Using EEG data to differentiate between schizophrenia and depression

Researchers found distinct neural signatures

Researchers at the Max Planck Institute of Psychiatry analyzed EEG recordings with supervised machine learning to identify distinct neural signatures of schizophrenia, major depressive disorder, and abnormal aging. The analysis showed that it is possible to differentiate between these mental health conditions using resting-state EEG, and also introduced a novel normative Electrophysiological Age Gap Estimation (EphysAGE) model to measure brain aging processes in healthy people and patients with neuropsychiatric conditions.

Key highlights of the paper:

• Robust classification models separating schizophrenia and depression from healthy controls using EEG data.
• Identification of alpha power decreases as a predictive feature for schizophrenia and depression.
• The development of the EphysAGE model, revealing how aging processes affect the brain differently in mental health disorders.
• Insights into how aging impacts diagnostic separability, paving the way for potential clinical biomarkers.