Elsevier

NeuroImage

Volume 52, Issue 2, 15 August 2010, Pages 571-582
NeuroImage

Mapping sources of correlation in resting state FMRI, with artifact detection and removal

https://doi.org/10.1016/j.neuroimage.2010.04.246Get rights and content

Abstract

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.

Introduction

Connectivity analysis of human brain using resting-state (RS) functional magnetic resonance imaging (FMRI) has gained wide use since its discovery, reporting brain regions fluctuating together with a significant degree of temporal correlation (Biswal et al., 1995). RS connectivity has been used for observing functional networks covarying (Biswal et al., 1995, Greicius et al., 2003, Raichle and Snyder, 2007), segregating functionally distinguishable brain regions (Kim et al., 2010, Mezer et al., 2009), and in comparative studies between typical and abnormal brains (Cherkassky et al., 2006, Greicius et al., 2004). To make these applications possible, the most commonly used approach uses the cross correlation of residual time series to estimate the strength of connection between a pair of voxels or regions of interest after possible artifacts are removed by linear regression from the original echo planar imaging (EPI) time series data.

For artifact removal, there are many existing methods, and we consider several types of non-random artifacts or noise sources that bias the connectivity results of blood-oxygen-level dependent (BOLD) FMRI data; these are (i) global artifacts (e.g. head motion, hardware drift signals) (Lund et al., 2006), (ii) physiological “noises” (true NMR signals with noise-like properties, caused by respiration and the heartbeat), and (iii) acute hardware malfunctions or instability. Given the nature of FMRI noise and the sampling constraints on FMRI time series, many components of RS FMRI show temporal structure that covaries over long spatial distances, but have little to do with the notion of neural connectedness (Birn et al., 2008). Especially, respiration and the heartbeat have long been targeted as a source of physiological noises in FMRI, and these sources are more problematic in RS FMRI than in task-induced FMRI (Birn et al., 2006, Chang et al., 2009, Shmueli et al., 2007, Wise et al., 2004). Retrospective image correction (RETROICOR) is a common approach to reduce the effects of heartbeat- and respiration-related changes (Glover et al., 2000, Jones et al., 2008).

However, there could be more artifact sources in residual signals after these physiological noises are modeled out. One considerable artifact source is the draining vessel system. The BOLD signal originates in the venous bed on the capillary side and moves through venules, and then veins, to the sinuses in sulcal cerebrospinal fluid (CSF) (Nencka and Rowe, 2007). With task FMRI, the temporal signature of the stimulus allows the localization of activation. Large vessels that show a significant fit to the task activation can be discounted. But in RS FMRI, this is less practicable. Large vessels contain signal sources that carry over long distances. Some of these signals are legitimate, gray matter (GM) BOLD signal, and some of these signals are also noise. Given the general orientation of venous vasculature draining in the direction of the pial surface and then coursing on the outer hull of the brain, there should be little reflection of GM-based RS signal changes in white matter (WM). Even if the above noise sources are not directly reflected in GM tissue, whole volume blurring operations can spread these noise signals into GM voxels, producing artifactual spatial coherence.

The large-scale effect of hardware drifts can largely be removed by baseline modeling. However, there can be remaining artifacts induced by hardware issues. One possible hardware artifact results in signals that fluctuate locally and are spatially correlated. An illustration of this can be seen in RS data obtained from a multi-channel array head coil. In this instance, one of the RF amplifier channels was intermittently and slightly faulty, and these fluctuations in the coil's input to the image reconstruction software caused small fluctuations in the RS EPI data that were spatially correlated over the spatial scale of the coil's sensitivity profile. However, visual image quality was unimpaired. As a result, a seed placed anywhere in the coil's sensitive zone results in a relatively high degree of correlation measurements elsewhere in that zone. In the most benign case, this spatially correlated noise would mask out true underlying connectivity. But unless accounted for, it could also bias the results of group comparisons where data acquisition could not be fully randomized because of subject population constraints. This artifact would have gone unnoticed, were it not for the interactive seed-based correlation maps that can be generated with AFNI's InstaCorr and GrpInCorr tools, both in the volume and on cortical surfaces with SUMA (Argall et al., 2006, Saad et al., 2004). These tools greatly facilitate RS data exploration by allowing immediate generation of single-subject (∼ 100 ms) or group-level (∼ 500–1500 ms) seed-based correlation statistics at the click of a mouse button.

In this work, we examine the anatomy of RS FMRI time series, identifying noise sources that contribute to RS FMRI signals and where in the brain are they manifested. In the process, we examine current methods for reducing noise contributions and their effects on connectivity maps, and offer improvements to some of these approaches. We attempt to reduce the effect of spurious sources of correlation that may bias RS connectivity studies. To control the effects of spatially correlated noise, we examine the spatiotemporal structure of RS FMRI data using high spatial resolution EPI data and anatomically segmented masks generated from T1-weighted images. Finally, we suggest a well-designed artifact correction method using anatomically modeled signals (ANATICOR), which can remove both local and global artifacts. The idea here is to model about as much of the artifactual signals as the anatomical masks are capable of, and use what left in the residual of the regression model to be used for functional connectivity analysis.

Section snippets

Data acquisition

Fifteen healthy volunteers (males, 18.27 ± 2.46 years) were instructed to fixate on a black cross in the center of the screen while keeping their mind clear, and were scanned using an 8 channel head coil array equipped GE 3 T scanner. RS FMRI time series were acquired using a T2-weighted gradient echo pulse sequence with high spatial resolution (1.719 × 1.719 × 3.000 mm3, TR = 3.5 s, TE = 27 ms, flip angle = 90°). Each of the RS FMRI scans lasted for a duration of 490 s or 140 volumes. A respiration belt was

Group average R2 maps

Fig. 3 shows group average R2 maps, thresholded at R2 > 0.04, for regression models GS, 3GS, 5NBT, ASGS, and ANATICOR from Eqs. (3; Model GS), (4; Model, (5; Model, (6; Model ASGS), (7; Model ANATICOR), respectively. These maps show that some components, such as MO (column 1), and RI (column 2), explain variance throughout the brain., while other components, such as GS (4th column, row 1), and NBT (4th column, row 2), model variance in anatomically restricted regions.

In general, MO regressors

Heart and respiration noise correction

Several physiological noise correction methods have been used to predict the effect of heart rate and respiration phase on BOLD signal (Birn et al., 2008, Glover et al., 2000, Shmueli et al., 2007). Averaged over gray matter, both RETROICOR and RVT explained little variance, and had negative adjusted R2 values for every regression model (see Fig. 4). This is largely due to the number of regressors involved compared to the number of samples (100 to 200) commonly used in RS experiments. The

Conclusion

We found most RS correlation in the brain to be positive, and largely restricted to gray matter and venous vasculature draining it. To the extent that signal correlation exists between other tissue types and gray matter, it appears due to PVE or hardware artifacts. In other terms, a regressor formed from a non-eroded white matter mask models largely the same variance as a regressor from gray matter, or a global signal regressor. We identified transient hardware artifacts specific to

Acknowledgments

The authors appreciate Marta Bianciardi and Jongho Lee for useful discussions about hardware-related and physiological noises, and Catie E. Chang and Gary H. Glover for code and assistance with RETROICOR. We especially wish to acknowledge the continuing encouragement and aid from Alex Martin. This research was supported by the NIMH and NINDS Intramural Research Programs of the NIH.

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