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WO2024263551A1 - Systèmes, procédés et produits programmes destinés à générer et à utiliser une connectomique - Google Patents

Systèmes, procédés et produits programmes destinés à générer et à utiliser une connectomique Download PDF

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Publication number
WO2024263551A1
WO2024263551A1 PCT/US2024/034442 US2024034442W WO2024263551A1 WO 2024263551 A1 WO2024263551 A1 WO 2024263551A1 US 2024034442 W US2024034442 W US 2024034442W WO 2024263551 A1 WO2024263551 A1 WO 2024263551A1
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disorder
human brain
computers
brain network
voxel
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PCT/US2024/034442
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WO2024263551A9 (fr
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Peter Thornton FOX
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The Board Of Regents Of The University Of Texas System
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention generally relates to systems, methods and program products for producing, identifying and utilizing neuroimaging biomarkers that quantify 7 brain network properties (connectomics) for the purpose of assessing alterations in networkbased structure and function in a wide variety of health-related conditions including neurologic and psychiatric disorders, and normal changes across the lifespan including development and aging.
  • connectomics 7 brain network properties
  • Mass univariate CBMA has been used to estimate the activation likelihood of certain areas of the brain. CBMA has also been used in some instances for biomarker discovery. Mass-umvariate analysis, however, suffers from technical challenges since it does not allow for investigation of the interaction between different voxels (or regional groups of voxels of the brain). Performing this analysis (which may be understood to be a form of ‘multivariate analysis’) of the interactions between voxels is typically impeded by the high transfer volume required for mass metaconnectonomics research, limited access to high powered computers (and optimization required to make effective use of them), and difficult-to-implement algorithms. Conventional data-distribution models in the field do not meet these challenges.
  • the present disclosure includes a novel approach to neuroimaging biomarker discovery which overcomes the challenges currently facing the field, providing a high- powered computing system and employing methods to lead to the discovery and validation of network-based (connectomic) brain models of normal brain function and of neurologic, psychiatric, developmental and system disorders.
  • the technology encompasses analyzing neuroimaging studies using whole-brain contrasts (task vs control or case vs control) of spatially normalized statistical parametric images to yield location coordinates (x-y-z addresses) of effect loci in Cartesian space referenced to a neuroanatomical atlas.
  • This format has exceptionally high information content and readily allows coordinate-based meta-analyses (CBMA) with either univariate (for effect spatial distributions) or multivanate (for network architecture) statistical methods.
  • CBMA coordinate-based meta-analyses
  • the present invention relates to the use of statistical parametric images (SPI) of the human brain.
  • SPI data typically has been 1) acquired over the whole brain; 2) analyzed in a voxel-wise manner as deviations from a null distribution; 3) and, reported as x-y-z locations of local maxima (foci) in a 3D space referenced to a neuroanatomical atlas within a Cartesian coordinate system, a standard first developed by Dr. Peter Fox. These measurements are often transformed into ‘standard space’ from ‘real space’ and were developed to allow scientists to more easily reproduce published experimental results. Coordinate-based meta-analysis (CBMA) has been performed on SPI data.
  • CBMA Coordinate-based meta-analysis
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected neurologic disorder or psychiatric disorder, the human brain network including a morphologic property and a physiologic property and, optionally, a temporal property, the method including: (a) obtaining, by one or more computers of a high-performance computing portal, a selected neurologic disorder or psychiatric disorder; (b) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinatebased spatially -normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, including: (i) a task-activation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxe
  • TA DB task
  • the techniques described herein may relate to a method of treating a subject for a neurologic disorder, a psychiatric disorder, a development disorder, or age-associated changes the method including: (a) imaging brain activity of said subject; (b) comparing said imaged brain activity to a database of virtual representations of brain networks associated with a neurologic disorder, a psychiatric disorder, a development disorder, or age-associated changes, each network including a morphologic property and a physiologic property and.
  • a temporal property wherein the virtual representations were generated by applying a multivariate coordinate-based meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinate-based quantified brain activity data related to specific human subject tasks and stored in a task-activation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (VBM DB); and (iii) voxel-based physiological data stored in a voxel-based physiology database (VBP DB); and (c) determining, based on the comparison, if the subject exhibits a brain network associated with a specific neurologic disorder, a specific psychiatric disorder, a specific development disorder, or specific age-associated changes, wherein if the subject does exhibit said brain network associated with the specific neurologic disorder, the specific psychiatric disorder, the specific development disorder, or specific age
  • the techniques described herein may relate to a method of determining if a neuromodulatory therapy is effective in ameliorating a neurologic disorder or a psychiatric disorder in a subject including: (a) providing a virtual representation of a human brain network associated with a neurologic disorder or a psychiatric disorder, wherein the virtual representations were generated by applying a multivariate coordinatebased meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinate-based quantified brain activity data related to specific human subject tasks and stored in a taskactivation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (VBM DB); and (iii) voxel-based physiological data stored in a voxel-based physiology database (VBP DB); and (b) identifying, from imaging brain activity, if the subject
  • the techniques described herein may relate to a method for improving effectiveness of a neurosurgery on a brain of a subject, the method including: (a) imaging brain activity of the subject prior to and/or during the neurosurgery; (b) comparing the imaged brain activity in real time to a database of virtual representations of brain networks associated with a neurologic disorder or a psychiatric disorder, the network including a morphologic property and a physiologic property and, optionally, a temporal property, wherein the virtual representations were generated by applying a multivariate coordinate-based meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinatebased quantified brain activity data related to specific human subject tasks stored in a taskactivation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (VBM DB); and (iii) vo
  • CBMA multivari
  • the neurological disorder comprises an epilepsy.
