Abstract #0063

A family-constrained local canonical correlation model to improve activation detection in fMRI

Xiaowei Zhuang¹, Zhengshi Yang¹, Tim Curran², and Dietmar Cordes^1,2

¹Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ²Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States

A family constrained CCA (cCCA) method was introduced to improve the accuracy of activation detection in noisy fMRI data. The cCCA was converted into a constrained multivariate multiple regression problem and solved efficiently with a numerical optimization algorithm. Results from both simulated data and real episodic memory data indicated that a higher detection sensitivity for a fixed specificity can be achieved with the proposed cCCA method as compared to the widely used mass-univariate or other conventional multivariate (CCA) approaches.

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