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Funder: Marmaduke Sheild Fund, Funder: Fearnsides Fund, Funder: Addenbrooke's Charitable Trust; Id: http://dx.doi.org/10.13039/501100002927, Down's syndrome is associated with pathological ageing and a propensity for early-onset Alzheimer's disease. The early symptoms of dementia in people with Down's syndrome may reflect frontal lobe vulnerability to amyloid deposition. Auditory predictive processes rely on the bilateral auditory cortices with the recruitment of frontal cortices and appear to be impaired in pathologies characterized by compromised frontal lobe. Hence, auditory predictive processes were investigated to assess Down's syndrome pathology and its relationship with pathological ageing. An auditory electroencephalography (EEG) global-local paradigm was presented to the participants, in which oddball stimuli could either violate local or higher level global rules. We characterised predictive processes in individuals with Down's syndrome and their relationship with pathological ageing, with a focus on the EEG event-related potential called Mismatch Negativity (MMN) and the P300. In Down's syndrome, we also evaluated the EEG components as predictor of cognitive decline 1 year later. We found that predictive processes of detection of auditory violations are overall preserved in Down's syndrome but also that the amplitude of the MMN to local deviancies decreases with age. However, the 1-year follow-up of Down's syndrome found that none of the ERPs measures predicted subsequent cognitive decline. The present study provides a novel characterization of electrophysiological markers of local and global predictive processes in Down's syndrome.

Mental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of “seeing with the mind’s eye”. In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli has not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here we present a novel way to disentangle perception from report using eye-movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroen-cephalography, we show that measures of integration and differentiation based on information theory closely follow participants’ perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perceptionper se.

Funder: Comisión Nacional de Investigación Científica y Tecnológica; doi: http://dx.doi.org/10.13039/501100002848, The overt or covert ability to follow commands in patients with disorders of consciousness is considered a sign of awareness and has recently been defined as cortically mediated behaviour. Despite its clinical relevance, the brain signatures of the perceptual processing supporting command following have been elusive. This multimodal study investigates the temporal spectral pattern of electrical brain activity to identify features that differentiated healthy controls from patients both able and unable to follow commands. We combined evidence from behavioural assessment, functional neuroimaging during mental imagery and high-density electroencephalography collected during auditory prediction, from 21 patients and 10 controls. We used a penalised regression model to identify command following using features from electroencephalography. We identified seven well-defined spatiotemporal signatures in the delta, theta and alpha bands that together contribute to identify DoC subjects with and without the ability to follow command, and further distinguished these groups of patients from controls. A fine-grained analysis of these seven signatures enabled us to determine that increased delta modulation at the frontal sensors was the main feature in command following patients. In contrast, higher frequency theta and alpha modulations differentiated controls from both groups of patients. Our findings highlight a key role of spatiotemporally specific delta modulation in supporting cortically mediated behaviour including the ability to follow command. However, patients able to follow commands nevertheless have marked differences in brain activity in comparison with healthy volunteers.

Accurate early prognostication is vital for appropriate long-term care decisions after traumatic brain injury. While measures of resting-state EEG oscillations and their network properties, derived from graph theory, have been shown to provide clinically useful information regarding diagnosis and recovery in patients with chronic disorders of consciousness, little is known about the value of these network measures when calculated from a standard clinical low-density EEG in the acute phase post-injury. To investigate this link, we first validated a set of measures of oscillatory network features between high-density and low-density resting-state EEG in healthy individuals, thus ensuring accurate estimation of underlying cortical function in clinical recordings from patients. Next, we investigated the relationship between these features and the clinical picture and outcome of a group of 18 patients in acute post-traumatic unresponsive states who were not following commands 2 days+ after sedation hold. While the complexity of the alpha network, as indexed by the standard deviation of the participation coefficients, was significantly related to the patients’ clinical picture at the time of EEG, no network features were significantly related to outcome at 3 or 6 months post-injury. Rather, mean relative alpha power across all electrodes improved the accuracy of outcome prediction at 3 months relative to clinical features alone. These results highlight the link between the alpha rhythm and clinical signs of consciousness and suggest the potential for simple measures of resting-state EEG band power to provide a coarse snapshot of brain health for stratification of patients for rehabilitation, therapy and assessments of both covert and overt cognition., Accurate early prognostication is vital for appropriate long-term care decisions after traumatic brain injury. O’Donnell et al. observe a link between alpha band power in the resting-state electroencephalogram of patients in acute post-traumatic unresponsive states and outcome at 3 months post-injury, indicating the value of electroencephalography for augmenting prognoses., Graphical Abstract Graphical Abstract

