In Silico Prediction of Changes in Intrinsic Network Functional Connectivity Following Repetitive Transcranial Magnetic Stimulation

Title

In Silico Prediction of Changes in Intrinsic Network Functional Connectivity Following Repetitive Transcranial Magnetic Stimulation

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Publication Date

Summer 8-6-2020

College, Institute, or Department

MD/PhD Scholars Program

Faculty Mentor

Dr. David Warren

Research Mentor

Connor Phipps

Document Type

Poster

Abstract

Targeted transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique that can influence brain activity, psychiatric features, and cognitive performance. While TMS has been reported to affect cognitive performance across a variety of domains, substantial gaps in knowledge remain regarding the association of these TMS-based cognitive changes and functional connectivity in the brain. In this project, we aim to build a computational model to simulate the effects of TMS on functional associations between a stimulated brain region and the brain networks connected to that brain region. Using data from the Human Connectome Project as the basis for the brain’s functional connectivity, we built a prototypical matrix that models all cortical brain regions (“parcels”) and the functional connections between them. From this starting point, our computational model predicts the effects of stimulating a specific parcel with TMS. Further, our model includes variable parameters to support estimation of functional effects associated with differences in TMS delivery (applying excitatory or inhibitory stimulation), stimulation intensity, stimulation duration (days of TMS), etc. Finally, the model supports estimation of not only local results, but also whole-brain changes in functional connectivity. Based on the quantitative changes in the model estimates associated with different parameter settings, we were able to predict the effects on functional connectivity both within the parcel’s network as well as across the entire cortex. Our approach is an important first step toward individualized in silico computation of how TMS affects the brain, and our work may have implications for developing more effective treatment with TMS.

Keywords

Transcranial Magnetic Stimulation (TMS), Network Functional Connectivity, Connectomics

In Silico Prediction of Changes in Intrinsic Network Functional Connectivity Following Repetitive Transcranial Magnetic Stimulation

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