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.
The default mode network (DMN) supports self-referential thinking, memory, and understanding others. Its functional connectivity with frontoparietal and dorsal attention networks is anti-correlated: when attention turns outward, the DMN deactivates. This switching between internal and external modes, mediated by salience networks, may indicate cognitive health. Resting-state fMRI enables large-scale datasets for standardized connectivity benchmarks. This review examines DMN connectivity metrics as potential biomarkers of cognitive state in attention, mind wandering, meditation, and clinical conditions like anxiety, depression, ADHD, and PTSD. It also addresses reliability issues and offers recommendations for using connectivity measures as biomarkers of cognitive health.