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Author Notes:

Svjetlana Miocinovic, Email: svjetlana.miocinovic@emory.edu

Bryan Howell: Conceptualization; Formal analysis; Investigation; Methodology; Software; Visualization; Roles/Writing - original draft; Writing - review & editing. Cameron C. McIntyre: Conceptualization; Funding acquisition; Methodology; Resources; Writing - review & editing. Philip A: Starr: Conceptualization; Funding acquisition; Resources; Writing - review & editing. Svjetlana Miocinovic: Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Visualization; Roles/Writing - original draft; Writing - review & editing. All other authors: Conceptualization, Writing - review & editing.

The authors thank Angela Noecker for assistance with visualization of the connectomic model data. This work made use of the High-Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.

Bryan Howell is a paid consultant for Abbott Laboratories. Robert E. Gross is a paid consultant for Medtronic, PLC and Abbot Laboratories. Philip A: Starr has research supported by Medtronic, PLC and Boston Scientific, Co. Jon T. Willie is a paid consultant for Medtronic, PLC and Neuropace, Inc. Cameron C. McIntyre is a paid consultant for Boston Scientific, Co., receives royalties from Hologram Consultants, Neuros Medical, and Qr8 Health, and is a shareholder in the following companies: Hologram Consultants, Surgical Information Sciences, CereGate, Autonomic Technologies, Cardionomic, Enspire DBS. All other authors have no competing interests.


Research Funding:

This work was supported by grants from the National Institute of Neurological Disorders and Stroke (K23NS097576, R01NS105690, and R01NS069779) and the National Institute of Mental Health (R01MH102238) of the National Institutes of Health.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Clinical Neurology
  • Neurosciences
  • Neurosciences & Neurology
  • Deep brain stimulation
  • Subthalamic nucleus
  • Electrocorticography
  • Evoked potentials
  • Biophysical modeling
  • Parkinson&apos
  • s disease&amp
  • nbsp

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


Journal Title:



Volume 14, Number 3


, Pages 549-563

Type of Work:

Article | Post-print: After Peer Review


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.

Copyright information:

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/rdf).
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