Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease.
Overview
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science Fudan University Shanghai China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration Shanghai China
- Shanghai Clinical Medical Center of Neurosurgery Shanghai China
- National Center for Neurological Disorders Shanghai China
- Institute of Science and Technology for Brain‐Inspired Intelligence Fudan University Shanghai China
- Department of Nursing, Huashan Hospital Fudan University Shanghai China
- Department of Neurosurgery, Third Division General Hospital Xinjiang Production and Construction Corps Tumushuke Xinjiang China
- Tianqiao and Chrissy Chen Institute Clinical Translational Research Center Shanghai China
Abstract
Background and Objectives: Deep brain stimulation of the subthalamic nucleus (STN‐DBS) is effective for medication‐refractory Parkinson's disease (PD) motor symptoms, but clinical response varies across symptom domains, particularly tremor and gait. Accurate preoperative stratification is clinically important, especially for early post‐programming outcomes.
Methods: We retrospectively enrolled 155 patients with PD undergoing bilateral STN‐DBS and 43 healthy controls. Preoperative structural magnetic resonance imaging and diffusion‐weighted imaging were used to quantify brain morphometry and glymphatic markers, including diffusion tensor imaging along the perivascular space (DTI‐ALPS) and choroid plexus volume (CPV). Total motor response was evaluated in all 155 patients, tremor response in 133 patients with complete tremor subscores, and an exploratory data‐driven gait‐improvement phenotype in 66 patients with paired instrumented gait assessments. Machine‐learning models were developed using fold‐wise feature selection and hyperparameter tuning and were evaluated by fivefold cross‐validation.
Results: Best‐performing trimodal models yielded AUCs of 0.850 ± 0.045 (95% CI, 0.794–0.906) for total motor response, 0.861 ± 0.047 (95% CI, 0.803–0.919) for tremor response, and 0.970 ± 0.019 (95% CI, 0.946–0.994) for the exploratory gait‐improvement phenotype. Morphometric‐only models retained substantial predictive performance, with maximum AUCs of 0.830, 0.849, and 0.955 for the motor, tremor, and gait‐related endpoints, respectively. For the exploratory gait phenotype, clinical‐plus‐morphometr
Conclusion: Preoperative cerebral morphometry, complemented by selected glymphatic markers and baseline clinical variables, may help stratify short‐term post‐programming STN‐DBS response in PD. These findings support further development of imaging‐informed DBS outcome prediction, while external validation and longer‐term follow‐up remain necessary before clinical implementation.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.
The paper's code and data availability statement is in the Data section.
Tracing map
A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.
Data
No dataset and no data link were found in the paper.
Data Availability Statement
The raw data of the present study are available from the corresponding author upon reasonable request after consideration and approval of local IRB. Code Availability: The statistical analysis code used in this study is available from the corresponding author upon reasonable request.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 17 MeSH terms, 9 funders, 37 references.
