Effects of model-based physiological noise correction on default mode network anti-correlations and correlations
Introduction
Functional magnetic resonance imaging (fMRI) studies of the brain have revealed temporal synchrony between the blood oxygen level dependent (BOLD) signals of anatomically distinct regions during the absence of an explicit task, i.e. in the resting state (for reviews, see Buckner and Vincent, 2007, Fox and Raichle, 2007). Several such sets of temporally correlated regions, known as resting-state networks, can be identified consistently across human subjects (Beckmann et al., 2005, Damoiseaux et al., 2006, De Luca et al., 2006) and are presumed to reflect a basic functional organization of the brain.
A particular resting-state network, known as the default-mode network (DMN), has received much recent attention (see Buckner et al., 2008 for a review). The DMN comprises brain regions that, in addition to having coherent fluctuations in the absence of a task, are commonly deactivated (exhibit sub-baseline signal deflections) in response to a broad range of cognitive and attentional tasks (Mazoyer et al., 2001, Raichle et al., 2001, Shulman et al., 1997) and may underlie self-referential processing (Gusnard et al., 2001) (see Buckner et al., 2008 for a review). Core regions of the DMN include the precuneus/posterior cingulate cortex (PCC), medial prefrontal cortex, ventral anterior cingulate, lateral parietal cortex, inferior temporal cortex, and parahippocampal cortex.
It has been reported that the spontaneous, resting-state time course of the DMN is negatively correlated (“anti-correlated”) with that of the task-positive network (TPN), a set of regions that are (positively) activated in response to cognitive and attentional tasks (Fox et al., 2005, Fransson, 2005). The TPN includes dorsolateral prefrontal cortex (DLPFC), supramarginal gyrus and posterior parietal cortex, insula, premotor cortex, and supplementary motor area (SMA). The finding of negative correlations between the two networks has been hypothesized to reflect a competitive relationship between internally- and externally-oriented processes, or between mind wandering and focused attention, and mirrors the opposing signal changes displayed by the two networks during controlled tasks (Fox et al., 2005, Fransson, 2005, Greicius et al., 2003). Subsequent studies have asserted that the strength of the negative correlations between the DMN and TPN mediates behavioral variability (Clare Kelly et al., 2008) and may distinguish Alzheimer's patients from healthy controls (Wang et al., 2007).
However, studies reporting significant negative correlations between the DMN and the TPN in resting-state have removed a whole-brain average time series from the data, either by proportional scaling (time-point-wise division), regressing it out as a pre-processing step, or including it as a nuisance covariate when assessing functional connectivity using the general linear model (Clare Kelly et al., 2008, Fair et al., 2008, Fox et al., 2005, Fransson, 2005, Fransson, 2006, Greicius et al., 2003, Tian et al., 2007, Uddin et al., 2009, Wang et al., 2007). The intent behind removing a global signal is to eliminate undesired non-neural fluctuations that affect large numbers of voxels. Unfortunately, since the global signal is derived from the data itself and is an unknown mixture of neural and non-neural fluctuations, these steps alter inter-regional correlations and complicate their interpretation. Furthermore, if the global signal has been regressed out of each voxel's time series, the resulting correlation coefficients between a seed voxel and all other voxels in the brain will have a mean value that is less than or equal to zero, necessarily introducing spurious negative correlations (Fox et al., 2008, Murphy et al., 2009). Consequently, it has been hypothesized that global signal removal may be the cause of the observed negative correlations between the DMN and TPN (Murphy et al., 2009). Studies by Golland et al. (2007) and Murphy et al. (2009) did not employ global signal removal and failed to observe negative correlations at any statistical threshold, but Fransson (2005) noted that when re-analyzing data without performing global signal regression, negative correlations in some TPN regions were present when the statistical threshold was lowered from p < 0.01 to p < 0.07 (corrected), and Uddin et al. (2009) showed that bilateral clusters in the insula were negatively correlated with the PCC when data were re-analyzed without global signal regression. The question of whether negative correlation between the two networks is a real phenomenon, or simply an artifact of global signal removal, is currently under debate (Buckner et al., 2008).
The present study aimed to determine the spatial extent and magnitude of negative and positive correlations with the DMN when model-based physiological noise corrections, instead of global signal removal, are applied. Respiration and cardiac processes are known to modulate the BOLD signal (Biswal et al., 1993, Dagli et al., 1999, Jezzard et al., 1993, Shmueli et al., 2007, Weisskoff et al., 1993, Wise et al., 2004), and their effects can be reduced by monitoring respiratory and cardiac cycles throughout the scan and filtering them out of the fMRI data using a priori models (Birn et al., 2006, Birn et al., 2008, Chang et al., 2009, de Munck et al., 2008, Glover et al., 2000). In this way, known sources of noise that affect functional connectivity can be reduced in an unbiased manner. A previous study, employing the same physiological noise corrections applied here, demonstrated increased spatial specificity of positive correlations within the DMN after correction (Chang et al., 2009). It was thus hypothesized that such corrections might increase the sensitivity of detecting regions that are negatively correlated with the DMN as well.
