Neural signature of the conscious processing of auditory regularities

Strategic and Non-Strategic Semantic Expectations Hierarchically Modulate Neural Processing

Perception is facilitated by a hierarchy of expectations generated from context and prior knowledge. In auditory processing, violations of local (within-trial) expectations elicit a mismatch negativity (MMN), while violations of global (across-trial) expectations elicit a later positive component (P300). This result is taken as evidence of prediction errors ascending through the expectation hierarchy. However, in language comprehension, there is no evidence that violations of semantic expectations across local-global levels similarly elicit a sequence of hierarchical error signals, thus drawing into question the putative link between event-related potentials (ERPs) and prediction errors. We investigated the neural basis of such hierarchical expectations of semantics in a word-pair priming paradigm. By manipulating the overall proportion of related or unrelated word-pairs across the task, we created two global contexts that differentially encouraged strategic use of primes. Across two experiments, we replicated behavioral evidence of greater priming in the high validity context, reflecting strategic expectations of upcoming targets based on "global" context. In our preregistered EEG analyses, we observed a "local" prediction error ERP effect (i.e., semantic priming) ∼250 ms post-target, which, in exploratory analyses, was followed 100 ms later by a signal that interacted with the global context. However, the later effect behaved in an apredictive manner, i.e., was most extreme for fulfilled expectations, rather than violations. Our results are consistent with interpretations of early ERPs as reflections of prediction error and later ERPs as processes related to conscious access and in support of task demands.

Auditory and Somatosensory P3 Are Complementary for the Assessment of Patients with Disorders of Consciousness

The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awareness. Some patients, although with remaining cognitive abilities, are not able to exhibit overt voluntary responses at the bedside and may be misdiagnosed as UWS. Several studies investigated functional neuroimaging and neurophysiology as an additional tool to evaluate the level of consciousness and to detect covert command following in DOC. Most of these studies are based on auditory stimulation, neglecting patients suffering from decreased or absent hearing abilities. In the present study, we aim to assess the response to a P3-based paradigm in 40 patients with DOC and 12 healthy participants using auditory (AEP) and vibrotactile (VTP) stimulation. To this end, an EEG-based brain-computer interface was used at DOC patient's bedside. We compared the significance of the P3 performance (i.e., the interpretation of significance of the evoked P3 response) as obtained by 'direct processing' (i.e., theoretical-based significance threshold) and 'offline processing' (i.e., permutation-based single subject level threshold). We evaluated whether the P3 performances were dependent on clinical variables such as diagnosis (UWS and MCS), aetiology and time since injury. Last we tested the dependency of AEP and VTP performances at the single subject level. Direct processing tends to overestimate P3 performance. We did not find any difference in the presence of a P3 performance according to the level of consciousness (UWS vs. MCS) or the aetiology (traumatic vs. non-traumatic brain injury). The performance achieved at the AEP paradigm was independent from what was achieved at the VTP paradigm, indicating that some patients performed better on the AEP task while others performed better on the VTP task. Our results support the importance of using multimodal approaches in the assessment of DOC patients in order to optimise the evaluation of patient's abilities.

Magnetoencephalographic signatures of hierarchical rule learning in newborns

Estimating the extent to which newborn humans process input from their environment, especially regarding the depth of processing, is a challenging question. To approach this problem, we measured brain responses in 20 newborns with magnetoencephalography (MEG) in a "local-global" auditory oddball paradigm in which two-levels of hierarchical regularities are presented. Results suggest that infants in the first weeks of life are able to learn hierarchical rules, yet a certain level of vigilance seems to be necessary. Newborns detected violations of the first-order regularity and displayed a mismatch response between 200-400 ms. Violations of the second-order regularity only evoked a late response in newborns in an active state, which was expressed by a high heart rate variability. These findings are in line with those obtained in human adults and older infants suggesting a continuity in the functional architecture from term-birth on, despite the immaturity of the human brain at this age.

Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality

By recording neural activity directly from the human brain, researchers gain unprecedented insight into how neurocognitive processes unfold in real time. We first briefly discuss how intracranial electroencephalography (iEEG) recordings, performed for clinical practice, are used to study human cognition with the spatiotemporal and single-trial precision traditionally limited to non-human animal research. We then delineate how studies using iEEG have informed our understanding of issues fundamental to human cognition: auditory prediction, working and episodic memory, and internal cognition. We also discuss the potential of iEEG to infer causality through the manipulation or 'engineering' of neurocognitive processes via spatiotemporally precise electrical stimulation. We close by highlighting limitations of iEEG, potential of burgeoning techniques to further increase spatiotemporal precision, and implications for future research using intracranial approaches to understand, restore, and enhance human cognition.

