Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease
Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson's disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This w...
| Autori principali: | , , , , |
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| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
Frontiers Media S.A.
2020-10-01
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| Serie: | Frontiers in Computational Neuroscience |
| Soggetti: | |
| Accesso online: | https://www.frontiersin.org/articles/10.3389/fncom.2020.571527/full |
| _version_ | 1828930740764540928 |
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| author | Ruihong Shang Le He Xiaodong Ma Yu Ma Xuesong Li |
| author_facet | Ruihong Shang Le He Xiaodong Ma Yu Ma Xuesong Li |
| author_sort | Ruihong Shang |
| collection | DOAJ |
| description | Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson's disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This work aims to investigate whether the topological network of functional connectivity states can predict the outcome of DBS without medication. Fifty patients were recruited to extract the features of the brain related to the improvement rate of PD after STN-DBS and to train the machine learning model that can predict the therapy's effect. The functional connectivity analyses suggested that the GBRT model performed best with Pearson's correlations of r = 0.65, p = 2.58E−07 in medication-off condition. The connections between middle frontal gyrus (MFG) and inferior temporal gyrus (ITG) contribute most in the GBRT model. |
| first_indexed | 2024-12-14T00:37:03Z |
| format | Article |
| id | doaj.art-9d2d6977e1054c7a956dce2aa974fb0f |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-14T00:37:03Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-9d2d6977e1054c7a956dce2aa974fb0f2022-12-21T23:24:36ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882020-10-011410.3389/fncom.2020.571527571527Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's DiseaseRuihong Shang0Le He1Xiaodong Ma2Yu Ma3Xuesong Li4School of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaDepartment of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, ChinaCenter for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United StatesDepartment of Neurosurgery, Tsinghua University Yuquan Hospital, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaSubthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson's disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This work aims to investigate whether the topological network of functional connectivity states can predict the outcome of DBS without medication. Fifty patients were recruited to extract the features of the brain related to the improvement rate of PD after STN-DBS and to train the machine learning model that can predict the therapy's effect. The functional connectivity analyses suggested that the GBRT model performed best with Pearson's correlations of r = 0.65, p = 2.58E−07 in medication-off condition. The connections between middle frontal gyrus (MFG) and inferior temporal gyrus (ITG) contribute most in the GBRT model.https://www.frontiersin.org/articles/10.3389/fncom.2020.571527/fulldeep brain stimulation (DBS) surgeryParkinson's diseasemachine learningbrain networkrs-fMRI |
| spellingShingle | Ruihong Shang Le He Xiaodong Ma Yu Ma Xuesong Li Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease Frontiers in Computational Neuroscience deep brain stimulation (DBS) surgery Parkinson's disease machine learning brain network rs-fMRI |
| title | Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease |
| title_full | Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease |
| title_fullStr | Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease |
| title_full_unstemmed | Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease |
| title_short | Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease |
| title_sort | connectome based model predicts deep brain stimulation outcome in parkinson s disease |
| topic | deep brain stimulation (DBS) surgery Parkinson's disease machine learning brain network rs-fMRI |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2020.571527/full |
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