System and method for determining a probability of progression to schizophrenia for a subject and for intervention target identification

Abstract

A method includes receiving MR data for a subject, generating a map of atrophy coordinates for the subject using a set of volumetric MR data from the MR data, defining a plurality of regions of interest (ROIs) for the subject based on the atrophy coordinates in the map of atrophy coordinates, generating a connectivity map for each ROI using a set of functional connectivity data from the MR data, retrieving a unified schizophrenia network connectivity map of atrophy patterns, determining a probability of progression to schizophrenia for the subject based on the connectivity map for each ROI and the unified schizophrenia network connectivity map of atrophy patterns, and generating a report including at least the probability of progression to schizophrenia for the subject. The method can further include identifying target(s) for a proactive intervention for the subject based on the connectivity maps for each ROI.

Description

BWH 2024-554-02Q&B 129319.01117SYSTEM AND METHOD FOR DETERMINING A PROBABILITY OF PROGRESSION TO SCHIZOPHRENIA FOR A SUBJECT AND FOR INTERVENTION TARGET IDENTIFICATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on, claims priority to, and incorporates herein by reference in its entirety U.S. Serial No. 63 / 699,281 filed September 26, 2024, and entitled “A System For Predicting Probability of Progression To Schizophrenia In Individuals At High Risk For Psychosis."STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] N / ABACKGROUND

[0003] Schizophrenia is among the major causes of disability worldwide. While neuroimaging has contributed to our understanding of the neural correlates of schizophrenia, there remains significant heterogeneity across different studies and modalities. For example, some studies have reported decreases in regional gray matter volume in the prefrontal cortex of patients with schizophrenia, while others have also found reductions in regions of the temporal lobe, occipital cortex, and subcortical structures. Similarly, while some studies have found increased functional connectivity within specific brain networks (default mode network, salience network, and frontoparietal network), others have reported decreased connectivity. Recent efforts by the Enhancing Neuro-Imaging Genetics through Meta-Analysis (ENIGMA) consortium have provided valuable insights into the neuroanatomical variations associated with schizophrenia, offering a more consolidated view of its neuroanatomical correlates. These studies harnessed large-scale, multi-site data to minimize the effects of methodological discrepancies that have historically contributed to heterogeneous findings. Nevertheless, prior heterogeneous results may still offer valuable information - when different methods yield different answers in a similar patient population, it may not be necessary to discard this information entirely. If theseBWH 2024-554-02Q&B 129319.01117 heterogeneous results share something in common, this would complement the valuable insights offered by large consortium studies. The heterogeneity in neuroimaging findings has made it challenging to develop reliable biomarkers or neuroanatomically targeted interventions for schizophrenia. For instance, while therapeutic brain stimulation is effective for various neuropsychiatric disorders, treatment targets for schizophrenia remain unclear.

[0004] In addition, existing techniques for target identification and treatment (e.g., neuromodulation) are typically directed to the treatment of patients (or subjects) that have already displayed psychiatric symptoms of a schizophrenia and / or been diagnosed with schizophrenia. There is a need for systems and methods for determining a probability of progression to schizophrenia in subjects at high risk for psychosis and for identifying targets for interventions in subjects at high risk for psychosis.SUMMARY

[0005] In accordance with an embodiment, a method includes receiving, using a processor device, MR data for a subject, generating, using the processor device, a map of atrophy coordinates for the subject using a set of volumetric MR data from the MR data for the subject, defining, using the processor device, a plurality of regions of interest (ROIs) for the subject based on the atrophy coordinates in the map of atrophy coordinates, generating, using the processor device, a connectivity map for each region of interest using a set of functional connectivity data from the MR data for the subject, retrieving, using the processor device, a unified schizophrenia network connectivity map of atrophy patterns, determining, using the processor device, a probability of progression to schizophrenia for the subject based on the connectivity map for each region of interest and the unified schizophrenia network connectivity map of atrophy patterns, and generating, using the processor device, a report including at least the probability of progression to schizophrenia for the subject.

[0006] In accordance with another embodiment, a system includes a memory that stores one or more computer readable media that includes instructions, and one or more processor devices configured to execute the instructions of the computer readable media to receive MR data for a subject, generate a map of atrophy coordinates for the subject using a set of volumetric MR data from the MR data for the subject, define a plurality of regions of interest (ROIs) for the subject based on the atrophy coordinates in the map of atrophy coordinates, generate a connectivity mapBWH 2024-554-02Q&B 129319.01117 for each region of interest using a set of functional connectivity data from the MR data for the subject, retrieve a unified schizophrenia network connectivity map of atrophy patterns, determine a probability of progression to schizophrenia for the subject based on the connectivity map for each region of interest and the unified schizophrenia network connectivity map of atrophy patterns, and generate a report including at least the probability of progression to schizophrenia for the subject.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present disclosure will hereafter be described with reference to the accompanying drawings, wherein like reference numerals denote like elements.

