Access denied: Plato's cave and the epistemic limits of cellular life.

The rhythms of trance: Cultural phenomenology and neural mechanisms of music-induced non-ordinary states of consciousness.

Functional connectivity disruptions and topological structure alterations centered around the sensorimotor network in disorders of consciousness: A functional near-infrared spectroscopy study.

A critical review of brain entropy as a biomarker of the psychedelic state.

Emergency Nursing Recognition of Atypical Acute Myocardial Infarction: Associations With Functional Status and Level of Consciousness-A Retrospective Cohort Study.

Steady-state visual evoked potentials as diagnostic tool for patients with severe disorders of consciousness - a proof of concept study.

Psychometric validation of the French version of the five-dimensional altered states of consciousness questionnaire (5D-ASC) and associated 11 OAV subscales.

This multicenter retrospective cohort study aimed to develop 3 predictive models to estimate consciousness status 3 months after admission. These models were designed to serve as complementary prognostic tools for patients diagnosed with unresponsive wakefulness syndrome (UWS) using the Coma Recovery Scale-Revised (CRS-R). We retrospectively collected data from 154 patients with UWS across 2 clinical centers, encompassing demographic, clinical, and laboratory biomarkers. Variable selection was performed using adaptive least absolute shrinkage and selection operator (LASSO) and ridge regression. The final predictive models were constructed using binary logistic regression, while additional machine-learning algorithms (Random Forest, SVM, and XGBoost) were applied for comparative evaluations of predictive performance. Of the patients studied, 88 (57%) regained responsiveness within 3 months, while 66 (43%) remained in UWS. Model UWS-base, incorporating clinical factors such as traumatic brain injury (TBI), hypoxic encephalopathy, hydrocephalus, and diffuse injury, demonstrated utility for outpatient preliminary screening. Model UWS-plus achieved superior accuracy (AUC = 0.84) by integrating key biomarkers, including fT3, albumin score, lymphocyte count, and alkaline phosphatase. Model UWS-lite, retaining only lymphocyte count alongside clinical variables, maintained robustness in resource-limited settings (AUC = 0.83). Notably, these biomarkers emerged as factors potentially associated with recovery, generating hypotheses for future interventional studies. We propose a biomarker-integrated model that complements the prognosis of UWS. Our findings also encourage a more holistic approach to clinical practice, wherein hydrocephalus management and systemic biomarkers may be considered alongside traditional scales to inform prognosis and guide therapeutic decisions.

Aphantasia was initially characterised as a pictorial imaginative deficit, but many people with Aphantasia also report having imaginative inabilities for other sensory modalities, such as for imagined sensations of audition, taste, smell, touch, and of body movements. Moreover, patterns of imaginative inability across different types of imagined experience are individually specific to different Aphantasics. Many Aphantasics also report having visual dreams, which has prompted the suggestion that Aphantasia might be an imaginative inability specific to volitional waking imagined sensations. Until now, no study had investigated the multisensory profiles of Aphantasics' dreams, to assess how these might relate to their volitional waking imagined experiences. Here, we make that assessment and find that there is typically a correspondence between the reported multisensory contents of Aphantasics' dreams and their volitional waking imagined experiences. However, there is considerable heterogeneity, with tight associations between the reported multisensory content of some Aphantasic's dreams and their volitional waking imagined sensations, and little or no correspondence between the multisensory profiles of imagined sensations across different states of consciousness in other Aphantasics. Our data challenge researchers and theorists of Aphantasia to explain why different Aphantasics report having different contingencies between their volitional waking and dreamt imagined sensations - with one possibility being that neurological differences exist that account for these diverse outcomes.

Multimodal assessment of minimally conscious state and cognitive motor dissociation in neurocritical care: A critical review.

