Using multivariate predictive models to identify brain mechanisms underlying the benefits of mindfulness meditation is a promising methodology that departs from conventional brain mapping. Two strategies—state induction and neuromarker identification—are highlighted, with examples distinguishing focused attention from mind wandering and showing effects of mindfulness interventions on somatic pain and drug-related cravings. Future research must address tradeoffs between personalized and population-based predictive modeling.
Multivariate predictive models—such as multivariate decoding, predictive classification, and model-based analyses—offer a powerful alternative to conventional brain mapping for studying the neural mechanisms behind mindfulness and meditation. These approaches can distinguish internally directed focused attention from mind wandering and reveal how mindfulness interventions affect somatic pain and drug-related cravings. Future research must weigh tradeoffs between personalized and population-based predictive modeling.
Mindfulness and compassion meditation are thought to promote prosocial behavior, but prior research has lacked diversity, limiting generalizability. To address this, researchers propose Intersectional Neuroscience, a framework that adapts research procedures to be inclusive of underrepresented groups through community engagement and individualized multivariate methods. Partnering with a diverse U.S. meditation center, they adapted fMRI screening and recruitment to include racial/ethnic minorities, gender and sexual minorities, people with disabilities, neuropsychiatric disorders, and lower-income individuals. They scanned 15 diverse meditators (80% racial/ethnic minorities, 53% gender and sexual minorities) and used individualized machine learning on fMRI data to decode mental states during breath-focused meditation. All participants' unique brain patterns were recognized significantly above chance. This demonstrates feasibility of inclusive neuroscience to develop individualized neural metrics of meditation.
Multi-voxel pattern analysis of fMRI data can recognize five internal attentional states—breath attention, mind wandering, self-referential processing, attention to feet, and attention to sounds—in individual participants with accuracy well above chance (over 41% vs. 20% chance). In a mixed sample of 16 experienced meditators and novices, classifiers trained on a directed attention task successfully identified these states in 87.5% of participants. When applied to a separate 10-minute meditation session, the classifiers indicated that participants spent more time attending to breath than mind wandering or self-referential processing. The findings suggest that objective, participant-level measurement of mental states during meditation is feasible, potentially improving assessment of internally-oriented attention cultivated by meditation.