Neural signature of the conscious processing of auditory regularities

Empirically Integrating the Evidence for Different Predictive Coding Components Using Auditory False Perception

Perception is a probabilistic estimation of the sensory information we receive at any given time and is shaped by an internal model generated by the brain by assimilating information over the life course. This predictive system in the brain has several components-(i) the internal model, (ii) the model-based prediction called priors, (iii) the weighted difference between the prior and sensory input called prediction error (PE) and (iv) the weighted sum of the prior and input called perceptual inference. Until now, different studies have explored the independent components of this predictive coding system, and we, for the first time to our knowledge, integrate them. To do this, we induce a conditioned hallucination (CH) illusion by means of a multisensory integration paradigm and use this as a model to study the behavioral and electrophysiological responses to this experience. Additionally, we also probe their predictive coding system using a well-established local-global auditory oddball paradigm. By comparing the behavioral and electrophysiological components of people more and less likely to perceive an illusion in the two paradigms, we observed that high perceivers place more confidence in their internal model and low perceivers in the sensory information. Furthermore, high perceivers were more sensitive than low perceivers to PEs that were generated by a change in the context of the sensory information, which served as a measure of a change in the internal model itself. As an exploratory analysis, we also observed that the objective likelihood of perceiving an illusion was corrected to the self-reported likelihood of perceiving an illusion in a day-to-day setting, which disappears when controlled for the perceptual threshold. These results taken together start to give us an idea as to how a person's innate bias-either towards a learned model or external information may-affect their perception in a sensory context.

Cardiac signals inform auditory regularity processing in the absence of consciousness

In healthy awake individuals, the neural processing of bodily signals is not only essential for survival but can also influence perception and compete with external stimulus processing. Yet, the mechanism underlying this bidirectional processing of bodily and external stimuli, as well as its persistence or modulation in unconscious states, remains largely unknown. Here, we investigated the role of cardiac activity on auditory regularity processing in coma. We recorded continuous electroencephalography and electrocardiography in 48 comatose patients on the first day after cardiac arrest during a closed-loop auditory paradigm. We tested whether sounds presented in synchrony with the ongoing heartbeat and sounds presented with fixed, isochronous intervals, would facilitate auditory processing, compared to an asynchronous sequence with variable heartbeat-to-sound and sound-to-sound intervals and a baseline without auditory stimulation. To assess sound prediction based on sequence regularity, we introduced sound omissions within the sequences, violating expected auditory patterns. In coma survivors only, the neural omission response differed in the synchronous against both control conditions. These results were corroborated by a multivariate decoding analysis of the single-trial neural responses to the synchronous omissions and baseline wherein survivors exhibited a higher degree of cardio-audio regularity encoding compared to nonsurvivors. Furthermore, omissions within the synchronous sequence elicited a heart rate deceleration exclusively in coma survivors, which was predictive of patient outcome. We show that the unconscious human brain infers on the temporal relationship across cardiac and auditory inputs and that the neural and cardiac correlates of cardio-audio regularity encoding are predictive of patient outcome.

Clinical translation of ultrasoft Fleuron probes for stable, high-density, and bidirectional brain interfaces

Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome these trade-offs, by introducting Fleuron, a family of ultrasoft, ultra-low-k dielectric materials compatible with thin-film scalable microfabrication techniques. We successfully integrate up to 1,024 sites within a single minimally-invasive Fleuron depth electrode. The combination of the novel implant material and geometry enables single-unit level recordings for 18 months in rodent models, and achieves a large number of units detected per electrode across animals. 128-site Fleuron probes, that cover 8x larger tissue volume than state-of-the-art polyimide counterparts, can track over 100 single-units over months. Stability in neural recordings correlates with reduced glial encapsulation compared to polyimide controls up to 9-month post-implantation. Fleuron probes are integrated with a low-power, mixed-signal ASIC to achieve over 1,000 channels electronic interfaces and can be safely implanted in depth using minimally-invasive surgical techniques via a burr hole approach without requiring specialized robotics. Fleuron probes further create a unique contrast in clinical 3T MRI, allowing for post-operative position confirmation. Large-animal and ex vivo human tissue studies confirm safety and functionality in larger brains. Finally, Fleuron probes are used for the first time ever intraoperatively during planned resection surgeries, confirming in-human usability, and demonstrating the potential of the technology for clincical translation in iBCIs.

Direct brain recordings reveal implicit encoding of structure in random auditory streams

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

Motor-based prediction mediates implicit vocal imitation

Phonetic convergence-the unconscious adaptation of one's speech to resemble that of an interlocutor-is thought to arise from predictive mechanisms. Two types of predictions have been proposed to modulate others' speech: memory-based predictions, which reduce sensitivity to acoustic features reflecting a speaker's vocal identity, and motor-based predictions, which are grounded in the listener's own vocal characteristics. Compared to a relatively well-established role of memory-based predictions, whether motor-based predictions suppress or enhance sensitivity to listener-matched predicted features and how they contribute to phonetic convergence remain unclear. In the present study, we examined these processes using a novel speaking oddball task in which participants were randomly prompted to repeat words they heard. Auditory mismatch negativity served as a neural index of mismatch detection. Prior to the oddball task, participants were divided into a shadow group-engaging in an additional shadowing task to promote vocal convergence-and a non-shadow group that did not receive such exposure. EEG analyses revealed that motor-based predictions enhance sensitivity to listener-matched predicted features following convergence behaviour, with this enhancement correlating with greater vocal convergence. Our novel oddball design provided an efficient method for revealing the dynamic interplay between internal predictive signals and external inputs that mediates phonetic convergence. These findings challenge the view that motor-based predictions only suppress neural responses to predicted features, and instead highlight their potential role in enhancing perceptual learning and guiding vocal adjustments. Motor-based predictions orchestrate sensorimotor interaction and memory-based operations to mediate implicit learning behaviour in a social context.

