Birthing consciousness and the flow experience during physiological childbirth.

PROBLEM: It has been demonstrated that birth without medical intervention conveys significant physical and psychological benefits to the mother and her newborn baby. However, there is a need to include women's subjective experience of physiological birth to understand and promote it. BACKGROUND: The theoretical concept of "birthing consciousness" hypothesizes that women during natural childbirth sometimes experience a specific altered state of consciousness, which is a positive peak experience that resembles "flow" in many aspects. AIM: To investigate the underexplored connection between the physiological mode of childbirth and altered states of consciousness during childbirth. METHODS: Israeli women with childbirth experience were recruited through social media (Facebook groups with a focus on childbirth and motherhood). Participants (n = 766) completed an online survey: the Flow State Scale (FSS) and a demographic questionnaire. FINDINGS: Differences were found between modes of birth as to flow state, as women who experienced physiological childbirth (i.e., with no epidural anesthesia or instrumental interventions) had a higher flow state during birth. DISCUSSION: This link empirically confirms the phenomenon of birthing consciousness. All nine dimensions of the mental state of flow apply to childbirth: challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration on the task, sense of control, loss of self-consciousness, transformation of time, and autotelic experience. CONCLUSION: Understanding a women's subjective experience during physiological birth can enhance clinical understanding of physiological birth thus promoting positive physiological birth experiences - which has crucial health benefits. We propose that more studies need to be done to promote experiencing flow during physiological birth.

Spinal cord electrical stimulation for severe disturbance of consciousness after traumatic brain injury: A case report.

Background: Currently, the use of spinal cord electrical stimulations for patients with severe disorders of consciousness after traumatic brain injury remains limited, and long-term follow-up studies are even scarcer. To date, there have been few reports using near-infrared spectroscopy to evaluate the clinical effects and optimal parameters of spinal cord electrical stimulation for severe consciousness disorders. This report describes a case of a patient with severe disturbance of consciousness after traumatic brain injury who underwent spinal cord electrical stimulation implantation. Advanced near-infrared spectroscopy was employed to monitor and evaluate postoperative efficacy. The findings of this case report will provide a reference for the clinical treatment of severe consciousness disturbances. Methods: A patient diagnosed with a severe disturbance of consciousness following traumatic brain injury presented symptoms of coma and lack of voluntary activity. The treatment regimen included conventional approaches (medication combined with rehabilitation training) and adjustments to the spinal cord electrical stimulation parameters. Advanced functional near-infrared spectroscopy (fNIRS) was used to explore changes in brain functional connectivity strength and assess clinical efficacy. Results: The integration of conventional treatment and continuous modification of spinal cord electrical stimulation parameters, combined with fNIRS monitoring, demonstrated that conventional treatment and spinal cord electrical stimulation displayed a positive effect on increasing brain functional strength connection. The Glasgow Coma Scale(GCS) score significantly improved from the baseline. Optimal results were observed with spinal cord stimulation settings at 4.5 V amplitude, 210 µs pulse width, and 70 Hz frequency, operating from 8:00-20:00 in a cycling mode of 15 min on and 15 min off, where improvements in consciousness were markedly evident. Conclusions: Patients with severe disturbances of consciousness after traumatic brain injury recover slowly. Conventional treatment combined with spinal cord electrical stimulation can improve the degree of disturbance of consciousness and promote recovery from the condition.

The assessment of consciousness status in primary brainstem hemorrhage (PBH) patients can be achieved by monitoring changes in basic vital signs.

