Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to examine functional connectivity (FC) alterations in neurological disorders such as Alzheimer's disease (AD). Traditional studies either employ whole-brain analyses or focus on specific regions, yet the vast number of FCs and their interrelations complicate interpretation. This study adopts a data-driven, hypothesis-free approach to detect altered functional subnetworks in AD.

Independent component analysis (ICA) was applied to FC matrices from 34 AD patients and 49 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After pruning, significant subnetworks distinguishing AD from HC were identified. Graph theoretical parameters were computed for each subnetwork, and their associations with Mini-Mental State Examination (MMSE) scores were assessed.

Three subnetworks effectively differentiated AD patients from HCs. One subnetwork showed significant group differences in network strength, clustering coefficient, and local efficiency, despite no whole-brain differences. Abnormal functional lateralization also emerged within subnetworks. Moreover, FC weights in the identified subnetworks positively correlated with MMSE scores, linking cognitive performance to subnetwork connectivity.

These results demonstrate the utility of a data-driven approach in detecting AD-specific altered subnetworks. By providing a modular perspective, this method facilitates targeted examination of connectivity changes, improves interpretability, and deepens understanding of functional disruptions in AD.