Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.
DBS programming
Parkinson disease
deep brain stimulation
local field potentials
subthalamic nucleus
Journal
Neuromodulation : journal of the International Neuromodulation Society
ISSN: 1525-1403
Titre abrégé: Neuromodulation
Pays: United States
ID NLM: 9804159
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
received:
06
08
2021
revised:
24
12
2021
accepted:
13
01
2022
pubmed:
28
2
2022
medline:
8
2
2023
entrez:
27
2
2022
Statut:
ppublish
Résumé
Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.
Sections du résumé
BACKGROUND
BACKGROUND
Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease.
OBJECTIVE
OBJECTIVE
In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold).
MATERIALS AND METHODS
METHODS
STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature.
RESULTS
RESULTS
Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction.
CONCLUSION
CONCLUSIONS
LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.
Identifiants
pubmed: 35219571
pii: S1094-7159(22)00038-1
doi: 10.1016/j.neurom.2022.01.017
pmc: PMC7614142
mid: EMS144707
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
320-332Subventions
Organisme : Medical Research Council
ID : MC_UU_12024/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00003/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V00655X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0901503
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/P012272/1
Pays : United Kingdom
Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.
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