Brain Network Graph Health Score Generation
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Solution Overview
Problem
Complex and high-dimensional brain data from patients, such as functional magnetic resonance imaging (fMRI) and tractography data, is difficult for clinicians to manually inspect and parse, making it challenging to determine brain health and plan appropriate treatments.
Innovation Solution
A system that represents the brain as a network graph, using nodes for sub-regions and edges for connections, calculates health scores based on the number of tracts, tract health, and centrality measures like PageRank, to automatically determine brain health and inform treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If brain data is represented as a complete correlation matrix with all pairs of locations, then comprehensive brain connectivity information is captured, but the data becomes extremely complex and difficult for clinicians to manually inspect and parse
Solution Approach 1:
The patent segments the complex brain network into modular functional components or communities using graph theory algorithms. Instead of presenting the entire correlation matrix, the system divides it into meaningful sub-networks (e.g., language network, motor network, default mode network) that are easier to interpret clinically while preserving the essential connectivity information.
Solution Approach 2:
The system extracts key structural properties and metrics from the complete brain network data, such as centrality measures, connectivity strength, and network efficiency indices. These extracted features represent the essential brain health information in a condensed form that is manageable for clinical decision-making without losing critical diagnostic value.
2Measurement precision
If clinicians manually inspect and parse brain data to determine brain health, then detailed analysis can be performed, but the process is time-consuming and inefficient
Solution Approach 1:
The patent introduces an intermediary computational system that acts as a bridge between raw brain imaging data and clinical interpretation. This system automatically calculates graph theory metrics, identifies abnormal network patterns, and generates preliminary diagnostic recommendations, thereby reducing the time clinicians need to spend on manual analysis while maintaining assessment accuracy.
Solution Approach 2:
The system performs preliminary processing and analysis of brain data before it reaches the clinician, including quality control checks, normalization, feature extraction, and initial pattern recognition. This preliminary action prepares the data in advance, allowing clinicians to focus their expertise on interpretation and decision-making rather than raw data processing.
3Reliability
If detailed brain data analysis is performed to inform treatment decisions, then treatment accuracy can be improved, but the process becomes more complex and harder to standardize
Solution Approach 1:
The patent transforms complex brain network data into standardized quantitative parameters and scores based on graph theory metrics (e.g., network efficiency, modularity, characteristic path length). These parameter changes convert qualitative network properties into measurable, comparable values that can be used to objectively inform treatment decisions and establish standardized protocols across different clinical settings.
Solution Approach 2:
The system incorporates feedback mechanisms where treatment outcomes and patient responses are fed back into the analysis framework. This allows the system to refine its predictions and recommendations over time, improving treatment decision reliability through iterative learning while maintaining a structured, standardized approach to clinical decision-making.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a mental health prediction for a patient. One of the methods includes obtaining brain data captured by one or more sensors characterizing a brain of a patient; determining a network graph from the brain data, wherein: a node of the network graph corresponds to a parcellation in the brain of the patient, and a subset of nodes of the network graph corresponds to a particular functional area of the brain; generating, for each of a plurality of nodes in the network graph, a measure of centrality of the node; generating, for the particular functional area of the brain and using the generated measures of centrality, a health score representing a measure of health of the functional area of the brain; generating a mental health prediction for the patient using the health score.