EEG correlates of time-varying BOLD functional connectivity

Chang, Catie; Li, Zhong‐Ming; Chen, Michael C.; Liu, Xiao; Duyn, Jeff H.

doi:10.1016/j.neuroimage.2013.01.049

Cited by 317 publications

(295 citation statements)

References 73 publications

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“…Finally, it is important to note that while fluctuations of dynFC might be driven by true non-stationarities and interactions of the time series, the origin of these signals could be both neurological and nonneurological. Ongoing and future research should further validate to what extent these origins can be disentangled (Chang et al, 2013); e.g., using concurrent measurements such as electroencephalography (EEG) and non-neurophysiological signals. …”

Section: Resultsmentioning

confidence: 99%

On spurious and real fluctuations of dynamic functional connectivity during rest

2015

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Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.

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Section: Resultsmentioning

confidence: 99%

On spurious and real fluctuations of dynamic functional connectivity during rest

2015

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“…Brain activity during task-free fMRI experiments does not remain in a stationary resting state (33,34). It has been established that spontaneous fluctuations in intrinsic FC are not simply noise (35) and can be correlated with physiological markers, such as EEG or MEG power at different frequency bands (19,20), as well as with heart rate variability (21). Shifts in EEG power in the α-and θ-bands correspond to changes in arousal (36)(37)(38).…”

Section: Discussionmentioning

confidence: 99%

“…The association between BOLD signal fluctuation in the default mode network (DMN) and mind-wandering episodes (15)(16)(17) has prompted investigations into the behavioral correlates of spontaneous resting-state FC fluctuations (11,18). Although these fluctuations in FC have been shown to correlate with several physiological markers, such as electroencephalogram (EEG) power, magnetoencephalography (MEG) power, and heart rate variability (19)(20)(21), their behavioral significance remains unclear.…”

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confidence: 99%

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Wang

Ong

Patanaik

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

193

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Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced withinnetwork functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. T he existence of large-scale functional brain networks is evidenced by well-defined spatial patterns of correlated bloodoxygenation level-dependent (BOLD) signal fluctuation in fMRI data (1). Recent work has shown that functional connectivity (FC) within and between brain networks is dynamic, corresponding to the observation that even while we are performing a task, our mental focus fluctuates (2). Fluctuation of fMRI-based FC occurs over tens of seconds (3, 4) and exhibits different patterns across conscious and unconscious states (5, 6). Furthermore, just as interindividual differences in stationary FC relate to variation in human behavior and cognition (7-10), it seems likely that recurring patterns (11) of fluctuating FC have behavioral significance.Temporal fluctuations in FC can arise from conscious mental activity (12), episodes of random synchrony (3), or simply timevarying levels of physiological noise (13,14). The association between BOLD signal fluctuation in the default mode network (DMN) and mind-wandering episodes (15-17) has prompted investigations into the behavioral correlates of spontaneous resting-state FC fluctuations (11,18). Although these fluctuations in FC have been shown to correlate with several physiological markers, such as electroencephalogram (EEG) power, magnetoencephalography (MEG) power, and heart rate variability (19-21), their behavioral significance remains unclear.A key obstacle to elucidating clear FC...

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“…Chang et al (2013b) recently linked heart rate variability to dynamic FC in regions associated with vigilance and arousal, suggesting the contribution of autonomic factors to the observed nonstationarity. Chang and colleagues also showed that alpha band power in EEG was related to FC fluctuations between the DMN and the dorsal attention network (Chang et al, 2013a). Further concurrent EEG-fMRI studies will be crucial in clarifying the relationship between these fluctuations and neural dynamics, as well as how they relate to attentional states.…”

Section: Timescales and Potential Confounds Of Dynamic Fcmentioning

confidence: 97%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

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EEG correlates of time-varying BOLD functional connectivity

Cited by 317 publications

References 73 publications

On spurious and real fluctuations of dynamic functional connectivity during rest

On spurious and real fluctuations of dynamic functional connectivity during rest

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

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