclosure, and therefore can be considered to constitute examples of modes for its practice. However, in light of the present disclosure, it will be appreciated by those skilled in the art that many changes can be made in the specific embodiments disclosed without departing from the spirit and scope of the present disclosure, and similar or similar results can still be obtained.

[0156] Example 1

[0157] Overview

[0158] The primary motor cortex (M1) is thought to form a continuous somatotopic homunculus extending from the precentral gyrus down the representation of the feet to the face. Despite evidence for concentric functional zones and complex action maps, the motor homunculus remains a textbook mainstay of functional neuroanatomy. Using our highest precision functional magnetic resonance imaging (fMRI) data and methods, we found that the classic homunculus is interrupted by regions that are distinct in connectivity, structure, and function, alternating with effector-specific (foot, hand, mouth) regions. These inter-effector regions exhibit reduced cortical thickness, as well as strong functional connectivity with each other and with prefrontal, insular, and subcortical regions of the cingulate-opercular network (CON), which is critical for executive action and physiological control, arousal, and the processing of errors and pain. This mutual digitization of action control connections and motor effector regions was independently validated in the three largest fMRI datasets. Precise fMRI in macaques and pediatrics (neonates, infants, children) reveals potential cross-species analogs and developmental precursors of the inter-effector system. A large number of motor and action fMRI tasks record concentric somatotopes for each effector, separated by inter-effector regions connected by the CON. Inter-effector regions lack motor specificity and are co-activated during both action planning (hand-foot coordination) and axial body movements (e.g., abdomen, brow). These results, along with previous work demonstrating stimulation-evoked complex movements and connectivity with visceral organs (e.g., adrenal medulla), propose that M1 is interrupted by an integrated system for achieving whole-body action planning. Thus, two parallel systems are intertwined in the motor cortex to form a fully segregated paradigm: effector-specific regions for segregated fine motor control (feet, hands, mouth), and a mind-body interface (MBI) for integrated whole-organism coordination of goal, physiology, and body movements.

[0159] main

[0160] Beginning in the 1930s, Penfield and colleagues mapped the human M1 using direct cortical stimulation to elicit movements from about half of its parts, primarily the feet, hands, or mouth. Although the representations used for specific body parts overlapped substantially, these maps produced the textbook view of the organization of the M1 as a continuous homunculus from head to toe.

[0161] In nonhuman primates, organizational features inconsistent with motor homunculus have been described. Structural connectivity studies divide the M1 into anterior gross motor, "old" M1 (few direct projections to spinal motor neurons) and posterior gross motor, "new" M1 (many direct motor neuron projections). Stimulation studies in nonhuman primates have shown that the body is represented in the anterior M1 and that motor effectors (tail, feet, hands, mouth) are represented in the posterior M1. Such studies have also proposed that the limbs are represented in concentric functional zones that progress from the fingers in the center to the shoulders in the periphery. Moreover, stimulation may elicit increasingly complex and multi-effector movements when moving from the posterior to the anterior M1.

[0162] During natural behavior, voluntary movements are part of goal-directed actions and are initiated and controlled by executive areas in the CON. Neural activity preceding voluntary movements can first be detected in the dorsal anterior cingulate cortex (dACC) or rostral cingulate areas, then in the presupplementary motor area (preSMA) and SMA, followed by the M1. These areas all project to the spinal cord, with the M1 acting as the main transmitter of motor commands under the corticospinal tract. The primary somatosensory cortex (S1), cerebellum, and striatum receive effective copies of movements for online correction and learning. Tracer injections in nonhuman primates have demonstrated direct projections from the anterior M1 / CON to visceral organs (e.g., adrenal medulla) for preparatory sympathetic arousal in anticipation of an action. Error and pain signals following movement are primarily relayed to the insular and cingulate areas of the CON, which update future action planning.

[0163] Resting-state functional connectivity (RSFC) fMRI noninvasively delineates functional networks of the brain. Precision functional mapping (PFM) studies rely on large amounts of multimodal data (e.g., RSFC, task) to map individual-specific brain organizations in as much detail as possible. Early PFM studies identified separate foot, hand, and mouth M1 regions and their corresponding cerebellar and striatal targets. These foot / hand / mouth motor circuits are characterized by strong intra-circuit connectivity and effector specificity in task fMRI. However, these circuits are relatively isolated and do not include functional connectivity to control networks (such as CON), which can support the integration of movements with global behavioral goals. A recent study showed that prolonged dominant arm fixation strengthened functional connectivity between disused M1 and CON, suggesting that the role of CON may extend beyond abstract action control and into motor coordination.

[0164] Here, the latest PFM iteration uses higher resolution (2.4 mm) and a larger volume of fMRI (RSFC: 172-1813 min / participant; task: 353 min / participant) and diffusion data to map M1 and its connections in the highest detail. Results are validated in group-averaged data from the three largest fMRI studies (Human Connectome Project [HCP], Adolescent Brain Cognitive Development [ABCD] Study, UK Biobank [UKB]; total n ~50,000). Furthermore, PFM data are used to place findings in cross-species (macaques vs humans), developmental (neonates, infants, children, and adults), and clinical (perinatal stroke) contexts.

[0165] Two distinct functional systems alternate in the motor cortex

[0166] Precise functional mapping of the primary motor cortex

[0167] Figure 5A and5B Depicted are resting-state functional connectivity (RSFC) seeded at consecutive linear cortical locations in the left precentral gyrus of a single sample participant (P1; 356 min resting-state fMRI). The six example seeds shown represent all of the different connectivity patterns observed. Functional connectivity seeded from these locations shows classic primary motor cortical connections for regions representing the foot (1), hand (3), and mouth (5), as well as an interleaved set of strongly interconnected regions (2, 4, 6).

[0168] For all highly sampled participants see Figure 6 , for within-participant replication see Figure 7 , and for group average data see Figure 8 .

[0169] Fig. 9 Depicted are discrete functional networks parcellated using a whole-brain, data-driven hierarchical approach (see Methods) applied to resting-state fMRI data that defined the spatial extent of the network (black outline). Regions defined by RSFC were functionally labeled using a classic block-design fMRI motor task involving separate movements of the foot, hand, and tongue. The figure shows an example participant (P1; for other participants see Fig.10 ) are activated by the movements of the feet (green), hands (cyan), and mouth (orange).

[0170] Fig.11 Depicted is how the inter-effector connectivity pattern becomes more distinct from surrounding effector-specific motor regions as the connectivity threshold increases from the 80th to the 97th percentile. The RSFC threshold required to detect the inter-effector pattern is lower in the individual-specific data (top) than in the group-averaged data (ABCD study, bottom).

[0171] High-level PFM reveals connectivity that is distinct from the typical gnotonomic organization of M1. Two contrasting patterns of functional connectivity in primary motor cortex alternate ( Figure 5A and 5B As described previously, the expected pattern for M1 foot, hand, and mouth representations consisted of three regions (in each hemisphere), with cortical connections restricted to the homotopic contralateral M1, SMA, and adjacent S1 ( Figure 5B , seeds 1, 3, 5). This set of RSFC-defined regions corresponded to task-evoked activity during foot, hand, and tongue movements ( Fig. 9 ; For other participants see Fig.10 ).

[0172] Interleaved between the known foot / hand / mouth M1 regions were three regions that were strongly connected to each other on both the contralateral and ipsilateral sides, forming a previously unrecognized cross-link in the precentral gyrus ( Figure 5B, seeds 2, 4, 6). Motifs from three M1 inter-effector regions were observed in adults at each height sampled ( Figure 6 ) and replicated within individuals in separate data from the same participants ( Figure 7 Importantly, the inter-effector pattern was also evident in all large-N group-averaged datasets (UKB, ABCD, HCP, WU120; Figure 8 ; See Table 1 for the coordinates of the region of interest).

[0173] Table 1: Inter-effector coordinates. Location of centroids of inter-effector and effector-specific regions within the HCP data. Coordinates are expressed as [XYZ] in MNI space.

[0174] Table 1

[0175]

[0176]

[0177] M1 inter-effector functional connectivity motifs were most evident in individual-specific maps, but once identified, they were also clearly identifiable in group-averaged data when visualized using stringent connectivity thresholds ( Fig.11 ).

[0178] The inter-effector region is evident relatively early in development. Although PFM data from newborns failed to reveal an inter-effector motif, it was detectable in infants as young as 11 months and was nearly as abundant in children as in adults at 9 years of age ( Fig.12 A-12E). Despite severe bilateral perinatal stroke that destroyed large parts of M1, even inter-effector regions could be identified in individuals who preserved motor function ( Fig.12 F).

[0179] M1 functional connectivity in pediatric participants and participants with perinatal stroke

[0180] Functional connectivity map seeded along the precentral gyrus from fMRI data averaged from 262 human newborns, all scanned shortly after birth ( Fig.12 A); A newborn scanned at 13 days of age ( Fig.12 B); 11-month-old infant ( Fig.12 C); Children aged 9 years ( Fig.12 D); Adult participant P01( Fig.12 E, from Figure 6 and Figure 7 ); and adolescents who underwent extensive cortical reorganization after severe bilateral perinatal stroke ( Fig.12F, black damaged cortex). The right hemisphere is shown in a stroke patient because the left hemisphere M1 is completely lost. The example seed map shown here shows the observed effector-specific (first three rows) and inter-effector (fourth row) connections. In infants, children, adults and stroke patients, effector-specific and inter-effector regions show clear boundaries within M1, but not in newborns. Due to differences in data collection and treatment and inherent differences in populations, the visualization threshold across different data sets varies between 0.3 and 0.5.

