Theories of consciousness

Consciousness beyond the human case

There is growing interest in the relationship been AI and consciousness. Joseph LeDoux and Jonathan Birch thought it would be a good moment to put some of the big questions in this area to some leading experts. The challenge of addressing the questions they raised was taken up by Kristin Andrews, Nicky Clayton, Nathaniel Daw, Chris Frith, Hakwan Lau, Megan Peters, Susan Schneider, Anil Seth, Thomas Suddendorf, and Marie Vanderkerckhoeve.

Don't forget the boundary problem! How EM field topology can address the overlooked cousin to the binding problem for consciousness

The boundary problem is related to the binding problem, part of a family of puzzles and phenomenal experiences that theories of consciousness (ToC) must either explain or eliminate. By comparison with the phenomenal binding problem, the boundary problem has received very little scholarly attention since first framed in detail by Rosengard in 1998, despite discussion by Chalmers in his widely cited 2016 work on the combination problem. However, any ToC that addresses the binding problem must also address the boundary problem. The binding problem asks how a unified first person perspective (1PP) can bind experiences across multiple physically distinct activities, whether billions of individual neurons firing or some other underlying phenomenon. To a first approximation, the boundary problem asks why we experience hard boundaries around those unified 1PPs and why the boundaries operate at their apparent spatiotemporal scale. We review recent discussion of the boundary problem, identifying several promising avenues but none that yet address all aspects of the problem. We set out five specific boundary problems to aid precision in future efforts. We also examine electromagnetic (EM) field theories in detail, given their previous success with the binding problem, and introduce a feature with the necessary characteristics to address the boundary problem at a conceptual level. Topological segmentation can, in principle, create exactly the hard boundaries desired, enclosing holistic, frame-invariant units capable of effecting downward causality. The conclusion outlines a programme for testing this concept, describing how it might also differentiate between competing EM ToCs.

Primary states of consciousness: A review of historical and contemporary developments

Primary states of consciousness are conceived as phylogenetically older states of consciousness as compared to secondary states governed by sociocultural inhibition. The historical development of the concept in psychiatry and neurobiology is reviewed, along with its relationship to theories of consciousness. We suggest that primary states of consciousness are characterized by a temporary breakdown of self-control accompanied by a merging of action, communication, and emotion (ACE fusion), ordinarily segregated in human adults. We examine the neurobiologic basis of this model, including its relation to the phenomenon of neural dedifferentiation, the loss of modularity during altered states of consciousness, and increased corticostriatal connectivity. By shedding light on the importance of primary states of consciousness, this article provides a novel perspective on the role of consciousness as a mechanism of differentiation and control. We discuss potential differentiators underlying a gradient from primary to secondary state of consciousness, suggesting changes in thalamocortical interactions and arousal function. We also propose a set of testable, neurobiologically plausible working hypotheses to account for their distinct phenomenological and neural signatures.

Hybrid predictive coding: Inferring, fast and slow

Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors"-the differences between predicted and observed data. Implicit in this proposal is the idea that successful perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception-including complex forms of object recognition-arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforward sweep can be understood as performing amortized inference (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner by describing both in terms of a dual optimization of a single objective function. We show that the resulting scheme can be implemented in a biologically plausible neural architecture that approximates Bayesian inference utilising local Hebbian update rules. We demonstrate that our hybrid predictive coding model combines the benefits of both amortized and iterative inference-obtaining rapid and computationally cheap perceptual inference for familiar data while maintaining the context-sensitivity, precision, and sample efficiency of iterative inference schemes. Moreover, we show how our model is inherently sensitive to its uncertainty and adaptively balances iterative and amortized inference to obtain accurate beliefs using minimum computational expense. Hybrid predictive coding offers a new perspective on the functional relevance of the feedforward and recurrent activity observed during visual perception and offers novel insights into distinct aspects of visual phenomenology.

Serial dependence in visual perception: A meta-analysis and review

Positive sequential dependencies are phenomena in which actions, perception, decisions, and memory of features or objects are systematically biased toward visual experiences from the recent past. Among many labels, serial dependencies have been referred to as priming, sequential dependencies, sequential effects, or serial effects. Despite extensive research on the topic, the field still lacks an operational definition of what counts as serial dependence. In this meta-analysis, we review the vast literature on serial dependence and quantitatively assess its key diagnostic characteristics across several different domains of visual perception. The meta-analyses fully characterize serial dependence in orientation, face, and numerosity perception. They show that serial dependence is defined by four main kinds of tuning: serial dependence decays with time (temporal-tuning), it depends on relative spatial location (spatial-tuning), it occurs only between similar features and objects (feature-tuning), and it is modulated by attention (attentional-tuning). We also review studies of serial dependence that report single observer data, highlighting the importance of individual differences in serial dependence. Finally, we discuss a range of outstanding questions and novel research avenues that are prompted by the meta-analyses. Together, the meta-analyses provide a full characterization of serial dependence as an operationally defined family of visual phenomena, and they outline several of the key diagnostic criteria for serial dependence that should serve as guideposts for future research.

Distinct ventral stream and prefrontal cortex representational dynamics during sustained conscious visual perception

Instances of sustained stationary sensory input are ubiquitous. However, previous work focused almost exclusively on transient onset responses. This presents a critical challenge for neural theories of consciousness, which should account for the full temporal extent of experience. To address this question, we use intracranial recordings from ten human patients with epilepsy to view diverse images of multiple durations. We reveal that, in sensory regions, despite dramatic changes in activation magnitude, the distributed representation of categories and exemplars remains sustained and stable. In contrast, in frontoparietal regions, we find transient content representation at stimulus onset. Our results highlight the connection between the anatomical and temporal correlates of experience. To the extent perception is sustained, it may rely on sensory representations and to the extent perception is discrete, centered on perceptual updating, it may rely on frontoparietal representations.

Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review

Introduction

Disorders of consciousness (DOC) has been an object of numbers of research regarding the diagnosis, treatment and prognosis in last few decades. We believe that the DOC could be considered as a disconnection syndrome, although the exact mechanisms are not entirely understood. Moreover, different conceptual frameworks highly influence results interpretation. The aim of this systematic review is to assess the current knowledge regarding neurophysiological mechanisms of DOC and to establish possible influence on future clinical implications and usage.

Methods

We have conducted a systematic review according to PRISMA guidelines through PubMed and Cochrane databases, with studies being selected for inclusion via a set inclusion and exclusion criteria.

Results

Eighty-nine studies were included in this systematic review according to the selected criteria. This includes case studies, randomized controlled trials, controlled clinical trials, and observational studies with no control arms. The total number of DOC patients encompassed in the studies cited in this review is 1,533.

Conclusion

Connectomics and network neuroscience offer quantitative frameworks for analysing dynamic brain connectivity. Functional MRI studies show evidence of abnormal connectivity patterns and whole-brain topological reorganization, primarily affecting sensory-related resting state networks (RSNs), confirmed by EEG studies. As previously described, DOC patients are identified by diminished global information processing, i.e., network integration and increased local information processing, i.e., network segregation. Further studies using effective connectivity measurement tools instead of functional connectivity as well as the standardization of the study process are needed.

Temporospatial Nestedness in Consciousness: An Updated Perspective on the Temporospatial Theory of Consciousness

Time and space are fundamental elements that permeate the fabric of nature, and their significance in relation to neural activity and consciousness remains a compelling yet unexplored area of research. The Temporospatial Theory of Consciousness (TTC) provides a framework that links time, space, neural activity, and consciousness, shedding light on the intricate relationships among these dimensions. In this review, I revisit the fundamental concepts and mechanisms proposed by the TTC, with a particular focus on the central concept of temporospatial nestedness. I propose an extension of temporospatial nestedness by incorporating the nested relationship between the temporal circuit and functional geometry of the brain. To further unravel the complexities of temporospatial nestedness, future research directions should emphasize the characterization of functional geometry and the temporal circuit across multiple spatial and temporal scales. Investigating the links between these scales will yield a more comprehensive understanding of how spatial organization and temporal dynamics contribute to conscious states. This integrative approach holds the potential to uncover novel insights into the neural basis of consciousness and reshape our understanding of the world-brain dynamic.

Consciousness and its hard problems: separating the ontological from the evolutionary

Few of the many theories devised to account for consciousness are explicit about the role they ascribe to evolution, and a significant fraction, by their silence on the subject, treat evolutionary processes as being, in effect, irrelevant. This is a problem for biological realists trying to assess the applicability of competing theories of consciousness to taxa other than our own, and across evolutionary time. Here, as an aid to investigating such questions, a consciousness "machine" is employed as conceptual device for thinking about the different ways ontology and evolution contribute to the emergence of a consciousness composed of distinguishable contents. A key issue is the nature of the evolutionary innovations required for any kind of consciousness to exist, specifically whether this is due to the underappreciated properties of electromagnetic (EM) field effects, as in neurophysical theories, or, for theories where there is no such requirement, including computational and some higher-order theories (here, as a class, algorithmic theories), neural connectivity and the pattern of information flow that connectivity encodes are considered a sufficient explanation for consciousness. In addition, for consciousness to evolve in a non-random way, there must be a link between emerging consciousness and behavior. For the neurophysical case, an EM field-based scenario shows that distinct contents can be produced in the absence of an ability to consciously control action, i.e., without agency. This begs the question of how agency is acquired, which from this analysis would appear to be less of an evolutionary question than a developmental one. Recasting the problem in developmental terms highlights the importance of real-time feedback mechanisms for transferring agency from evolution to the individual, the implication being, for a significant subset of theories, that agency requires a learning process repeated once in each generation. For that subset of theories the question of how an evolved consciousness can exist will then have two components, of accounting for conscious experience as a phenomenon on the one hand, and agency on the other. This reduces one large problem to two, simplifying the task of investigation and providing what may prove an easier route toward their solution.

The Current of Consciousness: Neural Correlates and Clinical Aspects

PURPOSE OF REVIEW

In this review, we summarize the current understanding of consciousness including its neuroanatomic basis. We discuss major theories of consciousness, physical exam-based and electroencephalographic metrics used to stratify levels of consciousness, and tools used to shed light on the neural correlates of the conscious experience. Lastly, we review an expanded category of 'disorders of consciousness,' which includes disorders that impact either the level or experience of consciousness.

RECENT FINDINGS

Recent studies have revealed many of the requisite EEG, ERP, and fMRI signals to predict aspects of the conscious experience. Neurological disorders that disrupt the reticular activating system can affect the level of consciousness, whereas cortical disorders from seizures and migraines to strokes and dementia may disrupt phenomenal consciousness. The recently introduced memory theory of consciousness provides a new explanation of phenomenal consciousness that may explain better than prior theories both experimental studies and the neurologist's clinical experience. Although the complete neurobiological basis of consciousness remains a mystery, recent advances have improved our understanding of the physiology underlying level of consciousness and phenomenal consciousness.