It is important to note, however, that the determination of sensitivity and specificity in functional connectivity analysis is not straightforward. In ROI-based functional connectivity analysis, where the time series of both the reference (seed ROI) and each voxel in the brain are considered to contain both a neuronal component and a noise component, the null hypothesis (H0) is that there is no correlation between the respective neuronal components of the seed ROI and a given voxel. Accordingly, a Type I error (false positive) occurs when there is an absence of neuronal correlation, and yet the noise components correlate strongly enough so that the overall correlation coefficient exceeds a threshold; a Type II error (false negative) occurs when the neuronal components of the seed ROI and a given voxel are correlated, but the correlation is not detected because of noise in the seed ROI and/or the queried voxel. In contrast with a standard analysis of task activation, wherein the reference time series is the expected noise-less neuronal time course (often modeled by a binary stimulus waveform convolved with a hemodynamic response function), the reference time series in functional connectivity is derived from a region of the brain itself, and it is not known which part of the signal is due to neural activity and which is due to noise. Nevertheless, physiological noise correction – by reducing the noise component of BOLD signal time series – aims to improve the estimation of neuronally-driven correlations between regions of the brain. Here, after correction, it was hypothesized that: (1) positive correlations with the DMN seed time series will diminish, as correlations will be reduced in voxels that had been falsely coupled due to the common influence of respiratory and cardiac noise, and that (2) negative correlations with the DMN seed time series, if they exist, will increase due to the reduction of respiratory and cardiac noise that can obscure these dynamics.
Section snippets
Subjects
Participants included 15 healthy adults (7 female, aged 29 ± 11.5 years). All subjects provided written, informed consent, and all protocols were approved by the Stanford Institutional Review Board.
Imaging parameters
Magnetic resonance imaging was performed at 3.0 T using a GE whole-body scanner (GE Healthcare Systems, Milwaukee, WI). Seven of the 15 subjects were scanned on a GE Signa HDX (rev. 12M5) using a custom quadrature birdcage head coil, while the remaining 8 subjects were scanned on a GE Signa 750 (rev.
Motion
Across subjects, the peak and RMS excursion in head motion were 0.67 ± 0.42 mm and 0.10 ± 0.07 mm, respectively (mean ± SD).
Comparison of physiological noise regressors, white/CSF signals, and the global signal
Correlation coefficients between the global signal and both respiratory and cardiac RVHRCOR regressors are shown in Table 1. Correlations with the global signal ranged from − 0.10 to 0.69 for RVx, and from − 0.15 to 0.49 for HRx. The white matter and CSF ROIs also demonstrated significant correlations with the global signal (Table 1), ranging from − 0.02 to 0.57 for white matter
Discussion
In the present study, negative correlations between the DMN and multiple key regions of the TPN were observed in the resting-state, despite the fact that no global signal removal or scaling steps were performed in pre-processing. While regions of negative correlation were observed in some individual subjects prior to any noise correction, the use of model-based corrections for respiratory and cardiac noise substantially increased their magnitude and extent. The present study indicates that DMN
Conclusions
The current findings indicate that negative correlations between DMN and task-positive regions can be observed in the absence of global signal removal. A group-level random-effects analysis revealed clusters at an uncorrected (p < 0.05) threshold (though none were significant at FDR = 0.05). Physiological noise correction, based on respiratory and cardiac monitoring in conjunction with a priori models, both increased the extent of negative correlations and decreased the extent of positive
Acknowledgments
The authors gratefully acknowledge support from NIH grants F31-AG032168 (CC) and P41-RR09784 (GHG), and thank Moriah Thomason for helpful consultation regarding brain anatomy. We are also grateful to two anonymous reviewers for insightful comments.
References (50)
- et al.
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Neuroimage
(2007) - 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.
Unrest at rest: default activity and spontaneous network correlations
Neuroimage
(2007) - et al.
Influence of heart rate on the BOLD signal: the cardiac response function
Neuroimage
(2009) - et al.
Fear conditioning in humans: the influence of awareness and autonomic arousal on functional neuroanatomy
Neuron
(2002) - et al.
Localization of cardiac-induced signal change in fMRI
Neuroimage
(1999) - et al.
fMRI resting state networks define distinct modes of long-distance interactions in the human brain
Neuroimage
(2006) - et al.
A study of the brain's resting state based on alpha band power, heart rate and fMRI
Neuroimage
(2008) - et al.
A core system for the implementation of task sets
Neuron
(2006)
How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations
Neuropsychologia
Deconvolution of impulse response in event-related BOLD fMRI
Neuroimage
Cortical networks for working memory and executive functions sustain the conscious resting state in man
Brain Res. Bull.
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?
Neuroimage
Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal
Neuroimage
Advances in functional and structural MR image analysis and implementation as FSL
Neuroimage
Controlled inspiration depth reduces variance in breath-holding-induced BOLD signal
Neuroimage
The relationship within and between the extrinsic and intrinsic systems indicated by resting state correlational patterns of sensory cortices
Neuroimage
Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal
Neuroimage
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. R. Soc. Lond. B. Biol. Sci.
Time-frequency analysis of functional EPI time-course series
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
The brain's default network: anatomy, function, and relevance to disease
Ann. N. Y. Acad. Sci.
Competition between functional brain networks mediates behavioral variability
Neuroimage
Control of goal-directed and stimulus-driven attention in the brain
Nat. Rev. Neurosci.
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