Systems and methods for whole-brain circuit-based nerve stimulation for treating brain disorders

Abstract

Provided herein is a targeted and individualized method for determining a specific treatment site based on an individual patient. Embodiments described herein may use functional and / or structural connectivity, and combinations thereof, to create a brain map of a patient and / or a control group having the same measurement criteria as the patient, in determining a target location for stimulatory treatment.

Description

Technical Field

[0001] Transcranial magnetic stimulation (TMS) is a non-invasive and non-pharmacological treatment for neuropsychiatric disorders, which uses a magnetic coil to stimulate specific regions of the brain. TMS uses short magnetic field pulses to induce an electric current in the underlying cortical tissue. When magnetic pulses are sent to a coil placed on a patient's skull, the TMS coil induces an electric current at a predetermined target within the brain by electromagnetic induction.

Background Art

[0002] In the field of psychiatry, TMS has been used successfully in the treatment of depression, anxiety disorders including panic disorder, and obsessive-compulsive disorder (OCD). TMS has also been studied in many neuropsychiatric disorders such as autism, eating disorders, drug abuse and dependence, post-traumatic stress disorder (PTSD), Alzheimer's disease, coma recovery, stroke, tinnitus, multiple sclerosis, and neurorehabilitation. For treatment-resistant major depressive disorder, TMS has been most widely and effectively used in the high-frequency (HF) mode on the left prefrontal lobe region of the brain called the dorsolateral prefrontal cortex (DLPFC). It has also been used on the right DLPFC in the low-frequency (LF) mode, and complementary effects have been obtained.

[0003] TMS for the left dorsolateral prefrontal cortex (L-DLPFC) is a treatment approved by the FDA for treatment-resistant depression (TRD). This treatment has only a partial effect, with a response rate of 41.2% and a remission rate of 35.3%. In the FDA-approved protocol for TRD, the left DLPFC stimulation site is identified by moving the coil 5 cm in front of the "hand motor hotspot" (motor cortex) along the curvature of the scalp. This approach only provides a rough targeting of the left DLPFC and cannot make a consistent distinction between sub-regions of the DLPFC. Although 10 years have passed since TMS therapy was approved by the FDA, the clinical success rate has not improved much.

Summary of the Invention

[0004] The embodiments described in this specification are merely exemplary. By the features of these embodiments, examples of various combinations of features are provided. However, the present disclosure also encompasses variations of the features of various combinations.

[0005] The embodiments of this specification include functional connectivity analysis between regions, between a region and a network, and between networks for individualizing TMS treatment. The embodiments include determining a plurality of treatment positions for performing TMS treatment.

[0006] Embodiments of determining treatment positions for treatment include identifying one or more treatment regions and networks by comparing patient information with a healthy control group and determining regions that are outside the desired range in comparison with the healthy control group. Embodiments of determining treatment positions for treatment include identifying one or more treatment regions having the highest change in a predetermined region from before treatment to after treatment for an individual having the same characteristics as the patient. These characteristics can be any combination of the patient's gender, age, age range, ethnicity, weight, symptoms, diagnosis, previous treatment, response to previous treatment, genetic factors, previous medical conditions, symptoms, and / or diagnosis.

[0007] Embodiments of determining treatment positions include comparing attributes of fMRI data including the amplitude and frequency variations of spontaneous blood oxygenation level that vary over time. The comparison of brain regions and networks is performed based on the co-variation of the variations of spontaneous blood oxygenation level over time. The co-variation may include changes in the frequency and amplitude of blood oxygenation level over time. The determination of treatment positions for treatment includes comparing, for example, by taking the absolute value of the difference in co-variation of each brain region with respect to each other region of the brain, each network of the brain, and each network with respect to each other network.

[0008] Determining a treatment location for treatment involves using the amplitude and frequency of brain activity within and between networks, regions, and / or between regions and networks. Embodiments herein may use resting state functional connectivity to evaluate brain activity. The systems and methods herein may include a magnetic resonance imaging (MRI) system. Resting state functional connectivity may be based on changes in blood oxygen concentration in the brain over time. In embodiments herein, blood oxygenation level dependent (BOLD) imaging may be used to generate images in functional magnetic resonance imaging (fMRI).

[0009] Provided herein are TMS treatment systems and methods for determining networks and regions within a patient's brain for applying TMS treatment. Although described herein with respect to TMS treatment, the preferred embodiments are not limited thereto. In preferred embodiments, low intensity focused ultrasound (LIFUS) may be included additionally or alternatively in the treatment. In preferred embodiments, if the determined brain regions and / or networks for treatment are identified as cortical brain circuits, their locations may be stimulated with TMS. If the brain regions and / or networks for treatment are identified as subcortical brain circuits, their locations may be stimulated with LIFUS. Other protocols and treatments such as electrical stimulation may also be used.

[0010] The system and method may be configured to analyze changes in frequency, amplitude, and relative frequency (or vice versa) with respect to amplitude, based on naturally occurring blood oxygenation level dependent fluctuations measured from an MRI of the patient's brain. The comparison is based on regions and networks of the brain. The system may be configured to include, and / or the method may include, calculations of activation (amplitude of naturally occurring BOLD fMRI fluctuations over time), correlation (frequency of naturally occurring BOLD fMRI fluctuations over time), and / or co-variation (correlation with respect to activation of naturally occurring BOLD fMRI fluctuations over time) for various brain networks and / or regions.

[0011] Provided herein is a non-transitory computer-accessible medium storing a system, method, and computer-executable instructions for determining one or more target regions for TMS treatment of a patient. The system, method, and instructions are configured to receive fMRI data of the patient's head; analyze the functional connectivity of the patient's brain through analysis of the fMRI data by measuring changes in any combination of a first variation that is a variation in the amplitude of the fMRI image method data, a second variation that is a variation in the frequency of the fMRI data, or a third variation that is a variation in the relative frequency with respect to the amplitude of the fMRI data; and determine one or more target regions for TMS treatment of the patient based on the measurement of any combination of the first variation, the second variation, or the third variation.

[0012] The system, method, and instructions may be configured to compare the measurement of any combination of the first variation, the second variation, or the third variation with the measurements of a healthy control group that match one or more characteristics of the patient. Examples of patient characteristics include the patient's gender, age, age range, ethnicity, weight, symptoms, diagnosis, prior treatment, response to prior treatment, genetic factors, previous medical conditions, and any combination of symptoms and / or diagnoses. The system, method, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and co-variation matrices between different brain networks of the patient's brain. The system, method, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and co-variation matrices between regions within a network.

[0013] Systems, methods, and instructions may analyze the functional connectivity of a patient's brain by determining activation, correlation, and co-variation matrices between different brain regions within a network and all other regions within the same network. Systems, methods, and instructions may analyze the functional connectivity of a patient's brain by determining activation, correlation, and co-variation matrices between different brain regions of different networks. Systems, methods, and instructions may select a first plurality of regions with small changes in amplitude, frequency, and relative frequency with respect to amplitude for a larger number of brain networks or regions. Systems, methods, and instructions may select a second plurality of regions with large changes in amplitude, frequency, and frequency with respect to amplitude for a larger number of brain networks or regions.

[0014] Systems, methods, and instructions may compare the first plurality of regions and the second plurality of regions with regions from a healthy control group that match the patient's characteristics, and select regions that exceed a first threshold and regions that are less than a second threshold to create a potential target group of regions for treatment. Systems, methods, and instructions may compare the potential target group of regions for treatment with a second control group that matches the patient's symptoms or diagnosis, and determine a subset from the potential target group of regions by determining in which regions the change in fMRI data from before treatment to after treatment in the second control group is the greatest. Systems, methods, and instructions may include a comparison of brain regions and networks outside the DLPhFC.

[0015] Systems, methods, and instructions may include selecting a plurality of target locations for treatment based on the analysis. Systems, methods, and instructions may also include analyzing changes in frequency as well as amplitude in determining the target locations for treatment. Systems, methods, and instructions may include early-stage diagnosis and treatment options for detecting areas at risk of brain damage before structural detection becomes apparent.

[0016] Systems, methods, and instructions may be used to determine regions of a patient's brain that are negatively correlated with many other areas of the brain. The systems, methods, and instructions may provide early detection of future brain damage by using fMRI to compare the functional connectivity of brain regions and identify brain regions that are functionally disconnected but have not yet experienced neurological death or physical deterioration.

[0017] A system, method, and non-transitory computer-accessible medium storing computer-executable instructions for determining one or more target regions for TMS treatment of a patient may be configured to receive MRI data of the patient's head; analyze the structural connectivity of the patient's brain through analysis of the MRI data by generating a brain structural connectivity matrix constructed based on white matter tractography from the whole brain. In one embodiment, structural data is used to perform a structural connectivity analysis (tractography). The structural data may be T1w / T2w. T1-weighted (T1w) and T2-weighted (T2w) are MRI sequence contrast scans, where in T1w MRI, the signal of adipose tissue is enhanced and the signal of water can be suppressed, while in T2w MRI, the signal of water can be enhanced. The structural connectivity matrix may include a comparison of the strength of white matter connections between each combination of two of a plurality of parcels. For the strength of white matter connections, streamline tractography of diffusion MRI may be used. Using voxel-specific directional diffusion information from diffusion-weighted MRI (dMRI), computational tractography generates three-dimensional trajectories through white matter within the MRI volume, called streamlines. The connection between corresponding regions of interest (ROIs) may be quantified as the number of streamlines between them. The system and method may include selecting a target position for stimulation, and may select a region with a greater number of white matter connections compared to other regions. The system and method may include selecting a target position for stimulation, and may select the region with the greatest number of white matter connections from a pool of targets based on the functional connectivity of fMRI.

[0018] Using the systems and methods described herein, determination of target regions for stimulation may be performed using any combination of functional connectivity and / or structural connectivity. Structural and functional connectivity analyses may be used separately or in combination. When used in combination, a first analysis (either structural or functional connectivity) may be used to identify a first set of target regions, and then sequential analyses may be performed to focus using the other analysis (the other of functional or structural connectivity) of those first sets of target regions. In this example, the other analysis may be used against the results from the first analysis, thereby further narrowing the results from the first analysis to create a target set of regions for stimulatory treatment. When used in combination, analyses may be performed simultaneously such that a first analysis (either structural or functional connectivity) is used to identify a first set of target regions and a second analysis (either functional or structural connectivity) is used to identify a second set of target regions. The final determination of targets for stimulation may be a combination of regions derived from either or both of the first and second analyses.

[0019] Embodiments described herein may use a more anatomically specific model of (functional and / or structural) brain connectivity that is constructible for an individual patient for targeted stimulation.

Brief Description of the Drawings

[0020] The accompanying drawings, which are incorporated herein and constitute a part of this specification, illustrate specific embodiments of the disclosure and, together with the description below, serve to explain the scope of the disclosure. The drawings are not to scale and are intended to be used in conjunction with the description in the following detailed description.

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Best Mode for Carrying Out the Invention

[0021] In the following discussion, conventional features of the disclosed technology that are obvious to those skilled in the art will be omitted or simply described. References to various embodiments are not intended to limit the scope of the appended claims. Also, the examples described herein are all intended to be non-limiting and merely illustrate some of the many possible embodiments of the appended claims. Additionally, the specific features described herein can be used in combination with other described features in each of various possible combinations and orders. Methods of using the present invention in combination with routine experimentation to achieve other results not specifically disclosed in the examples or embodiments will be known to those skilled in the art.

[0022] Unless otherwise defined herein, all terms shall be construed as broadly as possible to include any implied meaning from the specification, the meaning understood by those skilled in the art, and / or the meaning defined in dictionaries, treatises, etc. Unless otherwise defined, all technical and scientific terms used herein shall have the same meaning as commonly understood by those skilled in the art in the field of the disclosed technology. Also, the singular forms "a", "an", and "the" used in this specification and the appended claims shall include plural referents unless otherwise stated, and the terms "includes" and / or "including" used herein are intended to specify the presence of the stated features, elements, and / or components while not excluding the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. Further, methods, apparatuses, and materials similar or equivalent to those described herein may also be used in the practice or testing of the disclosed technology.

[0023] Since a patient's mental state is the result of electrical signals generated throughout the brain in response to commands, the way the brain learns to perform a task is to adjust these electrical signals and bundle them based on frequency. Therefore, if the pace of the electrical signal oscillations is disrupted for other members of the network, the network loses its ability to function properly. Depending on which network has malfunctioned, the individual experiences the consequences of deterioration in cognition, emotion, and behavior, which are classical features of mental disorders.

[0024] The systems and methods of network-guided stimulation provided herein include an individualized approach for modifying the connections of a patient's brain. Embodiments of regional and / or network-guided stimulation use fMRI as an induction of a revolutionary treatment for restoring a patient's health. Since there is no universal treatment approach for brain diseases, the disclosed systems and methods may include advanced fMRI techniques for treating complex brain diseases by sophisticated approaches using stimulation such as TMS. Since there are individual differences in brain tissue, it may be necessary to apply stimulation at slightly different positions among individuals. Therefore, one of the important goals of stimulation therapy is to induce stimulation targeting based on individualized criteria in order to improve the consistency of targeting among individuals. Although described herein as a treatment method using TMS, other stimulation methods such as ultrasonic waves and / or electricity may be used.

[0025] The systems and methods for network-guided stimulation such as TMS described herein include "network / circuit" detection used as a target for performing a stimulation therapy (such as TMS). The circuit or network is detected based on the activation and connectivity measured based on the images by fMRI technology. The systems and methods of network-guided stimulation (such as TMS) use network-guided stimulation to target a specific circuit rather than a single region of a patient's brain; use network-guided stimulation for not only psychiatric conditions but also traumatic brain injury and other neurological conditions such as stroke and dementia; use network-guided stimulation for psychiatric conditions using a network-based post-targeting treatment that is improved over single-region application of TMS.

[0026] FIG. 1 shows a process diagram of methods 100, 150 using a network-guided stimulation approach. The system and method may use any combination of functional connections 100 and structural connections 150 between brain areas (including regions and networks) to determine individualized locations for stimulation therapy. As shown, the system and method include: step 102 of acquiring a high-resolution fMRI image of a patient's brain; step 152 of acquiring a dMRI image of the patient's brain; step 104 of analyzing the fMRI image to identify the circuits of the patient's brain to be treated from the analyzed fMRI image; step 154 of analyzing the dMRI image to identify white matter tractography; step 106 of calculating appropriate stimulation based on the analyzed fMRI image and / or dMRI image; and step 108 of applying appropriate stimulation to the patient's brain based on the appropriate stimulation.

[0027] In step 102 of FIG. 1, method 100 may include acquiring an image for analysis. The image may be an image of the patient's brain. This image may also include acquiring a high-resolution fMRI brain image. Thereby, for each individual, a unique pattern of activated brain areas (nodes) and node-to-node connections (circuits) including various networks with different functions can be detected. Also, in embodiments, dMRI images may be used additionally or alternatively to evaluate target locations for stimulation therapy.

[0028] The system and method may include receiving a patient's detailed medical and psychiatric history. The method may include creating a detailed map of the brain where the problem exists. The method may include comfortably seating the patient in a chair similar to a dental chair and attaching a sensor cap. A tracker (sensor) attached to a band is worn around the patient's head. And the tracker can receive signals from the patient. Embodiments include obtaining an fMRI, which is a radiation scan that shows in real time how blood is flowing in the brain. If blood flow to a particular brain region is too much or too little compared to normal, it may be associated with clinical symptoms. The area of abnormal blood flow has been determined to indicate an area with reduced function and related dysfunction.

[0029] The system and method may include obtaining a resting-state fMRI (rsfMRI). RsfMRI is an imaging technique that can detect functionally connected brain regions. When multiple high-speed MRI images are obtained, an fMRI map can be constructed by finding the temporal variations in each brain region (voxel) over time. This is achieved by the sensitivity of fMRI to natural BOLD contrast variations. Since the BOLD signals between different interconnected brain regions are temporally correlated when the subject is at rest (when no specific stimulus or task is presented to the subject), fMRI can reveal defects in various brain networks.

[0030] The system and method may include receiving fMRI images. The system may be configured using a magnetic resonance imaging apparatus. The MRI apparatus may be configured to detect changes related to blood flow in various parts of the brain. The system may communicate with the MRI apparatus to capture and receive fMRI images. The system and method may be configured to receive fMRI images that were previously captured or separately captured prior to TMS treatment. In this case, the system may be configured to communicate via the Internet, email, electronic transfer, or file sharing, electronic transfer via a storage device, or other means to receive an electronic file having the fMRI image captured by the MRI apparatus prior to the TMS procedure.

[0031] In step 104 of FIG. 1, method 100 may include analyzing an fMRI image to determine a target position of a treatment site. The resting state network (RSN) is composed of a set of brain regions having coherent and natural activity fluctuations. fMRI makes it possible to examine the functional organization of the brain and to find dysfunctions in neurological or psychiatric diseases by examining differences from normal control. And functional connectivity (FC) may be calculated by finding statistical dependencies between different regions (voxels). Second-order dependencies (i.e., co-variation or correlation) for constructing a functional connectivity map are found for the Gaussian distribution of the data.