As without, so within: how the brain's temporo-spatial alignment to the environment shapes consciousness

Consciousness is constituted by a structure that includes contents as foreground and the environment as background. This structural relation between the experiential foreground and background presupposes a relationship between the brain and the environment, often neglected in theories of consciousness. The temporo-spatial theory of consciousness addresses the brain-environment relation by a concept labelled 'temporo-spatial alignment'. Briefly, temporo-spatial alignment refers to the brain's neuronal activity's interaction with and adaption to interoceptive bodily and exteroceptive environmental stimuli, including their symmetry as key for consciousness. Combining theory and empirical data, this article attempts to demonstrate the yet unclear neuro-phenomenal mechanisms of temporo-spatial alignment. First, we suggest three neuronal layers of the brain's temporo-spatial alignment to the environment. These neuronal layers span across a continuum from longer to shorter timescales. (i) The background layer comprises longer and more powerful timescales mediating topographic-dynamic similarities between different subjects' brains. (ii) The intermediate layer includes a mixture of medium-scaled timescales allowing for stochastic matching between environmental inputs and neuronal activity through the brain's intrinsic neuronal timescales and temporal receptive windows. (iii) The foreground layer comprises shorter and less powerful timescales for neuronal entrainment of stimuli temporal onset through neuronal phase shifting and resetting. Second, we elaborate on how the three neuronal layers of temporo-spatial alignment correspond to their respective phenomenal layers of consciousness. (i) The inter-subjectively shared contextual background of consciousness. (ii) An intermediate layer that mediates the relationship between different contents of consciousness. (iii) A foreground layer that includes specific fast-changing contents of consciousness. Overall, temporo-spatial alignment may provide a mechanism whose different neuronal layers modulate corresponding phenomenal layers of consciousness. Temporo-spatial alignment can provide a bridging principle for linking physical-energetic (free energy), dynamic (symmetry), neuronal (three layers of distinct time-space scales) and phenomenal (form featured by background-intermediate-foreground) mechanisms of consciousness.

Epistemological challenges for neural correlates of consciousness: A defense of medical research on consciousness

Recent work in the field of consciousness science has predominantly focused on the search for neural correlates of consciousness (NCC). However, despite significant technological advances in recent decades, defining NCC remains an ambitious goal in consciousness research. The main difficulty stems from an epistemological challenge known as the "Problem of coordination", which hinders or at least slows down the experimental process inherent to the study of consciousness. Fundamental research has mainly focused on a content-based conception of consciousness, often referred to as a "local" conception of consciousness. This approach suffers from the Problem of coordination and its consequences. However, an alternative, more reliable approach could be considered, namely, the global or "state-based" approach, which is grounded in clinical research on consciousness disorders.

Laminar specificity of the auditory perceptual awareness negativity: A biophysical modeling study

How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-5 (L5) pyramidal neurons. In turn, this leads to increased local field potential activity, increased spiking activity in L5 pyramidal neurons, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity.

Cerebral Organoid Research Ethics and Pinning the Tail on the Donkey

The risk of creating cerebral organoids/assembloids conscious enough to suffer is a recurrent concern in organoid research ethics. On one hand, we should, apparently, avoid discovering how to distinguish between organoids that it would be permissible (non-conscious) and impermissible (conscious) to use in research, since if successful we would create organoids that suffer. On the other, if we do not, the risk persists that research might inadvertently continue to cause organoids to suffer. Moreover, since modeling some brain disorders may require inducing stress in organoids, it is unclear how to eliminate the risk, if we want to develop effective therapies. We are committed to harm avoidance but hamstrung by a presumption that we should avoid research that might tell us clearly when suffering occurs. How can we negotiate this challenge and maximize the therapeutic benefits of cerebral organoid research? The author interrogates the challenge, suggesting a tentative way forward.

Surge of neurophysiological coupling and connectivity of gamma oscillations in the dying human brain

The brain is assumed to be hypoactive during cardiac arrest. However, animal models of cardiac and respiratory arrest demonstrate a surge of gamma oscillations and functional connectivity. To investigate whether these preclinical findings translate to humans, we analyzed electroencephalogram and electrocardiogram signals in four comatose dying patients before and after the withdrawal of ventilatory support. Two of the four patients exhibited a rapid and marked surge of gamma power, surge of cross-frequency coupling of gamma waves with slower oscillations, and increased interhemispheric functional and directed connectivity in gamma bands. High-frequency oscillations paralleled the activation of beta/gamma cross-frequency coupling within the somatosensory cortices. Importantly, both patients displayed surges of functional and directed connectivity at multiple frequency bands within the posterior cortical "hot zone," a region postulated to be critical for conscious processing. This gamma activity was stimulated by global hypoxia and surged further as cardiac conditions deteriorated in the dying patients. These data demonstrate that the surge of gamma power and connectivity observed in animal models of cardiac arrest can be observed in select patients during the process of dying.

Scale-free dynamics in the core-periphery topography and task alignment decline from conscious to unconscious states

Scale-free physiological processes are ubiquitous in the human organism. Resting-state functional MRI studies observed the loss of scale-free dynamics under anesthesia. In contrast, the modulation of scale-free dynamics during task-related activity remains an open question. We investigate scale-free dynamics in the cerebral cortex's unimodal periphery and transmodal core topography in rest and task states during three conscious levels (awake, sedation, and anesthesia) complemented by computational modelling (Stuart-Landau model). The empirical findings demonstrate that the loss of the brain's intrinsic scale-free dynamics in the core-periphery topography during anesthesia, where pink noise transforms into white noise, disrupts the brain's neuronal alignment with the task's temporal structure. The computational model shows that the stimuli's scale-free dynamics, namely pink noise distinguishes from brown and white noise, also modulate task-related activity. Together, we provide evidence for two mechanisms of consciousness, temporo-spatial nestedness and alignment, suggested by the Temporo-Spatial Theory of Consciousness (TTC).

