Systems and methods for whole brain circuit-based neurostimulation to treat brain conditions

Abstract

Provided herein are targeting and personalization methods for determining a specific treatment site based on an individual patient. Some embodiments described herein may use functional and structural connections to create brain mappings for a patient and / or a control group having the same metric as the patient, and combinations thereof, to determine a target location for stimulation therapy.

Description

Background Art

[0001] Transcranial magnetic stimulation (TMS) is a non-invasive, non-pharmacological procedure used to treat neuropsychiatric disorders in which a magnetic coil is used to stimulate specific areas of the brain. TMS uses short magnetic field pulses to induce electric currents in the underlying cortical tissue. Through electromagnetic induction, when magnetic pulses are delivered to a coil placed on top of the patient's skull, the TMS coil induces electric currents at predetermined targets within the brain.

[0002] In psychiatry, TMS has been successfully used to treat depression, anxiety disorders (including panic disorder and obsessive-compulsive disorder (OCD)). TMS has also been studied with a large number of neuropsychiatric disorders, including autism, eating disorders, substance abuse and addiction, posttraumatic stress disorder (PTSD), Alzheimer's disease, coma recovery, stroke, tinnitus, multiple sclerosis and neurorehabilitation. For treatment-resistant major depression, TMS has been most widely and effectively used in high-frequency (HF) mode on the left frontal region of the brain (called dorsolateral prefrontal cortex (DLPFC)). TMS is also used in low-frequency (LF) mode with complementary effectiveness on the right DLPFC.

[0003] TMS on the left dorsolateral prefrontal cortex (L-DLPFC) is an FDA-approved treatment for treatment-refractory depression (TRD). The treatment is only partially effective with response and remission rates of 41.2% and 35.3%, respectively. The FDA-approved regimen for TRD determines the left DLPFC stimulation site by moving the coil along the curvature of the scalp to 5 cm in front of the "hand motor hotspot" (motor cortex). This method only provides approximate targeting of the left DLPFC, with no consistent distinction between DLPFC subregions. Ten years after the FDA approved TMS treatment, clinical success has not increased significantly. Summary of the invention

[0004] The embodiments described herein are exemplary only. The features of the embodiments provide examples of different combinations of features. However, the present disclosure also encompasses feature variations in different combinations.

[0005] Embodiments herein include functional connectivity analysis between regions, between regions and networks, and between networks to personalize TMS treatment.Some embodiments include determining more than one treatment location for administering TMS treatment.

[0006] Some embodiments of determining treatment locations for treatment include determining one or more treatment areas and networks by comparing patient information to a healthy control group to determine those areas that are outside of a desired range compared to the healthy control group. Some embodiments of determining treatment locations for treatment include determining one or more treatment areas with the highest change in a given area from before treatment to after treatment for an individual with the same characteristics as the patient. The characteristics can be any combination of the patient's gender, age, age range, race, weight, symptoms, diagnosis, previous treatment, response to previous treatment, genetic factors, previous medical conditions, symptoms, and / or diagnosis.

[0007] Some embodiments of determining treatment locations include comparing properties of fMRI data including changes in amplitude and frequency of spontaneous blood oxygen level fluctuations over time. Comparison of brain regions and networks is based on covariation of changes in spontaneous blood oxygen levels over time. Covariation can include changes in frequency and amplitude of blood oxygen levels over time. Determination of treatment locations for treatment includes comparing the covariation of each brain region with each other region, each brain region with each other network, and each network with each other network, for example by taking the absolute value of the covariation difference of each brain region with each other region, each brain region with each other network, and each network with each other network.

[0008] Determination of treatment locations for treatment includes using amplitude and frequency of brain activity within and between networks, between regions, and / or between regions and networks. Some embodiments herein may use functional connectivity at rest to assess brain activity. Systems and methods herein may include a magnetic resonance imaging (MRI) system. Functional connectivity at rest may be based on changes in brain blood oxygen levels over time. Some embodiments herein may use blood oxygenation level dependent (BOLD) imaging to produce images in functional magnetic resonance imaging (fMRI).

[0009] Provided herein are TMS treatment systems and methods for determining networks and regions in a patient's brain for administering TMS treatment. Although explained herein in terms of TMS treatment, exemplary embodiments are not limited thereto. In some exemplary embodiments, treatment may also or alternatively include low intensity focused ultrasound (LIFUS). In an exemplary embodiment, if the resulting brain region and / or network for treatment is determined to be a cortical brain circuit, the location may be stimulated with TMS. If the brain region and / or network for treatment is determined to be a subcortical brain circuit, the location may be stimulated with LIFUS. Other regimens and treatments, such as electrical stimulation, may also be used.

[0010] Systems and methods may be configured to analyze frequency, amplitude, and changes in frequency relative to amplitude (or vice versa) based on spontaneous blood oxygen level-dependent fluctuations as determined from an MRI of a patient's brain. Comparisons based on brain regions and networks. Systems may be configured and / or methods may include calculating activation (amplitude of spontaneous BOLD fMRI fluctuations over time), correlation (frequency of spontaneous BOLD fMRI fluctuations over time), and / or covariation (correlation of activation with spontaneous BOLD fMRI fluctuations over time) of various brain networks and / or regions.

[0011] Provided herein are systems, methods, and non-transitory computer-accessible media having computer-executable instructions stored thereon for determining one or more target regions for TMS treatment of a patient. The systems, methods, and instructions may be configured to: receive fMRI data of a patient's head; analyze the functional connectivity of the patient's brain by analyzing the fMRI data by determining changes in any combination of: a first fluctuation in the amplitude of the fMRI imaging data, a second fluctuation in the frequency of the fMRI data, or a third fluctuation in the frequency relative to the amplitude of the fMRI data; and determine one or more target regions for TMS treatment of the patient based on the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation.

[0012] The systems, methods, and instructions may be configured to compare the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation with measurements of a healthy control group that matches one or more characteristics of the patient. Exemplary characteristics of the patient may include any combination of the following: the patient's gender, age, age range, race, weight, symptoms, diagnosis, previous treatment, response to previous treatment, genetic factors, previous medical conditions, symptoms, and / or diagnosis. The systems, methods, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and covariation matrices between different brain networks of the patient's brain. The systems, methods, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and covariation matrices between regions within a network.

[0013] The systems, methods, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and covariation matrices between different brain regions within a network and all other regions within the same network. The systems, methods, and instructions may analyze the functional connectivity of the patient's brain by determining activation, correlation, and covariation matrices between different brain regions of different networks. The systems, methods, and instructions may select a first plurality of regions having low variations in amplitude, frequency, and frequency relative to amplitude in the case of a larger number of brain networks or regions. The systems, methods, and instructions may select a second plurality of regions having high variations in amplitude, frequency, and frequency relative to amplitude in the case of a large number of brain networks or regions.

[0014] The systems, methods, and instructions may compare the first plurality of regions and the second plurality of regions to regions from a healthy control group matching the patient's characteristics and select regions above a first threshold and below a second threshold to create a set of potential target regions for treatment. The systems, methods, and instructions may compare the set of potential target regions for treatment to a second control group matching the patient's symptoms or diagnosis and determine a subgroup from the set of potential target regions by determining which region from the second control group has the greatest change in fMRI data from pre-treatment to post-treatment. The systems, methods, and instructions may include comparisons of brain regions and networks outside of the DLPhFC.

[0015] The systems, methods, and instructions may include selecting more than one target location for treatment based on the analysis. The systems, methods, and instructions may include analyzing frequency changes and amplitudes in the determined target locations for treatment. The systems, methods, and instructions may include early diagnosis and treatment options for detecting areas at risk for brain damage before structural detection becomes apparent.

[0016] The systems, methods and instructions can be used to determine a patient's brain region that is negatively correlated with a large number of other regions of the brain. Using fMRI to compare the functional connectivity of brain regions, the systems, methods and instructions can provide early detection of future brain damage by determining brain regions that are functionally disconnected but have not yet experienced neural death or physical decline.

[0017] Systems, methods, and non-transitory computer-accessible media having computer-executable instructions for determining one or more target areas for TMS treatment of a patient stored thereon may be configured to: receive MRI data of a patient's head; analyze the structural connectivity of the patient's brain by analyzing the MRI data by generating a brain structural connectivity matrix constructed based on white matter tractography from the whole brain. In one embodiment, the structural data is used to run a structural connectivity analysis (tractography). The structural data may be T1w / T2w. T1-weighted (T1w) and T2-weighted (T2w) are MRI sequence weighted scans, where T1w MRI can enhance the signal of fat tissue and suppress the signal of water, while T2w MRI can enhance the signal of water. The structural connectivity matrix may include a comparison of the strength of white matter connections between each combination of two partitions in a plurality of parcels. The strength of white matter connections can be performed using diffusion MRI streamline tractography. Using voxel-specific directional diffusion information from diffusion-weighted MRI (dMRI), computational fiber tractography produces three-dimensional trajectories, called streamlines, through the white matter within the MRI volume. The connections between corresponding regions of interest (ROI) can be quantified as the number of streamlines therebetween. Systems and methods may include selecting a target location for stimulation, which may include selecting a region with a greater number of white matter connections than other regions. Systems and methods may include selecting a target location for stimulation, which may include selecting a region with the greatest number of white matter connections from a target pool based on fMRI functional connectivity.

[0018] The systems and methods of the present invention can be used to determine target areas for stimulation using any combination of functional connectivity and / or structural connectivity. Structural connectivity and functional connectivity analyses can be used alone or in combination. When used in combination, the analyses can be run sequentially, such that a first set of target areas is determined using a first analysis (whether structural connectivity or functional connectivity), and then another analysis (the other of functional connectivity or structural connectivity) of those first set of target areas is used to focus. In this case, another analysis can be used on the outcome from the first analysis to further improve the results from the first analysis, thereby creating a target area group for stimulation therapy. When used in combination, the analyses can be run simultaneously, such that a first set of target areas is determined using a first analysis (whether structural connectivity or functional connectivity), and a second set of target areas is determined using a second analysis (whether functional connectivity or structural connectivity). The target for stimulation that is ultimately determined can be a combination of areas from one or both of the results of the first and second analyses.

[0019] Embodiments described herein may utilize more anatomically specific models of brain connectivity (functional and / or structural) that can be constructed for individual patients for targeted stimulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated herein and constitute a part of this specification, are illustrative of some specific embodiments of the present disclosure and do not limit the scope of the present disclosure. The drawings are not drawn to scale and are intended to be used in conjunction with the explanations in the following detailed description.

[0021] Figure 1 A process diagram illustrating an embodiment of a method using a network-guided stimulation approach.

[0022] Figure 2 is an illustrative working model explanation of the approach described in this article.

[0023] about Figures 3A to 8B , the figure with "A" provides an exemplary flow chart according to the algorithm described in this article, while the figure with "B" shows a representative brain segmentation to explain the flow chart. Accordingly, Figures 3A to 3B An exemplary analysis for determining the covariation of each network within a patient's brain is shown. Figures 4A to 4B An exemplary analysis for determining covariation for each region within the brain is shown. Figures 5A to 5B An exemplary analysis for comparing covariation between intracerebral networks is shown. Figures 6A to 6B An exemplary analysis of covariation for comparing each region with each external network from the network containing the compared region is shown. Figures 7A to 7B An exemplary analysis for comparing covariation between regions within the same network is shown. Figures 8A to 8B An exemplary analysis of covariation for comparing regions within one network to regions within other networks is shown. The systems and methods can use any combination of analyses involving different covariation calculations and comparisons.

[0024] Figures 9A to 9F Shown are different views of an fMRI brain scan acquired from a patient before receiving TMS treatment. Figures 10A to 10F Shows that after TMS treatment, Figures 9A to 9F Different views of an fMRI brain scan of a patient.

[0025] Figures 11A to 124D , 126A to D, and 128A to 147D show different views of fMRI scans of a control group and a patient before and after treatment for comparison according to some exemplary embodiments of the method for determining a target location for stimulation therapy. The figures of the brain scans are each identified by a letter to identify different views of the same brain scan with the same number.

[0026] Figures 11A to 11B is a set of scans at baseline, which shows the target treatment area at the rSFC region STSdp; Figures 12A to 12B is a set of scans after fMRI-guided TMS according to the methods described herein; and Figures 13A to 13B is a set of scans from a healthy control group matched to the patient for the same age and sex.

[0027] Figures 14A to 16B is a set of scans at baseline, which shows the target treatment area at RSFC area TPO1; Figures 15A to 15B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 16A to 16B These are scans of healthy controls matched to the patients for the same age and sex.

[0028] Figures 17A to 17B is a set of scans at baseline, which shows the target treatment area at RSFC region STV; Figures 18A to 18B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 19A to 19B These are scans of healthy controls matched to the patients for the same age and sex.

[0029] Figures 20A to 20B is a set of scans at baseline, which shows the target treatment area at the RSFC region SFL; Figures 21A to 21B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 22A to 22B These are scans of healthy controls matched to the patients for the same age and sex.

[0030] Figures 23A to 23B is a set of scans at baseline, which shows the target treatment area at RSFC area 55b; Figures 24A to 24B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 25A to 25B The patients were matched with healthy controls of the same age and sex.

[0031] Figures 26A to 26B is a set of scans at baseline, which shows the target treatment area at RSFC area 44; Figures 27A to 27B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 28A to 28B The patients were matched with healthy controls of the same age and sex.

[0032] Figures 29A to 29B is a set of scans at baseline, which shows the target treatment area at RSFC area 45; Figures 30A to 30B is a scan after fMRI-guided TMS according to the method described in this article; and Figures 31A to 31B The patients were matched with healthy controls of the same age and sex.

[0033] Figures 32A to 32D is a set of scans at baseline, which shows the target treatment area at RSFC area p9-46v; Figures 33A to 33D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 34A to 34D The patients were matched with healthy controls of the same age and sex.

[0034] Figures 35A to 35D is a set of scans at baseline, which shows the target treatment area at RSFC area STSdp; Figures 36A to 36D This is the scan after fMRI-guided TMS according to the method in this article; Figures 37A to 37D The patients were matched with healthy controls of the same age and sex.

[0035] Figures 38A to 38D is a set of scans at baseline, which shows the target treatment area at RSFC area TPOJ1; Figures 39A to 39D This is the scan after fMRI-guided TMS according to the method in this article; Figures 40A to 40D The patients were matched with healthy controls of the same age and sex.

[0036] Figures 41A to 41D is a set of scans at baseline, which shows the target treatment area at the left side of RSFC area 46; Figures 42A to 42D is a set of scans after fMRI-guided TMS according to the methods described herein, which includes individual target planning based on the complete circuit; Figures 43A to 43D is a set of scans following conventional single treatment of targeted TMS therapy based on comparison of fMRI data of area 46 left within the DLPFC only; and Figures 44A to 44D is a set of scans from a healthy control group matched to the patient for the same age and sex.

[0037] FIG. 45A to FIG. 45D is a set of scans at baseline, which shows the target treatment area at the left side of RSFC area PF; Figures 46A to 46D is a set of scans after fMRI-guided TMS according to the methods described herein, which includes individual target planning based on the complete circuit; Figures 47A to 47D is a set of scans after conventional single treatment of targeted TMS therapy based on comparing only fMRI data of area PF left within the DLPFC; and FIGS. 48A to 48D are a set of scans of a healthy control group of the same age and gender matched to the patients.

[0038] Figures 49A to 49D is a set of scans at baseline, which shows the target treatment area at the right side of RSFC area 46; Figures 50A to 50D is a set of scans after fMRI-guided TMS according to the methods described herein, which includes individual target planning based on the complete circuit; Figures 51A to 51D is a set of scans following conventional single treatment of targeted TMS therapy based on comparison of fMRI data of area 46 to the right within the DLPFC only; and Figures 52A to 52D is a set of scans from a healthy control group matched to the patient for the same age and sex.

[0039] Figures 53A to 53D is a set of scans at baseline, which shows the target treatment area at the right side of RSFC area PF; Figures 54A to 54D is a set of scans after fMRI-guided TMS according to the methods described herein, which includes individual target planning based on the complete circuit; Figures 55A to 55D is a set of scans following conventional single treatment of targeted TMS therapy based on comparison of fMRI data of area PF right within DLPFC only; and Figures 56A to 56D is a set of scans from a healthy control group matched to the patient for the same age and sex.

[0040] Figures 57A to 57D is a set of scans at baseline, which shows the target treatment area at RSFC area STGa; Figures 58A to 58D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 59A to 59D The patients were matched with healthy controls of the same age and sex.

[0041] Figures 60A to 60D The target treatment area at RSFC area STSda is shown at baseline; Figures 61A to 61D is a set of scans after fMRI-guided TMS according to the methods described herein; and Figures 62A to 62D The patients were matched with healthy controls of the same age and sex.

[0042] Figures 63A to 63D is a set of scans at baseline, which shows the target treatment area at RSFC area STSva; Figures 64A to 64D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 65A to 65D The patients were matched with healthy controls of the same age and sex.

[0043] Figures 66A to 66D is a set of scans at baseline, which shows the target treatment area at RSFC area STSdp; Figures 67A to 67D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 68A to 68D The patients were matched with healthy controls of the same age and sex.

[0044] Figures 69A to 69D is a set of scans at baseline, which shows the target treatment area at RSFC region STSvp; Figures 70A to 70Dis a scan after fMRI-guided TMS according to the method described in this article; and Figures 71A to 71D The patients were matched with healthy controls of the same age and sex.

[0045] Figures 72A to 72D is a set of scans at baseline, which shows the target treatment area at RSFC area FOP5; Figures 73A to 73D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 74A to 74D The patients were matched with healthy controls of the same age and sex.

[0046] Figures 75A to 75D is a set of scans at baseline, which shows the target treatment area at RSFC area FOP4; Figures 76A to 76D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 77A to 77D The patients were matched with healthy controls of the same age and sex.

[0047] Figures 78A to 78D is a set of scans at baseline, which shows the target treatment area at RSFC area FOP3; Figures 79A to 79D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 80A to 80D The patients were matched with healthy controls of the same age and sex.

[0048] Figures 81A to 83D is a set of scans at baseline, which shows the target treatment area at RSFC area FOP2; Figures 82A to 82D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 83A to 83D The patients were matched with healthy controls of the same age and sex.

[0049] Figures 84A to 84D is a set of scans at baseline, which shows the target treatment area at RSFC area TE1a; Figures 85A to 85D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 86A to 86D The patients were matched with healthy controls of the same age and sex.

