ArticleHuman brain mapping2020
Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Brain connectivity markers in advanced Parkinson's disease for predicting mild cognitive impairment.European radiology · 2021Trial
- Brain connectome-based imaging markers for identifiable signature of migraine with and without aura.Quantitative imaging in medicine and surgery · 2024Article
- Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review.Frontiers in human neuroscience · 2024Review
- Association of clinical outcomes and connectivity in awake versus asleep deep brain stimulation for Parkinson disease.Journal of neurosurgery · 2023Article
- A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders.Brain communications · 2023Review
- Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease.Neurology and therapy · 2022Article
- Effect of deep brain stimulation on brain network and white matter integrity in Parkinson's disease.CNS neuroscience & therapeutics · 2022Article
- Neuroimaging Advances in Deep Brain Stimulation: Review of Indications, Anatomy, and Brain Connectomics.AJNR. American journal of neuroradiology · 2020Review
- Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation.Human brain mapping · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole-brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.
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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.