Feedback information sharing in the human brain reflects bistable perception in the absence of report

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

Complex of global functional network as the core of consciousness

Finding the neural basis of consciousness is challenging, and the distribution location of the core of consciousness remains inconclusive. Integrated information theory (IIT) argues that the posterior part of the brain is the hot zone of consciousness, especially phenological consciousness. The IIT has proposed a "main complex", a set of elements determined such that the information loss in a hierarchical partition approach is the largest among those of any other supersets and subsets, as the core of consciousness in a dynamic system. This approach may be applicable not only to phenomenal but also to access-consciousness. This study estimated the main complex of brain dynamics using functional magnetic resonance imaging in Human Connectome Project (HCP) and sleep datasets. The complex analyses revealed the common networks across various tasks and rest-state in HCP, composed of executive control, salience, and dorsal/ventral attention networks. The set of networks of the main complex was maintained during sleep. However, compared with the wakefulness stage, the amount of information of these networks and the default mode network, was reduced for the hypnagogic stage. The global interconnected structure composed of major functional networks can comprise the core of consciousness.

Towards a pluralistic neurobiological understanding of consciousness

Theories of consciousness are often based on the assumption that a single, unified neurobiological account will explain different types of conscious awareness. However, recent findings show that, even within a single modality such as conscious visual perception, the anatomical location, timing, and information flow of neural activity related to conscious awareness vary depending on both external and internal factors. This suggests that the search for generic neural correlates of consciousness may not be fruitful. I argue that consciousness science requires a more pluralistic approach and propose a new framework: joint determinant theory (JDT). This theory may be capable of accommodating different brain circuit mechanisms for conscious contents as varied as percepts, wills, memories, emotions, and thoughts, as well as their integrated experience.

Revealing robust neural correlates of conscious and unconscious visual processing: activation likelihood estimation meta-analyses

Our ability to consciously perceive information from the visual scene relies on a myriad of intrinsic neural mechanisms. Functional neuroimaging studies have sought to identify the neural correlates of conscious visual processing and to further dissociate from those pertaining to preconscious and unconscious visual processing. However, delineating what core brain regions are involved in eliciting a conscious percept remains a challenge, particularly regarding the role of prefrontal-parietal regions. We performed a systematic search of the literature that yielded a total of 54 functional neuroimaging studies. We conducted two quantitative meta-analyses using activation likelihood estimation to identify reliable patterns of activation engaged by i. conscious (n = 45 studies, comprising 704 participants) and ii. unconscious (n = 16 studies, comprising 262 participants) visual processing during various task performances. Results of the meta-analysis specific to conscious percepts quantitatively revealed reliable activations across a constellation of regions comprising the bilateral inferior frontal junction, intraparietal sulcus, dorsal anterior cingulate, angular gyrus, temporo-occipital cortex and anterior insula. Neurosynth reverse inference revealed conscious visual processing to be intertwined with cognitive terms related to attention, cognitive control and working memory. Results of the meta-analysis on unconscious percepts revealed consistent activations in the lateral occipital complex, intraparietal sulcus and precuneus. These findings highlight the notion that conscious visual processing readily engages higher-level regions including the inferior frontal junction and unconscious processing reliably recruits posterior regions, mainly the lateral occipital complex.

Embodying Consciousness through Interoception and a Balanced Time Perspective

This review presents current research and scientific knowledge in body mind sciences through the lens of interoception, as a representative of the body; and time perspective, as the representative of the mind. This intertwining dichotomy has been a subject of discourse in many fields, all having the common denominator of consciousness. Our aim is to expand on the congruities of these seemingly deconstructed worlds-of science and philosophy, of the body and the mind, to show that the place of consciousness lies in the zone between these two. Being aware of the body in the present moment. We introduce interoception and time perspective, focusing on how interoceptive signals are depicted in autonomic nervous system (ANS) regulation, and how this relates to the concept of a balanced time perspective (BTP), a highly adaptive psychological characteristic. Time perspective and interoception are also reviewed in the case of clinical conditions. We assess findings on interoceptive pathways in the body, finding convergence with balanced time perspective through the neuroanatomical lens. We conclude with findings that both dysregulated interoceptive states and a time perspective disbalance are recognized as defining features of mental disorders, proposing prospective practical therapeutic approaches, as well as implications for further research in the field.

