上海科技大学知识管理系统

Noise and artifacts significantly corrupt fMRI data. We present a deep learning-based Automatic fMRI Scrubbing via Graph Attention (ASGA), to perform fMRI data “scrubbing” by automatically identifying and removing contaminated volumes. To achieve this, we firstly design an easy-to-implement carpet plot-based labeling tool for human labelling, which is fed to ASGA model training. By applying ASGA to two large-cohort studies (BCP and CBCP), our method effectively removed noise-contaminated volumes without human interference. It also has a significant potential in enhancing research outcomes for challenging populations like children and older subjects whose data prone to have noise, facilitating reliable fMRI studies.