Altered brain network dynamics and functional connectivity in subjective cognitive decline: an edge-centric network study

TL;DR

This study explores brain network changes in people with subjective cognitive decline, revealing altered connectivity patterns that could serve as biomarkers.

Contribution

The study introduces an edge-centric network framework to uncover brain network alterations in subjective cognitive decline.

Findings

SCD patients showed reduced peak amplitude and longer trough-to-trough duration compared to healthy controls.

High-amplitude frame networks identified more altered brain regions and a subnetwork linked to SCD.

Brain network metrics like clustering coefficient and global efficiency were significantly reduced in SCD.

Abstract

To explore neurodynamic bases underlying subjective cognitive decline (SCD) based on edge-centric functional network.

211 SCD patients and 210 healthy controls (HC) were recruited from the Alzheimer’s Disease Neuroimaging Initiative. Edge time series (ETS) were obtained based on resting-state functional magnetic resonance data. The top 10% co-fluctuation signals of all time points in ETS were extracted to construct the high-amplitude frame networks, and the co-fluctuation signals from the remaining time points were used to construct the low-amplitude frame networks. In both network states, the graph theory and network-based statistics (NBS) analyses were used to compare SCD and HC. The correlation of the imaging indicators with cognitive scores and apolipoprotein E (APOE) ε4 genes was performed by Spearman correlation analysis.

SCD exhibited lower peak amplitude and longer trough-to-trough duration (TTD) compared to HC. In both network states, the normalized clustering coefficient, normalized characteristic path length, small-worldness, and global efficiency of SCD were significantly reduced, and the altered nodal centralities of SCD predominantly exhibited a decreasing trend. However, the high-amplitude frame network identified more altered brain regions compared to the low-amplitude frame network. Furthermore, a SCD-related subnetwork was found in the high-amplitude frame network, which was composed of 11 brain regions and 13 edges. TTD was positively related to the number of APOE ε4 genes; the normalized characteristic path length, the betweenness centrality of right postcentral gyrus, and the connection between bilateral angular gyrus were correlated with cognitive scores.

Our findings demonstrate that the edge-centric network framework reveals details of brain network alterations in SCD through different perspectives, and these alterations hold potential as novel biomarkers for SCD.

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