[0008] FIG. 1 illustrates a method for determining a probability of progression to schizophrenia for a subject and for intervention target identification for the subject in accordance with an embodiment;

[0009] FIG. 2 illustrates a method for generating connectivity maps for a plurality of regions of interest of a subject in accordance with an embodiment;

[0010] FIG. 3 illustrates a method for generating a schizophrenia network using a connect ome database in accordance with an embodiment;

[0011] FIG. 4 is a block diagram of an example computer system in accordance with an embodiment;

[0012] FIG. 5 is a block diagram of an example magnetic resonance imaging (MRI) system in accordance with an embodiment; and

[0013] FIG. 6 is a block diagram of an example transcranial magnetic stimulation (TMS) system in accordance with an embodiment.DETAILED DESCRIPTION

[0014] The present disclosure describes systems and methods for determining a probability of progression to schizophrenia in subjects at high risk for psychosis and for identifying targets for proactive or prophylactic interventions for the subject. Accordingly, the identified targets can be used to enable an intervention (e.g., neuromodulation) for a subject at high risk for psychosis before the onset of symptoms to try to prevent or delay the development of symptoms of schizophrenia. The disclosed systems and methods can utilize a unified schizophrenia networkBWH 2024-554-02Q&B 129319.01117 connectivity map of atrophy patterns and a set of connectivity maps for a plurality of regions of interest to determine the probability of progression to schizophrenia for the subject. The determined probability of progression to schizophrenia for the subject can be used to determine how closely to follow up with a particular subject based on the level of risk they will develop schizophrenia.

[0015] FIG. 1 illustrates a method for determining a probability of progression to schizophrenia for a subject and for intervention target identification for the subject in accordance with an embodiment. Although the blocks of the process shown in FIG. 1 are illustrated in a particular order, in some embodiments, one or more blocks may be executed in a different order than illustrated in FIG. 1 or may be bypassed. One or more aspects of the method illustrated in FIG. 1 may be performed by or implemented on a processing system including at least one electronic processor (or processor device), where the at least one electronic processor may be or include a processor as described herein.

[0016] At block 102, magnetic resonance (MR) data of a brain of a subject (e.g., a subject at high risk for psychosis / schizophrenia based on, for example, clinical factors, genetic factors, etc.) may be retrieved or received. In some embodiments, the MR data can include volumetric MR data and functional MRI (fMRI) data. In some embodiments, the MR data of the brain of the subject may be acquired using an MRI system (e.g., MRI system 500 shown in FIG. 5) using known acquisition techniques and protocols. For example, in some embodiments, the MR data can be acquired using structural volumetric MRI acquisition techniques and fMRI acquisition techniques (e.g., resting-state fMRI (rs-fMRI). In some embodiments, the MR data may be acquired by and received from an MRI system (e.g., MRI system 500 shown in FIG. 5) in real time. In some embodiments, the MR data can be retrieved (or received) from data storage of an MRI system (e.g., MRI system 500 shown in FIG. 5) or data storage of other computer systems (e.g., storage device 416 of computer system 400 shown in FIG. 4). In some embodiments, the MR data of the brain of the subject can be converted from a Digital Imaging and Communications in Medicine (DICOM) format to a Neuroimaging Informatics Technology Initiative (NIFTI) format.

[0017] At block 104, a map of atrophy coordinates can be generated for the subject using the volumetric MR data of the brain of the subject received at block 102. In some embodiments, automated segmentation techniques can be used to compute gray matter volumes. Areas of grayBWH 2024-554-02Q&B 129319.01117 matter volume reduction (e.g., atrophy) can then be identified relative to age- and gender- matched normative reference maps. In some embodiments, the normative reference maps can be existing reference maps. In some embodiments, the normative reference maps can be calculated using known methods. The identified areas of atrophy can then be used to generate a map of atrophy coordinates of the subject. In some embodiments, the map of atrophy coordinates for the subject can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). At block 106, regions of interest for the subject can be identified using the map of atrophy coordinates. In some embodiments, a region of interest (ROI), or seed ROI, can be created for each atrophy coordinate in the map of atrophy coordinates. For example, a 4-mm spherical seed ROI can be created for each atrophy coordinate. In some embodiments, additional ROIs can be defined as, for example, a whole brain continuous map of atrophy locations, a normative map of expected atrophy locations, a schizophrenia causal circuit, or any other potential way of defining the actual or expected location of atrophy in the patient. In some embodiments, the ROIs defined for the subject can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0018] At block 108, a connectivity map can be generated for each region of interest defined at block 106. In some embodiments, the connectivity map for each ROI (or seed ROI) can be generated using the fMRI data of the brain of the subject received at block 102. For example, resting-state fMRI data of the subject can be used to calculate functional connectivity. An embodiment of a method for generating connectivity maps for a plurality of regions of interest of a subject is discussed further below with respect to FIG. 2. In some embodiments, the connectivity map generated for each ROI defined for the subject can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). At block 110, a unified schizophrenia network connectivity map of atrophy patterns (otherwise referred to herein as a schizophrenia network or Schizophrenia Conversion Circuit (Scz-CC)) can be retrieved. For example, in some embodiments, the unified schizophrenia network connectivity map of atrophy patterns may be retrieved (or received) from data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). In some embodiments, the schizophrenia network can be generated using existing data using data from studies of atrophy in schizophrenia and the human connectome as described further below with respect to FIG. 3. In some embodiments, theBWH 2024-554-02Q&B 129319.01117 schizophrenia network can unite the results (data) from existing studies of atrophy in schizophrenia and identify a connectivity pattern for atrophy in schizophrenia.

