A community-based loving-kindness meditation program helped diverse LGBTQ+ adults build belonging, compassion, and resilience during the COVID-19 pandemic. Participants reported that the practice supported them in coping with both personal stressors and structural oppression. Three main themes emerged: the community of practice fostered belonging; metta meditation cultivated compassion and equanimity; and the practice helped navigate harmful situations. The findings suggest that community spaces designed for belonging can connect contemplative practice with social change efforts.
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.
A research framework called Intersectional Neuroscience, which adapts research procedures to be more inclusive of underrepresented groups, was tested in partnership with a diverse U.S. meditation center. The approach used community engagement and individualized multivariate neuroscience methods. With person-centered screening, 15 diverse meditators (80% racial/ethnic minorities, 53% gender and sexual minorities) were recruited and scanned. Machine learning algorithms recognized each meditator's unique brain patterns during breath-focused meditation significantly above chance levels, allowing individual-level attention profiles to be compiled. The study demonstrates feasibility of including diverse participants and developing individualized neural metrics of meditation practice.