Cite
This paper
Wang, Y., Zhang, X., Yu, W., Yang, Y., Cai, J., He, J., Miao, H., Li, L., Lang, L., Hu, J., Qi, Z., & Chen, L. (2026). Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease. CNS neuroscience & therapeutics, 32(8), e71043. https://
BibTeX
@article{wang2026multimo
author = {Wang, Yining and Zhang, Xin and Yu, Wenwen and Yang, Yiyao and Cai, Jiajun and He, Juanjuan and Miao, Huie and Li, Lingjuan and Lang, Liqin and Hu, Jie and Qi, Zengxin and Chen, Liang},
title = {{Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease}},
journal = {CNS neuroscience \& therapeutics},
year = {2026},
month = aug,
volume = {32},
number = {8},
pages = {e71043},
publisher = {Wiley},
issn = {1755-5930},
doi = {10.1002/
url = {https://
pmid = {42631338},
pmcid = {PMC13499007}
}
RIS
TY - JOUR
AU - Wang, Yining
AU - Zhang, Xin
AU - Yu, Wenwen
AU - Yang, Yiyao
AU - Cai, Jiajun
AU - He, Juanjuan
AU - Miao, Huie
AU - Li, Lingjuan
AU - Lang, Liqin
AU - Hu, Jie
AU - Qi, Zengxin
AU - Chen, Liang
TI - Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease
T2 - CNS neuroscience & therapeutics
J2 - CNS Neurosci Ther
PY - 2026
DA - 2026/
VL - 32
IS - 8
SP - e71043
SN - 1755-5930
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1002/
"type": "article-journal",
"title": "Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease",
"container-title": "CNS neuroscience & therapeutics",
"author": [
{
"family": "Wang",
"given": "Yining"
},
{
"family": "Zhang",
"given": "Xin"
},
{
"family": "Yu",
"given": "Wenwen"
},
{
"family": "Yang",
"given": "Yiyao"
},
{
"family": "Cai",
"given": "Jiajun"
},
{
"family": "He",
"given": "Juanjuan"
},
{
"family": "Miao",
"given": "Huie"
},
{
"family": "Li",
"given": "Lingjuan"
},
{
"family": "Lang",
"given": "Liqin"
},
{
"family": "Hu",
"given": "Jie"
},
{
"family": "Qi",
"given": "Zengxin"
},
{
"family": "Chen",
"given": "Liang"
}
],
"container-title-short":
"volume": "32",
"issue": "8",
"page": "e71043",
"DOI": "10.1002/
"PMID": "42631338",
"PMCID": "PMC13499007",
"ISSN": "1755-5930",
"publisher": "Wiley",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1073/pnas.2516601123 [code]
- Unveiling the glymphatic system's role in brain aging: A comprehensive biomarker and modifiable intervention target.Journal: Proceedings of the National Academy of Sciences of the United States of AmericaIn common: structural MRI / diffusion, clinical / translational, 4 references
- [2] doi:10.1162/imag.a.1303
- Composite reaction time and variability correlate with whole-brain white-matter characteristics.Journal: Imaging neuroscience (Cambridge, Mass.)In common: structural MRI / diffusion, 5 references
- [3] doi:10.1162/nol.a.265 [code]
- Structural Brain Correlates of Poor Reading Comprehension.Journal: Neurobiology of language (Cambridge, Mass.)In common: structural MRI / diffusion, 4 references
- [4] doi:10.3389/fneur.2026.1879894
- Perivascular diffusion and white matter free water improve identification of cognitive impairment in cerebral small vessel disease.Journal: Frontiers in neurologyIn common: structural MRI / diffusion, clinical / translational, 3 references
- [5] doi:10.1371/journal.pone.0348957 [code]
- Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.Journal: PloS oneIn common: Parkinson's, clinical / translational, 2 references
- [6] doi:10.1016/j.isci.2026.116581
- Peripheral inflammation impairs glymphatic function, contributing to neurodegeneration in Alzheimer's disease.Journal: iScienceIn common: structural MRI / diffusion, 3 references
- [7] doi:10.3389/fpain.2026.1850836 [code]
- Diffusion tensor imaging in chronic tension-type headache.Journal: Frontiers in pain research (Lausanne, Switzerland)In common: structural MRI / diffusion, 4 references
- [8] doi:10.3389/fpsyt.2026.1848053
- Neuroanatomical substrates of perivascular space index: a morphometric and tractography study.Journal: Frontiers in psychiatryIn common: structural MRI / diffusion, 3 references
- [9] doi:10.3389/frai.2026.1771088 [code]
- Few-shot deployment of pretrained MRI transformers in brain imaging tasks.Journal: Frontiers in artificial intelligenceIn common: structural MRI / diffusion, 3 references
- [10] doi:10.1002/mds.70355 [code]
- Gray Matter Microstructure Measured Using Diffusion Imaging as a Biomarker of Severity in Lewy Body Diseases.Journal: Movement disorders : official journal of the Movement Disorder SocietyIn common: Parkinson's, structural MRI / diffusion, clinical / translational, 2 references
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.