Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment - Balderston, Nicholas L; Beer, Joanne C; Darsol Seok; Makhoul, Walid; Zhi-De Deng; Girelli, Tommaso; Teferi, Marta; Smyk, Nathan; Jaskir, Marc; Oathes, Desmond J; Sheline, Yvette I (bioRxiv, 2021)
Abstract Background Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. Methods In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products 1) the estimated connectivity/symptom coefficients, 2) a vectorized e-field model for site/orientation, 3) the pre-treatment rsFC matrix, scaled by a proportionality constant. Results In AM patients, our pre-stimulation connectivity-based model predicted a significant decrease depression for sites near BA46, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. Discussion These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact. Competing Interest Statement The authors have declared no competing interest.