Unresponsive but not necessarily unconscious: An introduction to the Special Focus

peer reviewe

Towards modern post-coma care based on neuroscientific evidence.

peer reviewed[en] BACKGROUND: Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients. METHOD: This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years. RESULTS: Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics. CONCLUSIONS: Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside

Distinct Oscillatory Frequencies Underlie Excitability of Human Occipital and Parietal Cortex.

Transcranial magnetic stimulation (TMS) of human occipital and posterior parietal cortex can give rise to visual sensations called phosphenes. We used near-threshold TMS with concurrent EEG recordings to measure how oscillatory brain dynamics covary, on single trials, with the perception of phosphenes after occipital and parietal TMS. Prestimulus power and phase, predominantly in the alpha band (8-13 Hz), predicted occipital TMS phosphenes, whereas higher-frequency beta-band (13-20 Hz) power (but not phase) predicted parietal TMS phosphenes. TMS-evoked responses related to phosphene perception were similar across stimulation sites and were characterized by an early (200 ms) posterior negativity and a later (>300 ms) parietal positivity in the time domain and an increase in low-frequency ( approximately 5-7 Hz) power followed by a broadband decrease in alpha/beta power in the time-frequency domain. These correlates of phosphene perception closely resemble known electrophysiological correlates of conscious perception of near-threshold visual stimuli. The regionally differential pattern of prestimulus predictors of phosphene perception suggests that distinct frequencies may reflect cortical excitability in occipital versus posterior parietal cortex, calling into question the broader assumption that the alpha rhythm may serve as a general index of cortical excitability.SIGNIFICANCE STATEMENT Alpha-band oscillations are thought to reflect cortical excitability and are therefore ascribed an important role in gating information transmission across cortex. We probed cortical excitability directly in human occipital and parietal cortex and observed that, whereas alpha-band dynamics indeed reflect excitability of occipital areas, beta-band activity was most predictive of parietal cortex excitability. Differences in the state of cortical excitability predicted perceptual outcomes (phosphenes), which were manifest in both early and late patterns of evoked activity, revealing the time course of phosphene perception. Our findings prompt revision of the notion that alpha activity reflects excitability across all of cortex and suggest instead that excitability in different regions is reflected in distinct frequency bands

Tracking dynamic interactions between structural and functional connectivity : a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain

Measuring consciousness in severely damaged brains.

Significant advances have been made in the behavioral assessment and clinical management of disorders of consciousness (DOC). In addition, functional neuroimaging paradigms are now available to help assess consciousness levels in this challenging patient population. The success of these neuroimaging approaches as diagnostic markers is, however, intrinsically linked to understanding the relationships between consciousness and the brain. In this context, a combined theoretical approach to neuroimaging studies is needed. The promise of such theoretically based markers is illustrated by recent findings that used a perturbational approach to assess the levels of consciousness. Further research on the contents of consciousness in DOC is also needed

La transe cognitive auto-induite : caractéristiques et applications thérapeutiques potentielles.

peer reviewe

A mean field approach to model levels of consciousness from EEG recordings

We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications

A mean field approach to model levels of consciousness from EEG recordings

We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications.Comment: 23 pages, 6 figures. Accepted for publication in Journal of Statistical Mechanics: Theory and Experimen