Automatic sleep staging using fMRI functional connectivity data

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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...

Section: Discussionsupporting

confidence: 86%

“…Best results from prior studies showed classification accuracies around 80% for durations of 60 s or more (17,19). For shorter windows, when reported, accuracy dropped quickly.…”

Section: Discussionmentioning

confidence: 93%

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

Gonzalez-Castillo

Hoy

Handwerker

et al. 2015

322

341

“…Although specific patterns of large-scale connectivity are characteristic of different NREM sleep stages (37) (in fact, allowing fMRI-based sleep staging) (38), ample evidence suggests that canonical RSNs are grossly preserved during sleep (7, 8) (SI Appendix, Fig. S8.1), possibly with weakened intranetwork connectivity in the case of the DMNs (11,12).…”

Section: Discussionmentioning

confidence: 99%

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Tagliazucchi

Wegner

Morzelewski

et al. 2013

Self Cite

217

194

The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as longterm memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.T he human brain displays complex spatiotemporal patterns of energy-consuming activity, even in the absence of an explicit task or stimulation (1). Large efforts have been devoted to the study of spontaneous neural activity encoded in the slow (∼0.1 Hz) fluctuations of the blood oxygen level-dependent (BOLD) signal, which are measured with functional MRI (fMRI). Nontrivial spatial organization of functional brain activity in resting state networks (RSNs) was consistently shown (2-4), comprising brain regions with high BOLD signal coherence and anatomical consistency with systems activated during task performance or stimulation (5).Remarkably, although human nonrapid eye movement (NREM) sleep is characterized by impaired awareness and reduced conscious mentation, organization into RSNs is preserved in light sleep (6) and to a large extent, deeper sleep stages (7, 8) (SI Appendix, Fig. S8.1). In particular, the default mode network (DMN; a set of task-deactivated regions implied with internal conscious cognitive processes) (9, 10) was repeatedly observed during deep sleep, albeit with reduced frontal connectivity (11,12). Although brain modules are preserved, even in the absence of conscious awareness, their functional integration is greatly impaired (8,13,14), which was predicted by an information integration account of consciousness (15). These results suggest that ongoing conscious mentation is not the only origin of RSN activity, whereas the level of consciousness is reflected in the interaction of functional networks.However, brain activity is not completely characterized in the spatial domain only. fMRI BOLD signals display rich temporal organization, including scale-free 1/f power spectra and long-range temporal autocorrelations (16)(17)(18), with activity at any given time being influenced by the previous history of the system up to several minutes into the past. These landmarks of complex information processing and rapid adaptability are shared by many systems found in nature (19,20). Evidence for such properties is also manifest...

“…Using the technique described in ref. 30, it was discovered that light sleep is surprisingly prevalent in resting-state scans in which subjects were intended to stay awake (5). Together, these findings indicate that substantial changes in arousal occur in routine, several-minutelong fMRI scans and imply that changes in functional connectivity caused by arousal may be attributed erroneously to core differences in functional organization.…”

mentioning

confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

285

289

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...