Topological and geometric signatures of brain network dynamics in Alzheimer's disease.

This study explores magnetic resonance imaging (MRI) as a promising non-invasive approach to monitor Alzheimer's disease (AD) and related dementias. We investigate whether dynamic functional connectivity (dFC), which captures time-varying neural interactions, can reveal sex-specific brain network disruptions in AD that conventional static connectivity analyses may miss.We analyzed dFC in the Open Access Series of Imaging Studies (OASIS-3) dataset across three diagnostic groups (normal cognition, mild cognitive impairment, dementia), stratified by sex, and regressed out age. We evaluated group differences using multiple distance metrics sensitive to various aspects of network structure, with statistical significance assessed via permutation testing.Distinct sex-specific patterns emerged across diagnostic groups, with each metric sensitive to different aspects of network disruption. Peak connectivity states, rather than mean levels, more effectively reflected brain network dynamics.By emphasizing network dynamics, our findings highlight promising signatures for early detection and longitudinal biomarkers. Future work will explore metrics tailored to specific demographic or clinical subpopulations.Dynamic connectivity reveals sex-specific brain disruptions in Alzheimer's disease (AD). Peak-based analysis improves sensitivity over mean-based connectivity measures. Topological and geometric metrics capture distinct network disruptions by sex. Mild cognitive impairment shows less consistent connectivity changes due to diagnostic instability. Findings support dynamic magnetic resonance imaging (MRI) metrics as early AD biomarkers in future studies.

Duke Scholars

Author Tananun Songdechakraiwut Computer Science