[0032] The systems and methods also include determining which locations (positions) in the brain have reduced function and correlating that with highly refined software. Based on the poor functional connectivity of the brain, a treatment plan based on the (functional) brain circuitry may be devised, and wiring and readjustment of the target path by magnetic beams may be performed. The systems and methods herein may also identify unique patterns of abnormal node function and / or network abnormalities that may include abnormal inter-node and intra-node connections after examining fMRI images of thousands of individuals. Embodiments may identify and extract the amplitude and frequency of brain activity within and / or between brain networks and / or brain regions to analyze the connectivity between circuits. The systems and methods may also include algorithms for identifying and extracting the amplitude and frequency of brain activity within and / or between brain networks and / or brain regions for precise and individualized stimulation treatments such as TMS therapy.

[0033] The system and method may use the BOLD imaging method to measure brain activity. The BOLD images may be subdivided into regions and networks, and various comparisons may be made of the patient's fMRI data and / or between the patient and one or more healthy groups and / or control groups. The regions of the brain may be divided based on recognized regions such as Brodmann areas. Other subdivisions of the brain may be made based on different functional, connectivity, and / or developmental criteria, etc. The regions may be defined by the Glasser atlas. A region may be considered a broader brain area. For example, the DLPFC may be a brain region that is divided into smaller brain regions such as a9-46v, 9-46, etc. based on multimodal techniques. Glasser refers to these subdivided ones within a region as areas. Glasser divides the brain into a total of 22 brain regions (bilateral) and 360 brain areas. The networks of the brain may be identified as a set of regions. The set of regions may be based on the functional connectivity by statistical analysis of the patient's fMRI BOLD signals and / or based on the recognized networks of healthy patients. The networks may be identified by other recording methods such as EEG, PET, or MEG. A network may be defined as a group of brain regions that are functionally connected. Functional connectivity may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, etc. The network may be defined based on an individual's resting state and may include a resting state network (RSN). The network may include, for example, any combination such as medial frontoparietal, midcingulo-insular, dorsal frontoparietal, lateral frontoparietal, peria central, occipital, limbic, auditory, cerebellar, spatial attention, language, lateral visual, temporal, visual, left / right executive, etc. The network is defined by the Cole-Anticevic atlas. The network may be a group of (brain areas that may be different from brain regions and that are) interconnected (structurally and / or functionally) (or a subset of areas within a region).Preferred embodiments may relate to the identification of regions for a TMS target treatment location and / or the use of brain regions in a comparison to identify a target region. Embodiments may also use an area for a target TMS treatment and / or use an area to determine a target location. Thus, embodiments may be applied to an area as described herein with respect to the area.

[0034] Using a system and method, individual brain areas may be targeted based on functional connectivity. In an embodiment, the concept of a circuit may be used to refer to multiple brain areas of the same and / or different networks for the purpose of targeting a circuit to restore network integrity or interactions between networks. Comparison of BOLD images may be performed by comparing different regions and networks of a patient's brain. Analysis of regions and networks may be performed by comparing activation, correlation, and co-variation matrices of different BOLD regions and / or networks.

[0035] Using a system and method, individual brain areas may be targeted based on structural connectivity. In an embodiment, dMRI tractography may be used. In a structural connectome, dMRI tractography may be used in addition to, or in combination with, that obtained by other imaging methods (such as functional MRI) to study and identify the underlying white matter tracts of cortical regions shown to be defective in a functional connectivity map (i.e., based on activation, co-variation, and / or correlation). Specific brain regions may first be selected as potential stimulation targets using functional data, and then priority targets may be selected by narrowing down from the potential stimulation targets using structural data analysis. fMRI data may be used as a guide for structural analysis, such as when the co-occurrence of mental disorders, stroke, traumatic brain injury, neurodevelopmental disorders, or neurodegenerative disorders is unclear. Structural data may be used as a guide for functional analysis, such as when a patient has a stroke, traumatic brain injury, neurodevelopmental disorder, or neurodegenerative disorder. Analysis of structural connectivity may be used alone or in combination with analysis of functional connectivity.

[0036] When functional analysis is used as a guide for structural analysis, the process may include the following: (1a) constructing activation, correlation, and co-variation matrices using fMRI data and selecting targets with stronger (positive and / or negative) activation / connectivity values for a larger group of brain regions, and / or (1b) constructing activation, correlation, and / or co-variation matrices using fMRI data and selecting regions with weaker (closer to 0) activation values for connectivity for a larger group of brain regions; and (2) constructing a structural connectivity matrix using structural data and selecting regions with a larger number of white matter connections from 1(a) / 1(b).

[0037] When structural analysis is used as a guide for functional analysis, the process may include the following: (1) constructing a structural connectivity matrix using structural data and selecting regions with fewer white matter connections; and (2a) constructing activation, correlation, and / or covariance matrices using fMRI data and selecting, from (1), regions with stronger (positive and / or negative) activation-to-connectivity (activation / connectivity) values for a larger group of brain regions, and / or (2b) constructing activation, correlation, and covariance matrices using fMRI data and selecting, from (1), regions with weaker (closer to 0) activation-to-connectivity (activation / connectivity) values for a larger group of brain regions.

[0038] Structural analysis may begin with obtaining the patient's dMRI image at step 152. At step 154, the dMRI image is analyzed to construct a brain structural connectivity matrix. The target location for stimulation may be determined based on the structural connectivity matrix of step 154. The process may proceed to the calculation of the stimulation parameters and the application of stimulation of steps 106 and 108.

[0039] Using the results from the functional connectivity-based analysis for selecting a first set of potential targets, and using structural connectivity, prioritize targets and / or additional targets may be identified and a set of targets for stimulatory treatment may be defined by selecting from within and / or in addition to the first set of potential targets.

[0040] Using fMRI-guided stimulation therapy, the rate of increase from baseline for the following whole-brain measurements may be shown: total cortical gray matter volume and white surface total area. Structural analysis may be combined with functional analysis and used for patients suffering from neurodevelopmental disorders such as autism spectrum disorder, as well as neurodegenerative diseases such as dementia and Alzheimer's disease. In an embodiment, structural analysis may be used for disorders or conditions of patients in which structural changes occur in the brain. In an embodiment, structural analysis may be used as an additional processing step to identify target positions for stimulation such as TMS therapy based on structural connectivity.

[0041] In step 152, the process may include acquiring dMRI images. In the structural processing step, dMRI tractography is used. In the functional connectivity map, dMRI tractography may be used (alone or in addition to those obtained by other imaging methods such as fMRI described herein) to study the causative white matter tracts of cortical regions shown to be defective based on activation, co-variation, and correlation, using the constructed structural connectome.

[0042] In step 154, the process may include analyzing the dMRI images to construct a brain structural connectivity matrix. The brain structural connectivity matrix may be generated based on whole-brain white matter tractography. The matrix is created with rows and columns representing regions of interest (ROIs) (parcels) of brain gray and white matter. An example of parcellation is Glasser-based parcellation of brain gray and white matter, and the value of an element of the matrix is the strength of the white matter connection between two corresponding ROIs, quantified as the number of streamlines. Using voxel-specific diffusion information from diffusion-weighted MRI (dMRI), computational tractography generates three-dimensional trajectories through white matter within the MRI volume, called streamlines.

[0043] In step 156, a target position for treatment is selected. The selection of a target position for stimulation may include the selection of a region of interest having a greater number of white matter connections. The selection of a greater number of white matter connections may be a selection from potential targets preselected from another analysis method, such as functional connectivity analysis. Selecting a greater number of white matter connections may be independent of any other analysis and may provide additional target positions for stimulation. The selection of a target for stimulation includes selecting a predetermined number of target positions and / or selecting a plurality of regions of interest where the number of white matter connections exceeds a threshold. The selection of a target for stimulation includes selecting a region of interest having a greater number of white matter connections that form a target pool based on the functional connectivity of fMRI.

[0044] As shown, the system and method may include acquiring an image, such as a dMRI image, in step 152. Next, the system and method may analyze the dMRI image in step 156 to construct a brain structural connectivity matrix in order to identify or select a position for stimulation in step 156. The structural analysis may be performed before and / or after the functional analysis (steps 102 and 104). A first set of targets identified by a first process, such as a functional analysis (e.g., from step 104), is determined, and then that first set of identified targets is used in the structural analysis so that the final selection of targets is based on the priority of the first set of identified targets based on the structural analysis. Alternatively, the functional analysis and the structural analysis may be performed separately, and the overall set of targets may be determined based on the combination of the analyses between the functional analysis and the structural analysis.

[0045] In step 106 of FIG. 1, method 100 may include selecting an appropriate TMS coil and an appropriate TMS stimulation once the network to be treated has been identified. This calculation may include parameters that determine the site of stimulation and the result of the stimulation (e.g., increase or decrease in the strength of nodes and networks). The parameters may include the strength of the magnetic pulses, as well as their frequencies and the number of pulses given during a session and subsequent sessions, or any combination thereof. By applying repetitive pulses (repetitive TMS) at high frequencies (e.g., above 5 Hz), the underlying cortical activity can be excited, and at low frequencies (e.g., below 5 Hz), inhibitory changes can be brought about. Since the effects of TMS can propagate beyond the stimulation site through connectivity and affect the networks distributed in brain regions, the use of resting-state functional connectivity (rsFC) is a powerful tool for evaluating connectivity and guiding the optimal coil position regarding the target region.

[0046] When determining the intensity of the pulse, the system and method may use pulses with an intensity that exceeds the patient's motor threshold (MT) by 100-120%. Depending on the coil used, theta burst stimulation may be applied 5 times per second as a 50 Hz triplet burst for 1 second. In an embodiment, intermittent theta burst stimulation (iTBS) may be used. This means that stimulation can be given in a cycle of about 2 seconds on and 8 seconds off over a period of 3 minutes. In a typical stimulation session, the patient receives a total of 600 pulses and 200 bursts. This treatment is known to increase the neural firing in a specific region and, as a result, increase the brain activity and functional connectivity of the target region that regulates the neural circuit.

[0047] Inhibitory theta burst stimulation may be applied 5 times per second for 1 second with a 50 Hz triplet burst. Continuous theta burst stimulation (cTBS) may be used. This means continuously delivering a total of 600 pulses of stimulation over about 40 seconds. This protocol may be used to reduce neural firing and, as a result, reduce regional brain activity and functional connectivity in brain regions where regulation of neural circuits due to a slowdown of neural circuits is required. In step 108 of FIG. 1, method 100 may include stimulating the patient according to the parameters calculated in step 106. The system and method for stimulating the patient may include using a TMS device and / or a navigation system. Using the navigation system may improve the proper placement of the stimulation device in the first and subsequent treatment sessions. Also, other stimulation methods may be used, such as, for example, ultrasound and / or electrical stimulation.

[0048] Methods and systems are provided for determining networks and regions within a patient's brain for TMS treatment. Although described herein with respect to TMS treatment, the preferred embodiments are not limited thereto. In preferred embodiments, low-intensity focused ultrasound (LIFUS) may be included additionally or alternatively in the treatment. In preferred embodiments, if the brain regions and / or networks determined for treatment are identified as cortical brain circuits, their locations may be stimulated with TMS. If the brain regions and / or networks for treatment are identified as subcortical brain circuits, their locations may be stimulated with LIFUS. Other protocols and treatments, such as electrical stimulation, may also be used.

[0049] TMS is a non-invasive (does not enter the body) brain stimulation method used to change brain activity using magnetic fields or correct abnormal activity due to diseases. This magnetic field can pass through the skull and reach the patient's brain, and induce an electric current at the stimulation site (focal). TMS can regulate the resting-state activity of the brain and finely adjust the plasticity of the DMN. The direction (increase or decrease of activity) and degree of this regulation depend on the design of a specific rTMS protocol tailored to the brain activity of each patient. TMS involves using a magnetic coil placed on the top surface of the head and sending magnetic pulses into the brain through the skull (cranium).

[0050] The disclosure provided herein may provide unique aspects different from conventional systems, for example, as follows: (1) distinguishing and individualizing each patient's brain based on the recognition that each brain is different; (2) finding treatment targets from images of the patient's brain; (3) delivering treatment with high precision to functionally incomplete sites.

[0051] Consistency is important. In each TMS treatment, it is desirable for the network to be "re-educated" by the application of TMS pulses to receive the same treatment at the same location so that the normalization of its associated network connectivity is initiated. Therefore, it is desirable for the location of treatment citation to be accurate in order to bring about better success and provide longer-lasting health. Different from standard TMS that uses a uniform approach for the selection and indication of the TMS coil, in an individualized TMS approach, invariant anatomical features (landmarks) of an individual's face may be used for registration, and real-time navigation may be used to indicate treatment. With this technology, the system and method can accurately provide treatment to the location required for each treatment. The navigation system operates based on optical (infrared) tracking and improves accuracy and precision. The accuracy of this optical navigation system is higher than that of other systems because registration and real-time navigation only depend on the visibility of the tracker and probe to the infrared camera.

[0052] The systems and methods of this specification may use a precise neuronavigation guidance system capable of achieving millimeter-level accuracy. The guidance system may use functional near-infrared spectroscopy (fNIRS) and neuronavigation to perform high-precision targeting for TMS treatment related to a specified brain network. In near-infrared spectroscopy, infrared light may be used that is delivered through optical fibers to the scalp and into the brain through the skull. The infrared light is scattered or reflected by brain tissue and blood. A secondary set of optical fibers on the scalp captures the infrared light exiting the head. By detecting the concentration changes of oxygenated hemoglobin and deoxygenated hemoglobin in the blood shown in fMRI studies, a specific navigation map of the patient's head can be created in relation to the network used to determine the target TMS treatment location.

[0053] This system and method precisely irradiates magnetic beams onto nodes of a brain network or circuit that is functionally isolated from normal areas. Embodiments attempt to use brain mapping techniques to return the brain circuit to a healthier state and alleviate the underlying abnormalities. Figure 2 illustrates an explanatory working model of the method described in this specification. In method 200, at step 202, the patient may be caused to obtain a magnetic resonance image (MRI) of the brain at an MRI center. Next, the method may perform a functional and / or structural connectivity analysis on the fMRI image at step 212 and provide an output at step 214.

[0054] As illustrated and described above, the system may receive MRI data in various ways. The MRI information may be functional MRI (fMRI) and / or diffusion MRI (dMRI). fMRI is a type of MRI that measures changes in blood flow associated with brain activity. fMRI shows regional temporal changes in the temporal changes of cerebral blood flow. dMRI is a type of MRI in which the image contrast is based on the diffusion of water molecules in tissue. The MRI scan may be obtained in various ways. For example, the system may simply generate an input from an MRI center to functional connectivity analysis (using fMRI) and / or structural connectivity analysis (using dMRI). The transfer may be performed via any method for communication of information, including but not limited to direct connection or communication via a network such as an online platform. The system may be configured to communicate via the file transfer protocol and / or select preprocessing for the image data before using the MRI / fMRI / dMRI data as an input to the functional connectivity analysis in step 212. For example, the system may utilize public domain information such as the Human Connectome MRI protocol for data acquisition. In an embodiment, the HCP pipeline may be used for preprocessing of the data. In an embodiment, the Glasser and Cole-Anticevic atlas may be used, or other atlases that become generally available after development may be used. After the participating site 204 uses its respective or desired file transfer protocol 206 and executes the desired computational algorithm 208 to perform preprocessing of the MRI data, analysis may be performed on functional and / or structural connectivity in step 212.

[0055] In step 212, the system may receive the preprocessed MRI (fMRI and / or dMRI) data in accordance with the description herein, steps 204, 206, 208.

[0056] The system may analyze the functional and / or structural connectivity within a patient's brain. Using the preprocessed data, activation, correlation, and co-variation matrices for the whole brain of an individual subject may be calculated. Measuring the whole brain activation includes measuring the change in amplitude of fMRI data over time. Measuring the whole brain correlation includes measuring the change in frequency of fMRI data over time. Measuring the whole brain co-variation includes measuring the relative change in frequency with respect to the change in amplitude over time. Covariation as used herein is understood to be the ratio of one of correlation or activation and the other of activation or correlation. Thus, although generally described herein as correlation with respect to activation, covariation is understood to include its reciprocal and remain within the scope of the present disclosure and definition.

[0057] Embodiments may also additionally or alternatively have access to available data for the evaluation or determination of a healthy control group, and the data may include age, gender, ethnicity, or other genetic information related to diagnosis and treatment in pathology and / or prevention. Using preprocessing data from healthy controls along an age range and pre-post fMRI / TMS in neuropsychiatry, the average whole brain activation, correlation, and co-variation matrices of a healthy population may be calculated.

[0058] For example, at 210, the MRI center may provide fMRI information of healthy patients and patient information regarding relationships with individual patients, such as age, gender, ethnicity, etc. The system may hold patient information including fMRI data, patient information regarding relationships, TMS protocol, and patient outcomes such as responses to TMS treatment based on predetermined TMS target locations. This information may be stored in a database and provided as the input 210 for the functional connectivity analysis at step 212.

[0059] The functional connectivity analysis performed herein includes receiving preprocessed fMRI data from a patient and a healthy control dataset having metrics related to the patient. The metrics may include any combination such as age, gender, ethnicity, etc. The metrics related to the patient may include those among the characteristics of the healthy control group that are the same as or within a predetermined range of the patient's characteristics. For example, gender is determined based on whether the patient's gender is the same as that of anyone in the healthy control group. For age, the healthy control group may be selectable when having ages within the range of the patient's age. For example, when the patient and the control group are within a predetermined range (such as in 5-year or 10-year increments), or within a predetermined range from the patient's age (such as 5 or 10 years older or younger than the patient), etc. The age range may be based on those recognized as developmental stages of the brain, such as adolescence, adulthood, etc., or may be based on deterioration stages / periods based on age or a predetermined disease or condition, etc.

