Novel electric-field modelling approach to quantify changes in resting state functional connectivity following theta burst stimulation

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.

Grant Number: 4UF1MH130447-04
NIH Institute/Center: NIH
Principal Investigator: nicholas balderston