Down’s Syndrome (DS) is associated with premature and accelerated ageing and a propensity for early-onset Alzheimer’s disease (AD). The early symptoms of dementia in people with DS may reflect frontal lobe vulnerability to amyloid deposition. The Mismatch Negativity (MMN) is a frontocentral component elicited by auditory violations of expected sensory input and it reflects sensory memory and automatic attention switching. In the typically developing (TD) population, the MMN response has been found to decrease with age. In the cross-sectional phase of this study the MMN was used to investigate the premature neurological ageing hypothesis of DS. In the longitudinal phase, we evaluated the MMN as a potential predictor of cognitive decline. The study found that age predicted MMN amplitude in DS but not in those who are TD, showing that the MMN reflects accelerated ageing in DS. However, a follow-up of 34 adults with DS found that neither amplitude nor latency of the MMN predicted cognitive decline one year later.

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs – the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) – to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 minutes of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior “hot zone”, frontoparietal connections, and between-RSN connections. We estimate – for the first time – a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.HighlightsModelling shows that connectivity within hot zone tracks change of conscious stateSeparately, frontoparietal connections support maintenance of conscious stateStrength of frontoparietal connections predicts conscious state in unseen dataBoth parietal hot zone and frontoparietal connectivity important for consciousnessFundingThis work was supported by the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Belgian National Funds for Scientific Research (FRS-FNRS), the University and University Hospital of Liege, the Fund Generet, the King Baudouin Foundation, the AstraZeneca Foundation, the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), DOCMA project (EU-H2020-MSCA–RISE–778234), the BIAL Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Center-TBI project (FP7-HEALTH-602150), the Public Utility Foundation ‘Université Européenne du Travail’, “Fondazione Europea di Ricerca Biomedica”, the Mind Science Foundation, the European Commission, and the Special Research Fund of Ghent University. O.G. is research associate and S.L. is research director at the F.R.S-FNRS.Declaration of interestNone.Significance StatementVarious connectivity studies have suggested multiple network-level mechanisms driving changes in the state of consciousness, such as the posterior hot zone, frontal-, and large-scale frontoparietal networks. Here, we computationally evaluate evidence for these mechanisms using dynamic causal modeling for resting EEG recorded before and during propofol-anaesthesia, and demonstrate that, particularly, connectivity in the posterior hot zone is impaired during propofol-induced unconsciousness. With a robust cross-validation paradigm, we show that connectivity in the large-scale frontoparietal networks can consistently predict the state of consciousness and further generalise these findings to an unseen state of recovery. These results suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.

Mental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of "seeing with the mind's eye". In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli has not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

Highlights • pDOC patients are assessed for 2-years longitudinally with both the CRS-R and EEG. • Theta power and alpha clustering correlated strongest with changes in CRS-R scores. • EEG combined with CRS-R improved predictive power of future CRS-R scores. • Early changes in EEG outperformed early changes in CRS-R in terms of prognostic power. • Regular and repeated bedside EEG is feasible and has clinical utility for pDOC., Providing an accurate prognosis for prolonged disorder of consciousness (pDOC) patients remains a clinical challenge. Large cross-sectional studies have demonstrated the diagnostic and prognostic value of functional brain networks measured using high-density electroencephalography (hdEEG). Nonetheless, the prognostic value of these neural measures has yet to be assessed by longitudinal follow-up. We address this gap by assessing the utility of hdEEG to prognosticate long-term behavioural outcome, employing longitudinal data collected from a cohort of patients assessed systematically with resting hdEEG and the Coma Recovery Scale-Revised (CRS-R) at the bedside over a period of two years. We used canonical correlation analysis to relate clinical (including CRS-R scores combined with demographic variables) and hdEEG variables to each other. This analysis revealed that the patient’s age, and the hdEEG theta band power and alpha band connectivity, contributed most significantly to the relationship between hdEEG and clinical variables. Further, we found that hdEEG measures recorded at the time of assessment augmented clinical measures in predicting CRS-R scores at the next assessment. Moreover, the rate of hdEEG change not only predicted later changes in CRS-R scores, but also outperformed clinical measures in terms of prognostic power. Together, these findings suggest that improvements in functional brain networks precede changes in behavioural awareness in pDOC. We demonstrate here that bedside hdEEG assessments conducted at specialist nursing homes are feasible, have clinical utility, and can complement clinical knowledge and systematic behavioural assessments to inform prognosis and care.