Modulation of mind wandering using transcranial direct current stimulation: A meta-analysis based on electric field modeling

Mind wandering (MW) is a heterogeneous construct involving task-unrelated thoughts. Recently, the interest in modulating MW propensity via non-invasive brain stimulation techniques has increased. Single-session transcranial direct current stimulation (tDCS) in healthy controls has led to mixed results in modulating MW propensity, possibly due to methodological heterogeneity. Therefore, our aim was to conduct a systematic meta-analysis to examine the influence of left dorsolateral prefrontal cortex (lDLPFC) and right inferior parietal lobule (rIPL) targeted tDCS on MW propensity. Importantly, by computational modeling of tDCS-induced electric fields, we accounted for differences in tDCS-dose across studies that varied strongly in their applied methodology. Fifteen single-session, sham-controlled tDCS studies published until October 2021 were included. All studies involved healthy adult participants and used cognitive tasks combined with MW thought-probes. Heterogeneity in tDCS electrode placement, stimulation polarity and intensity were controlled for by means of electric field simulations, while overall methodological quality was assessed via an extended risk of bias (RoB) assessment. We found that RoB was the strongest predictor of study outcomes. Moreover, the rIPL was the most promising cortical area for influencing MW, with stronger anodal electric fields in this region being negatively associated with MW propensity. Electric field strength in the lDLPFC was not related to MW propensity. We identified several severe methodological problems that could have contributed to overestimated effect sizes in this literature, an issue that needs urgent attention in future research in this area. Overall, there is no reliable evidence for tDCS influencing MW in the healthy. However, the analysis also revealed that increasing neural excitability in the rIPL via tDCS might be associated with reduced MW propensity. In an exploratory approach, we also found some indication that targeting prefrontal regions outside the lDLPFC with tDCS could lead to increased MW propensity.

Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Functional Neurologic Disorders, disorders to be managed by neurologists, or are neurologists wandering in a dangerous field with inadequate resources?

In recent years, some neurologists reconsidered their approach to Medically Unexplained Symptoms and proposed Functional Neurologic Disorders (FND) as a new entity, claiming that neurology could offer alternative treatment options to the psychotherapies provided in psychiatry settings. FNDs, for this purpose, should include only the disorders listed as Conversion from the Somatic Symptom and Related Disorders (SSRD) group. The present review analyzes the rationale of this position and challenges the arguments provided for its support. The review also discusses the systematization of these disorders as provided by public health systems. It outlines risks stemming from economic support and public funding uncertainty, given their negligible epidemiological dimensions resulting from the parcellation of SSRD. The review underlines the unresolved issue of Factitious Disorders, which are in the same SSRD category of the international classification but are, nonetheless, overlooked by the theoretical proponents of the FND entity. Comorbidity with other psychiatric disorders is also analyzed. We propose a model that supports the continuum between different SSRD conditions, including Factitious Disorders. The model is based on the emergence of feigned death reflex and deception from frontal lobe dysfunction. Finally, the paper summarizes the wealth of historical psychiatric and psychodynamic approaches and critical reviews. The study also puts in context the categorization and interpretation efforts provided by the most eminent researchers of the past century.

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning

The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin-Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.

Bistability of prefrontal states gates access to consciousness

Access of sensory information to consciousness has been linked to the ignition of content-specific representations in association cortices. How does ignition interact with intrinsic cortical state fluctuations to give rise to conscious perception? We addressed this question in the prefrontal cortex (PFC) by combining multi-electrode recordings with a binocular rivalry (BR) paradigm inducing spontaneously driven changes in the content of consciousness, inferred from the reflexive optokinetic nystagmus (OKN) pattern. We find that fluctuations between low-frequency (LF, 1-9 Hz) and beta (∼20-40 Hz) local field potentials (LFPs) reflect competition between spontaneous updates and stability of conscious contents, respectively. Both LF and beta events were locally modulated. The phase of the former locked differentially to the competing populations just before a spontaneous transition while the latter synchronized the neuronal ensemble coding the consciously perceived content. These results suggest that prefrontal state fluctuations gate conscious perception by mediating internal states that facilitate perceptual update and stability.

Entropy and Cross-Level Orderliness in Light of the Interconnection between the Neural System and Consciousness

Despite recent advances, the origin and utility of consciousness remains under debate. Using an evolutionary perspective on the origin of consciousness, this review elaborates on the promising theoretical background suggested in the temporospatial theory of consciousness, which outlines world-brain alignment as a critical predisposition for controlling behavior and adaptation. Such a system can be evolutionarily effective only if it can provide instant cohesion between the subsystems, which is possible only if it performs an intrinsic activity modified in light of the incoming stimulation. One can assume that the world-brain interaction results in a particular interference pattern predetermined by connectome complexity. This is what organisms experience as their exclusive subjective state, allowing the anticipation of regularities in the environment. Thus, an anticipative system can emerge only in a regular environment, which guides natural selection by reinforcing corresponding reactions and decreasing the system entropy. Subsequent evolution requires complicated, layered structures and can be traced from simple organisms to human consciousness and society. This allows us to consider the mode of entropy as a subject of natural evolution rather than an individual entity.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS

Background

Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients.

Methods

We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups.

Results

Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores.

Conclusion

This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible nonequilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to Functional magnetic resonance imaging brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.

An integrated information theory index using multichannel EEG for evaluating various states of consciousness under anesthesia

BACKGROUND

The integrated information theory (IIT) of consciousness introduces a measure Φ to quantify consciousness in a physical system. Directly related to this, general anesthesia aims to induce reversible and safe loss of consciousness (LOC). We sought to propose an electroencephalogram (EEG)-based IIT index ΦEEG to evaluate various states of consciousness under general anesthesia.

METHODS

Based on the definition of mutual information, we estimated the ΦEEG by maximizing the integrated information under various time lags. We used the binning method to cut the nonGaussian EEG data for estimating mutual information. We tested two EEG databases collected from propofol- (n=20) and sevoflurane-induced (n=15) anesthesia, and especially, we compared the ΦEEG of drowsy (n=7) and responsive participants (n=13) under propofol anesthesia. We compared the effectiveness of ΦEEG with the estimated bispectral index (eBIS).

