Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

Bryan Howell; Faical Isbaine; Jon T Willie; Enrico Opri; Robert E Gross; Coralie De Hemptinne; Philip A Starr; Cameron C McIntyre; Svjetlana Miocinovic

doi:10.1016/j.brs.2021.03.009

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

Brain Stimul. 2021 May-Jun;14(3):549-563. doi: 10.1016/j.brs.2021.03.009. Epub 2021 Mar 20.

Authors

Bryan Howell¹, Faical Isbaine², Jon T Willie², Enrico Opri³, Robert E Gross², Coralie De Hemptinne⁴, Philip A Starr⁵, Cameron C McIntyre¹, Svjetlana Miocinovic⁶

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, USA.
² Department of Neurosurgery, Emory University, USA.
³ Department of Neurology, Emory University, USA.
⁴ Department of Neurology, University of Florida, USA.
⁵ Department of Neurological Surgery, University of California San Francisco, USA.
⁶ Department of Neurology, Emory University, USA. Electronic address: svjetlana.miocinovic@emory.edu.

Abstract

Background: Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

Objective: Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

Methods: Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

Results: Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

Conclusions: Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

Keywords: Biophysical modeling; Deep brain stimulation; Electrocorticography; Evoked potentials; Parkinson’s disease; Subthalamic nucleus.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Deep Brain Stimulation*
Evoked Potentials
Humans
Neural Pathways
Parkinson Disease* / therapy
Subthalamic Nucleus*
Subthalamus*

Abstract

Publication types

MeSH terms

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