[0019] At block 112, a probability of progression to schizophrenia for the subject can be determined. In some embodiments, the probability of progression to schizophrenia can be determined by comparing the connectivity maps for each region of interest determined at block 108 and the unified schizophrenia network connectivity map of atrophy patterns (or schizophrenia network or Scz-CC) retrieved at block 110. For example, in some embodiments, a spatial coefficient of determination (eta squared) can be used to compare each ROI connectivity map to the schizophrenia network. In some embodiments, each ROI connectivity map can also be compared to other brain regions, for example, the other defined ROIs, the whole brain, etc. In some embodiments, the comparison of each ROI connectivity map to the schizophrenia network can be compared using other methods of assessing the similarity between two maps such as, for example, a spatial Pearson correlation, a spatial Spearman correlation, Dice coefficient, composite multiplication, weighted composite multiplication, etc. For each region of interest, the comparison can generate a single value for how well the subject matches the schizophrenia network. In one example, high risk subjects with atrophy patterns specifically connected to the anterior cingulate cortex (ACC) and the medial temporal lobe can be more likely to progress to schizophrenia compared to subject’s that do not exhibit these patterns. Each of the ROI specific values can be combined to generate a composite probability (or risk) score for probability (or likelihood) of progression to schizophrenia. In some embodiments, the probability score can be expressed as a percentage risk or probability. In some embodiments, the probability score can be computed based on model calibration. In some embodiments, the probability score for the subject can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0020] At block 114, one or more targets (or target locations or candidate targets) in the brain of the subject to be targeted by, for example, neuromodulation, can be identified based on the connectivity maps for each region of interest for the subject. In some embodiments, one or more targets can be identified for the subject regardless of the probability score determined for the subject at block 112. In some embodiments, one or more targets can be identified for a subject when the probability score for the subject is greater than a predetermined threshold. In other words, one or more targets can be identified for a subject with a probability of progression toBWH 2024-554-02Q&B 129319.01117 schizophrenia that is greater than a predetermined percentage probability or risk. In some embodiments, the ROI connectivity maps of the subject determined at block 108 can be used for identifying targets for intervention. The intervention can be neuromodulation, for example, transcranial magnetic stimulation (TMS). In some embodiments, the target (e.g., a target for stimulation using TMS) can be a schizophrenia causal circuit. The schizophrenia causal circuit can be a network derived based on brain lesions that cause psychosis. In some embodiments, the schizophrenia causal circuit can be personalized, for example, the location of the normative schizophrenia causal circuit can be individualized to the subject using personalization techniques such as, for example, weighted seed-based functional connectivity, iterative parcellation, etc. In some embodiments, the target can be an atrophy-connected region within the schizophrenia network as defined by each ROI for the subject and the ROI’s associated connectivity map. Accordingly, the different seed ROIs (or maps) for the subject can be considered as potential targets. In some embodiments, applying TMS to the selected target(s) (e.g., the schizophrenia causal circuit) can prevent or delay onset of schizophrenia. In some embodiments, a target can be identified by comparing an ROI connectivity map of the subject to a plurality of hypothetical stimulation sites throughout the brain. In some embodiments the plurality of hypothetical stimulation sites throughout the brain can be defined using dimple spherical models, complex decaying spheres, more complex biophysical electric field models, or any other method. In some embodiments, the hypothetical stimulation sites with the greatest intersection to the target circuit can be considered candidate stimulation sites. In some embodiments, the identified target(s) can be stored in data storage (e g., storage device 416 of computer system 400 shown in FIG. 4). In some embodiments, the identified target or targets can be provided to a TMS system (e.g., TMS system 600 shown in FIG. 6) and / or to a neuronavigation system. A neuronavigation system can be configured to, for example, visualize a subject's brain based on imaging data, for example, MRI data, and to track and monitor the position of a coil (e.g., a TMS coil) on the visualization of the subject's head or brain. In some embodiments, the neuronavigation system 202 can track the position of the TMS coil relative to a target or relative to an anatomical area of interest. In some embodiments, a neuronavigation system may be coupled to a display (e.g., display 418 of computer system 400 shown in FIG. 4) to display images of a subject's head and brain, tracking of a coil used for TMS, and other information associated with the subject being treated with TMS.BWH 2024-554-02Q&B 129319.01117

[0021] At block 116, a report can be generated. In some embodiments, the report can include, for example, the identified atrophy locations (e.g., the map of atrophy coordinates), the connectivity findings to the schizophrenia network, the probability of progression to schizophrenia for the subject (e.g., a percentage risk), and identified (or candidate) targets for proactive or prophylactic interventions for the subject (e.g., TMS). In some embodiments, the report may include a display that includes a visual indicator identifying the target location on, for example, an image or map of the subject’s brain. In some embodiments, the report may include a connectome of the subject’s brain. The generated report may be displayed on a display (for example, display 418 of computer system 400 shown in FIG. 4, displays 504, 536, 544 of MRI system 500 shown in FIG. 5). In some embodiments, the report can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). As mentioned, in some embodiments, the target location can be used for an intervention such as neuromodulation. In some embodiments, the report may be used to perform interventional planning, such as, for example, treatment planning. In some embodiments, the neuromodulation may be, for example, transcranial magnetic stimulation (TMS) and performed using a TMS system such as, for example, the TMS system 600 described below with respect to FIG. 6.

[0022] As mentioned above, at block 108, a connectivity map can be generated for each for each region of interest defined for the subject at block 106. FIG. 2 illustrates a method for generating connectivity maps for a plurality of regions of interest of a subject in accordance with an embodiment. Although the blocks of the process shown in FIG. 2 are illustrated in a particular order, in some embodiments, one or more blocks may be executed in a different order than illustrated in FIG. 2 or may be bypassed. One or more aspects of the method illustrated in FIG. 2 may be performed by a processing system including at least one electronic processor, where the at least one electronic processor may be or include a processor as described herein.

