Brain map portal for alcohol use, obesity, temporal LOBE epilepsy, and alzheimer's

A high-performance computing system using Low-d M-ICA and CBMA generates low-dimensional virtual representations of brain networks, addressing the limitations of current neuroimaging methods to identify clinically relevant biomarkers for neurological and psychiatric disorders.

WO2026143175A1 Publication Date: 2026-07-02BOARD OF RGT THE UNIV OF TEXAS SYST
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Patent Information

Application Number
PCT/US2025/061242
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-12-23
Filing Date
2025-12-23
Publication Date
2026-07-02

AI Technical Summary

Technical Problem

Current neuroimaging methods struggle to identify robust, context-specific neural networks governing individual behaviors or diseases due to limitations in generalizability, computational expense, and lack of theoretical basis for low-dimensional applications, making it difficult to develop clinically relevant neuroimaging biomarkers.

Method used

A high-performance computing system employing a Low-dimensionality Meta-analytic Independent Component Analysis (Low-d M-ICA) algorithm analyzes neuroimaging data to discover and validate network-based brain models, using multivariate coordinate-based meta-analysis (CBMA) to generate low-dimensional virtual representations of brain networks applicable to individual patients.

Benefits of technology

Enables the discovery and validation of clinically relevant neuroimaging biomarkers for various disorders, including epilepsy and psychiatric conditions, by accurately delineating context-specific neural networks, overcoming computational and generalizability challenges.

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Abstract

The present invention provides systems and methods for treating a subject for a neurologic disorder, a psychiatric disorder, a development disorder, or age-associated changes including comparing the imaged brain activity to a database of virtual representations of brain networks, wherein the virtual representations were generated by applying a multivariate coordinate-based meta-analysis (CBMA) algorithm, wherein the CBMA algorithm comprises a Low-dimensionality Meta-analytic Independent Component Analysis (Low-d M-ICA) algorithm; and making one or more determinations based on the results of the comparison, including where to place a treatment device.
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