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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.