Brain network topology predicts participant adherence to mental training programs

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Adherence is crucial for the success of mental training programs.
• The neurobiological underpinnings of adherence remain largely unknown.
• Understanding adherence predictors can inform the design of new learning regimens.

Purpose of the Study:

• To investigate whether brain network configurations in resting-state fMRI scans predict adherence to meditation and creative writing programs.
• To identify specific brain regions and network properties associated with adherence.

Main Methods:

• Resting-state functional magnetic resonance imaging (fMRI) was used to analyze brain network patterns.
• Network segregation and clustering metrics were calculated across various thresholds.
• Machine learning models were employed to assess the predictive accuracy of brain metrics for adherence.

Main Results:

• Increased system segregation and clustering in brain networks predicted higher adherence (practice sessions, class participation) in both programs.
• Subcortical regions (striatum, accumbens) showed adherence-predictive connectivity across all participants.
• Meditation adherence correlated with connectivity in the anterior insula and default mode network; writing adherence linked to frontal and temporal regions.

Conclusions:

• Brain connectivity patterns, specifically network organization, are associated with an individual's capacity for adherence to mental training.
• Neurobiological factors offer potential targets for enhancing engagement in learning programs.
• These findings highlight the link between brain structure and motivational aspects of learning.