Abstract #3821

A Novel Model-based Segmentation Approach for Improved Activation Detection in fMRI studies

Wei-Chen Chen¹ and Ranjan Maitra²

¹pbdR Core Team, Silver Spring, MD, United States, ²Statistics, Iowa State University, Ames, IA, United States

Functional Magnetic Resonance Imaging (fMRI) provides a popular approach to imaging cerebral activation in response to stimuli. Reliably detecting activation is, however, not an easy proposition because only a very small proportions of voxels show true activation. These truly activated voxels are known to be spatially localized, yet incorporating this information is challenging to implement practically. We provide a model-based approach that incorporates spatial context in a practical and methodologically sound manner while postulating our a priori expectation that a certain proportion of voxels is truly active. Results on simulation experiments for different noise levels are uniformly encouraging. The methodology is also illustrated on a sports imagination experiment and shows its potential in making possible the adoption of fMRI as a clinical tool to detect awareness and improve treatment in individual patients in persistent vegetative state, such as traumatic brain injury survivors.

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