[0023] At block 202, functional connectivity can be calculated for each seed ROI (defined at block 106 of FIG. 1) using the resting-state fMRI (rs-IMRI) data from the subject (e.g., retrieved at block 102 of FIG. 1). In some embodiments, the rs-fMRI MR data for the subject \can be retrieved (or received) from data storage of an MRI system (e.g., MRI system 500 shown in FIG. 5) or data storage of other computer systems (e.g., storage device 416 of computer system 400 shown in FIG. 4). In some embodiments, the functional connectivity calculated for each ROI can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). AtBWH 2024-554-02Q&B 129319.01117 block 204, an fMRI time course can be generated for every location in each ROI. For example, in some embodiments, for every location in each ROI, the spontaneous fluctuation in brain activity can be estimated using the fMRI data to yield the fMRI time course for every location in each ROI. In some embodiments, the fMRI time course for each location in an ROI can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0024] At block 206, for each ROI, the fMRI time course generated for all the locations in the ROI can be used to generate a composite time course for the ROI. Accordingly, a composite time course can be generated for each ROI. In some embodiments, a composite time course for an ROI can be generated by combining the fMRI time course for each location in the ROI into a weighted mean (e.g., weighted by the magnitude of atrophy on the ROI). In some embodiments, the composite time course generated for each ROI can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). At block 208, a connectivity map for each ROI can be generated based on the composite time course for the region of interest. For example, in some embodiments, the composite time course for an ROI can be compared to the time course of every other voxel in the brain (e.g., the time courses for each voxel in the MR data of the brain of the subject) using, for example, Pearson correlation, to generate a connectivity map for the ROI. Pearson correlation is a conventional methods for computing whole brain functional connectivity. As mentioned, a connectivity map can be generated for each ROI. At block 210, the connectivity map generated for each ROI can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0025] As mentioned above, a unified schizophrenia network connectivity map of atrophy patterns (or schizophrenia network or Scz-CC) can be used to determine a probability of progression to schizophrenia for a subject at block 112 of FIG. 1. FIG. 3 illustrates a method for generating a schizophrenia network using a connectome database in accordance with an embodiment. Although the blocks of the process shown in FIG. 3 are illustrated in a particular order, in some embodiments, one or more blocks may be executed in a different order than illustrated in FIG. 3 or may be bypassed. One or more aspects of the method illustrated in FIG. 3 may be performed by a processing system including at least one electronic processor, where the at least one electronic processor may be or include a processor as described herein.

[0026] At block 302, existing schizophrenia data (e.g., MRI data, images, maps, etc.) related to atrophy for a set of studies can be retrieved. In some embodiments, the schizophrenia data or aBWH 2024-554-02Q&B 129319.01117 plurality of existing studies of atrophy in schizophrenia can be retrieved (or received) from data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4). At block 304, atrophy coordinates and atrophy maps can be determined for each study based on the data from each study. In some embodiments, coordinates of significant atrophy can be plotted after multiple comparisons correction across all studies of schizophrenia on a common brain atlas and then the overlap of individual atrophy locations (1) with anatomical lobes and (2) with functional networks can be quantified. To assess structural heterogeneity across anatomical lobes, in some embodiments the percentage of coordinates within each lobe can be compared to the percentage of brain gray matter volume covered by that lobe using a binomial test. In some embodiments, to assess heterogeneity across functional networks, each of the coordinates can be localized to specific networks as defined by, for example, the consensus seven -network Yeo parcellation. In some embodiments, the percentage of coordinates within each network can be compared to the percentage of the parcellation covered by that network using a binomial test. In some embodiments, the atrophy coordinates and atrophy maps for each study can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0027] At block 306, a connectivity pattern map of atrophy locations for each study can be generated using coordinate network mapping (CNM) and the human connectome. Given the widespread and heterogeneous patterns of atrophy in schizophrenia, CNM can be utilized. CNM is a a meta-analysis approach used to identify and map the functional connectivity of brain regions. By leveraging the human connectome, a wiring diagram of the brain, CNM can map specific coordinates onto broader brain circuits, providing a more comprehensive understanding of connectivity patterns of atrophy locations across the brain. To apply the CNM technique, the normative functional connectivity of atrophy locations identified in cohorts of patients with schizophrenia can be estimated using a normative connectome database (n = 1000). This analysis can yield a functional connectivity map of each study’s reported pattern of atrophy. In some embodiments, the analysis can include combining each study in the plurality of studies into a single analysis. For each study, in some embodiments, a standard 4- mm spherical seed can be created at each identified coordinate of significant atrophy after multiple comparisons correction. Studies that reported Talairach coordinates can be converted into MNI coordinates using known methods. Then, the spherical seeds for a study can be combined to generate a study-level seed. The resting-state functional connectivity data from the 1000-parti cipant large normativeBWH 2024-554-02Q&B 129319.01117 connectome database can be utilized to compute seed-based functional connectivity between study-level atrophy coordinates and the rest of the brain. Specifically, fMRI time courses can be extracted for each brain region and for all coordinates of the study-level seed map. Then, Pearson’s correlation coefficient between time- courses can be calculated. Using Fisher’s r to z transform, r values can be converted to a normal distribution. Afterwards, Fisher z values can be averaged across all 1000 subjects in the normative functional connectivity dataset. This process can generate 1,000 functional connectivity maps for each study. These functional connectivity maps from the connectome can then be merged to form a unified connectivity map per study, referred to as a ‘coordinate network’ (or a connectivity pattern map of atrophy locations) for each study-level seed. In some embodiments, the connectivity pattern map of atrophy locations for each study can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0028] At block 308, a unified schizophrenia network connectivity map of atrophy patterns (or schizophrenia network or Scz-CC) can be generated. In some embodiments, the study-level maps from block 306 can be thresholded at, for example, |t|>5 and then the study -level functional connectivity maps can be combined into one composite sensitivity map representing the voxelwise overlap of connectivity maps across all studies. In some embodiments, to test for brain regions that were significantly consistent across all studies, the unthresholded maps can also be combined into a composite consistency t-map using a voxel-wise one-sample t-test with threshold-free cluster enhancement (TFCE) to correct for multiple comparison. Finally, the results of the sensitivity and consistency analyses can be combined in one map (sensitivityconsistency map) showing only areas that were statistically significant in both analyses (sensitive and consistent) after correcting for multiple comparisons. The significant brain regions in this map can include, for example, the left insula and bilateral anterior cingulate cortex (ACC).

