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Abstract #3446

An Eigenmode-Based GLM Method For Task-fMRI Data Analysis

Fang Cai1, Jieying Zhang1, Yishi Wang2, Wenzhang Liu1, Bo Hong1, and Tianyi Qian1
1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2MR Research Collaboration Team, Siemens Healthcare Ltd., Beijing, China

Synopsis

Keywords: fMRI Analysis, Data Analysis, Cortical eigenmode, GLM analysis

Motivation: In traditional fMRI experiments, the BOLD signal is influenced by the spatial distribution of veins, which is closely linked to the morphological characteristics of the cortex.

Goal(s): Cortical eigenmode decomposition represents a frequency-domain approach for analyzing brain structures, yielding a set of spatial bases for dissecting large-scale brain activities.

Approach: In this study, we introduced an eigenmode-based General Linear Model method to investigate the influence of spatial patterns on the activation of specific fMRI tasks.

Results: The results reveal a strong correlation in spatial distribution between the reconstructed z-map and the conventional activation map.

Impact: Quantitative cortical eigenmode analysis offers a frequency-domain perspective for integrating structural and functional neuroimages. Eigenmodes encode connectivity patterns within the cortical structure, offering a promising avenue for unveiling implicit connections across cortical surface through their application to brain activity analysis.

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