Introduced in 2014 and revised in 2018, the entropic brain hypothesis has accrued a wealth of supportive evidence. The hypothesis states that-along a dimension of the size of phenomenal consciousness-expansive states reliably exhibit increased brain entropy whereas the inverse applies for states of no or reduced consciousness. Examples of expansive states include expert meditation, flicker light stimulation, near-death-like experiences, atypical breathing, rapid-eye-movement sleep, the pre-ictal aura, unmedicated early psychosis and psychedelic drug states. Examples of states of no or reduced consciousness with low brain entropy, include disorders of consciousness, deep sleep, the anesthetized state, seizure, post-stroke, ageing, cognitive impairment, and neurodegenerative illness. It is shown that the entropic brain has convergent, correlative, predictive, discriminative and external validity. Regarding its predictive validity, increased brain entropy under psilocybin (in a supportive context) predicts subsequent improvements in mental health (improved wellbeing 1-month post-dose). Regarding its discriminative validity, changes in brain entropy selectively index the breadth of subjective experience versus alternative dimensions, such as arousal. Regarding portability/external validity, an entropy-related function is applied in generative artificial intelligence. In conclusion, the entropic brain is a useful model of conscious states.

Critical real-world neurocognitive processes, such as settling into a conversation and neurophysiological fluctuations, vary over minutes-to-days in real-world environments. We harnessed simultaneous multi-electrode intracranial and video recordings in twenty people during a week of unconstrained, spontaneous behavior. Using dynamical deep learning algorithms, we found neurodynamics linked to circadian rhythm, heart rate, and multiple aspects of behavior (socializing, watching a screen, sleep depth, etc.). Transitioning between behaviors was associated with bursts of rapid, chaotic neural exploration that stabilized into new states. Despite this chaos, large-scale dynamics anchored to a stabilizing center manifold associated with neurophysiological and conscious states, with a central attractor involving default mode network activation. When perturbed by sleep deprivation, neural transitions were more chaotic and dynamics around the central attractor were suppressed, suggesting diminished neurodynamic control due to lack of sleep. These findings highlight how the brain chaotically transitions around a stabilizing equilibrium to balance dynamic exploration and stable equilibria during real-world behavior.

ABSTRACTThe human brain exhibits inherent multistability, with Energy Landscape Analysis (ELA) providing effective frameworks for investigating this property through BOLD signals. However, traditional amplitude‐based approaches fundamentally neglect critical phase synchronization dynamics that mediate large‐scale neural coordination, while existing phase‐based methods like Leading Eigenvector Dynamic Analysis (LEiDA) lack thermodynamic formalism for state stability quantification. Here, we introduce Energy‐based Phase‐Locking State Analysis (EPLSA), a transformative computational framework that synergistically integrates instantaneous phase‐coupling dynamics with rigorous energy landscape principles, addressing fundamental limitations of conventional methodologies. Comprehensive validation across two independent neuroimaging datasets (HCP and Natural Sleep) demonstrated EPLSA's marked superiority over LEiDA and conventional ELA in terms of test–retest reliability, task‐specific brain state differentiation, and individual‐level classification performance. To demonstrate the physiological and clinical utility of the proposed method, sleep–wake analysis was performed to reveal EPLSA's enhanced sensitivity to consciousness state transitions, identifying decreased primary state occupancy and increased minor state prevalence during sleep, with significantly reduced direct transition probabilities. Furthermore, application to patients with Alzheimer's disease using the OASIS‐3 dataset identified shortened dwell time and occurrence frequency for the frontoparietal control network‐default mode network (FPCN‐DMN) co‐activation state, and prolonged dwell time and occurrence frequency for the visual network‐limbic network (VIS‐LMN) co‐activation state, with these metrics significantly correlating with cognitive impairment. By unifying phase‐coupling and thermodynamic principles, EPLSA provides novel insights into neurodynamic mechanisms across cognitive tasks, consciousness states, and neurodegenerative conditions, offering a transformative analytical tool for investigating brain function in health and disease with particular promise for early detection and monitoring of neurological disorders.

Consciousness intuitions are illusory.

