Author: Roberto Guidotti1, Antea D'Andrea1, Alessio Basti1, Antonino Raffone2, Vittorio Pizzella3,4, Laura Marzetti1,5
1 Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy.
2 Department of Psychology, "La Sapienza" University Rome, 00185, Rome, Italy.
3 Department of Neuroscience, Imaging and Clinical Sciences, "Gabriele d'Annunzio" University Chieti- Pescara, Via dei Vestini 33, 66013, Chieti, Italy. firstname.lastname@example.org.
4 Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University Chieti-Pescara, 66013, Chieti, Italy. email@example.com.
5 Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University Chieti-Pescara, 66013, Chieti, Italy.
Conference/Journal: Brain Topogr
Date published: 2023 Mar 28
Other: Special Notes: doi: 10.1007/s10548-023-00950-3. , Word Count: 227
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.
Keywords: FMRI; Focused attention mediation; Functional connectivity; Machine learning; Mindfulness; Open monitoring meditation.
PMID: 36977909 DOI: 10.1007/s10548-023-00950-3