Mapping sources of correlation in resting state FMRI, with artifact detection and removal
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.
References (32)
Adaptive cyclic physiologic noise modeling and correction in functional MRI
J. Neurosci. Meth.
(2010)- et al.
Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI
Neuroimage
(2006) - et al.
The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration
Neuroimage
(2008) - et al.
Influence of heart rate on the BOLD signal: the cardiac response function
Neuroimage
(2009) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Comput. Biomed. Res.
(1996)- et al.
Removal of confounding effects of global signal in functional MRI analyses
Neuroimage
(2001) - et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Neuron
(2002) - et al.
Spatial accuracy of fMRI activation influenced by volume- and surface-based spatial smoothing techniques
Neuroimage
(2007) - et al.
Artificial shifting of fMRI activation localized by volume- and surface-based analyses
Neuroimage
(2008) - et al.
Integration of motion correction and physiological noise regression in fMRI
Neuroimage
(2008)
Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method
Neuroimage
Non-white noise in fMRI: does modelling have an impact?
Neuroimage
Cluster analysis of resting-state fMRI time series
Neuroimage
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?
Neuroimage
Reducing the unwanted draining vein BOLD contribution in fMRI with statistical post-processing methods
Neuroimage
Aberrant functional connectivity in autism: evidence from low-frequency BOLD signal fluctuations
Brain Res.
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