Use of Resting-State Networks to Predict the Ideal Site for Brain Stimulation

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DESCRIPTION (provided by applicant): Focal brain stimulation, including deep brain stimulation (DBS) and repetitive trans cranial magnetic stimulation (rTMS), can have therapeutic benefit in patients with an increasing number of brain disorders including Parkinson's, post-stroke deficits, and potentially even minimally conscious states. However it is often unclear what stimulation site will work best for a given patient or disease, limiting efficacy and extension to new disorders. Choosing an ideal stimulation site is difficult in part because focal brain stimulation propagates through connections to impact a distributed network of brain regions, and these network effects can determine the clinical response. Thus deciding where to stimulate depends in part on our ability to predict where stimulation will propagate. Advances in neuroimaging technology, such as resting-state functional connectivity MRI (rs-fcMRI) allow us to visualize brain networks in humans with unprecedented clarity. This project tests the hypothesis that networks seen with rs-fcMRI can predict how focal brain stimulation will propagate, thus facilitating selection of an ideal stimulation site to eventually target specific networks in specific patients. I propose to test this hypothesis in the motor network, where propagation of focal brain stimulation can be measured with transcranial magnetic stimulation (TMS). TMS to primary motor cortex (M1) results in a measurable muscle contraction, the strength of which depends on the underlying neuronal activity in M1. If one applies TMS to a connected region that then propagates to and alters M1, it will impact the strength of the TMS-induced muscle contraction. The current study will therefore examine whether rs-fcMRI can be used to identify sites from which the effects of TMS will propagate to and affect M1 in a predictable manner. It will further clarify what properties of rs-fcMRI are most useful and whether stimulation sites selected based on individualized rs-fcMRI patterns are superior to those selected based on group data. If this project is successful, future efforts will extend the approach to identify TMS and DBS sites most likely to specifically affect networks implicated in different diseases. Potential applications include identifying parietal TMS sites most likely to propagate to attention networks and improve spatial neglect, DBS sites most likely to impact motor networks without affecting cognitive or mood networks in a patient with Parkinson's, or candidate stimulation sites in minimally conscious states most likely to impact networks involved in arousal and consciousness.