Abstract #1595

Self-Organizing Group Level Independent Component Analysis Reveals Task-Related Activity as Well as Resting State Networks During Auditory Stimulation

Elizabeth Quattrocki Knight^1,2, Xiaoying Fan³, Blaise Frederick⁴, Marc Kaufman⁴, Bruce Cohen^2,3

¹Psychiatry, McLean Hospital, Belmont, MA, United States; ²Psychiatry, Harvard Medical School, Boston, MA, United States; ³Frazier Research Institute, McLean Hospital, Belmont, MA, United States; ⁴Brain Imaging Center, McLean Hospital, Belmont, MA, United States

Although numerous fMRI studies have examined visual processing, less work has focused on the auditory system. With the exception of sparse sampling techniques, interference from scanner noise can impede the study of auditory processing. Independent component analysis (ICA), by isolating and removing components in the data representing extraneous sources of noise, can facilitate fMRI data analysis. Here, we compare results of a self-organizing group level ICA (SogICA) to a random effects (RFX) general linear model in an auditory listening study. SogICA identifies not only more extensive task-related activity, but also reveals underlying resting state networks.