[0050] Figures 87A to 87D is a set of scans at baseline, which shows the target treatment area at RSFC area TE1m; Figures 88A to 88D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 89A to 89D The patients were matched with healthy controls of the same age and sex.

[0051] Figures 90A to 90D is a set of scans at baseline, which shows the target treatment area at RSFC area TE1p; Figures 91A to 91D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 92A to 92D The patients were matched with healthy controls of the same age and sex.

[0052] Figures 93A to 93D is a set of scans at baseline, which shows the target treatment area at the RSFC region PHT; Figures 94A to 94D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 95A to 95D The patients were matched with healthy controls of the same age and sex.

[0053] Figures 96A to 96D is a set of scans at baseline, which shows the target treatment area at RSFC area TE2a; Figures 97A to 97D is a scan after fMRI-guided TMS according to the method described in this article; and Figures 98A to 98D The patients were matched with healthy controls of the same age and sex.

[0054] Figures 99A to 99D is a set of scans at baseline showing the left side of the inferior parietal cortex area PGp with high negative correlation / covariation with a large set of brain regions; Figures 100A to 100D is a set of scans after treatment according to the methods described herein; and Figures 101A to 101D is a set of scans for an exemplary control group based on age and gender.

[0055] Figures 102A to 102D is a set of scans at baseline, which shows a high negative correlation / covariation with a large group of brain regions at the parieto-occipital sulcus (is POS1, left side); Figures 103A to 103D is a set of scans after treatment according to the methods described herein; and Figures 104A to 104D is a set of scans of an exemplary control group based on age and gender according to embodiments described herein.

[0056] Figures 105A to 105D is a set of scans at baseline showing the frontal polar cortex (area a47r, right) with high negative correlation / covariation with a large set of brain regions using the methods described herein. Figures 106A to 106D is a scan after treatment according to the methods described herein; and Figures 107A to 107D is a set of scans for an exemplary control group based on age and gender.

[0057] Figures 108A to 108D is a set of scans at baseline showing target stimulation at the parieto-occiptial sulcus region (area POS1, right) with high negative correlation / covariation with a large set of brain regions; Figures 109A to 109D After treatment according to the methods described herein; and Figures 110A to 110Dis a set of scans for an exemplary control group based on age and gender.

[0058] Figures 111A to 111D is a set of scans at baseline showing the dorsolateral prefrontal cortex (area 9p, right) with high negative correlation / covariation with a large set of brain regions; Figures 112A to 112D is a set of scans after treatment according to the methods described herein; and Figures 113A to 113D is a set of scans for an exemplary control group based on age and gender.

[0059] Figures 114A to 114D is a set of scans at baseline showing the superior parietal cortex (area 7PC, right) with high negative correlation / covariation with a large set of brain regions; Figures 115A to 115D is a set of scans after treatment according to the methods described herein; and Figures 116A to 116D is a set of scans for an exemplary control group based on age and gender.

[0060] Figures 117A to 117D is a set of scans at baseline showing an exemplary inferior frontal cortex (area 45, right) with high negative correlation / covariation with a large set of brain regions; Figures 118A to 118D is a scan after treatment according to the methods described herein; and Figures 119A to 119D is a set of scans for an exemplary control group based on age and gender.

[0061] Figures 120A to 120D is a set of scans at baseline showing an exemplary frontal polar cortex (area 10pp, right) with high negative correlation / covariation with a large set of brain regions; and Figures 121A to 121B This is a scan after treatment according to the method described in this article.

[0062] Figures 122A to 122D is a set of scans at baseline showing an exemplary frontal polar cortex (area TE1m, right) with high negative correlation / covariation with a large set of brain regions; and Figures 123A to 123D This is after treatment according to the method in this article.

[0063] Figures 124A to 127 It's a brain scan of a person who's experienced a stroke. Figures 124A to 124D 126A to 126D are fMRI brain scans from a patient experiencing a stroke, showing functional connectivity close to zero. Fig.125 and 127 Corresponding images are shown to illustrate the corresponding structural degeneration of the brain area damaged by the stroke.

[0064] Figures 128A to 147D are fMRI brain scans of patients or controls before and after treatment according to the exemplary embodiments described herein.

[0065] Figure 128A to 1 28D is a set of scans at baseline (before patient treatment) to show an exemplary RSFC region p9-46v identified as a potential target for stimulation; and Figures 129A to 129D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0066] Figures 130A to 130D is a set of scans at baseline to illustrate exemplary RSFC regions as potential targets for stimulation, right hemisphere, dorsolateral prefrontal, area a9-46v, right side; and Figures 131A to 131D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0067] Figures 132A to 132D is a set of scans at baseline to illustrate an exemplary RSFC area right hemisphere, anterior cingulate, area d32, right side as a potential target for stimulation. Figures 133A to 133D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0068] Figures 134A to 134D is a set of scans at baseline to show exemplary RSFC areas as potential targets for stimulation right hemisphere, anterior cingulate, area a32pr, right side; Figures 135A to 135D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0069] Figures 136A to 136D is a set of scans at baseline to show exemplary RSFC areas as potential targets for stimulation right hemisphere, anterior cingulate, area 9m, right side; Figures 137A to 137D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0070] Figures 138A to 138D is a set of scans at baseline to illustrate an exemplary RSFC area in the left hemisphere parieto-occipital sulcus, area POS2, left side, which shows the target location for stimulation; Figures 139A to 139D This is a scan after fMRI-guided TMS according to the method in this article.

[0071] Figures 140A to 140D is a set of scans at baseline to show potential targets for stimulation in the RSFC area, right hemisphere, parieto-occipital sulcus area, area POS2, right side; Figures 141A to 141D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0072] Figures 142A to 142D is a set of scans at baseline to show potential targets for stimulation in the right hemisphere in the RSFC area, the parieto-occipital sulcus area, and area POS1 on the right side; Figures 143A to 143D This is a set of scans after fMRI-guided TMS according to the method in this article.

[0073] Figures 144A to 144D 14A to 145D are a set of scans after fMRI-guided TMS according to the methods described herein.

[0074] Figures 146A to 146D is a set of scans at baseline to show potential targets for stimulation in the right hemisphere of RSFC area, superior parietal lobule, area 7Am, right side; Figures 147A to 147D This is a set of scans after fMRI-guided TMS according to the method in this article. DETAILED DESCRIPTION

[0075] The following discussion omits or only briefly describes conventional features of the disclosed technology that are obvious to those skilled in the art. Reference to multiple embodiments does not limit the scope of the claims appended hereto. In addition, any examples set forth in this specification are intended to be non-limiting and only set forth some of the many possible embodiments of the appended claims. In addition, the specific features described herein can be used in combination with other described features in each of a variety of possible combinations and arrangements. Those of ordinary skill in the art will know how to use the present invention in conjunction with routine experimentation to achieve other outcomes not specifically disclosed in the examples or embodiments.

[0076] Unless otherwise specifically defined herein, all terms will be given their broadest possible interpretation, including the meaning implied from the specification and the meaning understood by those skilled in the art and / or the meaning defined in dictionaries, treatises, etc. Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the disclosed technical field. It must also be noted that, as used in this specification and the appended claims, nouns modified by quantifiers, unless otherwise specified, represent one / kind or more / kind, and when used in this specification, the term "including" or its variations specify the presence of the features, elements and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components and / or groups thereof. In addition, methods, equipment and materials similar or equivalent to the methods, equipment and materials described herein can also be used in the practice or testing of the disclosed technology.

[0077] Because the patient's thoughts are the result of electrical signals generated on command around the brain, the way the brain learns to perform tasks is by coordinating these electrical signals and grouping them together based on their frequency. Therefore, if the oscillation of the electrical signals is not synchronized with the other members of the network, the network loses its ability to function properly. Depending on which network malfunctions, the individual experiences the consequences through poor cognition, emotions, and behaviors, which are the classic hallmarks of mental disorders.

[0078] The systems and methods for network-guided stimulation provided herein include personalized methods for correcting patient brain connections. Some embodiments of regional and / or network-guided stimulation use fMRI as a guide for revolutionary treatments to restore the health of patients. Because brain diseases do not follow a one-size-fits-all treatment method, the disclosed systems and methods may include advanced fMRI technology to treat complex brain diseases using advanced precision methods using stimulation (e.g., such as TMS). Individual differences in brain tissue may require stimulation to be applied to slightly different locations in different individuals. Therefore, an important goal of stimulation therapy is to guide stimulation targeting on a personalized basis to improve the consistency of targeting between individuals. Although described herein as a treatment method using TMS, other stimulation methods may also be used, such as ultrasound and / or electricity.

[0079] The systems and methods described herein for network-guided stimulation (e.g., TMS) include detection of "networks / circuits" that serve as targets for application of stimulation (e.g., TMS) therapy. Circuits or networks are detected based on activation and connectivity as determined based on images from fMRI technology. The systems and methods for network-guided stimulation (e.g., TMS) can: (1) use network-guided stimulation to target specific circuits rather than a single area of ​​a patient's brain; (2) use the network-guided stimulation herein for traumatic brain injury and other neurological disorders, such as stroke, dementia, etc., and not just psychiatric disorders; (3) use network-guided stimulation for psychiatric disorders where network-based targeted post-treatment is improved over single-area application of TMS.

[0080] Figure 1A process diagram of a method 100, 150 using a network-guided stimulation approach is shown. The system and method can use any combination of functional 100 and structural 150 connections between brain regions (including regions and networks) to determine personalized locations for stimulation treatment. As shown, the system and method may include acquiring a high-resolution fMRI image of the patient's brain, step 102; acquiring a dMRI image of the patient's brain, step 152; analyzing the fMRI image to identify the patient's brain circuit to be treated from the analyzed fMRI image, step 104; analyzing the dMRI image to identify white matter fiber tract imaging, step 154; calculating appropriate stimulation based on the analyzed fMRI and / or dMRI image, step 106; and applying appropriate stimulation to the patient's brain based on the appropriate stimulation, step 108.

[0081] Figure 1 , step 102, method 100 may include acquiring images for analysis. The images may be images of the patient's brain. The images may include acquiring high-resolution fMRI brain images, which allows for detection of unique patterns of active brain regions (nodes) and connections between nodes (circuits) for each individual, including multiple networks, each with different functions. Some embodiments may also or alternatively use dMRI images to assess target locations for stimulation therapy.

[0082] The systems and methods may include receiving a detailed medical and psychiatric history of the patient. The methods may also include creating a detailed map of the brain where the problem is. The methods may include having the patient sit comfortably in a chair similar to a dental chair and fitted with a sensor cap. A tracker (sensor) connected to a belt is placed around the patient's head. The tracker can then receive signals from the patient. Some embodiments include obtaining an fMRI, which is a radiological scan that shows in real time how blood flows through the brain. If blood flow to a specific brain area is too high or too low compared to normal, this will correlate with clinical manifestations. Areas of abnormal blood flow have been determined to represent areas of poor function and their associated dysfunction.

[0083] Systems and methods may include obtaining a resting state fMRI (rsfMRI). rsfMRI is an imaging technique that can detect functionally connected brain regions. If multiple fast MRI images are obtained, an fMRI map can be constructed by finding temporal fluctuations in the time series of each brain region (voxel). This is achieved through the sensitivity of fMRI to spontaneous BOLD contrast fluctuations. Since the BOLD signals between different brain regions working together when the subject is at rest (no specific stimulus or task is presented to the subject) are temporally correlated, fMRI can reveal defects in various brain networks.

[0084] The systems and methods may include receiving fMRI images. The system may be configured with a magnetic resonance imaging machine. The MRI machine may be configured to detect changes associated with blood flow in different parts of the brain. The system may communicate with the MRI machine to capture and receive fMRI images. The systems and methods may also be configured to receive fMRI images captured separately or previously captured prior to TMS treatment. In this case, the system may be configured to communicate, such as via the Internet, email, electronic transmission or file sharing, electronic transmission via a storage device, or other methods, to receive an electronic file with fMRI images captured with the MRI machine prior to the TMS procedure.

[0085] Figure 1 , step 104, method 100 may include analyzing fMRI images to determine target locations for treatment sites. Resting-state networks (RSNs) are made from a set of brain regions with coherent spontaneous fluctuations in activity. fMRI enables the functional organization of the brain to be explored and its differences from normal controls to be examined to find dysfunctions in neurological or psychiatric diseases. Functional connectivity (FC) can then be calculated by finding statistical dependencies between different regions (voxels). For a Gaussian distribution of data, second-order dependencies (i.e., covariance or correlation) are found to be used to construct a functional connectivity map.

[0086] Systems and methods may include determining which sites (positions) of the brain are dysfunctional and associating them with very complex software. Based on the poor functional connections of the brain, a treatment plan can be designed based on brain circuits (functionality), with magnetic beams used to route and rearrange target pathways. After viewing fMRI images of thousands of individuals, the systems and methods herein can identify unique patterns of network abnormalities, including abnormal node functions and / or abnormal internodes and internode connections. Some embodiments can determine and extract the amplitude and frequency of brain activity within brain networks and / or brain regions and / or between brain networks and / or brain regions to analyze the connectivity between circuits. Systems and methods may also include algorithms for determining and extracting the amplitude and frequency of brain activity within brain networks and / or regions and between brain networks and / or regions, which are used for precise and personalized stimulation therapy, such as TMS therapy.

[0087] Systems and methods can use BOLD imaging to measure brain activity. BOLD images can be subdivided into regions and networks to make different comparisons of fMRI data between patients and / or patients and one or more healthy and / or control groups. Brain regions can be divided based on recognized regions (e.g., Brodmann regions). The brain can also be subdivided in other ways, such as based on different functional, connection and / or developmental criteria. Regions can be defined by Glasser atlas. Regions can be considered as broader brain areas (brain areas), for example, DLPFC can be a brain area that is divided into smaller brain areas based on multimodal technology, such as a9-46v, 9-46, etc. Glasser uses the term "area" to refer to these subdivisions within a region. Glasser divides the brain into 22 brain regions (bilateral) and a total of 360 brain regions. Brain networks can be determined as a collection of regions. A collection of regions can be based on functional connectivity through statistical analysis of the patient's fMRI BOLD signal and / or based on a network of recognized healthy patients. The network can also be determined by other recording methods, such as EEG, PET or MEG. The network can be defined as a group of functionally connected brain regions. Functional connections can be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), and seed based algorithms. The network can be defined based on the resting state of an individual, and can include a resting state network (RSN). The network can include, for example, any combination of the following: frontoparietal, midcingulo-insular, dorsal frontoparietal, lateral frontoparietal, pericentral, occipital, limbic, auditory, cerebellum, spatial attention, language, lateral visual, time, visual perception, left / right execution. The network is defined by the Cole-Anticevic atlas. The network can be a group of (structurally and / or functionally) interconnected brain regions (which may be different from brain regions) (or a subgroup of regions within a region). Some exemplary embodiments can be about determining the region of the TMS target treatment position, and / or using the brain region in the comparison to determine the target region. Some embodiments may also use zones for target TMS treatment and / or use zones for determining target locations. Thus, embodiments may be applied to zones as described herein with respect to regions.

[0088] Systems and methods can be used to target individual brain regions based on functional connectivity. Some embodiments can use the concept of circuits to refer to multiple brain regions from the same and / or different networks to target circuits to restore network integrity or interactions between networks. Comparison of BOLD images can be performed by comparing different regions and networks of the patient's brain. Regions and networks can be analyzed by comparing activation, correlation, and covariation matrices of different BOLD regions and / or networks.

[0089] Systems and methods can be used to target individual brain regions based on structural connectivity. Some embodiments can use dMRI fiber bundle imaging. In addition to those obtained by other imaging forms (e.g., functional MRI) or in combination with those obtained by other imaging forms (e.g., functional MRI), the structural connectome can use dMRI fiber bundle imaging to study and determine the potential white matter tracts (i.e., based on activation, covariation and / or correlation) of the cortical regions that are displayed as defective in the functional connectivity map. Functional data can be initially used to select certain brain regions as potential stimulation targets, and then structural data analysis can be used to refine and select priority targets from potential stimulation targets. fMRI data can be used to guide structural analysis, such as when mental disorders can occur together with unknown complications of stroke, traumatic brain injury, neurodevelopment or neurodegenerative diseases. Structural data can be used to guide functional analysis, such as when there is stroke, traumatic brain injury, neurodevelopment or neurodegenerative diseases in patients. The analysis of structural connectivity can be used alone or in combination with the analysis of functional connectivity.

[0090] If functional analysis is used to guide structural analysis, the process may include: (1a) using fMRI data to construct activation, correlation, and covariation matrices and selecting targets with stronger (positive and / or negative) activation / connectivity values ​​with larger groups of brain regions, and / or (1b) using fMRI data to construct activation, correlation, and / or covariation matrices and selecting regions with weaker (close to 0) activation / connectivity values ​​with larger groups of brain regions; and (2) using structural data to construct a structural connectivity matrix and selecting regions from 1(a) / 1(b) with a higher number of white matter connections.

[0091] If structural analysis is used to guide functional analysis, the process may include: (1) using structural data to construct a structural connectivity matrix and selecting regions with a lower number of white matter connections; and (2a) using fMRI data to construct activation, correlation, and / or covariation matrices and selecting regions from (1) that have stronger (positive and / or negative) activation-to-connectivity values ​​(activation / connectivity) with a larger group of brain regions; and / or (2b) using fMRI data to construct activation, correlation, and covariation matrices and selecting regions from (1) that have weaker (close to 0) activation-to-connectivity values ​​(activation / connectivity) with a larger group of brain regions.

[0092] The structural analysis may begin by acquiring a dMRI image of the patient in step 152. In step 154, the dMRI image is analyzed to construct a brain structural connectivity matrix. Target locations for stimulation may be determined based on the structural connectivity matrix of step 154. The process may proceed from steps 106 and 108 to calculate stimulation parameters and apply stimulation.

[0093] Using the results from the functional connectivity-based analysis to select a first set of potential targets, structural connectivity can be used to determine priority targets and / or additional targets by selecting a set within and / or in addition to the first set of potential targets, thereby defining a target set for stimulation therapy.

[0094] fNRI-guided stimulation therapy can be used to show the percentage increase from baseline in the following whole-brain measures: total cortical gray matter volume and total white surface area. For patients experiencing neurodevelopmental disorders (e.g., autism spectrum disorders) and neurodegenerative diseases (e.g., dementia and Alzheimer's disease), structural analysis can be used in combination with functional analysis. Some embodiments may use structural analysis for disorders or conditions in patients with structural changes in the brain. Some embodiments may use structural analysis as an additional processing step to determine the target location for stimulation (e.g., TMS treatment) based on structural connectivity.

