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.
Focal brain stimulation can provide therapeutic benefit for patients with an increasing number of neurological diseases including Parkinson's, neglect, and possibly even minimally conscious states. However its efficacy and its use in new disorders are limited by our ability to identify the best place to stimulate. The current proposal will test wheter a brain MRI technique, resting-state functional connectivity, can identify the ideal stimulation sie for a particular patient or disease by predicting how focal stimulation will propagate to affect brain networks.
| Darby, R Ryan; Horn, Andreas; Cushman, Fiery et al. (2018) Lesion network localization of criminal behavior. Proc Natl Acad Sci U S A 115:601-606 |
| Weigand, Anne; Horn, Andreas; Caballero, Ruth et al. (2018) Prospective Validation That Subgenual Connectivity Predicts Antidepressant Efficacy of Transcranial Magnetic Stimulation Sites. Biol Psychiatry 84:28-37 |
| Boes, Aaron D; Fischer, David; Geerling, Joel C et al. (2018) Connectivity of sleep- and wake-promoting regions of the human hypothalamus observed during resting wakefulness. Sleep 41: |
| Boes, Aaron D; Uitermarkt, Brandt D; Albazron, Fatimah M et al. (2018) Rostral anterior cingulate cortex is a structural correlate of repetitive TMS treatment response in depression. Brain Stimul 11:575-581 |
| Darby, R Ryan; Joutsa, Juho; Burke, Matthew J et al. (2018) Lesion network localization of free will. Proc Natl Acad Sci U S A 115:10792-10797 |
| Horn, Andreas; Reich, Martin; Vorwerk, Johannes et al. (2017) Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol 82:67-78 |
| Drysdale, Andrew T; Grosenick, Logan; Downar, Jonathan et al. (2017) Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:264 |
| Darby, R Ryan; Laganiere, Simon; Pascual-Leone, Alvaro et al. (2017) Finding the imposter: brain connectivity of lesions causing delusional misidentifications. Brain 140:497-507 |
| Fasano, Alfonso; Laganiere, Simon E; Lam, Susy et al. (2017) Lesions causing freezing of gait localize to a cerebellar functional network. Ann Neurol 81:129-141 |
| Caulfield, Kevin A; Bernstein, Margo H; Stern, Adam P et al. (2017) Antidepressant Effect of Low-Frequency Right-Sided rTMS in Two Patients with Left Frontal Stroke. Brain Stimul 10:150-151 |
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