A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior

Casali, Adenauer G.; Gosseries, Olivia; Rosanova, Mario; Boly, Mélanie; Sarasso, Simone; Casali, Karina Rabello; Casarotto, Silvia; Bruno, Marie Aurélie; Laureys, Steven; Tononi, Giulio; Massimini, Marcello

doi:10.1126/scitranslmed.3006294

Cited by 998 publications

(1,289 citation statements)

References 63 publications

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“…Our results are also in agreement with several theories of consciousness (22,24) and earlier observations in sleep, anesthesia, and VS (18,(40)(41)(42)(43). Several theories of consciousness posit that distributed functional networks support conscious states and that loss of consciousness is indexed by alterations of these network patterns (22,24,42).…”

Section: Discussionsupporting

confidence: 81%

“…If so, how should functional connectivity data be processed to extract signatures of the conscious state, i.e., features of resting state activity that are only present in the awake state and disappear with the loss of consciousness? Identifying such signatures may have important consequences for clinical practice, as it would add to the small number of brain-imaging paradigms that are currently available to diagnose residual consciousness in VS patients (15,(18)(19)(20).…”

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confidence: 99%

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Signature of consciousness in the dynamics of resting-state brain activity

Barttfeld

Uhrig

Sitt

et al. 2015

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

618

696

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Section: Discussionsupporting

confidence: 81%

mentioning

confidence: 99%

Signature of consciousness in the dynamics of resting-state brain activity

Barttfeld

Uhrig

Sitt

et al. 2015

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

618

696

View full text Add to dashboard Cite

“…Further, as patients recover beyond MCS, it appears that both positive and negative correlations of activity within and between networks also reappear (Thibaut et al, 2012;Di Perri et al, 2016). 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).…”

Section: Discussionmentioning

confidence: 99%

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

Chennu

Annen²,

Wannez³

et al. 2017

Brain

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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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“…In the minimally conscious state, the PCI has intermediate values. In patients suffering from locked-in syndrome, the index is as high as in healthy awake subjects, a strong indication of full consciousness experienced by these patients (Casali et al, 2013). The PCI is an improvement over previous TMS/EEG measures, which are not always able to detect smaller graded changes in the level of consciousness (Rosanova et al, 2012).…”

Section: Implications Of Findingsmentioning

confidence: 77%

What Is Lost During Dreamless Sleep: The Relationship Between Neural Connectivity Patterns and Consciousness

Klímová

2014

Journal of European Psychology Students

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Non-rapid eye movement (NREM) sleep is characterised by reduced consciousness; thus, studying its neural characteristics acts as a useful indication of what is needed for conscious experience. The integrated information theory (Tononi, 2008) states that the ability of different thalamocortical regions to interact is crucial for consciousness, thereby motivating research concerning connectivity changes in the thalamocortical system that accompany changing consciousness levels. This review aims to discuss investigations of functional connectivity of resting-state and large-scale brain networks, applying correlational approaches to neuroimaging data as well as studies that used brain stimulation to investigate effective connectivity. Most findings suggest a reorganisation of functional brain networks where interregion connectivity is reduced and intra-region connectivity is stronger in deep sleep than wakefulness.

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A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior

Abstract: A theory-derived index of consciousness, which quantifies the complexity of the brain’s response to a stimulus, measures the level of consciousness in awake, sleeping, anesthetized, and brain-damaged subjects.

Cited by 998 publications

References 63 publications

Signature of consciousness in the dynamics of resting-state brain activity

Signature of consciousness in the dynamics of resting-state brain activity

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

What Is Lost During Dreamless Sleep: The Relationship Between Neural Connectivity Patterns and Consciousness

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