[0181] Inter-effector regions are functionally connected to the cingulate-opercular control network

[0182] Functional connectivity and cortical thickness of effector-interacting motifs in motor cortex

[0183] Fig.13 Depicted are brain regions with the strongest functional connectivity to the left middle intereffector region (exemplary seed) in the cortex, striatum, thalamus (horizontal slices; centromedian (CM) nucleus shown), and cerebellum (plan view) in an exemplary participant (P1); for other participants see Fig.14 .

[0184] Using a whole-brain data-driven hierarchical community detection approach applied to resting-state fMRI data, discrete functional networks were delineated within each subject in M1 and S1. Communities were matched to known large-scale networks and inter-effector regions. Networked inter-effector regions were observed in all participants.

[0185] Fig.15 Any foot / hand / mouth areas used for other participants were depicted (P1; Fig.16 A) Brain regions that are more functionally connected to inter-effector regions than to the cingulate-opercular network (CON: individual-specific) are outlined in purple. The central sulcus is masked because it exhibits great variability in definition.

[0186] Whole-brain functional connectivity of effector-interacting motifs in the precentral gyrus across subjects

[0187] The medial cortex ( Fig.16 A) striatum ( Fig.16 B, Lateral view of left and right striatum), thalamus ( Fig.16 C, axial view) and cerebellum ( Fig.16D) The intermediate inter-effector region in the brain region with the strongest functional connectivity. In the cortex, functional connectivity values ​​were thresholded at Z(r)>0.35. Due to variations in subcortical signal-to-noise ratio across individuals, subcortical functional connectivity values ​​were thresholded at different levels in each subject. The thresholds were chosen to illustrate the strongest subcortical connections. Specific thresholds shown here: P01-Z(r)>0.15; P03,04,06,07-Z(r)>0.10; P02-Z(r)>0.04; P05-Z(r)>0.03. Connections were calculated between each network and the inter-effector and effector-specific M1 regions. The figure shows the minimum difference between inter-effector connectivity and any effector-specific connectivity (standard error bars). This difference is greater for CON than for any other network (*:P<0.05).

[0188] refer to Fig.17 , the inter-network relationships were visualized in network space using a spring embedding plot, where connected regions are pulled together and disconnected regions are pushed apart. Connecting lines indicate functional connectivity (r>0.2) (P1; Fig.18B for all participants).

[0189] Fig.19 Depicted are inter-effector and effector-specific regions tested for systematic differences in the temporal order of their subslow fMRI signals (<0.1 Hz). The figure shows the average signal order across participants for CON, inter-effector, and effector-specific regions (standard error bars; *: P<0.05). Previous electrophysiological work has proposed that late subslow activity (here CON) corresponds to early delta band (0.5-4 Hz) activity.

[0190] refer to Fig. 20 , within each participant (individual points), inter-effector regions exhibited lower cortical thickness than all effector-specific regions (**: P < 0.01).

[0191] In addition to their interconnectedness, the three inter-effector areas are functionally connected to the dACC and pre-SMA, which are thought to be important for goal-oriented cognitive control. Subcortically, the inter-effector areas are most strongly connected to the dorsolateral putamen, the ventral intermediate nucleus (VIM), the central median nucleus (CM), the ventral posteromedial nucleus (VPM), and the ventral posteroinferior nucleus (VPI) of the thalamus ( Fig.13 ; For other participants see Fig.14 ). Inter-effector regions are more strongly connected to surrounding cerebellar regions, but are distinct from effector-specific cerebellar regions ( Fig.21 ).

[0192] In all highly sampled individuals (n=7), the inter-effector region had stronger connections to CON than any of the foot / hand / mouth regions ( Fig.15 ; For all participants Fig.16 ; Across participants: all paired t > 4.75; all P < 0.01; Fig.18A ). The inter-effector and foot / hand / mouth differences were greater for CON than for any other network (all paired t > 2.8; all P < 0.03; Fig.15 In network space, the inter-effector region is located between the CON and the foot / hand / mouth regions ( Fig.17 ; Fig.18B The inter-effector region was also more strongly connected to the middle insula, which is known to process pain and intraocular signals. Fig.18B ; all paired t>2.7; all P<0.03); lateral cerebellar lobule V and vermis Crus II, lobule VIIb, and lobule VIIIa (all paired t>3.7, all P<0.01); dorsolateral putamen, which is critical for motor function (all paired t>3.7; all P<0.01); and sensorimotor areas of the thalamus (VIM; CM; VPM; all paired t>3.0, all P<0.02).

[0193] Comparison of the relative timing of resting-state fMRI signals (lag structure) showed that ultraslow (<0.1 Hz) fMRI signals in CON and inter-effector networks lagged behind those in effector-specific regions ( Fig.19 (CON vs. foot: paired t = 2.38, P = 0.055; vs. hand and mouth: all t > 2.84, all P < 0.03; effector vs. foot / hand / mouth: all t > 2.5, all P < 0.05). Interregional lags in ultraslow (<0.1 Hz) signals were associated with propagation of higher-frequency delta activity (0.5-4 Hz) in the opposite direction, suggesting that high-frequency signals may arise earlier in the CON than in M1—consistent with electrical recordings during voluntary movements—but that such signals reach the effector before the foot, hand, and mouth regions.

[0194] As expected, the M1 foot / hand / mouth region is strongly functionally connected to the adjacent S1 ( Figure 5A and 5B , Fig.22A ), which is consistent with the known functional connectivity between M1 and S1. In contrast, the inter-effector region exhibited lower connectivity with adjacent S1 ( Fig.22H ; all paired t>3.2, all P<0.02). More specifically, inter-effector functional connectivity extended to the floor of the central sulcus representing proprioception ( Fig. 22B ; Brodmann area [BA]3a), but does not extend into the postcentral gyrus (BA3b / 1 / 2) that represents touch stimulation of the skin.

[0195] Converging with these functional differences, measures of brain structure differed systematically between effector-inter and effector-specific regions. Inter-effector regions exhibited lower cortical thickness (all paired t > 3.6; all P ≤ 0.01; Fig. 20 ), more similar to the prefrontal cortex but with higher fractional anisotropy (2 mm below the cortex; all paired t > 5.3; all P < 0.05; Fig.22J ). The inter-effector region also had higher intracortical myelin content than the foot region (paired t = 6.8, P < 0.001) but lower than the hand region (paired t = 4.8, P = 0.003; Figure 22K ).

[0196] Functional connectivity and structural MRI measures of the precentral gyrus region

[0197] In each individual participant, measures were derived from each of the foot, hand, mouth, and inter-effector motor areas. Colored lines connect inter-effector and effector-specific areas in the same participant to facilitate comparison. Functional connectivity strength between M1 areas and the individual-specific cingulate-opercular networks ( Fig.22A ); the functional connectivity strength between the M1 region and the intermediate insula ( Fig. 22B ); Functional connectivity strength with the vermis of lobule VIIIa of the cerebellum ( Fig. 22C ); the functional connectivity strength between the M1 region and the dorsal putamen ( Fig.22D ). The functional connectivity strength between the M1 region and the thalamic nuclei: ventral intermediate nucleus ( Fig.22E ); parafascicular nucleus ( Fig.22F );( Figure 22G ) ventrolateral anterior nucleus. The functional connectivity strength between the M1 region and the adjacent postcentral gyrus ( Fig.22H ). Cortical thickness in the M1 region ( Fig.22I ). The fractional anisotropy within 2 mm below the cortex in the M1 region ( Fig.22J In the cortex of the M1 region, intracortical myelin was indexed by the T1 / T2 ratio and normalized across cortices ( Figure 22K ). *p<.05; **p<.01; ***p<.001.

[0198] Concentric effector body structures separated by inter-effector body / action regions

[0199] Individual task-specific activation in the primary motor cortex

[0200] Fig.23Task fMRI activations during the motor task group test (P1, P2) are depicted, including movements of the toes, ankles, knees, gluteal muscles, abdomen, shoulders, elbows, hands, eyebrows, eyelids, tongue, and swallowing (244 min / participant). Each cortical vertex is colored according to the movement that elicits the strongest task activation (winner takes all) and is shown on a flat representation of the cortical surface. Background shading indicates groove depth.

[0201] Fig.24 Activation intensities for each movement calculated along the dorsoventral axis within M1 are depicted. A bimodal Gaussian curve was fit to each movement activation (see Methods). Fitted curves are shown for movements of the abdomen, shoulder, elbow, wrist, and hand. Peak locations (hash on the right) are arranged concentrically around the hand peak. For all movements, see Fig.25 and 26 .

[0202] Inter-effector and effector-specific regions in the precentral and postcentral gyri

[0203] In each participant, Brodmann areas in M1 (BAs 4a, 4p) and S1 (BAs 1, 2, 3a, 3b) are shown on the cerebral cortex, tilted around the Y and Z axes to show S1. Overlaid are the somatomotor hand areas ( Fig.25 ) and the inter-effector region ( Fig.26 ).

[0204] exist Fig. 27 , co-activation of inter-effector regions during abdominal contractions.

