Transcranial magnetic stimulation (TMS) is a powerful investigational tool for in vivo manipulation of regional or network activity, with a growing number of potential clinical applications. Unfortunately, the vast majority of targeting strategies remain limited by their reliance on non-realistic brain models and assumptions that anatomo-functional relationships are 1:1. Here, we present an integrated framework that combines anatomically realistic finite element models of the human head with resting functional MRI to predict functional networks targeted via TMS at a given coil location and orientation. Using data from the Human Connectome Project, we provide an example implementation focused on dorsolateral prefrontal cortex (DLPFC). Three distinct DLPFC stimulation zones were identified, differing with respect to the network to be affected (default, frontoparietal) and sensitivity to coil orientation. Network profiles generated for DLPFC targets previously published for treating depression revealed substantial variability across studies, highlighting a potentially critical technical issue.
Bibliographical noteFunding Information:
This work was supported in part by grants from NIH ( U01MH099059 ) and the Child Mind Institute ( 1FDN2012-1 ) to Michael P. Milham, and gifts to the Child Mind Institute (Michael P. Milham) from Phyllis Green, Randolph Cowen, and Joseph P. Healey. Michael D. Fox was supported in part from NIH grants K23 NS083741 and R21 MH099196 , and the Sidney Baer Foundation .
© 2015 Elsevier Inc.