[0060] In step 212, the method includes separately generating whole-brain activation, whole-brain correlation, and whole-brain covariation matrices for patient data and a healthy control dataset having metrics related to the patient. That is, using the preprocessed fMRI data of the patient, for each brain region, whole-brain activation (change in amplitude over time), correlation (change in frequency over time), and covariation (relative change in frequency with respect to the change in amplitude over time) are calculated. Embodiments include receiving preprocessed data from healthy controls along any combination of metrics including age range, gender, ethnicity, etc. For the preprocessed data from healthy controls, the fMRI data may be processed before and after (pre / post) TMS treatment. Embodiments include calculating, for each brain region, whole-brain activation (change in amplitude over time), correlation (change in frequency over time), and covariation (relative change in frequency with respect to the change in amplitude over time) for each of the pre- and post-treatment fMRI datasets of healthy controls having metrics within a predetermined range from the patient.

[0061] After obtaining the activation, correlation, and co-variation matrices for the patient and healthy control groups, in-network analysis by comparing network coherence and network interactions within portions of the patient's brain, and / or between-network analysis by comparing network coherence and interactions between the patient and other subjects (healthy control groups and / or groups of subjects with the same diagnosis), the matrices may be evaluated in a variety of ways. Evaluation of the patient's activation, correlation, and co-variation matrices may include a first inter-network comparison. In this example, the method may include calculating changes between activation, correlation, and co-variation matrices between brain networks. Accordingly, embodiments may calculate changes in amplitude, frequency, relative frequency to amplitude, or any combination thereof between different brain networks to generate network-to-network, between-network comparisons. For example, a first network may be compared to a second network, then separately to a third network, and comparison may continue with an nth network. Thereafter, the second network may be compared to the third network, then to the fourth network, and comparison may continue with the nth network, and so on until each network is compared to every other network.

[0062] The evaluation of the activation, correlation, and co-variance matrices of a patient may include within-network comparisons. In this example, the method may include calculating changes between activation, correlation, and co-variance matrices between cortical and subcortical regions within each network. Accordingly, embodiments may include calculating changes in amplitude, changes in frequency, changes in frequency relative to amplitude, or any combination thereof, between different brain regions within a network. Network within-comparisons may be generated using the calculation of changes between different brain regions within a network and the combination of all other members of that network (region vs. network). For example, a first area of a network is compared to the combination of all members of that network (including and / or excluding the first area), and then a second area of that network is compared to the combination of all other members of that network (including and / or excluding the second area), and so on, until each area of that network and all networks of the brain have been analyzed.

[0063] The evaluation of the activation, correlation, and covariance matrices of a patient may include an intra-network comparison in a second network. In this example, the method may include calculating changes between activation, correlation, and covariance matrices between cortical and subcortical regions within each network, similar to the inter-network comparison in the first network. However, in this case, instead of a region-to-network comparison, a region-to-region comparison may be performed. Embodiments may include calculating changes in amplitude, changes in frequency, changes in frequency relative to amplitude, or any combination thereof, between different brain regions within the same network. Embodiments may include calculating changes in amplitude, changes in frequency, changes in frequency relative to amplitude, or any combination thereof, between different brain regions and other brain regions within the network (region-to-region) to generate an intra-network analysis. For example, a first brain region of a first brain network may be compared to a second brain region of the first brain network, then to a third brain region of the first brain network, and further comparisons may continue with the nth brain region of the first brain network. Thereafter, the second brain region of the first brain network may be compared to the third brain region of the first brain network, and further comparisons may continue with each other remaining region of the first brain network, the nth region. Thereafter, the remaining brain regions may be compared to all other brain regions within that brain network that have not yet been compared. Next, the method may perform the next brain network comparison by comparing the first brain region of the second brain network to each other remaining brain region of the second brain network, and this may be done until each brain region within each network has been compared to each other remaining region of that same network.

[0064] The evaluation of the activation, correlation, and co-variation matrices of a patient may include a second inter-network comparison and a second region-to-region comparison. However, in this case, the method includes calculating changes between activation, correlation, and co-variation matrices between brain networks, similar to the first inter-network comparison, but the calculation may be performed between different regions of those networks. That is, embodiments may include calculating changes between activation, correlation, and co-variation matrices between brain regions of different brain networks. Embodiments may include calculating changes in amplitude, changes in frequency, changes in frequency relative to amplitude, or any combination thereof between different regions of different brain networks and generating a region-to-region, inter-network analysis. For example, a first region of a first network may be compared to a first region of a second network, then separately to a second region of the second network, and further comparisons may continue with the nth region of the second network. Thereafter, the first region may be compared to a first region of a third network and further compared to all regions of the third network, and so on, until the first region of the first network has been compared to all regions of all other networks outside the first network. Thus, compared to region-to-region intra-network analysis, each region may be compared to each other region of the brain, thereby providing an analysis of the entire brain.

[0065] Embodiments of the present disclosure may additionally or alternatively include various comparisons of a patient to other groups, where the other groups are, for example, healthy control groups or groups of subjects with the same diagnosis, which may or may not be selected based on their relationship to patient metrics such as age, ethnicity, gender, condition / disease, treatment history, treatment effect, or any combination thereof.

[0066] The measured values of the patient may be statistically compared with the same measured values of healthy controls. The healthy control group or the group of subjects with the same diagnosis has the same age, gender, diagnosis, or symptoms as the patient. However, other criteria metrics may be used to select members of the healthy control group from a larger control group database. For example, the measured values of the patient may be compared with a healthy control group that matches the same relational metrics as the patient (e.g., having the same age and gender, etc.), which may include network-to-network network analysis of any combination of activation, correlation, and co-variation assessment; within-network analysis of region-to-network; within-network analysis of region-to-region; and / or between-network analysis of region-to-region; and may include a comparison between the patient and the healthy control group.

[0067] The comparison of a patient to a healthy control group or a group of subjects with the same diagnosis may be to one or more different control groups based on one or more different relational metrics. For example, the patient's measurements may be compared to a first healthy control group where a first set of relational metrics such as age and gender match the patient, and this may include network-to-network inter-network analysis of any combination of activation, correlation, co-variation assessment; region-to-network intra-network analysis; region-to-region intra-network analysis; and / or region-to-region inter-network analysis; of the patient and the first healthy control group. The patient's measurements may be additionally or alternatively compared to a second control group where the relational metrics match the patient, such as the same age and diagnosis or symptoms, and this may include network-to-network inter-network analysis of any combination of activation, correlation, co-variation assessment; region-to-network intra-network analysis; region-to-region intra-network analysis; and / or region-to-region inter-network analysis; of the patient and the second control group. Different combinations of relational metrics for the control groups may be used, and these combinations are within the scope of the present disclosure. For example, the first healthy control group may include the same age and gender as the patient; the second control group may include the same age and gender as the patient, and the symptom specification or diagnosis may also overlap; the third control group may include the same age, and the symptom specification or diagnosis may also overlap; the fourth control group may include only those having the same symptoms and / or diagnosis as the patient. The between-subject analysis may be based on the comparison of one, two, three, or more different control groups. The healthy control group may be an individual without a personal and / or family history of neurological or psychiatric conditions.

[0068] The system may be configured to perform any combination of the following functional connectivity analyses: (1) Network-to-network inter-network analysis by calculating changes in amplitude, changes in frequency, and relative frequency to amplitude for different brain networks of the same patient for each network of the patient; (2)Calculating, for each region of the patient, changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between a brain region having a network and all other members of that network of the same patient, for within-network analysis of region-to-network; (3)Calculating, for each region of the patient, changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between different brain regions within a network and all other members of that network of the same patient, for within-network analysis of region-to-region; (4)Calculating, for each region of the patient, changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between different brain regions of different networks of the same patient, for between-network analysis of region-to-region; (5)Statistical comparison between measurements of any combination of (1) to (4) from the patient and similar functional connectivity analysis of a healthy control group including individuals whose age and gender match the patient; (6)Statistical comparison between measurements of any combination of (1) to (4) from the patient and similar functional connectivity analysis of a healthy control group including individuals whose age, gender, and diagnosis and / or symptoms match the patient.

[0069] The system may be configured to output a circuit recommended for stimulation after performing various calculations and comparisons between and / or within patients to evaluate the functional connectivity of the patient's brain. Optionally, the TMS protocol may be customized based on the TMS protocol from a healthy control group. An exemplary protocol for TMS may include applying TMS at a frequency between 0 and 100 Hz with an amplitude of 50 to 150% of the individual's motor threshold.

[0070] The system may analyze the functional and / or structural connectivity within a patient's brain. As illustrated in FIG. 1, the structural analysis may be performed independently of, and / or in combination with, the functional analysis. For example, the functional analysis may be used to evaluate the patient and also to determine a target treatment location for performing steps 102, 104, 110, 106, and 108. In another example, the structural analysis may be used to evaluate the patient and to determine a target location for performing steps 152, 154, 156, 106, and 108. The functional analysis and the structural analysis may be used in combination. First, the functional analysis may be performed, where steps 102, 104, and 110 are executed to identify a first set of potential target locations. The first set of potential target locations may then be prioritized or analyzed according to an embodiment of the structural analysis to identify a target location from within the first set of potential target locations, where steps 152, 154, and 156 are executed to determine final parameters at step 106 and to perform a stimulation treatment at step 108. The process may be switched to generate a first set of potential target locations by first performing the structural analysis and then using it in the functional analysis to identify a target location for treatment from the first set of potential target locations. These processes may be performed in parallel (either simultaneously or sequentially) so that one treatment process does not encroach on the other, but the entire brain is analyzed according to each embodiment and the resulting target locations are prioritized together to identify the final target location.

[0071] The system may be configured at 212 to analyze the structural connectivity of the brain. This may include obtaining dMRI images of the patient from the MRI center 202. The dMRI images are analyzed to construct a brain structural connectivity matrix. A target location for stimulation may be determined based on the structural connectivity matrix (structural connectivity (SC) analysis of algorithm 212). The process may proceed to calculate stimulation parameters as the output of 214.

[0072] Using the results from an analysis based on functional connectivity to select a first set of potential targets, and selecting targets from within and / or in addition to the first set of potential targets using structural connectivity, priority targets and / or additional targets may be identified and a set of targets for stimulatory treatment may be defined.

[0073] Using MRI-guided stimulatory treatment, the rate of increase from baseline in the following whole-brain measurements may be shown: total cortical gray matter volume and total white surface area. Structural analysis may be used in combination with functional analysis for patients suffering from neurodevelopmental disorders such as autism spectrum disorder, and neurodegenerative diseases such as dementia and Alzheimer's disease. In embodiments, structural analysis may be used for disorders or conditions in patients where structural changes occur in the brain. Structural analysis may include additional processing steps for identifying target locations for stimulation such as TMS treatment based on structural connectivity.

[0074] The 212 system may include analyzing dMRI images to construct a brain structural connectivity matrix. The brain structural connectivity matrix may be generated based on whole-brain white matter tractography. The matrix is created with rows and columns representing regions of interest (ROIs) (parcels) of the brain gray matter. An example of parcelation is Glasser-based brain gray matter parcelation, and the value of an element of the matrix is the strength of the white matter connection between two corresponding ROIs, quantified as the number of streamlines. Using voxel-specific diffusion information from diffusion-weighted MRI (dMRI), computational tractography generates three-dimensional trajectories through the white matter within the MRI volume, called streamlines.

[0075] The structural connectivity of a patient's brain may be analyzed using MRI data to create a brain structural connectivity matrix. Structural connectivity analysis (tractography) may be performed using the MRI data. Structural connectivity analysis (tractography) is performed using the structural data. The structural data may be T1w / T2w. T1-weighted (T1w) and T2-weighted (T2w) are MRI sequence weighted scans, where in T1w MRI, the signal of adipose tissue is emphasized and the signal of water can be suppressed, while in T2w MRI, the signal of water can be emphasized. The structural data (such as T1w / T2w, etc.) is used for structural connectivity analysis (such as tractography, etc.) to study the structural composition of the whole brain.

[0076] The system may be configured to output a target position 214 for a treatment selected from algorithm 212. The selection of the target position for stimulation may include the selection of a region of interest having a greater number of white matter connections. The selection of a greater number of white matter connections may be a selection from potential targets preselected from another analysis method, such as functional connectivity analysis. Selecting a greater number of white matter connections may be independent of any other analysis and may provide additional target positions for stimulation. The selection of the target for stimulation includes selecting a predetermined number of target positions and / or selecting a large number of regions of interest where the number of white matter connections exceeds a threshold. The selection of the target for stimulation includes selecting a region of interest with a greater number of white matter connections that form a target pool based on the functional connectivity of fMRI.

[0077] In step 214, the output may include the selection of the region to which TMS is applied. To obtain the selection of the region, the system may be configured to analyze various calculations performed to determine functional and / or structural connectivity and the comparisons executed in step 212. For each step of the analysis executed in step 212, the system may be configured to select a threshold percentage of the brain regions that meet the selection criteria, where the functional connectivity is determined by the analysis of the fMRI data regions and / or networks, and the region with a larger number of white matter connections is determined to determine the structural connectivity. The calculations performed to determine the functional connectivity may include, for example, (1) network-to-network analysis between networks; (2) intra-network analysis of a region to a network; (3) intra-network analysis of a region to a region; (4) inter-network analysis of a region to a region; and (5) one or more statistical comparisons (the control group may be selected for one or more combinations of relational metrics such as age, gender, symptoms, diagnosis, ethnicity, etc.) comparing the measurements from the patient of any combination of (1) to (4) above with the same functional connectivity analysis of the control group; and may include any combination thereof. The calculations performed to determine the structural connectivity may include, for example, (1) generating a structural connectivity matrix of the brain based on white matter tractography from the whole brain; (2) generating a structural connectivity matrix of the brain based on white matter tractography from a subset of the brain based on another brain analysis method (such as functional connectivity); (3) creating a matrix indicating the gray and white matter regions of interest in the brain based on a desired parcellation; (4) determining the value of the element of the matrix as the strength of the white matter connection between the two corresponding regions of interest; and / or (5) generating a three-dimensional trajectory (streamline) through the white matter using the voxel-specific diffusion information from diffusion-weighted MRI; and may include any combination thereof.

[0078] The system may be configured to select a threshold percentage of the brain regions that meet the following conditions from the above calculations performed to determine the patient's functional connectivity based on the within-patient comparison. 7. Select a first threshold of the region where the change in amplitude, frequency, relative frequency with respect to amplitude, or a combination thereof, is the smallest for the largest number of brain networks and regions. 8. Select a second threshold of the region where the change in amplitude, frequency, relative frequency with respect to amplitude, or a combination thereof, is the largest for the largest number of brain networks and regions of the patient.

[0079] These comparisons are intended to identify regions of the patient's brain that are outliers of activity with the smallest or largest changes relative to the other most regions and networks of the patient's brain. That is, each of the above calculations 1-4 can be ranked for each region of the brain based on changes in amplitude, changes in frequency, and relative frequency with respect to amplitude. Regions below the third threshold can be grouped together, and the total number of each region below the third threshold (the frequency distribution of regions below the third threshold) can be determined. Similarly, the frequency distribution of regions above the fourth threshold can be determined. Then, the region with the highest frequency of occurrence in each group (regions below the third threshold or above the fourth threshold) is used for the determination in steps 7 and 8 above. These comparisons, and the regions identified based on the within-patient data, provide a within-patient output, in which a large number of regions are identified based on the analysis of activation, correlation, and co-variation information of the patient's data.

[0080] Thresholds may be used to divide the number of regions identified. The networks identified may be determined based on different thresholds. For example, the first and second thresholds for determining the final number of regions may be the top 1%, 5%, 10%, 15%, 25%, or other thresholds. These thresholds may be the same or different. Different comparisons may be made using different thresholds. For example, when determining the maximum and minimum changes in amplitude, when dividing the regions for the frequency distribution, the regions may be divided in a 50% range, and the upper half of the regions may be analyzed to determine the maximum change, and the lower half of the regions may be analyzed to determine the minimum change.

[0081] The system may be configured to select a threshold percentage of brain regions that satisfy the following conditions from the above calculations performed to determine the functional connectivity of a patient by patient - to - patient comparison, which is a comparison against one or more healthy control groups. 9. Compare the output within the subject (e.g., steps 7 and 8) with the output of a healthy control group having the same first metric relationship (in the example using steps 5 - 6, the same age and gender), and select regions that deviate from the determined normal range in the comparison with the healthy control group. 10. Compare the output within the subject (e.g., steps 7 and 8, or 9) with the output of a control group having the same second metric relationship (in the example using steps 5 - 6, the same diagnosis and symptoms), and select regions where the changes in activation, correlation, and co - variation before and after TMS treatment are maximized.

[0082] To save calculations, the order of steps may be different and executed for different inputs. For example, the last step 10 above may be based on the regions identified from steps 7 and 8, or the regions narrowed down by the filter from step 9.

