Exploring aperiodic, complexity and entropic brain changes during non-ordinary states of consciousness
Victor Oswald, Karim Jerbi, Corine Sombrun, Hamza Abdelhedi, Annen Jitka, Charlotte Martial, Audrey Vanhaudenhuyse, Olivia Gosseries
arXiv (Cornell University) September 23, 2025 preprint DOI: 10.48550/arxiv.2509.19254 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort |
|---|---|
| Sample size | 27 |
| Population | Trained participants who can enter Auto-Induced Cognitive Trance |
| Intervention | Auto-Induced Cognitive Trance |
| Topics | Default mode network Altered states of consciousness |
| Keywords | Discriminative model Electroencephalography Consciousness Sample entropy Posterior cingulate Neural activity Cognitive psychology Mind-wandering Artificial intelligence Aperiodic graph Anterior cingulate cortex Pattern recognition psychology Trance Functional connectivity Posterior parietal cortex Brain mapping Artificial neural network Prefrontal cortex |
| Key findings | Auto-Induced Cognitive Trance engages frontal regions, the posterior cingulate cortex, and the left parietal cortex, with the aperiodic component of the power spectrum being the strongest discriminator from rest. |
Abstract
Non-ordinary states of consciousness (NOC) provide an opportunity to experience highly intense, unique, and perceptually rich subjective states. The neural mechanisms supporting these experiences remain poorly understood. This study examined brain activity associated with a self-induced, substance-free NOC known as Auto-Induced Cognitive Trance (AICT). Twenty-seven trained participants underwent high-density electroencephalography (EEG) recordings during rest and AICT. We analyzed the aperiodic component of the power spectrum (1/f), Lempel-Ziv complexity, and sample entropy from five-minute signal segments. A machine learning approach was used to classify rest and AICT, identify discriminative features, and localize their sources. We also compared EEG metrics across conditions and assessed whether baseline activity predicted the magnitude of change during AICT. Classification analyses revealed condition-specific differences in spectral exponents, complexity, and entropy. The aperiodic component showed the strongest discriminative power, followed by entropy and complexity. Source localization highlighted frontal regions, the posterior cingulate cortex, and the left parietal cortex as key contributors to the AICT state. Baseline neural activity in frontal and parietal regions predicted individual variability in the transition from rest to AICT. These findings indicate that AICT engages brain regions implicated in rich subjective experiences and provide mechanistic insights into how self-induced trance states influence neural functioning.