Predictive coding and attention in developmental cognitive neuroscience and perspectives for neurodevelopmental disorders

Sensory prediction and repetition suppression are closely related cognitive mechanisms that allow the brain to form predictions about the environment, and guide perception in synergy with attention. Predictive coding is a theory of the fundamental role of predictive mechanisms in brain functions. Authors have proposed a central role of predictive impairments in autism and possibly other neurodevelopmental disorders. However, little is known about predictive mechanisms in typical development, and how they co-develop with attention. Here we review experimental support for predictive coding and its links with attention in healthy adults' brains, the first experimental works performed in typically developing children and infants, and theoretical accounts of neurodevelopmental disorders using a predictive coding framework. We propose future directions for predictive coding research in development. Finally, we describe the first predictive coding experiments in neonates and provide research perspectives for using this framework in searching for early markers of atypical neurodevelopment.

Sub-Second Fluctuation between Top-Down and Bottom-Up Modes Distinguishes Diverse Human Brain States

Information continuously flows between regions of the human brain, exhibiting distinct patterns that dynamically shift across states of consciousness, cognitive modes, and neuropsychiatric conditions. In this study, we introduce Relative Phase Analysis (RPA), a method that leverages phase-lead/lag relationships to reveal the real-time dynamics of dominant directional patterns and their rapid transitions. We demonstrate that the human brain switches on a sub-second timescale between a top-down mode-where anterior regions drive posterior activity-and a bottom-up mode, characterized by reverse directionality. These dynamics are most pronounced during full consciousness and gradually become less distinct as awareness diminishes. Furthermore, we find from simultaneous EEG-fMRI recordings that the top-down mode is expressed when higher-order cognitive networks are more active while the bottom-up mode is expressed when sensory systems are more active. Moreover, comparisons of an attention deficit hyperactivity disorder (ADHD) inattentive cohort with typically developing individuals reveal distinct imbalances in these transition dynamics, highlighting the potential of RPA as a diagnostic biomarker. Complementing our empirical findings, a coupled-oscillator model of the structural brain network recapitulates these emergent patterns, suggesting that such directional modes and transitions may arise naturally from inter-regional neural interactions. Altogether, this study provides a framework for understanding whole-brain dynamics in real-time and identifies sub-second fluctuations in top-down versus bottom-up directionality as a fundamental mechanism underlying human information processing.

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals

Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.

Statistical learning dynamically shapes auditory perception

Humans and other animals use information about how likely it is for something to happen. The absolute and relative probability of an event influences a remarkable breadth of behaviors, from foraging for food to comprehending linguistic constructions -- even when these probabilities are learned implicitly. It is less clear how, and under what circumstances, statistical learning of simple probabilities might drive changes in perception and cognition. Here, across a series of 29 experiments, we probe listeners' sensitivity to task-irrelevant changes in the probability distribution of tones' acoustic frequency across tone-in-noise detection and tone duration decisions. We observe that the task-irrelevant frequency distribution influences the ability to detect a sound and the speed with which perceptual decisions about its duration are made. The shape of the probability distribution, its range, and a tone's relative position within that range impact observed patterns of suppression and enhancement of tone detection and decision making. Perceptual decisions are also modulated by a newly discovered perceptual bias, with lower frequencies in the distribution more often and more rapidly perceived as longer, and higher frequencies as shorter. Perception is sensitive to rapid distribution changes, but distributional learning from previous probability distributions also carries over. In fact, massed exposure to a single point along the dimension results in seemingly maladaptive loss of sensitivity - occurring entirely in the absence of feedback or reward - along a range of subsequently encountered frequencies. This points to a gain mechanism that suppresses sensitivity to regions along a perceptual dimension that are less likely to be encountered.

Learning of the mean, but not variance, of color distributions cues target location probability

Humans are good at picking up statistical regularities in the environment. Probability cueing paradigms have demonstrated that the location of a target can be predicted based on spatial regularities. This is assumed to rely on flexible spatial priority maps that are influenced by visual context. We investigated whether stimulus features such as color distributions differing in mean and variance can cue location regularities. In experiment 1, participants searched for an oddly colored target diamond in a 6 × 6 set. On each trial, the distractors were drawn from one of two color distributions centered on different color averages. Each distribution was associated with different target location probabilities, one distribution where the target had an 80% chance to appear on the left (the rich location), while the rich location would be on the right for the other distribution. Participants were significantly faster at locating the target when it appeared in the rich location for both distributions, demonstrating learning of the relationship between color average and location probability. In experiments 2 and 3, observers performed a similar search task, but the distributions had different variances with the same average color. There was no evidence that search became faster when the target appeared in a rich location, suggesting that contingencies between target probabilities and color variance were not learned. These results demonstrate how statistical location learning is flexible, with different visual contexts leading to different spatial priority maps, but they also reveal important limits to such learning.

The neural activity of auditory conscious perception

Although recent work has made headway in understanding the neural temporospatial dynamics of conscious perception, much of that work has focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we test within the auditory domain. Participants completed an auditory threshold task while undergoing intracranial electroencephalography. Recordings from >2,800 grey matter electrodes were analyzed for broadband gamma power (a range which reflects local neural activity). For perceived trials, we find nearly simultaneous activity in early auditory regions, the right caudal middle frontal gyrus, and the non-auditory thalamus; followed by a wave of activity that sweeps through auditory association regions into parietal and frontal cortices. For not perceived trials, significant activity is restricted to early auditory regions. These findings show the cortical and subcortical networks involved in auditory perception are similar to those observed with vision, suggesting shared mechanisms for conscious perception.

Unpacking the complexities of consciousness: Theories and reflections

As the field of consciousness science matures, the research agenda has expanded from an initial focus on the neural correlates of consciousness, to developing and testing theories of consciousness. Several theories have been put forward, each aiming to elucidate the relationship between consciousness and brain function. However, there is an ongoing, intense debate regarding whether these theories examine the same phenomenon. And, despite ongoing research efforts, it seems like the field has so far failed to converge around any single theory, and instead exhibits significant polarization. To advance this discussion, proponents of five prominent theories of consciousness-Global Neuronal Workspace Theory (GNWT), Higher-Order Theories (HOT), Integrated Information Theory (IIT), Recurrent Processing Theory (RPT), and Predictive Processing (PP)-engaged in a public debate in 2022, as part of the annual meeting of the Association for the Scientific Study of Consciousness (ASSC). They were invited to clarify the explananda of their theories, articulate the core mechanisms underpinning the corresponding explanations, and outline their foundational premises. This was followed by an open discussion that delved into the testability of these theories, potential evidence that could refute them, and areas of consensus and disagreement. Most importantly, the debate demonstrated that at this stage, there is more controversy than agreement between the theories, pertaining to the most basic questions of what consciousness is, how to identify conscious states, and what is required from any theory of consciousness. Addressing these core questions is crucial for advancing the field towards a deeper understanding and comparison of competing theories.

