EEG microstate temporal Dynamics Predict depressive symptoms in College Students

Area of Science:

• Neuroscience
• Psychiatry
• Biomarkers

Background:

• Electroencephalogram (EEG) parameters are explored as potential biomarkers for depressive disorders.
• Previous research has not focused on resting-state EEG microstates for early detection in preclinical individuals.
• College students represent a population vulnerable to developing depressive symptoms.

Purpose of the Study:

• To investigate the association between resting-state EEG microstate temporal dynamics and depressive symptoms in college students.
• To identify potential EEG microstate biomarkers for early detection of depression.

Main Methods:

• EEG microstate analysis was conducted on eyes-closed resting-state EEG data (approx. 5 min) from 34 undergraduates with high depressive symptoms and 34 matched controls.
• Five microstate classes (A-E) were identified.
• Statistical analyses compared microstate parameters (duration, occurrence, coverage, transition probabilities) and correlated them with Beck Depression Inventory-II (BDI-II) scores.

Main Results:

• Individuals with high depressive symptoms showed increased mean duration, occurrence, and coverage of microstate class B, and decreased occurrence and coverage of classes D and E compared to controls.
• Microstate class B presence positively correlated with BDI-II scores, while classes D and E negatively correlated.
• Altered transition probabilities between microstate classes were observed in the high-symptom group.

Conclusions:

• Resting-state EEG microstate temporal dynamics, particularly classes B, D, and E, serve as potential biomarkers for early depression detection in college students.
• These findings support the use of EEG microstates for timely diagnosis and intervention in at-risk individuals.
• Further research can validate these microstate patterns for clinical application in mental health screening.