  • the epilepsy is frontal lobe epilepsy.
  • the neurosurgery is a resective surgery.
  • the neurosurgery is performed on one of the temporal lobes. This is an area of the brain that controls visual memory, language comprehension and emotions.
  • the neurosurgery is a laser interstitial thermal therapy (LITT).
  • the neurosurgery is a corpus callosotomy.
  • the neurosurgery is a hemispherectomy.
  • the neurosurgery is a functional hemispherectomy.
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age-associated change, the human brain network including a morphologic property and a physiologic property and, optionally, a temporal property 7 , the method including: (a) obtaining, by one or more computers of a high- performance computing portal, the selected developmental disorder or systemic disorder; (b) accessing, by the one or more computers, a plurality of databases storing group- averaged coordinate-based spatially-normalized data populated from a plurality 7 of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel
  • TA DB task
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected disorder, the human brain network including a morphologic property' and a physiologic property and, optionally, a temporal property’, the method including: (a) obtaining, by one or more computers of a high-performance computing portal, the selected disorder; (b) accessing, by 7 the one or more computers, a plurality’ of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality 7 of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel -based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology 7 database (VBP DB)
  • TA DB task
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory' wherein the memory stores computer readable instructions including program code that, when executed, cause the one or more computers to perform the steps of: (a) obtaining, by the one or more computers of the high-performance computing portal, a selected neurologic disorder or psychiatric disorder; (b) accessing, by the one or more computers, a plurality' of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer- readable media, including: (i) a task-activation database (TA DB) including coordinatebased quantified brain activity data related to specific human subject tasks; (ii) a voxelbased morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology’ database (VBP DB) including
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory yvherein the memory stores computer readable instructions including program code that, when executed, cause the one or more computers to perform the steps of: (a) obtaining, by one or more computers of a high-performance computing portal, a selected developmental disorder, systemic disorder, or age-associated changes; (b) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially -normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, including: (i) a task-activation database (TA DB) including coordinate-based quantified brain activity- data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory- yvherein the memory stores computer readable instructions including program code that, yvhen executed, cause the one or more computers to perform the steps of: (a) obtaining, by one or more computers of a high-performance computing portal, a selected disorder; (b) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality’ of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel -based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB) including voxe
  • TA DB task
  • FIG. 1 is a depiction of exemplary BrainMap HPC Components.
  • BrainMap HPC assembles & coordinates 3 BrainMap databases, CBMA network-modeling pipelines, user fMRI/sMRI primary data, and primary data pipelines in a TACC HPC science gateway to accelerates connectome-biomarker discovery'. Users build and test models. Developers create and expand pipelines.
  • TACC manages the Gateway.
  • BrainMap manages CBMA data and analytics.
  • FIG. 2 is a depiction of exemplary BrainMap HPC Portal Architecture.
  • the portal architecture interfaces & integrates Topware, Midware, and Deepware.
  • Topware provides the User Interface to portal resources.
  • Midware bridges the User Interface with HPC Deepware. Deepware supports resources accessed by but not edited or directly called by users.
  • This architecture provides comprehensive, sophisticated, network-model development and validation capabilities within user-friendly, secure, cloud-computing environment.
  • FIG. 3 is an exemplary BrainMap HPC Portal Example Workflow.
  • a typical disease network-modeling example workflow is illustrated. (Other workflows are supported.)
  • Node-and-edge model creation begins with a disorder-specific coordinate-based literature, here. Multiple Sclerosis. Node creation applies GingerALE. progresses from the coordinate-based literature (left), through Model Estimation and Model Validation to clinical application. All stages of the biomarker-discovery workflow are supported by the Portal Components ( Figure 1) and Portal Architecture ( Figure 2) described above.
  • FIG. 4 is an exemplary’ flow chart of a method for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age-associated change.
  • FIG. 5 is an exemplary' flow chart of a method for generating a virtual representation of a human brain network associated with a selected disorder, the human brain network comprising a morphologic property and a physiologic property and, optionally, a temporal property’.
  • FIG. 6 is an exemplary’ system for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age-associated change.
  • the present invention generally relates to systems, methods and program products for producing, identifying and validating neuroimaging biomarkers that quantify brain network properties (connectomics) for the purpose of assessing alterations in networkbased structure and function in a wide variety of health-related conditions including neurologic and psychiatric disorders, and normal changes across the lifespan including development and aging. More generally, the present invention relates to the use of statistical parametric images (SPI) of the human brain.
