Evidence map›Paper›PMID 40402036›Full record

ArticleJournal of cognitive neuroscience2025

Integrated Phenomenology and Brain Connectivity Demonstrate Changes in Nonlinear Processing in Jhana Advanced Meditation.

Ruby M Potash, Sean D van Mil, Mar Estarellas, Andres Canales-Johnson, Matthew D Sacchet

Abstract read
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In one paragraph

Article in Journal of cognitive neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Ruby M PotashMassachusetts General Hospital, Harvard Medical School.
Sean D van MilUniversity of Amsterdam.
Mar EstarellasQueen Mary University of London.
Andres Canales-JohnsonUniversity of Cambridge.ORCID 0000-0002-2747-8894
Matthew D SacchetMassachusetts General Hospital, Harvard Medical School.

Funding

Individualized brain systems and depressionR01MH125850 · NIMH · MCLEAN HOSPITAL · PI SACCHET, MATTHEW D · 2022 to 2024
$1.3M
Ad Astra Chandaria FoundationANID/FONDECYT Regular 1240899ANID/FONDECYT Regular 1251273Dimension Giving FundNIMH NIH HHS R01MH125850Tan Teo Charitable Foundation
6 · The paper itself

Abstract

We present a neurophenomenological case study investigating distinct neural connectivity regimes during an advanced concentrative absorption meditation series 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 non-oscillatory dynamics, and linear (weighted phase lag index) connectivity metrics, capturing oscillatory synchrony. Results demonstrate ACAM-J are better distinguished by non-oscillatory 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.

Indexed as

AttentionBrainBrain WavesConnectomeMeditationNerve NetNonlinear DynamicsAdultElectroencephalographyFemaleHumansMale

Identifiers

PMID40402036

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.