Search

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness., Competing Interests: O.G. is a research associate and S.L. is a research director at the F.R.S-FNRS., (Copyright: © 2024 Dagnino et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or 'information structure'), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision., Competing Interests: The authors have declared that no competing interests exist.

Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states., (© 2022. The Author(s).)

Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics., (© 2021. The Author(s).)

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness., Competing Interests: The authors have declared that no competing interests exist.

Objective: Brain-injured patients who are unresponsive at the bedside (ie, vegetative state/unresponsive wakefulness syndrome - VS/UWS) may present brain activity similar to patients in minimally conscious state (MCS). This peculiar condition has been termed "non-behavioural MCS" or "MCS*". In the present study we aimed to investigate the proportion and underlying brain characteristics of patients in MCS*., Methods: Brain 18 F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) was acquired on 135 brain-injured patients diagnosed in prolonged VS/UWS (n = 48) or MCS (n = 87). From an existing database, relative metabolic preservation in the fronto-parietal network (measured with standardized uptake value) was visually inspected by three experts. Patients with hypometabolism of the fronto-parietal network were labelled "VS/UWS", while its (partial) preservation either confirmed the behavioural diagnosis of "MCS" or, in absence of behavioural signs of consciousness, suggested a diagnosis of "MCS*". Clinical outcome at 1-year follow-up, functional connectivity, grey matter atrophy, and regional brain metabolic patterns were investigated in the three groups (VS/UWS, MCS* and MCS)., Results: 67% of behavioural VS/UWS presented a partial preservation of brain metabolism (ie, MCS*). Compared to VS/UWS patients, MCS* patients demonstrated a better outcome, global functional connectivity and grey matter preservation more compatible with the diagnosis of MCS. MCS* patients presented lower brain metabolism mostly in the posterior brain regions compared to MCS patients., Interpretation: MCS* is a frequent phenomenon that is associated with better outcome and better brain preservation than the diagnosis of VS/UWS. Complementary exams should be provided to all unresponsive patients before taking medical decisions. ANN NEUROL 2021;90:89-100., (© 2021 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

The neural monitoring of visceral inputs might play a role in first-person perspective (i.e., the unified viewpoint of subjective experience). In healthy participants, how the brain responds to heartbeats, measured as the heartbeat-evoked response (HER), correlates with perceptual, bodily, and self-consciousness. Here we show that HERs in resting-state EEG data distinguishes between postcomatose male and female human patients ( n  = 68, split into training and validation samples) with the unresponsive wakefulness syndrome and in patients in a minimally conscious state with high accuracy (random forest classifier, 87% accuracy, 96% sensitivity, and 50% specificity in the validation sample). Random EEG segments not locked to heartbeats were useful to predict unconsciousness/consciousness, but HERs were more accurate, indicating that HERs provide specific information on consciousness. HERs also led to more accurate classification than heart rate variability. HER-based consciousness scores correlate with glucose metabolism in the default-mode network node located in the right superior temporal sulcus, as well as with the right ventral occipitotemporal cortex. These results were obtained when consciousness was inferred from brain glucose met`abolism measured with positron emission topography. HERs reflected the consciousness diagnosis based on brain metabolism better than the consciousness diagnosis based on behavior (Coma Recovery Scale-Revised, 77% validation accuracy). HERs thus seem to capture a capacity for consciousness that does not necessarily translate into intentional overt behavior. These results confirm the role of HERs in consciousness, offer new leads for future bedside testing, and highlight the importance of defining consciousness and its neural mechanisms independently from behavior., Competing Interests: The authors declare no competing financial interests., (Copyright © 2021 the authors.)

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks. Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups. Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness., Methods: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets., Results: Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM., Interpretation: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853., (© 2018 American Neurological Association.)

Introduction: Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness., Methods: Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames., Results: Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

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., (© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Making an accurate diagnosis in patients with disorders of consciousness remains challenging. 18 F-fluorodeoxyglucose (FDG)-PET has been validated as a diagnostic tool in this population, and allows identifying unresponsive patients with a capacity for consciousness. In parallel, the perturbational complexity index (PCI), a new measure based on the analysis of the electroencephalographic response to transcranial magnetic stimulation, has also been suggested as a tool to distinguish between unconscious and conscious states. The aim of the study was to cross-validate FDG-PET and PCI, and to identify signs of consciousness in otherwise unresponsive patients., Methods: We jointly applied the Coma Recovery Scale-Revised, FDG-PET and PCI to assess 24 patients with non-acute disorders of consciousness or locked-in syndrome (13 male; 19-54 years old; 12 traumatic; 9 unresponsive wakefulness syndrome, 11 minimally conscious state; 2 emergence from the minimally conscious state, and 2 locked-in syndrome)., Results: FDG-PET and PCI provided congruent results in 22 patients, regardless of their behavioural diagnosis. Notably, FDG-PET and PCI revealed preserved metabolic rates and high complexity levels in four patients who were behaviourally unresponsive., Conclusion: We propose that jointly measuring the metabolic activity and the electrophysiological complexity of cortical circuits is a useful complement to the diagnosis and stratification of patients with disorders of consciousness.