  • SPI statistical parametric images
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected neurologic disorder or psychiatric disorder, the human brain network including a morphologic property and a physiologic property 7 and, optionally, a temporal property, the method including: (a) obtaining, by one or more computers of a high-performance computing portal, a selected neurologic disorder or psychiatric disorder; (b) accessing, by the one or more computers, a plurality 7 of databases storing group-averaged coordinatebased spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, including: (i) a task-activation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry 7 database (VBM DB) including voxel-based morphometric data; and (iii) a vo
  • TA DB task
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the techniques described herein may relate to a method of treating a subject for a neurologic disorder, a psychiatric disorder, a development disorder, or age-associated changes the method including: (a) imaging brain activity of said subject; (b) comparing said imaged brain activity to a database of virtual representations of brain networks associated with a neurologic disorder, a psychiatric disorder, a development disorder, or age-associated changes, each network including a morphologic property and a physiologic property' and, optionally, a temporal property, wherein the virtual representations were generated by applying a multivariate coordinate-based meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinate-based quantified brain activity data related to specific human subject tasks and stored in a task-activation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (CBMA) algorithm to information
  • the techniques described herein may relate to a method of determining if a neuromodulatory therapy is effective in ameliorating a neurologic disorder or a psychiatric disorder in a subject including: (a) providing a virtual representation of a human brain network associated with a neurologic disorder or a psychiatric disorder, wherein the virtual representations were generated by applying a multivariate coordinatebased meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinate-based quantified brain activity data related to specific human subj ect tasks and stored in a taskactivation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (VBM DB); and (iii) voxel-based physiological data stored in a voxel-based physiology database (VBP DB); and (b) identifying, from imaging brain activity
  • CBMA multivari
  • the transcranial magnetic brain stimulation is employed to treat identified PTSD as a monotherapy. In embodiments, the transcranial magnetic brain stimulation is employed to treat a substance use disorder.
  • the techniques described herein may relate to a method for improving effectiveness of a neurosurgery on a brain of a subject, the method including: (a) imaging brain activity of the subject prior to and/or during the neurosurgery; (b) comparing the imaged brain activity in real time to a database of virtual representations of brain networks associated with a neurologic disorder or a psychiatric disorder, the network including a morphologic property and a physiologic property and, optionally, a temporal property, wherein the virtual representations were generated by applying a multivariate coordinate-based meta-analysis (CBMA) algorithm to information associated with the neurologic disorder or the psychiatric disorder, the information including: (i) coordinatebased quantified brain activity data related to specific human subject tasks stored in a taskactivation database (TA DB); (ii) including voxel-based morphometric data stored in a voxel-based morphometry database (VBM DB); and (iii) vo
  • CBMA multivari
  • the imaging brain activity of said subject may include MRI. In embodiments, the imaging brain activity of said subject may include rs-fMRI. [0034] In embodiments, the human brain network may be associated with a neurologic disorder, and/or with a psychiatric disorder.
  • the neurologic disorder comprises an epilepsy and the method is employed to effect presurgical mapping in epilepsy. In embodiments, the method further comprises performing an epilepsy surgery based thereon.
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age associated-change, the human brain network including a morphologic property and a physiologic property and, optionally, a temporal property, the method including: (a) obtaining, by one or more computers of a high- performance computing portal, the selected developmental disorder or systemic disorder; (b) accessing, by the one or more computers, a plurality of databases storing group- averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel -based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based
  • TA DB task
  • the human brain network may be associated with a developmental disorder, with a systemic disorder, and/or with age-associated changes.
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the techniques described herein may relate to a method for generating a virtual representation of a human brain network associated with a selected disorder, the human brain network including a morphologic property' and a physiologic property’ and, optionally, a temporal property’, the method including: (a) obtaining, by one or more computers of a high-performance computing portal, the selected disorder; (b) accessing, by’ the one or more computers, a plurality’ of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality’ of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii)
  • TA DB taskactivation database
  • the selected disorder may be one of a neurologic, psychiatric, developmental, or systemic disorder.
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory' wherein the memory' stores computer readable instructions including program code that, when executed, cause the one or more computers to perform the steps of: (a) obtaining, by’ the one or more computers of the high-performance computing portal, a selected neurologic disorder or psychiatric disorder; (b) accessing, by the one or more computers, a plurality' of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer- readable media, including: (i) a task-activation database (TA DB) including coordinatebased quantified brain activity data related to specific human subject tasks; (ii) a voxelbased morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB) including
  • the human brain network may be associated with a neurologic disorder and/or a psychiatric disorder.
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory wherein the memory stores computer readable instructions including program code that, when executed, cause the one or more computers to perform the steps of: (a) obtaining, by one or more computers of a high-performance computing portal, a selected developmental disorder, systemic disorder, or age-associated changes; (b) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially -normalized data populated from a plurality' of human brain subject, each stored on one or more computer-readable media, including: (i) a task-activation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB)
  • TA DB task
  • the human brain network may be associated with a developmental disorder, a systemic disorder, and/or with age-associated changes.
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the techniques described herein may relate to a system including: one or more computers of a high-performance computing portal including memory wherein the memory stores computer readable instructions including program code that, when executed, cause the one or more computers to perform the steps of: (a) obtaining, by one or more computers of a high-performance computing portal, a selected disorder; (b) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, including: (i) a taskactivation database (TA DB) including coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel -based morphometry database (VBM DB) including voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB) including voxel-based physiological data; (c
  • TA DB task
  • the selected disorder may be one of a neurologic, psychiatric, developmental, or systemic disorder.
  • the presenting step (E) may include transmitting the virtual representation of said human brain network to a user device of the user and/or causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • At least one of the plurality of databases may include restingstate functional MRI data.
  • Examples of a task-activation database for human brains, voxel-based morphometry database and voxel-based physiology database can be found e.g. at brainmap.org.
  • BrainMap is a database of published task and structural neuroimaging experiments with coordinate-based results (x,y,z) in Talairach or MNI space.
  • BrainMapWeb at apps.rii.uthscsa.edu/bmapWeb/ is a web application for searching and retrieving data from the task database.
