Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research...

Area of Science:

• Neuroscience
• Network Science
• Psychiatry

Background:

• Brain network temporal stability, quantified via functional magnetic resonance imaging (fMRI) and dynamic network models, is increasingly recognized for its predictive potential in altered brain functions.
• Various metrics exist to assess temporal stability, including variance of dynamic functional connectivity, temporal variability, flexibility, and temporal clustering coefficient, yet no single measure has achieved consensus.
• Factors such as sex, age, cognition, head motion, circadian rhythms, and data processing strategies can influence brain network temporal stability and require careful consideration in clinical research.

Purpose of the Study:

• To review current knowledge on brain network temporal stability.
• To summarize clinical research progress and discuss future perspectives.
• To highlight the association between temporal stability and psychiatric disorders.

Main Methods:

• Utilizing functional magnetic resonance imaging (fMRI) data.
• Applying multilayer dynamic network models.
• Analyzing various temporal stability measures (e.g., variance, variability, flexibility, clustering coefficient).

Main Results:

• Altered temporal stability is linked to psychiatric disorders like schizophrenia, major depressive disorder, and bipolar disorder, particularly during resting states.
• Both decreased and increased temporal stability may indicate disorder-related brain dysfunction.
• Divergent results across studies currently limit the clinical diagnostic application of temporal stability measures.

Conclusions:

• Brain network temporal stability is a promising biomarker for brain function but requires standardized measurement.
• Further research with larger, transdiagnostic samples is necessary to validate findings and enable clinical translation.
• Addressing confounding factors and methodological variations is crucial for advancing the clinical utility of temporal stability in neuropsychiatric disorders.