Neural correlates of consciousness: progress and problems

Koch, Christof; Massimini, Marcello; Boly, Mélanie; Tononi, Giulio

doi:10.1038/nrn.2016.22

Cited by 1,169 publications

(1,166 citation statements)

References 247 publications

(278 reference statements)

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“…Notably, TMS perturbations revealed complex cortical responses (PCI max > PCI*) also in 5 MCS patients, who according to the clinical classification of the resting EEG were characterized by a severely abnormal pattern (see Fig 3B, C). This suggests that assessing the joint presence of integration and differentiation through direct cortical perturbations17, 32 reveals additional information that is not immediately captured by conventional EEG descriptors and may direct further refinement of the classification of EEG resting activity.…”

Section: Discussionmentioning

confidence: 99%

Stratification of unresponsive patients by an independently validated index of brain complexity

Casarotto

Comanducci

Rosanova

et al. 2016

Annals of Neurology

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ObjectiveValidating objective, brain‐based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousness—the Perturbational Complexity Index (PCI)—in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs).MethodsThe benchmark population encompassed 150 healthy controls and communicative brain‐injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS]).ResultsWe found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition.InterpretationGiven its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high‐PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior. Ann Neurol 2016;80:718–729

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Section: Discussionmentioning

confidence: 99%

Stratification of unresponsive patients by an independently validated index of brain complexity

Casarotto

Comanducci

Rosanova

et al. 2016

Annals of Neurology

Self Cite

371

436

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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: 99%

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

Chennu

Annen²,

Wannez³

et al. 2017

Brain

236

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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“…The core behaviour systems (CBS) of the midbrain provide the substrate for primary consciousness driving both the neocortex (1) and the brainstem activating system (BAS) that in turn provide the activation and valuation of the thalamus (2) and neocortex (3). The neocortex (CRT) shares direct and indirect recurrent connections with the thalamus (4) and normative virtualization memory of secondary consciousness is implemented by the cortico-cortical and thalamo-cortical system of the fronto-parietal network (4,5). Cortical networks in turn can exert control over BAS (6) and CBS (1) closing a normative valuation feedback loop.…”

Section: (C) Empirical and Methodological Consequencesmentioning

confidence: 99%

“…For instance, Edelman proposes primary and secondary forms of consciousness that relate to the expanding temporal horizon in which the self operates, from the instantaneous physical experience (primary) to the imagined future and remembered past (secondary). Metzinger has further elaborated this notion where the self progresses from a globalized identification with the body or first person perspective (1PP), to (1), wheels (2), a gripper (3) and five proximity sensors (4). The environment contains a home base (yellow), obstacles (green) and two kinds of rewards (blue and red).…”

Section: The Dimensions Of Consciousnessmentioning

confidence: 99%

“…The former proposed to identify the Neural Correlate(s) of Consciousness (NCC) [1], while the latter advanced a theory-based approach including the use of synthetic systems or, so-called, brain-based devices [2]. The study of the NCC has given rise to a flurry of research, which unsurprisingly has uncovered a large universe of correlations between states of mind and brain and consciousness (see [3,4] for recent reviews), while the latter approach has dwindled. Here I will attempt to rectify this imbalance and advance the distributed adaptive control theory of consciousness (DACtoc).…”

Section: Introduction 'That Is Very Hokey!'mentioning

confidence: 99%

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Synthetic consciousness: the distributed adaptive control perspective

Verschure

2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

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Neural correlates of consciousness: progress and problems

Cited by 1,169 publications

References 247 publications

Stratification of unresponsive patients by an independently validated index of brain complexity

Stratification of unresponsive patients by an independently validated index of brain complexity

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

Synthetic consciousness: the distributed adaptive control perspective

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