[0083] In step 9, the system may compare the output within the target from steps 7 and 8 to obtain regions having values below a fifth threshold and above a sixth threshold of the average value of the healthy control group that match the first metrics relationship. The fifth and sixth thresholds may be the same or different. The fifth and sixth thresholds may be based on the number of standard deviations, such as being separated from the average by 1, 2, 2.5, or 3 standard deviations. The identified region from step 9 may then be compared in step 10 to a pool of patients that match the second metrics relationship. In this case, a subset of the region from step 9 may be selected for the regions having the greatest changes in activation, correlation, and co-variation before and after TMS treatment from a pool of patients of a control group having the second metrics relationship. That is, the regions of the control group that match the second metrics relationship (e.g., same diagnosis and / or symptoms, with the same or different age, gender, and / or ethnicity) are compared by taking the absolute value of the difference before and after treatment for each patient in the control group. And the region having the greatest change, the greatest absolute value, is identified as the output of step 10. A seventh threshold may be used to determine a cut-off, such as the top 1 percent, 5 percent, 10 percent, top 1 region, 2 regions, 3 regions, 4 regions, or more regions.

[0084] The system may be configured to use structural connectivity to perform selection of target locations from potential selections based on functional connectivity.

[0085] Embodiments may include an output including a report for the patient and / or operator receiving TMS treatment. The report may include one or more images of the patient's brain. For example, a comparison may be provided of activation, correlation, co-variation, or other combinations of images used herein, such as of and BOLD images. The report may include a visual representation of a brain map. The report may include a statistical representation. The report may include regions, networks, and / or circuits recommended for stimulation by TMS treatment. The report may include a TMS protocol customized as needed based on the comparisons made herein.

[0086] Stimulus parameters that can be individualized for the patient may also be provided. Once the area to which TMS is to be applied has been identified from the above steps, an excitatory (>5 Hz) or inhibitory (<5 Hz) TMS protocol may be selected. The treatment coil may be selected based on focality and depth. Focality is the width (horizontal axis) of the stimulation area, and depth is the distance (vertical axis) from the scalp to the target area. Additional or alternative TMS treatment parameters may be determined from the parameters of a group of prior patients who showed the best results from before to after TMS treatment. From the comparison in step 10, treatment parameters from the patient who showed the largest change before and after in the brain region may be used to notify the treatment parameters for application to the patient. For example, the treatment parameters may include an average value, a weighted average value, an average value after filtering, or treatment parameters from a patient or group of patients who showed the largest change for the identified area. Also, the system may be one that determines treatment parameters by comparing data of a healthy control group as well. For example, once the area is identified, using the control group, a third relational metric (which may or may not be the same as the first or second relational metric) may be used to identify the patient with the largest change before and after treatment in that area. For example, using this control group, in a comparison with the entire control group or a subset of the control group that matches the same gender, age, ethnicity, or a combination thereof (or other combinations of relational metrics), a group of patients with the largest change before and after treatment for a given area may be identified. By using the parameters from the treatment of the identified healthy control group alone or in combination, for example as an average value, a treatment protocol customized for the patient may be created.

[0087] Figures 3A - 8B are flowcharts for explaining exemplary functional connectivity analysis and corresponding visualized brain diagrams. The system may be configured to perform any combination of the following functional connectivity analyses: 1. Network-to-network inter-network analysis by calculating changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude for different brain networks of the same patient for each network of the patient; 2. Region-to-network intra-network analysis by calculating changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between a brain region having a network and all other members of that network of the same patient for each region of the patient; 3. Region-to-region intra-network analysis by calculating changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between different brain regions within a network and all other members of that network of the same patient for each region of the patient; 4. Region-to-region inter-network analysis by calculating changes in amplitude, changes in frequency, and changes in relative frequency with respect to amplitude between different brain regions of different networks of the same patient for each region of the patient; 5. Statistical comparison between the measurement values of any combination of (1) to (4) above from a patient and a similar functional connectivity analysis of a healthy control group including individuals whose age and gender match those of the patient; 6. Statistical comparison between the measurement values of any combination of (1) to (4) above from a patient and a similar functional connectivity analysis of a healthy control group including individuals whose age, gender, and diagnosis and / or symptoms match those of the patient.

[0088] The system and method may include measuring changes in amplitude, changes in frequency, and / or changes in relative frequency to amplitude between different brain network-to-network, region-to-region, or region-to-network. This difference is determined from the natural fluctuations of the BOLD contrast when the subject is at rest (when no specific stimuli or tasks are presented to the subject). The fluctuations of the BOLD contrast may be determined from fMRI by comparing different brain regions and networks. The brain may be divided into different regions. The regions may be based on recognized brain regions, or on other subdivisions based on various functional, connectivity, and / or developmental criteria, or on the activity of brain regions as shown in fMRI images of patients and / or healthy patient groups. The brain networks may be regarded as a set of regions. The set of regions may be based on the functional connectivity by statistical analysis of the fMRI BOLD signals of the patient, and / or based on the recognized networks of healthy patients. Other delineations of regions and networks may also be used. FIG. 3B shows an exemplary brain subdivided into four exemplary networks identified as n = 1, n = 2, n = 3, and n = 4. FIG. 3D shows the exemplary brain of FIG. 4B further subdivided into individual regions such that each network includes a plurality of regions. The number of regions per network may be the same or different. For example, the first network n = 1 in FIG. 3B is subdivided into four regions, while the second network n = 2 is subdivided into three regions. The illustrated regions and networks are provided only for explaining the related flowchart and are not intended to limit the actual brain regions and / or networks.

[0089] Exemplary functional connectivity analysis includes various analyses of changes in frequency, changes in amplitude, and changes in relative frequency with respect to amplitude, based on naturally occurring blood oxygenation level-dependent fluctuations determined from fMRI. The comparisons are based on brain regions and networks. Thus, FIG. 3A shows a flowchart for calculating the activation (amplitude of natural BOLD fMRI fluctuations), correlation (frequency of natural BOLD fMRI fluctuations), and covariation (correlation with respect to activation of natural BOLD fMRI fluctuations) of various brain networks, while FIG. 4A shows the calculation of activation, correlation, and covariation of various brain regions.

[0090] In FIG. 3A, for each network of the brain, the BOLD fluctuations of the first brain network area are determined. The network may be associated with a predetermined value by determining a median, mean, or other statistical value related to the distribution, and the amplitude and / or frequency of the BOLD fluctuations may be associated with a predetermined network. As shown, the analysis begins with the first network, n = 1. If not all networks have been analyzed (except for the first network case), the system calculates the activation of the first network and the correlation of the first network. From these values, the system can calculate the covariation as the ratio of the correlation with respect to activation (or vice versa). The system then increments to the next network and determines its activation, correlation, and covariation. The system proceeds for each network until all networks (n ≦ N) have associated values of activation, correlation, and covariation.

[0091] In FIG. 4A, for each region of the brain, the BOLD fluctuation of the first brain region is determined. The region may be associated with a predetermined value by determining a median, mean, or other statistical value related to the distribution, and the amplitude and / or frequency of the BOLD fluctuation may be associated with a predetermined area. As shown, the analysis starts from the first region. If not all regions have been analyzed (except for the first region), the system calculates the activation of the first network and the correlation of the first network. From these values, the system can calculate the covariation as the ratio of the correlation (or vice versa) to the activation. Then, the system increments to the next region and determines its activation, correlation, and covariation. The system proceeds for each region until all nodes (n ≦ R max ) have the associated activation, correlation, and covariation values.

[0092] As used herein, all of the activation, correlation, and / or covariation of each network and / or region may be calculated and / or stored in a database. As described herein with respect to FIGS. 3A - 8B, covariations may be stored and analyzed between various regions and networks. Thus, activation and correlation may be calculated during the calculation of covariation, while it may not be necessary to separate, determine, and / or store it in the system. Such intermediate determinations are understood to be included in the definition of the activation and / or correlation calculations used herein.

[0093] As shown, each network is identified by a serial number, and each region is identified by the serial number of its network and the region. The system and method may use any indexing system to associate the activation, correlation, and / or covariation of a predetermined network and / or region. Thus, as explained in the associated flowchart of FIG. 4A, the predetermined value of R is not necessarily limited when comparing r ≦ Rmax. Rather, this comparison is intended to illustrate that the current region is one of the regions that need to be analyzed and has not been analyzed yet.

[0094] Preferred embodiments include comparing each of the brain networks with each of the other brain networks. Illustrated in FIG. 5A is a first network selected for comparison with all of the other networks. For example, n a =(n = 1) from FIG. 3B is shown hatched in FIG. 5B for this network. The first network is then compared with the next network (n = 2) by taking the difference in the co-variation values from the information determined from the algorithms described with respect to FIGS. 3A and 3B. The first network is then compared with the next network (n = 3) by taking the difference in their respective co-variation values. After the first network has been compared with all of the other networks, the next network (n = 2) is compared against all of the other networks. The differences from the previous comparisons need not be repeated. Thus, if the difference from n2 to n1 has already been determined, the difference from n1 to n2 is not necessarily determined. Thus, the next network is compared against all of the other networks that are higher order or that have not been previously compared. Thus, the comparison loop is illustrated such that for n b = n a + 1, all of the networks that have not been previously compared with n a can be compared with n a . This loop continues until all of the networks have been compared with all of the other networks. In FIG. 5B, only the inner loop where n a = 1 is compared with all of the other networks (n b = n a + 1 = 2 to n b = N) is shown.

[0095] An embodiment may include comparing each of the brain regions with each of the other brain regions. The comparison may be processed in two segments: an intra-network comparison of regions within a network with all other regions within the same network, as illustrated in FIGS. 7A-7B; and an inter-network comparison of regions within a network with all other regions within another network, as illustrated in FIGS. 8A-8B. By combining these two loops, each region will be compared with each other region of the brain by taking the difference in the respective covariation values between the regions. That is, a first region selected for comparison with all other regions, such as r a =(r n=1 =1) from FIG. 4B, is indicated by the projection lines to this region in FIGS. 7B and 8B. In FIG. 8B, that first region is then compared with the next region within the network (r b =(r n=1 =2)) by taking the difference in the covariation values from the information determined from the algorithms described with respect to FIGS. 4A and 4B. In FIG. 8B, that first region is also compared with the next region within the next network (r b =(r n=2 =1)) by taking the difference in the covariation values from the information determined from the algorithms described with respect to FIGS. 4A and 4B. When the algorithm loops through either FIG. 7A or 8A, that first region is compared with the next region within the first network (FIG. 7B) or the next network (FIG. 8B). Each loop continues until each region is compared with each other region by determining the difference in covariation between the regions. a =(r n=1 =1) b =(r n=1 =2)) b =(r n=2 =1))

[0096] An embodiment may include comparing each of the brain regions with each of the other brain networks that do not include that brain region. In FIG. 6A, a first region is selected for comparison with all other networks. For example, r=(r n=1 n=1=(1) is indicated by hatching for this region in FIG. 6B. Then, the first region is compared by taking the difference in the co-variation values from the information determined from the next network (n = 2) and the algorithms described with respect to FIGS. 3A - 4B. Then, the same first region is compared with the next network (n = 3) by taking the difference in their respective co-variation values. After comparing the first region with all other networks, the next region (r = (r n=1 = 2)) is compared against all other networks. The loop continues until all regions have been compared with all networks that do not include the region being compared. This loop continued until all regions had been compared with all networks, regardless of whether the networks included the regions. In FIG. 6B, only the inner loop where r = (r n=1 = 1) is compared with all other networks (n b = n a + 1 = 2 to n b = N) is shown.

[0097] After all of the above comparisons (network - to - network, region - to - region, and region - to - network) have been determined, a statistical distribution is created, and a first subset of regions and / or networks where the difference (difference in co - variation) from the above results is minimum for the largest number of brain networks / regions, and a second subset of regions and / or networks where the difference (difference in co - variation) from the above results is maximum for the largest number of brain networks / regions may be obtained.

[0098] Next, compare the first and second subsets of the regions and / or networks to a healthy control group of the same age and / or gender as the patient. Identify a third subset of the regions and / or networks from the first and second subsets where the regions are outside a threshold from the mean value of the difference in co-variation from the healthy group. For example, those regions are where the difference in the corresponding co-variation is outside the range of two standard deviations (greater or less) from the mean of the difference in co-variation from healthy individuals of the same gender and age. The first threshold may exceed the mean value of the healthy control group by within two standard deviations, and the second threshold may be below the mean value of the healthy control group by within two standard deviations. Matching of the healthy control group may be done for the patient based on various parameter ranges, as understood by those skilled in the art. For example, a perfect match comparison may be made such that the gender and age of the patient are the same as the control group. The same age may be based on the year of birth. This comparison may also be for what is understood by those skilled in the art. For example, in medical comparisons, medical age groups are used. Thus, the same age may also be the same age within the same age group. The age categories may be, for example, 18 - 21 years old, 22 - 35 years old, 36 - 55 years old, 56 - 65 years old, and 66 years old and above. Other categories may be used, for example, 18 - 24 years old, 25 - 45 years old, 46 - 65 years old, and over 65 years old. Other comparison ranges may also be used. For example, a deviation such as 1 year or 5 years may be added or subtracted from the same age based on the year of birth. The matching may also be based on ranges based on the age range and on the conditions and changes that the patient typically experiences.

[0099] The first and second subsets of regions and / or networks may additionally or alternatively be compared to a healthy control group having the diagnosis / symptoms before and after TMS treatment. For the network shown to have the largest change in covariation values from before to after TMS treatment for a pool of patients having the same diagnosis and / or symptoms, a fourth subset of regions and / or networks is identified from the first and second subsets of regions and / or networks, or from a third subset of regions and / or networks. In other words, an exemplary input to the system includes a comparison pool of patient data taking the covariation difference before and after TMS for each region and / or network of the patient's brain. The comparison pool may also include the diagnosis and / or symptoms for the patient. Next, each of the regions and / or networks from the third subset of regions is compared to the comparison pool to determine the difference in the relevant covariation for the relevant regions and / or networks for the treated patients. Thus, a further subset identifies the regions that have a significant change with TMS and are most likely to provide the outcome of the TMS treatment.

[0100] Embodiments may include comparisons of different features of fMRI from different regions of the brain, networks, etc. The use of relative comparisons such as high, low, large, small, etc. is within the scope of the methods, systems, algorithms used herein. One of ordinary skill in the art will understand the scope of these relative terms by referring to the purpose of the comparison and the relative values within the set of values for which the relative terms are used. For example, "high" compared to a dataset may be 50% of the value at the upper end of the available data range, or 75%, 80%, 90%, 95%, or other margin. One of ordinary skill in the art will understand that the selection of the range may be clarified by the description of the purpose for which the comparison is made. Relative terms are equal to, above, or below a set value (threshold) or range that one of ordinary skill in the art would understand to be based on the characteristics of the values, the purpose of the comparison, the function of the treatment or algorithm, the normal or average values of a healthy control group, or the normal or average values of a dataset with respect to any feature (such as comparison with other data within a control group, healthy group, disease, dataset). The setting of the threshold may be done based on statistical variation and / or when selecting the desired number of target positions for treatment.

[0101] One embodiment includes a first input comparison control group that includes patient information such as the patient's age, gender, and / or other relevant diagnostic and / or treatment parameters. The first input comparison control group includes a database of data from healthy patients that includes region-to-region, region-to-network, and network-to-network covariation comparisons (differences). Next, the in-target outputs may be compared using the first input comparison control group to determine regions outside a desired range from the patient as compared to the healthy control group represented by the first input comparison control group. Comparisons may be made in the healthy control group for similar differences, and the region with the largest difference from normal (the healthy group) is selected as the target. In other words, if a large covariation difference occurs in the patient in region 1 of network 1 due to the comparison from region 1 of network 2, a comparison of healthy patients for a similar difference (region 1 of network 1 vs. region 1 of network 2) may be made. Embodiments may also make comparisons based on individuals in the control group, an average across the control group (e.g., averaging the healthy control group across all similar region-to-network and region-to-region differences), or other comparison bases.

[0102] One embodiment includes a second input comparison control group that includes patient information such as the patient's age, gender, and / or other relevant diagnostic and / or treatment parameters. The second input comparison control group includes a database of data from patients having the same symptoms and / or diagnosis, the data including the covariation of each treatment region of the patient's brain compared before and after TMS treatment. In other words, the second input comparison control group includes information regarding changes that occur in regions of the patient's brain based on the application of TMS treatment to those regions, and the covariation is determined before and after treatment to those regions.

[0103] Embodiments may include comparing regions and networks of a patient's brain to other regions and networks. The comparison may be performed in various ways. For example, as shown and described in the loops of FIGS. 3A - 8B, each region may be compared to each other region, each network may be compared to each other network, each region may be compared to each network (excluding or excepting the network in which the region itself exists), or any combination thereof. When comparing one region to another region or another network, or a network to another network, the combination of other regions or networks may be a group. For example, each region may be compared individually to each other region, or it may be a comparison as a group. The comparison by group may take the average value of the regions across a group of regions and perform a comparison between a single region and the group of regions. One region is compared to a group of other regions existing within the same network. One region is compared to a group of regions that include a network or a part of a network, and this group of regions and the network are outside the region or network in which the single region is located. The region is compared to multiple groups of regions such that the region is compared to all other regions of the brain.

