Extracting interpretable signatures of whole-brain dynamics through systematic comparison

Area of Science:

• Neuroscience
• Systems Biology
• Data Science

Background:

• Brain dynamics are complex and often analyzed with limited statistical measures.
• Existing methods may not capture the full picture of brain activity, especially in disorders.

Purpose of the Study:

• To systematically compare diverse features of brain activity and functional coupling from rs-fMRI data.
• To develop a data-driven method for identifying interpretable dynamical signatures in complex time-series data.
• To apply this method to case-control studies of neuropsychiatric disorders.

Main Methods:

• Systematic comparison of intra-regional activity and inter-regional functional coupling from rs-fMRI.
• Application of linear time-series analysis techniques.
• Demonstration using case-control comparisons across four neuropsychiatric disorders.

Main Results:

• Linear time-series analysis techniques are generally effective for rs-fMRI case-control studies.
• Simple statistical properties of fMRI dynamics performed well, but combining them with inter-regional coupling enhanced performance.
• Identified new, informative dynamical fMRI structures.

Conclusions:

• A comprehensive, data-driven method can systematically identify and interpret quantitative dynamical signatures.
• Combining intra-regional and inter-regional analyses provides a more complete understanding of brain dynamics in neuropsychiatric disorders.
• The method has broad applicability to time-varying systems beyond neuroimaging.