Abstract #4080

Group-level adaptive-analysis of task fMRI data

Xiaowei Zhuang^1,2, Zhengshi Yang¹, Tim Curran³, Rajesh Nandy⁴, Mark Lowe⁵, and Dietmar Cordes^1,3

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

Synopsis

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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