[0029] Next, a specificity analysis can be performed. The connectivity maps of atrophy patterns in schizophrenia can be compared to 10 control groups comprising connectivity of atrophy patterns in normal aging and nine brain disorders namely major depressive disorder, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. In some embodiments, a two- sample t-test can be used to identify voxel clusters that are specific to schizophrenia. After multiple comparisons correction using TFCE, this analysis can yieldBWH 2024-554-02Q&B 129319.01117 significant clusters in, for example, the left superior temporal gyrus and right somatosensory cortex. The statistically significant region for schizophrenia versus psychiatric disorders was the left ACC, bordering the pregenual and dorsal regions. In some embodiments, the statistically significant region for schizophrenia versus neurodegenerative disorders and aging included the left amygdala. Each individual psychiatric and neurological disorder showed statistically significant brain regions that were distinct from schizophrenia after multiple comparisons correction, with the exception of bipolar disorder and major depression.

[0030] In some embodiments, the sensitivity / consistency map and the specificity map described above can be combined into a conjunction map, which can be referred to as the ‘schizophrenia network’. By definition, this network includes the connectivity of atrophy profiles that are sensitive, consistent, and specific to schizophrenia. Brain regions in this map included, for example, the dorsal ACC and mid-insula bilaterally. The schizophrenia network unites heterogenous published atrophy coordinates by a specific pattern of connectivity to one common network. In other words, the schizophrenia network can provide a common brain network that links heterogeneous atrophy patterns associated with schizophrenia. In addition, the schizophrenia network can indicate that different symptoms clusters in schizophrenia can localize to similar brain networks. In some embodiments, atrophy patterns in high risk subjects who progressed to schizophrenia can show more connectivity to the medial temporal lobe and ACC. At block 310, the schizophrenia network can be stored in data storage (e.g., storage device 416 of computer system 400 shown in FIG. 4).

[0031] As mentioned above, one or more aspects of the methods illustrated in FIGs. 1, 2 and 3 may be performed by or implemented on a processing system including at least one electronic processor (or processor device), where the at least one electronic processor may be or include a processor as described herein. In an example, the at least one electronic processor can be part of a computer system such as, for example, a general purpose computing system or device such as a personal computer, workstation, cellular phone, smartphone, laptop, tablet, or the like. As such, the computer system may include any suitable hardware and components designed or capable of carrying out a variety of [processing and control tasks, including steps for implementing aspects of the process described in FIGs. 1, 2 and 3. For example, the computer system may include a programmable processor or combination of programmable processors, such as central processing units (CPUs), graphics processing units (GPUs), and the like (e.g., as discussed further belowBWH 2024-554-02Q&B 129319.01117 with respect to FIG. 4). In some implementations, the one or more processors (or processor devices) of the computer system may be configured to execute instructions stored in a non- transitory computer readable media. In this regard, the computer system may be any device or system designed to integrate a variety of software, hardware, capabilities and functionalities. Alternatively, and by way of particular configurations and programming, the computer system may be a special-purpose system or device. For example, such special-purpose system or device may include one or more dedicated processing units or modules that may be configured (e.g., hardwired, or programmed) to cappy out steps in accordance with the present disclosure.

[0032] FIG. 4 is a block diagram of an example computer system in accordance with an embodiment. Computer system 400 may be used to implement the systems and methods described herein. In some embodiments, the computer system 400 may be a workstation, a notebook computer, a tablet device, a mobile device, a multimedia device, a network server, a mainframe, one or more controllers, one or more microcontrollers, or any other general-purpose or application-specific computing device. The computer system 400 may operate autonomously or semi -autonomously, or may read executable software instructions from the memory or storage device 416 or a computer-readable medium (e.g., a hard drive, a CD-ROM, flash memory), or may receive instructions via the input device 420 from a user, or any other source logically connected to a computer or device, such as another networked computer or server. Thus, in some embodiments, the computer system 400 can also include any suitable device for reading computer-readable storage media.

[0033] Data, such as data acquired with an imaging system (e.g., a magnetic resonance imaging (MRI) system) may be provided to the computer system 400 from a data storage device 416, and these data are received in a processing unit 402. In some embodiment, the processing unit 402 includes one or more processors. For example, the processing unit 402 may include one or more of a digital signal processor (DSP) 404, a microprocessor unit (MPU) 406, and a graphics processing unit (GPU) 408. The processing unit 402 also includes a data acquisition unit 410 that is configured to electronically receive data to be processed. The DSP 404, MPU 406, GPU 408, and data acquisition unit 410 are all coupled to a communication bus 412. The communication bus 412 may be, for example, a group of wires, or a hardware used for switching data between the peripherals or between any components in the processing unit 402.BWH 2024-554-02Q&B 129319.01117

[0034] The processing unit 402 may also include a communication port 414 in electronic communication with other devices, which may include a storage device 416, a display 418, and one or more input devices 420. Examples of an input device 420 include, but are not limited to, a keyboard, a mouse, and a touch screen through which a user can provide an input. The storage device 416 may be configured to store data, which may include data such as, for example, MRI data, maps of atrophy coordinates, connectivity maps, a schizophrenia network, probabilities of progression to schizophrenia, intervention targets, etc. whether these data are provided to, or processed by, the processing unit 402. The display 418 may be used to display images and other information, such as magnetic resonance images, patient health data, and so on.

