Altered Dynamic Network Stability in Remitted Late-Life Depression Associated With Depression Recurrence

Late-life depression (LLD) is associated with negative outcomes including high rates of recurrence and cognitive decline. However, the neurobiological changes influencing such outcomes in LLD are not well understood. Disequilibrium in large-scale brain networks may contribute to LLD-related cognitive decline.

Never-depressed older adults and participants in early remission from LLD were recruited as part of the REMBRANDT study. At study entry, participants completed a resting-state functional magnetic resonance imaging scan and neuropsychological testing and were subsequently monitored over 2 years for depression recurrence. Using a previously described algorithm, recurring whole-brain states of spatial coactivation were identified by k-means consensus clustering. Co-occurring network state properties from never-depressed participants (n = 40) were then compared with participants with LLD who remained in remission (n = 50) or experienced depression recurrence (n = 33).

A 3-network solution overlapping anatomically with the default mode network, cognitive control network, and anterior salience network best explained recurring network states. Compared with never-depressed older adults, participants who remitted from LLD exhibited decreased network resilience and altered transitions between networks. The stability of specific networks was associated with baseline clinical and neuropsychological markers in never-depressed participants and participants in sustained remission but was blunted for participants who experienced depression recurrence.

Collectively, these data suggest that LLD alters dynamic network stability, with effects that last into remission. Furthermore, the stability of specific network states is associated with clinical and neuropsychological markers that may predict the likelihood of a recurrent episode of LLD.