Electrophysiological and Neuroimaging Studies - During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review

A severe brain injury may lead to a disorder of consciousness (DOC) such as coma, vegetative state (VS), minimally conscious state (MCS) or locked-in syndrome (LIS). Till date, the diagnosis of DOC relies only on clinical evaluation or subjective scoring systems such as Glasgow coma scale, which fails to detect subtle changes and thereby results in diagnostic errors. The high rate of misdiagnosis and inability to predict the recovery of consciousness for DOC patients have created a huge research interest in the assessment of consciousness. Researchers have explored the use of various stimulation and neuroimaging techniques to improve the diagnosis. In this article, we present the important findings of resting-state as well as sensory stimulation methods and highlight the stimuli proven to be successful in the assessment of consciousness. Primarily, we review the literature based on (a) application/non-use of stimuli (i.e., sensory stimulation/resting state-based), (b) type of stimulation used (i.e., auditory, visual, tactile, olfactory, or mental-imagery), (c) electrophysiological signal used (EEG/ERP, fMRI, PET, EMG, SCL, or ECG). Among the sensory stimulation methods, auditory stimulation has been extensively used, since it is easier to conduct for these patients. Olfactory and tactile stimulation have been less explored and need further research. Emotionally charged stimuli such as subject’s own name or narratives in a familiar voice or subject’s own face/family pictures or music result in stronger responses than neutral stimuli. Studies based on resting state analysis have employed measures like complexity, power spectral features, entropy and functional connectivity patterns to distinguish between the VS and MCS patients. Resting-state EEG and fMRI are the state-of-the-art techniques and have a huge potential in predicting the recovery of coma patients. Further, EMG and mental-imagery based studies attempt to obtain volitional responses from the VS patients and thus could detect their command-following capability. This may provide an effective means to communicate with these patients. Recent studies have employed fMRI and PET to understand the brain-activation patterns corresponding to the mental imagery. This review promotes our knowledge about the techniques used for the diagnosis of patients with DOC and attempts to provide ideas for future research.

Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect

Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) 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 GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task—an experimental paradigm that engages a predictive hierarchy, which encompasses the GW. 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 (GNW; 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 PC explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the GNW.

Concealed information revealed by involuntary eye movements on the fringe of awareness in a mock terror experiment

Involuntary eye movements during fixation are typically inhibited following stimulus onset (Oculomotor Inhibition, OMI), depending on the stimulus saliency and attention, with an earlier and longer OMI for barely visible familiar faces. However, it is still unclear whether OMI regarding familiarities and perceptual saliencies differ enough to allow a reliable OMI-based concealed information test (CIT). In a “mock terror” experiment with 25 volunteers, 13 made a concealed choice of a “terror-target” (one of eight), associated with 3 probes (face, name, and residence), which they learned watching text and videos, whereas 12 “innocents” pre-learned nothing. All participants then watched ~ 25 min of repeated brief presentations of barely visible (masked) stimuli that included the 8 potential probes, as well as a universally familiar face as a reference, while their eye movements were monitored. We found prolonged and deviant OMI regarding the probes. Incorporated with the individual pattern of responses to the reference, our analysis correctly identified 100% of the terror targets, and was 95% correct in discriminating “terrorists” from “innocents”. Our results provide a “proof of concept” for a novel approach to CIT, based on involuntary oculomotor responses to barely visible stimuli, individually tailored, and with high accuracy and theoretical resistance to countermeasures.

Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study

Long-range cortico-cortical functional connectivity has long been theorized to be necessary for conscious states. In the present work, we estimate long-range cortical connectivity in a series of intracranial and scalp EEG recordings experiments. In the two first experiments intracranial-EEG (iEEG) was recorded during four distinct states within the same individuals: conscious wakefulness (CW), rapid-eye-movement sleep (REM), stable periods of slow-wave sleep (SWS) and deep propofol anaesthesia (PA). We estimated functional connectivity using the following two methods: weighted Symbolic-Mutual-Information (wSMI) and phase-locked value (PLV). Our results showed that long-range functional connectivity in the delta-theta frequency band specifically discriminated CW and REM from SWS and PA. In the third experiment, we generalized this original finding on a large cohort of brain-injured patients. FC in the delta-theta band was significantly higher in patients being in a minimally conscious state (MCS) than in those being in a vegetative state (or unresponsive wakefulness syndrome). Taken together the present results suggest that FC of cortical activity in this slow frequency band is a new and robust signature of conscious states.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

The global workspace theory, the phenomenal concept strategy, and the distribution of consciousness

Peter Carruthers argues that the global workspace theory implies there are no facts of the matter about animal consciousness. The argument is easily extended to other cognitive theories of consciousness, posing a general problem for consciousness studies. But the argument proves too much, for it also implies that there are no facts of the matter about human consciousness. A key assumption is that scientific theories of consciousness must explain away the explanatory gap. I criticize this assumption and point to an alternative strategy for defending scientific theories of consciousness, one that better reflects the ongoing scientific practice. I argue there are introspectable inferential connections from phenomenal concepts to functional concepts that scientists can use to individuate the global workspace in terms of capacities that animals and humans share.

Habituation of auditory startle reflex is a new sign of minimally conscious state

Neurological examination of non-communicating patients relies on a few decisive items that enable the crucial distinction between vegetative state (VS)-also coined unresponsive wakefulness syndrome (UWS)-and minimally conscious state. Over the past 10 years, this distinction has proven its diagnostic value as well as its important prognostic value on consciousness recovery. However, clinicians are currently limited by three factors: (i) the current behavioural repertoire of minimally conscious state items is limited and restricted to a few cognitive domains in the goldstandard revised version of the Coma Recovery Scale; (ii) a proportion of ∼15-20% clinically VS/UWS patients are actually in a richer state than VS/UWS as evidenced by functional brain imaging; and (iii) the neurophysiological and cognitive interpretation of each minimally conscious state item is still unclear and debated. In the current study we demonstrate that habituation of the auditory startle reflex (hASR) tested at bedside constitutes a novel, simple and powerful behavioural sign that can accurately distinguish minimally conscious state from VS/UWS. In addition to enlarging the minimally conscious state items repertoire, and therefore decreasing the low sensitivity of current behavioural measures, we also provide an original and rigorous description of the neurophysiological basis of hASR through a combination of functional (high density EEG and 18F-fluorodeoxyglucose PET imaging) and structural (diffusion tensor imaging MRI) measures. We show that preservation of hASR is associated with the functional and structural integrity of a brain-scale fronto-parietal network, including prefrontal regions related to control of action and inhibition, and meso-parietal areas associated with minimally conscious and conscious states. Lastly, we show that hASR predicts 6-month improvement of consciousness. Taken together, our results show that hASR is a cortically-mediated behaviour, and suggest that it could be a new clinical item to clearly and accurately identify non-communicating patients who are in the minimally conscious state.