The objective of the study was to explore the association between basic vital signs and consciousness status in patients with primary brainstem hemorrhage (PBH). Patients with PBH were categorized into two groups based on Glasgow Coma Scale (GCS) scores: disturbance of consciousness (DOC) group (GCS=3-8) and non-DOC group (GCS=15). Within DOC group, patients were further divided into behavioral (GCS=4-8) and non-behavioral (GCS=3) subgroups. Basic vital signs, such as body temperature, heart rate, and respiratory rate, were monitored every 3 hours during the acute bleeding phase (1st day) and the bleeding stable phase (7th day) of hospitalization. The findings revealed a negative correlation between body temperature and heart rate with GCS scores in DOC group at both time points. Moreover, basic vital signs were notably higher in the DOC group compared to non-DOC group. Specifically, the non-behavioral subgroup within DOC group exhibited significantly elevated heart rates on the 1st day of hospitalization and moderately increased respiratory rates on the 7th day compared to the control group. Scatter plots illustrated a significant relationship between body temperature and heart rate with consciousness status, while no significant correlation was observed with respiratory rate. In conclusion, the study suggests that monitoring basic vital signs, particularly body temperature and heart rate, can serve as valuable indicators for evaluating consciousness status in PBH patients. These basic vital signs demonstrated variations corresponding to lower GCS scores. Furthermore, integrating basic vital sign monitoring with behavioral assessment could enhance the assessment of consciousness status in PBH patients.

A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind.

There have been impressive advancements in the field of natural language processing (NLP) in recent years, largely driven by innovations in the development of transformer-based large language models (LLM) that utilize "attention." This approach employs masked self-attention to establish (via similarly) different positions of tokens (words) within an inputted sequence of tokens to compute the most appropriate response based on its training corpus. However, there is speculation as to whether this approach alone can be scaled up to develop emergent artificial general intelligence (AGI), and whether it can address the alignment of AGI values with human values (called the alignment problem). Some researchers exploring the alignment problem highlight three aspects that AGI (or AI) requires to help resolve this problem: (1) an interpretable values specification; (2) a utility function; and (3) a dynamic contextual account of behavior. Here, a neurosymbolic model is proposed to help resolve these issues of human value alignment in AI, which expands on the transformer-based model for NLP to incorporate symbolic reasoning that may allow AGI to incorporate perspective-taking reasoning (i.e., resolving the need for a dynamic contextual account of behavior through deictics) as defined by a multilevel evolutionary and neurobiological framework into a functional contextual post-Skinnerian model of human language called "Neurobiological and Natural Selection Relational Frame Theory" (N-Frame). It is argued that this approach may also help establish a comprehensible value scheme, a utility function by expanding the expected utility equation of behavioral economics to consider functional contextualism, and even an observer (or witness) centric model for consciousness. Evolution theory, subjective quantum mechanics, and neuroscience are further aimed to help explain consciousness, and possible implementation within an LLM through correspondence to an interface as suggested by N-Frame. This argument is supported by the computational level of hypergraphs, relational density clusters, a conscious quantum level defined by QBism, and real-world applied level (human user feedback). It is argued that this approach could enable AI to achieve consciousness and develop deictic perspective-taking abilities, thereby attaining human-level self-awareness, empathy, and compassion toward others. Importantly, this consciousness hypothesis can be directly tested with a significance of approximately 5-sigma significance (with a 1 in 3.5 million probability that any identified AI-conscious observations in the form of a collapsed wave form are due to chance factors) through double-slit intent-type experimentation and visualization procedures for derived perspective-taking relational frames. Ultimately, this could provide a solution to the alignment problem and contribute to the emergence of a theory of mind (ToM) within AI.

What is it like to be a bot? The world according to GPT-4.