[0104] Region - to - region, region - to - network, and network - to - network comparisons need not include all other regions and / or networks within the brain. Region - to - region comparison includes comparing a region to regions within the same network and / or regions outside the same network. However, if a part of the brain is known to be functioning normally or is known to be unrelated to a particular disease or symptom, the region or network may be excluded from the comparison or from the regions and networks. However, for a more complete brain evaluation to determine the targeting of TMS treatment, it remains preferable to compare a region to other regions outside the same network, a network to a network, or a region to a network outside the network of that region.

[0105] The embodiments may be used for the treatment of various conditions. For example, the embodiments may be used for the treatment of various mental disorders such as Alzheimer's disease, anxiety disorder, obsessive-compulsive disorder (OCD), PTSD, schizophrenia, cognitive impairment caused by stroke and other brain lesions, cognitive impairment caused by traumatic brain injury (TBI), insomnia, eating disorders, drug addiction, depression, attention deficit hyperactivity disorder (ADHD), attention deficit disorder (ADD), bipolar disorder, autism spectrum disorder, neurodevelopmental disorders, and psychosis. By using preferred embodiments, the decline in cognitive function may be suppressed.

[0106] Embodiments of the present disclosure may be used for patients suffering from psychosis (auditory hallucinations). For example, as an average among 5 patients suffering from auditory hallucination (AH) psychosis, FIGS. 9A-9F show before TMS treatment, FIGS. 10A-10F show after TMS treatment, and the target brain regions identified for TMS treatment are shown by thick-line contours. The illustrated images are by comparison of the co-variation (amplitude vs. frequency) of BOLD fMRI data. The circuits for treatment identified according to exemplary regions for treatment included the following: left hemisphere: PGs, IP1, MIP, 7PL, VIP, IPS1, 10v 10r, 25; and right hemisphere: 7PL, MIP, IP1, VIP, 7PC, OFC, pOFC, 25. As shown in FIGS. 10A-10F, the circuits after TMS treatment are normalized. The patients had a change in PANSS score from severe (6 points) to mildest (2 points) in areas such as P1 (delusions), P2 (disorder of concept integration), P3 (behavior due to hallucinations), P6 (suspicion / persecutory feeling), and P7 (hostility). Also, the patients were able to reduce their medication by up to 50% compared to during fMRI scans.

[0107] The scans in FIGS. 9A-10F are an average of 5 patients suffering from auditory hallucination (AH) psychosis so that the individual scans are not disclosed. As shown, the bright areas in FIGS. 9A-9F are targeted for stimulation treatment, and improvements as shown in the improved areas illustrated in FIGS. 10A-10F were found.

[0108] Embodiments of the present disclosure may be used for patients suffering from TBI. For example, FIGS. 11A-11B show exemplary baseline RSFC area maps before TMS treatment according to an embodiment, and FIGS. 12A-12B show exemplary RSFC maps after fMRI-guided treatment. FIGS. 13A-13B show an exemplary control group having the same age and gender as the patients of FIGS. 11A-12B. The figures are an average of 5 exemplary subjects for illustrative purposes. RSFC analysis by the exemplary methods described herein was performed on 5 patients. According to a preferred embodiment, a circuit for treatment by TMS may be identified that includes the following regions within the language network for stimulation: left hemisphere: STSdp, TPOJ1, STV, SFL, 55b, 44, 45. A preferred embodiment of the treatment protocol was excitatory and at an amplitude of 130% of the individual motor threshold. The patients experienced symptoms including severe (score 6) to mild (score 3) limitations in language production (conversation comprehension, articulation, and rate) and language comprehension (meaning processing). By way of example, exemplary maps provided for different RSFC areas are provided. See FIGS. 11A-31B.

[0109] TBI can be caused by a sudden blow or vibration to the head. The functional impairments of these injuries have been particularly studied in the intrinsic connectivity network (ICN). TBI substantially disrupts the function of the ICN associated with cognitive impairment. Two major ICNs, the salience network (SN) and the default mode network (DMN), are thought to be related to the treatment of TBI. In addition to the above findings, it is thought that the effectiveness of standard TMS in TBI treatment can be substantially improved by utilizing a brain network-based guidance system.

[0110] Resting-state functional connectivity MRI (rsFC MRI) is an imaging technique that can reveal the brain's networks and the dynamics of their interactions. In embodiments, rsFC MRI may be used to visualize the brain networks of each patient. RsFC MRI maps may also be used to find appropriate targets that "normalize" the function of brain networks and their interactions with other networks. This is an effective way to treat TBI injuries. For example, the activities of ICNs are usually tightly coupled, which is important for attentional control. The ability to restore the normal activities of ICNs can reverse the tight coupling of ICNs that was lost as a result of damage to the structural connectivity of these networks. When the normal correlations between networks are lost, abnormalities may occur in network function and cognitive control.

[0111] Conventionally, to determine the severity of TBI, it was necessary for the patient or someone near the patient to report the symptoms. In some cases, physical detection may also be made by brain structural lesions. A brain in which neurological death has occurred shows lesions in the areas that have suffered structural damage from TBI in the form of a reduction in the volume of brain tissue. Basically, these areas have no connectivity with other areas of the brain. Detection of structural lesions can be done by identifying areas that show no functional connectivity with multiple brain regions and suggest widespread effects through comparison of rsFC MRI comparisons, by the methods, systems, and instructions described herein. Other methods described herein may use structural analysis to identify the total cortical gray and white matter volumes and the total white surface area to identify structural changes associated with TBI. Embodiments may be used to provide an alternative diagnostic pathway for identifying structural lesions that is decoupled from physical detection based on loss of brain tissue.

[0112] The systems, methods, and instructions provided herein may also be used to diagnose brain damage that would lead to structural lesions prior to neurological death, thereby treating or reducing damage to brain areas. For example, functional lesions may be identified by the systems, methods, and instructions provided herein. A functional lesion can be an area of the brain that is functionally disconnected from multiple brain regions, but is not necessarily in a zero state or necessarily suffering from physical loss. Areas with functional lesions may exhibit irregular or episodic behavior and may have a high degree of negative correlation / covariation less than zero. For example, when a region is compared to other regions of the brain using rsFC MRI, that region may show a high degree of negative correlation, such that one area may be considered to have a very high degree of negative correlation with other areas of the brain. In this case, the brain area may be considered a functional lesion that, if left untreated, is likely or at risk of leading to structural lesions and / or neurological death of the brain area.

[0113] In functional lesions, widespread disruptive behavior may be detected due to the inactivation of other brain regions that are negatively correlated with them. Functional lesion regions are highly negatively correlated, which means that this region inactivates multiple brain regions in an attempt to reconnect itself to the rest of the brain and shuts down regions in a negative correlation relationship. The high degree of negative correlation / covariation with multiple brain regions may be used to identify functional lesions. These high degrees of negative correlation / covariation are less than zero and indicate a large-scale decoupling effect (inactivation of a large group of brain regions).

[0114] Systems, methods, and instructions may thus include early stage diagnostic and treatment options for detecting areas at risk of brain injury before or before structural detection becomes apparent or available. The systems, methods, and instructions may provide early detection of future brain injury by using fMRI to compare the functional connectivity of brain areas and identify areas of the brain that are functionally disconnected but have not yet experienced neurological death and / or physical degradation. Thus, embodiments may provide early detection such that treatment can intervene before neurological death, such that damage can be limited, and / or such that damage can be reversed.

[0115] Accordingly, embodiments may be used for the diagnosis and treatment of TBI. Embodiments may not rely on the patient's physical symptoms or physical brain lesions to diagnose the severity of TBI. Embodiments may be used for early detection and / or treatment prior to brain injury, prior to severe brain injury, or prior to neurological death. Structural lesions may have zero or near-zero functional connectivity with many brain regions, e.g., may have reduced or near-zero functional connectivity. FIGS. 19A-20B show brain scans of patients who have experienced a stroke. FIGS. 19A and 20A show images from fMRI brain scans of patients suffering from a stroke, indicating near-zero functional connectivity. FIGS. 19B and 20B show corresponding images to show the corresponding structural degradation. In the scans of the patients, no correlation / covariation with a large group of brain regions is seen. Preferred embodiments exhibit correlation / covariation values of 0 or near 0.

[0116] At stage 1 before the nerve cells die, the correlations / covariations with a number of brain regions become less than zero, showing a wide decoupling effect, and a number of brain regions are inactivated and may be classified as functional lesions. At stage 2 where brain damage is usually present, the correlations / covariations with a number of brain regions are close to zero or zero, showing a wide pattern of disconnection of structural lesions. The changes in the connectivity of the areas for identifying TBI may be based on the embodiments described herein, in which case, when an area of the brain is compared with all other areas of the brain, that area of the brain is outside the statistical deviation from the rest as a structural lesion with low functional connectivity or as a functional lesion with a high negative correlation. This comparison may additionally or alternatively be based on other patients diagnosed with and treated for TBI.

[0117] In embodiments of the method, sophisticated techniques for modeling brain dynamics may be utilized to gain insights into network dysfunction. For example, in embodiments, rsFC MRI may be used to examine how damage to the structural network due to axonal injury gives rise to functional irregularities, and this may be used to determine the targets for TMS treatment. In other examples, embodiments may detect "functional lesions" to detect and / or treat areas of the brain of a patient having a structurally undetected TBI. Embodiments may be used to identify a "lesion" when the brain area does not appear structurally in the form of volume reduction (nerve cell death). By treating these early-diagnosed lesions through early treatment provided by early detection, structural damage may be limited, halted, and even reversed.

[0118] Using the embodiments of this specification, structural lesions may be identified by finding brain areas with no functional connectivity to other areas of the brain. In this case, these areas of the brain may be targeted for treatment and / or excluded from treatment. For example, these areas may have already undergone neurological death in the brain region and may not be receptive to treatment, and thus may be excluded from the algorithms of this specification. Without excluding these areas from the selection, they may be identified by the above algorithms based on the above criteria. This is because, due to their connectivity disorders, they may be identified as preferred treatment areas. In another example, when neurological death has not yet occurred, it may be highly desirable to treat these areas before such physical damage is completed or continues. The connectivity patterns of structural lesions and functional lesions are very different.

[0119] Using the embodiments of fMRI-guided TMS treatment in this specification, treatment of functional lesions that may result from TBI may be performed. The brain maps of functional lesions are usually extremely abnormal (irregular or epileptic behavior of nerve cells) and usually show extensive effects with a high negative correlation / covariation with a large number of brain regions. Usually, these maps are extremely colorful on the negative side of the spectrum, such as light blue, green, purple, gray, etc. When these areas are targeted with TMS treatment, complete recovery or improvement of function may occur. FIGS. 18A-18B show the prefrontal cortex (area TE1m, right) having a high negative correlation / covariation with a large group of brain regions (light blue and green areas). FIG. 18A shows the patient at baseline, and FIG. 18B shows the patient after treatment according to the embodiments described in this specification.

[0120] Using the embodiments of MRI-guided TMS treatment in this specification, treatment of structural lesions that may result from TBI may be performed. The brain maps of structural lesions usually are not very colorful, are dark red or brown close to black, and show a lack of connectivity with a large number of regions. In some cases, some activity may be detected in these areas. If so, after treatment with TMS, these areas may partially recover their function.

[0121] TBI patients may exhibit either or both functional and / or structural lesions. Using the embodiments herein, either or both may be treated by fMRI-guided TMS. Embodiments of the present disclosure may be used in patients to reduce further injury from TBI or to reduce brain injury from functional disconnection. By using the embodiments herein to identify and / or treat functional lesions, early diagnosis and treatment of brain areas prior to structural brain injury may be provided.

[0122] Embodiments of the present disclosure using fMRI may enable early detection of brain abnormalities prior to behavioral or structural signs (i.e., image-based biomarkers) for prevention and early intervention of neurological and psychiatric conditions. Embodiments may also enable intervention at a later stage of diseases / conditions with cortical atrophy and volume loss by (1) compensating for loss of function by recruiting healthy brain regions to the affected pathway; (2) stimulating atrophied areas to delay further disease worsening and progression (when applied to neurodegenerative diseases such as Parkinson's disease or Alzheimer's disease where the condition worsens over time).

[0123] Embodiments may show and explain methods for identifying brain regions having a high degree of negative correlation and co-variation with a large group of brain regions. These types of functional lesions are detectable in any brain region. The embodiments illustrated herein show a single-case scenario for illustration purposes. See FIGS. 99A - 127. Embodiments may be used to help prevent late complications of TBI such as Alzheimer's disease and chronic traumatic encephalopathy. This may be due to the fact that axonal injury can interact with neuroinflammation and neurodegeneration and contribute to the formation of chronic complications. Embodiments may provide information for the diagnosis, prognosis, and treatment planning of TBI using network-level imaging methods.

[0124] Embodiments of the present disclosure may be used in patients to reduce or prevent age-related cognitive decline. For example, FIGS. 32A-32D show baseline RSFC area maps before TMS treatment, and FIGS. 33A-33D show RSFC maps after fMRI-guided treatment. FIGS. 34A-34D show a control group having the same age and gender as the patients of FIGS. 32A-33D. The figures are averaged over 5 subjects for illustrative purposes. RSFC analysis according to the methods herein was performed on 5 patients. Embodiments herein identified circuits for treatment by TMS, including the following regions within the fronto-parietal and language networks for stimulation: left hemisphere: p9-46v, STSdp, TPOJ1. Embodiments of the treatment protocol were excitatory and at an amplitude of 130% of the individual motor threshold. Since the patients were asymptomatic during TMS treatment, there was no change in the symptom score. Patients were selected based on risk factors for neurodegenerative diseases (cardiovascular and genetic factors). By way of example, exemplary maps provided for different RSFC areas are provided. See FIGS. 32A-38D.

[0125] Aging can cause cognitive impairment, such as disorders caused by vascular deterioration. Vascular cognitive impairment (VCI) is caused by occlusion of the microvasculature, resulting in a decrease in cognitive function due to the inability to supply oxygen and nutrients to various brain regions. This ability may also be lost due to cerebrovascular disorders. In addition to vascular dementia, there is also degenerative dementia (most common in Alzheimer's disease). Combining these two types covers most cases of dementia.

[0126] TMS can be used to prevent cognitive decline by enhancing brain activity and connectivity in the most vulnerable regions, such as specific locations within the frontal and temporal lobes, detected by the inventors' fMRI-based biomarkers. The methods herein may be successful in treating MCI patients using an MRI-guided TMS approach. Also, using the embodiments, a new protocol may be developed to prevent or reduce everyday memory decline in asymptomatic patients at risk of MCI and Alzheimer's disease due to family history or cardiovascular disease.

[0127] Embodiments of the protocol may include: (1) analyzing resting-state fMRI (rsfMRI) images of a patient's brain to construct an intracranial network; (2) using biomarkers to detect the most vulnerable brain regions in each individual; and (3) delivering MRI-guided TMS over 20 sessions. The patient can undergo a second MRI after the final session to compare the baseline and post-treatment brain maps and calculate a measure of effectiveness. Additionally, the patient can undergo MRI again at one-year follow-up to observe changes in brain activity and connectivity, and further, the biomarker can be used to evaluate whether another cycle of 20 sessions of MRI-guided TMS is needed and deliver it lifelong if necessary. The methods herein for use in MCI patients suggest that MRI-guided TMS is an effective treatment for preventing cognitive decline and may be a tool to slow down deterioration.

[0128] Embodiments of the present disclosure may be used for patients suffering from depression. For example, FIGS. 41A-41D show baseline RSFC area maps before TMS treatment according to embodiments of the present specification, and FIGS. 42A-42D show RSFC area maps after whole-circuit-based fMRI-guided treatment according to embodiments of the present specification. FIGS. 43A-43D show RSFC area maps after single-region treatment of area 46 in the DLPFC by conventional treatment. FIGS. 44A-44D show RSFC area maps from a healthy control group matching the same age and gender as the patients in FIGS. 41A-42D. The figures are averaged over 10 subjects for illustrative purposes. RSFC analysis was performed according to the method of the present specification on 10 depressed patients who did not respond to a standard TMS approach based on the 5 cm rule. According to embodiments of the present specification, circuits were identified that included the following regions within the cingulo-opercular network for stimulation: left hemisphere: 46, PF; right hemisphere: 46, PF. Embodiments of the treatment protocol were excitatory and had an amplitude of 120% of the individual motor threshold. Change symptomatic depression scores based on HAMD started from a baseline score of HAMD<7 in all subjects, and included remission after circuit-based fMRI-TMS, and HAMD>7 in all subjects after single-region fMRI-TMS (although a 50% improvement in the HAMD score was achieved in all subjects, no subjects achieved remission). None of the patients included in these analyses responded to standard TMS (5 cm rule). By way of example, exemplary maps provided for different RSFC areas are provided. Refer to FIGS. 41A-7A-1 to 56D.

[0129] Major depressive disorder (MDD) may be characterized by abnormal functional connectivity of brain networks. Studies have revealed disruptions in network connectivity in the major networks in MDD, namely, the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Abnormalities in cerebellar and thalamic circuits have also been reported. The method may address the qualitative aspects of the symptoms of MDD using unique tools (resting-state functional connectivity MRI, fMRI) that can reveal changes in brain networks.

