Integrated Phenomenology and Brain Connectivity Demonstrate Changes in Nonlinear Processing in Jhana Advanced Meditation.
Ruby M. Potash, Sean D. Van Mil, Mar Estarellas, Andrés Canales-Johnson, Matthew D. Sacchet
Journal of Cognitive Neuroscience May 14, 2025 DOI: 10.1162/jocn.a.50 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Case study Case report Peer reviewed |
|---|---|
| Sample size | 1 |
| Population | A meditator with over 20,000 hours of practice |
| Duration | 29 sessions |
| Topics | Meditation Philosophy of mind |
| Keywords | Contemplation Deep meditation Advanced meditation Brain science Neurobiology Cognitive science Brain function Neural activity Consciousness Awareness Mind states Altered states Subjective experience Profound conscious states Brain processing Cognitive processing Information processing Mental processing Information handling Neural processing |
| Citations | 7 |
| Key findings | Advanced concentrative absorption meditation (jhana) is better distinguished by nonoscillatory neural dynamics than by oscillatory synchrony, and deeper absorption involves an equalization of feedback and feedforward processes. |
Abstract
We present a neurophenomenological case study investigating distinct neural connectivity regimes during an advanced concentrative absorption meditation called jhana (ACAM-J), characterized by highly stable attention and mental absorption. Using EEG recordings and phenomenological ratings (29 sessions) from a meditator with +20,000 hr of practice, we evaluated connectivity metrics tracking distinct large-scale neural interactions: nonlinear (weighted symbolic mutual information and directed information), capturing nonoscillatory dynamics, and linear (weighted phase lag index) connectivity metrics, capturing oscillatory synchrony. Results demonstrate ACAM-J are better distinguished by nonoscillatory compared with oscillatory dynamics across multiple frequency ranges. Furthermore, combining attention-related phenomenological ratings with weighted symbolic mutual information improves Bayesian decoding of ACAM-J compared with neural metrics alone. Crucially, deeper ACAM-J indicate an equalization of feedback and feedforward processes, suggesting a balance of internally and externally driven information processing. The results from this intensively sampled case study are a promising initial step in revealing the distinct neural dynamics during ACAM-J, offering insights into refined conscious states and highlighting the value of nonlinear neurophenomenological approaches to studying attentional states.