Predictive coding: a more cognitive process than we thought?

In predictive coding (PC), higher-order brain areas generate predictions that are sent to lower-order sensory areas. Top-down predictions are compared with bottom-up sensory data, and mismatches evoke prediction errors. In PC, the prediction errors are encoded in layer 2/3 pyramidal neurons of sensory cortex that feed forward. The PC model has been tested with multiple recording modalities using the global-local oddball paradigm. Consistent with PC, neuroimaging studies reported prediction error responses in sensory and higher-order areas. However, recent studies of neuronal spiking suggest that genuine prediction errors emerge in prefrontal cortex (PFC). This implies that predictive processing is a more cognitive than sensory-based mechanism - an observation that challenges PC and better aligns with a framework we call predictive routing (PR).

Decoding Analyses Show Dynamic Waxing and Waning of Event-Related Potentials in Coma Patients

Background/Objectives: Coma prognosis is challenging, as patient presentation can be misleading or uninformative when using behavioral assessments only. Event-related potentials have been shown to provide valuable information about a patient's chance of survival and emergence from coma. Our prior work revealed that the mismatch negativity (MMN) in particular waxes and wanes across 24 h in some coma patients. This "cycling" aspect of the presence/absence of neurophysiological responses may require fine-grained tools to increase the chances of detecting levels of neural processing in coma. This study implements multivariate pattern analysis (MVPA) to automatically quantify patterns of neural discrimination between duration deviant and standard tones over time at the single-subject level in seventeen healthy controls and in three comatose patients. Methods: One EEG recording, containing up to five blocks of an auditory oddball paradigm, was performed in controls over a 12 h period. For patients, two EEG sessions were conducted 3 days apart for up to 24 h, denoted as day 0 and day 3, respectively. MVPA was performed using a support-vector machine classifier. Results: Healthy controls exhibited reliable discrimination or classification performance during the latency intervals associated with MMN and P3a components. Two patients showed some intervals with significant discrimination around the second half of day 0, and all had significant results on day 3. Conclusions: These findings suggest that decoding analyses can accurately classify neural responses at a single-subject level in healthy controls and provide evidence of small but significant changes in auditory discrimination over time in coma patients. Further research is needed to confirm whether this approach represents an improved technology for assessing cognitive processing in coma.

Predicted missing information biases ensemble perception of temporally ordered facial expressions

By observing dynamically changing facial expressions, humans can use a specialized capacity known as ensemble coding to effortlessly obtain a summary representation of an individual's emotional state. However, few studies have examined whether the missing expression informed by the statistical regularity in the changing facial expressions can be sampled and then influence the perceptual averaging process. In Experiment 1a and 1b, we manipulated the amount of prior information from local regularity by varying the position of the missing expression in the temporal sequence (1a: Neutral to Disgust and/or Disgust to Neutral,1b: Neutral to Happy and/or Happy to Neutral) within a trial. Results showed that ensemble estimates were towards the mean of expressions including both the presented and the missing faces, only when sufficient predictability (e.g. a missing expression in the late position) informed by local regularity. In Experiment 2, we added prior information from global regularities to help boost the predictability of an early missing expression by keeping the emotional direction consistent in a block. However, estimates were not towards the mean of expressions including both the presented and the missing expressions as expected. Although the generalizability may be limited, these findings suggest that prior information at different levels of hierarchical predictive coding may exert qualitatively different influences on the perceptual averaging of temporally ordered facial expressions with missing items.

Markers of consciousness in infants: Towards a 'cluster-based' approach

As recently as the 1980s, it was not uncommon for paediatric surgeons to operate on infants without anaesthesia. Today, the same omission would be considered criminal malpractice, and there is an increased concern with the possibility of consciousness in the earliest stage of human infancy. This concern reflects a more general trend that has characterised science since the early 1990s of taking consciousness seriously. While this attitude shift has opened minds towards the possibility that our earliest experiences predate our first memories, convincing demonstrations of infant consciousness remain challenging given that infants cannot report on their experiences. Furthermore, while many behavioural and neural markers of consciousness that do not rely on language have been validated in adults, no one specific marker can be confidently translated to infancy. For this reason, we have proposed the 'cluster-based' approach, in which a consensus of evidence across many markers, all pointing towards the same developmental period, could be used to argue convincingly for the presence of consciousness. CONCLUSION: We review the most promising markers for early consciousness, arguing that consciousness is likely to be in place by 5 months of age if not earlier.

Mismatch Negativity and P300 in the Diagnosis and Prognostic Assessment of Coma and Other Disorders of Consciousness

BACKGROUND

The objective of this study was to investigate the value of mismatch negativity (MMN) and P300 event-related potentials for discriminating the consciousness state and predicting improvement of consciousness at 6 months in patients with coma and other disorders of consciousness (DOC).

METHODS

We performed MMN and P300 on 42 patients with DOC with a mean onset time of 40.21 ± 19.43 days. These patients with DOC were categorized into coma, unresponsive wakefulness syndrome (UWS), minimal consciousness minus (MCS-), and minimal consciousness plus (MCS +) groups according to neurobehavioral assessment and the Coma Recovery Scale-Revised score. The primary outcome was the improvement of consciousness at 6 months in patients with DOC. We assessed the efficacy of MMN and P300 in quantitatively predicting the prognosis at 6 months and the capability of MMN and P300 parameters to differentiate between DOC.

RESULTS

At least one significant difference in either MMN or P300 parameters was displayed among the DOC groups, but not between the MCS- and MCS+ groups (significance level: 0.05). Both MMN and P300 amplitudes showed desirable predictive accuracy at 6 months, with areas under the curve (AUCs) of 0.859 and 0.856, respectively. The optimal thresholds for MMN and P300 amplitudes were 2.044 and 1.095 μV. However, the combined MMN-P300 amplitude showed better 6-month predictive accuracy (AUC 0.934, 95% confidence interval 0.860-1.000), with a sensitivity of 85% and a specificity of 90.9%.

