Evidence map›Paper›PMID 40738500›Full record

ArticleJournal of movement disorders2025

Connectivity-Based Analysis of the Stimulation Effects of Globus Pallidus Interna Deep Brain Stimulation in Parkinson's Disease: A Focus on Freezing of Gait.

Sungyang Jo, Moongwan Choi, Jihyun Lee, Sangjin Lee, Hwon Heo, Chong Hyun Suh, Woo Hyun Shim, Junhyung Kim, Sang Ryong Jeon, Hyunna Lee and 1 more

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In one paragraph

Article in Journal of movement disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Sungyang JoDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Moongwan ChoiDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Jihyun LeeDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sangjin LeeDepartment of Neurology, Chung-Ang University College of Medicine, Seoul, Korea.
Hwon HeoDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Chong Hyun SuhDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Woo Hyun ShimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Junhyung KimDepartment of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sang Ryong JeonDepartment of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Hyunna LeeBig Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.
Sun Ju ChungDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Funding

Korea Movement Disorder Society
6 · The paper itself

Abstract

objectiveFreezing of gait (FOG) significantly affects quality of life and increases the risk of falls in patients with Parkinson's disease (PD). Although deep brain stimulation (DBS) of the globus pallidus interna (GPi) is effective in managing motor complications, its efficacy in treating FOG remains inconsistent. This study aimed to determine whether preoperative structural brain connectivity can predict both the presence of FOG and its postoperative improvement following GPi DBS.

methodsWe retrospectively analyzed 58 patients with PD who underwent GPi DBS. Preoperative diffusion tensor imaging was used to assess structural connectivity between the volume of activated tissue (VAT) and 82 cortical regions. Machine learning models were developed to predict baseline FOG and postoperative FOG improvement (defined as a ≥1- or ≥2-point reduction) using demographic and connectivity features.

resultsMachine learning models incorporating structural connectivity features between the VAT and cortical regions-including the prefrontal, cingulate, and premotor cortices-outperformed models based solely on demographic variables in predicting both the presence of preoperative FOG and postoperative improvement. For example, the support vector machine model to predict FOG improvement (≥1-point improvement) achieved an accuracy of 0.65 with demographic data alone, which increased to 0.77 with the addition of structural connectivity features. Similar performance enhancements were observed in sensitivity analyses using stricter FOG thresholds (≥2-point improvement).

conclusionPreoperative structural connectivity between the GPi and key cortical regions involved in cognitive control and motor planning predicts FOG responsiveness to DBS. These.

resultshighlight the utility of connectomic biomarkers for personalizing DBS strategies and optimizing therapeutic outcomes in patients with advanced PD.

Indexed as

Deep brain stimulationDiffusion tensor imagingFreezing of gaitGlobus pallidus internaParkinson’s diseaseStructural connectivity

Identifiers

PMID40738500
PMCPMC12580727

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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.