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Connectome-Based Predictive Modeling of Trait Mindfulness.

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  • 1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

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|January 9, 2025
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Summary
This summary is machine-generated.

This study explored the brain connections linked to trait mindfulness, finding distinct neural patterns for "Acting with Awareness" and "Non-judging" facets. However, robust, generalizable brain markers for mindfulness remain challenging to identify.

Keywords:
attentionconnectomemulti‐sitepredictive modelsresting‐state fMRItrait mindfulness

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Area of Science:

  • Neuroscience
  • Psychology
  • Cognitive Science

Background:

  • Trait mindfulness, the tendency towards present-moment, non-judgmental awareness, is linked to positive mental health.
  • Neural underpinnings of trait mindfulness are not well understood, with prior studies focusing on default-mode (DMN), fronto-parietal (FPN), and salience networks.
  • Existing research lacks clarity on the generalizability of findings, the relationship to mindfulness components, and the involvement of other brain networks.

Purpose of the Study:

  • To investigate the neural correlates of trait mindfulness using the largest resting-state functional magnetic resonance imaging (fMRI) study to date.
  • To identify generalizable brain connectivity models for trait mindfulness and its subscales, specifically 'Acting with Awareness' and 'Non-judging'.
  • To explore the relationship between mindfulness neural models and mind-wandering connectome models.

Main Methods:

  • A pre-registered, connectome-based predictive modeling analysis was conducted on 367 meditation-naïve adults across three independent samples.
  • Resting-state fMRI data were analyzed to identify patterns of brain connectivity associated with trait mindfulness and its subscales.
  • Models were trained and tested for generalization across different sites and scanners.

Main Results:

  • No overall trait mindfulness predictive model was found, but neural models for 'Acting with Awareness' (FPN-involved) and 'Non-judging' (DMN-involved) positive networks were identified.
  • Negative network models, showing inverse connectivity, overlapped significantly across subscales and involved widespread brain regions including somatomotor, visual, and DMN networks.
  • Only negative network models showed partial out-of-sample generalization for subscale scores, and predictions were negatively correlated with mind-wandering models.

Conclusions:

  • Preliminary neural evidence suggests distinct connectivity models for specific facets of trait mindfulness.
  • The limited and inconsistent generalization across sites highlights the difficulty in establishing robust brain markers for mindfulness.
  • Future research should address model stability and overlap to better understand the neural basis of mindfulness.