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
bioRxiv (Cold Spring Harbor Laboratory) preprint DOI: 10.1101/2024.11.29.626048 (opens in new tab)
Summary
AI-generated from the abstractDuring a deep meditative state called jhana, the brain's non-oscillatory, nonlinear neural activity—rather than oscillatory synchrony—best distinguishes the state from ordinary waking consciousness. In a single highly experienced meditator (over 20,000 hours of practice) studied across 29 sessions, EEG recordings showed that combining subjective ratings of attention with a nonlinear connectivity metric improved the ability to decode the meditative state compared to using neural measures alone. Deeper jhana states were marked by a balance between feedback and feedforward neural processes, indicating an equalization of internally and externally directed information processing. These findings suggest that refined conscious states involve distinct large-scale neural dynamics not captured by traditional oscillatory measures.
Study at a glance
| Characteristics | Case study Case report |
|---|---|
| Sample size | 1 |
| Population | A single meditator with over 20,000 hours of practice in concentrative absorption meditation (jhana) |
| Duration | 29 sessions |
| Citations | 4 |
| Key finding | Non-oscillatory neural dynamics better distinguish the jhana meditative state than oscillatory synchrony, and deeper states show an equalization of feedback and feedforward information processing. |
Abstract
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 hours of practice, we evaluated connectivity metrics tracking distinct large-scale neural interactions: nonlinear (WSMI and Directed Information), capturing non-oscillatory dynamics; and linear (WPLI) connectivity metrics, capturing oscillatory synchrony. Results demonstrate ACAM-J are better distinguished by non-oscillatory compared to oscillatory dynamics across multiple frequency ranges. Furthermore, combining attention-related phenomenological ratings with WSMI improves Bayesian decoding of ACAM-J compared to 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.