  • a task-activation database is accessible at portal.brainmap.org
  • Voxel-based morphometry of the human brain/CNS is established (see. e.g., Whitwell. J Neurosci.
  • Physiology parameters include cerebral blood volume (CBV), cerebral blood flow' (CBF), cerebral metabolic rate of oxygen (CMRO2), and oxygen extraction fraction (OEF).
  • CBV cerebral blood volume
  • CBF cerebral blood flow'
  • CMRO2 cerebral metabolic rate of oxygen
  • OEF oxygen extraction fraction
  • the techniques described herein may relate to a system, the user may be located remotely from the computer system.
  • the subject, or subjects is/are human.
  • all data sets and data subsets are obtained from human subjects.
  • a predefined condition is a condition or status decided or determined beforehand.
  • 30 to 70 includes the subset of 30 to 35, the subset of 40-60, etc. as well as every individual integer value, e g. 30, 31, 32, 33, and so on.
  • “And/or” as used herein, for example with option A and/or option B, encompasses the separate embodiments of (i) option A, (ii) option B, and (iii) option A plus option B.
  • BrainMap HPC Portal (“BrainMap High Performance Computing Portal”).
  • BrainMap HPC Portal is a cloud-based environment designed to promote the development of neuroimaging biomarkers that quantify brain network properties (i.e., connectomics) for the purpose of assessing alterations in network-based structure and function in a wide variety of health- related conditions including neurologic and psychiatric disorders, and normal changes across the lifespan including development and aging.
  • BrainMap HPC Portal can achieve its goals via meta-analytic modeling of data gleaned from the coordinate-reporting neuroimaging literature, a large and well- standardized corpus.
  • data for meta-analytic modeling are provided by three BrainMap Databases (DBs): Task-activation database (TA DB), voxel-based morphometry database (VBM DB), and voxel-based physiology database (VBP DB).
  • TA DB Task-activation database
  • VBM DB voxel-based morphometry database
  • VBP DB voxel-based physiology database
  • the BrainMap DBs are steadily expanding, being supplemented by BrainMap team efforts and community contributions. All data are curated by the BrainMap team, including selection, coordination and arrangement.
  • the BrainMap DBs are copyrighted by the Board of Regents, University' of Texas System.
  • each Database may be configured to store data from publication based upon a structured standardized coding scheme. Data may be sourced from published human neuroimaging experimental results using the coding scheme.
  • the BrainMap HPC Portal is a customized instance of the Texas Advanced Computing Center (“TACC”) “Core Experience Portal’’ (CEP) framework.
  • TACC Texas Advanced Computing Center
  • CEP Core Experience Portal
  • the CEP framework is a cloneable “plain vanilla’’ portal developed by TACC as a starting point for portal-project development.
  • the CEP codebase is open-source, but in this instance has been extensively customized for BrainMap HPC.
  • Unique to the BrainMap Community Portal are several enhancements made over the Core Experience Portal.
  • Tapis is an open source framework which was developed at TACC.
  • the Tapis API also provides a fully functional command line interface to the assets in the BrainMap Community Portal (as a programmatic alternative to the point-and- click web interface).
  • the BrainMap Portal environment contains: 1) extensive (and extensible) data resources in the form of BrainMap records, each representing peer-reviewed publications reporting data adhering to the strict quality standards of coordinate-based meta-analysis; 2) extensive (and extensible) analytic resources in the form of mass-univariate and multivariate statistical -analysis pipelines implemented in formats optimized for CBMA; and 3) high-performance and high- throughput computing (HPC, HTC).
  • HPC high-performance and high- throughput computing
  • the systems and methods provided herein in non-limiting embodiments can be employed to: create a computer environment designed for discovery and validation of network-based (connectomic), brain models of normal brain function and of neurologic, psychiatric, developmental, and systemic disorders; create a computer environment to create connectomic models of brain disorders for use as neuroimaging biomarkers, by which is meant quantitative indices of brain physiology or pathophysiology to be used for diagnosis, prognosis, disease-progression monitoring, or disease risk prediction either clinically or in the context of clinical trials; create a computer environment to create connectomic models of brain disorders for use in treatment planning neuromodulation therapy, including: transcranial magnetic brain stimulation; direct current brain stimulation; alternating current brain stimulation; deep brain stimulation; focused ultrasound brain stimulation; or other neuromodulation techniques not yet described or discovered; create a computer environment to create connectomic models of brain disorders for use in preoperative planning of neurosurgical interventions; create a computer environment in manner that integrates coordinate-based, group-averaged data gleaned from the neuroimagina network-
  • FIG. 1 is a depiction of the BrainMap HPC Components.
  • An exemplary system embodiment, BrainMap HPC Portal 100 (FIG. 1), shows a fundamentally new' approach to neuroimaging biomarker identification and generation.
  • BrainMap HPC Portal 100 intersects: 1) three curated BrainMap DBs 102; 2) CBMA network-modeling pipelines TACC 104; 3) sMRI/fMRI model-validation pipelines 106; 4) connectome researchers (BrainMap HPC users) 108; 5) HPC gateway architecture & resources 110; and 6) pipeline refinement by in-house and community pipeline developers 112.
  • the system results in connectomic, neuroimaging biomarkers for a host of brain disorders.
  • BrainMap HPC 100 assembles & coordinates 3 BrainMap databases 102, CBMA network-modeling pipelines 104, user fMRI/sMRI primary data 106, and primary data pipelines 108 in a TACC HPC science gateway 110 to accelerate connectome-biomarker discovery.