[0205] exist Fig.28 In the 2017 study, inter-effector regions exhibited more extensive evoked activity during movement. Movement specificity was calculated as the difference in activation between the first and second most preferred movements under the six conditions that best activated each discrete region (toe, abdomen, hand, eyelid, tongue, swallow).

[0206] Fig.29 Depicted are event-related task fMRI data during a motor planning task with separate planning and execution phases for movements of the hands and feet (see Methods). M1 activity in the planning phase was higher in inter-effector regions than in the execution phase, but not in effector-specific regions.

[0207] To better understand the function of inter-effector motifs, fMRI data were collected during blocked performance of 25 different movements in two highly sampled individuals (64 runs; 244 min / participant) and during a novel event-related task with separate planning and execution phases for coordinating movements of the hands and feet (12 runs; 132 min / participant). According to the dwarf model of M1, activation when moving a given body part should exhibit a single peak within the precentral gyrus. If M1 were instead organized into concentric functional areas, then all movements except those in the center (i.e., toes, fingers, tongue) should exhibit two peaks (upper and lower). Within each of the three effector-specific regions, a topography of preferred movements—movements that elicited maximal activation at each vertex ( Fig.23 )—is more consistent with concentric organization (distal-proximal; e.g., toe in center with concentric areas around ankle-knee-hip) than the canonical linear toe-to-face dwarf model.

[0208] To formally test for concentric organization, unimodal and bimodal Gaussian curves were fit to the task activation profiles along the dorsomedial to ventrolateral axis of M1. The bimodal curve fit was significantly better for all movements except the hand in (F-test comparing models: all F>6.9, all P<0.001) ( Fig.25 ). Curve fitting reveals that for distal motion (hand ( Fig.24 ), toes and tongue( Fig.26 )) of the cervical spine, and expands outward to more proximal movements (shoulders, glutes, jaw). Concentric activation rings from the center of the discrete foot / hand / mouth intersect at the top and middle inter-effector regions.

[0209] Some exercises that require less fine motor control, such as isometric contractions of the abdomen ( Fig. 27 ) or raising an eyebrow, co-activating multiple inter-effector regions and CON( Figure 30-33 ). In contrast, foot and hand movements activated only the corresponding effector-specific regions ( Figure 30-33 Unlike effector-specific regions, effector internodes exhibit only weak motor specificity, with minimal differences in activation between their first and second most preferred motors ( Fig.28 ).

[0210] Fig.30 Plots of movement-driven activation versus dorsoventral location within left hemisphere M1 for all movement tasks. For most movements, the bimodal curve modeled as a double Gaussian (blue) fit the data better than the unimodal curve modeled as a single Gaussian (red) (ps < .001).

[0211] Fig.31 All modeled double-peaked curves for P01 and P02 are depicted. Most movements, except for the movements of the most distal body parts (toes, hands, tongue), show a clear double peak. The peak positions (hashed on the right) are arranged concentrically around the peak of the most distal body part.

[0212] Fig.32 Figure 3. Inter-effector regions and CON were active for each participant during the abdominal flexion task and the eyebrow raising task. In contrast, activation was more specific to a single region of the somatomotor cortex during the toe and hand movement tasks. Across all tasks, CON activation was consistently similar to activation in inter-effector regions (correlations between CON and inter-effector activation: all rs > 0.81, ps < 10-5), but not consistently similar to activation in hand (CON vs hand: rs > 0.05, ps < 0.82) or foot (CON vs foot: rs > 0.33, ps < 0.13) regions, and more weakly similar to activation in mouth regions (CON vs mouth: rs > 0.61, ps < 0.003). Activation values ​​plotted were averaged across participants and sorted based on CON activation.

[0213] Regions in the CON instantiate action planning, suggesting that connections between the CON and effectors may carry general action planning signals. In a novel coordination task of foot and hand movements, effector regions showed greater activity during action planning than during movement execution, but effector-specific regions did not ( Fig.29 ), which suggests that the implementation of action planning may be enabled in part by intereffector regions in M1.

[0214] Precentral gyrus functional connectivity in nonhuman primates

[0215] Fig.34 A depicts a functional connectivity map seeded from points in the precentral gyrus in macaques. Left: Seed locations are shown relative to the macaque “simiculus” (Woolsey et al., 1952). Middle: Maps seeded from locations in the posterior precentral gyrus corresponding to the feet (green), hands (blue), and mouth (orange) exhibit clear boundaries within the macaque M1, but no cross-sectional distributed connectivity patterns were detected between the foot, hand, and mouth regions. Right: Seeds in the anterior precentral gyrus (maroon) exhibit strong connections distributed along the dorsoventral axis within the precentral gyrus, as well as homologs to human CON regions, including the inferior frontal gyrus, anterior inferior parietal cortex, and anterior dorsomedial prefrontal cortex. These anterior seeds correspond to the body and neck, as well as regions involved in complex movements (Graziano 2016) and regions that project to visceral organs (Dum et al., 2018). This connectivity suggests that anterior motor regions in macaques may be analogous to inter-effector regions in humans.

[0216] Fig.34 B depicts schematic representations of proposed analogous regions in macaques (left) and humans (right). The foot region (green) is displaced to the medial wall in humans, while the hand (cyan) and face (orange) regions maintain their relative positions. The anterior precentral gyrus region of macaques (maroon) is displaced posteriorly to the posterior part of the precentral gyrus in humans, interleaved between the foot, hand, and face regions.

[0217] Similarities between human inter-effector motifs and non-human primate pre-M1

[0218] When examining PFM functional connectivity data from macaques ( Fig.34 A), Seeds in the posterior portion of the precentral gyrus M1 revealed foot, hand, and mouth effector-specific functional connectivity patterns consistent with those seen in humans. Anterior precentral gyrus regions revealed functional connectivity with each other and with the rostral cingulate region, anterior SMA, and parietal cortex similar to human CON ( Fig.34 A).

[0219] In macaques, distinct patterns of corticospinal connectivity distinguish anterior and posterior M1. The phylogenetically newer posterior M1 represents effector-backward projections, projects primarily to the cervical and lumbar enlargements of the spinal cord, and contains projections that make more direct synapses onto muscle-innervating spinal neurons for fine motor control. In contrast, the older anterior M1 represents the body and more complex movements, projects bilaterally throughout the spinal cord, and connects to visceral organs such as the adrenal medulla and stomach. Non-human primate electrophysiological studies reporting motor planning signals typically record from sites that overlap with proposed anteriorly located macaque inter-effector and CON analogs. Thus, direct stimulation, electrophysiological recordings, structural, and functional connectivity data have all revealed similarities between macaque premotor cortex and human inter-effector motifs ( Fig.34 B).

[0220] The discontinuous dwarf--an integration / segregation model of behavioral and motor control

[0221] Fig.35 Penfield's classic gnome depicting a sequential diagram of the body in the primary motor cortex.

[0222] like Fig.36 In the integration-segregation model of primary motor cortical organization, as depicted in Figure 2, effector-specific (feet [green], hands [cyan], mouth [orange]) functional areas are represented by concentric rings, with proximal body parts surrounding relatively more separable distal body parts (toes, fingers, tongue). Inter-effector areas (maroon) are located at the intersection of these fields and form part of the mind-body interface (MBI) for integrated, non-homeostatic whole-body control.

[0223] Distinct systems for effector segregation and integrated action interleaving in the motor cortex

[0224] Penfield conceptualized his direct stimulation findings in M1 as a continuous map of the body—the homunculus—an organizing principle that dominated for nearly 100 years ( Fig.35 Based on new and existing data, we propose a dual-system, integrated segregation model of behavioral control in which effector segregation and whole-organism action implementation are regionally alternating ( Fig.36 ). This model better fits the human imaging data presented here, which demonstrate contrasting structural, functional, and connectivity patterns within M1. Inter-effector patterns emerge during infancy and are preserved even in the presence of severe perinatal cortical damage ( Fig.12 ). In the integrated segregation model, the regions for foot / hand / mouth fine motor skills are organized on the somatotope as three concentric functional zones, with the distal portions of the effectors (toes, fingers, tongue) located in the center and the proximal portions (knees, shoulders, jaw) located in the periphery. Inter-effector regions at the edges of the effector regions coordinate with each other and with the CON to accomplish overall whole-body functions in the service of action execution. Current research proposes that these functions include action implementation, as well as postural and gross motor control of axial muscles, while previous research in humans and nonhuman primates has proposed that these circuits may also regulate arousal and control of internal processes and organs (i.e., blood pressure, stomach, adrenal medulla), consistent with circuits for whole-body, metabolic, and physiological control. Thus, the inter-effector system fulfills the role of a mind-body interface (MBI). The MBI combines with upstream executive control operations of the CON to form part of a unified action control system to coordinate gross movements and muscle groups (e.g., trunk, eyebrows) and to provide top-down control of posture and internal physiology in preparation for action. These proposed functions converge with the concept of allostatic regulation, whereby the brain anticipates upcoming changes in physiological demands based on planned actions and exerts top-down preparatory control over the body.