Applied potential of task-free event-related paradigms for assessing neurocognitive functions in disorders of consciousness

Diagnosing patients with disorders of consciousness is immensely difficult and often results in misdiagnoses, which can have fatal consequences. Despite the severity of this well-known issue, a reliable assessment tool has not yet been developed and implemented in the clinic. The main aim of this focused review is to evaluate the various event-related potential paradigms, recorded using EEG, which may be used to improve the assessment of patients with disorders of consciousness; we also provide a brief comparison of these paradigms with other measures. Notably, most event-related potential studies on the topic have focused on testing a small set of components, or even just a single component. However, to be of practical use, we argue that an assessment should probe a range of cognitive and linguistic functions at once. We suggest a novel approach that combines a set of well-tested auditory event-related potential components: N100, mismatch negativity, P3a, N400, early left anterior negativity and lexical response enhancement. Combining these components in a single, task-free design will provide a multidimensional assessment of cognitive and linguistic processes, which may help physicians make a more precise diagnosis.

Electrophysiological evidence of preserved hearing at the end of life

This study attempts to answer the question: “Is hearing the last to go?” We present evidence of hearing among unresponsive actively dying hospice patients. Individual ERP (MMN, P3a, and P3b) responses to deviations in auditory patterns are reported for conscious young, healthy control participants, as well as for hospice patients, both when the latter were conscious, and again when they became unresponsive to their environment. Whereas the MMN (and perhaps too the P3a) is considered an automatic response to auditory irregularities, the P3b is associated with conscious detection of oddball targets. All control participants, and most responsive hospice patients, evidenced a “local” effect (either a MMN, a P3a, or both) and some a “global” effect (P3b) to deviations in tone, or deviations in auditory pattern. Importantly, most unresponsive patients showed evidence of MMN responses to tone changes, and some showed a P3a or P3b response to either tone or pattern changes. Thus, their auditory systems were responding similarly to those of young, healthy controls just hours from end of life. Hearing may indeed be one of the last senses to lose function as humans die.

Finding hidden treasures: A child-friendly neural test of task-following in individuals using functional Transcranial Doppler ultrasound

Despite growing interest in the mental life of individuals who cannot communicate verbally, objective and non-invasive tests of covert cognition are still sparse. In this study, we assessed the ability of neurotypical children to understand and follow task instructions by measuring neural responses through functional transcranial Doppler ultrasound (fTCD). We recorded blood flow velocity for the two brain hemispheres of twenty children (aged 9 to 12) while they performed either a language task or a visuospatial memory task, on identical visual stimuli. We extracted measures of neural lateralisation for the two tasks separately to investigate lateralisation, and we compared the left-minus-right pattern of activation across tasks to assess task-following. At the group level, we found that neural responses were left-lateralised when children performed the language task, and not when they performed the visuospatial task. However, with statistically robust analyses and controlled paradigms, significant lateralisation in individual children was less frequent than expected from the literature. Nonetheless, the pattern of hemispheric activation for the two tasks allowed us to confirm task-following in the group of participants, as well as in over half of the individuals. This provides a promising avenue for a covert and inexpensive test of children's ability to follow task instructions and perform different mental tasks on identical stimuli.

Brain dynamics for confidence-weighted learning

Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15–30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations.

Learning in a changing and uncertain world is difficult. In this context, facing a discrepancy between my current belief and new observations may reflect random fluctuations (e.g. my commute train is unexpectedly late, but it happens sometimes), if so, I should ignore this discrepancy and not change erratically my belief. However, this discrepancy could also denote a profound change (e.g. the train company changed and is less reliable), in this case, I should promptly revise my current belief. Human learning is adaptive: we change how much we learn from new observations, in particular, we promote flexibility when facing profound changes. A mathematical analysis of the problem shows that we should increase flexibility when the confidence about our current belief is low, which occurs when a change is suspected. Here, I show that human learners entertain rational confidence levels during the learning of changing probabilities. This confidence modulates intrinsic properties of the brain state (oscillatory activity and neuromodulation) which in turn amplifies or reduces, depending on whether confidence is low or high, the neural responses to discrepant observations. This confidence-weighting mechanism could underpin adaptive learning.

Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

How does information from seconds earlier affect neocortical responses to new input? We found that when two groups of participants heard the same sentence in a narrative, preceded by different contexts, the neural responses of each group were initially different but gradually fell into alignment. We observed a hierarchical gradient: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. What computations explain this hierarchical temporal organization? Linear integration models predict that regions that are slower to integrate new information should also be slower to forget old information. However, we found that higher-order regions could rapidly forget prior context. The data from the cortical hierarchy were instead captured by a model in which each region maintains a temporal context representation that is nonlinearly integrated with input at each moment, and this integration is gated by local prediction error.

