Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

Tagliazucchi, Enzo; Wegner, Frederic von; Morzelewski, Astrid; Brodbeck, Verena; Laufs, Helmut

doi:10.3389/fnhum.2012.00339

Cited by 266 publications

(300 citation statements)

References 89 publications

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“…Similarly, it may be that resting-state functional connectivity is influenced not only by stable trait-like characteristics, but also by mental state, food or caffeine intake, daily exercise routines, awake-sleep patterns, seasonal change, and circadian and diurnal rhythms, which are more likely to be different when the scans are months apart than within the same session. Such a dependence of rs-fcMRI on mental state has been observed following a task stimulus (Grigg and Grady, 2010;Lewis et al, 2009;Stevens et al, 2010), when subjects were in different moods or emotional states (Eryilmaz et al, 2011), and with differences in vigilance (Tagliazucchi et al, 2012;Wong et al, 2013).…”

Section: Discussionmentioning

confidence: 84%

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

et al. 2014

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The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac-and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points-two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

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Section: Discussionmentioning

confidence: 84%

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

et al. 2014

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“…However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies (Handwerker et al, 2012;Keilholz et al, 2013). A few labs have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals (Magri et al, 2012;Pan et al, 2011Pan et al, , 2013Scholvinck et al, 2010;Shmuel and Leopold, 2008;Thompson et al, 2013aThompson et al, , 2013b and in humans (Chang et al, 2013;Tagliazucchi et al, 2012;Wu et al, 2010). In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in the interpretation of these studies.…”

Section: Introductionmentioning

confidence: 99%

The Neural Basis of Time-Varying Resting-State Functional Connectivity

2014

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Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.

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“…This dynamic behavior has been observed in awake and sleeping humans (5-8), as well as in anesthetized animals (9, 10). Several studies involving simultaneous fMRI and electrophysiological recordings have suggested that FC dynamics may be driven by neurophysiological sources rather than noise (6,11,12). Furthermore, FC dynamics exhibit rich spatiotemporal structure.…”

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confidence: 99%

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

Gonzalez-Castillo

Hoy

Handwerker

et al. 2015

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

340

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Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to grouplevel, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.fMRI | connectivity dynamics | functional connectivity states | cognitive states | classification R esting state functional MRI (rs-fMRI) focuses on spatial patterns of blood oxygenation level dependent (BOLD) signal cofluctuations recorded in the absence of externally driven tasks or stimulation. These patterns, known as functional connectivity (FC) patterns, are usually computed on the basis of an entire scan (often >6 min). Their cognitive significance (1) and long term reproducibility (2) are well established, and preliminary data suggest that they have potential clinical value (3). However, recent studies have shown that FC patterns are highly dynamic at shorter temporal scales (4) (i.e., tens of seconds), adding yet another challenge to developing fMRI-based protocols with sufficient single-subject specificity and sensitivity to inform clinical decisions.FC patterns computed with 1-to 2-min portions of a scan can vary substantially around a mean FC pattern obtained using complete 6-to 20-min scans. This dynamic behavior has been observed in awake and sleeping humans (5-8), as well as in anesthetized animals (9, 10). Several studies involving simultaneous fMRI and electrophysiological recordings have suggested that FC dynamics may be driven by neurophysiological sources rather than noise (6,11,12). Furthermore, FC dynamics exhibit rich spatiotemporal structure. Connections between higher order cognitive regions are more variable than those between primary sensorymotor regions (13-15), and a limited set of whole-brain, quasistable FC configurations-known as FC states-reliably recur both within and across subjects at rest (13,16).Given that cognition is supported by highly dynamic brain processes, it has been hypothesized that FC states may reflect changes in ongoing cognitive states during rest (13). Initial task-based studies have been able to differentiate between a limited set of mental tasks (17, 18) and arousal levels (19) on the basis of l...

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Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

Cited by 266 publications

References 89 publications

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

The Neural Basis of Time-Varying Resting-State Functional Connectivity

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

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