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Abstract #5468

An automated method for assessing the accuracy of cross-modal registration in high-field fMRI

Cheryl A Olman1, Kimberly B Weldon2, Andrea N Grant2, Philip C Burton3, and Essa Yacoub2

1Department of Psychology, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 3Office of the Associate Dean for Research, College of Liberal Arts, University of Minnesota, Minneapolis, MN, United States

In this work, we developed a method for evaluating the quality of cross-modal registration of functional and anatomical MRI datasets that obviates the need for subjective human judgments. In brief, we propose that the overlap of an activation mask derived from the functional data with a binary GM mask derived from the reference anatomical volume is a useful metric for overall registration quality. In addition, we promote the use of activation consistency throughout the gray matter as an inclusion criterion for regions of interest when computing laminar (depth-dependent) profiles, provided that the activation is computed in a robust, independent localizer.

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