Decoding self-reported meditative depth from EEG recordings is feasible. Expert Vipassana meditators (34 people) reported their depth on a 1–5 scale during two sessions, using either traditional probing or a novel spontaneous emergence method. Machine learning models fused spatial, spectral, and connectivity information from theta, alpha, and gamma bands to predict depth across unseen sessions. The spontaneous emergence method produced more frequent reports and correlated better with post-session outcomes than probing. No single EEG channel or default mode network region captured the complex neural dynamics; multivariate patterns were necessary. The findings suggest potential improvements for neurofeedback in meditation.
Mindfulness-based cognitive therapy (MBCT) alters brain network dynamics during depressive rumination in people with recurrent depression. In a randomized controlled trial with 48 participants, those who received MBCT plus treatment as usual showed changes in the occurrence frequency of a 'salience-somatomotor' brain state during rumination compared to those receiving treatment as usual alone. These neural changes were linked to reduced rumination after treatment and fewer depressive symptoms three months later. The findings suggest that MBCT may work by modifying how the brain's somatosensory and salience networks interact during rumination, offering clues about the brain mechanisms underlying treatment response.