Split-Brain: What We Know Now and Why This is Important for Understanding Consciousness

Recently, the discussion regarding the consequences of cutting the corpus callosum (“split-brain”) has regained momentum (Corballis, Corballis, Berlucchi, & Marzi, Brain, 141(6), e46, 2018; Pinto et al., Brain, 140(5), 1231–1237, 2017a; Pinto, Lamme, & de Haan, Brain, 140(11), e68, 2017; Volz & Gazzaniga, Brain, 140(7), 2051–2060, 2017; Volz, Hillyard, Miller, & Gazzaniga, Brain, 141(3), e15, 2018). This collective review paper aims to summarize the empirical common ground, to delineate the different interpretations, and to identify the remaining questions. In short, callosotomy leads to a broad breakdown of functional integration ranging from perception to attention. However, the breakdown is not absolute as several processes, such as action control, seem to remain unified. Disagreement exists about the responsible mechanisms for this remaining unity. The main issue concerns the first-person perspective of a split-brain patient. Does a split-brain harbor a split consciousness or is consciousness unified? The current consensus is that the body of evidence is insufficient to answer this question, and different suggestions are made with respect to how future studies might address this paucity. In addition, it is suggested that the answers might not be a simple yes or no but that intermediate conceptualizations need to be considered.

Pearls and pitfalls in brain functional analysis by event-related potentials: a narrative review by the Italian Psychophysiology and Cognitive Neuroscience Society on methodological limits and clinical reliability-part I

Event-related potentials (ERPs) are obtained from the electroencephalogram (EEG) or the magnetoencephalogram (MEG, event-related fields (ERF)), extracting the activity that is time-locked to an event. Despite the potential utility of ERP/ERF in cognitive domain, the clinical standardization of their use is presently undefined for most of procedures. The aim of the present review is to establish limits and reliability of ERP medical application, summarize main methodological issues, and present evidence of clinical application and future improvement. The present section of the review focuses on well-standardized ERP methods, including P300, Contingent Negative Variation (CNV), Mismatch Negativity (MMN), and N400, with a chapter dedicated to laser-evoked potentials (LEPs). One section is dedicated to proactive preparatory brain activity as the Bereitschaftspotential and the prefrontal negativity (BP and pN). The P300 and the MMN potentials have a limited but recognized role in the diagnosis of cognitive impairment and consciousness disorders. LEPs have a well-documented usefulness in the diagnosis of neuropathic pain, with low application in clinical assessment of psychophysiological basis of pain. The other ERP components mentioned here, though largely applied in normal and pathological cases and well standardized, are still confined to the research field. CNV, BP, and pN deserve to be largely tested in movement disorders, just to explain possible functional changes in motor preparation circuits subtending different clinical pictures and responses to treatments.

How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Exploitation of local and global information in predictive processing

While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, probabilistic information at critical points in time (so-called checkpoints) has been suggested to be sampled in order to evaluate the internal model, particularly in uncertain contexts. This way, initial model-based expectations are iteratively reaffirmed under uncertainty, even in the absence of prediction errors. Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global uncertainty information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences (i.e., 3-4-5-6-7) that had a regular length of five digits and were occasionally extended to seven digits. Over time, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues. Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. As for model-compliant information, multivariate pattern decoding within the P3b time frame demonstrated effective exploitation of local (adjustment cues vs non-informative analogues) and global information (high vs low uncertainty regular endings) sampled from probabilistic events. Finally, superior fit of a global model (disregarding local adjustments) compared to a local model (including local adjustments) in a representational similarity analysis underscored the precedence of global reference frames in hierarchical predictive processing. Overall, results suggest that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Following the hierarchical organisation of predictive processing, model evaluation too can occur at several levels of the processing hierarchy.

Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system

Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: The mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.

The developing predictive brain: How implicit temporal expectancy induced by local and global prediction shapes action preparation across development

Human behavior is continuously shaped not just as a function of explicitly responding to external world events but also by internal biases implicitly driven by the capacity to extract statistics from complex sensory patterns. Two possible sources of predictability engaged to generate and update temporal expectancy are the implicit extraction of either local or global statistical contingencies in the events' temporal structure. In the context of action preparation the local prediction has been reported to be stable from the age of 6. However, there is no evidence about how the ability to extract and use global statistical patterns to establish temporal expectancy changes across development. Here we used a new, child-friendly reaction time task purposely designed to investigate how local (within-trial expectancy bias) and global (between-block expectancy bias) prediction interplay to generate temporal expectancy and consequently shape action preparation in young (5- to 6-year-old), middle-aged (7- to 8-year-old) and old (9- to 10-year-old) typically developing children. We found that while local temporal prediction showed stable developmental trajectories, the ability to use a global rule to action preparation in terms of both accuracy and speed becomes stable after the age of seven. These findings are discussed by adopting a neuroconstructivist-inspired theoretical account, according to which the developmental constraints on learning from hierarchically nested levels of sensory complexity may constitute a necessary prerequisite for mastering complex domains.

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness.

Conscious Processing and the Global Neuronal Workspace Hypothesis

We review the central tenets and neuroanatomical basis of the global neuronal workspace (GNW) hypothesis, which attempts to account for the main scientific observations regarding the elementary mechanisms of conscious processing in the human brain. The GNW hypothesis proposes that, in the conscious state, a non-linear network ignition associated with recurrent processing amplifies and sustains a neural representation, allowing the corresponding information to be globally accessed by local processors. We examine this hypothesis in light of recent data that contrast brain activity evoked by either conscious or non-conscious contents, as well as during conscious or non-conscious states, particularly general anesthesia. We also discuss the relationship between the intertwined concepts of conscious processing, attention, and working memory.

