Abstract #3912

Multivariate group-level analysis to detect fMRI task activation

Xiaowei Zhuang¹, Zhengshi Yang¹, Tim Curran², Rajesh Nandy³, and Dietmar Cordes^1,4

¹Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ²Departments of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States, ³Departments of Biostatistics and Epidemiology, University of North Taxes, Denton, TX, United States, ⁴University of Colorado Boulder, Boulder, CO, United States

A constrained multivariate model is introduced for fMRI group-level analysis to increase the sensitivity in group activation detection by incorporating local neighboring information. Results from both simulated data and real episodic memory data indicate that a higher detection sensitivity for a fixed specificity can be achieved in group-level activation detection with the proposed method. Applying multivariate analysis in both subject and group levels of analysis can further improve the activation detection performance. Statistical thresholds for significance of the group-inferences in the multivariate method are computed non-parametrically.

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