Abstract #0614

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

Jingyuan E. Chen^1,2, Anna I. Blazejewska^1,2, Nina E. Fultz¹, Bruce R. Rosen^1,2,3, Laura D. Lewis^1,4, and Jonathan R. Polimeni^1,2,3

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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