The recent explosion of Large Language Models (LLMs) has provoked lively debate about "emergent" properties of the models, including intelligence, insight, creativity, and meaning. These debates are rocky for two main reasons: The emergent properties sought are not well-defined; and the grounds for their dismissal often rest on a fallacious appeal to extraneous factors, like the LLM training regime, or fallacious assumptions about processes within the model. The latter issue is a particular roadblock for LLMs because their internal processes are largely unknown - they are colossal black boxes. In this paper, I try to cut through these problems by, first, identifying one salient feature shared by systems we regard as intelligent/conscious/sentient/etc., namely, their responsiveness to environmental conditions that may not be near in space and time. They engage with subjective worlds ("s-worlds") which may or may not conform to the actual environment. Observers can infer s-worlds from behavior alone, enabling hypotheses about perception and cognition that do not require evidence from the internal operations of the systems in question. The reconstruction of s-worlds offers a framework for comparing cognition across species, affording new leverage on the possible sentience of LLMs. Here, we examine one prominent LLM, OpenAI's GPT-4. Inquiry into the emergence of a complex subjective world is facilitated with philosophical phenomenology and cognitive ethology, examining the pattern of errors made by GPT-4 and proposing their origin in the absence of an analogue of the human subjective awareness of time. This deficit suggests that GPT-4 ultimately lacks a capacity to construct a stable perceptual world; the temporal vacuum undermines any capacity for GPT-4 to construct a consistent, continuously updated, model of its environment. Accordingly, none of GPT-4's statements are epistemically secure. Because the anthropomorphic illusion is so strong, I conclude by suggesting that GPT-4 works with its users to construct improvised works of fiction.

Promoting the social implementation of digital and mobile health: effects of regulation on user and non-user behavior in East Asia.

This study conducted policy and regulation analyses and user acceptance surveys in three East Asian countries with developed telecommunication infrastructure (China, South Korea, and Japan) to determine the most effective way to implement mobile health (mHealth). Regional differences in users' emphasis on the purpose of mHealth, including medical information referral or health management, appear to be influenced by regional regulation, thus making regulation analysis important when considering mHealth penetration strategies. Potential mHealth users have high expectations for medical information and correspondence, which is crucial for the pharmaceutical industry in terms of providing information and retaining patients. Furthermore, potential users are willing to use the system medically, which is beneficial to the pharmaceutical industry when introducing mHealth and prescriptions in combination.

Parabrachial nucleus Vglut2 expressing neurons projection to the extended amygdala involved in the regulation of wakefulness during sevoflurane anesthesia in mice.

AIMS: The parabrachial nucleus (PBN) promotes wakefulness states under general anesthesia. Recent studies have shown that glutamatergic neurons within the PBN play a crucial role in facilitating emergence from anesthesia. Our previous study indicates that vesicular glutamate transporter 2 (vglut2) expression neurons of the PBN extend into the extended amygdala (EA). However, the modulation of PBNvglut2-EA in general anesthesia remains poorly understood. This study aims to investigate the role of PBNvglut2-EA in alterations of consciousness during sevoflurane anesthesia. METHODS: We first validated vglut2-expressing neuron projections from the PBN to the EA using anterograde tracing. Then, we conducted immunofluorescence staining of c-Fos to investigate the role of the EA involved in the regulation of consciousness during sevoflurane anesthesia. After, we performed calcium fiber photometry recordings to determine the changes in PBNvglut2-EA activity. Lastly, we modulated PBNvglut2-EA activity under sevoflurane anesthesia using optogenetics, and electroencephalogram (EEG) was recorded during specific optogenetic modulation. RESULTS: The expression of vglut2 in PBN neurons projected to the EA, and c-Fos expression in the EA was significantly reduced during sevoflurane anesthesia. Fiber photometry revealed that activity in the PBNvglut2-EA pathway was suppressed during anesthesia induction but restored upon awakening. Optogenetic activation of the PBNvglut2-EA delayed the induction of anesthesia. Meanwhile, EEG recordings showed significantly decreased δ oscillations and increased ß and γ oscillations compared to the EYFP group. Furthermore, optogenetic activation of the PBNvglut2-EA resulted in an acceleration of awakening from anesthesia, accompanied by decreased δ oscillations on EEG recordings. Optogenetic inhibition of PBNvglut2-EA accelerated anesthesia induction. Surprisingly, we found a sex-specific regulation of PBNvglut2-EA in this study. The activity of PBNvglut2-EA was lower in males during the induction of anesthesia and decreased more rapidly during sevoflurane anesthesia compared to females. Photoactivation of the PBNvglut2-EA reduced the sensitivity of males to sevoflurane, showing more pronounced wakefulness behavior and EEG changes than females. CONCLUSIONS: PBNvglut2-EA is involved in the promotion of wakefulness under sevoflurane anesthesia. Furthermore, PBNvglut2-EA shows sex differences in the changes of consciousness induced by sevoflurane anesthesia.