  • users (not depicted) build and test models via the BrainMap HPC Portal.
  • users may not have direct access to the specific database but may build and test models via computer query entered into a user interface.
  • developers 112 may create and expand pipelines (e.g., steps of performing certain analysis using the BrainMap HPC portal).
  • a party other than the users e.g., the Texas Advanced Computing Center, or “TACC”
  • another party e.g., BrainMap
  • CBMA data and analytics may be manage the CBMA data and analytics.
  • Some advantages of the BrainMap HCP portal project are to provide users with: comprehensive access to the three BrainMap coordinate-literature databases: TA, VBM, VBP;
  • - primary-data (fMRI, sMRI) pipelines for out-of-sample model validation on with user-provided primary data in disease & control populations; refinements of CBMA and primary -data pipelines by BrainMap, TACC and community developers; an intuitive, powerful, secure HPC user experience through a TACC Science Gateway architecture.
  • the 3 BrainMap Databases have been mirrored within Tapis architecture using the Oracle® Goldengate function.
  • the BrainMap suite of established Java CBMA applications have been implemented with the portal.
  • Multiple CBMA model-construction applications have been implemented as containerized python pipelines.
  • Primary data (fMRI, sMRI) model-validations pipelines are optionally included.
  • FIG. 2 is a depiction of BrainMap HPC Portal Architecture 200.
  • the portal architecture 200 interfaces & integrates Top ware 240, Midware 260, and Deepware 260.
  • Topware 240 provides a user interface to portal resources.
  • Midware 260 bridges the User Interface 240 with HPC Deepware 260.
  • Deepware 260 supports resources accessed by but not edited or directly called by users. This architecture provides comprehensive, sophisticated, network-model development and validation capabilities within user-friendly, secure, cloudcomputing environment.
  • the BrainMap HPC Portal Architecture 200 is three-tiered.
  • Topware 240 (1st tier), connectome researchers can access BrainMap HPC via a web-based interface (e.g., DB interface 242 ).
  • a point-and-click menu provides access to a suite of tools for accessing BrainMap data by SQL query 7 (e.g., Sleuth - software designed to search the BrainMap databases, create workspace data sets, plots and export the subjects and locations for meta-analysis) and constructing command-line jobs to perform a variety of mass univariate and multivariate CBMA workflows (FSL MELODIC, MACM - meta-analytic connective modeling, CBP - coactivation-based parcellation, GTM - graph theory 7 modelling).
  • tools for image-data visualization Mango.Papaya, FSL View
  • metadata-informed interpretation can be launched to view and analyze job outcomes in place (using, e.g., Model Interface 244).
  • Functionality is also provided to facilitate uploading User Files 246, such as structural MRI (sMRI) and functional MRI (fMRI).
  • a purpose of primary data upload is out-of- sample validation of meta-analytic network models, which is done by assessing model goodness-of-fit, model-based discrimination of patients from controls, and other metrics.
  • download of network models, validation data and other user-created intellectual property' is also supported.
  • the yveb-based interface 240 may be an instance of TACC's open-source "Core Experience Portal" code base, which has been extensively customized for BrainMap HPC.
  • midware 260 may use Tapis (a framework providing a hosted, unified yveb-based API for securely managing computational yvorkload) to provide its HPC integration and data-management capabilities.
  • Tapis provides user-authentication 261, job management 262, user notification 263, and load balancing 264.
  • for Brainmap HPC Tapis provides routines for association with the BrainMap web portal ("tenancy") 265, data-use request and data-access management and monitoring functions 266 for the BrainMap SQL DBs, and application definitions 267 for CBMA and primary data pipelines. Data provenance and job histories are tracked in Tapis log files 268.
  • the Tapis API also provides a fully functional command-line interface (CLI) to all the assets of the BrainMap HPC portal, as an alternative to the point- and-click interface for more experienced users.
  • CLI command-line interface
  • the BrainMap HPC CLI enables advanced users to script and automate tasks, run many jobs simultaneously, and augment the portal offerings by adding new applications and workflows.
  • Tapis (like the Core Experience Portal) is an open-source codebase.
  • Deepware 280 may be made of assets accessed by users only via the Tapis midware level and may consistent of hardware and software.
  • These include TACC high performance computers 285 (where the CBMA applications 284 run) and virtual machines supporting various functions (e.g., the landing page and taxonomy server, the three BrainMap SQL DBs 281, and precomputed images and co-occurrence matrices 282 supporting MACM, CBP and other applications 283).
  • the MACM, CBP and other applications 283 may consist of modules configured to perform certain algorithms or calculations running on the high performances computers.
  • developers and system administrators may have access to all levels of the architecture.
  • other developers e.g., Brainmap developers
  • BrainMap software Sleuth, Mango, Papaya
  • data structures BrainMap SQL databases, co-occurrence matrices.
  • Community developers can add new analysis applications, in cooperation with TACC and BrainMap developers.
  • Community developers can build new analysis pipelines independently, which can be made openly accessed by TACC developers, for example by using Developer Tools 290.
  • FIG. 3 is a BrainMap HPC Portal Example Workflow.
  • a typical disease network-modeling example workflow is illustrated.
  • Node- and-edge model creation begins with a disorder-specific coordinate-based literature, here, Multiple Sclerosis (patent pending.)