Aspiration is a major safety concern among hospitalized older adults, yet clinical prevention practices remain inconsistent. Using the JBI Practical Application of Clinical Evidence System (JBI PACES) and guided by the Chinese Nursing Association group standard Prevention of Aspiration in Older Adults, this project aimed to implement a standardized evidence-based nursing program for aspiration risk prevention and to evaluate its impact on nurses' knowledge, adherence to audit criteria, and aspiration incidence. This evidence-based audit-and-feedback project employed a pre-post design at a tertiary hospital in Shanxi Province, China, from July 2024 to December 2025. Reporting followed the SQUIRE 2.0 guidelines. A multidisciplinary team systematically retrieved and appraised evidence, yielding 22 audit criteria. Barriers and facilitators were analyzed using the Ottawa Model of Research Use. Guided by Proctor's Implementation Outcomes Taxonomy, effects were assessed at three levels: implementation outcomes (audit criteria adherence), service outcomes (nurses' knowledge, high-risk identification accuracy, feeding plan compliance), and clinical outcomes (aspiration incidence, patient satisfaction). Binary logistic regression adjusted for potential confounders. Baseline characteristics were comparable between groups (P > 0.05). Aspiration incidence was lower in the post-implementation period (13.59%) compared with baseline (27.72%) (P = 0.015); after adjusting for age, sex, consciousness status, and nutritional route, aspiration risk remained significantly lower post-implementation (adjusted OR = 0.359, 95% CI: 0.157-0.820). Nurses' knowledge scores rose from 56.72 ± 8.29 to 85.03 ± 8.40 (P < 0.001), and high-risk identification accuracy increased from 34.65% to 81.55% (P < 0.001). Most audit criteria improved significantly: system-level criteria were fully adopted, positioning criteria reached 100%, and feeding behavior criteria showed the largest gains although absolute adherence remained lower. Feeding plan compliance improved from 41.58% to 77.67% (P < 0.001), and satisfaction scores from 80.35 ± 7.62 to 92.16 ± 4.85 (P < 0.001). This JBI PACES-based multi-component intervention was associated with improved adherence to aspiration prevention measures and lower aspiration incidence among hospitalized older adults. Positive changes were observed across implementation, service, and clinical outcome levels within Proctor's taxonomy. Given the pre-post design, these improvements should be interpreted as observed changes rather than confirmed causal effects. Future studies employing stepped-wedge or multicenter controlled designs are needed to strengthen causal inference and assess long-term sustainability. This evidencebased practice project was registered with the Fudan University Center for EvidenceBased Nursing, China (Registration No. ER20251074) on January 9, 2025. Not applicable.

Disorders of consciousness (DoC) following severe brain injury have limited therapeutic options. Noninvasive brain stimulation (NIBS), particularly transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), has shown promise in improving consciousness. However, the use of NIBS in patients with programmable ventriculoperitoneal (VP) shunt valves poses unique challenges, as these devices are susceptible to electromagnetic interference. While TMS may alter valve settings or induce heating, the safety of tDCS in such patients remains largely unexplored. We report the feasibility and safety of high-definition tDCS (HD-tDCS) in a 9-year-old girl with a minimally conscious state following severe traumatic brain injury and an implanted programmable VP shunt. Stimulation targeting the left dorsolateral prefrontal cortex was delivered using a neuronavigation-guided montage optimized through computational modeling. Sessions were conducted with continuous clinical monitoring and serial imaging to assess valve pressure. The patient completed 15 sessions with gradual titration of current intensity up to 1mA. No major adverse events were observed. Serial imaging confirmed stable VP shunt position and pressure settings throughout the intervention. Although no significant change was noted in Coma Recovery Scale-Revised scores, subtle clinical improvements were observed, including increased spontaneous movements, improved muscle tone, enhanced eye movements, and intermittent responsiveness to environmental stimuli. This case highlights the potential safety and feasibility of HD-tDCS in patients with programmable VP shunts. tDCS may represent a viable neuromodulatory alternative when TMS is contraindicated, warranting further systematic investigation.

Disorders of consciousness (DoC) are associated with large-scale abnormalities in brain function, but system-specific alterations in regional resting-state BOLD signal entropy remain poorly understood. Here, we examined regional entropy of resting-state fMRI signals in patients with minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). Resting-state fMRI was acquired in 23 MCS patients, 31 UWS patients, and 20 age-matched healthy controls. Regional entropy was estimated from voxel-wise BOLD signals within anatomically defined brain regions using a PCA-based framework, allowing quantification of covariance-based regional BOLD signal variability across the whole brain. Pairwise group comparisons were performed at both the ROI level and in grouped functionally relevant regional sets. Relative to healthy controls, MCS patients showed reduced regional entropy mainly in sensory- and memory-related regions, whereas UWS patients showed widespread reductions across sensory, memory-related, and high-order cognitive regions. In grouped regional-set analyses, entropy in sensory and memory systems showed a graded decrease from healthy controls to MCS and from MCS to UWS. In contrast, entropy in high-order cognitive systems remained relatively preserved in MCS and was significantly reduced only in UWS, with the exception of the posterior cingulate cortex and precuneus. These findings demonstrate system-specific reductions in resting-state BOLD signal entropy in DoC and reveal distinct patterns of altered regional BOLD signal variability across sensory, memory-related, and high-order cognitive systems in MCS and UWS. Regional BOLD signal entropy may provide a quantitative regional description of altered brain dynamics in DoC and complement existing work on large-scale communication and network dysfunction.

