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Madelynn Park

2 papers in the library · 9 citations · publishing 2024-2025

Papers

Connectome-Based Predictive Modeling of Trait Mindfulness.

Human brain mapping January 1, 2025 Isaac N Treves, Aaron Kucyi, Madelynn Park et al. 8 citations

Trait mindfulness—the tendency to attend to present 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 meditation-naïve adults across three sites, no connections predicted overall trait mindfulness. However, neural models for two subscales, Acting with Awareness and Non-judging, were identified. Positive networks for these subscales involved distinct fronto-parietal and default-mode networks, while negative networks overlapped across subscales and included somatomotor, visual, and default-mode regions. Only negative networks generalized to predict subscale scores in some out-of-sample tests. Predictions negatively correlated with a mind-wandering model. The findings provide preliminary evidence for generalizable connectivity models of mindfulness facets, but incomplete generalization across sites and model overlap highlight the challenge of identifying robust brain markers.

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.