RESULTS

In all EEG frequency bands, we observed a negative correlation between ΦEEG and end-tidal sevoflurane concentration under sevoflurane-induced anesthesia (p<0.001,BF10>6000). Under propofol-induced anesthesia, drowsy participants in moderate sedation (6.96±0.26(mean±SD)) showed decreased alpha-band ΦEEG compared with baseline (7.40±0.53,p=0.016,BF10=3.58), no significant difference was observed for responsive participants. Oppositely, the responsive participants in moderate sedation (-5.32±0.38) showed decreased eBIS compared with baseline (-4.94±0.40,p=0.03,BF10=2.41).

CONCLUSIONS

These findings may enable monitors of the anesthetic state that can distinguish consciousness and unconsciousness rather than the changes of anesthetic concentrations. The alpha-band ΦEEG is promising for deriving the gold standard for depth of anesthesia monitoring.

Imagination: The dawn of consciousness: Fighting some misconceptions in the discussion about consciousness

Several theories of consciousness (ToC) have been proposed, but it is hard to integrate them into a consensus theory. Each theory has its merits, in dealing with some aspects of the question, but the terminology is inconsistent, each ToC aims at answering a different question, and there is not even a reasonable agreement about what 'consciousness' is in the first place. Some common implicit assumptions, and the way some critical words - such as 'sensation', 'perception', 'neural correlate of consciousness' (NCC) - are thought to relate to consciousness, have introduced a series of misconceptions that make it difficult to pinpoint what consciousness consists in and how it arises in the brain. The purpose of this contribution is twofold: firstly, to discern the various steps that lead from the detection of a stimulus to a conscious experience, by redefining terms such as sensation and perception with an adequate operative meaning; secondly, to emphasize the inevitable contribution of emotions and the active role of imagination in this process. The diffuse view, for the layperson but among scientists as well, is that the brain produces an internal 'representation' of the external reality and of oneself. This tends to consign one to a Cartesian perspective, i.e., the idea that some entity must be there to witness and interpret such representation. This approach splits the conscious experience into brain activity, which generates a (possible) content of consciousness (still unconscious), and a vaguely defined entity or process that 'generates' consciousness and injects (or sheds the light of) consciousness onto the content of brain activity. This way, however, we learn nothing about how such consciousness would arise. We propose here that consciousness is the function that generates a subjectively relevant and emotionally coloured internal image of the experience one is living. In this process, endogenous, spontaneous activity (imaginative activity, consisting in recalling and reviving memories, prefiguring consequences, analysing conjectures) produces many vague and ambiguous hints, rich of symbolic links, which compete in giving rise to an implicit, emotionally characterized, and semantically pleiotropic, internal experience. Cognitive elaboration may extract from this a defined and univocal, complete and consistent, explicit experience, that can be verbally reported ('what it is like to...').

Feasibility of unconscious instrumental conditioning: A registered replication

The extent to which high-level, complex functions can proceed unconsciously has been a topic of considerable debate. While unconscious processing has been demonstrated for a range of low-level processes, from feature integration to simple forms of conditioning and learning, theoretical contributions suggest that increasing complexity requires conscious access. Here, we focus our attention on instrumental conditioning, which has been previously shown to proceed without stimulus awareness. Yet, instrumental conditioning also involves integrating information over a large temporal scale and distinct modalities in order to deploy selective action, constituting a process of substantial complexity. With this in mind, we revisit the question of feasibility of instrumental conditioning in the unconscious domain. Firstly, we address the theoretical and practical considerations relevant to unconscious learning in general. Secondly, we aim to replicate the first study to show instrumental conditioning in the absence of stimulus awareness (Pessiglione et al., 2008), following the original design and supplementing the original crucial analyses with a Bayesian approach (Experiment 1). We found that apparent unconscious learning took place when replicating the original methods directly and according to the tests of awareness used. However, we could not establish that the full sample was unaware in a separate awareness check. We therefore attempted to replicate the effect yet again with improved methods to address the issues related to sensitivity and immediacy (Experiment 2), including an individual threshold-setting task and a trial-by-trial awareness check permitting exclusion of individual aware trials. Here, we found evidence for absence of unconscious learning. This result provides evidence that instrumental conditioning did not occur without stimulus awareness in this paradigm, supporting the view that complex forms of learning may rely on conscious access. Our results provides support for the proposal that perceptual consciousness may be necessary for complex, flexible processes, especially where selective action and behavioural adaptation are required.

Conscious interpretation: A distinct aspect for the neural markers of the contents of consciousness

Progress in the science of consciousness depends on the experimental paradigms and varieties of contrastive analysis available to researchers. Here we highlight paradigms where the object is represented in consciousness as a set of its features but the interpretation of this set alternates in consciousness. We group experimental paradigms with this property under the label "conscious interpretation". We compare the paradigms studying conscious interpretation of the already consciously perceived objects with other types of experimental paradigms. We review previous and recent studies investigating this interpretative aspect of consciousness and propose future directions. We put forward the hypothesis that there are types of stimuli with a hierarchy of interpretations for which the rule applies: conscious experience is drawn towards higher-level interpretation and reverting back to the lower level of interpretation is impossible. We discuss how theories of consciousness might incorporate knowledge and constraints arising from the characteristics of conscious interpretation.

