Detecting brain state changes via fiber-centered functional connectivity analysis.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Systems Neuroscience

Background:

• Functional brain connectivity is often assumed to be temporally static.
• Emerging evidence indicates dynamic changes in functional brain connectivity across various timescales.
• Existing methods may not fully capture the temporal variability of brain states.

Purpose of the Study:

• To propose and validate a novel approach for modeling and detecting dynamic changes in functional brain states.
• To leverage multimodal functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data.
• To investigate temporal dynamics in both resting-state and task-based brain activity.

Main Methods:

• A sliding window approach is employed to analyze functional connectivity patterns.
• Functional connectivity data from fiber-connected cortical voxels are concatenated into feature vectors.
• Abrupt changes in these functional vector patterns are detected to identify brain state transitions.

Main Results:

• The proposed method successfully detected meaningful brain state change points in task-based, resting-state, and natural stimulus fMRI/DTI datasets.
• Detected change points in task-based fMRI aligned with the experimental stimulus paradigms.
• This provides partial validation for the brain state change detection approach.

Conclusions:

• The study presents a novel perspective on the dynamic nature of functional brain connectivity.
• The developed method offers a valuable tool for analyzing temporal brain state changes.
• This work serves as a foundation for future research into complex brain interaction dynamics.