Chaotic nature of the electroencephalogram during shallow and deep anesthesia: From analysis of the Lyapunov exponent.

In conscious states, the electrodynamics of the cortex are reported to work near a critical point or phase transition of chaotic dynamics, known as the edge-of-chaos, representing a boundary between stability and chaos. Transitions away from this boundary disrupt cortical information processing and induce a loss of consciousness. The entropy of the electroencephalogram (EEG) is known to decrease as the level of anesthesia deepens. However, whether the chaotic dynamics of electroencephalographic activity shift from this boundary to the side of stability or the side of chaotic enhancement during anesthesia-induced loss of consciousness remains poorly understood. We investigated the chaotic properties of EEGs at two different depths of clinical anesthesia using the maximum Lyapunov exponent, which is mathematically regarded as a formal measure of chaotic nature, using the Rosenstein algorithm. In 14 adult patients, 12 s of electroencephalographic signals were selected during two depths of clinical anesthesia (sevoflurane concentration 2% as relatively deep anesthesia, sevoflurane concentration 0.6% as relatively shallow anesthesia). Lyapunov exponents, correlation dimensions and approximate entropy were calculated from these electroencephalographic signals. As a result, maximum Lyapunov exponent was generally positive during sevoflurane anesthesia, and both maximum Lyapunov exponents and correlation dimensions were significantly greater during deep anesthesia than during shallow anesthesia despite reductions in approximate entropy. The chaotic nature of the EEG might be increased at clinically deeper inhalational anesthesia, despite the decrease in randomness as reflected in the decreased entropy, suggesting a shift to the side of chaotic enhancement under anesthesia.

Consciousness and education: contributions by Piaget, Vygotsky and Steiner.

The objective of this study is to gain an understanding of the development of consciousness and its relationship with education based on different theoretical models-namely those by Piaget, Vygotsky and Steiner. All three of them focus on different subcomponents of the educational process and there is hence a need for integrative discussion. Piaget and Vygotsky are fundamental references in the understanding of developmental and learning processes. Steiner was a pioneer in proposing a pedagogy that progresses by integrating feeling and wanting alongside thinking in the educational process. Their theories have important similarities but also differences and these will be essential for broadening the understanding of the construction of consciousness and its relationships with education.

Evaluating the explanatory power of the Conscious Turing Machine.

The recent "Conscious Turing Machine" (CTM) proposal offered by Manuel and Lenore Blum aims to define and explore consciousness, contribute to the solution of the hard problem, and demonstrate the value of theoretical computer science with respect to the study of consciousness. Surprisingly, given the ambitiousness and novelty of the proposal (and the prominence of its creators), CTM has received relatively little attention. We here seek to remedy this by offering an exhaustive evaluation of CTM. Our evaluation considers the explanatory power of CTM in three different domains of interdisciplinary consciousness studies: the philosophy of mind, cognitive neuroscience, and computation. Based on our evaluation in each of the target domains, at present, any claim that CTM constitutes progress is premature. Nevertheless, the model has potential, and we highlight several possible avenues of future research which proponents of the model may pursue in its development.

Unifying biophysical consciousness theories with MaxCon: maximizing configurations of brain connectivity.

