Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation.
Biophysical modeling
Deep brain stimulation
Electrocorticography
Evoked potentials
Parkinson’s disease
Subthalamic nucleus
Journal
Brain stimulation
ISSN: 1876-4754
Titre abrégé: Brain Stimul
Pays: United States
ID NLM: 101465726
Informations de publication
Date de publication:
Historique:
received:
18
10
2020
revised:
19
02
2021
accepted:
14
03
2021
pubmed:
25
3
2021
medline:
25
11
2021
entrez:
24
3
2021
Statut:
ppublish
Résumé
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. Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 33757931
pii: S1935-861X(21)00060-7
doi: 10.1016/j.brs.2021.03.009
pmc: PMC8164987
mid: NIHMS1688369
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
549-563Subventions
Organisme : NINDS NIH HHS
ID : K23 NS097576
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH102238
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS069779
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS105690
Pays : United States
Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest 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.
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