Edge centric dysfunction of default mode network in Alzheimer’s disease using resting state fMRI

Disrupted connectivity of the Default Mode Network (DMN) is observed in Alzheimer’s disease as reported in previous literatures. rs-fMRI analyses relying on global or nodal level connectivity measures may overlook dissimilar edge-level dysconnectivity. We postulated that network level disruptions are better captured at edge-level, where aggregated graph metrics suppress the localized connectivity. In this study, we extract the BOLD time series data from 26 DMN regions defined by the AAL3 Atlas, from rs-fMRI data from Alzheimer’s Disease Neuroimaging Initiative (ADNI).

We calculate 325 edge features for every participant using pairwise Pearson correlations between the 26 DMN regions. Node metrics and graph metrics, such as degree, local clustering, betweenness centrality, average neighbour degree, show no significant difference between groups (31 AD patients vs. 40 healthy controls). To find out discriminating edges between pair of regions, we have implemented random forest method.

To ensure no leakage of data occurred, we performed feature selection within each cross-validation (CV) fold, and we only used the absolute correlation values for the Mann-Whitney U test on the training data. We then used only the selected edges to train a Random Forest using Fisher Z-transformed values with a repeated stratified cross-validation. While none of the classic edge-wise statistical tests were significant, we achieved stable classification and consistently found a small set of informative edges when using this method.

Most connection which are found consistently disrupted involved pathways between hippocampus area and temporal regions, connections includes posterior cingulate regions, anterior cingulate regions, and fronto parietal associations. The data demonstrates that disruptions in the function of the DMN have very specific localizations and would not be identified using node-level connections alone. This edge-based approach thus provides clearer insight and provides biologically plausible evidence of disruptions to the DMN in Alzheimer’s disease.

Open access funding provided by Symbiosis International (Deemed University). School of Computer Science Engineering and Technology, Bennett University, Greater Noida, 201310, India Amartya Saran, Anuj Kumar Bharti & Mohd. Abuzar Sayeed Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, 412115, India The authors declare no competing interests.

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