Resting-State Functional Magnetic Resonance Imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but not to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences: Global Signal Regression (GSReg) – the practice of projecting out signal averaged over the entire brain – can change resting state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem with GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter inter-regional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true inter-regional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network’s regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.

Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T p 2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.resting state fMRI | human neuroimaging | time series R esting state experiments typically involve a short period (i.e., 10 min) of blood oxygen level-dependent (BOLD) functional MRI (fMRI) scanning while participants lie in the scanner without experimental control over brain function. The data show low-frequency (f ≤ 0.1 Hz) oscillations indicative of spontaneous brain activity. Functional connectivity between brain regions is then typically estimated by the correlation between time series (1). Unfortunately, resting state fMRI is highly susceptible to artifacts. It has recently been shown that small (≤1 mm) and transient movements of the subject's head during scanning can bias estimates of time series correlation for long distance anatomical connections, even after the data have been preprocessed by traditional methods (2-4). The effects of head motion and related artifacts are problematic especially for studies of very young or elderly subjects or patients with neuropsychiatric disorders, all of whom demonstrate a greater extent of head movement than healthy adults.Current pr...

Many components of resting-state (RS) FMRI show non-random structure that has little to do with neural connectivity but can covary over multiple brain structures. Some of these signals originate in physiology and others are hardware-related. One artifact discussed herein may be caused by defects in the receive coil array or the RF amplifiers powering it. During a scan, this artifact results in small image intensity shifts in parts of the brain imaged by the affected array components. These shifts introduce artifactual correlations in RS time series on the spatial scale of the coil's sensitivity profile, and can markedly bias RS connectivity results. We show that such a transient artifact can be substantially removed from RS time series by using locally formed regressors from white matter tissue. This is particularly important in arrays with larger numbers of coils, which may generate smaller artifact zones. In such a case, brain-wide average noise estimates would fail to capture the artifact. We also examine the anatomical structure of artifactual variance in RS FMRI time series, by identifying sources that contribute to these signals and where in the brain are they manifested. We consider current methods for reducing confounding sources (or noises) and their effects on connectivity maps, and offer an improved approach (ANATICOR) that can also reduce hardware artifacts. The methods described herein are currently available with AFNI, in addition to tools for rapid, interactive generation of seed based correlation maps at single-subject and group levels.