[0130] Using the embodiments herein, the severity of depression may be specified by changes found in the DMN, CEN, SN, frontal and thalamic brain regions, the insula, and the subgenual anterior cingulate cortex (sgACC). One reason standard TMS does not achieve highly successful results is that it cannot treat targets specific to each patient. The embodiments described herein may measure a patient's brain function and connectivity immediately before treatment begins and use sophisticated algorithms to identify specific brain regions that exhibit abnormal behavior.

[0131] Using the embodiments, the involvement of distinct functional connectivity in MDD, related to pathophysiology and treatment, may be shown. However, embodiments of the use of fMRI enable the embodiments of the methods described herein for discovering targets, delivering treatment, and evaluating its effectiveness at the end of treatment. The implementation of functional connectivity as a scientific biomarker may increase the likelihood of addressing the root cause of MDD. As with other diseases, it is important to diagnose the brain regions involved in depression and target those regions. The methods described herein may use fMRI to discover brain regions that are not functioning properly. The exemplary fMRI-guided TMS protocol for depression of the embodiments described herein may identify and treat these specific regions, most commonly referred to as the DLPFC. The inventors have found reduced activity and abnormal connectivity in the left DLPFC of depressed patients. This area is associated with the behavioral dysregulation commonly seen in depression (e.g., reduced energy, insomnia, appetite changes).

[0132] The involvement of the subgenual anterior cingulate cortex (sgACC) in the pathophysiology of depression and as a response predictor has also been shown. The decreased metabolism of the DLPFC may be secondary to the enhanced activity of the limbic system, and the connectivity (negative correlation) between the DLPFC and the limbic system regions is involved in the antidepressant response. The optimal target of TMS may widely cover the "anti-correlated" region of the sgACC in DLPFC 2-4. There are specific areas of the brain that may be affected when a patient is in a depressive state. For example, the dorsolateral prefrontal cortex, hippocampus, amygdala, lateral orbitofrontal cortex, anterior cingulate cortex and posterior cingulate cortex, parahippocampal gyrus, insula, temporal cortex, and precentral gyrus are all areas to be analyzed according to the methods described herein for the treatment of depression and visualized and treated areas according to the embodiments of the network. If the blood flow and connectivity of these sites deviate from the norm, further investigation may confirm the existence of areas with functional and / or structural excess or deficiency, as well as the possibility that the functional and / or structural connectivity may be increased or decreased. By setting the TMS coil to deliver magnetic pulses at a specific frequency to these specific regions and targeting the circuitry there, the specific regions of the brain essential for symptoms can regain their proper function and the patient can feel joy again.

[0133] The systems and methods described herein may be used with patients suffering from autism spectrum disorder. For example, FIGS. 57A-57D show baseline RSFC area maps before TMS treatment, and FIGS. 58A-58D show RSFC maps after fMRI-guided treatment. FIGS. 59A-59D show an exemplary control group having the same age and gender as the patients of FIGS. 57A-58D. The figures are averaged over 5 exemplary subjects for illustrative purposes. RSFC analysis was performed on 5 patients by the method. According to an embodiment, a circuit for treatment was identified that includes the following areas within the default and language networks for stimulation: left hemisphere: STGa, STSda, STSva, STSdp, STSvp. The embodiment of the treatment protocol was excitatory and had an amplitude of 110% of the individual motor threshold. Patients experienced symptoms including social interaction, social communication, and limitations in theory of mind, and improvement was brought about from severe (score 6) to mild (score 3) across all subjects. As an example, exemplary maps provided for different RSFC areas are provided. See FIGS. 57A-71D.

[0134] Autism in DSM5 is one of the so-called pervasive developmental disorders (PDD), and Asperger's disorder and other pervasive developmental disorders not specified herein are also included therein. These groups of disorders are collectively referred to as autism spectrum disorder (ASD). ASD is characterized by impairments in social skills, repetitive behaviors, and the use of verbal and non-verbal language for communication. Children who engage in restricted or repetitive behaviors share characteristics such as the complexity of the behavior and the inability to grasp concepts. ASD affects approximately 1 in 60 children, but there is currently no drug therapy targeting the core networks involved in ASD.

[0135] According to recent evidence, repetitive transcranial magnetic stimulation (rTMS) is likely to alleviate the main symptoms of patients with ASD. Patients with autism have a unique brain complexity. There is strong evidence that rTMS improves the symptoms of youths with ASD. As shown by the experience of the inventors herein, based on the embodiments described herein, applying rTMS to the DLPFC targeting individual regions specific to an individual is effective as a novel intervention for the EF deficit in ASD. By utilizing the methods of the rsFC MRI technology described herein, TMS can be administered to patients in a targeted and individualized manner. This enables direct treatment of dysfunctional regions, acquisition of neuroimaging measurements before and after treatment, and recording of the treatment effect on the brain structures essential for EF performance. Embodiments of this ability enable investigation of the neural mechanisms of the rTMS treatment effect. Embodiments may also include longitudinal follow-up investigations that enable assessment of the need for continuous intervention to maintain the treatment effect.

[0136] Similar to depression, standard rTMS in ASD uses treatment sites based on relatively fixed positions on the motor cortex. Since autism is a disorder of the association cortex, particularly a disorder of connectivity mainly involving intrahemispheric connectivity, identifying optimal TMS target coordinates in both bilateral DLPFCs is considered a targeting strategy for connectivity-based TMS to treat ASD. According to suggestions from the inventors' previous experiences, in ASD with predominant social and cognitive dysfunction, embodiments of the methods provided herein enable: (i) measuring changes in resting-state functional connectivity (rsFC) between nodes of the relevant network, such as nodes of the "theory of mind (ToM) network" in the prefrontal cortex, orbitofrontal cortex, supplementary areas, anterior cingulate cortex, posterior cingulate cortex, superior temporal cortex, superior and middle temporal gyri, and inferior parietal lobe regions, and the underlying deep nuclear regions, such as the ventroanterior nucleus of the thalamus (such comparisons yield correlations mediating treatment response); and (ii) identifying optimized bilateral targets within the prefrontal cortex for TMS treatment and individualizing treatment by applying a connectivity-based targeting approach at the single-subject level. Accordingly, the inventors provide a novel and innovative approach that enhances the effectiveness of TMS guided by rsFC MRI.

[0137] This approach is fundamentally different from conventional approaches that target without investigating the patient's brain or understanding differences in brain composition. Instead, the embodiments described herein stimulate the brain based on the rsFC pattern of the individual brain. The embodiments described herein emphasize the network structure of the human brain and therefore use rsFC MRI to construct the networks involved in each disorder. With respect to ASD, evidence such as the fact that the association cortex, other than the primary sensory and motor cortices and white matter, is generally dysfunctional, and in addition, the absence of clinical signs of focal brain dysfunction such as visuospatial disorders commonly seen in children with hypoxic-ischemic injury and cerebral palsy, etc., has pointed to abnormalities in the distributed nervous system. This is the reason why the inventors' treatment is network-based. There are also examples of guiding TMS treatment for ASD using EEG-based functional connectivity. In comparison, the results after applying the TMS treatment protocol using the method described herein indicate that patients experience longer-lasting clinical improvements under anatomically accurate techniques.

[0138] The systems and methods of this specification may be used with patients suffering from dementia. For example, FIGS. 72A-72D show baseline RSFC area maps prior to TMS treatment according to embodiments described in this specification, and FIGS. 73A-73D show baseline maps after fMRI-guided treatment according to embodiments described in this specification. FIGS. 74A-74D show an exemplary control group having the same age and gender as the patients of FIGS. 72A-73D. The figures are averages of five exemplary subjects for illustrative purposes. RSFC analysis according to the exemplary methods described in this specification was performed on five patients. According to embodiments described in this specification, circuits for targeted TMS treatment were identified that included the following regions within the cingulate-opercular, somatomotor, default, frontoparietal, and dorsal attention networks for stimulation: Left hemisphere: Semantic search: Lexical search: FOP5, FOP4, FOP3, FOP2; TE1a, TE1m, TE1p, PHT, TE2a. The treatment protocol was excitatory and at an amplitude of 130% of the individual motor threshold. Patients experienced symptoms including improvement in vocabulary / semantic information retrieval and improvement was brought about from moderate to severe (score 5) to mild (score 3) across all subjects. As an example, exemplary maps provided for different RSFC areas are provided. Refer to FIGS. 72A-98D.

[0139] The embodiments described herein are believed to be effective in halting, slowing, and / or reversing the effects of Alzheimer's disease (AD). To date, pharmacological treatments have not achieved favorable results, leaving room for non-pharmacological interventions for this disease. TMS can induce changes in brain activity and bring about long-term changes in damaged neural networks. Such an ability of TMS holds great potential in clinical interventions. Standard TMS can bring about changes in cortical excitability, enhance brain plasticity, and promote recovery through the reorganization of damaged neural networks that cause cognitive impairment. However, standard TMS does not provide an individualized targeting ability that can be modified by using advanced MRI technology. The method described herein maps the networks of the entire brain using fMRI and then performs a special analysis for each patient to discover the appropriate targets for TMS treatment and achieve the best results.

[0140] The fMRI according to the embodiments described herein may be used as a technique for creating biomarker-surrogates for neurodegenerative diseases. This may thereby identify the presence of AD, track its progression and severity, guide TMS treatment, and provide an assessment of the treatment effect. There is increasing evidence indicating that interventions in neurodegenerative disorders need to be carried out at an early stage of AD, or rather, before the symptoms appear. Therefore, by using a sensitive technique such as fMRI to monitor the progression of the disease based on quantitative metrics and combining it with clinical features, a highly reliable and easily trackable biomarker is provided in this field.

[0141] The embodiments described herein may be used to restore the patient's baseline and reduce or recover from anxiety such as generalized anxiety disorder (GAD). The fMRI techniques and methods described herein may clarify the patient's treatment plan by enabling insights into how the individual patient's brain functions.

[0142] The unique combination of TMS and fMRI focused on the network according to the embodiments described herein may help patients be freed from anxiety, depression, and many other brain disorders. Briefly, the MRI images and related MRI image analysis methods described herein may help identify the specific brain functions and create a treatment plan specific to the patient. As described herein, fMRI measures brain activity by detecting changes related to blood flow, and dMRI creates three-dimensional trajectories through white matter within the brain volume using computational tractography. During the use of a certain area of the brain, blood flow to that area increases. Since several parts of the brain are important factors in the generation of fear and anxiety, fMRI helps identify the areas affected in the patient's brain. The embodiments illustrated herein show the anxiety circuit. Two types of brain circuits show two different biomarkers for anxiety, including the prefrontal lobe or the parieto-occipital sulcus. Some patients have both characteristics, and some patients show a bias towards the prefrontal or parieto-occipital circuits.

[0143] For example, FIGS. 128A-128D show the baseline RSFC area maps before TMS treatment according to the embodiments described herein, and FIGS. 129A-129D show the RSFC maps after fMRI-guided treatment according to the embodiments described herein. The figures are an average of 5 subjects. RSFC analysis according to the method described herein was performed on 5 patients. According to the embodiments described herein, circuits for treatment by TMS were identified, including the following areas within the fronto-parietal, cingulo-opercular, and default mode networks for stimulation: Right hemisphere: dorsolateral prefrontal, area 9-46d, right. The treatment protocol was inhibitory and had an amplitude of 90-100% of the individual motor threshold (adjusted by depth of penetration and type of coil used). The patients experienced psychological / physiological anxiety and panic attacks, and their scores ranged from severe 6 to minimal 2. By way of example, exemplary maps provided for different RSFC areas are provided. Refer to FIGS. 128A-137D

[0144] For example, FIGS. 138A - 138D show baseline RSFC area maps before TMS treatment according to the embodiments described herein, and FIGS. 139A - 139D show SFC maps after fMRI - guided treatment. The figures are an average of 5 subjects. RSFC analysis according to the methods described herein was performed on 5 patients. According to the embodiments described herein, circuits for treatment by TMS were identified that included the following regions within the fronto - parietal, default mode, and cingulo - opercular networks for stimulation: Left hemisphere: the parieto - occipital sulcus area, area POS2, left. The embodiment of the treatment protocol was inhibitory and was at an amplitude of 90 - 100% of the individual motor threshold (adjusted by depth of penetration and type of coil used). The patients experienced psychological / physiological anxiety and panic attacks, and their scores were from severe 6 to minimal 2. Exemplary maps for different RSFC areas are provided. Refer to FIGS. 138A - 147D.

[0145] The systems and methods described herein may be used to treat bipolar disorder, also known as manic - depressive illness. Bipolar disorder is a brain disorder that causes abnormal changes in mood, energy, activity levels, and the ability to perform daily tasks. Types of bipolar disorder include bipolar I disorder, bipolar II disorder, cyclothymia, and other specified and unspecified bipolar and related disorders.

[0146] By the fMRI imaging method according to the embodiments described herein, non - normally functioning networks that cause the occurrence of manic episodes can be identified. The methods according to the embodiments described herein may, by this technique, accurately treat specific areas using individualized treatment plans and accurate neuronavigation.

[0147] The embodiments described herein may be used for the treatment of ADHD. Attention - Deficit / Hyperactivity Disorder (ADHD) is a disorder characterized by persistent and inappropriate levels of hyperactivity, impulsivity, and inattention.

[0148] ADHD may involve two partially separated attention networks, the dorsal attention network (DAN) and the ventral attention network (VAN). Both networks are deeply involved in the brain's attention control system. The DAN is centered around the bilateral intraparietal sulcus (IPS) and the junction of the precentral sulcus and the superior frontal sulcus (frontal eye field, FEF), and enables the control of spatial attention by selecting sensory stimuli based on internal goals or expectations and linking these goals to appropriate motor responses. The inability to ignore stimuli from the external world is one of the main symptoms of ADHD, which is associated with a loss of functional connectivity (FC) in the DAN. The VAN is located in the right temporoparietal junction (TPJ) and the ventral prefrontal cortex (VFC), and redirects attention to salient stimuli related to behavior. In this regard, fMRI studies of ADHD have revealed that the VAN and DAN are significantly hypoactivated compared to healthy controls, which has been shown to be associated with ADHD.

[0149] Task-based fMRI studies have made important contributions to the understanding of brain function in ADHD, but resting-state fMRI studies have also revealed reduced connectivity in the DAN and VAN in pediatric and adult ADHD. Therefore, the network-based causes of ADHD may be related to reduced connectivity in the attention networks, including the DAN and VAN.

[0150] The treatment according to the embodiments described herein may be induced by resting-state fMRI rather than task-based fMRI, because the pattern of network response to external stimuli (task-based studies) depends on the connectivity of the resting-state brain network (resting-state fMRI studies). Thus, the method can identify and regulate how these networks respond when engaged in tasks that require attention through computational mapping according to the embodiments described herein. Furthermore, resting-state fMRI is easier to perform and may be more sensitive in identifying functional brain networks than task-based fMRI.

[0151] The systems and methods described herein may be used for the treatment of drug addiction. Image studies have identified specific networks that cause the three stages of the addiction cycle. In the binge stage, the major components of the network center around the ventral tegmental area and the ventral striatum. The structure that plays a major role in the withdrawal stage is the amygdala.

[0152] Many networks are involved in the anticipation stage: Craving is controlled by the cingulate gyrus, orbitofrontal cortex, dorsal striatum, prefrontal cortex, amygdala, and hippocampus. Loss of inhibitory control is caused by dysfunction of the insula, dorsolateral prefrontal, and inferior frontal cortex.

[0153] A continuous neuroplastic event from the ventral to the striatum and orbitofrontal cortex causes dysregulation of the prefrontal cortex, cingulate gyrus, and amygdala, affecting the transition to addiction. Also, the identification of the neural networks involved in the transition stage of addiction has provided insights into vulnerability to addiction onset.

[0154] The fundamental mechanism by which the effects induced by TMS become an effective treatment for SUD patients is that it regulates the maladaptive brain networks of addiction. In embodiments of the methods described herein, this network-level action, supported by many preclinical and clinical findings, may be utilized to develop even more effective TMS techniques, including the use of resting-state functional connectivity MRI or fMRI-guided TMS.

[0155] The methods described herein include mapping the brain network and using network analysis focused on deep brain regions involved in addiction. Thereby, the methods described herein can determine the counterparts of the nodes on the dysfunctional network of the cerebral cortex that would be targets for neuromodulation-guided TMS treatment.

[0156] The embodiments described herein may include fMRI for assessing the severity of addiction and for guiding TMS treatment. This fMRI-guided TMS is a state-of-the-art and very complex technology that uses infrared technology to accurately target diseased structures in the brain. The embodiments described herein may provide multiple targets for applying TMS, which is very effective for quitting smoking and substance use, by this technology.

[0157] The method may include using fMRI to objectively evaluate the treatment effect after treatment completion. fMRI can be complemented with evidence-based self-help interventions, cognitive-behavioral interventions, and nicotine / substance replacement therapies. The main advantage of fMRI is that it is directly involved in the dysregulation of the motivation network.

[0158] Such dysregulation is caused by three stages: enhanced incentive salience and habit formation, reward deficits, and impaired executive function, so it can be identified by fMRI and involves networks that are accessible by TMS. It has been proven that changes in dopamine and opioid peptides in the basal ganglia are involved in the reward effects of substances, such as the expression of incentive salience and drug-seeking habits in the binge stage. Since the electric field strength is weak in deep brain regions, TMS cannot directly stimulate the basal ganglia. Therefore, targeting secondary regions of subcortical areas that are anatomically connected to the DLPFC is only possible with the information provided by fMRI.

