Depression biomarkers using non-invasive EEG: A review.

Area of Science:

• Neuroscience
• Psychiatry
• Biomedical Engineering

Background:

• Depression is a major global health issue, affecting over 300 million people and causing significant disability.
• Electroencephalography (EEG) offers a non-invasive method for studying neurological disorders like depression.
• Identifying reliable biomarkers is crucial for diagnosing and understanding depression.

Purpose of the Study:

• To systematically map and analyze recent research (2014-2018) utilizing non-invasive EEG for depression biomarker detection.
• To identify promising EEG-based biomarkers for diagnosing depression and its associated symptoms.

Main Methods:

• A systematic review and mapping of scientific literature from 2014 to 2018.
• Analysis of over 250 articles, focusing on 42 studies that employed non-invasive EEG to detect depression biomarkers.
• Discussion of findings related to brain network structure and specific signal characteristics.

Main Results:

• The depressed brain exhibits a more random network structure compared to non-depressed states.
• Promising diagnostic biomarkers include features within the gamma frequency band and measures of signal complexity.
• Certain EEG features may help detect specific depression-related symptoms, such as suicidal ideation.

Conclusions:

• Non-invasive EEG is a valuable tool for identifying depression biomarkers.
• EEG-derived network structure, gamma band activity, and signal complexity show potential for depression diagnosis.
• Further research into EEG biomarkers could improve diagnostic accuracy and targeted treatment for depression.