Fiber-based Leading Eigenvector Dynamic Analysis (FLEiDA) of Brain States

Motivation: Current Leading Eigenvector Dynamic Analysis (LEiDA) of brain states in functional magnetic resonance (fMRI) typically focuses on whole-brain grey matter regions. Goal(s): This study aims to identify the brain states of specific regions associated with a particular white matter fasciculus. Approach: We proposed a new approach called Fiber-based Leading Eigenvector Dynamic Analysis (FLEiDA), which selects the brain regions connected by specific fasciculus and performs leading eigenvector analysis, yielding structural coupled fiber-based brain states. Results: The findings indicate that fiber-based brain states can reveal insights which are not detected through whole-brain analysis. Impact: By employing FLEiDA analysis, we can identify distinct connectivity patterns within fiber-connected regions, thereby elucidating mechanisms of brain function and offering novel biomarkers for various diseases.