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Abstract #4080

Group-level adaptive-analysis of task fMRI data

Xiaowei Zhuang1,2, Zhengshi Yang1, Tim Curran3, Rajesh Nandy4, Mark Lowe5, and Dietmar Cordes1,3
1Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States, 2Interdisciplinary neuroscience PhD program, University of Nevada, Las Vegas, Las Vegas, NV, United States, 3University of Colorado Boulder, Boulder, CO, United States, 4University of North Texas Health Science Center at Fort Worth, Fort Worth, TX, United States, 5Cleveland Clinic, Cleveland, OH, United States


A task-fMRI group-level analysis method is proposed to incorporate spatial covariance structures in fMRI data using the subject-level steerable filter smoothing with various full-wide-half-maximums followed by a group-level one-step optimization. Subject-level smoothed time series are further orthogonalized to guarantee non-overlapped contributions to group-level activations. Using the proposed method, we are able to detect more accurate activations in both simulated data and during a real-fMRI episodic memory task.

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