Abstract #3915

Physiological noise removal in fast fMRI without separate physiological signal acquisition

Uday Agrawal¹, Emery Brown^1,2,3,4, and Laura Lewis^5,6,7

¹Harvard-MIT Division of Health Sciences and Technology (HST), Cambridge, MA, United States, ²Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ³Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁵Department of Biomedical Engineering, Boston University, Boston, MA, United States, ⁶Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁷Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States

Recent work has shown that commonly used methods to account for physiological noise and serial correlations in conventional fMRI are inadequate for fast (TR<500 ms) fMRI and may lead to incorrect inferences¹. We created a model of physiological noise based on harmonic regression with autoregressive noise that utilizes the enhanced sampling of fast fMRI to estimate physiological noise directly from the fMRI data; therefore, it does not require physiological reference signals such as respiration. We found that our model performs as well as gold standard reference-based approaches in removing physiological noise and improves the detection of task-driven fMRI activity.

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