Prospective development of a whole-brain connectivity and E-field guided TMS atlas for PTSD symptoms.

Background Although trauma-focused psychotherapy remains the first-line treatment for PTSD, 14% to 35% of individuals fail to achieve symptom remission. Transcranial magnetic stimulation (TMS) has shown promise as an adjunctive intervention, but its efficacy for PTSD remains inconsistent, and may be better informed by the incorporation of brain-based signals to guide stimulation. To address this, we developed a connectivity-based modeling framework to identify functional networks that predict symptom severity and to estimate where stimulation would most effectively reduce PTSD symptoms. Methods We used clinical, structural, and resting state functional data from 350 veterans with and without PTSD. Principal component analysis (PCA) was applied to functional connectivity matrices to reduce dimensionality and resulting component scores were entered into a lasso regression to identify connections associated with symptom severity. To simulate TMS effects, we generated electric field models for 200 randomly distributed cortical sites per participant and used these distributions to estimate connectivity changes proportional to the modeled fields. The estimated changes were combined with PCA regression weights to predict stimulation induced symptom changes at each site and were mapped across the cortex to create a whole brain TMS targeting atlas. Results The model identified two broad and opposing patterns. Stimulation expected to increase connectivity in the left prefrontal cortex were predicted to reduce symptoms, whereas stimulation expected to decrease connectivity in bilateral parietal and occipital regions were also predicted to reduce symptoms. Conclusions These findings provide a framework for individualized TMS targeting in PTSD and generate potential therapeutic targets for future clinical trials.