There is such a vast proliferation of scientific theories of consciousness that it is worrying some scholars. There are even competitions to test different theories, and the results are inconclusive. Consciousness research, far from converging toward a unifying framework, is becoming more discordant than ever, especially with respect to theoretical elements that do not have a clear neurobiological basis. Rather than dueling theories, an integration across theories is needed to facilitate a comprehensive view on consciousness and on how normal nervous system dynamics can develop into pathological states. In dealing with what is considered an extremely complex matter, we try to adopt a perspective from which the subject appears in relative simplicity. Grounded in experimental and theoretical observations, we advance an encompassing biophysical theory, MaxCon, which incorporates aspects of several of the main existing neuroscientific consciousness theories, finding convergence points in an attempt to simplify and to understand how cellular collective activity is organized to fulfill the dynamic requirements of the diverse theories our proposal comprises. Moreover, a computable index indicating consciousness level is presented. Derived from the level of description of the interactions among cell networks, our proposal highlights the association of consciousness with maximization of the number of configurations of neural network connections -constrained by neuroanatomy, biophysics and the environment- that is common to all consciousness theories.

Our brains sense the future through a new quantum-like implicit learning mechanism.

BACKGROUND: Imagine if our brains could unconsciously predict future events. This study explores this concept, presenting evidence for an inherent 'foreseeing' ability, termed anomalous cognition (AC). We introduce a new experimentally verifiable approach to explain anomalous information anticipation (AIA), a type of AC, based on an innovative, quantum-like model of implicit learning, grounded in Nonlocal Plasticity Theory (NPT). METHODS: Our research involved 203 participants using methods such as continuous flash suppression, random dot motion, and advanced 3D EEG neuroimaging, along with IBM quantum random event generators for precise measurements across 144 trials. These trials tested contingencies between undetectable sensory stimuli and dot movements, focusing on participants' prediction abilities. The design conditions were strictly experimental, violating fundamental classical learning principles, particularly reflex conditioning. If these principles were immutable, their violation would prevent any systematic behavioral changes, resulting in random responses. This violation was implemented through two quantum physics concepts: the mathematical principle of nonlocality and entanglement. RESULTS: Despite the sensory stimulus being inaccessible, our results showed a significant prediction between the contingencies and an increase in AIA accuracy, with explained variances between 25 % and 48 %. EEG findings supported this, showing a positive link between brain activity in specific regions and AIA success. Electrochemical activations were detected in the posterior occipital cortex, the intraparietal sulcus, and the medial temporal gyri. AIA hits exceeded the threshold value corresponding to one standard deviation above the expected mean, showing moderate effect sizes in the experimental group (Cohen's d = 0.461). Analyzing the learning curve using the derivation technique, we identified the acceleration point of the wave function, indicating systematic implicit learning. This result showed that from repetition 63 onwards, AIA hits increased significantly. CONCLUSIONS: The results suggest that, despite violating fundamental classical learning principles, cognitive processes produced changes in participants' responses susceptible to neuromodulation, considering quantum physics principles of nonlocality and entanglement (both present in NPT). We discuss (a) why the priming effect does not explain the significant results; (b) the potential discovery of a new form of quantum-like implicit learning, which could scientifically resolve phenomena associated with anomalous cognitions (e.g., AIA); and (c) future research directions, including potential applications and clinical impact.

Myelin modulates the process of isoflurane anesthesia through the regulation of neural activity.