[0095] In step 152, the process may include acquiring a dMRI image. The structural processing step uses dMRI fiber tractography. Structural connectomes constructed using dMRI fiber tractography (alone or in addition to those derived by other imaging modalities (e.g., fMRI as described herein)) can be used to investigate the underlying white matter tracts of cortical regions that appear to be deficient in functional connectivity maps, e.g., based on activation, covariation, and correlation.

[0096] In step 154, the process may include analyzing dMRI images to construct a brain structural connectivity matrix. A brain structural connectivity matrix may be generated based on white matter fiber bundle imaging from the whole brain. The matrix is ​​made of rows and columns representing gray matter regions of interest (ROIs) (partitions). An exemplary parcellation may be a gray matter parcellation based on Glasser, and the values ​​in the matrix elements are the strengths of the white matter connections between two corresponding ROIs, quantified as the number of streamlines. Using voxel-specific directional diffusion information from diffusion-weighted MRI (dMRI), computational fiber bundle imaging produces a three-dimensional trajectory of the white matter in the MRI volume, which is referred to as a streamline.

[0097] In step 156, target locations for treatment are selected. Selecting target locations for stimulation may include selecting a target region with a higher number of white matter connections. The higher number of white matter connections may be selected from pre-selected potential targets from another analysis method (e.g., such as functional connectivity analysis). Selecting a higher number of white matter connections may be independent of any other analysis and may provide additional target locations for stimulation. Selecting targets for stimulation includes selecting a predetermined number of target locations and / or selecting multiple target regions in which the number of white matter connections is above a threshold. Selecting targets for stimulation includes selecting a target region with a higher number of white matter connections from a pool of targets based on fMRI functional connectivity.

[0098] As shown, the systems and methods may include acquiring imaging, such as dMRI images, in step 152. Then, in step 156, the systems and methods may analyze the dMRI images to construct a brain structural connectivity matrix to determine or select locations for stimulation in step 156. The structural analysis may be performed before and / or after the analysis of the functional analysis, steps 102 and 104. A first set of determined targets (e.g., from step 104) may be determined by a first process (e.g., functional analysis), and then those first set of determined targets may be used within the structural analysis, such that the final selection of targets is based on the priority of the first set of determined targets based on the structural analysis. Alternatively, each functional and structural analysis may be used separately to determine an overall target set based on a combination of analyses between the functional analysis and the structural analysis.

[0099] Figure 1, step 106, method 100 may include once the network to be treated is determined, selecting an appropriate TMS coil and an appropriate TMS stimulation. The calculation may include the site of stimulation, as well as parameters that determine the outcome of the stimulation, for example, increasing or decreasing the strength of nodes and networks. The parameters may include the intensity of the magnetic pulses as well as their frequency and the number of pulses applied during one session and subsequent sessions, or any combination thereof. By applying repetitive pulses (repetitive TMS) at high frequencies (e.g., >5 Hz), underlying cortical activity may be stimulated, and low frequencies (e.g., <5 Hz) may result in inhibitory changes. The effects of TMS can propagate beyond the stimulation site through connectivity, affecting a distributed network of brain regions, making the use of resting state functional connectivity (rsFC) a powerful tool to assess connectivity and guide optimal coil placement with respect to target areas.

[0100] When determining the pulse intensity, the systems and methods may use pulses with an intensity that is 100% to 120% above the patient's motor threshold (MT). Depending on the coil used, theta burst stimulation can be applied in triple bursts of 50 Hz 5 times per second. Some embodiments may use intermittent Theta Burst Stimulation (iTBS), which means that stimulation can be delivered in a cycle of approximately 2 seconds on and 8 seconds off over a 3 minute period. During a typical stimulation session, a patient may receive a total of 600 pulses and 200 bursts. This treatment is known to increase neuronal firing in a given area, and therefore improve brain activity and functional connectivity in target areas that modulate neural circuits.

[0101] Inhibitory theta burst stimulation can be applied in triple bursts of 50 Hz 5 times per second. Continuous Theta Burst Stimulation (cTBS) can be used, which means that the stimulation is delivered continuously over a period of about 40 seconds, for a total of 600 pulses. This protocol can be used to reduce neuronal firing, which results in a decrease in regional brain activity and functional connectivity in the brain area that needs to be slowed down, thereby modulating neural circuits. Figure 1 , step 108, method 100 may include stimulating the patient according to the parameters calculated in step 106. Systems and methods for stimulating a patient may include using a TMS device and / or a navigation system. The use of a navigation system may improve the proper placement of the stimulator during the initial treatment session and subsequent treatment sessions. Other stimulation methods may also be used, including, for example, ultrasound and / or electrical stimulation.

[0102] Methods and systems for determining networks and regions within a patient's brain for administering TMS treatment are provided. Although TMS treatment is explained herein, some exemplary embodiments are not limited thereto. In some exemplary embodiments, treatment may also or alternatively include low-intensity focused ultrasound (LIFUS). In an exemplary embodiment, if the resulting brain region and / or network for treatment is determined to be a cortical brain circuit, the location may be stimulated with TMS. If the brain region and / or network for treatment is determined to be a subcortical brain circuit, the location may be stimulated with LIFUS. Other schemes and treatments may also be used, such as electrical stimulation, for example.

[0103] TMS is a non-invasive (not entering the body) brain stimulation technology that is used to change brain activity using a magnetic field and correct abnormal activity caused by disease. The magnetic field can pass through the skull to the patient's brain and induce current at the stimulation site (lesion). TMS can regulate the resting state activity of the brain and fine-tune DMN plasticity; the direction (increase or decrease activity) and degree of this regulation depend on the design of a specific rTMS program for the brain activity of each individual patient. TMS involves the use of a magnetic coil placed on the top side of the head, which passes through the skull (cranium) and sends magnetic pulses to the brain.

[0104] The disclosure provided herein may provide unique aspects compared to conventional systems, such as: (1) differentiation and personalization of each patient's brain, recognizing that each brain is different; (2) finding treatment targets from images of the patient's brain; and (3) delivering treatment to malfuctioning sites with laser precision.

[0105] Consistency is important; for each TMS treatment, the network that has the TMS pulses applied for "retraining" preferably has the same treatment at the same location to begin to normalize the relevant network connectivity. Therefore, the precise location of the treatment site is desirable to improve success rates and provide longer-lasting health. Unlike standard TMS, in which a one-size-fits-all approach is used to select and guide the TMS coil, the personalized TMS approach can use the unchanging anatomical features (landmarks) of the individual's face for registration and use real-time navigation to guide treatment. This technology allows the system and method to provide treatment exactly where it is needed for each treatment. The navigation system works based on optical (infrared) tracking, which improves precision and accuracy. The accuracy of this optical navigation system is higher than other systems because the registration and real-time navigation depend only on the visibility of the tracker and probe to the infrared camera.

[0106] The systems and methods herein can use a precise neuronavigation guidance system, where millimeter accuracy can be achieved. The guidance system can use functional near infrared spectroscopy (fNIRS) with neuronavigation to deliver precise targeting for TMS treatment associated with a determined brain network. Near infrared spectroscopy can use infrared light delivered to the scalp through an optical fiber and through the skull and into the brain. The infrared light is scattered or reflected by brain tissue and blood. When the infrared light leaves the head, a second set of optical fibers on the scalp captures the infrared light. By detecting changes in the concentration of oxyhemoglobin and deoxyhemoglobin in the blood shown by fMRI studies, a specific navigation map of the patient's head associated with the network used to determine the target TMS treatment location can be created.

[0107] Systems and methods finely target magnetic beams to nodes of brain networks or circuits that are functionally disconnected from normal areas. Using brain mapping techniques, some embodiments attempt to restore brain circuits to a healthier state and mitigate underlying abnormalities. Figure 2 200 may have a patient obtain a magnetic resonance image (MRI) of their brain at an MRI center in step 202. The method may then perform a functional and / or structural connectivity analysis on the fMRI image in step 212 and provide an output in step 214.

[0108] As shown and explained above, the system can receive MRI data in different ways. MRI information can be functional MRI (fMRI) and / or diffusion MRI (dMRI). fMRI is a type of MRI that measures changes in blood flow that occur in the presence of brain activity. fMRI shows regional changes in cerebral blood flow over time. dMRI is a type of MRI in which image contrast is based on the diffusion of water molecules in tissues. MRI scans can be obtained in different ways. For example, the system can simply generate inputs from the MRI center into functional connectivity analysis (using fMRI) and / or structural connectivity analysis (using dMRI). Transmission can be transmitted by any method, including but not limited to direct connection or transmission through a network (such as an online platform). The system can be configured to transmit through a file transfer protocol and / or preferably pre-process the image data before using MRI / fMRI / dMRI data as input into the functional connectivity analysis in step 212. For example, the system can use public domain information, such as the human connection group MRI protocol for data acquisition. Some embodiments can use the HCP pipeline for data preprocessing. Some embodiments may use the Glasser and Cole-Anticevic atlases, or other atlases that have been developed and made publicly available. Participating sites 204 may use a corresponding or desired file transfer protocol 206 to perform a desired computational algorithm 208 to pre-process the MRI data prior to performing functional and / or structural connectivity analysis in step 212.

[0109] In step 212, MRI (fMRI and / or dMRI) data may be received by the system and pre-processed as described herein (steps 204, 206, 208).

[0110] The system can analyze functional and / or structural connections within the patient's brain. The preprocessed data can be used to calculate whole-brain activation, correlation, and covariation matrices in individual subjects. Determining whole-brain activation includes determining changes in amplitude over time of fMRI data. Determining whole-brain correlation includes determining changes in frequency over time of fMRI data. Determining whole-brain covariation includes determining changes in frequency relative to changes in amplitude over time. As used herein, covariation is understood to be the ratio of one of correlation or activation to the other of activation or correlation. Therefore, although generally described herein as correlation to activation, covariation is also understood to include its inverse and is still within the scope of the present disclosure and definitions.

[0111] Some embodiments may also or alternatively access available data for assessing or identifying healthy control groups, which may include age, sex, race, or other genetic information relevant to diagnosis and treatment in pathology and / or prevention. Pre-processed data from healthy controls in neuropsychiatry across an age span and before and after fMRI / TMS can be used to calculate mean whole-brain activation, correlation, and covariation matrices for healthy populations.

[0112] For example, in 210, an MRI center can provide fMRI information of healthy patients and provide patient information related to the individual patient, such as age, gender, race, etc. The system can also maintain patient information, which includes fMRI data, patient information related to the relationship, TMS regimen, and patient outcomes including response to TMS treatment based on a given TMS target location. This information can be stored in a database and provided as input 210 to the functional connectivity analysis of step 212.

[0113] Functional connectivity analysis carried out in this article includes receiving the pre-treated fMRI data from the patient and the healthy control data set with the metric relevant to the patient.Metrics may include any combination of age, sex, race, etc.Metrics relevant to the patient may include the feature of the healthy control group identical with the patient characteristics or within the scope of a given patient characteristics.For example, gender may be determined based on whether the gender of the patient is identical with the gender of someone from the healthy control group.For age, if the age of the healthy control group is within the age range of the patient, such as the patient and the control group fall within a predetermined range (such as 5 or 10 years increment), or within a predetermined range relative to the patient's age (such as older or younger than the patient by 5 or 10 years), the healthy control group may be selective.Age range may be based on generally recognized brain development levels (such as adolescence, adulthood), or based on degenerative stages / duration based on age or given disease or illness, etc.

[0114] In step 212, the method includes generating whole-brain activation, whole-brain correlation, and whole-brain covariation matrices for patient data and healthy control data sets with patient-related metrics, respectively. Thus, the pre-processed patient fMRI data is used to calculate whole-brain activation (changes in amplitude over time), correlation (changes in frequency over time), and covariation (changes in frequency relative to changes in amplitude over time) for each brain region. Some embodiments include receiving pre-processed data from healthy controls along any combination of metrics including age span, gender, race, etc. For pre-processed data from healthy controls, fMRI data may be processed before and after (pre / post) TMS treatment. Some embodiments include calculating whole-brain activation (changes in amplitude over time), correlation (changes in frequency over time), and covariation (changes in frequency relative to changes in amplitude over time) for each brain region of each pre- and post-treatment fMRI data set of healthy controls with metrics within a given range from the patient.

[0115] After obtaining the activation, correlation, and covariation matrices of the patient and the healthy control group, the matrices can be evaluated in a variety of ways: by comparing the intra-subject analysis of network consistency and interaction within the patient's brain portion, and / or by comparing the inter-subject analysis of network consistency and interaction between other subjects (healthy controls and / or groups of subjects with the same diagnosis) and the patient. The evaluation of the patient's activation, correlation, and covariation matrices may include a first inter-network comparison. In this case, the method may include calculating the changes between the activation, correlation, and covariation matrices between brain networks. Therefore, some embodiments may include calculating the amplitude, frequency, frequency relative to the change in amplitude, or any combination thereof between different brain networks to generate a network-to-network (inter-network) comparison. For example, the first network may be compared with the second network, and then compared with the third network, respectively, until the nth network. The second network may then be compared with the third network, then compared with the fourth network, and until compared with the nth network, and so on, until each network is compared with each other network.

[0116] Evaluation of the patient's activation, correlation, and covariation matrices may include a first intra-network comparison. In this case, the method may include calculating changes between activation, correlation, and covariation matrices between cortical and subcortical regions within each network. Thus, some embodiments may include calculating amplitude, frequency, frequency relative to amplitude changes between different brain regions within the network, or any combination thereof. Calculating changes between different brain regions within the network and all other members of the combined network (regions and networks) can be used to generate intra-network comparisons. For example, a first region of a network can be compared to all members of the combined network (including and / or excluding the first region), and then a second region of the network can be compared to all other members of the combined network (including and / or excluding the second region), and so on, until each region of the network and all networks of the brain are analyzed.

[0117] The evaluation of the patient's activation, correlation, and covariation matrices may include a second intra-network comparison. In this case, the method may include calculating changes between activation, correlation, and covariation matrices between cortical and subcortical regions within each network, similar to the first intra-network comparison. However, in this case, a region-to-region comparison may now be performed instead of a region-to-network comparison. Some embodiments may include calculating the amplitude, frequency, frequency relative to amplitude changes, or any combination thereof between different brain regions within the same network. Some embodiments may include calculating the amplitude, frequency, frequency relative to amplitude changes, or any combination thereof between different brain regions and other brain regions (region-to-region) within the network to produce an intra-network analysis. For example, the first brain region of the first brain network may be compared with the second brain region of the first brain network, and then compared with the third brain region of the first brain network, until the nth brain region of the first brain network. Then, the second brain region of the first brain network may be compared with the third brain region of the first brain network, and each other region of the first brain network until the nth region. Thereafter, the remaining brain regions may be compared with all other brain regions that have not yet been compared within the brain network. The method can then compare the next brain network by comparing the first brain region of the second brain network to every other brain region of the second brain network until every brain region within each network has been compared to every other region of the same network.

[0118] The evaluation of the activation, correlation, and covariation matrices of the patient may include a second inter-network comparison and a second region-to-region comparison. However, in this case, the method may include calculating changes between activation, correlation, and covariation matrices between brain networks, similar to the first inter-network comparison, but between different regions of those networks. Therefore, some embodiments may include calculating changes between activation, correlation, and covariation matrices between brain regions of different brain networks. Some embodiments may include calculating amplitude, frequency, frequency relative to amplitude changes, or any combination thereof between different regions of different brain networks to produce a region-to-region, network-to-network analysis. For example, the first region of the first network may be compared with the first region of the second network, and then compared with the second region of the second network, respectively, until the nth region of the second network. The first region may then be compared with the first region of the third network, until all regions of the third network, and so on, until the first region of the first region is compared with all regions of all other networks outside the first network. Therefore, when compared with the region-to-region, intra-network analysis, each region can be compared with every other region of the brain, thereby providing a whole brain analysis.

[0119] Some embodiments of the present disclosure may also or alternatively include different comparisons of patients to other groups, such as healthy controls or groups of subjects with the same diagnosis, which may or may not be selected based on a relationship to patient metrics such as, for example, age, race, sex, condition / disease, treatment history, treatment efficacy, or any combination thereof.

[0120] The patient's measurements can be statistically compared with the same measurements performed for healthy controls. A healthy control group or a subject group with the same diagnosis matches the patient with the same age, sex, diagnosis or symptoms. However, other metrics can be formulated to select members of a healthy control group from a larger control group database. For example, the patient's measurements can be compared with a healthy control group that matches the same relationship metric (e.g., with the same age and sex) as the patient, and can include a comparison of any combination of activation, correlation, and covariation assessment, including network-to-network, inter-network analysis of the patient and the healthy control group; region-to-network, intra-network analysis; region-to-region, intra-network analysis, and / or region-to-region, inter-network analysis.

[0121] The comparison of a patient with a healthy control group or a group of objects with the same diagnosis can be based on one or more different relationship metrics for one or more different control groups. For example, the patient's measurements can be compared with a first healthy control group that matches the patient with a first set of relationship metrics (e.g., age and gender), and can include a comparison of any combination of activation, correlation, and covariation assessments, including network-to-network, inter-network analysis of the patient and the first healthy control group; region-to-network, intra-network analysis; region-to-region, intra-network analysis, and / or region-to-region, inter-network analysis. The patient's measurements can also or alternatively be compared with a second control group that matches the patient with a second set of relationship metrics (e.g., age and diagnosis or symptoms), and can include a comparison of any combination of activation, correlation, and covariation assessments, including network-to-network, inter-network analysis of the patient and the second control group; region-to-network, intra-network analysis; region-to-region, intra-network analysis, and / or region-to-region, inter-network analysis. Different combinations of relationship metrics of control groups can be used and fall within the scope of the present disclosure. For example, a first healthy control group may include the same age and gender as the patient; a second control group may include the same age and gender as the patient and may include overlapping identification of symptoms or diagnoses; a third control group may include the same age and may include overlapping identification of symptoms or diagnoses; a fourth control group may include only those with the same symptoms and / or diagnoses as the patient. Inter-subject analysis may be based on comparisons of one, two, three or more different control groups. A healthy control group may be an individual with no personal and / or family history of a neurological or psychiatric disorder.