[0225] Human stimulation evidence for an integrate-isolate model of motor cortex

[0226] Penfield proposed that the homunculus was an approximation of group-averaged intraoperative direct electrocortical stimulation data that showed significant overlap across patients and body parts. He later described his artistic rendering of the homunculus as "an aid to memory […] a cartoon of a representation that could not possibly achieve scientific accuracy". Reexamination of existing human stimulation data has raised doubts about the accuracy of the homunculus in individuals and revealed an equal or better fit to the overall segregation model. In some patients, as in nonhuman primates, concentric organization from distal to proximal is recorded for the upper limb, while facial movements can be elicited from regional representations of the dorsal side of the hand. In addition to focal movements, M1 stimulation often elicits several other response types, all of which are better explained by whole-organism control areas. Patients have reported an urge to move while being aware that they were holding still; they have reported a sense of movement, although no movement was detectable; or they have moved but denied doing so - an effect consistent with modulation of the system that also indicates action targets. These responses are similar to those commonly elicited by CON areas such as the dACC and preparietal cortex.

[0227] Stimulation almost never produces isolated trunk or shoulder movement, and a common outcome of stimulation is that no response is reported. Historically, there have been no documented cases of stimulation failing to elicit movement. However, reanalysis of motor stimulation from a recent large study by mapping motor stimulation to the cortex revealed an area that never elicited movement in any patient, corresponding to the inter-effector area ( Fig.37 ). These results suggest that stimulation intensities considered safe in humans do not typically elicit movements in the M1 mind-body interface region, similar to higher-order lateral and medial premotor (i.e., pre-SMA) regions. Human brain-computer interface (BCI) recordings in M1 have also demonstrated whole-body motor tuning, likely reflecting inter-effector activity, and suggest that inter-effector motifs could provide targets for whole-body BCI.

[0228] Inter-effector regions in human cortical stimulation and previous network mapping efforts

[0229] Inter-effector area maps were compared with published movements evoked by direct cortical stimulation. Cortical maps: Functional connectivity is shown seeded from the central inter-effector M1 region and averaged across all subjects in the HCP dataset (see also Figure 8 ). Stimulation Locations: MNI coordinates of stimulation locations and resulting evoked movements are reported for 100 patients who underwent awake surgical brain mapping. Each stimulation location that evoked any movement was mapped to the nearest cortical vertex on the group-averaged pial surface. Stimulation sites are colored according to whether they evoked facial movements (orange) or upper limb movements (cyan). Stimulation sites that evoked movement did not overlap with the central intereffector region.

[0230] Clinical evidence for separating motor isolation and integrating motor control systems

[0231] Brain lesion data further support the existence of a dual system for motor segregation and action integration, with partial redundancy in M1. Motor deficits following middle cerebral artery stroke are unilateral, more severe in most distal effectors, and without significant global organ control deficits. In contrast, lesions in MBI-related CON regions (dACC, anterior insula, aPFC) can lead to isolated volitional deficits, ranging from reduced fluency to aphasia to akinetic mutism, with preserved motor abilities but few self-generated movements. Similarly, anterior motor lesions in macaques can avoid visually guided movements while selectively disrupting internally generated movements. Animals with lesions in effector M1 typically recover total effector control very quickly, while fine finger motor deficits persist for longer periods of time. The more rapid recovery of total motor ability may be due in part to the proximal functions undertaken by the contralateral lesion MBI circuit enabled by its bilateral spinal cord connections. Therefore, persistent deficits may be more likely in functions uniquely supported by effector-specific circuits.

[0232] In a proof-of-principle patient with extensive bilateral perinatal stroke but typical motor abilities, extensive poststroke reorganization maintained the MBI pattern at the expense of a portion of the already reduced M1 hand region. The top third of M1 was destroyed, and the surviving cortex contained an M1 hand region that was displaced ventrally and was much smaller than typical controls. Surprisingly, the MBI region was identified above and below the surviving effector-specific hand region ( Fig.12 F), which highlights the importance of the mind-body interface for typical motor abilities.

[0233] Specific connections to thalamic motor nuclei are used as targets for clinical intervention (VIM, CM), and CON-connected MBI may be associated with various movement disorders, including dystonia or essential tremor. Parkinson's disease (PD) is particularly noteworthy. Many PD symptoms span motor, physiological, and volitional domains (e.g., postural instability, autonomic dysfunction, and reduced self-activity, etc.), reflecting the connectivity of MBI with areas related to postural control (cerebellar vermis), volition, and physiological regulation (CON).

[0234] Sensory and motor systems share common functional organizational features

[0235] Many of the features of motor cortical organization described here have clear parallels in sensory systems. Like the concentric somatotopic organization of central finger fine movements, the primary visual cortex represents higher visual acuity treatments centrally, transitioning concentrically to lower visual acuity in the periphery. Consistent with our integration / segregation dual-system model, visual processing streams are parallel and segregated in the thalamus, early visual cortex, and higher-order visual processing streams, with each level of processing maintaining a separation of different types of information (e.g., early: eccentricity vs. angle; late: faces vs. objects). Auditory processing may share similar features, as acoustic signals are processed at least in part in parallel for auditory and speech perception in the superior temporal gyrus. These findings suggest shared organizational principles in input and output processing streams across the brain. S1 may also have some concentric organizational elements, which should be explored in future work.

[0236] A network for mind-body integration

[0237] Two behavioral control systems are intertwined in the human motor cortex. One well-known system consists of effector-specific circuits for precise, independent movements of highly specialized appendages (fingers, toes, and tongue)—the type of dexterous movements needed for speech or manipulation of objects. A second, integrated output system, the mind-body interface (MBI), is more important for controlling the organism as a whole. The MBI integrates body control (both motor and voluntary) and action planning, consistent with the idea that aspects of higher-level executive control may derive from motor coordination. The MBI includes M1, SMA, thalamus (VIM, CM), posterior putamen, and specific regions of the postural cerebellum, and is functionally connected to the dACC, which is associated with free will, parietal regions that signal motor intention, and insular regions for processing somatosensory, pain, and intersensory visceral signals. The common factor across this rather broad set of processes is that they must be integrated if the organism is to achieve its goals through movement while avoiding injury and maintaining physiological dysregulation. The MBI provides the basis for this integration, which enables the anticipatory postural, cardiovascular, and arousal changes (e.g., shoulder tension, increased heart rate, upset stomach) that precede movement. The finding that movement and body control converge in a common circuit may help explain why mental and physical states interact so frequently.

[0238] Declaration of Interest:

[0239] DAF and NUFD have a financial interest in NOUS Imaging, Inc. and may benefit financially if the company successfully markets the FIRMM motion monitoring software product. DAF and NUFD may receive royalty income based on FIRMM technology developed by the University of Washington School of Medicine and Oregon Health & Science University and licensed to NOUS Imaging, Inc. DAF and NUFD are co-founders of NOUS Imaging, Inc. These potential conflicts of interest have been reviewed and managed by the University of Washington School of Medicine, Oregon Health & Science University, and the University of Minnesota. The other authors declare no competing interests.

[0240] method

[0241] Subjects

[0242] Individual Specific PFM Data – University of Washington Adult

[0243] Participants:

[0244] Data were collected from three healthy, right-handed adult participants (ages 35, 25, and 27 years; 1 female) as part of a study investigating the effects of arm immobilization on brain plasticity (previously published data). Two of the participants were authors (NUFD and ANN). Informed consent was obtained from all participants. The study was approved by the Human Research Committee and the Institutional Review Board at Washington University School of Medicine. The primary data employed here were collected either before (Participants 01, 03) or two years after (Participant 02) the immobilization intervention. Data collected immediately after the intervention were Fig.11 Rendered for intra-participant replication.

[0245] In two participants (Participants 01, 02), additional fMRI data were collected using the same sequence during the performance of two motor tasks: a somatotopic mapping task and a motor control task.

[0246] Body-specific area mapping task

[0247] The block design was adapted. In each run, participants were presented with a visual cue that guided them to perform one of five specific motor movements. Each block started with a 2.2s cue indicating which movement was to be performed. After this cue, a centrally presented caret replaced the instruction and flashed every 1.1 seconds (without temporal jitter). Each time the cursor flashed, the participant performed the appropriate movement. 12 movements were performed per block. Each block lasted 15.4 seconds, and each task run consisted of 2 blocks of each movement type and 3 resting fixation blocks. The movements performed within each run were as follows:

[0248]

[0249] Motion control and coordination tasks

[0250] An event-related design implemented using the JSpsych toolbox v6.3 was used to distinguish between planning and execution of limb movements. Within runs, participants were prompted to move a single limb or to move both limbs simultaneously. Fig.38 There are four possible movements - opening and closing of fingers or toes, left and right flexion of wrist / ankle, clockwise rotation of wrist / ankle and counterclockwise rotation of wrist / ankle - each of which can be performed by any of the four limbs (left or right upper / lower limb). Each movement / limb combination may be required alone or in combination with a second simultaneous movement. When participants see one or two gray movement symbols placed on the body shape (planning phase), they are prompted to prepare (one or more) movements, and then prompted to perform (one or more) movements when (one or more) gray symbols turn green (execution phase). Using random jitter, the planning phase can last from 2 to 6.5 seconds, followed by 4 to 8.5 seconds of movement execution. Each movement trial (planning and execution) is followed by a jitter fixation of up to 5 seconds. A rest block of 8.6 seconds is implemented every 12 movements. Two possible movements are requested during the task run and practiced before the task. The movement pairs are changed for each task run. A total of 12 runs are obtained for each participant.

