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An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-based Brain Decoding by Zhu, Jianfei; Wei, Baichun; Tian, Jiaru; Jiang, Feng; Yi, Chunzhi is a scholarly article available to read on EtoBox.

What is An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-based Brain Decoding about?

Brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5\% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method pr

Author

Zhu, Jianfei; Wei, Baichun; Tian, Jiaru; Jiang, Feng; Yi, Chunzhi

Published

2024

Language

EN