ArticleFrontiers in computational neuroscience2020
Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson's Disease.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive Models for Motor Outcomes From Deep Brain Stimulation in Parkinson's Disease: A Systematic Review.The European journal of neuroscience · 2026Pooled it
- Use of Functional MRI in Deep Brain Stimulation in Parkinson's Diseases: A Systematic Review.Frontiers in neurology · 2022Pooled it
- Targeting based on patient-specific 7 Tesla MRI connectivity analysis improves deep brain stimulation for Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease.Neurosurgery · 2026Article
- Functional and structural connectome-based predictive modelling of balance in elderly adults.Scientific reports · 2026Article
- Deep Brain Stimulation as a Rehabilitation Amplifier: A Precision-Oriented, Network-Guided Framework for Functional Restoration in Movement Disorders.Journal of clinical medicine · 2026Review
- Preoperative Functional Connectivity Predicts Antiparkinson Drug Change after Deep Brain Stimulation.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Hot Topics and Frontiers of Resting-State fMRI in Parkinson's Disease: Research Trends and Paradigm Shifts From a Bibliometric Perspective.Parkinson's disease · 2025Review
- Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.BMC neurology · 2024Review
- Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.Human brain mapping · 2023Article
- Multi-scale and cross-dimensional TMS mapping: A proof of principle in patients with Parkinson's disease and deep brain stimulation.Frontiers in neuroscience · 2023Article
- Identifying a whole-brain connectome-based model in drug-naïve Parkinson's disease for predicting motor impairment.Human brain mapping · 2022Article
- Big Data in the Clinical Neurosciences.Acta neurochirurgica. Supplement · 2022Article
- A multimodal machine learning model for predicting postoperative worsening of FOGQ in Parkinson's disease following STN-DBS.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.