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The study of resting state functional connectivity (RSFC) using functional MRI scans has rapidly become one of the most promising and widely used techniques for investigations of human brain function in both healthy and psychiatric populations. Two critical recent developments in this field are 1) the increasing use of simultaneous multi-slice fMRI (multiband) acceleration techniques, which dramatically improves the spatial and temporal resolution of fMRI data, and 2) the recognition that participant motion is a critical confound in RSFC studies, which requires careful denoising in order to obtain valid results. However, motion artifact denoising techniques were not developed with the temporal resolution of multiband fMRI in mind, which results in the capture of high-frequency respiration-related motion of participants during scanning. This respiration-related motion appears to negatively impact the performance of existing volume censoring approaches. Using publicly available multiband RSFC data from the Human Connectome Project, we developed a new volume censoring motion correction approach that addresses respiration-related motion separately from other sources of motion, and outperforms one of the most widely used denoising pipelines. We further show that the assumptions underlying some of the most commonly employed metrics for evaluating motion denoising pipelines (testing for significant differences in RSFC correlations between high-and low-motion participants, and so-called "QC-FC" based methods) are invalid. Specifically, the number of significant RSFC correlations between high-and low-motion groups is dramatically reduced by exclusion of participants exhibiting substance use or who have a family history of psychiatric or neurological disorder, indicating that individual differences in unmeasured third variables contribute to both higher motion and true differences in RSFC correlations, thereby invalidating this widely used metric, which assumes that no true differences in RSFC exist between high-and low-motion groups. Finally, we develop and present an empirical basis for selecting volume censoring thresholds in any multiband RSFC dataset, which are widely used in the field but have had an exclusively heuristic basis prior to this work. These findings thus have three major impacts: first, to present a substantively improved pipeline for motion denoising of multiband RSFC data; second, to raise concerns about a key metric used to evaluate motion denoising for RSFC data more generally; and third, to provide investigators with an empiricallygrounded estimate of the optimal volume censoring threshold to employ for any dataset.

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