[0251] Individual Specific PFM Data – Cornell Adult

[0252] Participants:

[0253] Data were collected from four healthy adult participants (ages 29, 38, 24, and 31; all male) as part of a previously published study. Two of the participants were authors (CJL and JDP). This study was approved by the Weill Cornell Medicine Institutional Review Board.

[0254] Individual Specific PFM Data – Neonates

[0255] Participants:

[0256] Data were collected from one healthy full-term newborn participant during sleep, beginning at 13 days of age, corresponding to 42 weeks of postmenstrual age. This study was approved by the Human Research Committee and the Institutional Review Board at Washington University School of Medicine.

[0257] MRI acquisition:

[0258] Subjects were scanned while sleeping over the course of 4 consecutive days using a Siemens Prisma 3T scanner at the Washington University School of Medicine. Each session consisted of the collection of high-resolution T2-weighted spin-echo images (TE=563 ms, TR=3200 ms, flip angle=120°, 208 slices with 0.8x0.8x0.8mm voxels). In each session, multiple 6-minute 45-second multi-echo resting-state fMRI runs were collected as a five-echo blood oxygen level-dependent (BOLD) contrast-sensitive gradient-echo planar sequence (flip angle=68°, resolution=2.0 mm isotropic, TR=1761 ms, multiband 6 acceleration, TE=120°, 120°, 208 slices with 0.8x0.8x0.8mm voxels). 1 :14.20ms,TE 2 :38.93ms,TE 3 :63.66ms,TE 4 :88.39ms,TE 5 :113.12ms). The number of BOLD runs collected in each session depended on the neonate's ability to remain asleep during that scan; a total of 23 runs were collected over the four days. Between every 3 BOLD runs or any time the participant was removed from the scanner, a pair of spin-echo EPI images with opposite phase encoding directions (AP and PA) but identical geometric parameters and echo spacing were acquired.

[0259] MRI processing

[0260] Structural and functional treatments followed the pipeline used for the Wash U dataset with two exceptions. First, due to the inverse image contrast observed in neonates, T2-weighted images (as assessed by visual inspection of the single highest quality T2 image) rather than T1-weighted images were used for segmentation, surface delineation, and atlas registration. Second, after multi-echo BOLD data were dewarped and normalized to atlas space, they were optimally combined before perturbation regression and plotting into cifti space. All fMRI scans from the second day of scanning were excluded due to registration abnormalities.

[0261] Individual Specific PFM Data - Infants

[0262] Participants:

[0263] Data were collected from an 11-month-old healthy sleeping infant. This study was approved by the Human Research Committee and the Institutional Review Board at the University of Washington School of Medicine.

[0264] MRI acquisition:

[0265] The subjects were scanned while they slept during the course of three sessions at the University of Washington School of Medicine using a Siemens Prisma 3T scanner. The first session included the collection of high-resolution T1-weighted MP-RAGE (TE=2.24ms, TR=2400ms, flip angle=8°, 208 slices with 0.8x0.8x0.8mm voxels) and T2-weighted spin echo images (TE=564ms, TR=3200ms, flip angle=120°, 208 slices with 0.8x0.8x0.8mm voxels). The second and third sessions included the collection of a total of 26 resting state fMRIs, each of which was a 6-minute 49-second long blood oxygen level dependent (BOLD) contrast-sensitive gradient echo planar sequence (flip angle=52°, resolution=3.0mm, isotropic, TE=30ms, TR=861ms, multi-band 4 acceleration). For each run, a pair of spin-echo EPI images with opposite phase encoding directions (AP and PA) but identical geometric parameters and echo spacing were acquired to correct for spatial distortions.

[0266] MRI processing

[0267] Structural processing followed the DCAN Labs processing pipeline (https: / / github.com / DCAN-Labs / abcd-hcp-pipeline), which was found to perform the best surface segmentation at this age. Functional processing followed the pipeline used for the Wash U dataset.

[0268] Individual Specific PFM Data - Children

[0269] Participants:

[0270] Data were collected from a healthy, awake male child aged 9 years. This study was approved by the Human Research Committee and the Institutional Review Board at the University of Washington School of Medicine.

[0271] MRI acquisition:

[0272] Subjects were scanned repeatedly while sleeping over the course of 12 sessions at the Washington University School of Medicine using a Siemens Prisma 3T scanner. These sessions included the collection of 14 high-resolution T1-weighted MP-RAGE images (TE=2.90 ms, TR=2500 ms, flip angle=8°, 176 slices with 1 mm isotropic voxels), 14 T2-weighted spin echo images (TE=564 ms, TR=3200 ms, flip angle=120°, 176 slices with 1 mm isotropic voxels), and a total of 26 resting-state fMRIs, each collected as a 10-minute long blood oxygen level-dependent (BOLD) contrast-sensitive gradient echo planar sequence (flip angle=84°, resolution=2.6 mm isotropic, 56 slices, TE=33 ms, TR=1100 ms, multi-band 4 acceleration). In each session, a pair of spin-echo EPI images with opposite phase encoding directions (AP and PA) but identical geometric parameters and echo spacing were acquired to correct for spatial distortions in the BOLD data.

[0273] MRI processing

[0274] Structural and functional processing followed the DCAN Labs processing pipeline (https: / / github.com / DCAN-Labs / abcd-hcp-pipeline).

[0275] Individual Specific PFM Data – Perinatal Stroke

[0276] Participants:

[0277] PS1 is a 13-year-old left-handed male who played on a competitive youth baseball team and was referred to an orthopedic surgeon due to difficulty using his right arm effectively. Ulnar neuropathy was considered and he was referred for physical therapy. However, PS1 was first seen by a child neurologist (NUFD) for further evaluation. Structural brain MRI revealed unexpectedly extensive bilateral cystic lesions consistent with perinatal infarction. Review of PS1's medical history revealed that the injury occurred within the perinatal period.

[0278] Data collection for PS1 was performed with approval from the Washington University Institutional Review Board. Written informed consent was provided by PS1's mother and assent was given by PS1 at the time of data collection.

[0279] For additional details regarding clinical history, neuropsychologic evaluation, motor assessment, or MR image acquisition or processing, see 35 .

[0280] Individual Specific PFM Data – Macaques

[0281] Animal preparation:

[0282] Data were collected from a sedated adult female macaque (Macaca fascicularis). Experimental procedures were performed in accordance with the standards for the care and use of nonhuman primates by the University of Minnesota Institutional Animal Care and Use Committee and the National Institute of Health. Subjects were fed ad libitum and housed in pairs in a colony room with controlled light and temperature. Animals were not restricted to water. Subjects did not have any previous implants or cranial surgery. Animals were fasted for 14-16 hours before imaging. On the scanning day, anesthesia was first induced by intramuscular injection of atropine (0.5 mg / kg), ketamine hydrochloride (7.5 mg / kg), and dexmedetomidine (13 μg / kg). Subjects were transported to the scanner antechamber and intubated with an endotracheal tube. Initial anesthesia was maintained using 1.0%-2% isoflurane mixed with oxygen (1 L / m during intubation and 2 L / m during scanning to compensate for the 12 m length of the tubing used). For functional imaging, the isoflurane level was reduced to 1%. Subjects were placed on a custom-made coil bed, in which head fixation was integrated by placing stereotactic ear bars into the ear canal. The position of the animal corresponds to the sphinx position. The experiment was performed in the case of free breathing of the animal. During the process, 4.5 μg / kg / hr dexmedetomidine was continuously administered using a syringe pump. Rectal temperature (~99.6F), respiration (10-15 breaths / min), end-tidal carbon dioxide (25-40), electrocardiogram (70-150bpm) and SpO2 (>90%) were monitored using an MRI compatible monitor (IRAD-IMED 3880MRI monitor, USA). A circulating water bath and a chemical heating pad and a pad for thermal insulation were used to maintain temperature.

[0283] MRI acquisition:

[0284] Data were acquired on a Simens Magnetom 10.5T Plus. A custom in-house built and designed RF coil was used with an 18 cm long 8-channel transmit / receive end-loaded dipole array coupled to a tightly fitting 16-channel loop receive array head cap and a 50 × 100 mm head mounted under the chin. 2The 8-channel loop receive array combination of size was used. B1+ (transmit B1) field maps were acquired using the flip angle mapping sequence provided by the vendor, and then power calibrated for each individual. After B1+ transmit calibration, an average of 3-5 T1-weighted MP-RAGEs were acquired (23 min) for anatomical processing (TR=3300 ms, TE=3.56 ms, TI=1140, flip angle=5°, slice=256, matrix=320×260, acquisition voxel size=0.5×0.5×0.5 mm3, in-plane acceleration GRAPPA=2). A resolution- and FOV-matched T2-weighted 3D turbo spin echo sequence was run to facilitate B1 inhomogeneity correction. Five images were acquired in two phase encoding directions (R / L and L / R) for offline EPI distortion correction. Six fMRI time series runs, each consisting of 700 consecutive 2D multi-band EPI functional volumes (TR=1110 ms; TE=17.6 ms; flip angle=60°, slices=58, matrix=108×154; FOV=81×115.5 mm; acquisition voxel size=0.75×0.75×0.75 mm), were acquired in left-right phase encoding directions using in-plane acceleration factor GRAPPA=3, partial Fourier=7 / 8th, and MB factor=2. As the macaques were scanned in the sphinx position, the orientation mentioned here is consistent (in terms of gradients) with typical human brain studies (head first), but translated differently to the actual macaque orientation.