[0122] The system can be configured to perform any combination of the following functional connectivity analyses:

[0123] (1) Network-to-network and inter-network analysis, which is performed for each network of the patient by calculating the amplitude, frequency, and the change of frequency relative to amplitude between different brain networks of the same patient;

[0124] (2) Region and network, intra-network analysis, which was performed for each region of the patient by calculating the amplitude, frequency, and changes in frequency relative to amplitude between the brain region with the network and all other members of the network in the same patient.

[0125] (3) region-by-region, within-network analysis, which was performed for each region of the patient by calculating the amplitude, frequency, and changes in frequency relative to amplitude between different brain regions within the network and all other members of the network in the same patient;

[0126] (4) Region-to-region and inter-network analysis, which is performed for each region of the patient by calculating the changes in amplitude, frequency, and frequency-relative amplitude between different brain regions of different networks in the same patient.

[0127] (5) a statistical comparison comparing any combination of measurements from (1) to (4) from the patient with a similar functional connectivity analysis from a healthy control group consisting of individuals matched to the patient for the same age and sex;

[0128] (6) A statistical comparison comparing any combination of measurements from (1) to (4) from a patient with a similar functional connectivity analysis from a healthy control group comprising individuals matched to the patient for the same age, sex, and diagnosis and / or symptoms.

[0129] After performing various calculations and comparisons between and / or within patients to assess the functional connectivity of the patient's brain, the system can be configured to output a recommended stimulation circuit. The TMS protocol can also be customized as needed based on a TMS protocol from a healthy control group. An exemplary protocol for TMS may include administering TMS at a frequency of 0 to 100 Hz with an amplitude of 50% to 150% of the individual's motor threshold.

[0130] The system can analyze the functional and / or structural connections within the patient's brain. Figure 1 As shown in , structural analysis can be performed independently of functional analysis and / or can be performed in conjunction with it. For example, functional analysis can be used to evaluate a patient and determine a target treatment location in which steps 102, 104, 110, 106, and 108 are performed. In another example, structural analysis can be used to evaluate a patient and determine a target location in which steps 152, 154, 156, 106, and 108 are performed. Functional analysis and structural analysis can also be used in combination. Functional analysis can be performed first, where steps 102, 104, 110 are performed to determine a first set of potential target locations. Then, those first set of potential target locations can be prioritized or analyzed according to some embodiments of structural analysis to determine target locations from within the first set of potential target locations, where steps 152, 154, and 156 are performed to determine the final parameters in step 106, and stimulation therapy is applied in step 108. These processes can be switched so that structural analysis is first performed to generate a first set of potential target locations, and then the first set of potential target locations is used within the functional analysis to determine target locations for treatment from the first set of potential target locations. These processes can be performed in parallel (either simultaneously or sequentially) so that one treatment process does not feed into another, but the entire brain is analyzed according to each embodiment and the resulting target locations are then prioritized together to determine the final target location.

[0131] The system may be configured to analyze the structural connectivity of the brain in 212. This may include acquiring a dMRI image of the patient from the MRI center 202. The dMRI image is analyzed to construct a brain structural connectivity matrix. Target locations for stimulation may be determined based on the structural connectivity matrix (Structural Connectivity (SC) analysis of algorithm 212). The process may continue with calculating stimulation parameters as output in 214.

[0132] Using the results from the functional connectivity-based analysis to select a first set of potential targets, structural connectivity can be used to determine priority targets and / or additional targets by selecting a set within and / or in addition to the first set of potential targets, thereby defining a target set for stimulation therapy.

[0133] MRI-guided stimulation therapy can be used to show the percentage of improvement from baseline in the following whole-brain measures: total cortical gray matter volume and total white surface area. For patients experiencing neurodevelopmental disorders (e.g., such as autism spectrum disorders) and neurodegenerative diseases (e.g., such as dementia and Alzheimer's disease), structural analysis can be used in combination with functional analysis. Some embodiments may use structural analysis for disorders or conditions of patients with structural changes occurring in the brain. Structural analysis may include additional processing steps to determine the target location for stimulation (e.g., TMS treatment) based on structural connectivity.

[0134] In 212, the system may include analyzing dMRI images to construct a brain structural connectivity matrix. A brain structural connectivity matrix may be generated based on white matter fiber bundle imaging from the whole brain. The matrix is ​​made of rows and columns representing gray matter regions of interest (ROIs) (partitions). An exemplary partition may be based on Glasser-based gray matter partitions, and the values ​​in the matrix elements are the strengths of the white matter connections between two corresponding ROIs, quantified as the number of streamlines. Using voxel-specific directional diffusion information from diffusion-weighted MRI (dMRI), computational fiber bundle imaging generates three-dimensional trajectories that pass through the white matter in the MRI volume, which are referred to as streamlines.

[0135] The structural connectivity of the patient's brain can be analyzed by MRI data to generate a brain structural connectivity matrix. MRI data can be used to run structural connectivity analysis (fiber tract imaging). Structural data is used to run structural connectivity analysis (fiber tract imaging). Structural data can be T1w / T2w. T1 weighted (T1w) and T2 weighted (T2w) are MRI sequence weighted scans, where T1wMRI can enhance the signal of fat tissue and suppress the signal of water, while T2w MRI can enhance the signal of water. Structural data (such as T1w / T2w) can be used for structural connectivity analysis (such as fiber tract imaging) to study the whole brain structural architecture.

[0136] The system can be configured to output 214 a target location for treatment selected from the algorithm 212. Selecting a target location for stimulation can include selecting a target region with a higher number of white matter connections. The higher number of white matter connections can be selected from pre-selected potential targets from another analysis method (e.g., such as functional connectivity analysis). Selecting a higher number of white matter connections can be independent of any other analysis and can provide additional target locations for stimulation. Selecting a target for stimulation includes selecting a predetermined number of target locations and / or selecting a number of target regions in which the number of white matter connections is above a threshold. Selecting a target for stimulation includes selecting a target region with a higher number of white matter connections from a target pool based on fMRI functional connectivity.

[0137] In step 214, the output may include a selection of regions for TMS administration. To obtain the selection of regions, the system may be configured to analyze a variety of calculations performed to determine functional connectivity and / or structural connectivity, and the comparisons performed in step 212. The system may be configured to select the highest threshold percentage of brain regions that meet the selection criteria for each step of the analysis performed in step 212, in which regions and / or networks of fMRI data are analyzed to determine functional connectivity and / or regions with a higher number of white matter connections are analyzed to determine structural connectivity. As an example, the calculations performed to determine functional connectivity may include any combination of the following: (1) network to network, between-network analysis, (2) region to network, within-network analysis; (3) region to region, within-network analysis; (4) region to region, between-network analysis; and (5) one or more statistical comparisons of any combination of the above (1) to (4) measurements from the patient with a similar functional connectivity analysis of a control group, wherein the control group may be selected based on one or more combinations of relationship metrics, such as age, sex, symptoms, diagnosis, race, etc. As an example, calculations performed to determine structural connectivity may include any combination of the following: (1) generating a brain structural connectivity matrix based on white matter fiber tractography from the whole brain; (2) generating a brain structural connectivity matrix based on white matter fiber tractography from brain subgroups based on another brain analysis method (e.g., functional connectivity); (3) creating a matrix representing gray matter target regions based on a desired partitioning; (4) determining the value in the matrix element as the strength of the white matter connection between two corresponding target regions; and / or (5) using voxel-specific directional diffusion information from diffusion-weighted MRI to generate three-dimensional trajectories (streamlines) through the white matter.

[0138] The system can be configured to select a threshold percentage of brain regions from the above calculations that meet the following conditions, the above calculations being suitable for determining functional connectivity of a patient based on a within-patient comparison:

[0139] 7. A first threshold of regions is selected where the lowest changes in amplitude, frequency, frequency relative to amplitude, or a combination thereof occur in the largest number of brain networks and regions.

[0140] 8. A second threshold for selecting regions where the highest changes in amplitude, frequency, frequency relative to amplitude, or a combination thereof occur in the largest number of brain networks or regions of the patient.

[0141] The comparison is intended to determine areas of abnormal activity in the patient's brain that have the lowest or highest changes compared to most other areas and networks of the patient's brain. In other words, each of the above calculations 1 to 4 can rank each area of ​​the brain based on amplitude, frequency, and changes in frequency relative to amplitude. Areas that appear below the third threshold can be grouped together, and the total number of each area below the third threshold (the frequency distribution of areas below the third threshold) can be determined. Similarly, the frequency distribution of areas above the fourth threshold can be determined. Then, in the determination of steps 7 and 8 above, the areas that appear most in each group (appearing below the third threshold or above the fourth threshold) are used. An intra-patient output is provided based on the comparison and determination of the intra-patient data, wherein multiple areas are determined based on an analysis of the activation, correlation, and covariation information of the patient data.

[0142] Thresholds may be used to divide the number of regions determined. The determined network may be determined based on different thresholds. For example, the first and second thresholds for determining the final number of regions may be, for example, the top 1%, 5%, 10%, 15%, 25%, or other thresholds. The thresholds may be the same or different. Different thresholds may be used for different comparisons. For example, when dividing the regions of the frequency distribution, the regions may be divided within a 50% range, so the top half of the region is analyzed to determine the highest change, and the bottom half of the region is analyzed to determine the lowest change.

[0143] The system may be configured to select a threshold percentage of brain regions from the above calculations that meet the following conditions, the above calculations being suitable for determining functional connectivity of a patient compared to one or more healthy controls (inter-patient comparison):

[0144] 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 (same age and gender in the instance using steps 5 to 6), and select regions that are outside the determined normal range when compared to the healthy control group.

[0145] 10. Compare the within-subject output (e.g., steps 7 and 8 or 9) to that of a control group with the same second metric relationship (using the same diagnosis and symptoms in the example of steps 5 to 6), and select the regions with the largest changes in activation, correlation, and covariation between pre- and post-TMS treatment.

[0146] To save computation, the sequence of steps can be run in a different order and on different inputs. For example, the last step 10 described above can be based on the regions determined from steps 7 and 8, or those reduced by the filter from step 9.

[0147] In step 9, the system can compare the within-subject outputs from steps 7 and 8 to obtain regions with values ​​below the fifth threshold and above the sixth threshold in the healthy control group matching the first metric relationship. The fifth and sixth thresholds can be the same or different. The fifth and sixth thresholds can be based on multiple standard deviations, such as 1, 2, 2.5, or 3 standard deviations from the mean. Thereafter, the determined regions from step 9 can be compared with the patient pool matching the second metric relationship in step 10. In this case, those regions of the subgroup of regions from step 9 that have the largest changes in activation, correlation, and covariation between pre-TMS treatment and post-TMS treatment can be selected from the patient pool of the control group with the second metric relationship. In other words, the regions of the control group matching the second metric relationship (e.g., the same diagnosis and / or symptoms, with or without the same age, sex, and / or race) are compared by taking the absolute value difference between pre-treatment and post-treatment for each patient in the control group. Then, the region with the largest change, the largest absolute value is determined as the output of step 10. The seventh threshold can be used to determine a cutoff value, such as the top 1%, 5%, 10%, top 1, 2, 3, 4, or more regions.

[0148] The system can be configured to use structural connectivity to select a target location from potential choices based on functional connectivity.

[0149] Some embodiments may include an output including a report for a patient and / or practitioner administering TMS therapy. The report may include one or more images of the patient's brain. For example, for BOLD images, a comparison of activations, correlations, covariations, or other combinations of the images used herein may be provided. The report may include a visual display of a brain map. The report may include a statistical display. The report may include recommended areas, networks, and / or circuits for stimulation by TMS treatment. The report may include a TMS regimen customized as needed based on the comparisons made herein.

[0150] Stimulation parameters that can be personalized for patients can also be provided. Once the area from the above steps is determined to apply TMS, an excitatory (>5Hz) or inhibitory (<5Hz) TMS scheme can be selected. The treatment coil can also be selected based on focus and depth. The focus can be the width of the stimulated area (horizontal axis), and the depth can be the distance from the scalp to the target area (vertical axis). Additional or alternative TMS treatment parameters can be determined by the parameters of the previous patient group with the best results before and after TMS treatment. According to the comparison of step 10, the treatment parameters from the patient who caused the greatest change in the brain area before and after can be used to inform the treatment parameters for the patient. For example, the treatment parameters may include an average, a weighted average, an average after filtering, or a treatment parameter from a patient with the greatest change in the determined area. The system can also compare the healthy control group data to determine the treatment parameters. For example, once a region is identified, the control group can be used to identify patients with the greatest pre- and post-treatment change for that region based on a third relationship metric (which may or may not be the same as the first or second relationship metric) - for example, the control group can be used to identify the group of patients with the highest pre- and post-change for a given region, either for the entire control group or compared to a subgroup of controls matched for the same sex, age, race, or combination thereof (or other combination of relationship metrics). Parameters from the identified healthy control group treatments can be used individually or in combination, such as averages, to create a customized treatment regimen for the patient.

[0151] Figures 3A to 8B A flow chart and corresponding brain visual for explaining an exemplary functional connectivity analysis are shown. The system can be configured to perform any combination of the following functional connectivity analyses:

[0152] 1. Network-to-network and inter-network analysis, which is performed for each network of the patient by calculating the amplitude, frequency, and the change of frequency relative to amplitude between different brain networks of the same patient;

[0153] 2. Region and network, intra-network analysis, which is performed for each region of the patient by calculating the amplitude, frequency, and the change of frequency relative to amplitude between the brain region with the network and all other members of the network of the same patient.

[0154] 3. Region-by-region, within-network analysis, which was performed for each region of the patient by calculating the amplitude, frequency, and changes in frequency relative to amplitude between different brain regions within the network and all other members of the network within the same patient;

[0155] 4. Region-to-region and inter-network analysis, which is performed for each region of the patient by calculating the amplitude, frequency, and the change in frequency relative to amplitude between different brain regions of different networks in the same patient.

[0156] 5. A statistical comparison comparing the measurements of any combination of (1) to (4) above from the patient with a similar functional connectivity analysis of a healthy control group comprising individuals matched to the patient for the same age and sex;

[0157] 6. A statistical comparison comparing the measurements of any combination of (1) to (4) above from the patient with a similar functional connectivity analysis of a healthy control group comprising individuals matched to the patient for the same age, gender, and diagnosis and / or symptoms.

[0158] Systems and methods may include determining amplitude, frequency, and / or changes in frequency relative to amplitude between different brain networks and networks, regions and regions, or regions and networks. Differences are determined based on spontaneous BOLD contrast fluctuations when the subject is at rest (no specific stimulus or task is presented to the subject). BOLD contrast fluctuations can be determined from fMRI by comparing different brain regions and networks. The brain can be divided into different regions. The regions can be based on recognized brain regions, or based on other subdivisions of brain region activity based on different functional, connectivity, and / or developmental criteria, or as shown in fMRI images of patients and / or healthy patient groups. Brain networks can be determined as a collection of regions. The collection of regions can be based on functional connectivity through statistical analysis of the patient's fMRI BOLD signal and / or based on recognized networks of healthy patients. Other depictions of regions and networks can be used. Figure 3B An exemplary brain is shown subdivided into exemplary four networks labeled n=1, n=2, n=3, and n=4. FIG3D shows Figure 4B , which is further subdivided into regions such that each network contains multiple regions. The number of regions for each network can be the same or different. For example, Figure 3B The first network (n=1) is subdivided into 4 regions, while the second network (n=2) is subdivided into 3 regions. The regions and networks shown are only used to explain the relevant flowcharts and are not intended to be limited to actual brain regions and / or networks.

[0159] Exemplary functional connectivity analyses include a variety of analyses based on the frequency, amplitude, and frequency-relative-amplitude changes of spontaneous blood oxygen level-dependent fluctuations as determined by fMRI. Comparisons are made based on brain regions and networks. Thus, Figure 3A A flowchart is shown for calculating activation (amplitude of spontaneous BOLD fMRI fluctuations), correlation (frequency of spontaneous BOLD fMRI fluctuations), and covariation (correlation of activation with spontaneous BOLD fMRI fluctuations) of various brain networks, and Figure 4A Computations of activation, correlation, and covariation of various brain regions are shown.

[0160] Reference Figure 3A, for each network of the brain, the BOLD fluctuations of the first brain network area are determined. A network can be associated with a given value by determining a median, mean, or other statistical value associated with the distribution to associate the amplitude and / or frequency of the BOLD fluctuations with a given network. As shown, the analysis starts with the first network (n=1). If not all networks have been analyzed (which will not be the case for the first network), the system calculates the activation of the first network and the correlation of the first network. From these values, the system can then calculate the covariation as the ratio of correlation to activation (or vice versa). The system then increments to the next network to determine its activation, correlation, and covariation. The system advances in each network until all networks (n≤N) have relevant activation, correlation, and covariation values.

[0161] Reference Figure 4A , for each region of the brain, the BOLD fluctuations of the first brain region are determined. Regions can be associated with given values ​​by determining a median, mean, or other statistical value associated with the distribution to associate the amplitude and / or frequency of BOLD fluctuations with a given region. As shown, the analysis begins with the first region. If not all regions have been analyzed (which would not be the case 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 then calculate the covariation as the ratio of correlation to activation (or vice versa). The system then increments to the next region to determine its activation, correlation, and covariation. The system advances in each region until all nodes (r≤R max ) have associated activation, correlation, and covariation values.

[0162] As used herein, all activations, correlations, and / or covariations for each network and / or region may be calculated and / or saved to a database. Figures 3A to 8B As described, covariation between multiple regions and networks can be saved and analyzed. Thus, activation and correlation can be calculated during the calculation of covariation, but cannot be determined and / or saved to the system separately. Such intermediate determination is understood to be included in the definition of calculating activation and / or correlation used in this article.

[0163] As shown, each network is identified as a numerical ordinal value, and each region is identified as its network and region ordinal value. The systems and methods may use any indexing system to associate activation, correlation, and / or covariation for a given network and / or region. Thus, as Figure 4A As shown in the relevant flow chart of , when comparing r≤Rmax, the given value of R is not necessarily limited. Instead, the comparison is intended to illustrate that the current area is one of the areas that need to be analyzed and has not been analyzed before.

[0164] Some exemplary embodiments include comparing each brain network to every other brain network. Figure 5A As shown in , the first network is selected to be compared with all other networks. a =(n=1)(from Figure 3B ),like Figure 5B The network is shown in cross-hatching. Then, by Figure 3A and 3B The algorithm determines the information by taking the difference in covariance values ​​and comparing this first network to the next network (n=2). Then, this same first network is compared to the next network (n=3) by taking the difference in the corresponding covariance values. Once the first network has been compared to all other networks, the next network (n=2) is then compared to all other networks. The differences between previous comparisons may not be repeated. Therefore, if the difference between n1 and n2 has been determined, the difference between n2 and n1 is not necessarily determined as well. Therefore, when the next network is compared to all other higher order or previously uncompared networks. Therefore, according to n b =n a +1 shows a comparison loop, which allows the a All previously uncompared networks with n a The loop continues until all networks have been compared with all other networks. Figure 5B Only the inner loop is shown, where n a = 1 has been connected to all other networks (n b =n a +1 = 2 to n b =N) were compared.

