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Abstract #2328

3D Spatially-Adaptive Canonical Correlation Analysis for Episodic Memory Task fMRI Data: Local and Global Methods

Zhengshi Yang1, Xiaowei Zhuang1, Karthik Sreenivasan1, Virendra Mishra1, Tim Curran2, Richard Byrd2, Rajesh Nandy3, and Dietmar Cordes1,2

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

Spatially adaptive multivariate methods based on local CCA have been used in fMRI data analysis to improve sensitivity of activation detection. To improve specificity, local CCA methods require spatial constraints. In the past, local CCA methods have been used exclusively in 2D applications because of limitations imposed by the computational time requirements for 3D neighborhoods. We have implemented an efficient algorithm to solve the 3D local constrained CCA problem and furthermore proposed a global kernel CCA method to analyze the time series of the whole brain simultaneously. Results show that global kernel CCA outperforms local CCA in detecting activations.

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