Automated Biosignature Extraction from Task-Based fMRI Data
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Solution Overview
Problem
Task-based fMRI studies generate vast amounts of data that are difficult to interpret effectively, lacking tools to automatically extract relevant information on brain circuit patterns and biomarkers for mental disorders, limiting diagnostic and therapeutic advancements.
Innovation Solution
A computer-implemented method and system for extracting biosignatures from task-based fMRI data using a multivariate, automated, systematic, and hierarchical searching algorithm (MASHA) that maps brain functional localizomes, selects and sorts brain regions, and searches for optimal classifiers to identify differential and co-activating brain regions, providing objective biosignature extraction and biomarker discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If task-based fMRI studies are conducted to assess brain circuit function, then comprehensive brain function information is obtained, but the data volume becomes overwhelming and difficult to interpret
Solution Approach 1:
The patent segments the overwhelming fMRI data into distinct functional circuits and networks by identifying specific brain regions and their interconnections. The system divides the complex brain data into manageable functional units (circuits, networks, nodes) that can be individually analyzed and interpreted, transforming the undifferentiated mass of data into structured, interpretable components.
Solution Approach 2:
The patent introduces computational algorithms and data processing intermediaries that act as mediators between the raw fMRI data and human interpretation. These computational tools serve as intermediaries that automatically process, filter, and organize the overwhelming data, making it accessible and interpretable for clinical and research purposes without requiring direct human analysis of the raw data volume.
2Measurement precision
If manual analysis methods are used to interpret fMRI data, then detailed examination is possible, but the process is time-consuming and lacks objectivity
Solution Approach 1:
The patent implements self-service through automated computational algorithms that perform data analysis independently without requiring continuous human intervention. The system automatically processes fMRI data, identifies brain circuits, and generates interpretations autonomously, eliminating the time-consuming manual analysis process while maintaining detailed examination capabilities through sophisticated computational methods.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with computational algorithms and automated processing systems. This substitution transforms the time-consuming manual examination process into rapid automated computation, preserving the ability to conduct detailed analysis while dramatically reducing the time required through the use of computer-based processing instead of human manual methods.
3Loss of information
If comprehensive brain mapping is performed, then complete functional information is obtained, but the complexity of processing and analyzing the data increases significantly
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific brain regions and functional circuits of clinical relevance rather than attempting uniform comprehensive analysis of the entire brain. The system identifies and analyzes specific functional nodes and circuits with detailed local processing, while using更高效 methods for other regions, thereby maintaining complete functional information while reducing overall processing complexity through selective detailed analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the identification of hidden brain circuit patterns and biomarkers, facilitating non-invasive diagnostics and targeted therapeutics for neuropsychiatric disorders by automating the interpretation of fMRI data, guiding diagnosis, treatment, and predicting outcomes.
Implementation Method 1
a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject
Implementation Method 2
a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field
Implementation Method 3
a radio frequency (RF) system configured to apply an RF field to the subject and to receive fMRI signals therefrom
Data Source
AI summary
An automated system and method is provided for biotype extraction and biomarker discovery from task-based fMRI imaging data. The system and method may include automatically mapping a localizome, such as a task-condition/contrast/population-specific brain functional localizome, based on fMRI data and automatically selecting and sorting brain regions or brain nodes to produce a subset of functional brain regions or brain nodes. A report may then be generated indicating that the subject has a particular brain circuit pattern of activity and connectivity associated with one or more symptoms of the given mental disorder, treatments, or associated with normal brain functions, based upon the extracted biosignatures by searching for the optimal multivariate classifier with least dimensionality in the brain functional localizome. These biosignatures and biomarkers that reveal hidden, implicit, and latent brain circuit patterns provoked by fMRI tasks, can also provide for the development of non-invasive diagnostics and targeted therapeutics in neuropsychiatric diseases.