Resting-state electroencephalogram and heart rate variability jointly predict intentional episodic memory control...

Area of Science:

• Neuroscience
• Psychophysiology
• Cognitive Science

Background:

• Deficits in intentional memory control are linked to psychiatric disorders.
• A dynamic systems approach suggests brain and autonomic function influence memory control.

Purpose of the Study:

• To investigate if resting-state electroencephalogram (EEG) and heart rate variability (HRV) predict intentional episodic memory control.
• To determine if combining EEG and HRV enhances prediction of memory control differences.

Main Methods:

• Collected resting-state EEG and ECG data from 42 healthy adults.
• Administered an item-method directed forgetting task to assess memory control.
• Applied machine learning to EEG and HRV features, and created fusion models.

Main Results:

• Both EEG and HRV unimodal models predicted memory control above chance.
• Five out of six EEG-HRV fusion models significantly outperformed EEG-only models.
• Autonomic signatures from HRV enhanced EEG-based prediction of memory control.

Conclusions:

• Resting-state EEG and HRV independently contain predictive information for intentional episodic memory control.
• Integrating autonomic measures (HRV) with central measures (EEG) improves the modeling of individual differences in memory control.
• This supports a combined central-autonomic physiological approach to understanding memory control.