ArticleCNS neuroscience & therapeutics2026
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.
Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
BACKGROUND AND
objectivesDeep 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.
methodsWe 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.
resultsBest-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-morphometric and trimodal models performed similarly, suggesting limited incremental value of glymphatic variables in this subgroup.
conclusionPreoperative 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.