Abstract #0530

Resting-State “Physiological” Networks

Jingyuan E. Chen^1,2, Laura D. Lewis^1,3, Catie Chang⁴, Nina E. Fultz¹, Ned A. Ohringer¹, Bruce R. Rosen^1,2,5, and Jonathan R. Polimeni^1,2,5

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Biomedical Engineering, Boston University, Boston, MA, United States, ⁴Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States, ⁵Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States

In this abstract, we offer a systemic characterization of the spatiotemporal patterns of fMRI signals subsequent to slow fluctuations in respiratory volume and heart rate. We show that these slow physiological dynamics contain structured network patterns that are somewhat consistent across individuals. We also show that global signal regression (GSR) may introduce anti-correlating patterns of the physiological dynamics to the final observations of functional connectivity.

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