Differential Effects of Awareness and Task Relevance on Early and Late ERPs in a No-Report Visual Oddball Paradigm

To date it is poorly understood how and when deviance processing interacts with awareness and task relevance. Furthermore, an important issue in the study of consciousness is the prevalent confound of conscious perception with the requirement of reporting it. This study addresses these topics using a no-report inattentional blindness paradigm with a visual oddball sequence of geometrical shapes presented to male and female human participants. Electrophysiological responses were obtained in three physically identical Phases A-C that differed only with respect to the instructions: (A) participants were uninformed about the shapes and attended an unrelated foreground task (inattentional blind), (B) were informed about the shapes but still attended the foreground task, and (C) attended the shapes. Conscious processing of shapes was indexed by the visual awareness negativity but not a P3. Deviance processing was associated with the visual mismatch negativity independently of consciousness and task relevance. The oddball P3, however, only emerged when the stimuli were task relevant, and was absent for consciously perceived but task irrelevant stimuli. The P3 thus does not represent a reliable marker of stimulus awareness. This result pattern supports the view of hierarchical predictive processing, where lower levels display automatic deviance processing, whereas higher levels require attention and task relevance.SIGNIFICANCE STATEMENT To react to potentially important changes in our environment it is fundamental to detect deviations from regularities of sensory input. It has yet to be understood how awareness and task relevance of this input interact with deviance processing. We investigated the role of awareness in deviance detection while at the same time circumventing the confound of awareness and report by means of a no-report paradigm. Our results suggest that early processes are elicited automatically, whereas, contrary to prominent theories, late processes do not depend on awareness but on task-based attention.

Interventions to improve outcomes in children and young people with unresponsive wakefulness syndrome following acquired brain injury: A systematic review

Unresponsive wakefulness syndrome (UWS) is a disorder of consciousness (DoC) which describes a state of wakefulness without evidence of self or environmental awareness, or interaction. There is currently no universally accepted evidence-based intervention for the treatment of UWS. This systematic review aimed to identify interventions to improve functioning in children and young people (0-25 years) with UWS following acquired brain injury (ABI). A systematic review of electronic databases was conducted, consisting of CINAHL, EMBASE, Medline, PsycINFO, PubMed, Cochrane Library, Scopus and Google Scholar. Eight studies met inclusion criteria. In these studies, the outcomes of interventions which aimed to improve quality of life, functional outcomes and/or increases in level of consciousness of paediatric patients in UWS were reported. Retrieved studies presented some evidence for improved consciousness and functional outcomes, following multi-component neurorehabilitation programmes, sensory stimulation or pharmacological interventions. Quality appraisal, using a modified version of the Downs and Black (1998) checklist, revealed risk of bias in a number of sources, including insufficient control over confounding variables, the use of inadequately validated outcome measures and concerns regarding diagnostic accuracy. More robust research is needed to adequately determine which interventions are most valuable at improving outcomes in paediatric UWS and to provide an improved evidence base for clinicians to draw upon when selecting treatment for patients.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness

BACKGROUND AND PURPOSE

Patients with acquired brain injury and acute or prolonged disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions on coma and other DoC is limited but accumulating. This guideline provides the state-of-the-art evidence regarding the diagnosis of DoC, summarizing data from bedside examination techniques, functional neuroimaging and electroencephalography (EEG).

METHODS

Sixteen members of the European Academy of Neurology (EAN) Scientific Panel on Coma and Chronic Disorders of Consciousness, representing 10 European countries, reviewed the scientific evidence for the evaluation of coma and other DoC using standard bibliographic measures. Recommendations followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The guideline was endorsed by the EAN.

RESULTS

Besides a comprehensive neurological examination, the following suggestions are made: probe for voluntary eye movements using a mirror; repeat clinical assessments in the subacute and chronic setting, using the Coma Recovery Scale - Revised; use the Full Outline of Unresponsiveness score instead of the Glasgow Coma Scale in the acute setting; obtain clinical standard EEG; search for sleep patterns on EEG, particularly rapid eye movement sleep and slow-wave sleep; and, whenever feasible, consider positron emission tomography, resting state functional magnetic resonance imaging (fMRI), active fMRI or EEG paradigms and quantitative analysis of high-density EEG to complement behavioral assessment in patients without command following at the bedside.

CONCLUSIONS

Standardized clinical evaluation, EEG-based techniques and functional neuroimaging should be integrated for multimodal evaluation of patients with DoC. The state of consciousness should be classified according to the highest level revealed by any of these three approaches.

