Brain Connectivity Normative Model Using fMRI and dMRI
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Solution Overview
Problem
Existing normative models in neuroimaging primarily focus on single-brain region measures like cortical surface area, thickness, and volume, failing to account for network-based connectivity information that is crucial for characterizing individual differences and brain disorders.
Innovation Solution
A multi-modal normative model is generated using functional MRI (fMRI) and diffusion MRI (dMRI) data to incorporate structural and functional connectivity between brain regions, employing graph theoretic analyses and gradient-based techniques to derive brain network connectivity measures, which are normalized and aligned to a template for individual comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If normative models focus on single-brain region measures (cortical surface area, thickness, volume), then the models are simpler and easier to compute, but they fail to capture network-based connectivity information that is crucial for characterizing individual differences and brain disorders
Solution Approach 1:
The patent merges structural connectivity data (from dMRI) and functional connectivity data (from fMRI) into a unified multi-modal normative model. This combination allows the model to capture both anatomical and functional network characteristics, resolving the contradiction by integrating previously separate types of information without creating an insurmountable complexity barrier.
Solution Approach 2:
The patent segments the brain into multiple regions and analyzes connectivity between these regions using graph theoretic measures. By dividing the complex whole-brain connectivity problem into manageable regional connections, the model becomes computationally feasible while still capturing essential network-based information that single-region measures miss.
2Measurement precision
If the model incorporates multi-modal data (fMRI and dMRI) and graph theoretic analyses, then the model provides comprehensive brain connectivity understanding, but the computational process becomes more complex and time-consuming
Solution Approach 1:
The patent performs preliminary actions by pre-processing and normalizing connectivity data from multiple subjects to create a normative model framework before individual analysis. This preliminary model building allows subsequent individual assessments to be performed more efficiently by comparing against established norms rather than performing all analyses from scratch.
Solution Approach 2:
The patent transforms connectivity data into standardized graph theoretic parameters (degree, betweenness centrality, clustering coefficient) that can be directly compared across subjects. This parameter transformation enables precise connectivity measurement while facilitating efficient computation by reducing the complexity of direct multi-modal data comparison.
3Adaptability or versatility
If the model uses graph theoretic analysis and gradient-based techniques to derive connectivity measures, then the model captures whole-brain network dynamics accurately, but the methodology becomes more complex
Solution Approach 1:
The patent develops a universal framework that can analyze both structural and functional connectivity using the same graph theoretic tools and gradient-based techniques. This multi-functionality allows the model to capture diverse network dynamics (structural, functional, and their interactions) through a unified methodology, reducing the need for separate specialized analyses.
Solution Approach 2:
The patent introduces graph theoretic measures and gradient-based representations as intermediary layers between raw imaging data and final connectivity assessments. These intermediaries simplify the relationship between complex multi-modal data and the questions being asked, making the methodology more manageable while preserving the ability to capture nuanced network dynamics.
Data Source
AI summary
Methods and systems for generating and using a multi-modal normative model of a brain are described. The method for generating the multi-modal normative model comprises receiving functional magnetic resonance imaging (fMRI) data and diffusion MRI (dMRI) data for each of a plurality of human subjects, generating, based on the fMRI data, functional connectivity data for each of the plurality of human subjects, generating, based on the dMRI data, structural connectivity data for each of the plurality of human subjects, determining, based on the structural connectivity data and/or the functional connectivity data, at least one brain network connectivity measure associated with each of a plurality of brain regions, and generating a multi-modal normative model that includes the at least one brain network connectivity measure associated with each of the plurality of brain regions.