[0285] MRI processing

[0286] Treatment followed the DCAN Labs non-human primate processing pipeline (https: / / github.com / DCAN-Labs / nhp-abcd-bids-pipeline) with minor modifications. Specifically, it was observed that the distortion from the 10T scanner was so extensive that the field map did not fully correct it. Therefore, instead of field map-based dewarping, computation-based field map warping was used as the initial starting point for Synth, a field map-free distortion correction algorithm that creates synthetic distortion-free BOLD images for registration with anatomical images. Synth substantially reduced the residual BOLD image distortion.

[0287] Group average dataset

[0288] Resting-state fMRI data were averaged across participants within each of the five large datasets.

[0289] UKB:

[0290] A group-averaged weighted eigenvector file for an initial batch of 4100 UKB participants scanned for 6 min using resting-state fMRI was downloaded from https: / / www.fmrib.ox.ac.uk / ukbiobank / . This file consists of the first 1200 weighted spatial eigenvectors from the group-averaged PCA. See and documented at https: / / biobank.ctsu.ox.ac.uk / crystal / ukb / docs / brain_mri.pdf for details of the acquisition and processing pipeline. This eigenvector file was plotted to the Conte69 surface template atlas using the band-constrained method in Connectome Workbench and the eigenvector time courses for all surface vertices were cross-correlated.

[0291] ABCD:

[0292] 20 minutes (4x 5 minute runs) of resting state fMRI data, as well as high-resolution T1-weighted and T2-weighted images, were collected from 3928 participants aged 9-10 years, selected as participants with at least 8 minutes of low-motion data from a larger scan sample. Data collection was performed across 21 sites within the United States and coordinated across Siemens, Philips, and GE3T MRI scanners. Data processing was performed using the ABCD-BIDS pipeline (https: / / github.com / DCAN-Labs / abcd-hcp-pipelines).

[0293] HCP:

[0294] The vertex-by-vertex group-averaged functional connectivity matrix from the HCP 1200 participant release was downloaded from db.humanconnectome.org. This matrix consists of the mean strength of functional connections across all 812 participants who completed four 15-min resting-state fMRI runs and had their raw data reconstructed using the newer “recon 2” software.

[0295] University of Washington:

[0296] Data were collected from 120 healthy young adult participants recruited from the University of Washington community during relaxed open-eye fixation (60 females, mean age = 25 years, age range = 19-32 years). Scanning was performed using a Siemens TRIO 3.0T scanner and included the collection of high-resolution T1-weighted and T2-weighted images, as well as an average of 14 minutes of resting-state fMRI. See details of the acquisition and processing pipeline.

[0297] Neonates (eLABE):

[0298] Mothers were recruited from two obstetric clinics at the University of Washington in the second or third trimester as part of the Early Life Adversity, Biological Embedding, and Risk of Developmental Precursors of Psychiatric Disorders (eLABE) study. Neuroimaging of full-term healthy neonatal offspring was performed shortly after birth (mean postmenstrual age of included participants was 41.4 weeks, range 38-45 years). Of the 385 participants scanned for eLABE, 262 were included in the current analyses. See Participants, Criteria for Exclusion, Scan Acquisition Protocol and Parameters, and Processing Pipeline for additional details.

[0299] analyze

[0300] Functional connectivity

[0301] For each single participant dataset, a vertex / voxel-wise functional connectivity matrix was calculated from the resting-state fMRI data as the Fisher-transformed pairwise correlations of the time series of all vertices / voxels in the brain. In the ABCD, WashU120, and eLABE datasets, a vertex / voxel-wise group-averaged functional connectivity matrix was constructed by first calculating the vertex / voxel-wise functional connectivity within each participant as the Fisher-transformed pairwise correlations of the time series of all vertices / voxels in the brain, and then averaging these values ​​across participants at each vertex / voxel.

[0302] Seed-based functional connectivity

[0303] A continuous seed line was defined beneath the left precentral gyrus by selecting each vertex on a continuous straight line on the cortical surface between the most ventral part of the medial motor area (approximate MNI coordinates [-4, -31, 54]) and the ventral lip of the precentral gyrus just above the operculum (approximate MNI coordinates [-58 4 8]). For each seed, its functional connectivity map was examined as the Fisher transformed correlation between the time course of that vertex and the time course of every other vertex / voxel in the brain.

[0304] Network detection in the somatomotor cortex

[0305] To define the somatomotor regions visually identified from the seed-based connectivity analysis for further exploration in an unbiased manner, data from each individual adult participant were fed into a data-driven network detection algorithm designed to identify subdivisions of networks that are hierarchically below the level of classical large-scale networks (e.g., those that give rise to hand / foot parcellations in somatomotor cortex). This approach identified subnetwork structures that converged with task-activated regions and known neuroanatomical systems.

[0306] In each adult participant, the analysis clearly identified network structures corresponding to motor representations of the feet, hands, and mouth; and it additionally identified network structures that corresponded exactly to previously unknown connectivity patterns identified from seed-based connectivity exploration as inter-effector regions. For simplicity, all inter-effector subnetworks were manually grouped together into a single putative network structure (labeled Inter-effector) for further analysis.

[0307] Finally, to identify classical large-scale networks in each participant, the Infomap algorithm was rerun on matrices thresholded at a range of denser thresholds (ranging from 0.2% to 5%), and additionally identified individual-specific networks corresponding to default, medial and lateral vision, cingulate-opercular, frontoparietal, dorsal attention, language, salience, parametrial memory, and contextual association networks.

[0308] Differences in functional connectivity between effectors and foot / hand / mouth regions

[0309] Within each adult human participant, the inter-effector connectivity map was calculated as the Fisher-transformed correlation between the mean time course of all cortical inter-effector vertices and the time course of every other vertex / voxel in the brain. This process was repeated to calculate connectivity maps for the foot, hand, and mouth regions.

[0310] To identify brain regions that are more strongly connected to the inter-effector regions than to other motor regions, the minimum positive difference between the inter-effector connections and any foot / hand / mouth connections was calculated in each voxel / vertex. This represents a conservative approach that identifies only regions of the brain that are more strongly connected to the inter-effector regions than to any other motor regions.

[0311] Functional connectivity with CON

[0312] Within each adult human participant, functional connectivity was calculated between each of the foot, hand, mouth, and inter-effector regions and CON. This was calculated as the Fisher-transformed correlation between 1) the mean time course across all vertices in the motor region and 2) the mean time course across all vertices in CON. Paired t-tests were performed across subjects to compare inter-effector connectivity to CON with foot / hand / mouth connectivity, correcting for the three tests performed.

[0313] Motion / CON Network Visualization

[0314] The network relationships were visualized using a spring embedding graph as implemented in Gephi (https: / / gephi.org / ). In each individual adult human participant, a node was defined as a node larger than 20 mm. 2Congruent clusters of the foot, hand, mouth, inter-effector, and CON networks. Pairwise connections between nodes were computed as Fisher-transformed correlations of their average time courses. For visualization purposes, the graph was constructed by thresholding the pairwise node-to-node connectivity matrix at 40% density (the overall appearance of the graph did not vary across a range of densities).

[0315] Functional connectivity with adjacent postcentral gyrus

[0316] In each adult human participant, the precentral and postcentral gyri were defined based on an individual-specific Brodmann area parcellation generated by Freesurfer, which was deformed into fs_LR_32k space to match the functional data. The precentral gyrus was considered to be the vertices labeled Brodmann areas 4a and 4p, while the postcentral gyrus was the vertices labeled Brodmann areas 3b and 2. Brodmann area 3a (base of the central sulcus) was not considered for this analysis. Since Freesurfer consistently classified the medial part of the somatomotor cortex (corresponding to the representation of the leg and foot) as BA 4a, the medial postcentral gyrus was defined as the cortical vertex with a y-coordinate that was more posterior than the median y-coordinate of the foot area (from the network plot above).

[0317] Within the precentral gyrus across participants, vertices were labeled as representing foot, hand, mouth, or inter-effector based on their labels from the network drawing process. The postcentral gyrus was partitioned into foot, hand, mouth, and inter-effector regions, depending on which precentral gyrus region each vertex was physically closest to. Finally, within each partition (foot, hand, mouth, and inter-effector), the average connectivity between the precentral and postcentral gyri was calculated as the Fisher-transformed correlation between the mean time courses of all vertices in each region. Paired t-tests were performed across subjects to compare inter-effector connectivity with adjacent S1 to each of the foot / hand / mouth connections of S1, with FDR correction for the three tests performed.

[0318] Functional connectivity with the intermediate insula

[0319] In each adult human participant, the middle insula was defined based on individual-specific Freesurfer gyral parcellations on the Destrieux atlas and warped into fs_LR_32k space to match the functional data. The middle insula was considered to be the upper segment of the circular sulcus labeled as insula or the vertex of the short insular gyrus. Functional connectivity between the bilateral feet, hands, mouth, and inter-effector regions and the bilateral middle insula was calculated. Paired t-tests were performed across subjects to compare inter-effector connectivity with the middle insula with each of the feet / hands / mouth, with FDR correction for the number of tests performed.