CONCLUSIONS

MMN and P300 may help discriminate among coma, UWS, and MCS, but not between patients with MCS- and patients with MCS+ . The MMN amplitude, P300 amplitude, and especially combined MMN-P300 amplitude at 6 months may be interesting predictors of consciousness improvement at 6 months in patients with DOC.

TRIAL REGISTRATION

Chinese Clinical Trial Registry identifier ChiCTR2400083798.

Adaptation, not prediction, drives neuronal spiking responses in mammalian sensory cortex

Predictive coding (PC) hypothesizes that the brain computes internal models of predicted events and that unpredicted stimuli are signaled with prediction errors that feed forward. We tested this hypothesis using a visual oddball task. A repetitive sequence interrupted by a novel stimulus is a "local" oddball. "Global" oddballs defy predictions while repeating the local context, thereby dissociating genuine prediction errors from adaptation-related responses. We recorded neuronal spiking activity across the visual hierarchy in mice and monkeys viewing these oddballs. Local oddball responses largely followed PC: they were robust, emerged early in layers 2/3, and fed forward. Global oddball responses challenged PC: they were weak, absent in most visual areas, more robust in prefrontal cortex, emerged in non-granular layers, and did not involve inhibitory interneurons relaying predictive suppression. Contrary to PC, genuine predictive coding does not emerge early in sensory processing, and is instead exclusive to more cognitive, higher-order areas.

Advances in neuroimaging in disorders of consciousness

Disorders of consciousness (DoC) are a heterogeneous spectrum of clinical conditions, including coma, unresponsive wakefulness syndrome, and minimally conscious state. DoC are clinically defined on the basis of behavioral cues expressed by the patients, on the assumption that such behavioral responses of the patient are representative of the patient's degree of consciousness impairment. However, many studies have highlighted the issues arising from formulating a DoC diagnosis merely on behavioral assessment. Overcoming the limitations of behavioral assessment, neuroimaging provides a direct window on the cerebral activity of the patient, bypassing the motor, perceptual, or cognitive deficits that might hamper the patient's ability to produce an appropriate behavioral response. This chapter provides an overview of available molecular, functional, and structural neuroimaging evidence in patients with DoC. This chapter introduces the neuroimaging tools available in the clinical settings of nuclear medicine and neuroradiology and presents the evidence on the role of neuroimaging tools to improve the clinical management of DoC patients, from the standpoint of differential diagnosis and prognosis. Last, we outline the open questions in the field, and point at actions that are urgently needed to fully exploit neuroimaging tools to advance scientific understanding and clinical management of DoC.

The Complex Pattern Mismatch Negativity as a Potential Indicator of Psychosis Across all Phases of Illness: A Meta-Analysis

Over the past decade, there has been extensive research on the mismatch negativity (MMN) and its promise as a biomarker of illness in people with schizophrenia (SZ). Nevertheless, when attempting to assess the early stages of illness progression, the utility of MMN has been inconsistent. Recently, researchers have been investigating a more advanced MMN paradigm (the complex MMN [cMMN]) which is believed to index higher-order cognitive processing and has been suggested to be a more effective indicator of the early phases of SZ. The cMMN is defined as a paradigm that relies on alterations within a pre-established pattern of stimuli. In this meta-analysis, we investigated cMMN deficits in individuals with SZ, including an analysis involving those in the first 5 years of illness. Our search also included individuals with bipolar disorder who experience psychosis; however, no related papers were found and thus, no findings are reported. Our findings indicate a small/moderate effect (d = 0.47), suggesting that individuals with SZ exhibit reduced cMMN amplitudes compared to individuals without SZ. Interestingly, this effect seems to be more pronounced in individuals within the first 5 years of their illness (d = 0.58), suggesting that cMMN might be a more sensitive biomarker in the early phases of SZ compared to traditional paradigms.

Evoked potentials in patients with disorders of consciousness

Acute coma in the intensive care unit and persistent disorders of consciousness (DoC) in neuro-rehabilitation are frequent in patients with hypoxic-ischemic encephalopathy after cardiac arrest (CA), traumatic brain injury, intracranial hemorrhage, or ischemic stroke. Reliable prognostication of long-term neurologic outcomes cannot be made by clinical examination alone in the early phase for many patients, and thus, additional investigations are necessary. Evoked potentials provide inexpensive, real-time, high temporal resolution, bedside, quantifiable information on different sensory pathways into the brain including local and global cortical processing. Short-latency somatosensory evoked potentials can reliably predict poor neurologic long-term outcome in the early phase after CA and are recommended by guidelines as one investigation within an early multimodal assessment. Middle-latency and event-related or cognitive evoked potentials provide information on the integrity of more advanced cortical processing, some closely related to consciousness. This information can help to identify those comatose patients with a good prognosis in the acute phase and help to better understand their precise clinical state and the chances of further recovery in patients with persistent DoC in neuro-rehabilitation. Further studies are necessary to improve the applicability of research findings in the clinical sphere.

Traumatic brain injury and disorders of consciousness

Trauma is one of the most common causes of disorders of consciousness (DOC) worldwide. Traumatic brain injury (TBI) leads to heterogeneous, multifocal injury via focal brain damage and diffuse axonal injury, causing an acquired network disorder. Recovery occurs through reemergence of dynamic cortical and subcortical networks. Accurate diagnostic evaluation is essential toward promoting recovery and may be more challenging in traumatic than non-traumatic brain injuries. Standardized neurobehavioral assessment is the cornerstone for assessments in the acute, prolonged, and chronic phases of traumatic DOC, while structural and functional neuroimaging, tractography, nuclear medicine studies, and electrophysiologic techniques assist with differentiation of DOC states and prognostication. Prognosis for recovery is better for patients with TBI than those with non-traumatic brain injuries, and the timeline for recovery is longer. The majority of patients experience improvement in their DOC within the first year post-injury, but recovery can continue for five and even ten years after TBI. Pharmacologic therapy and device-related neuromodulation represent important areas for future research.