[0159] Analysis of fMRI according to the embodiments described herein may find regions within the left DLPFC that can be used as target areas for treating SUD.

[0160] The embodiments may also be used to reduce drug consumption.

[0161] In the embodiments described herein, the same strategy can also be applied to the treatment of negative emotional states. Negative emotional states in the withdrawal symptom stage are caused by a decrease in the function of the dopamine component of the reward system and the mobilization of brain stress neurotransmitters such as corticotropin-releasing factor and dynorphin. In the amygdala network, seed-based calculations can be used to find effective cortical targets by fMRI, which may enhance the effectiveness of TMS in the treatment of negative emotional states.

[0162] Similarly, the same approach may be used to suppress cravings and executive function impairments in the anticipation stage. Here, based on the knowledge that dysregulation of the centripetal projections from the prefrontal cortex and the insula containing glutamate to the basal ganglia and the amygdala extension is involved in this stage, TMS targets are selected using fMRI. Therefore, the present system, method, and algorithm are very effective in the treatment of addiction.

[0163] The embodiments described herein are considered to be helpful in the support of eating disorders (ED). The network causing the eating disorder may be identified and corrected using fMRI TMS according to the embodiments of the method described herein.

[0164] When eating good-tasting food, the activation of reward-related regions such as the ventral and dorsal striatum, midbrain, amygdala, and orbitofrontal cortex increases. In ED, an increase or decrease in resting-state functional connectivity has also been observed when compared with the control group, and networks related to executive function, reward processing, and perception are considered to be involved. By choosing low-fat foods over high-fat foods, in patients with AN, the connectivity between the dorsal caudate and the DLPFC region increases. Since this is related to the actual food intake, the DLPFC is a very notable region.

[0165] By using fMRI imaging and the embodiments described herein to identify the causal network related to the disorder, the biological aspects of this disorder can be treated.

[0166] The embodiments described in this specification may be used for the treatment of OCD.

[0167] Neuroimaging studies have provided strong evidence indicating the involvement of neural circuits in OCD. The most promising candidate as a causal network in the pathophysiology of OCD is the cortico-striato-thalamo-cortical (CSTC) circuit.

[0168] The CSTC network is thought to be involved in multiple cognitive functions, such as the suppression of impulsive behavior, the regulation of motor activity, and the allocation of attention. Also, imaging studies have shown that the CSTC circuit has several interconnected circuits involving fronto-cortical and subcortical brain areas. These pathways are in opposition to each other and provide either an inhibitory function that reduces movement by activating the indirect pathway or a net excitation that increases movement by activating the direct pathway to the thalamus.

[0169] Various CSTC networks play a role in determining specific motor and cognitive functions. These selections are made by specific prefrontal cortex fields included in the network.

[0170] The relationship between the prefrontal cortex fields and the basal ganglia may determine which actions are selected and which are suppressed. The stimulation or suppression of appropriate behavioral sequences is determined by changes in the activity balance between the direct and indirect pathways. If a dysfunctional behavioral sequence cannot be excluded, OCD symptoms are caused.

[0171] OCD patients may have a dysfunction in the core neural processes performed by the CSTC circuit, such as response inhibition and sensorimotor gating. This means that OCD patients are biased towards performing habits at the expense of goal-directed behavior. Different populations of striatal neurons may exert different controls over the direct and indirect basal ganglia pathways, potentially causing stereotypical motor behaviors.

[0172] The direct pathway (i.e., the striatum, substantia nigra, internal segment of the globus pallidus) and the indirect pathway (i.e., the striatum, subthalamic nucleus, external segment of the globus pallidus) may contribute to the communication between the thalamus and the cerebral cortex and the generation of movement patterns. This understanding can explain why the symptoms of OCD may be caused by excessive activity in the direct or indirect OFC-subcortical network. fMRI can be used to detect the activity of the whole brain while paying special attention to the network involved in OCD. Then, using this method, targets specific to each individual patient may be determined based on the dysfunction of various regions. These different targets among patients are then treated by TMS to normalize the function of the related network.

[0173] The embodiments described herein may be used for the treatment of PTSD. PTSD is a disabling condition accompanied by symptoms of nightmares or flashbacks, avoidance, and hyperarousal after a traumatic experience.

[0174] The methods described herein, including fMRI, may be used to evaluate the roles of three basic brain networks in the understanding of higher cognitive functions, namely, the default mode network (DMN), central executive (CEN), and salience (SN). Therefore, technologies that can address the problem of dysfunction of these networks may bring about a significant change in PTSD. Although standard TMS in PTSD treatment has been successful, on the other hand, there is a fact that the neural network is not uniformly invaded, so individualized treatment is required. Some embodiments of the disclosed methods use resting-state functional connectivity MRI (rsFC MRI) as a guide for individualizing TMS therapy.

[0175] The embodiments may include fMRI-TMS with rTMS in a wide range of frequencies from 1 to 20 Hz, thereby performing theta burst (continuous / intermittent TBS) on specific targets within the right and / or left DLPFC. iTBS is high-frequency rTMS that delivers short trains of high-frequency pulses (50 Hz). The trains are delivered at a frequency of 5 Hz (every 200 milliseconds), which is within the theta region [4 - 7 Hz] of EEG. Since iTBS has many advantages such as being able to deliver an effective treatment in 3 minutes while the standard protocol for depression is 37 minutes, iTBS may be used.

[0176] The embodiments described herein may be used for the treatment of schizophrenia. TMS has been used for the treatment of schizophrenia. A type of TMS called deep TMS or dTMS, when performed on the DLPFC in schizophrenia, has shown significant improvement in negative symptoms. In this case, a treatment may be performed that applies high-frequency (18 Hz) bilateral stimulation to the DLPFC from both sides using a deep TMS coil. To ensure the reliability of the results, objective improvement scales such as the Scale for the Assessment of Negative Symptoms and the Positive and Negative Syndrome Scales may be used. Resting-state functional connectivity MRI, i.e., rsFC MRI, is an advanced imaging technique that provides the functional connectivity (FC) of the brain and may be used according to the embodiments described herein to measure the abnormalities of the brain network in schizophrenia.

[0177] Comparing schizophrenia patients with healthy controls, the connectivity between the posterior insula (PI) and the somatosensory cortex, and between the dorsal anterior insula (dAI) and the putamen was reduced in individuals at high clinical risk of psychosis (CHR) and schizophrenia patients. Furthermore, in schizophrenia patients, the connectivity between the dAI and the ventral anterior insula (vAI) and the visual cortex was enhanced compared to controls and CHR patients. FC presents evidence for the connectivity disorder hypothesis of schizophrenia.

[0178] Resting-state fMRI (rsfMRI) can map functional brain networks such as the default mode network (DMN), enabling the study of the system-level pathology of schizophrenia. In patients with schizophrenia, the connectivity of the DMN may be altered. Specifically, the features discovered by rsfMRI may include: increased connectivity of the DMN, which is a general consensus in rsFC MRI studies; changes in corticocortical-subcortical networks including the thalamocortical, fronto-limbic, and corticocerebellar networks; decreased connectivity of the prefrontal cortex (PFC), particularly within the PFC; and patterns of functional connectivity within the auditory / language network and the basal ganglia that correlate with specific clinical symptoms such as auditory-verbal hallucinations and delusions.

[0179] In the embodiments described herein, rsFC MRI and correlation analysis may be used to identify targets for TMS treatment of schizophrenia. The functional connectivity between dAI and the superior temporal gyrus may be positively correlated with the positive symptoms of CHR. Furthermore, the connectivity between vAI and DLPFC may be negatively correlated with the severity of symptoms in first-episode schizophrenia. The methods described herein may provide the ability to map the topology of the whole-brain network using rsFC MRI and utilize graph theory. The functional brain network of schizophrenia may be characterized by a decrease in small-worldness, a decrease in the connectivity of brain hubs, and a decrease in modularity.

[0180] The sensitivity of functional connectivity is sufficient to detect differences in unaffected relatives, suggesting that functional connectivity impairment is an endophenotype related to the genetic risk of schizophrenia. Since the connectivity impairment theory of schizophrenia is widely supported, this feature of rsFC MRI may be utilized to identify targets for TMS treatment.

[0181] The methods described herein may be used to treat stroke and other brain lesions. Repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC may have beneficial effects on the treatment of various neuropsychiatric or neuropsychological disorders.

[0182] In some embodiments, specific brain regions may be targeted by the disclosed methods. For example, the default mode network (DMN), cognitive control network (CCN), and affective network (AN) may involve an increase in functional connectivity (FC) related to depression in the same dorsal region (i.e., dorsal medial prefrontal) called the dorsal nexus. This result suggests that depressive symptoms are not associated with one specific network, but rather with dysfunction of several brain networks. Furthermore, PSD has been shown to cause changes in FC in the DMN and AN. These networks may be involved in the development of PSD.

[0183] Analysis of fMRI performance in acute ischemic stroke patients showed that the functional connectivity (FC) of the motor network in acute ischemic stroke was independently associated with functional outcome. Specifically, the FC between the primary motor cortex (M1) ipsilateral to the lesion and the dorsal premotor area (PMd) contralateral to the lesion was independently associated with unfavorable outcome, while the FC of the default mode network did not differ between groups. These results indicate that the interhemispheric FC of the motor network is an independent predictor of functional outcome in acute ischemic stroke patients. High-frequency rTMS to the left DLPFC may enhance low-frequency resting brain activity in the target and remote sites reflected by fALFF and FC. In this way, TMS may be used to reach remote sites affected by stroke.

[0184] Conversely, by using fMRI, the exact location of the cortical counterpart of a network with unreachable brain regions affected by stroke can be found. Embodiments described herein may include a complete brain evaluation to determine the target location for TMS treatment. Embodiments described herein include a comparison of the covariation of fMRI data between regions and networks of a patient's brain. Embodiments may include a complete evaluation of these regional networks according to the embodiments described herein. Embodiments may also focus on specific regions and networks based on known associated functional connectivity for a given state, symptom, or disease. This focus may be to reduce the necessary calculations in assessing the functional connectivity of the brain and / or in providing special attention, for example, by more finely partitioning regions to more specifically target regions that are already thought to be beneficial for TMS treatment in relation to a given symptom or disease. Using such prior knowledge of the functional relationships described herein, the regions and / or region sizes of the brain regions described herein may be determined and compared and evaluated. Using such prior knowledge of the functional relationships described herein, some comparisons may be excluded for areas of no interest or areas not thought to be functionally related or associated with a given symptom or disease, and / or the regions for comparison may be expanded.

[0185] Conventional TMS approaches approved by the FDA use a 5 cm rule for TMS targeting. This approach does not include any image guidance and does not individualize to account for differences in brain regions between individuals. In some attempts to use fMRI to guide TMS, the region of the DLPFC that shows the highest anti-correlation with the subgenual cingulate is determined to detect the target of TMS stimulation for depression. This approach focuses only on two specific regions of the brain and uses only a single relationship between these regions. The embodiments described herein may include a more complete brain evaluation for detecting brain circuits that include multiple targets for TMS and LIFUS. Embodiments may include an evaluation of the entire brain, as opposed to an approach that specifically focuses on the DLPFC. Cortical brain circuits may be selected for targeted stimulation by TMS. Subcortical brain circuits may be selected for targeted stimulation by LIFUS. The embodiments described herein may include a circuit-based approach that evaluates multiple interconnected brain regions for stimulation, as opposed to a single-region application to the DLPFC. The embodiments described herein may be used not only for depression, but also for other brain injuries such as TBI, or other brain lesions, etc. The embodiments described herein may identify and extract the amplitude and frequency of brain activity within / between brain networks to analyze the connectivity between circuits.

[0186] The specific brain states shown and described herein relate to functional connectivity analysis and the resulting regions presumed to be areas of interest for targeted therapy of stimulation. Specific examples are shown herein, but these are not intended to be limiting. As described herein, for any brain state, structural analysis may be added or used for the detection of abnormalities and / or determination of target locations for stimulation.

[0187] Total cortical gray matter volume and total white surface area in a prospective case series of neurodevelopmental disorders (such as autism spectrum disorder) and neurodegenerative diseases (such as dementia, Alzheimer's disease, etc.).

[0188] Tables 1-2 provided herein show the improvement of structural connectivity as an example of the successful use of structural connectivity in the analysis of neurodevelopmental states. [Table 1] [Table 2]

[0189] Table 1 provides a sample of 5 patients with autism spectrum disorder aged 18 to 22 years. In the case of the method using structural connectivity, an increase in total cortical gray matter volume was observed in all patients after completion of 2 months of fMRI-guided TMS treatment (5 times a week). Individual and average measurements at baseline and after treatment are shown. In healthy controls, the total cortical gray matter volume decreases by approximately 1% per year.

[0190] Table 2 provides a sample of 5 patients with autism spectrum disorder aged 18 to 22 years. After completion of 2 months of fMRI-guided TMS treatment (5 times a week treatment) using the structural connectivity analysis according to the embodiments described herein, all patients showed an increase in the total white surface area. Individual and average measurements at baseline and after treatment are shown. In healthy children, the total white surface area generally increases by 1.7% over 3 years (from 9 to 12 years).

[0191] Tables 3-4 provided herein show the improvement of structural connectivity as an example of the successful use of structural connectivity in the analysis of neurodegenerative disorders. [Table 3] [Table 4]

[0192] Table 3 provides a sample of 5 dementia (Alzheimer's disease) patients in the age range of 69 - 72 years. After completion of fMRI-guided TMS treatment for 5 weeks per week for 6 months, all patients showed an increase in total cortical gray matter volume. Individual and mean measurements at baseline and after treatment are shown. Regional gray matter reduction rate (5.3 ± 2.3% per year in AD vs. 0.9 ± 0.9% per year in controls).

[0193] Table 4 provides a sample of 5 dementia (Alzheimer's disease) patients in the age range of 68 - 72 years. After completion of fMRI-guided TMS treatment (5 times per week) for 6 months, all patients showed an increase in total white surface area. Individual and mean measurements at baseline and after treatment are shown. In healthy individuals ranging from 30 to 90 years of age, a 26% decrease in white matter tissue volume was observed compared to a 14% decrease in gray matter tissue volume.

[0194] The computing device described in this specification is a non-conventional system. The reason is, at least, using non-conventional components and / or using non-conventional algorithms, processes, and methods that are at least partially included in the programming instructions stored and / or executed by the computing device. For example, embodiments may include a configuration and process including a unique magnetic device for applying a magnetic field to a patient according to the methods and algorithms for specific treatment targeting described in this specification; a configuration and process including a unique transcranial magnetic stimulation device or a low frequency intensity focused ultrasound device, with and without using high-precision navigation; a unique process and algorithm for determining specific brain circuits of a patient's brain, individualized for each patient; a unique configuration and process for targeting and arranging TMS to be applied to a specific brain circuit, individualized for each patient; or a combination thereof. The systems and methods described in this specification also include algorithms for identifying and extracting the amplitude and frequency of brain activity within and between brain networks for precise and individualized TMS treatment. Embodiments may be used to identify target positions for TMS treatment of neurological and psychiatric conditions, examples of which include, but are not limited to, traumatic brain injury, brain lesions, mental illness, depression, maintenance, etc. In particular, autism and dementia are included. The preferred embodiments described in this specification may also be used, additionally or alternatively, not only for treating but also for preventing pathological conditions, for example, preventing cognitive decline due to aging. Embodiments may be used to improve the duration of the therapeutic effect so that the beneficial therapeutic effect lasts longer. Embodiments may include a combination of functional connectivity and / or structural connectivity and may be for determining a target treatment area individualized for an individual. By using a combination of functional connectivity and / or structural connectivity, a more complete analysis of an individual's brain function based on the individual's and / or brain's state may be provided, and a target position for stimulation that is most likely to improve the patient's state may be found.

[0195] An embodiment includes a non - transitory computer - accessible medium storing computer - executable instructions for determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient, the computer - executable instructions being configured to perform a method when executed by one or more processors. A method for determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient includes receiving functional magnetic resonance imaging (fMRI) data of the head of the patient; analyzing the functional connectivity of the patient's brain through the fMRI data by measuring a change in any combination of a first variation that is a variation in the amplitude of the fMRI image data, a second variation that is a variation in the frequency of the fMRI data, or a third variation that is a relative frequency variation with respect to the amplitude of the fMRI data; and determining one or more target regions for TMS treatment of the patient based on the measurement of any combination of the first variation, the second variation, or the third variation.

[0196] Embodiments of the non - transitory computer - accessible medium, instructions, or method may optionally include a comparison of the measurement of any combination of the first variation, second variation, or third variation with measurement values of a healthy control group that matches the patient's age range and gender.

[0197] Embodiments of the non - transitory computer - accessible medium, instructions, or method may include an analysis of the functional connectivity of the patient's brain including the first variation, second variation, or third variation, including determining a matrix of activation, correlation, co - variation, or a combination thereof between different brain networks of the patient's brain; a matrix between regions within a network of the patient's brain; a matrix between a region within a network and all other combinations of regions of that network; a matrix between different brain regions within a network and all other regions within the same network; and / or a matrix between different brain regions of different networks; in any combination.

