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Abstract #2326

Multivariate Second Level Analysis in fMRI with Canonical Correlation Analysis

Xiaowei Zhuang1, Zhengshi Yang1, Rajesh Nandy2, Tim Curran3, and Dietmar Cordes1,3

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of North Taxes, Fort Worth, TX, United States, 3University of Colorado, Boulder, CO, United States

A multivariate CCA method is introduced for fMRI 2nd level analysis to incorporate local neighboring information, and to improve the sensitivity in group activation and group difference detection in noisy fMRI data. Statistical thresholds for significance of the group-inferences in the multivariate method are computed non-parametrically. Results from both simulated data and real episodic memory data indicate that a higher detection sensitivity for a fixed specificity can be achieved in both 2nd level activation and difference detection with the proposed method, as compared to the widely used univariate techniques.

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