Characterizing PTSD Using Electrophysiology: Towards A Precision Medicine Approach

Area of Science:

• Neuroscience and Psychiatry
• Biomarkers and Physiological Measures

Background:

• Resting-state EEG and event-related potential (ERP) components show potential in distinguishing PTSD from controls.
• Previous meta-analyses show inconsistent findings, limiting understanding of PTSD biomarkers.
• A need exists for concise electrophysiological biomarkers applicable across psychiatric conditions and specific PTSD dimensions.

Purpose of the Study:

• To develop a concise set of electrophysiological biomarkers using neutral cognitive tasks.
• To identify biomarkers associated with anxiety and depression in post-traumatic stress disorder (PTSD).
• To assess the utility of EEG, ERP, and ECG measures for classifying PTSD and its symptom dimensions.

Main Methods:

• Continuous simultaneous electroencephalography (EEG) and electrocardiogram (ECG) recordings were obtained from veterans with PTSD (n=29) and healthy controls (n=62).
• Participants completed computerized tasks involving neutral cognitive stimuli.
• EEG, ERP, and heart rate variability measures were analyzed for group discrimination and correlation with psychological measures.

Main Results:

• The PTSD group exhibited faster alpha oscillations, reduced alpha power, and a flatter power spectrum compared to controls.
• Reduced alpha power correlated with higher trait anxiety, and a flatter slope correlated with increased depression severity in PTSD.
• ERP analysis revealed delayed/exaggerated early components and attenuated late positive potential (LPP) amplitudes in PTSD during visual memory and sustained attention tasks.

Conclusions:

• Distinct and complementary EEG signatures were identified across three cognitive tasks in individuals with PTSD.
• Multimodal biomarkers integrating EEG, cognitive ERPs, and ECG show promise for objective assessment.
• These biomarkers can aid in evaluating mood and anxiety disturbances within the PTSD spectrum.