Neuroscience has generated a number of recent advances in the search for the neural correlates of consciousness, but these have yet to find valuable real-world applications. Electroencephalography under anesthesia provides a powerful experimental setup to identify electrophysiological signatures of altered states of consciousness, as well as a testbed for developing systems for automatic diagnosis and prognosis of awareness in clinical settings. In this work, we use deep convolutional neural networks to automatically differentiate sub-anesthetic states and depths of anesthesia, solely from one second of raw EEG signal. Our results with leave-one-participant-out-cross-validation show that behavioral measures, such as the Ramsay score, can be used to learn generalizable neural networks that reliably predict levels of unconsciousness in unseen transitional anesthetic states, as well as in unseen experimental setups and behaviors. Our findings highlight the potential of deep learning to detect progressive changes in anesthetic-induced unconsciousness with higher granularity than behavioral or pharmacological markers. This work has broader significance for identifying generalized patterns of brain activity that index states of consciousness.Clinical Relevance- In the United States alone, over 100,000 people receive general anesthesia every day, from which up to 1% is affected by unintended intraoperative awareness [1]. Despite this, brain-based monitoring of consciousness is not common in the clinic, and has had mixed success [2]. Given this context, our aim is to develop and explore an automated deep learning model that accurately predicts and interprets the depth and quality of anesthesia from the raw EEG signal.

At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration is the property of experiencing a collection of objects as a unitary percept and differentiation is the property of experiencing these objects as distinct from each other. Here, we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability), neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology. Fil: Canales Johnson, Andrés. University of Cambridge; Estados Unidos. Universidad Catolica de Maule; Chile Fil: Billig, Alexander J.. University of Western Ontario; Canadá Fil: Olivares, Francisco. Universidad Diego Portales; Chile Fil: Gonzalez, Andrés. Universidad Diego Portales; Chile Fil: Garcia, Mariá Del Carmen. Hospital Italiano; Argentina Fil: Silva, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Hospital Italiano; Argentina Fil: Vaucheret Paz, Esteban Fabian. Hospital Italiano; Argentina Fil: Ciraolo, Carlos. Hospital Italiano; Argentina Fil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Adolfo Ibañez; Chile Fil: Huepe, David. Universidad Adolfo Ibañez; Chile Fil: Noreika, Valdas. University of Cambridge; Estados Unidos Fil: Chennu, Srivas. University of Cambridge; Estados Unidos. University Of Kent; Reino Unido Fil: Bekinschtein, Tristan A. University of Cambridge; Estados Unidos

Two important theories in cognitive neuroscience are predictive coding and the global workspace theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the global workspace). To address this question, we present a source-localisation of EEG responses evoked by the local-global task – an experimental paradigm that engages a predictive hierarchy, which encompasses the global workspace. The results of our source reconstruction suggest three-phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions; consistent with a global neuronal workspace (as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a predictive coding explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the global neuronal workspace.

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., 23 pages, 6 figures. Accepted for publication in Journal of Statistical Mechanics: Theory and Experiment