[0198] A non-transitory computer-accessible medium, instructions, or method may include any combination of: selecting a first plurality of brain regions having a small change in activation, correlation, co-variation, or a combination thereof, relative to a larger number of brain networks or regions; selecting a second plurality of brain regions having a large change in activation, correlation, co-variation, or a combination thereof, relative to a larger number of brain networks or regions; comparing the first and second pluralities of brain regions to brain regions from a healthy control group that match the patient's age range and gender, and / or selecting brain regions that exceed a first threshold and are less than a second threshold to create a potential target group of brain regions for TMS treatment.

[0199] A non-transitory computer-accessible medium, instructions, or method may include a first threshold that exceeds the mean of a healthy control group by within two standard deviations, and a second threshold that is less than the mean of a healthy control group by within two standard deviations.

[0200] A non-transitory computer-accessible medium, instructions, or method may include any combination of: determining a subset from the potential target group of brain regions by comparing the potential target group of brain regions for TMS treatment to a second control group that matches the patient's symptoms, diagnosis, or a combination thereof, and determining which of said regions has the greatest change in fMRI data from before to after TMS treatment in the second control group; and / or performing TMS treatment on a patient based on at least one parameter from the treatment plan of a member of the second control group having a brain region with the greatest change in fMRI data from before to after TMS treatment.

[0201] A non-transitory computer-accessible medium, instructions, or method may include receiving diffusion-weighted magnetic resonance imaging (dMRI) data of a patient's head; analyzing the structural connectivity of the patient's brain through analysis of the dMRI data by comparing the strength of white matter connections between combinations of parcels within the patient's brain; wherein determination of one or more target regions for TMS treatment of the patient is performed based on the analysis of the structural connectivity. The non-transitory computer-accessible medium, and / or method may include selecting a target location for stimulation wherein the determination of one or more target regions includes selecting a target having a greater number of white matter connections compared to other potential targets.

[0202] A non-transitory computer-accessible medium, instructions, or method may include performing the analysis of the structural connectivity of the patient's brain after analysis of the functional connectivity, and determining the one or more target regions may first be performed based on the functional connectivity and then using the analysis of the structural connectivity to select a final subset.

[0203] A non-transitory computer-accessible medium, instructions, or method may include: defining a plurality of brain networks and a plurality of brain regions of a patient; for each of the plurality of brain networks of the patient, comparing the covariation between one brain network and each other brain network of the plurality of brain networks; for each of the plurality of brain regions, comparing the covariation between a first brain region and each other brain region within the same brain network as the first brain region; for each of the plurality of brain regions, comparing the covariation between a second brain region and each other brain region within a different brain network from the second brain region; for each of the plurality of brain regions, comparing the covariation between a third brain region and each other brain network different from the brain network in which the third brain region is included; selecting a first set of regions with the smallest change in the comparison of covariation for the largest number of brain regions among the plurality of regions; selecting a second set of regions with the smallest change in the comparison of covariation for the largest number of brain regions among the plurality of regions; comparing the first set of regions and the second set of regions with a healthy control group, and determining a first set of potential targets outside a predetermined normal range in the comparison with the healthy control group from the first set of regions and the second set of regions; and determining a target set of regions from the comparison of the first set of regions and the second set of regions with the healthy control group; and may include performing functional connectivity analysis thereby.

[0204] A non-transitory computer-accessible medium, instructions, or method may include determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient to treat a mental disorder. The method includes receiving functional magnetic resonance imaging (fMRI) data of the patient's head; analyzing the functional connectivity of the patient's brain through analysis of the fMRI data by measuring a change in any combination of a first variation that is a variation in the amplitude of the fMRI image data, a second variation that is a variation in the frequency of the fMRI data, or a third variation that is a variation in the relative frequency with respect to the amplitude of the fMRI data; determining one or more target regions for TMS treatment of the patient based on the measurement of any combination of the first variation, the second variation, or the third variation; applying TMS treatment to the one or more target regions; and improving the patient's mental disorder.

[0205] The non-transitory computer-accessible medium, instructions, or method may include that the mental disorder is selected from Alzheimer's disease, anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, schizophrenia, insomnia, eating disorder, cognitive impairment, drug intoxication, depression, attention deficit hyperactivity disorder, attention deficit disorder, bipolar disorder, autism spectrum disorder, neurodevelopmental disorder, and psychosis.

[0206] The non-transitory computer-accessible medium, instructions, or method may include receiving diffusion tensor magnetic resonance imaging (dMRI) data of the patient's head; analyzing the structural connectivity of the patient's brain through analysis of the dMRI data by comparing the strength of white matter connections between combinations of parcels within the patient's brain; and determining one or more target regions for TMS treatment of the patient based on the analysis of the structural connectivity.

[0207] A non-transitory computer-accessible medium, instructions, or method may include receiving diffusion-weighted magnetic resonance imaging (dMRI) data of a patient's head; analyzing the structural connectivity of the patient's brain through analyzing the dMRI data by generating a brain structural connectivity matrix constructed based on white matter tractography from the whole brain; and determining, based on the analysis of the structural connectivity, one or more target regions for TMS treatment of the patient.

[0208] A non-transitory computer-accessible medium, instructions, or method may include receiving functional magnetic resonance imaging (fMRI) data; constructing a covariance matrix using the fMRI data; selecting a first set of potential targets with stronger covariance values for a first large group of brain regions, a second set of potential targets with weaker covariance for a second large group of brain regions, or a combination of the first set of potential targets and the second set of potential targets; receiving dMRI data; constructing a structural connectivity matrix using the dMRI data; and selecting a target region with a greater number of white matter connections from the first set of potential targets, the second set of potential targets, or the first and second sets of potential targets.

[0209] A non-transitory computer-accessible medium, instructions, or method may include receiving functional magnetic resonance imaging (fMRI) data; constructing a covariance matrix using the fMRI data; selecting a first set of potential targets with stronger covariance values for a first large group of brain regions, a second set of potential targets with weaker covariance for a second large group of brain regions, or a combination of the first set of potential targets and the second set of potential targets; receiving dMRI data; constructing a structural connectivity matrix using the dMRI data; and selecting a target region with a greater number of white matter connections from the first set of potential targets, the second set of potential targets, or the first and second sets of potential targets.

[0210] A non-transitory computer-accessible medium, instructions, or method may include receiving dMRI data; constructing a structural connectivity matrix using the dMRI data; selecting a set of potential targets having fewer white matter connections; receiving fMRI data; constructing a covariance matrix using the fMRI data; selecting a first set of targets from the set of potential targets having stronger covariance values for a first large group of brain regions, selecting a second set of targets from the set of potential targets having weaker covariance for a second large group of brain regions, or selecting a combination of the first set of targets and the second set of targets.

[0211] A non-transitory computer-accessible medium, instructions, or method may include constructing a structural connectivity matrix using structural data including T1w / T2w.

[0212] The systems described herein may be based on software and / or hardware. Although some specific embodiments of the invention have been shown, the invention is not limited to these embodiments. For example, most functions implemented by electronic hardware components can be replicated by software emulation. That is, a software program written to achieve the same functions may emulate the functionality of the hardware components within the input-output circuitry. It should be understood that the invention is not limited by the specific embodiments described herein, but only by the appended claims.

[0213] Although embodiments of the present invention have been fully described with reference to the accompanying drawings, various changes and modifications will be apparent to those skilled in the art. Such changes and modifications are understood to be included within the scope of the embodiments of the present disclosure as defined by the appended claims. Any of the disclosed components may be used in any combination. For example, any component, feature, step, or part may be integrated, separated, subdivided, removed, replicated, added, or used in any combination, and these remain within the scope of the present disclosure. The embodiments are merely illustrative and provide examples of combinations as illustrations of features, but are not limited thereto.

[0214] The above merely exemplifies the principles of the present disclosure. The examples described herein are all intended to be non-limiting and merely show some of the many possible embodiments for the appended claims. Those skilled in the art will readily recognize that various modifications and changes can be made without following the preferred embodiments and uses described and set forth herein and without departing from the true spirit and scope of the following claims.

[0215] All references cited and / or discussed in this specification are hereby incorporated by reference in their entirety to the same extent as if each reference were individually incorporated by reference.

Claims

1. A non - transitory computer - accessible medium storing computer - executable instructions for determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient, wherein the computer - executable instructions, when executed by one or more processors, receive functional magnetic resonance imaging (fMRI) data of the patient's head, analyze the functional connectivity of the patient's brain through the fMRI data by measuring changes in any combination of a first variation that is a variation in the amplitude of the fMRI image data, a second variation that is a variation in the frequency of the fMRI data, or a third variation that is a relative frequency variation with respect to the amplitude of the fMRI data, determine one or more target regions for TMS treatment of the patient based on the measurement of any combination of the first variation, the second variation, or the third variation, and are configured to perform a method comprising the above. The non - transitory computer - accessible medium.

2. The non - transitory computer - accessible medium according to claim 1, wherein the computer - executable instructions are further configured to compare the measurement of any combination of the first variation, the second variation, or the third variation with the measurement values of a healthy control group that matches the patient's age range and gender.

3. The analysis of the functional connectivity of the patient's brain including the first variation, second variation, or third variation determines a matrix of activation, correlation, co - variation, or a combination thereof between different brain networks of the patient's brain, determines a matrix of activation, correlation, co - variation, or a combination thereof between regions within a network of the patient's brain, determines a matrix of activation, correlation, co - variation, or a combination thereof between a region within a network and all other combinations of regions of the network, determines a matrix of activation, correlation, co - variation, or a combination thereof between different brain regions within a network and all other regions within the same network, or determines a matrix of activation, correlation, co - variation, or a combination thereof between different brain regions of different networks, and may include any combination of the above. The non - transitory computer - accessible medium according to claim 1 or 2.

4. The computer-executable instructions selecting a first plurality of brain regions having small changes in activation, correlation, co-variation, or combinations thereof for a greater number of brain networks or regions; selecting a second plurality of brain regions having large changes in activation, correlation, co-variation, or combinations thereof for a greater number of brain networks or regions; comparing the first plurality of brain regions and the second plurality of brain regions with brain regions from a healthy control group that matches the age range and gender of the patient, and selecting brain regions that exceed a first threshold and brain regions that are less than a second threshold to create a potential target group of brain regions for TMS treatment; or performing TMS treatment on the patient based on at least one parameter from the treatment plan of a member of a second control group having the brain region with the greatest change in fMRI data from before TMS treatment to after TMS treatment; executing and further configured to combine, the non-transitory computer-accessible medium according to any one of claims 1 to 3. **Claim 5** The non-transitory computer-accessible medium according to any one of claims 1 to 4, wherein the first threshold exceeds the average value of the healthy control group within two standard deviations, and the second threshold is below the average value of the healthy control group within two standard deviations. **Claim 6** The non-transitory computer-accessible medium according to any one of claims 1 to 5, wherein the potential target group of brain regions for TMS treatment is compared with a second control group that matches the patient's symptoms, diagnosis, or a combination thereof, and a subset is determined from the potential target group of brain regions by determining in which of the regions the change in fMRI data from before TMS treatment to after TMS treatment of the second control group is the greatest. **Claim 7** The computer-executable instructions receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the patient's head; analyzing the structural connectivity of the patient's brain through analysis of dMRI data by comparing the strength of white matter connections between combinations of parcels in the patient's brain; further configured to execute, wherein determination of one or more target regions for TMS treatment of the patient is performed based on the analysis of the structural connectivity. The non-transitory computer-accessible medium according to any one of claims 1 to 6. **Claim 8** The non - transitory computer - accessible medium according to any one of claims 1 to 7, wherein determining the one or more target regions includes selecting a target position for stimulation that has a greater number of white - matter connections compared to other potential targets.

9. The non - transitory computer - accessible medium according to any one of claims 1 to 8, wherein the analysis of the structural connections of the patient's brain is performed after the analysis of the functional connections, and the determination of the one or more target regions is first performed based on the functional connections and then a final subset is selected using the analysis of the structural connections.

10. Performing a treatment to improve the patient's condition using TMS stimulation at a target determined by any one of claims 1 to 9.

11. A method for determining one or more target regions for the stimulation treatment of a patient, comprising: defining a plurality of brain networks of the patient and a plurality of brain regions of the patient; for each of the plurality of brain networks of the patient, comparing the covariation between one brain network and each of the other brain networks of the plurality of brain networks; for each of the plurality of brain regions, comparing the covariation between a first brain region and each of the other brain regions within the same brain network as the first brain region; for each of the plurality of brain regions, comparing the covariation between a second brain region and each of the other brain regions within a different brain network from the second brain region; for each of the plurality of brain regions, comparing the covariation between a third brain region and each of the other brain networks different from the brain network in which the third brain region is included; selecting a first set of regions with the smallest change in the comparison of covariation for the largest number of brain regions among the plurality of regions; selecting a second set of regions with the smallest change in the comparison of covariation for the largest number of brain regions among the plurality of regions; comparing the first set of regions and the second set of regions with a healthy control group, and determining a first set of potential targets outside a predetermined normal range in the comparison with the healthy control group from the first set of regions and the second set of regions; determining a set of target regions from the comparison of the first set of regions and the second set of regions with the healthy control group; A method comprising performing a functional connectivity analysis.

12. A method for determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient for treating a mental disorder, comprising: Receiving functional magnetic resonance imaging (fMRI) data of the head of the patient; Analyzing the functional connectivity of the patient's brain through the analysis of the fMRI data by measuring changes in any combination of a first variation that is a variation in the amplitude of the fMRI image data, a second variation that is a variation in the frequency of the fMRI data, or a third variation that is a relative frequency variation with respect to the amplitude of the fMRI data; Determining one or more target regions for TMS treatment of the patient based on the measurement of any combination of the first variation, the second variation, or the third variation; Applying TMS treatment to the one or more target regions; Improving the mental disorder of the patient; A method comprising. **Claim 13** The method according to claim 12, wherein the mental disorder is selected from Alzheimer's disease, anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, schizophrenia, insomnia, eating disorder, cognitive impairment, drug intoxication, depression, attention deficit hyperactivity disorder, attention deficit disorder, bipolar disorder, autism spectrum disorder, neurodevelopmental disorder, and psychosis. **Claim 14** A non-transitory computer-accessible medium storing computer-executable instructions for determining one or more target regions for transcranial magnetic stimulation (TMS) treatment of a patient, wherein when the computer-executable instructions are executed by one or more processors, Receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the head of the patient; Analyzing the structural connectivity of the patient's brain through the analysis of the dMRI data by comparing the strength of white matter connections between combinations of parcels within the patient's brain; Determining one or more target regions for TMS treatment of the patient based on the analysis of the structural connectivity; A non-transitory computer-accessible medium configured to perform a method comprising. **Claim 15** Receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the head of the patient; Analyzing the structural connectivity of the patient's brain through the analysis of the dMRI data by generating a brain structural connectivity matrix constructed based on white matter tractography from the whole brain; Determining one or more target regions for TMS treatment of the patient based on the analysis of the structural connection; A method comprising.

16. The method according to claim 15, wherein the structural connectivity matrix may include a comparison of the strength of white matter connections between each combination of two parcels among a plurality of parcels.

17. The method according to any one of claims 15 or 16, wherein the analysis of the structural connection of the patient's brain includes using structural data to perform a structural connectivity analysis, and the structural data is from T1w to T2w.

18. The method according to any one of claims 15 to 17, further comprising selecting a target position for stimulation by selecting a region having a larger number of white matter connections compared to other regions.

19. The method according to any one of claims 15 to 17, further comprising selecting a target position for stimulation by selecting a region having the largest number of white matter connections from a pool of targets based on the functional connectivity of fMRI.

20. A method for determining a target position for stimulation, comprising: Receiving fMRI data; Constructing a covariance matrix using the fMRI data; Selecting a first set of potential targets having stronger covariance values for a first large group of brain regions, selecting a second set of potential targets having weaker covariance for a second large group of brain regions, or selecting a combination of the first set of potential targets and the second set of potential targets; Receiving dMRI data; Constructing a structural connectivity matrix using the dMRI data; Selecting a target region having a larger number of white matter connections from the first set of potential targets, the second set of potential targets, or the first and second sets of potential targets; A method comprising.

21. The method according to claim 20, wherein the structural connectivity matrix is constructed using structural data including T1w / T2w.

22. The method according to any one of claims 20 to 21, further comprising treating a mental disorder including a structural injury including any combination of stroke, traumatic brain injury, neurodevelopmental disorder, or neurodegenerative disorder.

23. A method for determining a target position for stimulation, comprising: Receiving dMRI data; Constructing a structural connectivity matrix using the dMRI data; Selecting a set of potential targets with fewer white matter connections, Receiving fMRI data, Constructing a covariance matrix using the fMRI data, Selecting, from the set of potential targets, a first set of targets with stronger covariance values for a first large group of brain regions, selecting, from the set of potential targets, a second set of targets with weaker covariance for a second large group of brain regions, or selecting a combination of the first set of targets and the second set of targets, A method comprising.

24. The method according to claim 23, wherein the structural connectivity matrix is constructed using structural data including T1w / T2w.

25. The method according to any one of claims 23 or 24, further comprising treating a patient who has experienced a stroke, traumatic brain injury, neurodevelopmental disorder, neurodegenerative disorder, or a combination thereof.

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