Graph-Based Analysis of Functional Brain Connectivity from fMRI Data
Abstract
Functional magnetic resonance imaging (fMRI) provides whole-brain measurements that can be converted into functional connectivity networks for quantitative analysis. Graph-based methods represent brain regions as nodes and statistical relationships among regional blood oxygen level-dependent signals as edges in this review. The above are the main steps in the analytical pipeline: preprocessing, brain parcellation, connectivity estimation, graph construction and calculation of nodal, global and community-level indicators. Representative studies are compared in terms of healthy brain organisation, neurological and psychiatric disorders, dynamic functional connectivity, and graph neural network applications. According to the above analysis, graph theory provides intuitive indicators of segregation, integration, efficiency, hub organisation and modularity, and graph learning methods can learn more complex patterns for prediction. However, the results are still sensitive to motion correction, atlas selection, connectivity measures, thresholding strategies, sample size, scanner differences and model interpretability. Future work will focus on building standardised and open pipelines, cross-site validation, multimodal data fusion, dynamic network models, and explainable graph neural networks. The above developments may enhance the reproducibility and clinical applicability of functional brain connectivity analysis.
