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Abstract #3406

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

Qiji Shi1, Xiaofeng Deng2, and Fangrong Zong1
1Beijing University of Posts and Telecommunications, Beijing, China, 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

Synopsis

Keywords: Data Processing, fMRI Analysis, structure and function coupling, dynamic functional analysis

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.

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