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John D. E. Gabrieli

2 papers in the library · 3 citations · publishing 2024

Papers

Mindfulness, cognition, and long-term meditators: Toward a science of advanced meditation

October 28, 2024 Sebastian Ehmann, Idil Sezer, Isaac N. Treves et al. 2 citations preprint

Long-term meditators show a distinct pattern of cognitive and brain changes, including enhanced integration of sensory and cognitive processes, reduced emotional reactivity, more rational decision-making, and altered self-awareness. Neuroimaging reveals increased activity in brain networks related to interoception and pain, along with reduced connectivity between executive and salience networks, decreased amygdala response to fear, and changes in default-mode network activity linked to emotional neutrality and non-ordinary states of consciousness. Methodological limitations, such as varied meditation practices among participants, prevent clear conclusions about specific cognitive changes over time. A unified research framework is needed to systematically study advanced meditation's unfolding stages and endpoints.

Connectome predictive modeling of trait mindfulness

bioRxiv (Cold Spring Harbor Laboratory) July 14, 2024 Isaac N. Treves, Aaron Kucyi, Madelynn Park et al. 1 citation preprint

Trait mindfulness—the tendency to attend to present-moment experience non-judgmentally—is linked to better mental health, but its neural basis remains unclear. In the largest resting-state fMRI study of trait mindfulness to date, involving 367 adults across three samples, researchers used connectome predictive modeling to test whether brain connectivity patterns could predict mindfulness scores. No connections predicted overall trait mindfulness, but models for two subscales—Acting with Awareness and Non-judging—were identified. Positive networks for these subscales involved fronto-parietal and default-mode networks, respectively. Negative networks, which overlapped across subscales, included somatomotor, visual, and default-mode connections. Only negative networks generalized to predict subscale scores in some out-of-sample datasets, and predictions correlated negatively with a mind-wandering model. The incomplete generalization and model overlap highlight the challenge of identifying robust brain markers for mindfulness facets.