[0165] Some embodiments include comparing each brain region to every other brain region. The comparison can be handled in two parts, where Figures 7A to 7B As shown, a region within a network is compared to all other regions within the same network (region vs region, within network), and as Figures 8A to 8B As shown in , each region within a network is compared to all other regions within other networks (region to region, network to network). The combination of these two loops results in each region being compared to every other region of the brain by taking the difference in the corresponding covariate values ​​between each region. In other words, the first region, e.g., r, is selected to be compared to all other regions. a =(r n＝1 =1)(from Figure 4B ), such as Figure 7B and 8B Then, refer to Figure 8B , by following the Figure 4A and 4BThe algorithm determines the information by taking the difference in covariance values ​​and comparing the first region with the next region in the network (r b =(r n＝1 =2)) for comparison. Figure 8B , by following the Figure 4A and 4B The algorithm determines the information by taking the difference of the covariance value and also compares the first region with the next region with the next network (r b =(r n＝2 =1)) for comparison. When the algorithm loops through Fig. 7A or Fig. 8A When the first area is associated with the first network ( Figure 7B ) or the next network ( Figure 8B ) is compared to the next region within . The corresponding loop continues until each region has been compared to every other region by determining the difference in covariance between each region.

[0166] Some embodiments include comparing each brain region to each brain network that does not include the brain region. Fig. 6A As shown in , the first region is selected to be compared with all other networks. For example, r = (r n＝1 =1)(from Figure 4B ), such as Figure 6B Then, by Figures 3A to 4B The algorithm determines the information by taking the difference in covariance values ​​and comparing this first region to the next network (n=2). Then, this same first region is compared to the next network (n=3) by taking the difference in the corresponding covariance values. Once the first region has been compared to all other networks, the next region (r=(r n＝1 =2)) and then compared with all other networks. The loop continues until all regions have been compared with all networks that do not contain the compared region. The loop continues until all regions have been compared with all networks, regardless of whether the region is included in the network. Figure 6B As shown in Figure 2, only the inner loop is shown, where r = (r n＝1 =1) has been connected to all other networks (n b =n a +1 = 2 to n b =N) were compared.

[0167] After determining all the above comparisons (network to network, region to region, and region to network), a statistical distribution can be created to obtain a first subgroup of regions and / or networks in which the difference (covariation difference) from the above results is minimal for the largest number of brain networks / regions, and a second subgroup of regions and / or networks in which the difference (covariation difference) from the above results is largest for the largest number of brain networks / regions.

[0168] Then, the first and second subgroups of the regions and / or networks are compared with a healthy control group having the same age and / or gender as the patient. A third subgroup of regions and / or networks is determined from the first and second subgroups, wherein the regions fall outside the threshold of the mean value of the covariation difference values ​​from the healthy group. For example, those regions with corresponding covariation differences, the covariation differences are outside (more or less than) two standard deviations of the mean value of the covariation differences from healthy individuals of the same gender and age. The first threshold may be within two standard deviations above the mean value of the healthy control group, and the second threshold may be within two standard deviations below the mean value of the healthy control group. As will be appreciated by those skilled in the art, the healthy control group may be matched to the patient based on parameters of different ranges. For example, the comparison may be a complete match, so that the patient's gender and age are the same as those from the control group. The same age may be based on year of birth. The comparison may also be within the range as will be appreciated by those skilled in the art. For example, medical age groups are used in medical comparisons. Therefore, the same age may be the same age within the same age grouping. Exemplary age groups may be 18 to 21 years old, 22 to 35 years old, 36 to 55 years old, 56 to 65 years old, 66 years old and above. Other groupings may also be used, such as, for example, 18 to 24 years, 25 to 45 years, 46 to 65 years, and over 65 years. Other comparison ranges may also be used. For example, the same age based on birth year plus or minus a deviation (e.g., one year or five years). Matching may be based on the range of symptoms and based on the changes that patients in the age range typically experience.

[0169] The first and second subgroups of regions and / or networks may also or alternatively be compared to a healthy control group having a diagnosis / symptom before / after TMS treatment. Those networks of a fourth subgroup of regions and / or networks are determined from the first and second subgroups or from the third subgroup of regions and / or networks, which are indicated as having the largest change in covariation value from before TMS treatment to after TMS treatment for a pool of patients with the same diagnosis and / or symptoms. In other words, an exemplary input to the system comprises a comparison pool of patient data, wherein the covariation difference before and after TMS is taken for each region and / or network of the patient's brain. The comparison pool also comprises the patient's diagnosis and / or symptoms. Each region and / or network from the third subgroup of regions is then compared to the comparison pool to determine the relevant covariation difference of the relevant regions and / or networks of the patient who has undergone treatment. Thus, further subgroups are determined by having significant changes due to TMS to determine those regions that are most likely to provide results for TMS treatment.

[0170] Some embodiments may include comparisons of different features of fMRI from different regions, networks, etc. of the brain. Relative comparisons may be used, where high, low, large, small, etc. are within the scope of the methods, systems, and algorithms used herein. Those skilled in the art will understand the scope of these relative terms by referring to the purpose of the comparison and the relative values ​​in the value set using the relative terms. For example, compared to the data set, "high" can be 50% of the higher end value of the available data range, or can be 75%, 80%, 90%, 95% or other limits (margin). Those skilled in the art will understand that the selection of the range can be informed by the purpose of the comparison therein. As those skilled in the art will understand based on the following, relative terms can be equal to, above or below a set number (threshold) or range: the characteristics of the value, the purpose of comparison, the function of the treatment or algorithm, the normal value and the average value of the healthy control group, the range of the normal value and the average value of the deviation feature data set (regardless of the control group, the healthy group, the disease, the comparison with other data in the data set, etc.) or one or more standard deviations. The setting of the threshold value can be based on statistical changes and / or in selecting the desired number of target positions for treatment.

[0171] One embodiment comprises a first input comparison control group, which comprises patient information including patient age, sex, gender and / or other relevant diagnosis and / or treatment parameters. The first input comparison control group comprises a database of data from healthy patients, which comprises covariation comparisons (differences) between regions and regions, regions and networks, and networks and networks. Then, the first input comparison control group can be used to compare the output within the object, to determine those regions from the patient that exceed the expected range from those compared with the healthy control group represented by the first output comparison control group. The healthy control group can be compared on the same difference, and the region with the largest difference from the normal state (healthy group) is selected as the target. In other words, if the patient produces a high covariation difference in region 1 from network 1 due to the comparison of region 1 from network 2, the healthy patient can be compared on the same difference (region 1, network 1 and region 1, network 2). Some embodiments can also be based on individuals from the control group, the average value on the control group (e.g., the healthy control group averages all the same regions and networks and region and region differences), or other comparison bases for comparison.

[0172] One embodiment includes a second input comparison control group that includes patient information including patient age, sex, gender, and / or other relevant diagnostic and / or treatment parameters. The second input comparison control group includes a database of data from patients with the same symptoms and / or diagnosis, wherein the data includes covariation for each treated region of the patient's brain compared before TMS treatment and after TMS treatment. In other words, the second input comparison control group includes information about changes that occur in a region of the patient's brain based on application of TMS treatment to that region, and the covariation is determined before and after treatment of that region.

[0173] Some embodiments may include comparing regions and networks of a patient's brain to other regions and networks. The comparison may be done in different ways. For example, Figures 3A to 8BAs shown and described in the loop of , each region can be compared to every other region, each network can be compared to every other network, each region can be compared to every network (without or without the network in which the region itself is located), or any combination thereof. When comparing a region with other regions or with other networks or a network with other networks, the combination of other regions or networks can be in a group. For example, each region can be compared to each other region individually or as a group. Comparison by group can be performed by taking the average of the regions within the group of regions, so that the comparison is performed by comparing a single region to a group of regions. Comparing a region to a group of other regions that occur within the same network. Comparing a region to a group of regions that includes a network or a portion of a network, where the group of regions and the network are outside the region or network in which the single region is located. Comparing a region to a group of multiple regions so that the region is compared to all other regions of the brain.

[0174] Comparisons of regions to regions, regions to networks, and networks to networks may not include all other regions and / or networks in the brain. Comparisons of regions to regions include comparing regions to regions within the same network and / or regions outside the same network. However, if a portion of the brain is known to function normally or is not associated with a given disease or symptom, the region or network may be removed from the comparison of regions and networks. However, it is still preferred to compare regions to other regions outside the same network, networks to networks, or regions to networks outside of the regional network to more completely assess the brain and determine the targeting of TMS treatment.

[0175] Some embodiments can be used to treat a variety of diseases. For example, some embodiments can be used to treat a variety of mental disorders, such as Alzheimer's disease, anxiety, 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. Some exemplary embodiments can also be used to reduce cognitive decline.

[0176] Some embodiments of the present disclosure can be used for patients suffering from psychosis (auditory hallucinations). For example, Figures 9A to 9F Shown are averages of five patients with auditory hallucination (AH) psychosis before TMS treatment, and Figures 10A to 10FShown is an average of five patients with auditory hallucination (AH) psychosis after TMS treatment, with the target brain regions identified for TMS treatment outlined in bold. The images shown are from a covariate (amplitude vs. frequency) comparison of BOLD fMRI data. Circuits identified for treatment based on exemplary regions for treatment include in the left hemisphere: PGs, IP1, MIP, 7PL, VIP, IPS1, 10v 10r, 25, and in the right hemisphere: 7PL, MIP, IP1, VIP, 7PC, OFC, pOFC, 25. As shown in FIG. Figures 10A to 10F As shown in , the circuits normalized after TMS treatment. The patient experienced a change in his PANSS score from severe (6 points) to minimal (2 points) in areas including P1 delusions, P2 conceptual confusion, P3 hallucinatory behavior, P6 suspicious / presecution, and P7 hostility. The patient was also able to reduce his medication to 50% after the time after the fMRI scan.

[0177] Figures 9A to 10F The scans were averaged across five patients with auditory hallucination (AH) psychosis to avoid making individual scans public. As shown, Figures 9A to 9F The brighter areas of the brain were targeted for stimulation therapy and found to be Figures 10A to 10F The improvements shown in the improvement area shown in .

[0178] Some embodiments of the present disclosure may be used in patients with TBI. For example, Figures 11A to 11B shows an exemplary baseline RSFC region map before TMS treatment according to some embodiments, and Figures 12A to 12B An exemplary RSFC map following fMRI-guided therapy is shown. Figures 13A to 13B Shown with Figures 11A to 12B of patients with an exemplary control group of the same age and sex. For illustrative purposes, the illustrations are averaged over 5 exemplary subjects. RSFC analysis according to the exemplary methods described herein was run on 5 patients. Some exemplary embodiments may identify a circuit for treatment using TMS that includes the following regions within the language network for stimulation: Left hemisphere: STSdp, TPOJ1, STV, SFL, 55b, 44, 45. Some exemplary embodiments of the treatment regimen are excitatory, at an amplitude of 130% of the individual's motor threshold. Symptoms experienced by patients included limitations in language production (speech comprehension, articulation, and speed) and language comprehension (semantic processing), ranging from severe (6 points) to mild (3 points). For example, exemplary graphs provided for different RSFC regions are provided. See Figures 11A to 31B .

[0179] A sudden blow or shake to the head can cause TBI. The functional impairment of these injuries has been specifically studied in the intrinsic connectivity network (ICN). TBI substantially disrupts ICN function, which is associated with cognitive impairment. Two major ICNs, the salience network (SN) and the default mode network (DMN), are thought to be relevant in treating TBI. In addition to the above findings, it is believed that the effectiveness of standard TMS in treating TBI can be significantly improved by equipping it with a brain network-based guidance system.

[0180] Resting-state functional connectivity MRI (rsFCMRI) is an imaging technique that can reveal the dynamics of brain networks and their interactions. Some embodiments may use rsFC MRI to visualize the brain networks of each patient. rsFC MRI images can also be used to find appropriate targets to "normalize" brain network function and interactions with other networks. This is an effective method for treating TBI disability because, for example, the activity of ICNs is usually tightly coupled, which is important for attention control. The ability to restore normal activity of ICNs can reverse the loss of tight coupling of ICNs due to impaired structural connectivity of these networks. The loss of normal correlations between networks can produce abnormalities in network function and cognitive control.

[0181] Routinely, the patient or someone close to the patient is required to provide symptoms to determine the severity of TBI. In some cases, physical detection can be performed by structural lesions in the brain. The brain that suffers from neurological death will show lesions in the form of brain tissue volume loss at the structurally damaged area of ​​TBI. In essence, these areas are not connected to other areas of the brain. Structural lesions can be detected by the following: the methods, systems, and instructions described herein are compared by rsFC MRI comparisons to indicate global effects by identifying areas with no functional connectivity with a large number of brain areas. Other methods described herein can use structural analysis to determine the total cortical gray matter volume and total white surface area to identify structural changes associated with TBI. Some embodiments can be used to provide alternative diagnostic pathways to identify structural lesions that are decoupled from physical detection by brain tissue loss.

[0182] The systems, methods and instructions provided herein can also be used to diagnose brain damage that will cause structural lesions before neural death, so that brain regions can be treated or the damage therein can be reduced. For example, the systems, methods and instructions provided herein can identify functional lesions. Functional lesions can be brain regions that are functionally disconnected from a large number of brain regions; but may not be zero, or may not experience physical loss. The region with functional lesions can show unstable or paroxysmic behavior, which has a high negative correlation / covariance less than zero. For example, compared with other regions of the brain using rsFC MRI, a region can show a high negative correlation, so that a region is determined to have a very high negative correlation with other regions of the brain. In this case, the brain region can be considered to be a functional lesion, which, if not treated, will have a high potential or possibility to cause structural lesions and / or neural death in the brain region.

[0183] In functional lesions, global disruptive behavior can be detected by inactivating other brain regions that are negatively correlated with it. Functional lesion regions are highly negatively correlated; this means that in an attempt to reconnect itself to the rest of the brain, the region inactivates a large number of brain regions, shutting down regions that are negatively correlated with it. High negative correlations / covariations with a large number of brain regions can be used to identify functional lesions; these high negative correlations / covariations are less than zero and show large-scale decoupling (inactivation of a large group of brain regions).

[0184] Thus, systems, methods, and instructions may include early stage diagnostics and treatment options for detecting areas at risk of brain damage before structural detection becomes apparent or available. Using fMRI to compare the functional connectivity of brain regions, systems, methods, and instructions may provide early detection of future brain damage by identifying brain regions that are functionally disconnected but have not yet undergone neural death and / or physical degeneration. Thus, some embodiments may provide early detection to allow for therapeutic intervention prior to neural death, thereby limiting damage and / or reversing damage.

[0185] Thus, some embodiments may be used to diagnose and treat TBI. Some embodiments may not need to rely on the patient's physical symptoms or physical brain lesions to diagnose the severity of TBI. Some embodiments may be used for early detection and / or treatment before brain damage or before significant brain damage or before neural death. Structural lesions may have zero functional connectivity, or nearly zero connectivity, including reduced or near zero functional connectivity, with many brain regions. Figures 19A to 20B Shown is a brain scan of a patient experiencing a stroke. Fig.19A and 20A An image from an fMRI brain scan of a patient experiencing a stroke is shown, showing near-zero functional connectivity. Fig.19B and 20BThe corresponding images are shown to show the corresponding structural degradation. The patient scans show no correlation / covariation with a large set of brain regions. An exemplary embodiment shows a correlation / covariation value of 0 or close to 0.

[0186] In stage 1, before neuronal death, the correlation / covariation with a large number of brain regions may be less than zero, indicating a global decoupling effect, deactivating a large number of brain regions and classifying them as functional lesions. In stage 2, when brain injury is generally present, the correlation / covariation with a large number of brain regions is close to or zero, which shows a global non-connectivity pattern of structural lesions. Changes in regional connectivity used to identify TBI can be according to some embodiments described herein, where one region of the brain is out of statistical deviation from the rest due to having low functional connectivity as a structural lesion or having a high negative correlation as a functional lesion compared to all other regions of the brain. Comparisons can also or alternatively be based on other patients who have been diagnosed and treated for TBI.

[0187] Some embodiments of the method may use advanced technology to simulate brain dynamics to gain insight into network dysfunction. For example, some embodiments may use rsFC MRI to find out how structural network damage caused by axonal damage produces functional irregularities, which can be used to determine targets for TMS treatment. As another example, some embodiments may detect "functional lesions" to detect and / or treat structurally undetected brain regions in certain patients with TBI. Some embodiments may be used to identify "lesions" when brain regions are not structurally manifested in the form of volume loss (neuronal death). Through early treatment provided by early detection, structural damage can be limited, stopped, or even reversed by treating these early diagnosed lesions.

[0188] Some embodiments herein can be used to identify structural lesions by finding brain regions that are not functionally connected to other regions of the brain. In this case, these regions of the brain can be targeted and / or eliminated by treatment. For example, these regions can be removed from the algorithm herein because the brain region may have experienced neural death and therefore cannot be treated. Without removing these regions from the selection, they can be identified by the above-mentioned algorithm based on the above-mentioned criteria because their dysconnectivity can determine these regions as preferred treatment regions. In other cases, if neural death has not yet occurred, these regions can be highly expected to be treated before such physical damage is completed or continued. The connectivity patterns of structural lesions are very different from those of functional lesions.

[0189] Some embodiments of fMRI-guided TMS therapy herein can be used to treat functional lesions that can be caused by TBI. Brain maps of functional lesions are usually very abnormal (unstable or paroxysmal behavior of neurons), usually with high negative correlation / covariation with a large number of brain regions, showing global effects. Typically, these maps are very colorful at the negative end of the spectrum, i.e., light blue, green, purple, gray, etc. When these areas are the target of TMS therapy, these lesions can completely restore or improve their function. Figures 18A to 18B The frontal cortex (region TE1m, right) is shown to have a high negative correlation / covariation with a large group of brain regions (light blue and green areas). Fig.18A Patients at baseline are shown; and Fig.18B Shown is a patient following treatment according to some embodiments described herein.