An adjunction hypothesis between qualia and reports

What are the nature of the relationship among qualia, contents of consciousness, and behavioral reports? Traditionally, this type of question has been only addressed via a qualitative and philosophical approach. Some theorists emphasize an incomplete and inaccurate nature of reports of one's own qualia to discourage formal research programs on qualia. Other empirical researchers, however, have made substantial progress in understanding the structure of qualia from such limited reports. What is the precise relationship between the two? To answer this question, we introduce the concept of “adjoint” or “adjunction” from the category theory in mathematics. We claim that the adjunction captures some aspects of the nuanced relationships between qualia and reports. The concept of adjunction allows us to clarify the conceptual issues with a precise mathematical formulation. In particular, adjunction establishes coherence between two categories that cannot be considered equivalent, yet has an important relationship. This rises in empirical experimental situations between qualia and reports. More importantly, an idea of adjunction naturally leads to various proposals of new empirical experiments to test the predictions about the nature of their relationship as well as other issues in consciousness research.

The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding

We present a unified computational theory of an agent's perception and memory. In our model, both perception and memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). The index layer encodes indices for concepts, predicates, and episodic instances. The representation layer broadcasts information and reflects the cognitive brain state; it is our model of what authors have called the "mental canvas" or the "global workspace." As a bridge between perceptual input and the index layer, the representation layer enables the grounding of indices by their subsymbolic embeddings, which are implemented as connection weights linking both layers. The propagation of activation to earlier perceptual processing layers in the brain can lead to embodiments of indices. Perception and memories first create subsymbolic representations, which are subsequently decoded semantically to produce sequences of activated indices that form symbolic triple statements. The brain is a sampling engine: only activated indices are communicated to the remaining parts of the brain. Triple statements are dynamically embedded in the representation layer and embodied in earlier processing layers: the brain speaks to itself. Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision making. "Future" episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. Perception is learning: episodic memories are constantly being formed, and we demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence.

Consciousness: Matter or EMF?

Conventional theories of consciousness (ToCs) that assume that the substrate of consciousness is the brain's neuronal matter fail to account for fundamental features of consciousness, such as the binding problem. Field ToC's propose that the substrate of consciousness is the brain's best accounted by some kind of field in the brain. Electromagnetic (EM) ToCs propose that the conscious field is the brain's well-known EM field. EM-ToCs were first proposed only around 20 years ago primarily to account for the experimental discovery that synchronous neuronal firing was the strongest neural correlate of consciousness (NCC). Although EM-ToCs are gaining increasing support, they remain controversial and are often ignored by neurobiologists and philosophers and passed over in most published reviews of consciousness. In this review I examine EM-ToCs against established criteria for distinguishing between ToCs and demonstrate that they outperform all conventional ToCs and provide novel insights into the nature of consciousness as well as a feasible route toward building artificial consciousnesses.

Leveraging conscious and nonconscious learning for efficient AI

Various interpretations of the literature detailing the neural basis of learning have in part led to disagreements concerning how consciousness arises. Further, artificial learning model design has suffered in replicating intelligence as it occurs in the human brain. Here, we present a novel learning model, which we term the "Recommendation Architecture (RA) Model" from prior theoretical works proposed by Coward, using a dual-learning approach featuring both consequence feedback and non-consequence feedback. The RA model is tested on a categorical learning task where no two inputs are the same throughout training and/or testing. We compare this to three consequence feedback only models based on backpropagation and reinforcement learning. Results indicate that the RA model learns novelty more efficiently and can accurately return to prior learning after new learning with less computational resources expenditure. The final results of the study show that consequence feedback as interpretation, not creation, of cortical activity creates a learning style more similar to human learning in terms of resource efficiency. Stable information meanings underlie conscious experiences. The work provided here attempts to link the neural basis of nonconscious and conscious learning while providing early results for a learning protocol more similar to human brains than is currently available.

Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia

Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity in ferrets under anesthesia before and after loss of consciousness (LOC) we show that anesthesia- related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ('anteriorization') also impacts local information processing-but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.

Repetitive transcranial magnetic stimulation over the posterior parietal cortex improves functional recovery in nonresponsive patients: A crossover, randomized, double-blind, sham-controlled study

Background

Recent studies have shown that patients with disorders of consciousness (DoC) can benefit from repetitive transcranial magnetic stimulation (rTMS) therapy. The posterior parietal cortex (PPC) is becoming increasingly important in neuroscience research and clinical treatment for DoC as it plays a crucial role in the formation of human consciousness. However, the effect of rTMS on the PPC in improving consciousness recovery remains to be studied.

Method

We conducted a crossover, randomized, double-blind, sham-controlled clinical study to assess the efficacy and safety of 10 Hz rTMS over the left PPC in unresponsive patients. Twenty patients with unresponsive wakefulness syndrome were recruited. The participants were randomly divided into two groups: one group received active rTMS treatment for 10 consecutive days (n = 10) and the other group received sham treatment for the same period (n = 10). After a 10-day washout period, the groups crossed over and received the opposite treatment. The rTMS protocol involved the delivery of 2000 pulses/day at a frequency of 10 Hz, targeting the left PPC (P3 electrode sites) at 90% of the resting motor threshold. The primary outcome measure was the JFK Coma Recovery Scele-Revised (CRS-R), and evaluations were conducted blindly. EEG power spectrum assessments were also conducted simultaneously before and after each stage of the intervention.

Result

rTMS-active treatment resulted in a significant improvement in the CRS-R total score (F = 8.443, p = 0.009) and the relative alpha power (F = 11.166, p = 0.004) compared to sham treatment. Furthermore, 8 out of 20 patients classified as rTMS responders showed improvement and evolved to a minimally conscious state (MCS) as a result of active rTMS. The relative alpha power also significantly improved in responders (F = 26.372, p = 0.002) but not in non-responders (F = 0.704, p = 0.421). No adverse effects related to rTMS were reported in the study.

