Depression links to unstable resting-state brain dynamics: insights from hidden markov models and functional network variability.

Background: Depression is closely associated with abnormalities in brain function. Traditional static functional connectivity analyses offer limited insight into the temporal variability of brain activity. Recent advances in dynamic analyses enable a deeper understanding of how depression relates to temporal fluctuations in brain activity.

Methods: This study utilized a large resting-state functional magnetic resonance imaging dataset ( = 696) to examine the association between brain dynamics and depression. Two complementary approaches were employed. Hidden Markov modeling (HMM) was used to identify discrete brain states and quantify their temporal switching patterns; temporal variability was computed within and between large-scale functional networks to capture time-varying fluctuations in functional connectivity.

Results: Depression scores were positively associated with switching rate and negatively associated with maximum fractional occupancy. Furthermore, depression scores were significantly associated with greater temporal variability both within and between networks, with particularly strong effects observed in the default mode network, ventral attention network, and frontoparietal network. Together, these findings suggest that individuals with higher depression scores exhibit more unstable brain dynamics.

Conclusion: Our findings reveal that individuals with higher depression levels exhibit greater instability in brain state transitions and increased temporal variability in functional connectivity across large-scale networks. This instability in brain dynamics may contribute to difficulties in emotion regulation and cognitive control. By capturing whole-brain temporal patterns, this study offers a novel perspective on the neural basis of depression.