[0320] Functional connectivity with the cerebellum

[0321] In each adult human participant, functional connectivity between the foot, hand, mouth, and inter-effector regions and each voxel of the cerebellum was calculated. The cerebellar connectivity strengths calculated in this way were then mapped onto a cerebellar atlas using the SUIT toolbox. Connectivity strengths were averaged within each of the 27 atlas regions. For each region, three pairwise t-tests were performed comparing inter-effector connectivity strengths with those of the foot, hand, and mouth, with FDR correction for the total number of tests performed. Regions were reported if the inter-effector connectivity strength was significantly higher than that of all other motor regions.

[0322] Functional connectivity with the putamen

[0323] In each adult human participant, each unilateral putamen was divided into quarters in each hemisphere by segmenting it based on the median of its Y (anterior-posterior) and Z (dorsal-ventral) coordinates. Functional connectivity between each of the foot, hand, mouth, and inter-effector regions and each putamen quarter was calculated.

[0324] For each putamen parcellation, paired t-tests were performed across subjects comparing the inter-effector connections with the putamen parcellation to each of the foot / hand / mouth connections, with FDR correction for the number of tests performed. Parcellations are reported where the inter-effector connections were significantly different from all three effector-specific connections.

[0325] Functional connectivity with the thalamus

[0326] To investigate thalamic subregions, the DISTAL atlas v1.1 was used, which contains many of the histological thalamic subregions identified by . The atlas was downsampled to 2 mm isotropic space for functional data. Functional connectivity maps seeded from the foot, hand, mouth, and inter-effector regions of each adult human participant were calculated, and mean connectivity values ​​within each atlas region were calculated. The atlas specifies multiple subregions for many nuclei; for the purposes of connectivity calculations, these subregions were combined and treated as a single nucleus.

[0327] For each adult human participant, connectivity seeded from the inter-effector region and from each of the foot, hand, and mouth regions was averaged across all voxels within each thalamic nucleus. For each thalamic nucleus, a paired t-test was performed across subjects comparing the mean of inter-effector to foot / hand / mouth connectivity, with Bonferroni correction for the number of thalamic nuclei tested.

[0328] Lag structure of RSFC

[0329] A previously published method was used to estimate relative time delays (“lags”) in fMRI data. Briefly, for each session in each adult participant, a lagged cross-covariance function (CCF) was calculated between each pair of vertex / voxel time courses within the motor system and the CON in the cortex. Lags were determined more precisely by estimating the cross-covariance extrema of the session-level CCF using three-point parabolic interpolation. The resulting set of lags was assembled into an antisymmetric matrix that captured all possible pairwise time delays (TD matrix) for each session and averaged across sessions to produce a participant-level TD matrix. Finally, the TD matrix for each participant was averaged across rows to summarize the average time shift from one vertex to all other vertices. The average time lags for all vertices in each precentral gyrus foot / hand / mouth / effector interregion and CON were then averaged.

[0330] Paired t tests were performed across subjects comparing 1) the mean lag in the inter-effector region to the mean lag in each of the foot / hand / mouth regions, and 3) the mean lag in the CON region to the mean lag in each of the foot, hand / mouth regions.

[0331] Structural MRI

[0332] Cortical thickness

[0333] Within each adult human participant, cortical thickness maps generated by Freesurfer segmentation were warped into fs_LR_32k space to match the functional data. The precentral gyrus foot, hand, mouth, and inter-effector regions were defined as described above, and mean cortical thickness within each region was calculated. Paired t-tests were performed across subjects to compare inter-effector thickness to each of the foot / hand / mouth thicknesses, correcting for the three tests performed.

[0334] Fractional Anisotropy

[0335] White matter fibers traced from different areas of the motor cortex using diffusion imaging rapidly converge into the internal capsule and become difficult to separate. Therefore, FA differences were tested in the white matter just below the precentral gyrus.

[0336] To calculate subcortical FA, a fs_LR_32k spatial surface was constructed 2 mm below each of the gray-white surfaces in adult human participants P1-P3. To achieve this, for each vertex on the surface, a 3D vector was calculated between the fs_LR_32k plane and the corresponding point on the gray-white surface, and this vector was additionally extended to an additional 2 mm beyond the gray-white surface in order to create a lower surface. FA values ​​were then mapped between the gray-white and 2 mm lower surfaces using a band-constrained approach. The result was FA values ​​mapped to a lower surface within the white matter that was aligned with the existing fs_LR_32k surface on which the functional data were mapped and motor areas defined.

[0337] The precentral gyrus foot, hand, mouth, and intereffector areas were defined as described above, and the mean FA underlying each cortical area was calculated.

[0338] Paired t tests were performed across subjects comparing the mean FA under the inter-effector region with the mean FA under each of the foot / hand / mouth thicknesses.

[0339] Myelin density

[0340] Within each adult human participant, a pyramidal map of intracortical myelin content was created. The precentral gyrus was defined as above. Across participants, baseline myelin density values ​​(both in the precentral gyrus and in the whole-brain myelin density maps) were found to vary strongly across participants in different datasets, likely based on differences in the T1 and T2 weighted sequences employed. Therefore, for optimal visualization of the results, in each participant, myelin density values ​​were normalized by dividing the calculated pyramidal myelin density in the precentral gyrus by the mean myelin density across the entire precentral gyrus. Finally, the precentral gyrus foot, hand, mouth, and inter-effector regions were defined as described above, and the mean normalized myelin density within each region was calculated. Paired t-tests were performed across subjects to compare the inter-effector myelin density to each of the foot / hand / mouth myelin densities, correcting for the three tests performed.

[0341] Task fMRI

[0342] Task-specific activation of body regions

[0343] The primary analysis of the somato-specific task data was performed using a within-participant block design. To calculate the overall degree of activation in response to each movement, data from each run were entered into a primary analysis within FSL's FEAT, in which each movement block was modeled as an event of 15.4 s duration, and the combined block waveform for each movement state was convolved with the hemodynamic response function to form a separate regressor in a GLM analysis testing the effects of multiple state regressors on the time course of activity within each vertex / voxel in the brain. Beta maps for each condition were extracted for each run and entered into a secondary analysis in which run-level condition betas were tested against the null hypothesis of zero activation in a one-sample t-test across runs (within participants). The resulting t-values ​​from each movement condition tested in this second-level analysis were converted to Z-scores. Z-score activation maps were smoothed with a geodesic 2D (for surface data) or Euclidean 3D (for volumetric data) Gaussian kernel with σ = 2.55 mm.

[0344] Winner takes all in specific areas of the body

[0345] For each vertex within the broad central sulcus region, the motion that produced the greatest activation strength (Z-score from the secondary analysis described above) was identified in that vertex and assigned to that vertex.

[0346] Body-specific region curve fitting

[0347] For each vertex within the precentral gyrus, its position was first calculated along the dorsoventral axis of M1 in the left hemisphere. This was done by identifying the closest point within a continuous line of points extending along the precentral gyrus (defined in the seed-based functional connectivity) and assigning the ordered position of this closest point within the line to the vertex.

[0348] For each movement, the dorsoventral M1 position was plotted against the Z-score activation in each vertex. Two curves were fit to each of these relationships. The first curve was a single Gaussian model of the form:

[0349] Activation=a1*exp(-((Position-b1) / c1)^2).

[0350] The second curve is a double Gaussian model of the following form:

[0351] Activation=a1*exp(-((Position-b1) / c1)^2)+a2*exp(-((Position-b2) / c2)^2).

[0352] The "a1" and "a2" parameters in each model were constrained to be positive (to force a positive peak). Curve fitting was restricted to the approximate vicinity of the activation region in order to avoid fitting the negative activation observed in the distal part of M1. For lower limb movements, this meant excluding the bottom third of M1; for upper limb movements, the bottom third of M1 plus the medial wall; and for facial movements, the anterior third of M1.

[0353] Finally, the unimodal or bimodal model was tested to determine which model fits the data better. This was done by performing an F test between the models, calculated as:

[0354] F=((SSE 1peak -SSE 2peaks ) / (df 1peak -df 2peaks )) / (SSE 2peaks / df 2peaks ).

[0355] By using the F-statistic continuous distribution function (fcdf.m) in Matlab and using (df 1peak –df 2peaks ) and df2peak as the numerator and denominator degrees of freedom, and calculate the p-value from this F.

[0356] Body region selectivity

[0357] Based on the results from the winner-take-all analysis described above, the most preferred movements were identified at the center of three effector-specific regions (toe movements, hand movements, tongue movements) and an inter-effector region (abdominal movements, eyelid movements, swallowing). The most central movements were selected to avoid problems with diffuse, overlapping activations near the boundaries of the effector-specific and inter-effector regions. For each vertex within the precentral gyrus, the activation intensity of the most preferred movement among the six movements at that vertex was compared to the activation intensity of the second most preferred movement. The difference between these activation intensities was considered the movement selectivity of that vertex.

[0358] Somatospecific coactivation

[0359] For each of the six resting-state derived foot, hand, mouth, and inter-effector regions in the precentral gyrus, the mean activation within that region for each motor movement was calculated, generating a motion activation intensity profile for that region. The mean activation within all CON vertices for each motor movement was also calculated. To determine the extent to which different motor regions interacted across movements, the activation intensity profile for each foot, hand, mouth, and inter-effector cluster was correlated with the profiles of all other clusters as well as the profile of the CON. Note: Visualization of the motion activation map revealed some streaking, suggesting that the open / closed mouth and flexed left knee conditions were partially affected by head movement; therefore, these conditions were excluded from the analysis, although their inclusion would not change the results.