[0190] Some embodiments of MRI-guided TMS therapy herein can be used to treat structural lesions that can be caused by TBI. Brain images of structural lesions are usually non-color, i.e., dark red or brown, almost black, showing a lack of connectivity with a large number of areas. In some cases, some activity can still be detected in these areas. If so, these areas can partially recover their function after treatment with TMS.

[0191] Patients with TBI may show one or both types of functional lesions and / or structural lesions. Some embodiments herein are used for one or both of treatments with fMRI-guided TMS. Some embodiments of the disclosure may be used for patients to reduce further injury from TBI or to reduce brain damage from functional disconnection. Some embodiments herein may be used to provide early diagnosis and treatment of brain regions prior to structural brain damage by identifying and / or treating functional lesions.

[0192] Some embodiments of the present disclosure using fMRI may allow for early detection of brain abnormalities prior to behavioral or structural manifestations (i.e., imaging-based biomarkers) for prevention and early intervention of neurological and psychiatric disorders. Some embodiments may also allow for intervention in later stages of the disease / disorder when cortical atrophy and volume loss is present by: (1) recruiting healthy brain regions in the affected pathways to compensate for the loss of function, and; (2) stimulating atrophic regions to delay further deterioration and progression of the disease (in the case of neurodegenerative diseases such as Parkinson's or Alzheimer's disease, where the disorder worsens over time).

[0193] Some embodiments may show and describe methods for identifying brain regions that have high negative correlations and covariations with a large set of brain regions. These types of functional lesions can be detected in any brain region. Some embodiments shown herein show a single case for illustration. See Figures 99A to 127 Some 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, leading to the development of chronic complications. Some embodiments may use network-level imaging to inform diagnosis, prognosis, and treatment planning for TBI.

[0194] Some embodiments of the present disclosure can be used in patients to reduce or prevent aging-based cognitive decline. For example, Figures 32A to 32D Baseline RSFC map before TMS treatment is shown, and Figures 33A to 33D Shown are RSFC maps after fMRI-guided treatment. Figures 34A to 34D Shown with Figures 32A to 33D The patients had a control group of the same age and sex. For illustrative purposes, the graphs are averaged over five (5) subjects. RSFC analysis according to the methods herein was run on five patients. Some embodiments herein identify a circuit for treatment using TMS that includes the following regions within the frontoparietal and language networks for stimulation: Left hemisphere: p9-46v, STSdp, TPOJ1. Some embodiments of the treatment regimen are excitatory, at an amplitude of 130% of the individual's motor threshold. Patients were asymptomatic at the time of TMS treatment and therefore no changes in symptom scores were produced. Patients were selected based on risk factors for neurodegenerative diseases (cardiovascular and genetic predisposition). For example, exemplary graphs are provided for different RSFC regions. See Figures 32A to 38D .

[0195] Aging can lead to cognitive impairment, such as that caused by vascular degeneration. Vascular cognitive impairment (VCI) is caused by blockage of the microvasculature; and the inability to supply oxygen and nutrients to different brain regions, leading to reduced cognitive abilities. Sometimes, this inability is caused by damage to the blood vessels of the brain. In addition to vascular-based dementias, there are also degenerative dementias (most common in Alzheimer's disease). Combining these two types covers most cases of dementia.

[0196] TMS can be used to prevent cognitive decline by strengthening connectivity and brain activity in the most vulnerable areas, such as specific locations within the frontal and temporal lobes detected by fMRI-based biomarkers. The methods herein can be successful in treating MCI patients using MRI-guided TMS methods. Some embodiments can also be used to develop new protocols for preventing or reducing daily memory decline in asymptomatic patients at risk for MCI and Alzheimer's disease due to family history or cardiovascular disease.

[0197] Some embodiments of the protocol may involve: (1) analyzing resting state fMRI (rsfMRI) images taken from the patient's brain to construct brain networks, (2) using biomarkers to detect the most susceptible brain regions in each individual, and (3) delivering MRI-guided TMS over the course of 20 sessions. The patient may receive a second MRI after the last session to compare baseline and post-treatment brain maps to calculate efficacy measures. The patient may also receive another MRI at a one-year follow-up to monitor the evolution of brain activity and connectivity, and the need for another 20-session cycle of MRI-guided TMS may be assessed by biomarkers and delivered as needed throughout the lifespan. The methods used in this article for MCI patients show that MRI-guided TMS can be an effective treatment for preventing cognitive decline and may be a tool to slow deterioration.

[0198] Some embodiments of the present disclosure can be used for patients suffering from depression. For example, Figures 41A to 41D shows a baseline RSFC area map before TMS treatment according to some embodiments herein, and Figures 42A to 42D Shown are RSFC area maps after whole circuit based fMRI-guided treatment according to some embodiments herein. Figures 43A to 43D Shown are RSFC area maps following single-region treatment of area 46 within the DLPFC according to conventional treatment. Figures 44A to 44D RSFC area maps from healthy controls and Figures 41A to 42DPatients were matched for the same age and gender. For illustrative purposes, the illustrations are averaged over ten (10) subjects. RSFC analyses according to the methods herein were run on ten patients with depression who did not respond to standard TMS methods based on the 5 cm rule. Some embodiments herein identify circuits including the following regions within the cingulo-opercular network for stimulation: left hemisphere: 46, PF; right hemisphere: 46, PF. Some embodiments of the treatment regimen are excitatory, at an amplitude of 120% of the individual's motor threshold. Changes in symptomatic depression scores based on HAMD included: starting from a baseline score of HAMD <7 for all subjects, remission after circuit-based fMRI-TMS, and HAMD>7 for all subjects after single-region fMRI-TMS, where a 50% improvement was achieved in the HAMD score for all subjects, but no one achieved remission. None of the patients included in these analyses responded to standard TMS (5 cm rule). For example, exemplary diagrams provided for different RSFC regions are provided. See Figure 41A-7A -1 to 56D.

[0199] Major depression disorder (MDD) can be characterized by abnormal functional connectivity of brain networks. Studies have revealed disrupted network connectivity in the major MDD networks, namely the default mode network (DMN), central executive network (CEN), and salience network (SN). Abnormalities in cerebellar and thalamic circuits have also been reported. Methods Qualitative aspects of MDD symptomatology can be addressed using a unique tool (resting-state functional connectivity MRI or fMRI) that can reveal changes in brain networks.

[0200] Some embodiments herein can be used to identify the severity of depression using changes found in the DMN, CEN, SN, frontal and thalamus brain regions, insula, and subgenual anterior cingulate cortex (sgACC). One reason why standard TMS cannot produce very high outcomes is because it cannot treat targets unique to each patient. Some embodiments described herein can measure the patient's brain function and connectivity shortly before treatment begins, and use complex algorithms to identify specific brain regions that show abnormal behavior.

[0201] Some embodiments can be used to show the involvement of clear functional connectivity in MDD related to its pathophysiology and treatment. However, some embodiments using fMRI enable some embodiments of the methods described herein to find targets, deliver treatments and evaluate their effectiveness after treatment is completed. The implementation of functional connectivity as a scientific biomarker can increase the chances of solving the root causes of MDD. Just like any other illness, it is important to diagnose brain regions involved in depression to target these brain regions. Using fMRI, the methods described herein can find brain regions that are not functioning properly. The exemplary fMRI-guided TMS schemes for depression of some embodiments described herein can identify and treat these specific regions, the most famous being called DLPFC. It was found that the left DLPFC of depressed patients had reduced activity and abnormal connectivity; and this region is associated with behavioral disorders common in depression (e.g., reduced energy, insomnia, changes in appetite).

[0202] The involvement of the subgenual anterior cingulate cortex (sgACC) in the pathophysiology of depression is shown, as well as its predictor of response. DLPFC hypometabolism may be secondary to limbic hyperfunction, and the connectivity (negative correlation) of DLPFC and limbic regions is responsible for antidepressant response. The best target of TMS may fall roughly in the sgACC "anti-correlation" region of DLPFC2-4. When a patient is depressed, certain areas in the brain may be affected, such as the dorsolateral prefrontal cortex, hippocampus, amygdala, lateral orbitofrontal cortex, anterior cingulate cortex and posterior cingulate cortex, parahippocampal gyrus, insula, temporal cortex and precuneus are all areas of visualization and treatment according to the embodiments of the area and network analyzed according to the method for depression treatment described herein. When the blood flow and connectivity in these areas are outside the normal range, further investigation can confirm that there are areas with excessive or insufficient functions and / or structures, and functional connectivity and / or structural connectivity can be increased or decreased. By setting up the TMS coil to deliver magnetic pulses in these specific areas at specific frequencies, thereby targeting the circuits there, the specific brain areas necessary for the symptoms can then resume normal function and the patient can feel pleasure again.

[0203] The systems and methods described herein can be used for patients with autism spectrum disorders. For example, Figures 57A to 57D Baseline RSFC map before TMS treatment is shown, and Figures 58A to 58D Shown are RSFC maps after fMRI-guided treatment. Figures 59A to 59D Shown with Figures 57A to 58Dof patients with an exemplary control group of the same age and sex. For illustrative purposes, the illustrations are averaged over 5 exemplary subjects. RSFC analysis according to the method was run on five patients. Some embodiments identify circuits for treatment that include the following regions within the default and language networks for stimulation: Left hemisphere: STGa, STSda, STSva, STSdp, STSvp. Some embodiments of the treatment regimen are excitatory, at an amplitude of 110% of the individual's motor threshold. In all subjects, the symptoms experienced by the patients included limitations in social interaction, social communication, theory of mind, starting from severe (6 points) and improving to mild (3 points). For example, exemplary graphs provided for different RSFC regions are provided. See Figures 57A to 71D .

[0204] Autism in DSM5 is characterized as one of the so-called pervasive developmental disorders (PDD), which also includes Asperger's disorder and other pervasive developmental disorders not detailed here. These groups of disorders are collectively called autism spectrum disorder (ASD). ASD is characterized by difficulties in social skills, repetitive behaviors, and the use of verbal and non-verbal language for communication. Children with restricted and repetitive behaviors share common characteristics, such as complex behaviors and an inability to grasp concepts. ASD affects approximately 1 in 60 children, but there are currently no drug treatments that can target the core networks involved in ASD.

[0205] Recent evidence shows that repetitive transcranial magnetic stimulation (rTMS) has a high potential to alleviate the main symptoms of individuals with ASD. Individuals with autism have unique brain complexity. There is strong evidence that rTMS improves symptoms in young people with ASD. Experience shows that rTMS applied to DLPFC, which targets a single area specific to an individual based on some embodiments described herein, has the effect of a new intervention for EF defects in ASD. By utilizing the method of rsFC MRI technology described herein, TMS can be applied to patients in a targeted and personalized manner, enabling them to directly treat areas of dysfunction, and obtain neuroimaging measurements before / after treatment to record the effect of treatment on brain structures that are critical for EF performance. The embodiment of this capability enables the neural mechanisms of rTMS treatment efficacy to be examined. Some embodiments may also include longitudinal follow-up that enables the assessment of continued intervention to maintain the need for therapeutic effects.

[0206] As with depression, standard rTMS for ASD uses a treatment site based on a fixed position relative to the motor cortex. Since autism is a disorder of the association cortex, and in particular a disorder of connectivity that primarily involves intrahemispheric connectivity, in order to treat ASD, a connectivity-based TMS targeting strategy determines the optimal TMS target coordinates in bilateral DLPFC. Previous experience has shown that in ASD dominated by abnormal social cognitive function, some embodiments of the methods provided herein enable: (i) measuring changes in resting state functional connectivity (rsFC) between nodes of the involved networks (e.g., prefrontal cortex, orbitofrontal cortex, supplementary area, anterior cingulate cortex, posterior cingulate gyrus, superior temporal cortex, superior / middle temporal gyrus, and inferior parietal regions) and culprit deep nuclei regions (e.g., ventral anterior nucleus of the thalamus). Such comparisons yielded correlates that mediate treatment responses; and (ii) a connectivity-based targeting approach applied at the single-subject level to identify bilateral targets optimized for TMS in the prefrontal cortex for personalized treatment. Thus, a new, innovative approach with enhanced efficacy of rsFC MRI-guided TMS is provided.

[0207] The method is fundamentally different from traditional methods, which target without investigating the patient's brain or understanding the differences in brain tissue. On the contrary, the embodiments described herein stimulate the brain based on the rsFC pattern of the individual brain. The embodiments described herein emphasize the network architecture of the human brain, and therefore use rsFC MRI to build a network related to each disease. About ASD, for example, the following evidence shows that the distributed nervous system is abnormal: widespread dysfunction of the association cortex, and the primary sensory and motor cortices and white matter are not affected, plus the lack of clinical signs (such as visual spatial defects) of focal brain dysfunction common in children with hypoxic-ischemic injury and cerebral palsy. This is why treatment is based on the network. Others have used EEG-based functional connectivity to guide their TMS treatment of ASD. In contrast, the results after applying the TMS treatment regimen using the method described herein show that patients will experience more lasting clinical improvement under anatomically precise techniques.

[0208] The systems and methods herein can be used for patients suffering from dementia. For example, Figures 72A to 72D shows a baseline RSFC area map before TMS treatment according to some embodiments described herein, and Figures 73A to 73D Shown is a baseline graph following fMRI-guided therapy according to some embodiments described herein. Figures 74A to 74D Shown with Figures 72A to 73Dof patients with an exemplary control group of the same age and sex. For illustrative purposes, the illustrations are averaged over 5 exemplary subjects. RSFC analysis according to the exemplary methods described herein was run on five patients. Circuits for targeted TMS treatment were identified according to some embodiments described herein, including the following regions within the opercular, somatomotor, default, frontoparietal, and dorsal attention networks for stimulation: Left Hemisphere: Semantic Retrieval: Lexical Retrieval: FOP5, FOP4, FOP3, FOP2; TE1a, TE1m, TE1p, PHT, TE2a. The treatment regimen was excitatory, at an amplitude of 130% of the individual's motor threshold. In all subjects, symptoms experienced by patients included improvements in lexical / semantic information retrieval, starting from moderately severe (5 points) to mild (3 points). For example, exemplary graphs provided for different RSFC regions are provided. See Figures 72A to 98D .

[0209] The embodiments described herein are considered to be effective in stopping, slowing down and / or reversing the effects of Alzheimer's disease (AD). So far, drug treatment has not obtained good results, and this has created space for non-drug interventions to be used for the disease. TMS can cause changes in brain activity and lead to long-term changes in damaged neural networks. Such abilities of TMS bring many prospects for clinical intervention. Standard TMS can cause changes in cortical excitability, increase brain plasticity, and promote recovery by restructuring the damaged neural networks responsible for cognitive impairment. However, standard TMS does not provide personalized targeting capabilities that can be corrected using advanced MRI technology. The method described herein uses fMRI to map the entire brain network, and then runs specific analysis for each patient to find the correct target for TMS treatment with the greatest outcome.

[0210] fMRI, as according to some embodiments described herein, can be used as a technique to generate biomarker surrogates for neurodegenerative diseases. It can identify the presence of AD and allow tracking of progression, severity, guide TMS treatment, and provide an assessment of therapeutic effects. There is increasing evidence that interventions for neurodegenerative disorders must be applied in the early or even presymptomatic stages of AD. Therefore, the use of sensitive techniques like fMRI to monitor disease progression based on quantitative measurements combined with clinical features provides a reliable and easily tracked biomarker in the field.

[0211] Some embodiments described herein can be used to restore a patient's foundation and reduce or recover from anxiety, such as generalized anxiety disorder (GAD). The fMRI techniques and methods described herein can clarify a patient's treatment plan by allowing insight into how an individual patient's brain works.

[0212] The unique combination of TMS and fMRI according to some embodiments described herein can help patients get relief from anxiety, depression and many other brain disorders. In short, the MRI images described herein and the related methods for analyzing MRI images can help identify unique brain functions to create a unique treatment plan for patients. As described herein, fMRI measures brain activity by detecting changes related to blood flow, and dMRI uses computational fiber bundle imaging to generate a three-dimensional trajectory of white matter passing through the brain volume. When a brain region is in use, blood flow to the region is increased. Since several parts of the brain are key factors in generating fear and anxiety, fMRI helps to determine the affected areas in the patient's brain. Some embodiments shown in this article show anxiety circuits. Two types of brain circuits show two different anxiety biomarkers, including frontal lobe or parieto-occipital sulcus. Some patients have two characteristics, and some patients are more inclined to frontal lobe or parieto-occipital circuits.

[0213] For example, Figure 128A to 1 28D shows a baseline RSFC area map before TMS treatment according to some embodiments described herein, and Figures 129A to 129D RSFC plots are shown following fMRI-guided treatment according to some embodiments described herein. The plots are averaged over five subjects. RSFC analysis according to the methods described herein was run on five patients. Some embodiments described herein identified a circuit for treatment using TMS that included the following regions within the frontoparietal, operculum, and default mode networks for stimulation: Right hemisphere: dorsolateral prefrontal, area 9-46d, right side. The treatment regimen was inhibitory, at 90% to 100% amplitude of individual motor threshold (adjusted based on penetration depth and coil type used). Patients experienced psychological / physiological anxiety and panic attacks ranging from severe scores of 6 to minimal scores of 2. For example, exemplary plots are provided for different RSFC regions. See Figures 128A to 137D .

[0214] For example, Figures 138A to 138D shows a baseline RSFC area map before TMS treatment according to some embodiments described herein, and Figures 139A to 139DSFC plots following fMRI-guided treatment are shown. The plots are averaged over five subjects. RSFC analysis according to the methods described herein was run on five patients. Some embodiments described herein identify circuits for treatment using TMS that include the following regions within the frontoparietal, default mode, and operculum networks for stimulation: left hemisphere, parieto-occipital sulcus area, area POS2, left side. Some embodiments of the treatment regimen are inhibitory, at 90% to 100% amplitude of the individual's motor threshold (adjusted based on penetration depth and type of coil used). Patients experienced psychological / physiological anxiety and panic attacks ranging from severe scores of 6 to minimal scores of 2. Exemplary plots of different RSFC regions are provided. See Figures 138A to 147D .

[0215] The systems and methods described herein can be used to treat bipolar disorder, also known as manic-depressive illness, which is a brain disorder that causes abnormal changes in mood, ability, activity level, and ability to perform daily tasks. Types of bipolar disorder include bipolar I disorder, bipolar II disorder, cyclothymic disorder, and other specified and unspecified bipolar and related disorders.

