Abstract #3865

Sparse Dictionary Learning and Deep Convolutional Auto-encoders as Alternative Methods to ICA for Resting-State Network Detection

Alice Shen¹, Gregory Simchick^2,3, Brandon Campbell¹, and Qun Zhao^2,3

¹University of Georgia, Athens, GA, United States, ²Physics and Astronomy, University of Georgia, Athens, GA, United States, ³Bio-Imaging Research Center, University of Georgia, Athens, GA, United States

Though Independent Component Analysis (ICA) is a commonly-used method for resting state network (RSN) detection from resting-state fMRI (rsfMRI) data, it is limited by its assumption of spatial independence requiring that detected networks be non-overlapping. This study investigates the use of Sparse Dictionary Learning (SDL) and Deep Convolutional Auto-Encoders (DCAE) as alternative methods for RSN detection using Human Connectome Project rsfMRI data. Using the Smith10 RSN Atlas as a ground truth, Pearson spatial correlation and spatial overlap scores were used as metrics of performance, and it was found that SDL and DCAE outperform ICA in detecting RSNs in single session analyses.

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