Near-Death Experiences (NDEs) are commonly portrayed as passing to an afterlife, but empirical research is recent and their definition remains debated. Questionnaires used to identify NDEs may be restrictive and subjective. To address this, researchers analyzed freely expressed narratives from 158 participants who reported a firsthand NDE, using automated text-mining. The analysis identified the most common words and, through hierarchical clustering, revealed three main clusters of features: visual perceptions, emotions, and spatial components. The authors suggest that this user-independent, data-driven approach can help build a more rigorous description and definition of NDEs.
Propofol, the most common general anesthetic, induces loss of consciousness through mechanisms that remain poorly understood. Using the generalized Ising model (GIM) to analyze fMRI data from healthy subjects listening to an audio clip, brain activity was modeled during wakefulness, mild sedation, deep sedation, and recovery. A novel inter-subject correlation technique captured common synchronization across participants. The GIM, modified to incorporate the naturalistic external stimulus, successfully fitted empirical task fMRI data at a temperature well above the critical temperature. This is the first mathematical modeling of human brain activity responding to real-life stimuli at different conscious levels, potentially aiding assessment of consciousness in patients with disorders of consciousness.