[0035] The processing unit 402 can also be in electronic communication with a network 422 to transmit and receive data and other information. The communication port 414 can also be coupled to the processing unit 402 through a switched central resource, for example the communication bus 412. The processing unit can also include temporary storage 424 and a display controller 426. The temporary storage 424 is configured to store temporary information. For example, the temporary storage 424 can be a random access memory.

[0036] FIG. 5 is a block diagram of an example magnetic resonance imaging (MRI) system in accordance with an embodiment. MRI system 500 that may be used to perform the methods described herein, for example, structural MRI scans, fMRI scans, and post-processing of acquired MR data. In some embodiments, the disclosed systems and methods may be designed to accompany the MRI system 500. The MRI system 500 includes an operator workstation 502, which may include a display 504, one or more input devices 506 (e.g., a keyboard and mouse), and a processor 508. The processor 508 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 502 provides the operator interface that facilitates entering scan parameters (e.g., a scan prescription) into the MRI system 500. The operator workstation 502 may be coupled to different servers, including, for example, a pulse sequence server 510, a data acquisition server 512, a data processing server 514, and a data store server 516. The operator workstation 502 and the servers 510, 512, 514, and 516 may be connected via a communication system 540, which may include any suitable network connection, whether wired, wireless, or a combination of both.

[0037] The pulse sequence server 510 functions in response to instructions provided by the operator workstation 502 to operate a gradient system 518 and a radiofrequency (“RF”) systemBWH 2024-554-02Q&B 129319.01117520. Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system 518, which excites gradient coils in an assembly 522 to produce the magnetic field gradients Gx, Gy, and Gzthat are used for spatially encoding magnetic resonance signals. The gradient coil assembly 522 forms part of a magnet assembly 524 that includes a polarizing magnet 526 and a whole-body RF coil 528 and / or a local coil (not shown).

[0038] RF waveforms are applied by the RF system 520 to the RF coil 528, or a separate local coil, to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 528, or a separate local coil, are received by the RF system 520. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 510. The RF system 520 includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server 510 to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the whole-body RF coil 528 or to one or more local coils or coil arrays.

[0039] The RF system 520 also includes one or more RF receiver channels. Each RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil 528 to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at any sampled point by the square root of the sum of the squares of the I and Q components:M = / l2+ Q2(3) and the phase of the received magnetic resonance signal may also be determined according to the following relationship:<P = tan-10). (4)

[0040] The pulse sequence server 510 may receive patient data from a physiological acquisition controller 530. By way of example, the physiological acquisition controller 530 may receive signals from a number of different sensors connected to the patient, such as electrocardiograph (“ECG”) signals from electrodes, or respiratory signals from a respiratory bellows or otherBWH 2024-554-02Q&B 129319.01117 respiratory monitoring device. Such signals are typically used by the pulse sequence server 510 to synchronize, or “gate,” the performance of the scan with the subject’s heartbeat or respiration.

[0041] The pulse sequence server 510 may also connect to a scan room interface circuit 532 that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 532, a patient positioning system 534 can receive commands to move the patient to desired positions during the scan.

[0042] The digitized magnetic resonance signal samples produced by the RF system 520 are received by the data acquisition server 512. The data acquisition server 512 operates in response to instructions downloaded from the operator workstation 502 to receive the real-time magnetic resonance data and provide buffer storage, such that no data is lost by data overrun. In some scans, the data acquisition server 512 passes the acquired magnetic resonance data to the data processor server 514. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server 512 may be programmed to produce such information and convey it to the pulse sequence server 510. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 510. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system 520 or the gradient system 518, or to control the view order in which k-space is sampled. In still another example, the data acquisition server 512 may also process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography (“MRA”) scan. For example, the data acquisition server 512 may acquire magnetic resonance data and process it in real-time to produce information that is used to control the scan.

[0043] The data processing server 514 receives magnetic resonance data from the data acquisition server 512 and processes it in accordance with instructions downloaded from the operator workstation 502. Such processing may include, for example, reconstructing two- dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or back-projection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images.

[0044] Images reconstructed by the data processing server 514 are conveyed back to the operator workstation 502 for storage. Real-time images may be stored in a database memory cache (notBWH 2024-554-02Q&B 129319.01117 shown in FIG. 5), from which they may be output to operator display 504 or a display 536. Batch mode images or selected real time images may be stored in a host database on disc storage 538. When such images have been reconstructed and transferred to storage, the data processing server 514 notifies the data store server 516 on the operator workstation 502. The operator workstation 502 may be used by an operator to archive the images, produce films, send the images via a network to other facilities, or post-processing of the acquired MR data or reconstructed images.

[0045] The MRI system 500 may also include one or more networked workstations 542. By way of example, a networked workstation 542 may include a display 544, one or more input devices 546 (e.g., a keyboard and mouse), and a processor 548. The networked workstation 542 may be located within the same facility as the operator workstation 502, or in a different facility, such as a different healthcare institution or clinic.

