Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning
Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.
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Nature Portfolio
2021
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oai:doaj.org-article:58ee235dc4e54cd085cabad46031c3242021-12-02T14:49:08ZPredicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning10.1038/s41467-021-23311-92041-1723https://doaj.org/article/58ee235dc4e54cd085cabad46031c3242021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23311-9https://doaj.org/toc/2041-1723Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.Alexandre BoutetRadhika MadhavanGavin J. B. EliasSuresh E. JoelRobert GramerManish RanjanVijayashankar ParamanandamDavid XuJurgen GermannAaron LohSuneil K. KaliaMojgan HodaieBryan LiSreeram PrasadAilish CoblentzRenato P. MunhozJeffrey AsheWalter KucharczykAlfonso FasanoAndres M. LozanoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q Alexandre Boutet Radhika Madhavan Gavin J. B. Elias Suresh E. Joel Robert Gramer Manish Ranjan Vijayashankar Paramanandam David Xu Jurgen Germann Aaron Loh Suneil K. Kalia Mojgan Hodaie Bryan Li Sreeram Prasad Ailish Coblentz Renato P. Munhoz Jeffrey Ashe Walter Kucharczyk Alfonso Fasano Andres M. Lozano Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| description |
Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings. |
| format |
article |
| author |
Alexandre Boutet Radhika Madhavan Gavin J. B. Elias Suresh E. Joel Robert Gramer Manish Ranjan Vijayashankar Paramanandam David Xu Jurgen Germann Aaron Loh Suneil K. Kalia Mojgan Hodaie Bryan Li Sreeram Prasad Ailish Coblentz Renato P. Munhoz Jeffrey Ashe Walter Kucharczyk Alfonso Fasano Andres M. Lozano |
| author_facet |
Alexandre Boutet Radhika Madhavan Gavin J. B. Elias Suresh E. Joel Robert Gramer Manish Ranjan Vijayashankar Paramanandam David Xu Jurgen Germann Aaron Loh Suneil K. Kalia Mojgan Hodaie Bryan Li Sreeram Prasad Ailish Coblentz Renato P. Munhoz Jeffrey Ashe Walter Kucharczyk Alfonso Fasano Andres M. Lozano |
| author_sort |
Alexandre Boutet |
| title |
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| title_short |
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| title_full |
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| title_fullStr |
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| title_full_unstemmed |
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning |
| title_sort |
predicting optimal deep brain stimulation parameters for parkinson’s disease using functional mri and machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/58ee235dc4e54cd085cabad46031c324 |
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