[0360] Motion Control and Coordination Task Analysis

[0361] The motor control tasks were analyzed using a within-participant event-related design. For each individual run, a GLM model was constructed in FEAT with separate regressors describing the initiation of 1) planning and 2) execution for each movement type (4 movements * 4 limbs). Each regressor was constructed as a 0-length event convolved with a canonical hemodynamic response, and the beta value for each regressor was estimated for each voxel in the brain. Therefore, a map of beta values ​​for each condition was calculated for each run and entered into a second-level analysis where t-tests across runs contrasted run-level planning betas with run-level execution betas. Movement type and dual-movement versus single-movement were not considered for the current analysis.

[0362] Direct electrical cortical stimulation site mapping in humans

[0363] Each reported stimulation location was individually mapped to the MNI spatial Conte69 atlas pial cortical surface by identifying the vertex with the minimum Euclidean distance to the MNI coordinates of the stimulation site. The movements generated by each site were classified as “lower limb,” “upper limb,” or “face” and colored accordingly (although lower limb movements were not reported in the left hemisphere shown).

[0364] Example 2

[0365] Clinical evidence for separable movement isolation and movement integration systems

[0366] The mind-body interface includes two important thalamic nuclei that serve as deep brain stimulation (DBS) targets for clinical pathologies: VIM for tremor (e.g., essential tremor, Parkinson's disease) and CM for generalized epilepsy (e.g., Lennox-Gastaut syndrome). The mechanisms of action underlying these clinical effects remain controversial, but it has been proposed that the antitremor effects of VIM may be mediated by its connections to the cerebellum and that the role of CM in arousal may explain its anti-epileptic effects.

[0367] The link with MBI may be crucial to the impact of neuromodulation on movement disorders. Tremor and arousal represent important aspects of integrated motor control. Physiological tremor (~10 Hz) is thought to time and coordinate movements, whereas physiological arousal and CON involvement are observed at the onset of goal-directed activity.

[0368] Many types of tremors are intention- or goal-dependent. For example, essential tremors are absent or minimal at rest and are evoked by intentional movements. Furthermore, most tremors disappear during sleep. Finally, tremors and generalized seizures are systemic phenomena that are not characterized by somatic specificity. Thus, the effects of VIM and CM-DBS are consistent with modulation of different aspects of whole-body motor control, movement timing, and arousal.

[0369] Parkinson's disease (PD) may be most specifically associated with MBI circuit dysfunction. PD symptoms span motor, physiological, and volitional domains (e.g., postural instability, autonomic dysfunction, and decreased self-activity, etc.), reflecting MBI connections to areas associated with postural control (cerebellar vermis), volition (dACC), and physiological regulation (insula). Clinton Woolsey's work documented that direct stimulation of the M1 of a PD patient temporarily eliminated his whole-body tremor and rigidity. This effect is difficult to explain as a result of stimulation of effector-specific regions, but as a result of MBI stimulation, the effect is quite simple. Neuronal death in the substantia nigra (SN) is one of the pathophysiological features of PD. Interestingly, the main target of SN projections is the dorsolateral putamen, which forms part of the mind-body interface. The inter-effector region is functionally strongly connected to the cerebellar vermis, which is important for postural control and is known to be structurally connected to M1 in NHPs. In addition, cortical projections that are important for coordinating physiology with motor planning (i.e., blood pressure, uprightness) originate primarily from the CON and anterior M1. Therefore, if PD is indeed a network disease caused by retrograde spread of synucleinopathy from the gut to the brainstem and beyond, then an appropriate candidate for the network most affected by the resulting degeneration is the mind-body interface.

[0370] Brain lesion data further support the existence of a dual system for motor segregation and action integration, with partial redundancy in M1. Motor deficits following middle cerebral artery stroke are unilateral, more severe in most distal effectors, and without significant global organ control deficits. In contrast, lesions leading to MBI-related CON regions (dACC, anterior insula, aPFC) can result in isolated volitional deficits, ranging from reduced fluency to aphasia to akinetic mutism, with preserved motor ability but few self-generated movements. Similarly, anterior motor lesions in macaques can avert visually guided movements while selectively disrupting internally generated movements. Interestingly, patients or animals with lesions in effector M1 often recover gross effector control rapidly, with persistent deficits in isolated fine finger movements. The more rapid recovery of gross motor ability may be due in part to the proximal functions assumed by the contralaterally lesioned MBI circuits enabled by their bilateral spinal cord connections. Therefore, persistent deficits may be more likely in functions uniquely supported by effector-specific circuits.

[0371] In a proof-of-principle patient with extensive bilateral perinatal stroke but typical motor abilities, extensive poststroke reorganization maintained the MBI pattern at the expense of a portion of the already reduced M1 hand region. The top third of M1 was destroyed, and the surviving cortex contained an M1 hand region that was displaced ventrally and was much smaller than typical controls. Surprisingly, the MBI region was identified above and below the surviving effector-specific hand region ( Fig.12 F), which highlights the importance of the mind-body interface for typical motor abilities.

Claims

1. A system for mapping a network in a subject's brain, the system comprising at least one processor in communication with at least one memory device, in, The at least one processor is programmed to: receiving task-based functional magnetic resonance imaging (fMRI) data of the subject's brain; receiving resting state fMRI data of the brain; localizing effector-specific regions of the brain and inter-effector regions of the brain based on the task-based fMRI data and the resting-state fMRI data; identifying a network in the brain based on functional connectivity with the inter-effector regions based on the resting-state fMRI data; as well as One or more graphs of the network are generated.

2. The system according to claim 1, in, The at least one processor is further programmed to: receiving the task-based fMRI data acquired during a motion planning task, wherein a planning phase of a movement is separated from an execution phase of the movement; and Based on the task-based fMRI data, the inter-effector region was localized as a region with higher activation in the planning phase than in the execution phase.

3. The system according to claim 1, in, The at least one processor is further programmed to: The inter-effector regions were localized based on functional connectivity with seeds selected from the precentral gyrus of the brain.

4. The system according to claim 1, in, The at least one processor is further programmed to: receiving structural MRI data of the brain; and Based on the structural MRI data, the inter-effector region is validated as a region having a lower thickness than the effector-specific region.

5. The system according to claim 1, in, The at least one processor is further programmed to: determining, based on the resting state fMRI data, functional connectivity of the inter-effector regions with voxels of the brain and functional connectivity of the effector-specific regions with the voxels of the brain; as well as The network is identified as a region comprising voxels that have higher functional connectivity with the inter-effector region than with the effector-specific region.

6. The system according to claim 5, in, The voxels were selected from the cingulate-opercular network.

7. The system according to claim 5, in, The voxels were selected from the thalamus.

8. The system according to claim 5, in, The voxels are selected from the postcentral gyrus, insula, putamen, and / or cerebellum.

9. The system according to claim 1, in, The at least one processor is further programmed to: Based on diffusion weighted imaging data, the network is identified.

10. A method of identifying treatment, include: receiving task-based functional magnetic resonance imaging (fMRI) data of the subject's brain; receiving resting-state fMRI data of the subject's brain; localizing effector-specific regions of the brain and inter-effector regions of the brain based on the task-based fMRI data and the resting-state fMRI data; identifying a network based on the resting-state fMRI data and based on functional connectivity with the inter-effector regions; generating one or more graphs of the network; and Based on the one or more graphs of the network, a treatment is selected.

11. The method according to claim 10, in, Treatment options further include: Based on the one or more maps, a therapeutic target is selected as the central median nucleus of the thalamus.

12. The method according to claim 10, in, The method further comprises: Receiving the task-based fMRI data further comprises: receiving the task-based fMRI data acquired during a motion planning task, wherein a planning phase of a movement is separated from an execution phase of the movement; and Based on the task-based fMRI data, the inter-effector region was localized as a region with higher activation in the planning phase than in the execution phase.

13. The method according to claim 10, in, The method further comprises: The inter-effector regions were localized based on functional connectivity with seeds selected from the precentral gyrus of the brain.

14. The method according to claim 10, in, The method further comprises: receiving structural MRI data of the brain; and Based on the structural MRI data, the inter-effector region is validated as a region having a lower thickness than the effector-specific region.

15. The method according to claim 10, in, The method further comprises: determining functional connectivity of the inter-effector region with voxels of the brain and functional connectivity of the effector-specific region with the voxels based on the resting-state fMRI data; and The network is identified as a region comprising voxels that have higher functional connectivity with the inter-effector region than with the effector-specific region.

16. The method according to claim 15, in, The voxels were selected from the cingulate-opercular network.

17. The method according to claim 15, in, The voxels are selected from the thalamus, putamen, and / or cerebellum.

18. The method according to claim 10, in, The method further comprises: Prior to the described treatment, a baseline network was mapped; After the treatment, plotting a post-treatment network; and The efficacy of the treatment is estimated based on the change in the one or more graphs of the post-treatment network compared to the one or more graphs of the baseline network.

19. The method according to claim 10, in, Options for treatment include: The treatment is selected to include a brain dysfunction comprising a neuropsychiatric symptom, a neuropsychiatric disorder, and / or a brain injury.

20. The method according to claim 10, in, The treatment is selected from invasive neuromodulation, non-invasive neuromodulation, and ablation techniques.

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