[0216] Using fMRI imaging according to some embodiments described herein, the malfunctioning networks that cause manic episodes to occur can be determined. Using this technology, the methods according to some embodiments described herein can accurately treat specific areas with personalized treatment plans and precise neuronavigation.

[0217] Some embodiments described herein can be used to treat ADHD.Attention deficit hyperactivity disorder (ADHD) is a condition characterized by persistent and inappropriate levels of excessive activity, impulsivity, and inattention.

[0218] ADHD may involve two partially separate attention networks: the dorsal attention network (DAN) and the ventral attention network (VAN). These two networks are closely related to the attention regulation system in the brain. The DAN is concentrated in the bilateral intraparietal sulcus (IPS) and the junction of the precentral and superior frontal sulci (FEF (frontal eye field)), and enables the control of spatial attention by selecting sensory stimuli based on internal goals or expectations and linking these goals with appropriate motor responses. One of the main symptoms of ADHD, the failure to ignore external stimuli, is related to the loss of functional connectivity (FC) in the DAN. The VAN is anchored in the right temporo-parietal junction (TPJ) and ventral frontal cortex (VFC) and redirects attention to significant behaviorally relevant stimuli. In this regard, fMRI studies of ADHD have revealed significant hypoactivation of the VAN and DAN compared with healthy controls, which is associated with ADHD.

[0219] Although task-based fMRI studies have made important contributions to the understanding of brain function in ADHD, resting-state fMRI studies have also revealed hypoconnectivity in the DAN and VAN in children and adults with ADHD. Therefore, the network causes of ADHD may be related to hypoconnectivity in the attention network that includes the DAN and VAN.

[0220] Treatment according to some embodiments described herein can be guided by resting state fMRI rather than task-based fMRI, because the way a network responds to external stimuli (task-based studies) depends on the connectivity of the brain network at rest (resting state fMRI studies). Therefore, by computational mapping according to some embodiments described herein, the method can determine and regulate the way these networks respond when participating in tasks that require attention. In addition, resting state fMRI can be more easily implemented, allowing functional brain networks to be identified with higher sensitivity than task-based fMRI.

[0221] The systems and methods herein can be used to treat drug addiction. Imaging studies have found specific networks that lead to the three stages of the addiction cycle. For the binge stage, the main components of the network are concentrated around the ventral tegmental area and ventral striatum. The structure that plays a leading role in the withdrawal period is the amygdala.

[0222] 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 cortex, and inferior frontal cortex.

[0223] A series of neuroplastic events from the ventral striatum to the orbitofrontal cortex leads to dysregulation of the prefrontal cortex, cingulate gyrus, and amygdala, controlling the transition to addiction. The identification of neural networks involved in the transitional stages of addiction also provides insight into the susceptibility to developing addiction.

[0224] The underlying mechanism of TMS-induced effects that makes it an effective treatment for SUD patients is that it modulates maladaptive brain networks of addiction. Some embodiments of the methods described herein can use this network-level action supported by a number of preclinical and clinical findings to develop even more effective TMS techniques, including the use of resting-state functional connectivity MRI or fMRI-guided TMS.

[0225] The method described herein involves mapping brain networks and subsequently using network analysis anchored in deep brain regions associated with addiction. This allows the method described herein to identify the counterparts of nodes on the dysfunctional network in the cortex that will become targets for neuronavigation-guided TMS treatment.

[0226] Some embodiments described herein may also include fMRI to assess the severity of addiction, and to guide TMS treatment. The fMRI-guided TMS is a state-of-the-art, highly sophisticated technology that uses infrared technology to precisely target affected structures in the brain. Through this technology, some embodiments described herein may provide multiple targets to apply TMS, which is highly effective in smoking and substance use cessation.

[0227] After treatment is completed, the approach may also include the use of fMRI to objectively assess the efficacy of treatment. fMRI can complement evidence-based self-help interventions, cognitive behavioral interventions, and nicotine / substance replacement therapy. The main advantage of fMRI is its direct involvement in dysregulation of motivational networks.

[0228] Since such disorders are caused by three phases of heightened incentive salience and habit formation, reward deficits, and impaired executive function, they engage networks that can be identified by fMRI and accessed by TMS. It has been established that the rewarding effects of substances, such as the incentive salience and drug-seeking habits that develop during the indulgence phase, involve changes in dopamine and opioid peptides in the basal ganglia. It is not possible to directly stimulate the basal ganglia by TMS due to its weak electric field strength in deep brain regions, and only through the information provided by fMRI is it possible to target secondary regions of the subcortical area that are anatomically connected to the DLPFC.

[0229] fMRI analysis according to some embodiments described herein may reveal regions within the left DLPFC that may serve as target areas for treating SUD.

[0230] Some embodiments may also be used to reduce drug consumption.

[0231] Some embodiments described herein can apply the same strategy in treating negative emotional states. Negative emotional states in the withdrawal phase are caused by reduced function of the dopamine component of the reward system and the recruitment of brain stress neurotransmitters such as corticotropin-releasing factor and dynorphin. In the amygdala network, seed-based computing can be used to find effective cortical targets through fMRI to improve the effectiveness of TMS in treating negative emotional states.

[0232] Similarly, the same approach can also be used to suppress craving and executive function deficits during the craving phase. Here, TMS targets were selected using fMRI, a phase known to involve dysregulation of afferent projections from the prefrontal cortex and insula (including glutamate) to the basal ganglia and extended amygdala. Therefore, the systems, methods, and algorithms are very effective in treating addiction.

[0233] Some embodiments described herein are believed to be helpful for eating disorders (ED).fMRI TMS according to some embodiments of the methods described herein can be used to identify and correct networks that contribute to eating disorders.

[0234] Consuming palatable foods increases activation of regions involved in reward, such as the ventral and dorsal striatum, midbrain, amygdala, and orbitofrontal cortex. Increases or decreases in functional resting-state connectivity have also been observed in ED compared to controls, suggesting networks associated with executive function, reward processing, and perception. In patients with AN, choosing a low-fat meal relative to a high-fat meal increases connectivity between the dorsal caudate and DLPFC regions, which is associated with actual food intake, thus making the DLPFC a region of considerable interest.

[0235] By identifying the underlying networks associated with a disorder through fMRI imaging and some embodiments described herein, biological aspects of the disorder can be treated.

[0236] Some embodiments described herein may be used to treat OCD.

[0237] Neuroimaging studies have provided strong evidence that OCD involves neural circuits. The most plausible candidate for the culprit network in the pathophysiology of OCD is the cortico-striato-thalamo-cortical (CSTC) circuit.

[0238] The CSTC network is thought to be involved in a variety of cognitive functions, such as inhibition of impulsive behavior, regulation of motor activity, and allocation of attention. Imaging studies have also shown that the CSTC circuit has several interconnected circuits that involve the frontal cortex and subcortical brain areas. These pathways oppose each other, giving the thalamus either an inhibitory function that leads to decreased movement through activation of the indirect pathway, or a net excitation that leads to increased movement through activation of the direct pathway.

[0239] Different CSTC networks are responsible for making decisions about specific motor and cognitive functions. These choices are made by specific frontal cortical areas included in the networks.

[0240] The relationship between the frontal cortical areas and the basal ganglia determines which actions are selected and which are inhibited. The stimulation or inhibition of appropriate behavioral sequences depends on changes in the balance of activity between the direct and indirect pathways. Failure to eliminate dysfunctional behavioral sequences leads to OCD symptoms.

[0241] OCD patients may have dysfunction in core neural processes executed by CSTC circuits, such as response inhibition and sensorimotor gating. This means that patients with OCD are biased to perform habits at the expense of goal-directed actions. It is possible that different populations of striatal neurons differentially regulate direct and indirect basal ganglia pathways, leading to stereotyped motor behaviors.

[0242] The direct pathway (striatum, substantia nigra, globus pallidus internal) and the indirect pathway (striatum, subthalamic nucleus, globus pallidus external) may contribute to the communication between the thalamus and the cortex and the generation of motor patterns. This understanding explains why OCD symptoms may be caused by excessive activity in the direct versus indirect OFC subcortical networks. fMRI can be used to detect activity throughout the brain with particular attention paid to the networks involved in OCD. Methods can then be used to determine targets specific to each individual patient based on the functional abnormalities of the different regions. These targets, which differ in the patient, will then be treated with TMS to normalize the function of the networks involved.

[0243] Some embodiments described herein are useful for treating PTSD, a disabling disorder with symptoms of nightmares or flashbacks, avoidance, and hyperarousal following a traumatic experience.

[0244] The methods described herein, including fMRI, can be used to assess the role of three fundamental brain networks, namely the default mode network (DMN), the central executive network (CEN), and the salience network (SN), in understanding higher cognitive functions. Therefore, technologies that can address the problem of impaired function of these networks have the opportunity to have a large impact on PTSD. Although standard TMS has been successful in treating PTSD, the fact that neural networks are not affected in the same way requires the selection of personalized treatments. Some embodiments of the disclosed methods use resting state functional connectivity MRI (rsFC MRI) as a guide for personalized TMS treatment.

[0245] Some embodiments may include a wide range of fMRI-TMS with frequencies of 1 to 20 Hz rTMS to short bursts of theta (continuous / intermittent TBS) on specific targets in the right and / or left DLPFC. iTBS is a high-frequency rTMS that delivers a short train of high-frequency pulses (50 Hz). The pulse train is delivered at a frequency of 5 Hz (every 200 milliseconds) within the theta range [4 to 7 Hz] of the EEG. iTBS can be used due to its many advantages, such as its ability to deliver effective treatment in 3 minutes compared to the 37-minute standard regimen for depression.

[0246] Some embodiments described herein can be used to treat schizophrenia.TMS has been used to treat schizophrenia.When the TMS version referred to as deep TMS or dTMS is applied to the DLPFC in schizophrenia, it shows a significant improvement in negative symptoms.In this case, the high-frequency (18Hz) bilateral stimulation applied on the bilateral DLPFC with a deep TMS coil can be used to treat the patient.Objective improvement measures (such as negative symptom assessment scales and positive and negative symptom scales) can be used to ensure the reliability of the results.Resting state functional connectivity MRI or rsFC MRI is an advanced imaging technique that can provide functional connectivity (FC) of the brain for measuring the abnormality of the brain network in schizophrenia according to some embodiments described herein.

[0247] Comparing schizophrenia patients with healthy controls, individuals at clinical high risk (CHR) for psychosis and schizophrenia patients showed hypoconnectivity between the posterior insula (PI) and somatosensory areas, and between the dorsal anterior insula (dAI) and the putamen. In addition, schizophrenia patients showed hyperconnectivity between the dAI and ventral anterior insula (vAI) and visual areas relative to controls and CHR individuals. FC provides evidence for the dysconnectivity hypothesis of schizophrenia.

[0248] Since resting state fMRI (rsfMRI) can map functional brain networks, such as the default mode network (DMN), it enables the study of the system-level pathology of schizophrenia. The connectivity of the DMN can be altered in patients with schizophrenia. Specifically, features found by rsfMRI may include: hyperconnectivity of the DMN is a common consensus of rsFC MRI studies; altered cortical-subcortical networks, including thalamocortical, frontolimbic, and cortical-cerebellar networks; reduced connectivity of the prefrontal cortex (PFC), especially connectivity within the PFC; patterns of functional connectivity within the auditory / language network and basal ganglia are associated with specific clinical symptoms (including auditory-verbal hallucinations and delusions).

[0249] Some embodiments described herein may use rsFC MRI and correlation analysis to determine targets for TMS treatment of schizophrenia. Functional connectivity of dAI with the superior temporal gyrus may be positively correlated with positive symptoms of CHR. In addition, connectivity of vAI with DLPFC may be negatively correlated with the severity of first-episode schizophrenia symptoms. The methods described herein may provide the ability to use rsFC MRI to map whole-brain network topology and use graph theory. Functional brain networks in schizophrenia may be characterized by reduced small-worldness, reduced connectivity of brain hubs, and reduced modularity.

[0250] The sensitivity of functional connectivity was sufficient to detect differences in unaffected relatives, suggesting that functional connectivity disturbances are an endophenotype associated with genetic risk for schizophrenia. As the inventors broadly support the connectivity disturbance theory of schizophrenia, this feature of rsFC MRI may be used to identify targets for TMS treatment.

[0251] The methods described herein may be used to treat stroke and other brain pathologies. Repetitive transcranial magnetic stimulation (rTMS) may have beneficial effects on the left DLPFC for the treatment of a variety of neuropsychiatric or neuropsychological disorders.

[0252] In some embodiments, the disclosed method can target specific brain regions. For example, the default mode network (DMN), cognitive control network (CCN) and affective network (AN) may include depression-related increases in functional connectivity (FC) in the same dorsal region (i.e., dorsal medial prefrontal cortex) called dorsal nexus. The results show that depressive symptoms are not related to specific networks, but rather to dysfunctions of several brain networks. In addition, PSD has been shown to cause changes in FC in DMN and AN. These networks may be involved in the pathogenesis of PSD.

[0253] fMRI performance was analyzed in patients with a first acute ischemic stroke attack, and functional connectivity (FC) of the motor network in acute ischemic stroke was found to be independently associated with functional outcome. Specifically, FC between the ipsilateral primary motor cortex (M1) and the contralateral dorsal premotor area (PMd) was independently associated with adverse outcome, whereas FC of the default mode network did not differ between groups. These results suggest that interhemispheric FC of the motor network is an independent predictor of functional outcome in patients with acute ischemic stroke. High-frequency rTMS over the left DLPFC enhanced low-frequency resting-state brain activity in both target and distal sites, as reflected by fALFF and FC. In this way, TMS can be used to reach distal sites affected by stroke.

[0254] Conversely, fMRI can be used to find the exact location of the cortical counterpart of the network, where the inaccessible brain area is affected by the stroke. Some embodiments described herein may include a complete brain assessment for determining the target location of TMS treatment. Some embodiments described herein include a comparison of the covariation of fMRI data between regions and networks of the patient's brain. Some embodiments may include a complete assessment of these regional networks according to some embodiments described herein. Some embodiments may also provide attention to specific regions and networks based on known relevant functional connections associated with a given condition, symptom or disease. Attention can be reduced to evaluate the functional connectivity of the brain and / or provide special attention required calculations, such as segmentation into smaller regions, for more specifically targeting areas that have been considered to be beneficial for TMS treatment associated with a given symptom or disease. When comparing and evaluating, such a prior understanding of functional relationships as described herein can be used to determine the region and / or region size of the brain region as described herein. Such a prior understanding of functional relationships as described herein can be used to eliminate some comparisons and / or expand the comparison area of ​​the area that is not of interest or is not considered to be functionally related or relevant to a given symptom or disease.

[0255] The traditional TMS method approved by the FDA uses the 5cm rule for TMS targeting. The method does not include any guidance through imaging and is not personalized to account for changes in brain regions between individuals. Some attempts to use fMRI to guide TMS have identified the area where the DLPFC is most anti-correlated with the subgenual cingulate gyrus to detect targets for TMS stimulation for depression. The method only focuses on two specific areas of the brain with only a single relationship between these areas. The current embodiments described herein may include a more complete brain assessment to detect brain circuits including multiple targets for TMS and LIFUS. Some embodiments may include an assessment of the entire brain, rather than a particularly focused approach to the DLPFC. Cortical brain circuits may be selected for target stimulation using TMS. Subcortical brain circuits may be selected for targeted stimulation using LIFUS. Some embodiments described herein may include a circuit-based approach that evaluates the use of multiple and interconnected brain regions for stimulation, rather than applying to a single area on the DLPFC. Some embodiments described herein may be used for depression, but may also be used for other brain injuries, such as TBI or other brain lesions. Some embodiments described herein identify and extract the amplitude and frequency of brain activity within / between brain networks to analyze connectivity between circuits.

[0256] Specific brain disorders are shown and described herein with respect to functional connectivity analysis and the resulting possible target therapeutic areas for stimulation. Some specific examples are provided herein, which are not intended to be limiting. For any brain disorder, structural analysis can be added or used as described herein to find abnormalities and / or determine target locations for stimulation.

[0257] Total cortical gray matter volume and total white surface area in prospective case series of neurodevelopmental disorders (e.g., autism spectrum disorders) and neurodegenerative diseases (e.g., dementia, Alzheimer's disease, etc.).

[0258] Tables 1-2 show improvements in structural connectivity, which are provided here as some examples of successful use of structural connectivity in analyzing neurodevelopmental disorders.

[0259]

[0260] Table 1 provides a sample of five (5) patients aged between 18 and 22 years with autism spectrum disorder. All patients showed an increase in total cortical gray matter volume after completing two months of fMRI-guided TMS treatment (5 times per week) when the method used structural connectivity. Individual and average measurements at baseline and after treatment are shown. In healthy controls, total cortical gray matter volume loss was approximately one (1) % per year.

[0261] Table 2 provides a sample of five (5) patients aged between 18 and 22 with autism spectrum disorder. Using structural connectivity analysis according to the embodiments described herein, all patients showed an increase in total white surface area after completing two months of fMRI-guided TMS treatment (5 times per week). Individual and average measurements at baseline and after treatment are shown. In healthy children, total white surface area typically increases by 1.7% over a three-year interval (from 9 to 12 years old).

[0262] Tables 3 to 4 show improvements in structural connectivity, which are provided here as some examples of successful use of structural connectivity in analyzing neurodegenerative disorders.

[0263]

[0264] Table 3 provides samples of five patients with dementia (Alzheimer's disease) with a patient age range of 69 to 72 years. All patients showed an increase in total cortical gray matter volume after completing six months of fMRI-guided TMS treatment (5 weeks per week). Individual and average measurements at baseline and after treatment are shown. Local gray matter loss rate (5.3% plus or minus 2.3% per year in AD and 0.9% plus or minus 0.9% per year in controls).

[0265] Table 4 provides samples of five patients with dementia (Alzheimer's disease) with an age range of 68 to 72 years. All patients showed an increase in total white surface area after completing six months of fMRI-guided TMS treatment (5 weeks per week). Individual and average measurements at baseline and after treatment are shown. In healthy individuals aged 30 to 90 years, a 26% decrease in white matter tissue volume was observed, relative to a 14% decrease in gray matter tissue volume.