Conclusions

This study suggests that 10 Hz rTMS over the left PPC can significantly improve functional recovery in unresponsive patients with DoC, with no reported side effects.

Clinical trial registration

www.ClinicalTrials.gov, identifier: NCT05187000.

Ten-Year Change in Disorders of Consciousness: A Bibliometric Analysis

Objectives: Disorders of consciousness (DoC) is a dynamic and challenging discipline, presenting intriguing challenges to clinicians and neurorehabilitation specialists for the lack of reliable assessment methods and interventions. Understanding DoC keeps pace with scientific research is urgent to need. We quantitively analyzed publications on DoC over the recent 10 years via bibliometrics analysis, to summarize the intellectual structure, current research hotspots, and future research trends in the field of DoC. Methods: Literature was obtained from the Science Citation Index Expanded of Web of Science Core Collection (WoSCC). To illustrate the knowledge structure of DoC, CiteSpace 5.8.R3 was used to conduct a co-occurrence analysis of countries, institutions, and keywords, and a co-citation analysis of references and journals. Also, Gephi 0.9.2 contributed to the author and co-cited author analysis. We found the most influential journals, authors, and countries and the most talked about keywords in the last decade of research. Results: A total of 1919 publications were collected. Over the past 10 years, the total number of annual publications has continued to increase, with the largest circulation in 2018. We found most DoC research and close cooperation originated from developed countries, e.g., the USA, Canada, and Italy. Academics from Belgium appear to have a strong presence in the field of DoC. The most influential journals were also mainly distributed in the USA and some European countries. Conclusions: This bibliometric study sheds light on the knowledge architecture of DoC research over the past decade, reflecting current hotspots and emerging trends, and providing new insights for clinicians and academics interested in DoC. The hot issues in DoC were diagnosing and differentiating the level of consciousness, and detecting covert awareness in early severe brain-injured patients. New trends focus on exploring the recovery mechanism of DoC and neuromodulation techniques.

Attentional capture mediates the emergence and suppression of intrusive memories

Intrusive memories hijack consciousness and their control may lead to forgetting. However, the contribution of reflexive attention to qualifying a memory signal as interfering is unknown. We used machine learning to decode the brain's electrical activity and pinpoint the otherwise hidden emergence of intrusive memories reported during a memory suppression task. Importantly, the algorithm was trained on an independent attentional model of visual activity, mimicking either the abrupt and interfering appearance of visual scenes into conscious awareness or their deliberate exploration. Intrusion of memories into conscious awareness were decoded above chance. The decoding accuracy increased when the algorithm was trained using a model of reflexive attention. Conscious detection of intrusive activity decoded from the brain signal was central to the future silencing of suppressed memories and later forgetting. Unwanted memories require the reflexive orienting of attention and access to consciousness to be suppressed effectively by inhibitory control.

Perceptual Awareness and Its Relationship with Consciousness: Hints from Perceptual Multistability

Many interesting theories of consciousness have been proposed, but so far, there is no "unified" theory capable of encompassing all aspects of this phenomenon. We are all aware of what it feels like to be conscious and what happens if there is an absence of consciousness. We are becoming more and more skilled in measuring consciousness states; nevertheless, we still "don't get it" in its deeper essence. How does all the processed information converge from different brain areas and structures to a common unity, giving us this very private "feeling of being conscious", despite the constantly changing flow of information between internal and external states? "Multistability" refers to a class of perceptual phenomena where subjective awareness spontaneously and continuously alternates between different percepts, although the objective stimuli do not change, supporting the idea that the brain "interprets" sensorial input in a "constructive" way. In this perspective paper, multistability and perceptual awareness are discussed as a methodological window for understanding the "local" states of consciousness, a privileged position from which it is possible to observe the brain dynamics and mechanisms producing the subjective phenomena of perceptual awareness in the very moment they are happening.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study

BACKGROUND AND OBJECTIVES

The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks.

METHODS

We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC.

RESULTS

We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups.

DISCUSSION

Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

Dissociable rhythmic mechanisms enhance memory for conscious and nonconscious perceptual contents

Understanding the neural mechanisms of conscious and unconscious experience is a major goal of fundamental and translational neuroscience. Here, we target the early visual cortex with a protocol of noninvasive, high-resolution alternating current stimulation while participants performed a delayed target–probe discrimination task and reveal dissociable mechanisms of mnemonic processing for conscious and unconscious perceptual contents. Entraining β-rhythms in bilateral visual areas preferentially enhanced short-term memory for seen information, whereas α-entrainment in the same region preferentially enhanced short-term memory for unseen information. The short-term memory improvements were frequency-specific and long-lasting. The results add a mechanistic foundation to existing theories of consciousness, call for revisions to these theories, and contribute to the development of nonpharmacological therapeutics for improving visual cortical processing.

Taking Conceptual Issues Really Seriously: One Next Step for the Cognitive Science of Consciousness

In this letter we focus on the cognitive science of consciousness. The general message is that, while this interdisciplinary area has made much progress in recent years, there is a tendency of downplaying conceptual issues, and therefore underestimating the difficulties of various problems. We briefly focus on a few prominent examples only, due to the space limit, but the general message should be clear: this recent tendency can be problematic for the progress of the consciousness branch of cognitive sciences.