An integrated framework for targeting functional networks via transcranial magnetic stimulation

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. •We present a principled, integrated framework for predicting the functional networks to be activated by TMS on an individual basis.•Initial application of the framework demonstrated systematic variation in the networks to be impacted by DLPFC stimulation, which depended on coil location and orientation.•Three distinct DLPFC stimulation zones were revealed, differing with respect to the network to be affected (default, frontoparietal) and sensitivity to coil orientation.•The network profiles generated for previously published depression targets varied substantially across studies.