Auditory localization (i.e. turning the head and/or the eyes towards an auditory stimulus) is often part of the clinical evaluation of patients recovering from coma. The objective of this study is to determine whether auditory localization could be considered as a new sign of minimally conscious state, using a multimodal approach. The presence of auditory localization and the clinical outcome at 2 years of follow-up were evaluated in 186 patients with severe brain injury, including 64 with unresponsive wakefulness syndrome, 28 in minimally conscious state minus, 71 in minimally conscious state plus and 23 who emerged from the minimally conscious state. Brain metabolism, functional connectivity and graph theory measures were investigated by means of 18F-fluorodeoxyglucose positron emission tomography, functional MRI and high-density electroencephalography in two subgroups of unresponsive patients, with and without auditory localization. These two subgroups were also compared to a subgroup of patients in minimally conscious state minus. Auditory localization was observed in 13% of unresponsive patients, 46% of patients in minimally conscious state minus, 62% of patients in minimally conscious state plus and 78% of patients who emerged from the minimally conscious state. The probability to observe an auditory localization increased along with the level of consciousness, and the presence of auditory localization could predict the level of consciousness. Patients with auditory localization had higher survival rates (at 2-year follow-up) than those without localization. Differences in brain function were found between unresponsive patients with and without auditory localization. Higher connectivity in unresponsive patients with auditory localization was measured between the fronto-parietal network and secondary visual areas, and in the alpha band electroencephalography network. Moreover, patients in minimally conscious state minus significantly differed from unresponsive patients without auditory localization in terms of brain metabolism and alpha network centrality, whereas no difference was found with unresponsive patients who presented auditory localization. Our multimodal findings suggest differences in brain function between unresponsive patients with and without auditory localization, which support our hypothesis that auditory localization should be considered as a new sign of minimally conscious state. Unresponsive patients showing auditory localization should therefore no longer be considered unresponsive but minimally conscious. This would have crucial consequences on these patients’ lives as it would directly impact the therapeutic orientation or end-of-life decisions usually taken based on the diagnosis., This study provides evidence that auditory localization should be considered as a new sign of minimally conscious state. The prevalence of auditory localization increases with the level of consciousness, and patients with unresponsive wakefulness syndrome and auditory localization have higher brain functioning and better outcome than unresponsive patients who do not show auditory localization., Graphical Abstract Graphical Abstract

Transcranial direct current stimulation (tDCS) applied over the dorsolateral prefrontal cortex (DLPFC) has induced promising behavioral improvement, both in acute and chronic minimally conscious state (MCS - [ 1 ]) patients [ 2 , 3 ]. We previously defined a tDCS-responder as a patient who demonstrates a new sign of consciousness following stimulation, which was neither present beforehand, nor before or after the sham stimulation [ 2 ]. In a study investigating the metabolic and structural differences between DLPFC-tDCS-responders and non-responders, we have identified that tDCS-responders presented a preservation of brain metabolism and grey matter integrity under the stimulated area, but also in the thalamus and the precuneus, areas involved in consciousness recovery [ 4 ]. Even if these results provided relevant insights into potential biomarkers of responsiveness, the access to such neuroimaging techniques (positron emission tomography - PET - and magnetic resonance imaging - MRI) remains limited. Recently, it has been demonstrated that high-density electroencephalography (hdEEG) network metrics in the alpha band correlates with the level of consciousness [ 5 ]. In addition, a strong correlation between brain metabolism and hdEEG network metrics was reported, making this bedside assessment a robust way to evaluate patients’ brain function.

Mental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of “seeing with the mind’s eye”. In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli have not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions.

Functional neuroimaging of covert perceptual and cognitive processes can inform the diagnoses and prognoses of patients with disorders of consciousness, such as the vegetative and minimally conscious states (VS;MCS). Here we report an event-related potential (ERP) paradigm for detecting a hierarchy of auditory processes in a group of healthy individuals and patients with disorders of consciousness. Simple cortical responses to sounds were observed in all 16 patients; 7/16 (44%) patients exhibited markers of the differential processing of speech and noise; and 1 patient produced evidence of the semantic processing of speech (i.e. the N400 effect). In several patients, the level of auditory processing that was evident from ERPs was higher than the abilities that were evident from behavioural assessment, indicating a greater sensitivity of ERPs in some cases. However, there were no differences in auditory processing between VS and MCS patient groups, indicating a lack of diagnostic specificity for this paradigm. Reliably detecting semantic processing by means of the N400 effect in passively listening single-subjects is a challenge. Multiple assessment methods are needed in order to fully characterise the abilities of patients with disorders of consciousness., Highlights • Event-related potentials (ERP) can identify covert auditory processes. • Some disorders of consciousness patients retain these auditory processes. • However, a lack of sensitivity suggests poor clinical utility.