[Applications and prospects of electroencephalography technology in neurorehabilitation assessment and treatment]

With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.

Complex auditory regularity processing across levels of consciousness in coma: Stage 1 Registered Report

A key question for the scientific study of consciousness is whether it is possible to identify specific features in brain activity that are uniquely linked to conscious experience. This question has important implications for the development of markers to detect covert consciousness in unresponsive patients. In this regard, many studies have focused on investigating the neural response to complex auditory regularities. One noteworthy example is the local global paradigm, which allows for the investigation of auditory regularity encoding at the 'global' level, based on the repetition of groups of sounds. The inference of global regularities is thought to depend on conscious access to such complex auditory stimuli as mostly shown in chronic stages of disorders of consciousness patients. However, whether global regularity encoding can identify covert consciousness along the consciousness spectrum including earlier stages of these disorders remains controversial. Here, we aim to fill this gap by investigating whether the inference of global auditory regularities can occur in acute coma, in the absence of consciousness, and how this may be modulated by the severity of the patients' clinical condition and consciousness level measured using the Full Outline of UnResponsiveness (FOUR) score. We will acquire 63-channel continuous electroencephalography to measure the neural response to global auditory regularity in comatose patients (N = 30) during the first day after cardiac arrest, when patients are unconscious, sedated and under normothermia, and during the second day (with reduced or absent sedation and body temperature control). We hypothesize that global regularity encoding will persist in the absence of consciousness independent of patient outcome, observed as above chance decoding of the neural response to global regularities using multivariate decoding analyses. We further hypothesize that decoding performance will positively correlate with the FOUR score, which indexes consciousness level, and typically improves between the first and second day after coma onset following cardiac arrest in patients with favourable outcome. In an exploratory analysis, we will also evaluate whether global regularity encoding may be influenced by the patients' clinical management, specifically sedation, also shown to affect global deviance detection. Our results will shed light on the neurophysiological correlates of complex auditory regularity processing in unconscious patients and on the link to residual levels of consciousness during the underexplored state of coma upon the first days after cardiac arrest.

Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex

The brain predicts regularities in sensory inputs at multiple complexity levels, with neuronal mechanisms that remain elusive. Here, we monitored auditory cortex activity during the local-global paradigm, a protocol nesting different regularity levels in sound sequences. We observed that mice encode local predictions based on stimulus occurrence and stimulus transition probabilities, because auditory responses are boosted upon prediction violation. This boosting was due to both short-term adaptation and an adaptation-independent surprise mechanism resisting anesthesia. In parallel, and only in wakefulness, VIP interneurons responded to the omission of the locally expected sound repeat at the sequence ending, thus providing a chunking signal potentially useful for establishing global sequence structure. When this global structure was violated, by either shortening the sequence or ending it with a locally expected but globally unexpected sound transition, activity slightly increased in VIP and PV neurons, respectively. Hence, distinct cellular mechanisms predict different regularity levels in sound sequences.

Decoding cortical chronotopy-Comparing the influence of different cortical organizational schemes

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Distinguishing expectation and attention effects in processing temporal patterns of visual input

The current study investigated how the brain sets up expectations from stimulus regularities by evaluating the neural responses to expectations driven implicitly (by the stimuli themselves) and explicitly (by task demands). How the brain uses prior information to create expectations and what role attention plays in forming or holding predictions to efficiently respond to incoming sensory information is still debated. We presented temporal patterns of visual input while recording EEG under two different task conditions. When the patterns were task-relevant and pattern recognition was required to perform the button press task, three different event-related brain potentials (ERPs) were elicited, each reflecting a different aspect of pattern expectation. In contrast, when the patterns were task-irrelevant, none of the neural indicators of pattern recognition or pattern violation detection were observed to the same temporally structured sequences. Thus, results revealed a clear distinction between expectation and attention that was prompted by task requirements. These results provide complementary pieces of evidence that implicit exposure to a stimulus pattern may not be sufficient to drive neural effects of expectations that lead to predictive error responses. Task-driven attentional control can dissociate from stimulus-driven expectations, to effectively minimize distracting information and maximize attentional regulation.

Predicting attentional focus: Heartbeat-evoked responses and brain dynamics during interoceptive and exteroceptive processing

Attention shapes our consciousness content and perception by increasing the probability of becoming aware and/or better encoding a selection of the incoming inner or outer sensory world. Engaging interoceptive and exteroceptive attention should elicit distinctive neural responses to visceral and external stimuli and could be useful in detecting covert command-following in unresponsive patients. We designed a task to engage healthy participants' attention toward their heartbeats or auditory stimuli and investigated whether brain dynamics and the heartbeat-evoked potential (HEP) distinguished covert interoceptive-exteroceptive attention. Exteroceptive attention yielded an overall flattening of the power spectral density (PSD), whereas during interoception, there was a decrease in complexity, an increase in frontal connectivity and theta oscillations, and a modulation of the HEP. Subject-level classifiers based on HEP features classified the attentional state of 17/20 participants. Kolmogorov complexity, permutation entropy, and weighted symbolic mutual information showed comparable accuracy in classifying covert attention and exhibited a synergic behavior with the HEP features. PSD features demonstrated exceptional performance (20/20). Command-following was assessed in five brain-injured patients with a modified version of the task. An unresponsive wakefulness syndrome/vegetative state patient and a locked-in syndrome patient demonstrated a willful modulation of the HEP and together with the explored brain markers suggest that patients were complying with task instructions. Our findings underscore the importance of attentional mechanisms in shaping interoceptive and exteroceptive sensory processing and expand the framework of heart-brain interactions employed for diagnostic purposes in patients with disorders of consciousness.