AIMS: The mechanism underlying the reversible unconsciousness induced by general anesthetics (GA) remains unclear. Recent studies revealed the critical roles of myelin and oligodendrocytes (OLs) in higher functions of the brain. However, it is unknown whether myelin actively participates in the regulation of GA. The aim of this study is to investigate the roles and possible mechanisms of myelin in the regulation of consciousness alterations induced by isoflurane anesthesia. METHODS: First, demyelination models for the entire brain and specific neural nuclei were established to investigate the potential role of myelination in the regulation of GA, as well as its possible regional specificity. c-Fos staining was then performed on the demyelinated nuclei to verify the impact of myelin loss on neuronal activity. Finally, the activity of neurons during isoflurane anesthesia in demyelinated mice was recorded by optical fiber photometric calcium signal. The related behavioral indicators and EEG were recorded and analyzed. RESULTS: A prolonged emergence time was observed from isoflurane anesthesia in demyelinated mice, which suggested the involvement of myelin in regulating GA. The demyelination in distinct nuclei by LPC further clarified the region-specific roles of isoflurane anesthesia regulation by myelin. The effect of demyelination on isoflurane anesthesia in the certain nucleus was consistent with that in neurons towards isoflurane anesthesia. Finally, we found that the mechanism of myelin in the modulation of isoflurane anesthesia is possibly through the regulation of neuronal activity. CONCLUSIONS: In brief, myelin in the distinct neural nucleus plays an essential role in regulating the process of isoflurane anesthesia. The possible mechanism of myelin in the regulation of isoflurane anesthesia is neuronal activity modification by myelin integrity during GA. Our findings enhanced the comprehension of myelin function, and offered a fresh perspective for investigating the neural mechanisms of GA.

Neuronal rhythmicity and cortical arousal in a mouse model of absence epilepsy.

OBJECTIVES: Absence seizures impair psychosocial function, yet their detailed neuronal basis remains unknown. Recent work in a rat model suggests that cortical arousal state changes prior to seizures and that single neurons show diverse firing patterns during seizures. Our aim was to extend these investigations to a mouse model with studies of neuronal activity and arousal state to facilitate future fundamental investigations of absence epilepsy. METHODS: We performed in vivo extracellular single unit recordings on awake head-fixed C3H/HeJ mice. Mice were implanted with tripolar electrodes for cortical electroencephalogram (EEG). Extracellular single unit recordings were obtained with glass micropipettes in the somatosensory barrel cortex, while animals ambulated freely on a running wheel. Signals were digitized and analyzed during seizures and baseline. RESULTS: Neuronal activity was recorded from 36 cortical neurons in 19 mice while EEG showed characteristic 7-8 Hz spike-wave discharges. Different single neurons showed distinct firing patterns during seizures, but the overall mean population neuronal firing rate during seizures was no different from pre-seizure baseline. However, the rhythmicity of neuronal firing during seizures was significantly increased (p < 0.001). In addition, beginning 10s prior to seizure initiation, we observed a progressive decrease in cortical high frequency (>40 Hz) EEG and an increase in lower frequency (1-39 Hz) activity suggesting decreased arousal state. SIGNIFICANCE: We found that the awake head-fixed C3H/HeJ mouse model demonstrated rhythmic neuronal firing during seizures, and a decreased cortical arousal state prior to seizure onset. Unlike the rat model we did not observe an overall decrease in neuronal firing during seizures. Similarities and differences across species strengthen the ability to investigate fundamental key mechanisms. Future work in the mouse model will identify the molecular basis of neurons with different firing patterns, their role in seizure initiation and behavioral deficits, with ultimate translation to human absence epilepsy.

A qualitative approach to explore the cognitive processes used by members of an adult learn-to-cycle program.

Introduction: Attaining movement proficiency under various constraints is well-researched; of particular interest here is how conscious processing and self-consciousness influence learning and performance. Current research relevant to these variables e.g., reinvestment, tends to utilize quantitative methods and thus overlooks a potentially rich source of understanding. Therefore, the purpose of this study was to apply a qualitative approach to explore the cognitive processes and self-consciousness within a less practiced population of adults participating in a non-competitive leisure activity. Methods: To achieve this, eleven semi-structured interviews were conducted with adult women learning or relearning to ride a bicycle. Results: Using thematic analysis, two distinct themes were evident and corroborated those illustrated in scales such as the movement-specific reinvestment scale. These themes included Conscious Processing, Recalling Experiences and Movement Analysis, in addition to Style of Movement, and Self-Consciousness. Anxiety also emerged as an additional and relevant theme to learning a new complex movement skill. Discussion: The findings indicated that participating women attuned to their movements to the extent that there was a conscious attempt to control their movements, but less so regarding movements being altered by self-awareness. Whilst further research is required, qualitative methods provide a promising basis for exploring the cognitive process involved with the conscious process involved in learning.

