The neural correlates of dreaming

Siclari, Francesca; Baird, Benjamin; Perogamvros, Lampros; Bernardi, Giulio; LaRocque, Joshua J.; Riedner, Brady A.; Boly, Mélanie; Postle, Bradley R.; Tononi, Giulio

doi:10.1038/nn.4545

Cited by 528 publications

(522 citation statements)

References 77 publications

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“…This relationship between the complexity of activity in brain networks and the state of consciousness has been demonstrated across mechanistically diverse natural, pharmacological and pathological modulations of consciousness using transcranial magnetic stimulation (TMS, Casali et al, 2013;Casarotto et al, 2016) as well as resting state EEG (Schartner et al, 2015). Further, recent literature has highlighted high frequency (20-50 Hz) activity in the parietal cortex (a 'posterior hot zone') as a neural correlate of conscious contents (Koch et al, 2016;Siclari et al, 2017). We complement this finding by highlighting frontoparietal connectivity in the alpha band as a potential correlate of the level of consciousness.…”

Section: Discussionmentioning

confidence: 96%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

249

255

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Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions. Keywords: disorders of consciousness; electroencephalography; positron emission tomography; resting state; brain networks Abbreviations: AUC = area under the curve; CRS-R = Coma Recovery Scale-Revised; dwPLI = debiased weighted phase lag index;GOS-E = Glasgow Outcome Scale-Extended; MCS = minimally conscious state; SVM = support vector machine; UWS = unresponsive wakefulness syndrome

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Section: Discussionmentioning

confidence: 96%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

249

255

View full text Add to dashboard Cite

show abstract

“…A similar idea is found in work on global states, background states, or modes of consciousness, where REM sleep/dreaming is contrasted with presumably unconscious NREM sleep (for details and critical discussion, see Bayne, Hohwy, & Owen, ). Because sleep isolates consciousness from potential confounds arising from outward behavior and external stimulus processing that accompany consciousness in waking, the contrast between dreamful and dreamless (presumably unconscious) sleep has also been proposed as a research model of consciousness (Churchland, ; Revonsuo, ) and led to specific experiments probing the neural correlates of consciousness (Siclari et al, ; Tononi & Massimini, ).…”

Section: The Traditional View Of Sleepmentioning

confidence: 99%

Consciousness in sleep: How findings from sleep and dream research challenge our understanding of sleep, waking, and consciousness

Windt

2020

Philosophy Compass

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Sleep is phenomenologically rich, teeming with different kinds of conscious thought and experience. Dreaming is the most prominent example, but there is more to conscious experience in sleep than dreaming. Especially in non‐rapid eye movement sleep, conscious experience, sometimes dreamful, sometimes dreamless, also alternates with a loss of consciousness. Yet while dreaming has become established as a topic for interdisciplinary consciousness science and empirically informed philosophy of mind, the same is not true of other kinds of sleep‐related experience, nor is it true of sleep itself. I argue that this is a mistake. Conscious experience in sleep is more diverse than dreaming and we need to explain its different forms as well as the alternation between conscious and unconscious sleep states. We also need to ask how different kinds of sleep‐related experience relate to foundational issues about sleep and wakefulness as well as sleep stages. I survey recent findings and theoretical developments from sleep and dream research to show how the traditional view of sleep and its relation to wakefulness and consciousness is flawed. I then suggest that by refining our frameworks of sleep‐related experiences and sleep staging in tandem, we can work toward a better view. As we are only beginning to understand the diversity of consciousness in sleep, an important aim is programmatic: We need a philosophy of sleep and of consciousness in sleep, not just a philosophy of dreaming, and a future theory of sleep needs to integrate phenomenological considerations with neuroscientific and behavioral evidence. Working toward such a theory will radically transform our understanding of sleep, wakefulness, and our conscious minds.

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“…Cartwright (2008), for one, has argued that dreams lost their prominence in the latter half of the 20th century as psychology attempted to become a more empirical science focused on observable behavior and mental activity and less reliant on memory. In the last decade, the distinctive brain patterns of dreaming have become more identifiable (Siclari et al, 2017) and research has amassed on the impact of dreams on waking life with links to mood (Cartwright, 2013), relationship health (Selterman et al, 2012) and decision-making (Morewedge and Norton, 2009). While scientists debate the purpose of dreams (Barrett, 2007;Cartwright et al, 2006), dreams continue to be a universal and time intensive experience across humanity.…”

Section: Introductionmentioning

confidence: 99%

In your wildest dreams: the language and psychological features of dreams

Niederhoffer¹,

Schler²,

Crutchley

et al. 2017

Proceedings of the Fourth Workshop on Computational Linguistics And Clinical Psychology –- From Linguistic Signal to

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In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.

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The neural correlates of dreaming

Cited by 528 publications

References 77 publications

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Consciousness in sleep: How findings from sleep and dream research challenge our understanding of sleep, waking, and consciousness

In your wildest dreams: the language and psychological features of dreams

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