Phenomenology of auto-induced cognitive trance using text mining: a prospective and exploratory group study

Auto-induced cognitive trance (AICT) is a modified state of consciousness derived from shamanic tradition that can be practised by individuals after specific training. The aim of this work was to characterize the phenomenological experiences of AICT, using text mining analysis. Free recalls of subjective experiences were audio-recorded in 27 participants after five pseudo-randomized experimental sessions: ordinary conscious resting state, with auditory stimulation and with an imaginary mental task, as well as during AICT with and without auditory stimulation. Recordings were transcribed, normalized total word counts were calculated for each condition, and analyses of content were performed using IRaMuTeQ software. Results showed that the length of the participants' reports was higher for AICT compared to the other conditions, and that the content could be categorized into four classes of discourse: AICT memory, AICT, ordinary conscious states, and AICT with and without stimulation. AICT was also characterized by specific content compared to rest, auditory stimulation, and imagination conditions. Content analysis of the narrative revealed nine categories encompassing the presence of nature, people, animals, positive and negative features, sensory perceptions, body modifications, metacognition, and difficulty of describing thoughts. Among these categories, AICT is specifically characterized by reports related to the presence of nature, animals, body modifications, as well as the difficulty of describing thoughts. These results suggest that a richer phenomenology was reported during AICT, compared to the other conditions, and that AICT constitutes a class of discourse on its own, with a clear dissociation from the other conditions.

Predictive learning shapes the representational geometry of the human brain

Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.

Neural signatures of visual awareness independent of postperceptual processing

What are the neural processes associated with perceptual awareness that are distinct from preconscious sensory encoding and postperceptual processes such as reporting an experience? Using electroencephalography and a no-report visual masking paradigm, we manipulated stimulus visibility by varying the time between stimuli and masks in linear steps (17, 33, 50, 67, and 83 ms). Awareness increased nonlinearly, with stimuli never seen at the two shortest intervals, always seen at the two longest, and 50% seen at the intermediate interval. Separate report and no-report conditions were used to isolate awareness from task performance. Our results revealed a neural signal closely linked to perceptual awareness, independent of the task: a fronto-central event-related potential that we refer to as the N2 (~250 to 300 ms). Earlier event-related potential signals reflected the linear manipulation of stimulus strength, while later signals like P3b and temporal generalization of decoding were tied to task performance, appearing only in the report condition. Taken together, these findings inform current debates regarding theories of consciousness and offer new avenues for exploring the neural mechanisms supporting conscious processing.

Revisiting the functional monitoring of brain development in premature neonates. A new direction in clinical care and research

The first 1000 days of life are of paramount importance for neonatal development. Premature newborns are exposed early to the external environment, modifying the fetal exposome and leading to overexposure in some sensory domains and deprivation in others. The resulting neurodevelopmental effects may persist throughout the individual's lifetime. Several neonatal neuromonitoring techniques can be used to investigate neural mechanisms in early postnatal development. EEG is the most widely used, as it is easy to perform, even at the patient's bedside. It is not expensive and provides information with a high temporal resolution and relatively good spatial resolution when performed in high-density mode. Functional near-infrared spectroscopy (fNIRS), a technique for monitoring vascular network dynamics, can also be used at the patient's bedside. It is not expensive and has a good spatial resolution at the cortical surface. These two techniques can be combined for simultaneous monitoring of the neuronal and vascular networks in premature newborns, providing insight into neurodevelopment before term. However, the extent to which more general conclusions about fetal development can be drawn from findings for premature neonates remains unclear due to considerable differences in environmental and medical situations. Fetal MEG (fMEG, as an alternative to EEG for preterm infants) and fMRI (as an alternative to fNIRS for preterm infants) can also be used to investigate fetal neurodevelopment on a trimester-specific basis. These techniques should be used for validation purposes as they are the only tools available for evaluating neuronal dysfunction in the fetus at the time of the gene-environment interactions influencing transient neuronal progenitor populations in brain structures. But what do these techniques tell us about early neurodevelopment? We address this question here, from two points of view. We first discuss spontaneous neural activity and its electromagnetic and hemodynamic correlates. We then explore the effects of stimulating the immature developing brain with information from exogenous sources, reviewing the available evidence concerning the characteristics of electromagnetic and hemodynamic responses. Once the characteristics of the correlates of neural dynamics have been determined, it will be essential to evaluate their possible modulation in the context of disease and in at-risk populations. Evidence can be collected with various neuroimaging techniques targeting both spontaneous and exogenously driven neural activity. A multimodal approach combining the neuromonitoring of different functional compartments (neuronal and vascular) is required to improve our understanding of the normal functioning and dysfunction of the brain and to identify neurobiomarkers for predicting the neurodevelopmental outcome of premature neonate and fetus. Such an approach would provide a framework for exploring early neurodevelopment, paving the way for the development of tools for earlier diagnosis in these vulnerable populations, thereby facilitating preventive, rescue and reparative neurotherapeutic interventions.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from the deep-layer cortex via alpha/beta (8 to 30 Hz) oscillations. They inhibit the gamma (40 to 100 Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in the sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late period (>200 ms from stimulus onset) spiking, and superficial layer sinks in the sensory cortex. LOC also resulted in diminished decodability of pattern-level prediction error signals in the higher-order cortex. Therefore, the auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in the auditory cortex, there was a loss of differential spiking to oddballs in the higher-order cortex. This may be a consequence of a loss of within-area and interareal spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

Effective Connectivity Network of Aberrant Prediction Error Processing in Auditory Phantom Perception

Introduction: Prediction error (PE) is key to perception in the predictive coding framework. However, previous studies indicated the varied neural activities evoked by PE in tinnitus patients. Here, we aimed to reconcile the conflict by (1) a more nuanced view of PE, which could be driven by changing stimulus (stimulus-driven PE [sPE]) and violation of current context (context-driven PE [cPE]) and (2) investigating the aberrant connectivity networks that are engaged in the processing of the two types of PEs in tinnitus patients. Methods: Ten tinnitus patients with normal hearing and healthy controls were recruited, and a local-global auditory oddball paradigm was applied to measure the electroencephalographic difference between the two groups during sPE and cPE conditions. Results: Overall, the sPE condition engaged bottom-up and top-down connections, whereas the cPE condition engaged mostly top-down connections. The tinnitus group showed decreased sensitivity to the sPE and increased sensitivity to the cPE condition. Particularly, the auditory cortex and posterior cingulate cortex were the hubs for processing cPE in the control and tinnitus groups, respectively, showing the orientation to an internal state in tinnitus. Furthermore, tinnitus patients showed stronger connectivity to the parahippocampus and pregenual anterior cingulate cortex for the establishment of the prediction during the cPE condition. Conclusion: These results begin to dissect the role of changes in stimulus characteristics versus changes in the context of processing the same stimulus in mechanisms of tinnitus generation. Impact Statement This study delves into the number dynamics of prediction error (PE) in tinnitus, proposing a dual framework distinguishing between stimulus-driven PE (sPE) and context-driven PE (cPE). Electroencephalographic data from tinnitus patients and controls revealed distinct connectivity patterns during sPE and cPE conditions. Tinnitus patients exhibited reduced sensitivity to sPE and increased sensitivity to cPE. The auditory cortex and posterior cingulate cortex emerged as pivotal regions for cPE processing in controls and tinnitus patients, indicative of an internal state orientation in tinnitus. Enhanced connectivity to the parahippocampus and pregenual anterior cingulate cortex underscores the role of context in tinnitus pathophysiology.

