Sign-specific stimulation 'hot' and 'cold' spots in Parkinson's disease validated with machine learning.
Parkinson’s disease
deep brain stimulation
machine learning
neuroimaging
subthalamic nucleus
Journal
Brain communications
ISSN: 2632-1297
Titre abrégé: Brain Commun
Pays: England
ID NLM: 101755125
Informations de publication
Date de publication:
2021
2021
Historique:
received:
20
10
2020
revised:
09
01
2021
accepted:
13
01
2021
entrez:
19
4
2021
pubmed:
20
4
2021
medline:
20
4
2021
Statut:
epublish
Résumé
Deep brain stimulation of the subthalamic nucleus has become a standard therapy for Parkinson's disease. Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific Parkinsonian signs when informed by precise locations of stimulation. We studied 275 Parkinson's disease patients who underwent subthalamic nucleus deep brain stimulation between 2003 and 2018. We selected pre-deep brain stimulation and best available post-deep brain stimulation scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to deep brain stimulation. Volumes of tissue activated were computed and weighted by (i) tremor, (ii) rigidity, (iii) bradykinesia and (iv) axial signs changes. Then, sign-specific sites of optimal ('hot spots') and suboptimal efficacy ('cold spots') were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (
Identifiants
pubmed: 33870190
doi: 10.1093/braincomms/fcab027
pii: fcab027
pmc: PMC8042250
doi:
Types de publication
Journal Article
Langues
eng
Pagination
fcab027Informations de copyright
© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.
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