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
Brain Topography March 28, 2023 Peer reviewed DOI: 10.1007/s10548-023-00950-3 via OpenAlex
Summary
Extensive meditation practice alters large-scale brain networks, as shown by a study comparing expert Theravada Buddhist monks and novice meditators. The machine learning classifier successfully distinguished meditation styles only among experts, highlighting the importance of the Anterior Salience and Default Mode networks in emotion and self-regulation. Specific connections related to attention regulation and somatosensory processing were also noted, with increased left inter-hemispheric connectivity observed during classification.
Study at a glance
| Design | observational cohort |
|---|---|
| Population | expert Theravada Buddhist monks and novice meditators |
| Key finding | Different meditation styles differentially affect connections in large-scale brain networks, with classification successful only in expert practitioners. |
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.