Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Introduction
Studies of the human brain using functional magnetic resonance imaging (fMRI) have revealed collections of distributed regions that exhibit low-frequency, temporally correlated BOLD signal fluctuations in the absence of an explicit task (resting state). Remarkably, these “resting-state networks” tend to comprise regions that are co-activated during tasks and are observed with consistency across subjects and scanning sessions (Damoiseaux et al., 2006, De Luca et al., 2006, Shehzad et al., 2009), suggesting a general principle of functional organization. In recent years, a large number of studies have emerged in an effort to understand the purpose of resting-state networks in subserving cognitive function or basic brain physiology (for reviews, see (Buckner and Vincent, 2007, Fox and Raichle, 2007)).
Currently, the analysis of resting-state networks across a single scanning session typically employs techniques that assume temporal stationarity: measures of linear dependence are computed over the entire scan and used to characterize the strength of connections across regions. Popular methods include seed-based region-of-interest (ROI) analysis, in which the time series of an ROI is used as a regressor to query regions of similar temporal behavior across the brain (Biswal et al., 1995, Greicius et al., 2003, Lowe et al., 1998), and independent component analysis (Beckmann et al., 2005, Kiviniemi et al., 2003, McKeown et al., 1998), which is a model-free approach for identifying spatial regions with temporally coordinated activity. Other methods for characterizing resting-state networks include partial correlation (Fransson and Marrelec, 2008), coherence and partial coherence (Salvador et al., 2005), phase relationships (Sun et al., 2005), clustering (Cordes et al., 2002, Mezer et al., 2009), and graph theory (Achard et al., 2006, Dosenbach et al., 2007).
Previous studies have demonstrated dynamic changes in network connectivity throughout development (Fair et al., 2008, Supekar et al., 2009), aging (Beason-Held et al., 2009), visual state (Bianciardi et al., 2009) and as a function of conscious awareness (Greicius et al., 2008, Horovitz et al., 2009, Martuzzi et al., 2009, Picchioni et al., 2008). Furthermore, there is evidence that inter-regional correlations can be modulated by cognitive processes that occur on time-scales of a typical (several-minute) scan. Task-based studies have revealed that attention (Esposito et al., 2006, Fransson, 2006), learning (Sun et al., 2007), and muscle fatigue (Deshpande et al., 2006) can effect changes in low-frequency dynamics and/or connectivity throughout the performance of the task and in subsequent resting-state scans (Barnes et al., 2009, Duff et al., 2008, Waites et al., 2005). Because the resting state is a condition of undirected wakefulness that may encompass varying levels of attention, mind-wandering, and arousal, one may hypothesize that the connectivity between and within networks may undergo substantial changes across the duration of a scan.
The aim of the present study was to investigate the temporal dynamics of connectivity between nodes of resting-state networks within the course of a 12- or 15-min scanning session. Here, we focus specifically on the default-mode network (DMN) and regions with which it has negative correlations (the “anticorrelated” network, also referred to as the “task-positive” or “executive-control” network) (Fox et al., 2005, Fransson, 2005, Greicius et al., 2003). These networks, comprised of spatially distinct brain regions, have been shown to have negative correlations in the resting state, a finding that mirrors the opposing signal changes displayed by the two networks during controlled tasks (Toro et al., 2008) and which may emerge in simulated cortical networks (Deco et al., 2009). However, compared with the positive correlations exhibited between nodes within the same network, the magnitude of negative correlations between the two networks have been reported to be weaker and much less consistent (Shehzad et al., 2009), particularly without the controversial processing step of global signal removal (Chang and Glover, 2009, Fox et al., 2009, Murphy et al., 2009).
A possible explanation for the relative weakness of negative correlations is that the presence of common noise across the brain, such as that due to respiration and cardiac processes (Birn et al., 2006, Chang et al., 2009, Shmueli et al., 2007, Wise et al., 2004), biases inter-regional correlation coefficients in the positive direction. A second possibility is that the phase difference between the two networks is variable in time, driven perhaps by cognitive state. Using local field potentials recorded in the feline homologues of the DMN and its anticorrelated network, (Popa et al., 2009) found variable phase differences between gamma power fluctuations of the two networks wherein negative correlations were more frequent during waking and REM sleep compared to slow-wave sleep.
As previous fMRI studies of temporal relationships between the DMN and its anticorrelated network have computed correlation coefficients across all time points in the scan, the observed relative weakness of the negative correlations raises a number of interesting questions. For instance, to what extent, and at what frequencies, do the magnitude and the phase relationships between the networks fluctuate over time? Are negative correlations between the two networks consistently weak, or marked by transient periods of strong negative correlation?
