Project Summary Transcranial magnetic stimulation (TMS) is currently approved by the FDA for the treatment of depression, obsessive compulsive disorder, and smoking cessation. Despite evidence that TMS improves symptoms by modulating brain connectivity, the few published studies that have measured brain connectivity before and after neuromodulatory TMS have been population-, dose-, and pattern-specific, with connectivity effects that are limited in scope to a handful a priori regions of interest. Accordingly, there is a critical need for generalized, comprehensive model that explains how functional brain connectivity changes at the whole-brain level following neuromodulatory TMS. Therefore, the objectives of this grant are to 1) develop a model using whole- brain estimates of the TMS-induced electric (e)-field to predict changes in resting state functional connectivity following neuromodulatory TMS, and 2) validate this model in a large cohort of healthy volunteers receiving multiple doses of either intermittent or continuous theta burst stimulation (iTBS and cTBS, respectively). Our central hypothesis is that changes in functional connectivity will vary systematically with the current density at the cortex, operationally defined using e-field modelling. We have pilot data suggesting that the variability in pre-post rsFC changes following TMS can be predicted using estimates of the current density at the cortex with a medium to large effect size. Our approach will be to measure rsFC in healthy volunteers before and after each of 3 doses (5 sessions/dose; 600 pulses/session) of iTBS or cTBS. Stimulation will be delivered to the left dlPFC, and targeting will be individualized based on fMRI data collected during the Sternberg working memory paradigm. Our primary outcome measure will be the percent of variability in pre-post rsFC accounted for by our model. Our rationale for this approach is that by collecting resting state data pre and post these doses of iTBS and cTBS, we will be able to quantify the effect of pattern (i.e. cTBS vs. iTBS) and dose (i.e. number of pulses) on functional connectivity changes. This work is innovative because it uses a novel application of e-field modelling to predict changes in rsFC data following TMS administration.