Meditation practices, which have shown therapeutic benefits for conditions like depression, pain, addiction, and anxiety, have been studied with neuroimaging over the past decade. However, existing neuroscientific models are based on small, heterogeneous datasets, limiting generalizability and replicability. The ENIGMA-Meditation consortium is the first worldwide collaborative effort to conduct systematic meta- and mega-analyses of globally distributed neuroimaging data using standardized methods. This framework aims to improve statistical power and address multidomain heterogeneity in meditation practice types, experience, and experimental design. The consortium will generate rigorous neuroscientific insights into the mechanisms underlying meditation's therapeutic effects on psychological and cognitive attributes.
Meditation practices, which have been adapted into manualized interventions for conditions like depression, pain, addiction, and anxiety, show therapeutic promise, but their neuroscientific basis remains unclear. Current neuroimaging studies rely on small, heterogeneous datasets that vary in practice types, participant experience, clinical targets, and imaging methods, limiting generalizability and replicability. To address this, the ENIGMA-Meditation consortium was formed as a global collaboration to conduct systematic meta- and mega-analyses of distributed neuroimaging data using standardized methods. This framework aims to improve statistical power and rigorously characterize the neural mechanisms underlying meditation's effects on psychological and cognitive attributes, advancing the field of contemplative neuroscience.
Focused attention on spontaneous body sensations requires interoceptive abilities and is impaired in disorders like illness-anxiety disorder. Fifteen experienced meditators performed a 20-minute attentional task to spontaneous sensations during fMRI scanning, with resting-state scans before and after the task. Dynamic functional connectivity and k-means clustering identified five recurring connectivity patterns across the dorsal attention, default mode, and frontoparietal networks. Subjects spent more time in a low-connectivity pattern during rest and more time in complex patterns during the task. A carry-over effect in connectivity after the task suggests an active role in learning from cognitive training. Focused attention to interoception involves a distinct dynamic brain organization compared to rest.