[0266] The computing devices described herein are unconventional systems, at least because of the use of unconventional components and / or the use of unconventional algorithms, processes, and methods that are at least partially embodied in programming instructions stored and / or executed by the computing devices. For example, some embodiments may use configurations and processes involving unique magnetic devices for applying magnetic fields to patients according to methods and algorithms for specific treatment targeting as described herein, configurations and processes involving unique transcranial magnetic stimulation devices or low-frequency intensity focused ultrasound devices with or without high-precision navigation, unique processes and algorithms for determining specific brain circuits of the patient's brain personalized for each patient, unique configurations and processes for targeting and placing TMS to be applied to specific brain circuits personalized for each patient, or a combination thereof. The systems and methods described herein also include algorithms for identifying and extracting the amplitude and frequency of brain activity within and between brain networks for precise and personalized TMS treatment. Some embodiments can be used to identify target locations for TMS treatment of neurological and psychiatric disorders, such as but not limited to traumatic brain injury, brain lesions, psychosis, depression, maintenance, autism, dementia, etc. Some exemplary embodiments described herein may also or alternatively be used for prevention, rather than just for pathology, such as prevention of cognitive decline due to age. Some embodiments may be used to improve the duration of the therapeutic effect, so that the beneficial therapeutic therapeutic effect can be sustained for a longer period of time. Some embodiments may include a combination of functional connections and / or structural connections to determine a target treatment area personalized to an individual. The use of a combination of functional connections and / or structural connections can provide a more complete analysis of individual brain function based on an individual and / or brain disorder, to find the target position for stimulation that is most likely to improve the patient's condition.

[0267] Some embodiments include a non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining one or more target areas for transcranial magnetic stimulation (TMS) treatment of a patient, wherein the computer-executable instructions are configured to perform a method when executed by one or more processors. The method for determining one or more target areas for transcranial magnetic stimulation (TMS) treatment of a patient may include the following steps: receiving functional magnetic resonance imaging (fMRI) data of a patient's head; analyzing the functional connectivity of the patient's brain by analyzing the fMRI data by determining changes in any combination of the following: a first fluctuation in the amplitude of the fMRI imaging data, a second fluctuation in the frequency of the fMRI data, or a third fluctuation in the frequency of the fMRI data relative to the amplitude; and determining one or more target areas for TMS treatment of the patient based on the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation.

[0268] Some embodiments of the non-transitory computer-accessible medium, instructions, or methods may include any combination of comparing the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation to measurements of a healthy control group matching the patient's age range and gender.

[0269] Some embodiments of the non-transitory computer-accessible medium, instructions, or methods may include analyzing the functional connectivity of a patient's brain including a first fluctuation, a second fluctuation, or a third fluctuation, the analysis comprising any combination of: determining a matrix of activation, correlation, covariation, 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 regions within a network and all other regions of the network combined; 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.

[0270] The non-transitory computer-accessible medium, instructions, or methods may include any combination of: selecting a first plurality of brain regions having low variations in activation, correlation, covariation, or a combination thereof in the case of a large number of brain networks or regions; selecting a second plurality of brain regions having high variations in activation, correlation, covariation, or a combination thereof in the case of a large number of brain networks or regions; comparing the first plurality of brain regions and the second plurality of brain regions to brain regions from a healthy control group of patients with an age range and gender matched, and / or selecting brain regions above a first threshold and below a second threshold to create a group of potential target brain regions for TMS treatment.

[0271] The non-transitory computer accessible medium, instructions, or method may include a first threshold within two standard deviations above the mean of a healthy control group, and a second threshold within two standard deviations below the mean of a healthy control group.

[0272] The non-transitory computer-accessible medium, instructions, or methods may include any combination of: comparing the group of potential target brain regions for TMS treatment with a second control group that matches the patient's symptoms, diagnosis, or a combination thereof, and determining a subgroup from the group of potential target brain regions by determining which region from the second control group has the greatest change in fMRI data from pre-TMS treatment to post-TMS treatment; and / or administering TMS treatment to the patient based on at least one parameter of the treatment plan for a member from the second control group, the member of the second control group having a brain region with the greatest change in fMRI data from pre-TMS treatment to post-TMS treatment.

[0273] The 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 via analyzing the dMRI data by comparing the strength of white matter connections between combinations of partitions within the patient's brain; wherein one or more target regions for TMS treatment of the patient are determined based on the analysis of the structural connectivity. The non-transitory computer-accessible medium and / or method may include determining one or more target regions, the determining including selecting a target location for stimulation that has a higher number of white matter connections compared to other potential targets.

[0274] The non-transitory computer-accessible medium, instructions, or method may include performing an analysis of structural connectivity of the patient's brain subsequent to the analysis of functional connectivity and first determining one or more target regions based on the functional connectivity and then selecting a final subgroup using the analysis of structural connectivity.

[0275] The non-transitory computer-accessible medium, instructions, or method may include performing a functional connectivity analysis by: defining a plurality of brain networks of the patient and a plurality of brain regions of the patient; including, for each of the plurality of brain networks of the patient, comparing the covariation between the one brain network and each of another brain network in the plurality of brain networks; for each of the plurality of brain regions, comparing the covariation between a first brain region and each of another brain region in 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 another brain region in a brain network different from the second brain region; for each of the plurality of brain regions, comparing the covariation between a third brain region and each of another brain network different from the brain network in which the third brain region is contained; selecting a first set of regions where the lowest change in the covariation comparison occurs in a maximum number of brain regions in the plurality of regions; selecting a second set of regions where the lowest change in the covariation comparison occurs in a maximum number of brain regions in 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 from the first set of regions and the second set of regions that are outside a determined normal range compared to the healthy control group; and 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.

[0276] Non-transitory computer-accessible media, instructions, or methods may include determining one or more target areas for transcranial magnetic stimulation (TMS) treatment of a patient for treating a psychiatric disorder, the method comprising: receiving functional magnetic resonance imaging (fMRI) data of the patient's head; analyzing the functional connectivity of the patient's brain by analyzing the fMRI data by determining changes in any combination of the following: a first fluctuation in the amplitude of the fMRI imaging data, a second fluctuation in the frequency of the fMRI data, or a third fluctuation in the frequency relative to the amplitude of the fMRI data; determining one or more target areas for TMS treatment of the patient based on determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation; applying TMS treatment at the one or more target areas; and improving the patient's psychiatric disorder.

[0277] The non-transitory computer-accessible medium, instructions, or methods may include a psychiatric disorder selected from the group consisting of Alzheimer's disease, anxiety, obsessive-compulsive disorder, post-traumatic stress disorder, schizophrenia, insomnia, eating disorders, cognitive disorders, drug addiction, depression, attention deficit hyperactivity disorder, attention deficit disorder, bipolar disorder, autism spectrum disorder, neurodevelopmental disorders, and psychosis.

[0278] Non-transitory computer-accessible media, instructions, or methods may include receiving diffusion-weighted magnetic resonance imaging (dMRI) data of a patient's head; analyzing the structural connectivity of the patient's brain by analyzing the dMRI data by comparing white matter connection strengths between combinations of partitions 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.

[0279] Non-transitory computer-accessible media, instructions, or methods may include receiving diffusion-weighted magnetic resonance imaging (dMRI) data of a patient's head; analyzing the structural connectivity of the patient's brain by analyzing the dMRI data by generating a brain structural connectivity matrix constructed based on white matter fiber bundle imaging from the whole brain; and determining one or more target areas for TMS treatment of the patient based on the analysis of the structural connectivity.

[0280] Non-transitory computer-accessible media, instructions, or methods may include receiving fMRI data; constructing a covariation matrix using the fMRI data; selecting a first group of potential targets having stronger covariation values ​​with a first large group of brain regions, selecting a second group of potential targets having weaker covariation with a second large group of brain regions, or selecting a combination of the first group of potential targets and the second group of potential targets; receiving dMRI data; using the dMRI data to construct a structural connectivity matrix; selecting a target region having a higher number of white matter connections from the first group of potential targets, the second group of potential targets, or the first and second groups of potential targets.

[0281] Non-transitory computer-accessible media, instructions, or methods may include receiving fMRI data; using the fMRI data to construct a covariation matrix; selecting a first group of potential targets having stronger covariation values ​​with a first large group of brain regions, selecting a second group of potential targets having weaker covariation with a second large group of brain regions, or selecting a combination of the first group of potential targets and the second group of potential targets; receiving dMRI data; using the dMRI data to construct a structural connectivity matrix; selecting a target region having a higher number of white matter connections from the first group of potential targets, the second group of potential targets, or the first group of potential targets and the second group of potential targets.

[0282] Non-transitory computer-accessible media, instructions, or methods may include receiving dMRI data; using the dMRI data to construct a structural connectivity matrix; selecting a group of potential targets having a lower number of white matter connections; receiving fMRI data; using the fMRI data to construct a covariation matrix; selecting a first group of targets from a group of potential targets having stronger covariation values ​​with a first large group of brain regions, selecting a second group of targets from a group of potential targets having weaker covariation values ​​with a second large group of brain regions, or selecting a combination of the first group of targets and the second group of targets.

[0283] The non-transitory computer accessible medium, instructions, or method may include structural data including T1w / T2w for constructing a structural connectivity matrix.

[0284] The systems described herein may be based on software and / or hardware. Although some specific embodiments of the present invention have been shown, the present invention is not limited to these embodiments. For example, most functions performed by electronic hardware components can be replicated by software simulation. Therefore, software programs written to achieve those same functions can simulate the functions of hardware components in input-output circuits. The present invention should be understood not to be limited by the specific embodiments described herein, but only by the scope of the appended claims.

[0285] Although some embodiments of the present invention have been fully described with reference to the accompanying drawings, various changes and modifications are obvious to those skilled in the art. Such changes and modifications should be understood to be included in the scope of the embodiments of the present disclosure as defined by the appended claims. Any disclosed component can be used in any combination. For example, any component, feature, step or part can be integrated, separated, subdivided, removed, copied, added or used in any combination and remain within the scope of the present disclosure. The embodiments are exemplary only and provide illustrative combinations of features, but are not limited thereto.

[0286] The foregoing merely illustrates the principles of the present disclosure. Any embodiments set forth in this specification are not intended to be limiting and merely set forth some of the many possible implementations of the appended claims. Those skilled in the art will readily appreciate that various modifications and variations may be made without following the exemplary implementations and applications illustrated and described herein, and without departing from the true spirit and scope of the appended claims.

[0287] All references cited and / or discussed in this specification are herein incorporated by reference in their entirety to the same extent as if each reference were incorporated by reference individually.

Claims

1. A non-transitory computer accessible medium having stored thereon computer executable instructions for determining one or more target areas for transcranial magnetic stimulation (TMS) treatment of a patient, wherein the computer executable instructions are configured to, when executed by one or more processors, perform a method comprising: receiving functional magnetic resonance imaging (fMRI) data of the patient's head; analyzing the functional connectivity of the patient's brain via analyzing the fMRI data by determining changes in any combination of: a first fluctuation in amplitude of the fMRI imaging data, a second fluctuation in frequency of the fMRI data, or a third fluctuation in frequency relative to amplitude of the fMRI data; as well as Based on the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation, one or more target regions for TMS treatment of the patient are determined.

2. The non-transitory computer-accessible medium of claim 1, wherein the computer-executable instructions are further configured to perform: comparing the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation with measurements of a healthy control group matching the age range and gender of the patient.

3. The non-transitory computer-accessible medium of claim 1 or 2, wherein analyzing the functional connectivity of the patient's brain including the first fluctuation, the second fluctuation, or the third fluctuation may include any combination of: determining a matrix of activations, correlations, covariations, or combinations thereof, between different brain networks of the patient's brain; determining a matrix of activation, correlation, covariation, or combinations thereof, between regions within a network of the patient's brain; Determine the activation, correlation, covariation, or combination matrix between a region within a network and all other regions of that network combined; Determine the activation, correlation, covariation, or combination matrix between different brain regions within a network and all other regions within the same network; or Determine the matrix of activations, correlations, covariations, or their combinations between different brain regions of different networks.

4. The non-transitory computer-accessible medium of any one of claims 1 to 3, wherein the computer-executable instructions are further configured to perform any combination of: In the case of a large number of brain networks or regions, selecting a first plurality of brain regions having low variance in activation, correlation, covariation, or a combination thereof; In the case of a large number of brain networks or regions, selecting a second plurality of brain regions having high variance in activation, correlation, covariation, or a combination thereof; comparing the first plurality of brain regions and the second plurality of brain regions to brain regions from a healthy control group matching the patient's age range and gender, and selecting brain regions above a first threshold and below a second threshold to create a group of potential target brain regions for TMS treatment; or TMS treatment is administered to the patient based on at least one parameter of the treatment plan for a member from a second control group having a brain region with the greatest change in fMRI data from pre-TMS treatment to post-TMS treatment.

5. The non-transitory computer-accessible medium of any one of claims 1 to 4, wherein the first threshold is within two standard deviations above the mean of a healthy control group, and the second threshold is within two standard deviations below the mean of a healthy control group.

6. The non-transitory computer-accessible medium of any one of claims 1 to 5, wherein the group of potential target 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 subgroup from the group of potential target brain regions is determined by determining which region in the second control group has the largest change in fMRI data from pre-TMS treatment to post-TMS treatment.

7. The non-transitory computer accessible medium of any one of claims 1 to 6, wherein the computer executable instructions are further configured to perform: receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the patient's head; analyzing the structural connectivity of the patient's brain by analyzing the dMRI data by comparing white matter connection strengths between combinations of subregions within the patient's brain; Wherein one or more target regions for TMS treatment of the patient are determined based on the analysis of structural connectivity.

8. The non-transitory computer-accessible medium of any one of claims 1 to 7, wherein the determining one or more target regions comprises selecting a target location for stimulation that has a higher number of white matter connections compared to other potential targets.

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

10. Performing a procedure using TMS stimulation at a target identified according to any one of claims 1 to 9 to improve a patient's condition.

11. A method for determining one or more target areas for stimulation therapy of a patient, comprising: Functional connectivity analysis was performed by: defining a plurality of brain networks of the patient and a plurality of brain regions of the patient; The method comprises: for each of the plurality of brain networks of the patient, comparing the covariation between one brain network and each of another brain network in the plurality of brain networks; for each of the plurality of brain regions, comparing the covariation between a first brain region and each of another brain region in 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 another brain region in a brain network different from the second brain region; for each of the plurality of brain regions, comparing the covariation between a third brain region and each of another brain network different from the brain network in which the third brain region is included; selecting a first set of regions where the lowest changes in the covariation comparison occur in a greatest number of brain regions among the plurality of regions; selecting a second set of regions where the lowest change in the covariation comparison occurs in a greatest number of brain regions among the plurality of regions; comparing the first set of regions and the second set of regions to a healthy control group, and identifying a first set of potential targets from the first set of regions and the second set of regions that are outside of a determined normal range compared to the healthy control group; as well as A target region set is determined from a comparison of the first set of regions and the second set of regions with the healthy control group.

12. A method for determining one or more target areas for transcranial magnetic stimulation (TMS) therapy of a patient for treating a psychiatric disorder, the method comprising: receiving functional magnetic resonance imaging (fMRI) data of the patient's head; Analyzing functional connectivity of the patient's brain via analyzing the fMRI data by determining changes in any combination of: a first fluctuation in amplitude of the fMRI imaging data, a second fluctuation in frequency of the fMRI data, or a third fluctuation in frequency relative to amplitude of the fMRI data; determining one or more target areas for TMS treatment of the patient based on the determination of any combination of the first fluctuation, the second fluctuation, or the third fluctuation; applying TMS treatment at the one or more target areas; Improve the patient's psychiatric condition.

13. The method of claim 12, wherein the psychiatric disorder is selected from the group consisting of Alzheimer's disease, anxiety, obsessive compulsive disorder, post-traumatic stress disorder, schizophrenia, insomnia, eating disorders, cognitive disorders, drug addiction, depression, attention deficit hyperactivity disorder, attention deficit disorder, bipolar disorder, autism spectrum disorder, neurodevelopmental disorders, and psychosis.

14. A non-transitory computer accessible medium having stored thereon computer executable instructions for determining one or more target areas for transcranial magnetic stimulation (TMS) treatment of a patient, wherein the computer executable instructions are configured to, when executed by one or more processors, perform a method comprising: receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the patient's head; analyzing the structural connectivity of the patient's brain by analyzing the dMRI data by comparing white matter connection strengths between combinations of subregions within the patient's brain; Based on the analysis of the structural connectivity, one or more target regions for TMS treatment of the patient are determined.

15. A method comprising: receiving diffusion-weighted magnetic resonance imaging (dMRI) data of the patient's head; analyzing the structural connectivity of the patient's brain by analyzing the dMRI data by generating a brain structural connectivity matrix constructed based on white matter tractography from the whole brain; as well as Based on the analysis of the structural connectivity, one or more target regions for TMS treatment of the patient are determined.

16. The method of claim 15, wherein the structural connectivity matrix comprises a comparison of white matter connection strengths between each combination of two partitions in a plurality of partitions.

17. The method of any one of claims 15 or 16, wherein analyzing the structural connectivity of the patient's brain comprises using structural data to run a structural connectivity analysis, the structural data being T1w versus T2w.

18. The method of any one of claims 15 to 17, further comprising selecting a target location for stimulation by selecting an area having a higher number of white matter connections compared to other areas.

19. The method of any one of claims 15 to 17, further comprising selecting a target location for stimulation by selecting a region with a highest number of white matter connections from a target pool based on fMRI functional connectivity.

20. A method of determining a target location for stimulation, comprising: Receiving fMRI data; constructing a covariation matrix using the fMRI data; Selecting a first group of potential targets having stronger covariation values ​​with a first large group of brain regions, selecting a second group of potential targets having weaker covariation values ​​with a second large group of brain regions, or selecting a combination of the first group of potential targets and the second group of potential targets; Receiving dMRI data; Use dMRI data to construct a structural connectivity matrix; A target region having a higher number of white matter connections is selected from the first group of potential targets, the second group of potential targets, or both the first group of potential targets and the second group of potential targets.

21. The method of claim 20, wherein structural data comprising T1w / T2w is used to construct the structural connectivity matrix.

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

23. A method of determining a target location for stimulation, comprising: Receiving dMRI data; Use dMRI data to construct a structural connectivity matrix; Selecting potential target groups with lower numbers of white matter connections; Receiving fMRI data; constructing a covariation matrix using the fMRI data; A first set of targets is selected from a group of potential targets having stronger covariation values ​​with a first large group of brain regions, a second set of targets is selected from a group of potential targets having weaker covariation values ​​with a second large group of brain regions, or a combination of the first set of targets and the second set of targets is selected.

24. The method of claim 23, wherein structural data comprising T1w / T2w is used to construct the structural connectivity matrix.

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

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