Impact of brain tissue filtering on neurostimulation fields: A modeling study
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Grodzinsky_Impact of brain.pdf
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Author(s)
Wagner, Tim
Eden, Uri
Rushmore, Jarrett
Russo, Christopher J.
Dipietro, Laura
Fregni, Felipe
Simon, Stephen
Rotman, Stephen
Pitskel, Naomi B.
Ramos-Estebanez, Ciro
Date Issued
July 2013
Journal
NeuroImage
Publisher
Elsevier
Citation
Wagner, Tim, Uri Eden, Jarrett Rushmore, Christopher J. Russo, Laura Dipietro, Felipe Fregni, Stephen Simon, et al. “Impact of Brain Tissue Filtering on Neurostimulation Fields: A Modeling Study.” NeuroImage 85 (January 2014): 1048–1057.
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Author's final manuscript
Abstract
Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson's disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Center for Biomedical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2013.06.079
