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Poster A53 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Connectome predictive modeling of trait mindfulness
Madelynn S. Park1 (), Isaac N. Treves1, Aaron Kucyi2, Tammi R.A. Kral3, Simon B. Goldberg3, Richard J. Davidson3, Melissa Rosenkranz3, Susan Whitfield-Gabrieli4, John D.E. Gabrieli1; 1McGovern Institute for Brain Research at the Massachusetts Institute of Technology, 2Drexel University, 3University of Wisconsin–Madison, 4Northeastern University
Trait mindfulness refers to one’s disposition or tendency to pay attention to experiences in a mindful way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. To explore the neural networks associated with trait mindfulness and their relationship to different facets, we conducted a pre-registered connectome predictive modeling analysis in 367 adults across three sites. This is the largest study to date examining trait mindfulness using resting-state fMRI. We identified significant neural models for two mindfulness subscales, Acting with Awareness (AA) and Non-judging (NJ). These models involved notable connections within the fronto-parietal network (FPN), default mode network (DMN), and somatomotor network (SMN). We determined that the AA model generalized to one dataset, while the NJ model generalized to another dataset. Thus, our results suggest that whole-brain functional connections can be used as markers of trait mindfulness.
Keywords: trait mindfulness connectome predictive models resting-state fMRI