Revisiting the Functional Anatomy of the Human Brain: Toward a Meta-Networking Theory of Cerebral Functions

For more than one century, brain processing was mainly thought in a localizationist framework, in which one given function was underpinned by a discrete, isolated cortical area, and with a similar cerebral organization across individuals. However, advances in brain mapping techniques in humans have provided new insights into the organizational principles of anatomo-functional architecture. Here, we review recent findings gained from neuroimaging, electrophysiological, as well as lesion studies. Based on these recent data on brain connectome, we challenge the traditional, outdated localizationist view and propose an alternative meta-networking theory. This model holds that complex cognitions and behaviors arise from the spatiotemporal integration of distributed but relatively specialized networks underlying conation and cognition (e.g., language, spatial cognition). Dynamic interactions between such circuits result in a perpetual succession of new equilibrium states, opening the door to considerable interindividual behavioral variability and to neuroplastic phenomena. Indeed, a meta-networking organization underlies the uniquely human propensity to learn complex abilities, and also explains how postlesional reshaping can lead to some degrees of functional compensation in brain-damaged patients. We discuss the major implications of this approach in fundamental neurosciences as well as for clinical developments, especially in neurology, psychiatry, neurorehabilitation, and restorative neurosurgery.

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

Surprise response as a probe for compressed memory states

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one’s memory of past stimuli. Thus, these responses may serve as a probe of different individuals’ remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework which suggests a more general, theoretically justifiable principle for biological and cognitive phenomena. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects’ recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons and the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.

Surprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we show the correspondence between the theoretical and physiological surprise, and calculate an estimate of the utilized memory. The generality of this framework allows it to be applied to different EEG features that reflect different modes and levels of the processing hierarchy, as well as other physiological measures of surprise responses. Future studies may use this framework to construct a handy diagnostic tool for a quantitative, individualized characterization of memory-related disorders.

Cortical mapping of mismatch responses to independent acoustic features

Predictive coding is an influential theory of neural processing underlying perceptual inference. However, it is unknown to what extent prediction violations of different sensory features are mediated in different regions in auditory cortex, with different dynamics, and by different mechanisms. This study investigates the neural responses to synthesized acoustic syllables, which could be expected or unexpected, along several features. By using electrocorticography (ECoG) in rat auditory cortex (subjects: adult female Wistar rats with normal hearing), we aimed at mapping regional differences in mismatch responses to different stimulus features. Continuous streams of morphed syllables formed roving oddball sequences in which each stimulus was repeated several times (thereby forming a standard) and subsequently replaced with a deviant stimulus which differed from the standard along one of several acoustic features: duration, pitch, interaural level differences (ILD), or consonant identity. Each of these features could assume one of several different levels, and the resulting change from standard to deviant could be larger or smaller. The deviant stimuli were then repeated to form new standards. We analyzed responses to the first repetition of a new stimulus (deviant) and its last repetition in a stimulus train (standard). For the ECoG recording, we implanted urethane-anaesthetized rats with 8 × 8 surface electrode arrays covering a 3 × 3 mm cortical patch encompassing primary and higher-order auditory cortex. We identified the response topographies and latencies of population activity evoked by acoustic stimuli in the rat auditory regions, and mapped their sensitivity to expectation violations along different acoustic features. For all features, the responses to deviant stimuli increased in amplitude relative to responses to standard stimuli. Deviance magnitude did not further modulate these mismatch responses. Mismatch responses to different feature violations showed a heterogeneous distribution across cortical areas, with no evidence for systematic topographic gradients for any of the tested features. However, within rats, the spatial distribution of mismatch responses varied more between features than the spatial distribution of tone-evoked responses. This result supports the notion that prediction error signaling along different stimulus features is subserved by different cortical populations, albeit with substantial heterogeneity across individuals.

Conscious and unconscious expectancy effects: A behavioral, scalp and intracranial electroencephalography study

OBJECTIVE

The scope of unconscious cognition stretched its limits dramatically during the last 40 years, yet most unconscious processes and representations that have been described so far are fleeting and very short-lived, whereas conscious representations can be actively maintained in working memory for a virtually unlimited period. In the present work we aimed at exploring conscious and unconscious lasting (>1 second) expectancy effects.

METHODS

In a series of four experiments we engaged participants in the foreperiod paradigm while using both unmasked and masked cues that were informative about the presence/absence of an upcoming target. We recorded behavioral responses, high-density scalp EEG (Exp. 2a), and intra-cranial EEG (Exp. 2b).

RESULTS

While conscious expectancy was associated with a large behavioral effect (~150 ms), unconscious expectancy effect was significant but much smaller (4 ms). Both conscious and unconscious expectancy Contingent Negative Variations (CNVs) originated from temporal cortices, but only the late component of conscious CNV originated from an additional source located in the vicinity of mesio-frontal areas and supplementary motor areas. Finally, only conscious expectancy was accessible to introspection.

CONCLUSIONS

Both unmasked and masked cues had an impact on response times and on brain activity.

SIGNIFICANCE

These results support a two-stage model of the underlying mechanisms of expectancy.

Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.

Neural Signal to Violations of Abstract Rules Using Speech-Like Stimuli

As the evidence of predictive processes playing a role in a wide variety of cognitive domains increases, the brain as a predictive machine becomes a central idea in neuroscience. In auditory processing, a considerable amount of progress has been made using variations of the Oddball design, but most of the existing work seems restricted to predictions based on physical features or conditional rules linking successive stimuli. To characterize the predictive capacity of the brain to abstract rules, we present here two experiments that use speech-like stimuli to overcome limitations and avoid common confounds. Pseudowords were presented in isolation, intermixed with infrequent deviants that contained unexpected phoneme sequences. As hypothesized, the occurrence of unexpected sequences of phonemes reliably elicited an early prediction error signal. These prediction error signals do not seemed to be modulated by attentional manipulations due to different task instructions, suggesting that the predictions are deployed even when the task at hand does not volitionally involve error detection. In contrast, the amount of syllables congruent with a standard pseudoword presented before the point of deviance exerted a strong modulation. Prediction error's amplitude doubled when two congruent syllables were presented instead of one, despite keeping local transitional probabilities constant. This suggests that auditory predictions can be built integrating information beyond the immediate past. In sum, the results presented here further contribute to the understanding of the predictive capabilities of the human auditory system when facing complex stimuli and abstract rules.

