Abstract #2230

Minimizing Spurious Functional Connectivity Findings from Resting State fMRI

Prantik Kundu¹, Noah Brenowitz¹, Souheil J. Inati², Ziad S. Saad³, Petra Vertes⁴, Yulia Worbe⁴, Valerie Voon⁴, Ed Bullmore⁴, Peter A. Bandettini⁵

¹Section on Functional Imaging Mehthods, NIMH, Bethesda, MD, United States; ²Functional MRI Core Facility, NIMH, Bethesda, MD, United States; ³Scientific and Statistical Computing Core, NIMH, Bethesda, MD, United States; ⁴Dept. of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom; ⁵Functional MRI Core Facility, National Institute of Mental Health, Bethesda, MD, United States

We present a solution to the critical problem for resting state fMRI (rs-fMRI) that functional connectivity estimates are severely biased by any level of in-scanner subject head movement. We show that separating BOLD from non-BOLD signals using T2* decay analysis of multi-echo fMRI and independent components analysis (ME-ICA) entirely removes both linear and non-linear manifestations of motion artifact. This denoising is achieved without arbitrary processing such as data censoring or band pass filtering, making ME-ICA the first physically and statistically principled approach for comprehensive denoising of rs-fMRI data. Our study gives a deeper understanding of rs-fMRI while solving a critical problem for this exciting methodology.