Accurate diagnosis and prognosis of disorders of consciousness is complicated by the variability amongst patients’ trajectories. However, the majority of research and scientific knowledge in this field is based on cross-sectional studies. The translational gap in applying this knowledge to inform clinical management can only be bridged by research that systematically examines follow-up. In this study, we present findings from a novel longitudinal study of the long-term recovery trajectory of 39 patients, repeatedly assessed using the Coma Recovery Scale-Revised once every 3 months for 2 years, generating 185 assessments. Despite the expected inter-patient variability, there was a statistically significant improvement in behaviour over time. Further, improvements began approximately 22 months after injury. Individual variation in the trajectory of recovery was influenced by initial diagnosis. Patients with an initial diagnosis of unresponsive wakefulness state, who progressed to the minimally conscious state, did so at a median of 485 days following onset—later than 12-month period after which current guidelines propose permanence. Although current guidelines are based on the expectation that patients with traumatic brain injury show potential for recovery over longer periods than those with non-traumatic injury, we did not observe any differences between trajectories in these two subgroups. However, age was a significant predictor, with younger patients showing more promising recovery. Also, progressive increases in arousal contributed exponentially to improvements in behavioural awareness, especially in minimally conscious patients. These findings highlight the importance of indexing arousal when measuring awareness, and the potential for interventions to regulate arousal to aid long-term behavioural recovery in disorders of consciousness., Patients with disorder of consciousness showed longitudinal improvements in behaviour consistent with a pattern of late recovery. Variability in recovery was predicted by initial diagnosis and age. It was progressively influenced by arousal, highlighting the importance of measuring arousal and the potential for arousal interventions to aid recovery., Graphical Abstract Graphical Abstract

Transcranial magnetic stimulation (TMS) has been widely used in human cognitive neuroscience to examine the causal role of distinct cortical areas in perceptual, cognitive and motor functions. However, it is widely acknowledged that the effects of focal cortical stimulation can vary substantially between participants and even from trial to trial within individuals. Recent work from resting state functional magnetic resonance imaging (fMRI) studies has suggested that spontaneous fluctuations in alertness over a testing session can modulate the neural dynamics of cortical processing, even when participants remain awake and responsive to the task at hand. Here we investigated the extent to which spontaneous fluctuations in alertness during wake-to-sleep transition can account for the variability in neurophysiological responses to TMS. We combined single-pulse TMS with neural recording via electroencephalography (EEG) to quantify changes in motor and cortical reactivity with fluctuating levels of alertness defined objectively on the basis of ongoing brain activity. We observed rapid, non-linear changes in TMS-evoked responses with decreasing levels of alertness, even while participants remained responsive in the behavioural task. Specifically, we found that the amplitude of motor evoked potentials peaked during periods of EEG flattening, whereas TMS-evoked potentials increased and remained stable during EEG flattening and the subsequent occurrence of theta ripples that indicate the onset of NREM stage 1 sleep. Our findings suggest a rapid and complex reorganization of active neural networks in response to spontaneous fluctuations of alertness over relatively short periods of behavioural testing during wake-to-sleep transition.

At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration is the property of experiencing a collection of objects as a unitary percept and differentiation is the property of experiencing these objects as distinct from each other. Here, we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability), neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.

Functional connectivity (FC) estimation methods are extensively used in neuroimaging to measure brain inter-regional interactions. The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent relatively robust exemplars of spectral (wPLI) and information-theoretic (wSMI) connectivity measures that recently gained increased popularity due to their relative immunity to volume conduction. wPLI and wSMI are posited to have different sensitivity to linear and nonlinear relationships between neural sources, but their performance has never been directly compared. Here, using simulated high-density (hd-)EEG data, we evaluated the accuracy of these two metrics for detecting distinct types of regional interdependencies characterised by different combinations of linear and nonlinear components. Our results demonstrate that while wPLI performs generally better at detecting functional couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI is able to detect exclusively nonlinear interaction dynamics. To evaluate the potential impact of these differences on real experimental data, we computed wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults obtained in wakefulness and deep (N3-)sleep. While both wPLI and wSMI revealed a relative decrease in alpha-connectivity during sleep relative to wakefulness, only wSMI identified a relative increase in theta-connectivity, while wPLI detected an increase in delta-connectivity, likely reflecting the occurrence of traveling slow waves. Overall, our findings indicate that wPLI and wSMI provide distinct but complementary information about functional brain connectivity, and that their combined use could advance our knowledge of neural interactions underlying different behavioural states.