Brain waves are ubiquitous phenomena of human brain activity. As they propagate, they coordinate neural communication, shaping conscious perception. Understanding how brain waves unfold across space and time is thus critical for uncovering the neural mechanisms that support and suppress consciousness. Here, we analyzed data from the Human Connectome Project alongside multiple independent human datasets of various states of consciousness collected during non-rapid eye movement sleep, propofol anesthesia, and psychedelic states produced by lysergic acid diethylamide, N,N-dimethyltryptamine, psilocybin, nitrous oxide, and ketamine. We then applied complex principal component analysis to map spatiotemporal propagation patterns of blood oxygen level-dependent activity across the human brain, under these diverse states of consciousness. We identified four dominant motifs of wave propagation: a global synchronized wave supporting unimodal-transmodal propagation, an anti-correlated unimodal-transmodal wave, an anti-correlated task-positive/task-negative wave, and an anti-correlated visual-somatomotor wave. Among them, the global wave exhibited the most pronounced state-dependent reconfiguration: in diminished states (sleep and anesthesia), the time needed for the wave to propagate across brain regions consistently increased and the distribution of regional contributions to the wave's power became more spatially concentrated and heterogeneous across individuals, indicating slower, more fragmented, and less stereotyped dynamics. In contrast, propagation duration decreased under psychedelic states, reflecting accelerated global wave dynamics alongside a trend towards more spatially distributed and uniform regional contributions, consistent with a more integrated global wave propagation pattern. Beyond this global mode, diminished states slowed propagation primarily along the unimodal-transmodal axis, whereas psychedelic states selectively accelerated propagation along the task-positive/task-negative axis. Together, our findings reveal that diminished (sleep and anesthesia) and psychedelic states alter the spatiotemporal structure of wave propagation across the brain in opposite and distinct ways, providing a unifying account of how macroscale brain dynamics are dynamically reshaped under pharmacological and endogenous perturbations of consciousness.

The influence of mild traumatic brain injury on attentional Bias: Preliminary evidence.

The post-traumatic confusional state (PTCS) is a period of recovery that follows traumatic brain injury (TBI), characterized by post-traumatic amnesia (PTA), impairments in attention, and behavioral dysregulation, among other clinical symptoms. The pathophysiology of PTCS is unknown, contributing to the absence of neurobiologically based diagnostic criteria, prognostic models, and treatments. The workgroup conducted a scoping review of the literature in MEDLINE/PubMed database and manual searches of references to synthesize the existing knowledge on structural, functional, electroencephalographic (EEG), molecular, and genetic biomarkers underlying PTCS diagnosis and prognosis through five Population, Intervention, Comparison, and Outcome (PICO) questions. The search yielded 3,333 abstracts of which 69 were retained and included. Teams of two workgroup members independently reviewed abstracts and articles. Most articles addressed whether biomarkers differentiated patients with PTCS/PTA from those not in PTCS/PTA, and whether biomarkers were associated with severity or duration of PTCS/PTA. Our findings suggest that transition through PTCS/PTA from lower to higher states of consciousness involves increased thalamic function, restoration of default mode network dynamics, and normalizing of excessive slow wave activity on quantitative EEG. For patients with mild TBI, PTCS/PTA was associated with greater TBI lesion burden on structural imaging. PTCS/PTA severity and duration were associated with lesion burden, reduced white matter integrity, and electrophysiological signatures. Results across studies were variable with many finding no relationship between PTCS/PTA and biomarkers. In summary, while it is premature to include biomarkers in the definition of PTCS/PTA, our findings provide avenues for future research that is designed specifically to address the pathophysiology of this condition.