Pain anticipation is a new behavioural sign of minimally conscious state

Probing cognition and consciousness in the absence of functional communication remains an extremely challenging task. In this perspective, we imagined a basic clinical procedure to explore pain anticipation at bedside. In a series of 61 patients with a disorder of consciousness, we tested the existence of a nociceptive anticipation response by pairing a somaesthetic stimulation with a noxious stimulation. We then explored how nociceptive anticipation response correlated with (i) clinical status inferred from Coma Recovery Scale-Revised scoring, (ii) with an EEG signature of stimulus anticipation-the contingent negative variation-and (iii) how nociceptive anticipation response could predict consciousness outcome at 6 months. Proportion of nociceptive anticipation response differed significantly according to the state of consciousness: nociceptive anticipation response was present in 5 of 5 emerging from minimally conscious state patients (100%), in 10 of 11 minimally conscious state plus patients (91%), but only in 8 of 17 minimally conscious state minus patients (47%), and only in 1 of 24 vegetative state/unresponsive wakefulness syndrome patients (4%) (χ 2  P < 0.0001). Nociceptive anticipation response correlated with the presence of a contingent negative variation, suggesting that patients with nociceptive anticipation response were more prone to actively expect and anticipate auditory stimuli (Fisher's exact test P = 0.05). However, nociceptive anticipation response presence did not predict consciousness recovery. Nociceptive anticipation response appears as a new additional behavioural sign that can be used to differentiate minimally conscious state from vegetative state/unresponsive wakefulness syndrome patients. As most behavioural signs of minimally conscious state, the nociceptive anticipation response seems to reveal the existence of a cortically mediated state that does not necessarily reflect residual conscious processing.

Language experience shapes predictive coding of rhythmic sound sequences

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language's word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker's linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term 'duration' prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.

Does Cognitive Load Affect Measures of Consciousness?

BACKGROUND

Developing and testing methods for reliably measuring the state of consciousness of individuals is important for both basic research and clinical purposes. In recent years, several promising measures of consciousness, grounded in theoretical developments, have been proposed. However, the degrees to which these measures are affected by changes in brain activity that are not related to changes in the degree of consciousness has not been well tested. In this study, we examined whether several of these measures are modulated by the loading of cognitive resources.

METHODS

We recorded electroencephalography (EEG) from 12 participants in two conditions: (1) while passively attending to sensory stimuli related to the measures and (2) during increased cognitive load consisting of a demanding working memory task. We investigated whether a set of proposed objective EEG-based measures of consciousness differed between the passive and the cognitively demanding conditions.

RESULTS

The P300b event-related potential (sensitive to conscious awareness of deviance from an expected pattern in auditory stimuli) was significantly affected by concurrent performance on a working memory task, whereas various measures based on signal diversity of spontaneous and perturbed EEG were not.

CONCLUSION

Because signal diversity-based measures of spontaneous or perturbed EEG are not sensitive to the degree of cognitive load, we suggest that these measures may be used in clinical situations where attention, sensory processing, or command following might be impaired.

Surprising sounds influence risky decision making

Adaptive behavior depends on appropriate responses to environmental uncertainty. Incidental sensory events might simply be distracting and increase errors, but alternatively can lead to stereotyped responses despite their irrelevance. To evaluate these possibilities, we test whether task-irrelevant sensory prediction errors influence risky decision making in humans across seven experiments (total n = 1600). Rare auditory sequences preceding option presentation systematically increase risk taking and decrease choice perseveration (i.e., increased tendency to switch away from previously chosen options). The risk-taking and perseveration effects are dissociable by manipulating auditory statistics: when rare sequences end on standard tones, including when rare sequences consist only of standard tones, participants are less likely to perseverate after rare sequences but not more likely to take risks. Computational modeling reveals that these effects cannot be explained by increased decision noise but can be explained by value-independent risky bias and perseveration parameters, decision biases previously linked to dopamine. Control experiments demonstrate that both surprise effects can be eliminated when tone sequences are presented in a balanced or fully predictable manner, and that surprise effects cannot be explained by erroneous beliefs. These findings suggest that incidental sounds may influence many of the decisions we make in daily life.

Chronic pain - A maladaptive compensation to unbalanced hierarchical predictive processing

The ability to perceive pain presents an interesting evolutionary advantage to adapt to an ever-changing environment. However, in the case of chronic pain (CP), pain perception hinders the capacity of the system to adapt to changing sensory environments. Similar to other chronic perceptual disorders, CP is also proposed to be a maladaptive compensation to aberrant sensory predictive processing. The local-global oddball paradigm relies on learning hierarchical rules and processing environmental irregularities at a local and global level. Prediction errors (PE) between actual and predicted input typically trigger an update of the forward model to limit the probability of encountering future PEs. It has been hypothesised that CP hinders forward model updating, reflected in increased local deviance and decreased global deviance. In the present study, we used the local-global paradigm to examine how CP influences hierarchical learning relative to healthy controls. As hypothesised, we observed that deviance in the stimulus characteristics evoked heightened local deviance and decreased global deviance of the stimulus-driven PE. This is also accompanied by respective changes in theta phase locking that is correlated with the subjective pain perception. Changes in the global deviant in the stimulus-driven-PE could also be explained by dampened attention-related responses. Changing the context of the auditory stimulus did not however show a difference in the context-driven PE. These findings suggest that CP is accompanied by maladaptive forward model updating where the constant presence of pain perception disrupts local deviance in non-nociceptive domains. Furthermore, we hypothesise that the auditory-processing based biomarker identified here could be a marker of domain-general dysfunction that could be confirmed by future research.

Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain

Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.

Habituation of Central and Electrodermal Responses to an Auditory Two-Stimulus Oddball Paradigm

The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; -SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task.

The developmental cognitive mechanism of learning algebraic rules from the dual-process theory perspective

Rule learning is an important ability that enables human beings to adapt to nature and develop civilizations. There have been many discussions on the mechanism and characteristics of algebraic rule learning, but there are still controversies due to the lack of theoretical guidance. Based on the dual-process theory, this study discussed the following arguments for algebraic rule learning across human and animal studies: whether algebraic rule learning is simply Type 1 processing, whether algebraic rule learning is a domain-general ability, whether algebraic rule learning is shared by humans and animals, and whether an algebraic rule is learned consciously. Moreover, we propose that algebraic rule learning is possibly a cognitive process that combines both Type 1 and Type 2 processing. Further exploration is required to establish the essence and neural basis of algebraic rule learning.

An interhemispheric frontoparietal network supports hypnotic states

Understanding the neural substrate of altered conscious states is an important cultural, scientific, and clinical endeavour. Although hypnosis causes strong shifts in conscious perception and cognition, it remains largely unclear how hypnosis affects information processing in cortical networks. Here we manipulated the depth of hypnotic states to study information processing between cortical regions involved in attention and awareness. We used high-density Electroencephalography (EEG) to record resting-state cortical activity from 30 hypnosis experts during two hypnotic states with different depth. Each participant entered a light and a deep hypnotic state as well as two well-matched control states. Bridging top-down and lateralisation models of hypnosis, we found that interhemispheric frontoparietal connectivity distinguished hypnosis and control conditions, while no difference was found between the two hypnotic states. Using a graph-theoretic measure, we revealed that the amount of information passing through individual nodes (measured via betweenness centrality) is reduced during hypnosis relative to control states. Finally, we found that theta power was enhanced during hypnosis. Our result contributes to the current discussion around a role for theta power in bringing about hypnotic states, as well as other altered conscious states. Overall, our findings support the notion that altered top-down control in frontoparietal regions facilitates hypnosis by integrating information between cortical hemispheres.

Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Functional and structural brain connectivity in disorders of consciousness

Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.

The influence of feature-based attention and response requirements on ERP correlates of auditory awareness

In search for the neural correlates of consciousness (NCCs), it is important to isolate the true NCCs from their prerequisites, consequences, and co-occurring processes. To date, little is known about how attention affects the event-related potential (ERP) correlates of auditory awareness and there is contradictory evidence on whether one of them, the late positivity (LP), is affected by response requirements. By implementing a GO-NOGO design with target and nontarget stimuli, we controlled for feature-based attention and response requirements in the same experiment, while participants rated their awareness using a perceptual awareness scale. The results showed a prolonged auditory awareness negativity (AAN) for aware trials, which was influenced neither by attention nor by response requirement. The LP was affected by both attention and response requirements. Consistent with the levels of processing hypothesis, the LP was related to consciousness as a correlate of the processing of higher-level stimulus features, likely requiring access to a "global workspace." Our findings further suggest that AAN is a proper ERP correlate of auditory consciousness and thus a true NCC in the auditory modality.

Content-state dimensions characterize different types of neuronal markers of consciousness

Identifying the neuronal markers of consciousness is key to supporting the different scientific theories of consciousness. Neuronal markers of consciousness can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize markers according to their dynamics in both the "state" and "content" dimensions. The 2D space is defined by the marker's capacity to distinguish the conscious states from non-conscious states (on the x-axis) and the content (e.g. perceived versus unperceived or different levels of cognitive processing on the y-axis). According to the sign of the x- and y-axis, markers are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of electroencephalography markers: markers of connectivity, markers of complexity, and spectral summaries. The neuronal markers of state are represented by the level of consciousness in (i) healthy participants during a nap and (ii) patients with disorders of consciousness. On the other hand, the neuronal markers of content are represented by (i) the conscious content in healthy participants' perception task using a visual awareness paradigm and (ii) conscious processing of hierarchical regularities using an auditory local-global paradigm. In both cases, we see separate clusters of markers with correlated and anticorrelated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining neuronal markers in a 2D space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Carrier-frequency specific omission-related neural activity in ordered sound sequences is independent of omission-predictability

Regularities in our surroundings lead to predictions about upcoming events. Previous research has shown that omitted sounds during otherwise regular tone sequences elicit frequency-specific neural activity related to the upcoming but omitted tone. We tested whether this neural response is depending on the unpredictability of the omission. Therefore, we recorded magnetencephalography (MEG) data while participants listened to ordered or random tone sequences with omissions occurring either ordered or randomly. Using multivariate pattern analysis shows that the frequency-specific neural pattern during omission within ordered tone sequences occurs independent of the regularity of the omissions. These results suggest that the auditory predictions based on sensory experiences are not immediately updated by violations of those expectations.

Perceptual Awareness in Human Infants: What is the Evidence?

Perceptual awareness in infants during the first year of life is understudied, despite the philosophical, scientific, and clinical importance of understanding how and when consciousness emerges during human brain development. Although parents are undoubtedly convinced that their infant is conscious, the lack of adequate experimental paradigms to address this question in preverbal infants has been a hindrance to research on this topic. However, recent behavioral and brain imaging studies have shown that infants are engaged in complex learning from an early age and that their brains are more structured than traditionally thought. I will present a rapid overview of these results, which might provide indirect evidence of early perceptual awareness and then describe how a more systematic approach to this question could stand within the framework of global workspace theory, which identifies specific signatures of conscious perception in adults. Relying on these brain signatures as a benchmark for conscious perception, we can deduce that it exists in the second half of the first year, whereas the evidence before the age of 5 months is less solid, mainly because of the paucity of studies. The question of conscious perception before term remains open, with the possibility of short periods of conscious perception, which would facilitate early learning. Advances in brain imaging and growing interest in this subject should enable us to gain a better understanding of this important issue in the years to come.