  • Node compute / creation 312 applies GingerALE (software designed to perform meta-analyses via the activation likelihood estimation (ALE) method), progresses from the coordinate-based literature 304 (left), through Model Estimation 320 and Model Validation 330 to clinical use/ application 340. All stages of the biomarker-discovery workflow are supported by the Portal Components ( Figure 1) and Portal Architecture ( Figure 2) descnbed above.
  • ALE activation likelihood estimation
  • BrainMap HPC Portal Workflows 300 are flexibly supported by the portal architecture and the rich array of data and computational resources assembled by the BrainMap/TACC development team.
  • connectomic models of network-based neural degeneration can be developed for any disorder represented in the coordinate-based neuroimaging literature, which may or may not be currently recognized as such.
  • connectomic models may be generated for neurologic, psychiatric, development, and systemic disorders, to name a few.
  • connectomic models may be generated for changes across a lifespan (e.g., age-associated changes and development).
  • such disorders and/or age-associated changes may or not be defined by the Diagnostic & Statistical Manual of Mental Disorders (DSM) and/or the International Classification of Disorders (ICD).
  • DSM Diagnostic & Statistical Manual of Mental Disorders
  • ICD International Classification of Disorders
  • the DSM-5 does not recognize Online Gaming Addiction
  • the ICD-10/11 does.
  • connectomic models may be generated for individuals with Online Gaming Addiction.
  • FIG. 3 An illustrated workflow (FIG. 3) is adapted from the Multiple Sclerosis biomarker developed by Chiang and colleagues (Chiang et al. 2019, 2020, 2021), which developed a relatively sparse node-and-edge model, mathematically formalized using structural equation modeling (SEM).
  • model nodes may be regions of reliably observed anatomical or physiological alterations computed by applying the ALE mass-univariate algorithm to experiments retrieved from databases or uploaded by the user.
  • Model edges may be between-node covariances (co-occurrence parameters) computed using data from one or more the BrainMap DBs, depending on the purpose of the model.
  • Model validation 330 may use primary data (sMRI. fMRI) 334 to assess model goodness of fit. edge weights, discrimination of patients from controls, or similar parameters. Following out-of-sample validation, promising models can be tested prospectively in clinical trials (Fox, 2023).
  • node computes and edge computes may be performed by the high computing resources of the brainmap portal.
  • a disease network-modeling Workflow 300 may begin with coordinate-based literature regarding a disease being collected in one or more database 304.
  • the databases may include one or more of the BrainMap DBs (e.g., the TA DB, the VBM DB. and/or the VBP DB, to name a few).
  • data may be input by Brainmap researchers.
  • data may be input by a user using softw are configured to interact with the database (e.g., Scribe - an application that is used to extract coordinates and meta-data from a published functional neuroimaging study).
  • a node compute 312 may apply an algorithm (e.g., an ALE mass-univariate algorithm) to the coordinate-based literature, to create a disease MetaMap 310.
  • a Disease Network Model Estimation 320 may then be generated. In embodiments, this may be generated from the MetaMap 310 using edge compute 322.
  • edge compute 322 may be performed using MACM and may be use data from databases 322 (e.g., the TA DB, the VBM DB, and the VBP database).
  • a Disease Network Model Validation 330 may be generated which may validate the Disease Network Model Estimation 320.
  • the Disease Network Model Validation 330 may be generated using algorithms (e.g., edge compute 3332 using one or more algorithms such as SEM or GTM) to assess model goodness of fit, edge weights, discrimination of patients from controls, or similar parameters, and may be based off of User Data 334 (e.g., sMRI, fMRI, PET scans).
  • User Data 334 e.g., sMRI, fMRI, PET scans.
  • Model Estimation 320 and/or Model Validation 330 may be used in Clinical Use 340. For example, they may be used in diagnosing disease 342, staging and monitoring 344, to target neuromodulation 346 and/or plan surgery' 348.
  • the Disease MetaMap 310 and Model Estimation 320 may be generated as virtual representations (or electronic correlates) of a human brain network.
  • Multivariate CBMA as used in the methods and systems computes interactions among voxels (or regional groups of voxels), treating each voxel (or region) as a variable (hence, multivariate).
  • each voxel may correspond to a three-dimensional area (e.g., an area bounded by a cube or rectangular prism) of imaged brain tissue.
  • Intervoxel co-occurrence patterns serve to identify brain networks. That brain functions arise from multi-regional, interconnected, neural networks has been presumed for more than a century. However, robust methods for mapping human neural networks in vivo fyconnectomics”) have been lacking. Multivariate CBMA overcomes this difficulty by extracting networks as emergent properties (latent variables) from BrainMap data.
  • Multivariate CBMA algorithms emerged in the mid-2000’s, being invented by BrainMap investigators (reviewed in Fox et al., 2014). These highly sophisticated analytics now include: independent components analysis (ICA; Smith, Fox et al.. 2009; Vanasse et al. 2018; 2021 ; Figure 3); meta-analytic connectivity modeling (MACM; Robinson et al., 2010, 2012); graph theory- modeling (GTM; Crossley et al., 2013, 2014, 2018; Cauda et al. 2018; Gray et al., 2020); connectivity-based parcellation (CBP; Bzdok et al.. 2012; Barron et al., 2014); author-topic modeling (ATM: Yeo et al..
