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Improved classification of Alzheimer’s disease and mild cognitive impairment through dynamic functional network analysis

Nicolás Rubido, Venia Batziou, Marwan Fuad, Vesna Vuksanovic Orcid Logo

arXiv

Swansea University Authors: Venia Batziou, Vesna Vuksanovic Orcid Logo

DOI (Published version): 10.48550/arXiv.2505.03458

Abstract

Brain network analysis using functional MRI has advanced our understanding of cortical activity and its disruption in neurodegenerative disorders underlying dementia. Recently, research has focused on dynamic (time-varying) brain networks that capture both spatial and temporal patterns of regional c...

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Published in: arXiv
Published:
URI: https://cronfa.swan.ac.uk/Record/cronfa69526
Abstract: Brain network analysis using functional MRI has advanced our understanding of cortical activity and its disruption in neurodegenerative disorders underlying dementia. Recently, research has focused on dynamic (time-varying) brain networks that capture both spatial and temporal patterns of regional cortical co-activity. However, this approach remains relatively unexplored across the Alzheimer’s disease (AD) spectrum. In this study, we analysed age- and sex-matched static and dynamic functional brain networks derived from resting-state fMRI data in 315 individuals with AD, mild cognitive impairment (MCI), and cognitively normal healthy controls (HC) from the ADNI-3 cohort. Functionalnetworks were constructed using the Juelich brain atlas, with static connectivity estimated from full time series and dynamic connectivity derived using a sliding-window approach. Group differences were assessed at both the link and node levels using non-parametric statistics and bootstrap resampling. While HC and MCI show similar static and dynamic patterns at the node level, clearer differences emerge in AD. We identified stable (stationary) differences in functional connectivity between white matter regions and parietal and somatosensory cortices, whereas temporally varying differences wereconsistently observed in connections involving the amygdala and hippocampal formation. In addition, node centrality analysis suggested that white matter connectivity differences are predominantly local in nature. Our results highlight shared and unique functional connectivity patterns in both static and dynamic functional networks, emphasising the importance of incorporating dynamic information into brain network analyses of the Alzheimer’s spectrum. Furthermore, we trained a Random Forest model on regional BOLD time series informed by dynamic and static network metrics, achieving robust classification of MCI, AD, and HC groups and demonstrating the diagnostic potential of time-varying connectivity.
Item Description: Preprint article before certification by peer review.
College: Faculty of Medicine, Health and Life Sciences

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