[0046] The networked workstation 542 may gain remote access to the data processing server 514 or data store server 516 via the communication system 540. Accordingly, multiple networked workstations 542 may have access to the data processing server 514 and the data store server 516. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server 514 or the data store server 516 and the networked workstations 542, such that the data or images may be remotely processed by a networked workstation 542. This data may be exchanged in any suitable format, such as in accordance with the transmission control protocol (TCP), the internet protocol (IP), or other known or suitable protocols.

[0047] FIG. 6 is a block diagram of an example transcranial magnetic stimulation (TMS) system in accordance with an embodiment. As mentioned above, targets identified using the systems and methods described herein may be used for an intervention performed using a TMS system 600. A TMS system 600 may include an input 602, a controller 604, a signal generator 606 (e.g., a signal stimulator) and an electromagnetic coil 608. The controller 604 is in communication with the signal generator 606 and is configured to direct the signal generator 606 to provide various signals to the coil 608. In some implementations, the controller 604 may be any general-purpose computing system or device, such as a personal computer, workstation, cellular phone, smartphone, laptop, tablet, or the like. As such, the controller 604 may include any suitable hardware and components designed or capable of carrying out a variety of processing and control tasks, including steps for optimizing and directing the signal generator 606 to provide variousBWH 2024-554-02Q&B 129319.01117 signals to the coil 608. For example, the controller 604 may include a programmable processor or combination of programmable processors, such as central processing units (CPUs), graphics processing units (GPUs), and the like. In some implementations, the controller 604 may be configured to execute instructions stored in a non-transitory computer readable-media. In this regard, the controller 604 may be any device or system designed to integrate a variety of software, hardware, capabilities and functionalities. Alternatively, and by way of particular configurations and programming, the controller 604 may be a special-purpose system or device. For instance, such special-purpose system or device may include one or more dedicated processing units or modules that may be configured (e.g., hardwired, or pre-programmed) to carry out steps, in accordance with aspects of the present disclosure.

[0048] The electromagnetic coil 608 is positioned proximate to and over the head, for example, the scalp 618, of a subject 612. The electromagnetic coil 608 may be insulated using known methods and materials. In some embodiments, the coil 608 may be positioned and held in place over the scalp 618 by an operator or using a mechanical arm (not shown). The position of the coil 608 over the scalp 618 can be selected to target and stimulate a specific area of the brain (e.g., a region, site or target in the brain). Accordingly, the coil 608 may be positioned over the region to be stimulated in the brain. Signal generator 606 is configured to generate and deliver electrical signals (e.g., electric current or voltage signals) to the coil 608. In some embodiments, the signal generator 106 may be based on capacitor banks, power amplifiers, or H-bridge type designs. The electric current delivered from the signal generator 606 and flowing through the coil 608 generates a magnetic field 614. The magnetic field 614 (e.g., magnetic pulses) passes through the skull 610 and into the brain 620 of the subject 612 and cause or induce electrical currents 616 that stimulate nerve cells in the targeted brain region. Different coil types may be used for coil 608 to elicit different magnetic field patterns. The strength and distribution of the time-varying magnetic fields 614 may be dependent on both the geometry and the amount of current traveling through the coil 608. The induced electric field 614 may also be dependent on fixed variables unique to individual subjects such as the geometry and electrical properties of anatomies in and around the brain. The induced electric field may also be triggered by the user or by the controller 104 based on external data such as magnetic resonance imaging (MRI) data, functional MRI (fMRI) data, electroencephalography (EEG) data, or the like.BWH 2024-554-02Q&B 129319.01117

[0049] In some embodiments, the signals generated by the signal generator 606 and provided to the coil 608 may be in the form of a pulse sequence having a plurality of pulses. The power, amplitude, duration, shape, and frequency of the pulses may be selected to achieve a desired level of or depth of stimulation, as well as to optimize heat or magnetic forces induced in the coil 608. An operator may select the specific type and characteristics of the electric pulses to be generated by the signal generator 606 using an input 602 coupled to the controller 604. The input 603 can be, for example, a keyboard, a mouse, a touch screen, etc.

[0050] As mentioned, the position of the coil 608 over the scalp 618 can be selected to target and stimulate a specific area of the brain (e.g., a region, site or target in the brain). In some embodiments, the coil may be positioned over the head (e.g., the scalp 618) of the subject 612 based on external landmarks and measurements, for example, techniques can be used that assume that the point with the shortest distance from the brain target to the skull 618 is where the coil 608 should be located. In some embodiments, a neuronavigation system (not shown) can be used in TMS to ensure that the relative position of the coil 608 and the head in real space matches the position in image space. Neuronavigation technology visualizes the subject’s brain based on imaging data, for example, MRI data, in order to navigate and correctly position the coil to target the desired brain structure of region. In addition, neuronavigation systems may be configured to track and monitor the position of the coil (e.g., using an infrared camera in combination with the imaging data) on a reconstruction of the subject's head or brain during the duration of a TMS stimulation session. Neuronavigation systems can track the position of the TMS coil relative to a target or relative to an anatomical area of interest. As mentioned, a candidate target or targets (e.g., a schizophrenic causal circuit) identified for an intervention such as TMS using the systems and methods described above with respect to FIG. 1-3 can be provided to the TMS system 600 and a neuronavigation system.

