Depression symptoms are associated with affective neural processing during sleep and rest

Area of Science:

• Neuroscience
• Sleep Science
• Affective Disorders

Background:

• Sleep is crucial for cognitive and emotional regulation.
• Abnormal sleep patterns are frequently observed in individuals with affective disorders.
• Maladaptive neural processing during offline periods may underlie affective symptoms.

Purpose of the Study:

• To investigate the relationship between neural processing during offline periods (sleep and rest) and affective symptoms.
• To determine if affective neural patterns re-emerge during sleep.
• To test the hypothesis that maladaptive neural processing during offline periods predicts depressive symptoms.

Main Methods:

• Used multivariate electroencephalography (EEG) decoding with cross-state classification.
• Trained a model on EEG data from participants (N=52) viewing emotional images to classify stimulus valence.
• Applied the trained model to EEG data collected during naps to detect affective neural patterns.

Main Results:

• Affective neural patterns, similar to those during wakeful emotional image viewing, re-emerged during sleep.
• Offline reinstatement of neural patterns associated with negative valence predicted higher depressive symptoms.
• This association was observed across different sleep stages and during wakeful rest, and included both cognitive-affective and somatic depression subscales.

Conclusions:

• Offline neural information processing during sleep and rest is linked to emotional well-being.
• Maladaptive negative biases can be perpetuated during rest, potentially contributing to the progression of affective disorders.
• These findings support a model where disrupted offline processing impacts emotional health.