Dynamic patterns of brain activity at rest distinguish conscious and unconscious states in humans.

Near-death experiences (NDEs) are complex subjective experiences, which have been previously associated with the psychedelic experience and more specifically with the experience induced by the potent serotonergic, N,N-Dimethyltryptamine (DMT). Potential similarities between both subjective states have been noted previously, including the subjective feeling of transcending one’s body and entering an alternative realm, perceiving and communicating with sentient ‘entities’ and themes related to death and dying. In this within-subjects placebo-controled study we aimed to test the similarities between the DMT state and NDEs, by administering DMT and placebo to 13 healthy participants, who then completed a validated and widely used measure of NDEs. Results revealed significant increases in phenomenological features associated with the NDE, following DMT administration compared to placebo. Also, we found significant relationships between the NDE scores and DMT-induced ego-dissolution and mystical-type experiences, as well as a significant association between NDE scores and baseline trait ‘absorption’ and delusional ideation measured at baseline. Furthermore, we found a significant overlap in nearly all of the NDE phenomenological features when comparing DMT-induced NDEs with a matched group of ‘actual’ NDE experiencers. These results reveal a striking similarity between these states that warrants further investigation.

Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a "resting" human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.

Objective: Brain‐injured patients who are unresponsive at the bedside (ie, vegetative state/unresponsive wakefulness syndrome – VS/UWS) may present brain activity similar to patients in minimally conscious state (MCS). This peculiar condition has been termed “non‐behavioural MCS” or “MCS*”. In the present study we aimed to investigate the proportion and underlying brain characteristics of patients in MCS*.Methods: Brain ¹⁸F‐fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) was acquired on 135 brain‐injured patients diagnosed in prolonged VS/UWS (n = 48) or MCS (n = 87). From an existing database, relative metabolic preservation in the fronto‐parietal network (measured with standardized uptake value) was visually inspected by three experts. Patients with hypometabolism of the fronto‐parietal network were labelled “VS/UWS”, while its (partial) preservation either confirmed the behavioural diagnosis of “MCS” or, in absence of behavioural signs of consciousness, suggested a diagnosis of “MCS*”. Clinical outcome at 1‐year follow‐up, functional connectivity, grey matter atrophy, and regional brain metabolic patterns were investigated in the three groups (VS/UWS, MCS* and MCS).Results: 67% of behavioural VS/UWS presented a partial preservation of brain metabolism (ie, MCS*). Compared to VS/UWS patients, MCS* patients demonstrated a better outcome, global functional connectivity and grey matter preservation more compatible with the diagnosis of MCS. MCS* patients presented lower brain metabolism mostly in the posterior brain regions compared to MCS patients.Interpretation: MCS* is a frequent phenomenon that is associated with better outcome and better brain preservation than the diagnosis of VS/UWS. Complementary exams should be provided to all unresponsive patients before taking medical decisions. ANN NEUROL 2021;90:89–100

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.