In the current study, we apply a time–frequency analysis–in particular, wavelet transform coherence–to examine temporal variability in the relationship between nodes of the DMN and its anticorrelated network. Time–frequency methods are popular in the analysis of EEG, MEG, and electrophysiological data (e.g. see (Le Van Quyen and Bragin, 2007, Mitra and Pesaran, 1999, Roach and Mathalon, 2008)). Wavelet transform coherence has previously been applied to the analysis of neural signals using spike trains and EEG (e.g. (Klein et al., 2006, Li et al., 2007, Zhan et al., 2006) and to fMRI data to study the temporal variability in the phase relationships between different brain regions in a visual task (Muller et al., 2004), and to study resting-state network nodes throughout the course of a working-memory task (Almeida et al., 2007). In addition, to explore regions of the brain having potentially interesting dynamic connectivity with the default-mode network, a whole-brain sliding correlation analysis was applied and regions with large temporal variability in connectivity were identified.
Section snippets
Subjects
Participants included 12 healthy adults (6 female, aged 27.7 ± 12.4 years) recruited from the Stanford University community. 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 GE whole-body scanners (GE Healthcare Systems, Milwaukee, WI). Four of the 12 subjects (those henceforth labeled as Subjects 1–4) were scanned on a GE Signa HDX (rev. 12M5) using a custom
Default-mode network and anticorrelations
Fig. 1 shows group-level t-maps of positive and negative correlation with the PCC ROI, where correlations for each subject were taken over all time points. The maps in Fig. 1 are displayed at a threshold identical to that used for obtaining the default-mode and anticorrelated clusters used as masks for deriving the subject-specific ROIs. Cluster locations (centroids and Brodmann areas) are provided in Table 1. The correlation coefficient (over all time points) between the PCC ROI and the
Discussion
This study presents a preliminary analysis of dynamic behavior between nodes of resting-state networks. We demonstrate the use of wavelet coherence analysis for analyzing time-varying interactions between brain regions in the resting state, focusing on the default-mode network and its negative correlations with regions of the “anticorrelated” network. In addition, regions of the brain with temporally-variable connectivity to the PCC, a major node of the default-mode network, were identified
Acknowledgments
This work was supported by NIH grants F31-AG032168 (CC) and P41-RR009784 (GHG). The authors gratefully acknowledge Jonathan Taylor, Ryan Tibshirani, Richard Olshen, Noah Simon, Mike Love, and Nelson Ray for valuable discussions regarding statistical methodology, and two anonymous reviewers for suggestions that have substantially improved this manuscript.
References (73)
- et al.
Modulation of spontaneous fMRI activity in human visual cortex by behavioral state
NeuroImage
(2009) - 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.
Effects of model-based physiological noise correction on default mode network anti-correlations and correlations
NeuroImage
(2009) - et al.
Influence of heart rate on the BOLD signal: the cardiac response function
NeuroImage
(2009) - et al.
Hierarchical clustering to measure connectivity in fMRI resting-state data
Magn. Reson. Imaging
(2002) - et al.
fMRI resting state networks define distinct modes of long-distance interactions in the human brain
NeuroImage
(2006) - et al.
Independent component model of the default-mode brain function: assessing the impact of active thinking
Brain Res. Bull.
(2006) How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations
Neuropsychologia
(2006)
The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis
NeuroImage
Independent component analysis of nondeterministic fMRI signal sources
NeuroImage
Analysis of dynamic brain oscillations: methodological advances
Trends Neurosci.
Interaction dynamics of neuronal oscillations analysed using wavelet transforms
J. Neurosci. Methods
Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations
NeuroImage
Cluster analysis of resting-state fMRI time series
NeuroImage
Analysis of dynamic brain imaging data
Biophys. J.
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?
NeuroImage
fMRI differences between early and late stage-1 sleep
Neurosci. Lett.
A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality
NeuroImage
Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal
NeuroImage
Measuring temporal dynamics of functional networks using phase spectrum of fMRI data
NeuroImage
Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal
NeuroImage
Detecting time-dependent coherence between non-stationary electrophysiological signals—a combined statistical and time–frequency approach
J. Neurosci. Methods
A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs
J. Neurosci.
Endogenous human brain dynamics recover slowly following cognitive effort
PLoS One
Stability of default-mode network activity in the aging brain
Brain Imaging Behav.
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. R. Soc. Lond. B. Biol. Sci.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
Control of goal-directed and stimulus-driven attention in the brain
Nat. Rev. Neurosci.
Consistent resting-state networks across healthy subjects
Proc. Natl. Acad. Sci. U. S. A.
Key role of coupling, delay, and noise in resting brain fluctuations
Proc. Natl. Acad. Sci. U. S. A.
Directed transfer function analysis of fMRI data to investigate network dynamics
Conf. Proc. IEEE Eng. Med. Biol. Soc.
Distinct brain networks for adaptive and stable task control in humans
Proc. Natl. Acad. Sci. U. S. A.
The power of spectral density analysis for mapping endogenous BOLD signal fluctuations
Hum. Brain Mapp.
Cited by (1465)
An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models
2024, Journal of Affective DisordersResolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder
2024, Progress in Neuro-Psychopharmacology and Biological Psychiatry