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

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Depression is linked to brain function abnormalities.
• Static functional connectivity analysis has limitations in capturing temporal brain activity.
• Dynamic analysis offers deeper insights into brain activity fluctuations in depression.

Purpose of the Study:

• To investigate the association between brain dynamics and depression using resting-state fMRI.
• To explore temporal variability and brain state transitions in individuals with depression.

Main Methods:

• Utilized a large resting-state fMRI dataset (N=696).
• Employed Hidden Markov modeling (HMM) to identify brain states and switching patterns.
• Calculated temporal variability within and between large-scale functional networks.

Main Results:

• Depression scores correlated positively with brain state switching rate.
• Higher depression scores were associated with greater temporal variability within and between networks, notably in the default mode, ventral attention, and frontoparietal networks.
• Individuals with higher depression exhibited more unstable brain dynamics.

Conclusions:

• Individuals with higher depression display increased instability in brain state transitions and functional connectivity.
• This brain dynamic instability may underlie difficulties in emotion regulation and cognitive control.
• The study provides a novel perspective on the neural basis of depression by analyzing whole-brain temporal patterns.