Post-traumatic stress disorder varies greatly in its clinical and biological features, making treatment difficult. The largest randomized trial of ketamine for PTSD found no overall benefit over placebo, highlighting the need to identify which patients might respond. Using pre-treatment blood DNA methylation profiles and clinical data from that trial, machine learning models predicted treatment response. A model based on 1,208 methylation sites outperformed models using only clinical variables, and combining both data types improved accuracy further. The methylation-derived score identified responders with 92.9% accuracy. Predictive methylation sites were near genes involved in glutamatergic signaling, immune regulation, and known PTSD risk loci, suggesting peripheral DNA methylation patterns can guide precision pharmacotherapy for PTSD.
The balance between excitatory and inhibitory (E/I) activity in the brain is important for normal function, and its disruption is linked to psychiatric disorders. In a randomized, double-blind, placebo-controlled study, healthy volunteers received low doses of ketamine (which shifts E/I balance toward excitation) and thiopental (which shifts it toward inhibition) while their brain activity was recorded with EEG. The drugs altered the aperiodic exponent of the power spectrum in opposite directions, matching computational predictions. Changes in the exponent correlated with subjective and cognitive effects, suggesting that this measure could serve as a noninvasive EEG biomarker for transient shifts in cortical E/I balance.