  • NDH Network Degeneration Hypothesis
  • Meta-ICA demonstrated a close correspondence between healthy, functional connectivity networks and the structural pathology networks observed in greater than 40 neurologic and psychiatric brain disorders (Vanasse et al.. 2018, 2021; Figure 4).
  • Neurological disorders exhibiting network pathology included both dementia-inducing disorders, and many others (Table 1). All psychiatric disorders with a sufficient volume of published studies for valid meta-ICA exhibited network degeneration (Table 1). This is unprecedented evidence that structural pathology propagating along functional connectivity networks is a general property’ of neuropsychiatric disorders.
  • BrainMap investigators have focused their efforts on meta-connectomic modeling of brain disorders.
  • a primary’ objective of initiative is to use meta-connectomics to create network-biomarkers adaptable to persubject application to rs-fMRI. as a neuroimaging adaptation of personalized medicine. This approach has worked for image-guided targeting of neuromodulation therapy for PTSD (Fox, Lancaster & Salinas, 2022; Fox, Salinas et al., 2023).
  • a diagnostic biomarker for Multiple Sclerosis (Chiang, et al, 2019) achieved sufficient confirmation in a small, out-of- sample.
  • primary-data validation (Chiang et al. 2021) to support patent applications (Chiang et al. 2020).
  • Metaconnectomics by multivariate CBMA can identify and validate connectomic biomarkers for a host of brain disorders (Table 1). Barriers have included prior data-distribution models. Data-transfer volumes required for mass metaconnectomics have been prohibitive also. HPC access is limited and requires optimization to this application. Algorithms are challenging to implement.
  • Table 1 non-limiting embodiments of neurologic disorders and psychiatric disorders:
  • the extant BrainMap workflow is insufficient to accelerate commumly- based, connectomic biomarker research.
  • a solution presented herein is a new research environment merging: 1) the BrainMap DBs; 2) multivariate meta-analytics; 3) high- performance computing; and 4) the connectome research & development communities. This concept is realized in the BrainMap HPC Portal.
  • Task activation can be, for example cognitive/emotion or sensory perception. Exemplary task activations are listed in Table 2:
  • FIG. 4 is an exemplary flow chart of a method for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age-associated change.
  • the method 400 comprises, at step S410, obtaining, by one or more computers of a high-performance computing portal, the selected developmental disorder or systemic disorder and, at step S420 accessing by the one or more computers, a plurality’ of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer- readable media.
  • the plurality of databases comprise a task-activation database (TA DB) comprising coordinate-based quantified brain activity data related to specific human subject tasks; a voxel-based morphometry database (VBM DB) comprising voxel-based morphometric data; and a voxel-based physiology database (VBP DB) comprising voxelbased physiological data;
  • the method 400 further comprises at step S430 obtaining, by the one or more computers, morphologic, physiologic, and optionally temporal, information from the plurality of databases, which information is associated with the selected developmental disorder, systemic disorder, or age-associated changes and, at step S440 applying, by the one or more computers, a multivariate coordinate-based meta-analysis (CBMA) algorithm to the obtained information so as to identify a human brain network comprising a morphologic property and a physiologic property' and, optionally, a temporal property' associated with the selected developmental disorder, systemic disorder, or age- associated changes, and
  • the human brain network is associated with a developmental disorder. In embodiments, the human brain network is associated with a systemic disorder. In embodiments, the human brain network is associated with age-associated changes.
  • the presenting step S450 comprises transmitting the virtual representation of said human brain network to a user device of the user. In embodiments, the presenting step S450 comprises causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • FIG. 5 is an exemplary flow chart of a method 500 for generating a virtual representation of a human brain network associated with a selected disorder, the human brain network comprising a morphologic property and a physiologic property and, optionally, a temporal property.
  • the method 500 comprises at step S510 obtaining, by one or more computers of a high-performance computing portal, the selected disorder and, at step S520 accessing, by the one or more computers, a plurality' of databases storing group- averaged coordinate-based spatially -normalized data populated from a plurality’ of human brain subject, each stored on one or more computer-readable media.
  • the databases comprises a task-activation database (TA DB) comprising coordinate-based quantified brain activity’ data related to specific human subject tasks, a voxel-based morphometry database (VBM DB) comprising voxel-based morphometric data, and a voxel-based physiology database (VBP DB) comprising voxel-based physiological data;
  • TA DB task-activation database
  • VBM DB voxel-based morphometry database
  • VBP DB voxel-based physiology database
  • the method 500 further comprises at step S530 obtaining, by the one or more computers, morphologic, physiologic, and optionally temporal, information from the plurality of databases, which information is associated with the selected disorder and, at step S540 applying, by the one or more computers, a multivariate coordinate-based metaanalysis (CBMA) algorithm to the obtained information so as to identify a human brain network comprising a morphologic property’ and a physiologic property and, optionally, a temporal property associated with the selected disorder, and generating a virtual representation of the human brain network.
  • the method 500 further comprises, at step S550 presenting, by the one or more computers, to a user the virtual representation of said human brain network.
  • the selected disorder is one of a neurologic, psychiatric, developmental, or systemic disorder.
  • the presenting step S550 comprises transmitting the virtual representation of said human brain network to a user device of the user. In embodiments, the presenting step S550 comprises causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • FIG. 6 is an exemplary system 600 for generating a virtual representation of a human brain network associated with a selected developmental disorder, systemic disorder, or age-associated change.
  • the system 600 comprises one or more computers 612 of a high- performance computing portal 610 comprising memory 614.