[0051] Computer-executable instructions for determining a probability of progression to schizophrenia for a subject and for intervention target identification for the subject according to the above-described methods may be stored on a form of computer readable media. Computer readable media includes volatile and nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer readable media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), electricallyBWH 2024-554-02Q&B 129319.01117 erasable programmable ROM (EEPROM), flash memory or other memory technology, compact disk ROM (CD-ROM), digital volatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired instructions and which may be accessed by a system (e.g., a computer), including by internet or other computer network form of access

[0052] The present technology has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

Claims

BWH 2024-554-02Q&B 129319.01117CLAIMS1. A method comprising: receiving, using a processor device, MR data for a subject; generating, using the processor device, a map of atrophy coordinates for the subject using a set of volumetric MR data from the MR data for the subject; defining, using the processor device, a plurality of regions of interest (ROIs) for the subject based on the atrophy coordinates in the map of atrophy coordinates; generating, using the processor device, a connectivity map for each region of interest using a set of functional connectivity data from the MR data for the subject; retrieving, using the processor device, a unified schizophrenia network connectivity map of atrophy patterns; determining, using the processor device, a probability of progression to schizophrenia for the subject based on the connectivity map for each region of interest and the unified schizophrenia network connectivity map of atrophy patterns; and generating, using the processor device, a report including at least the probability of progression to schizophrenia for the subject.

2. The method according to claim 1, wherein determining, using the processor device, a probability of progression to schizophrenia for the subject based on the connectivity map for each region of interest and the unified schizophrenia network connectivity map of atrophy patterns comprises comparing the connectivity map for each region of interest to the unified schizophrenia network connectivity map of atrophy patterns.

3. The method according to claim 1, further comprising: identifying, using the processor device, one or more targets for a proactive intervention for the subject based on the connectivity map for each region of interest; and wherein the generated report includes the one or more identified targets.

4. The method according to claim 1, wherein each atrophy coordinate identifies an area of gray matter volume reduction.BWH 2024-554-02Q&B 129319.011175. The method according to claim 1, wherein a region of interest is defined for each atrophy coordinate in the map of atrophy coordinates.

6. The method according to claim 1, wherein generating, using the processor device, a connectivity map for each region of interest using a set of functional connectivity data from the MR data for the subject comprises: calculating, using the processor device, functional connectivity for each region of interest for the subject; generating, using the processor device, an fMRI time course for each location in each region of interest in the plurality of regions of interest; generating, using the processor device, a composite time course for each region of interest based on the fMRI time course for each location in a region of interest; and generating, using the processor device, a connectivity map for each region of interest based on the composite time course for the region of interest.

7. The method according to claim 1, wherein the probability of progression to schizophrenia for the subject is a percentage probability.8 The method according to claim 3, wherein identifying, using the processor device, one or more targets for a proactive intervention for the subject based on the connectivity map for each region of interest comprises comparing the connectivity map for each region of interest to a hypothetical stimulation site in the brain.

9. The method according to claim 3, wherein the proactive intervention for the subject is neuromodul ati on .

10. The method according to claim 9, wherein the neuromodulation comprises transcranial magnetic stimulation (TMS).BWH 2024-554-02Q&B 129319.0111711. A system comprising: a memory that stores one or more computer readable media that includes instructions; and one or more processor devices configured to execute the instructions of the computer readable media to: receive MR data for a subject; generate a map of atrophy coordinates for the subject using a set of volumetric MR data from the MR data for the subject; define a plurality of regions of interest (ROIs) for the subject based on the atrophy coordinates in the map of atrophy coordinates; generate a connectivity map for each region of interest using a set of functional connectivity data from the MR data for the subject; retrieve a unified schizophrenia network connectivity map of atrophy patterns; determine a probability of progression to schizophrenia for the subject based on the connectivity map for each region of interest and the unified schizophrenia network connectivity map of atrophy patterns; and generate a report including at least the probability of progression to schizophrenia for the subject.

12. The system according to claim 11, wherein the one or more processor devices are configured to further execute the instructions of the computer readable media to determine a probability of progression to schizophrenia for the subject by comparing the connectivity map for each region of interest to the unified schizophrenia network connectivity map of atrophy patterns.

13. The system according to claim 11, wherein the one or more processor devices are configured to further execute the instructions of the computer readable media to: identify one or more targets for a proactive intervention for the subject based on the connectivity map for each region of interest; and wherein the generated report includes the one or more identified targets.BWH 2024-554-02Q&B 129319.0111714. The system according to claim 11, wherein each atrophy coordinate identifies an area of gray matter volume reduction.

15. The system according to claim 11, wherein a region of interest is defined for each atrophy coordinate in the map of atrophy coordinates.

16. The system according to claim 11, wherein the one or more processor devices are configured to further execute the instructions of the computer readable media to generate a connectivity map for each region of interest using a set of functional connectivity data from the MR data for the subject by: calculating functional connectivity for each region of interest for the subject; generating an fMRI time course for each location in each region of interest in the plurality of regions of interest; generating a composite time course for each region of interest based on the fMRI time course for each location in a region of interest; and generating a connectivity map for each region of interest based on the composite time course for the region of interest.

17. The system according to claim 11, wherein the probability of progression to schizophrenia for the subject is a percentage probability.18 The system according to claim 13, wherein identifying, using the processor device, one or more targets for a proactive intervention for the subject based on the connectivity map for each region of interest comprises comparing the connectivity map for each region of interest to a hypothetical stimulation site in the brain.

19. The system according to claim 13, wherein the proactive intervention for the subject is neuromodul ati on .

20. The system according to claim 19, wherein the neuromodulation comprises transcranial magnetic stimulation (TMS).

Images