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Abstract #0614

De-noising high-resolution fMRI data using cortical depth-dependent analysis

Jingyuan E. Chen1,2, Anna I. Blazejewska1,2, Nina E. Fultz1, Bruce R. Rosen1,2,3, Laura D. Lewis1,4, and Jonathan R. Polimeni1,2,3

1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States, 4Biomedical Engineering, Boston University, Boston, MA, United States

In this study, we exploit cortical depth-dependent information to de-noise high-resolution fMRI data. We have proposed a novel de-noising pipeline that can automatically differentiate neural and non-neural fluctuations according to cross-cortical-depth delay patterns. We also show that by excluding voxels intersecting the pial surface can reduce physiological effects and improve neuronal/spatial specificity.

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