  • the memory 614 stores computer readable instructions comprising program code that, when executed, cause the one or more computers 612 to perform the steps of: (A) obtaining, by the one or more computers of the high-performance computing portal, a selected neurologic disorder or psychiatric disorder; (B) accessing, by the one or more computers 612, a plurality of databases 620 storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer- readable media, comprising: (i) a task-activation database (TA DB) 622 comprising coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) 624 comprising voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB) 626 comprising voxel-based physiological data; (C) obtaining, by the one
  • the human brain network is associated with a neurologic disorder. Tn embodiments, the human brain network is associated with a psychiatric disorder.
  • the presenting step (E) comprises transmitting the virtual representation of said human brain network to a user device of the user. In embodiments, presenting step (E) comprises causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the memory 614 stores computer readable instructions comprising program code that, when executed, cause the one or more computers 612 to perform the steps of (A) obtaining, by one or more computers of a high- performance computing portal, a selected developmental disorder, systemic disorder, or age-associated changes; (B) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially-normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, comprising: (i) a task-activation database (TA DB) comprising coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) comprising voxel-based morphometric data; and (iii) a voxel-based physiology database (VBP DB) comprising voxel-based physiological data; (C) obtaining, by the one or more tasks, a task-activation
  • the human brain network is associated with a developmental disorder. In embodiments, the human brain network is associated with a systemic disorder. In embodiments, the human brain network is associated with age-associated changes.
  • the presenting step (E) comprises transmitting the virtual representation of said human brain network to a user device of the user. In embodiments, the presenting step (E) comprises causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • the memory 614 stores computer readable instructions comprising program code that, when executed, cause the one or more computers 612 to perform the steps of: (A) obtaining, by one or more computers of a high- performance computing portal, a selected disorder; (B) accessing, by the one or more computers, a plurality of databases storing group-averaged coordinate-based spatially- normalized data populated from a plurality of human brain subject, each stored on one or more computer-readable media, comprising: (i) a task-activation database (TA DB) comprising coordinate-based quantified brain activity data related to specific human subject tasks; (ii) a voxel-based morphometry database (VBM DB) comprising voxelbased morphometric data; and (iii) a voxel-based physiology database (VBP DB) comprising voxel-based physiological data; (C) obtaining, by the one or more computers, morphologic, physiologic, and
  • the selected disorder is one of a neurologic, psychiatric, developmental, or systemic disorder.
  • the presenting step (E) comprises transmitting the virtual representation of said human brain network to a user device of the user.
  • the presenting step (E) comprises causing the virtual representation of said human brain network to be displayed on a user device of the user.
  • at least one of the plurality of databases comprises resting-state functional MRI data.
  • the user is located remotely from the computer system.
  • inventive concepts may be embodied as a non-transitory computer readable storage medium (or multiple non-transitory computer readable storage media) (e.g., a computer memory of any suitable type including transitory or non-transitory digital storage units, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above.
  • the software code may be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
  • a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer, as non-limiting examples. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smartphone or any other suitable portable or fixed electronic device.
  • PDA Personal Digital Assistant
  • Such computers such as the computers of the HPC, may be capable of performing the methods and algorithms in this disclosure. In embodiments, such methods and algorithms may be incapable of being performed by hand - e g., with pen and paper.
  • a computer may have one or more communication devices, which may be used to interconnect the computer to one or more other devices and/or systems, such as, for example, one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet.
  • networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks or wired networks.
  • a computer may have one or more input devices and/or one or more output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that may be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that may be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible formats.
  • the non-transitory computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various one or more of the embodiments described above.
  • computer readable media may be non- transitory media.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or distributed as desired in various embodiments.
  • Databases may include computer readable memory' (also referred to as ‘memory').
  • data storage space 3memlN may be and/or include computer readable memory', used to store data as described in the disclosure.
  • Memory may be embodied by suitable hardware, including but not limited to the following: hard disk drives, serial advanced technology' attachment (SATA) hard drives, SATA solid state drives (SSDs). non-volatile memory express (NVMe) SSDs, tape drives.
  • SATA serial advanced technology' attachment
  • SSDs SATA solid state drives
  • NVMe non-volatile memory express
  • databases may be stored in computer-readable media in any suitable form.
  • databases may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

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Abstract

L'invention concerne des systèmes, des procédés et des produits programmes destinés à produire, à identifier et à utiliser des biomarqueurs de neuro-imagerie qui quantifient des propriétés de réseau cérébral (une connectomique) dans le but d'évaluer des altérations dans une structure et une fonction basées sur un réseau pour une grande variété d'états de santé comprenant des troubles neurologiques et psychiatriques, et des changements normaux tout au long d'une vie comprenant le développement et le vieillissement. Un ou plusieurs ordinateurs d'un portail informatique à haute performance peuvent obtenir un changement associé à l'âge ou un trouble sélectionné. On peut accéder à une pluralité de bases de données stockant des données provenant d'une pluralité de sujets à cerveau humain. La pluralité de bases de données peut comprendre des données relatives à (i) une activation de tâche, (ii) une morphométrie à base de voxel, et (iii) une physiologie à base de voxel. Un algorithme de méta-analyse basée sur des coordonnées (CBMA) à variables multiples peut être appliqué aux informations obtenues de façon à identifier des propriétés de réseau cérébral humain associées. Une représentation virtuelle du réseau cérébral humain peut être générée et présentée à un utilisateur du portail.
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