Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study.
Journal
Annals of clinical and translational neurology
ISSN: 2328-9503
Titre abrégé: Ann Clin Transl Neurol
Pays: United States
ID NLM: 101623278
Informations de publication
Date de publication:
07 2019
07 2019
Historique:
received:
13
11
2018
revised:
19
02
2019
accepted:
26
03
2019
entrez:
30
7
2019
pubmed:
30
7
2019
medline:
14
5
2020
Statut:
ppublish
Résumé
The selection of optimal deep brain stimulation (DBS) parameters is time-consuming, experience-dependent, and best suited when acute effects of stimulation can be observed (e.g., tremor reduction). To test the hypothesis that optimal stimulation location can be estimated based on the cortical connections of DBS contacts. We analyzed a cohort of 38 patients with Parkinson's disease (24 training, and 14 test cohort). Using whole-brain probabilistic tractography, we first mapped the cortical regions associated with stimulation-induced efficacy (rigidity, bradykinesia, and tremor improvement) and side effects (paresthesia, motor contractions, and visual disturbances). We then trained a support vector machine classifier to categorize DBS contacts into efficacious, defined by a therapeutic window ≥2 V (threshold for side effect minus threshold for efficacy), based on their connections with cortical regions associated with efficacy versus side effects. The connectivity-based classifications were then compared with actual stimulation contacts using receiver-operating characteristics (ROC) curves. Unique cortical clusters were associated with stimulation-induced efficacy and side effects. In the training dataset, 42 of the 47 stimulation contacts were accurately classified as efficacious, with a therapeutic window of ≥3 V in 31 (66%) and between 2 and 2.9 V in 11 (24%) electrodes. This connectivity-based estimation was successfully replicated in the test cohort with similar accuracy (area under ROC = 0.83). Cortical connections can predict the efficacy of DBS contacts and potentially facilitate DBS programming. The clinical utility of this paradigm in optimizing DBS outcomes should be prospectively tested, especially for directional electrodes.
Sections du résumé
BACKGROUND
The selection of optimal deep brain stimulation (DBS) parameters is time-consuming, experience-dependent, and best suited when acute effects of stimulation can be observed (e.g., tremor reduction).
OBJECTIVES
To test the hypothesis that optimal stimulation location can be estimated based on the cortical connections of DBS contacts.
METHODS
We analyzed a cohort of 38 patients with Parkinson's disease (24 training, and 14 test cohort). Using whole-brain probabilistic tractography, we first mapped the cortical regions associated with stimulation-induced efficacy (rigidity, bradykinesia, and tremor improvement) and side effects (paresthesia, motor contractions, and visual disturbances). We then trained a support vector machine classifier to categorize DBS contacts into efficacious, defined by a therapeutic window ≥2 V (threshold for side effect minus threshold for efficacy), based on their connections with cortical regions associated with efficacy versus side effects. The connectivity-based classifications were then compared with actual stimulation contacts using receiver-operating characteristics (ROC) curves.
RESULTS
Unique cortical clusters were associated with stimulation-induced efficacy and side effects. In the training dataset, 42 of the 47 stimulation contacts were accurately classified as efficacious, with a therapeutic window of ≥3 V in 31 (66%) and between 2 and 2.9 V in 11 (24%) electrodes. This connectivity-based estimation was successfully replicated in the test cohort with similar accuracy (area under ROC = 0.83).
CONCLUSIONS
Cortical connections can predict the efficacy of DBS contacts and potentially facilitate DBS programming. The clinical utility of this paradigm in optimizing DBS outcomes should be prospectively tested, especially for directional electrodes.
Identifiants
pubmed: 31353863
doi: 10.1002/acn3.784
pmc: PMC6649384
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1142-1150Subventions
Organisme : Discovery Themes Initiative
Pays : International
Organisme : Ohio State University
Pays : International
Informations de copyright
© 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.
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