Clinicians are regularly faced with the difficult challenge of diagnosing consciousness after severe brain injury. As such, as many as 40% of minimally conscious patients who demonstrate fluctuations in arousal and awareness are known to be misdiagnosed as unresponsive/vegetative based on clinical consensus. Further, a significant minority of patients show evidence of hidden awareness not evident in their behaviour. Despite this, clinical assessments of behaviour are commonly used as bedside indicators of consciousness. Recent advances in functional high-density electroencephalography (hdEEG) have indicated that specific patterns of resting brain connectivity measured at the bedside are strongly correlated with the re-emergence of consciousness after brain injury. We report case studies of four patients with traumatic brain injury who underwent regular assessments of hdEEG connectivity and Coma Recovery Scale-Revised (CRS-R) at the bedside, as part of an ongoing longitudinal study. The first, a patient in an unresponsive wakefulness state (UWS), progressed to a minimally-conscious state several years after injury. HdEEG measures of alpha network centrality in this patient tracked this behavioural improvement. The second patient, contrasted with patient 1, presented with a persistent UWS diagnosis that paralleled with stability on the same alpha network centrality measure. Patient 3, diagnosed as minimally conscious minus (MCS-), demonstrated a significant late increase in behavioural awareness to minimally conscious plus (MCS+). This patient’s hdEEG connectivity across the previous 18 months showed a trajectory consistent with this increase alongside a decrease in delta power. Patient 4 contrasted with patient 3, with a persistent MCS- diagnosis that was similarly tracked by consistently high delta power over time. Across these contrasting cases, hdEEG connectivity captures both stability and recovery of behavioural trajectories both within and between patients. Our preliminary findings highlight the feasibility of bedside hdEEG assessments in the rehabilitation context and suggest that they can complement clinical evaluation with portable, accurate and timely generation of brain-based patient profiles. Further, such hdEEG assessments could be used to estimate the potential utility of complementary neuroimaging assessments, and to evaluate the efficacy of interventions.

While many studies have linked prediction errors and event related potentials1\ud at a single processing level, few consider how these responses interact across\ud levels. In response, we present a factorial analysis of a multi-level oddball task\ud – the local-global task – and we explore it when participants are sedated\ud versus recovered. We found that the local and global levels in fact interact.\ud This is of considerable current interest, since it has recently been argued that\ud the MEEG response evoked by the global effect corresponds to a distinct\ud processing mode that moves beyond predictive coding. This interaction\ud suggests that the two processing modes are not distinct. Additionally, we\ud observed that sedation modulates this interaction, suggesting that conscious awareness may not be completely restricted to a single (global) processing\ud level.

Objective The P300 component of a sensory event-related potential is one of the major electrophysiological markers used to explore remnants of cognitive function in patients with disorders of consciousness (DoC). However, measuring the P300 in patients is complicated by significant inter-trial variability commonly observed in levels of arousal and awareness. To overcome this limitation, we analyzed single-trial modulation of power in the delta and theta frequency bands, which underlie the P300. Methods In a preliminary cross-sectional study using a 24-channel EEG and a passive own-name oddball paradigm, we analyzed event-related synchronization (ERS) across trials in the delta and theta bands in a sample of 10 control and 12 DoC subjects. Results In comparison to controls, DoC subjects presented a low percentage of trials where delta ERS was observed. In particular, coordinated modulation between delta and theta in response to the stimulus was absent, with a high percentage of trials where only theta ERS was observed. Further, we found a positive correlation between the percentage of epochs with delta ERS and the strength of the P300. Conclusions Reduced modulation of spectral activity in the delta band in response to stimuli indicates a dissociation in the activity of the neural networks that oscillate in delta and theta ranges and contribute to the generation of the P300. Significance The reduction in spectral modulation observed in DoC provides a deeper understanding of neurophysiological dysfunction and the means to develop a more fine-grained marker of residual cognitive function in individual patients.