A Network Analysis Approach to fMRI Condition-Specific Functional Connectivity

TL;DR

This paper introduces a network analysis framework to compare and classify condition-specific brain connectivity patterns in fMRI data, revealing systematic differences between processing abstract and concrete concepts.

Contribution

The study presents a novel network-based method for analyzing and classifying fMRI functional connectivity patterns across conditions, improving understanding of brain network differences.

Findings

Identified topological and functional differences in brain networks for abstract vs. concrete concepts.

Successfully classified unseen connectivity matrices based on the proposed network features.

Demonstrated the method's ability to distinguish cognitive conditions using fMRI data.

Abstract

In this work we focus on examination and comparison of whole-brain functional connectivity patterns measured with fMRI across experimental conditions. Direct examination and comparison of condition-specific matrices is challenging due to the large number of elements in a connectivity matrix. We present a framework that uses network analysis to describe condition-specific functional connectivity. Treating the brain as a complex system in terms of a network, we extract the most relevant connectivity information by partitioning each network into clusters representing functionally connected brain regions. Extracted clusters are used as features for predicting experimental condition in a new data set. The approach is illustrated on fMRI data examining functional connectivity patterns during processing of abstract and concrete concepts. Topological (brain regions) and functional (level of connectivity and information flow) systematic differences in the ROI-based functional networks were identified across participants for concrete and abstract concepts. These differences were sufficient for classification of previously unseen connectivity matrices as abstract or concrete based on training data derived from other people.