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Updated: May 17, 2026

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory
Published on: May 11, 2020
Cognitive-affective neural plasticity following active-controlled mindfulness intervention
Micah Allen1, Martin Dietz, Karina S Blair
1MINDLab, Center of Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark. micah@cfin.dk
Mindfulness training (MT) enhances self-regulation by improving attention and reducing emotional conflict. Deeper practice recruits specific brain regions, suggesting personalized MT approaches are most effective.
Area of Science:
- Neuroscience
- Psychology
- Cognitive Science
Background:
- Mindfulness training (MT) is known to improve self-regulation.
- Neural mechanisms underlying MT's effects on self-regulation are not fully understood.
- Conflicting evidence exists regarding the roles of interoceptive salience and attentional control.
Purpose of the Study:
- To investigate the neural mechanisms of mindfulness training (MT) on self-regulation.
- To differentiate the roles of interoceptive salience and attentional control in MT.
- To examine the effects of a 6-week randomized MT intervention using an active control group.
Main Methods:
- Randomized controlled trial with an active control group over 6 weeks.
- Functional MRI (fMRI) and behavioral metacognition assessed during an affective Stroop task.
- Analysis of blood oxygenation level-dependent (BOLD) signals and behavioral performance.
Main Results:
- Mindfulness training (MT) group showed reduced affective Stroop conflict and increased dorsolateral prefrontal cortex activity.
- Both groups improved response inhibition, but only MT group showed this with increased practice.
- Greater practice correlated with improved response inhibition and recruitment of ACC, mPFC, and right anterior insula.
Conclusions:
- Active control is crucial for mindfulness training (MT) research.
- Neural mechanisms for MT are progressive and depend on practice duration.
- A personalized, context-dependent approach to MT may be more effective than a one-size-fits-all model.
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