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Abstract #2100

Grading Resting-State FMRI Datasets by Reweighted L1 Regression

Chia-Jung Yeh1, Yu-Sheng Tseng1, Teng-Yi Huang1, Shang-Yueh Tsai2, 3

1Dept. of EE, National Taiwan University of Science and Technology, Taipei, Taiwan; 2Graduate Institute of Applied Physics, National Chengchi University, Taipei, Taiwan; 3Reasearch Center for Mind, Brain and Learning, National Chengchi University, Taipei, Taiwan

The rest-state fMRI (rsfMRI) detects the dynamic neuronal activity using a long series of BOLD imaging and measures the connectivity of brain functional areas using either correlation analysis or independent component analysis. However, in our experience, rsfMRI sometimes shows unstable results even if we preformed study using the same imaging protocols and data analysis methods. In this study, we proposed to use reweighted L1 regression, a form of robust regression, to find the outliers of the rsfMRI time series in the default-mode network (DMN) and developed a grading method for rsfMRI datasets.