Evidence map›Paper›PMID 36977909›Full record

ArticleBrain topography2023

Long-Term and Meditation-Specific Modulations of Brain Connectivity Revealed Through Multivariate Pattern Analysis.

Roberto Guidotti, Antea D'Andrea, Alessio Basti, Antonino Raffone, Vittorio Pizzella, Laura Marzetti

Abstract read
In one paragraph

Article in Brain topography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Observational
  2. Review
  3. Decoding Mindfulness With Multivariate Predictive Models.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025
    Review
  4. ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Roberto GuidottiDepartment of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy.
Antea D'AndreaDepartment of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy.
Alessio BastiDepartment of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy.
Antonino RaffoneDepartment of Psychology, "La Sapienza" University Rome, 00185, Rome, Italy.
Vittorio PizzellaDepartment of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy. vittorio.pizzella@unich.it.
Laura MarzettiDepartment of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroimaging studies have provided evidence that extensive meditation practice modifies the functional and structural properties of the human brain, such as large-scale brain region interplay. However, it remains unclear how different meditation styles are involved in the modulation of these large-scale brain networks. Here, using machine learning and fMRI functional connectivity, we investigated how focused attention and open monitoring meditation styles impact large-scale brain networks. Specifically, we trained a classifier to predict the meditation style in two groups of subjects: expert Theravada Buddhist monks and novice meditators. We showed that the classifier was able to discriminate the meditation style only in the expert group. Additionally, by inspecting the trained classifier, we observed that the Anterior Salience and the Default Mode networks were relevant for the classification, in line with their theorized involvement in emotion and self-related regulation in meditation. Interestingly, results also highlighted the role of specific couplings between areas crucial for regulating attention and self-awareness as well as areas related to processing and integrating somatosensory information. Finally, we observed a larger involvement of left inter-hemispheric connections in the classification. In conclusion, our work supports the evidence that extensive meditation practice modulates large-scale brain networks, and that the different meditation styles differentially affect connections that subserve style-specific functions.

Indexed as

MeditationAttentionBrainBrain MappingEmotionsHumansFMRIFocused attention mediationFunctional connectivityMachine learningMindfulnessOpen monitoring meditation

Identifiers

PMID36977909
PMCPMC10164028

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Registered trials

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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.