Search asymmetry in a serial auditory task: Neural source analyses of EEG implicate attention strategies

Here we report a detailed analysis of the fast network dynamics underlying P3a and P3b event-related potential (ERP) subcomponents generated during an unconventional serial auditory search paradigm. We dissect the electroencephalographic (EEG) data from an earlier study of ours, using a variety of advanced signal processing techniques, in order to discover how the brain is processing auditory targets differently when they possess a rare, salient, unpredictable feature not shared with distractors than when targets lack this feature but distractors have it. We find that brain regions associated with the Ventral Attention Network (VAN) are the primary neural generators of the P3a subcomponent in response to feature-present targets, whereas regions associated with the Dorsal Attention Network (DAN), as well as regions associated with detecting auditory oddball stimuli (ODD), may be the primary neural generators of the P3b, in the context of our study, and perhaps in search paradigms in general. Moreover, measurements of the time courses of oscillatory power changes and inter-regional synchronization in theta and low-gamma frequency bands were consistent with the early activation and synchronization within the VAN associated with the P3a subcomponent, and with the later activation and synchronization within the DAN and ODD networks associated with the P3b subcomponent. Implications of these finding for the mechanisms underlying search asymmetry phenomena are discussed.

Auditory deviance detection in the human insula: An intracranial EEG study

The human insula is known to be involved in auditory processing, but knowledge about its precise functional role and the underlying electrophysiology is limited. To assess its role in automatic auditory deviance detection we analyzed the EEG high frequency activity (HFA; 75-145 Hz) and ERPs from 90 intracranial insular channels across 16 patients undergoing pre-surgical intracranial monitoring for epilepsy treatment. Subjects passively listened to a stream of standard and deviant tones differing in four physical dimensions: intensity, frequency, location or time. HFA responses to auditory stimuli were found in the short and long gyri, and the anterior, superior, and inferior segments of the circular sulcus of the insular cortex. Only a subset of channels in the inferior segment of the circular sulcus of the insula showed HFA deviance detection responses, i.e., a greater and longer latency response to specific deviants relative to standards. Auditory deviancy processing was also later in the insula when compared with the superior temporal cortex. ERP results were more widespread and supported the HFA insular findings. These results provide evidence that the human insula is engaged during auditory deviance detection.

General Anesthesia: A Probe to Explore Consciousness

General anesthesia reversibly alters consciousness, without shutting down the brain globally. Depending on the anesthetic agent and dose, it may produce different consciousness states including a complete absence of subjective experience (unconsciousness), a conscious experience without perception of the environment (disconnected consciousness, like during dreaming), or episodes of oriented consciousness with awareness of the environment (connected consciousness). Each consciousness state may potentially be followed by explicit or implicit memories after the procedure. In this respect, anesthesia can be considered as a proxy to explore consciousness. During the recent years, progress in the exploration of brain function has allowed a better understanding of the neural correlates of consciousness, and of their alterations during anesthesia. Several changes in functional and effective between-region brain connectivity, consciousness network topology, and spatio-temporal dynamics of between-region interactions have been evidenced during anesthesia. Despite a set of effects that are common to many anesthetic agents, it is still uneasy to draw a comprehensive picture of the precise cascades during general anesthesia. Several questions remain unsolved, including the exact identification of the neural substrate of consciousness and its components, the detection of specific consciousness states in unresponsive patients and their associated memory processes, the processing of sensory information during anesthesia, the pharmacodynamic interactions between anesthetic agents, the direction-dependent hysteresis phenomenon during the transitions between consciousness states, the mechanisms of cognitive alterations that follow an anesthetic procedure, the identification of an eventual unitary mechanism of anesthesia-induced alteration of consciousness, the relationship between network effects and the biochemical or sleep-wake cycle targets of anesthetic agents, as well as the vast between-studies variations in dose and administration mode, leading to difficulties in between-studies comparisons. In this narrative review, we draw the picture of the current state of knowledge in anesthesia-induced unconsciousness, from insights gathered on propofol, halogenated vapors, ketamine, dexmedetomidine, benzodiazepines and xenon. We also describe how anesthesia can help understanding consciousness, we develop the above-mentioned unresolved questions, and propose tracks for future research.

Hierarchical modulation of auditory prediction error signaling is independent of attention

The auditory system is tuned to detect rhythmic regularities in the environment which can occur on different timescales. Event-related potentials such as mismatch negativity (MMN) and P3b are thought to index local and global deviance, respectively. However, it is not clear how these hierarchical levels interact and to what extent attention modulates this interaction. In this EEG study with 17 healthy young adults, we used a hierarchical oddball paradigm with local (sequence-level) and global (block-level) violations in attended and unattended conditions. Amplitude of N2 and P3b were analyzed in a 2*2*2 factorial model (local status, global status, attention condition). We found a significant interaction between the local and global status on the N2 amplitude, while there was no significant three-way interaction with attention, together demonstrating that lower-level prediction error is modulated by detection of higher-order regularity but expressed independently of attention. By contrast, higher-level prediction error, indexed by P3b, was sensitive to global regularity violations if the auditory stream was attended. The results demonstrate the capacity of our auditory perception to preattentively resolve conflicts between different levels of predictive hierarchy even across longer time intervals as indexed by MMN modulation, while P3b represents a different, attention-dependent system.