Conversion disorder after general anaesthesia for cystoscopy: A case report.

Conversion disorder (CD) is a rare complication after general anesthesia and can be presented with sensory and or motor deficits. The organic or metabolic cause should be excluded first before considering CD diagnosis. A 62-year-old female was admitted to the high-dependency unit with quadriplegia and disturbed level of consciousness secondary to CD upon emergence from general anesthesia.

A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.

Objective: This study pursued a meta-analysis to evaluate the predictive accuracy of machine learning (ML) models in determining disorders of consciousness (DOC) among patients with traumatic brain injury (TBI). Methods: A comprehensive literature search was conducted to identify ML applications in the establishment of a predictive model of DOC after TBI as of August 6, 2023. Two independent reviewers assessed publication eligibility based on predefined criteria. The predictive accuracy was measured using areas under the receiver operating characteristic curves (AUCs). Subsequently, a random-effects model was employed to estimate the overall effect size, and statistical heterogeneity was determined based on I2 statistic. Additionally, funnel plot asymmetry was employed to examine publication bias. Finally, subgroup analyses were performed based on age, ML type, and relevant clinical outcomes. Results: Final analyses incorporated a total of 46 studies. Both the overall and subgroup analyses exhibited considerable statistical heterogeneity. Machine learning predictions for DOC in TBI yielded an overall pooled AUC of 0.83 (95% CI: 0.82-0.84). Subgroup analysis based on age revealed that the ML model in pediatric patients yielded an overall combined AUC of 0.88 (95% CI: 0.80-0.95); among the model subgroups, logistic regression was the most frequently employed, with an overall pooled AUC of 0.85 (95% CI: 0.83-0.87). In the clinical outcome subgroup analysis, the overall pooled AUC for distinguishing between consciousness recovery and consciousness disorders was 0.84 (95% CI: 0.82-0.85). Conclusion: The findings of this meta-analysis demonstrated outstanding accuracy of ML models in predicting DOC among patients with brain injuries, which presented substantial research value and potential of ML application in this domain.

States of consciousness and interoceptive hypersensibility: A study in patients with insomnia disorder.

This exploratory study aimed to investigate the relationship between interoceptive sensibility and quality of consciousness in individuals with insomnia disorder, in order to understand how the modulation of internal states may contribute to modifying the experience of consciousness during sleep difficulties. A total of 37 patients with insomnia disorder (mean age = 46.05 ± 18.16) and 41 healthy good sleepers (mean age = 50.2 ± 12.99) underwent a psychometric sleep and interoceptive sensibility assessment, using Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Multidimensional Assessment of Interoceptive Awareness (MAIA). Moreover, patients with insomnia disorder also completed a quality of consciousness evaluation, using the Phenomenology of Consciousness Inventory (PCI). Patients with insomnia disorder exhibited heightened interoceptive sensibility, particularly in noticing body sensations (p < 0.0001) and emotional awareness (p = 0.032), along with diminished abilities in attention regulation (p = 0.040), not-worrying (p = 0.001), and trusting (p = 0.002). Furthermore, correlations between interoceptive sensibility and multiple aspects of the consciousness state during the insomnia night were identified. Specifically, higher emotional awareness was linked to a 2.49-fold increase in the likelihood of subjectively experiencing altered consciousness states during insomnia. The study sheds light on the relationship between interoceptive sensibility and the subjective state of consciousness during insomnia, emphasising the importance of exploring and considering interoception as part of the therapeutic process for insomnia disorder. Given the exploratory nature of the study and the increased risk of type-I error from numerous correlations, the results should be interpreted with caution. Further research is needed to validate and confirm their robustness.

Explaining recovery from coma with multimodal neuroimaging.

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.