Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI.

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) is crucial for studying dynamic functional connectivity and brain states.
• Existing research often assumes instantaneous brain state transitions, overlooking gradual changes.
• The complexity of brain function and high dimensionality of dynamic attributes necessitate advanced analysis methods.

Purpose of the Study:

• To challenge the instantaneous brain state transition assumption by proposing a gradual transition model.
• To develop a brain state conversion rate model for observing transition trends.
• To identify distinct time-varying brain network patterns in healthy versus autistic children.

Main Methods:

• Construction of a brain state conversion rate model to quantify transition dynamics.
• Development of a brain state network model incorporating both steady and transition states.
• Application of the network topological overlap coefficient to analyze time-varying network features.

Main Results:

• Brain state transitions were observed to occur gradually over several time points.
• A novel brain state network model effectively captured both steady and transition states.
• Distinct regular patterns in time-varying brain network characteristics were identified in healthy children but not in children with autism.

Conclusions:

• Brain state transitions are gradual processes, not instantaneous events.
• The developed model and analysis methods can differentiate between healthy and autistic children based on brain network dynamics.
• This approach offers a potential biomarker for autism spectrum disorder in children.