Low- and medium-rate auditory steady-state responses in patients with prolonged disorders of consciousness correlate with Coma Recovery Scale - Revised score

Diagnosis of consciousness in patients with prolonged disorders of consciousness (PDOC) remains challenging since their responsiveness is often very impaired, while their assessment depends on observable behavior. The aim of this proof-of-concept study was to evaluate whether low- and medium-rate amplitude-modulated (AM) auditory steady-state responses (ASSRs) can be sensitive to the state of PDOC patients and may thus serve as a diagnostic tool which does not explicitly depend on a patient's cooperation. EEG was recorded from nine unresponsive wakefulness syndrome/vegetative state (UWS/VS) and eight minimally conscious state (MCS)/emergence from MCS patients during stimulation with two-minute trains of simple tones, amplitude modulated (AM) by 4 Hz, 6 Hz, 8 Hz, 12 Hz, 20 Hz, 40 Hz. The obtained ASSRs were then related to the Coma Recovery Scale - Revised (CRS-R) diagnosis and its total score. We observed significant correlations between mean inter-trial phase coherence (PC) (averaged across all stimulation frequencies) and total CRS-R score, as well as between 40 Hz relative power (RP) and total CRS-R score. Moreover, both parameters significantly differed between the patient groups. Our preliminary results suggest that a passive auditory stimulation protocol consisting of low- and medium-rate ASSRs might be used as an objective estimate of the level of neural dysfunction in PDOC patients. Consequently, the integrity of the auditory system appears to be an important predictor of the actual state of consciousness in PDOC patients.

The Role of Top-Down Modulation in Shaping Sensory Processing Across Brain States: Implications for Consciousness

Top-down, feedback projections account for a large portion of all connections between neurons in the thalamocortical system, yet their precise role remains the subject of much discussion. A large number of studies has focused on investigating how sensory information is transformed across hierarchically-distributed processing stages in a feedforward fashion, and computational models have shown that purely feedforward artificial neural networks can even outperform humans in pattern classification tasks. What is then the functional role of feedback connections? Several key roles have been identified, ranging from attentional modulation to, crucially, conscious perception. Specifically, most of the major theories on consciousness postulate that feedback connections would play an essential role in enabling sensory information to be consciously perceived. Consequently, it follows that their efficacy in modulating target regions should drastically decrease in nonconscious brain states [non-rapid eye movement (REM) sleep, anesthesia] compared to conscious ones (wakefulness), and also in instances when a given sensory stimulus is not perceived compared to when it is. Until recently, however, this prediction could only be tested with correlative experiments, due to the lack of techniques to selectively manipulate and measure the activity of feedback pathways. In this article, we will review the most recent literature on the functions of feedback connections across brain states and based on the presence or absence of perception. We will focus on experiments studying mismatch negativity, a phenomenon which has been hypothesized to rely on top-down modulation but which persists during nonconscious states. While feedback modulation is generally dampened in nonconscious states and enhanced when perception occurs, there are clear deviations from this rule. As we will discuss, this may pose a challenge to most theories of consciousness, and possibly require a change in how the level of consciousness in supposedly nonconscious states is assessed.

Robust EEG-based cross-site and cross-protocol classification of states of consciousness

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

Interactions between Conscious and Subconscious Signals: Selective Attention under Feature-Based Competition Increases Neural Selectivity during Brain Adaptation

Efficient perception in natural environments depends on neural interactions between voluntary processes within cognitive control, such as attention, and those that are automatic and subconscious, such as brain adaptation to predictable input (also called repetition suppression). Although both attention and adaptation have been studied separately and there is considerable knowledge of the neurobiology involved in each of these processes, how attention interacts with adaptation remains equivocal. We examined how attention interacts with visual and auditory adaptation by measuring neuroimaging effects consistent with changes in either neural gain or selectivity. Male and female human participants were scanned with functional magnetic resonance imaging (fMRI) first while they discriminated repetition of morphed faces or voices and either directed their attention to stimulus identity or spatial location. Attention to face or voice identity, while ignoring stimulus location, solely increased the gain of respectively face- or voice-sensitive cortex. The results were strikingly different in an experiment when participants attended to voice identity versus stimulus loudness. In this case, attention to voice while ignoring sound loudness increased neural selectivity. The combined results show that how attention affects adaptation depends on the level of feature-based competition, reconciling prior conflicting observations. The findings are theoretically important and are discussed in relation to neurobiological interactions between attention and different types of predictive signals.SIGNIFICANCE STATEMENT Adaptation to repeated environmental events is ubiquitous in the animal brain, an automatic typically subconscious, predictive signal. Cognitive influences, such as by attention, powerfully affect sensory processing and can overcome brain adaptation. However, how neural interactions occur between adaptation and attention remains controversial. We conducted fMRI experiments regulating the focus of attention during adaptation to repeated stimuli with perceptually balanced stimulus expectancy. We observed an interaction between attention and adaptation consistent with increased neural selectivity, but only under conditions of feature-based competition, challenging the notion that attention interacts with brain adaptation by only affecting response gain. This demonstrates that attention retains its full complement of mechanistic influences on sensory cortex even as it interacts with more automatic or subconscious predictive processes.