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Accurate preoperative stratification is clinically important, especially for early post\u2010programming outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively enrolled 155 patients with PD undergoing bilateral STN\u2010DBS and 43 healthy controls. Preoperative structural magnetic resonance imaging and diffusion\u2010weighted imaging were used to quantify brain morphometry and glymphatic markers, including diffusion tensor imaging along the perivascular space (DTI\u2010ALPS) 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\u2010driven gait\u2010improvement phenotype in 66 patients with paired instrumented gait assessments. Machine\u2010learning models were developed using fold\u2010wise feature selection and hyperparameter tuning and were evaluated by fivefold cross\u2010validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Best\u2010performing trimodal models yielded AUCs of 0.850\u2009\u00b1\u20090.045 (95% CI, 0.794\u20130.906) for total motor response, 0.861\u2009\u00b1\u20090.047 (95% CI, 0.803\u20130.919) for tremor response, and 0.970\u2009\u00b1\u20090.019 (95% CI, 0.946\u20130.994) for the exploratory gait\u2010improvement phenotype. Morphometric\u2010only models retained substantial predictive performance, with maximum AUCs of 0.830, 0.849, and 0.955 for the motor, tremor, and gait\u2010related endpoints, respectively. For the exploratory gait phenotype, clinical\u2010plus\u2010morphometric and trimodal models performed similarly, suggesting limited incremental value of glymphatic variables in this subgroup.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Preoperative cerebral morphometry, complemented by selected glymphatic markers and baseline clinical variables, may help stratify short\u2010term post\u2010programming STN\u2010DBS response in PD. These findings support further development of imaging\u2010informed DBS outcome prediction, while external validation and longer\u2010term follow\u2010up remain necessary before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.71043","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T08:42:30Z","timestamp":1787388150000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Neuroimaging\u2010Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short\u2010Term Post\u2010Programming\n                    <scp>STN<\/scp>\n                    \u2010\n                    <scp>DBS<\/scp>\n                    Motor Response in Parkinson's Disease"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9210-1552","authenticated-orcid":false,"given":"Yining","family":"Wang","sequence":"first","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1480-6015","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"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"},{"name":"Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration  Shanghai China"},{"name":"Shanghai Clinical Medical Center of Neurosurgery  Shanghai China"},{"name":"National Center for Neurological Disorders  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1027-0090","authenticated-orcid":false,"given":"Wenwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain\u2010Inspired Intelligence Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3470-5014","authenticated-orcid":false,"given":"Yiyao","family":"Yang","sequence":"additional","affiliation":[{"name":"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"},{"name":"Department of Nursing, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Cai","sequence":"additional","affiliation":[{"name":"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"},{"name":"Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration  Shanghai China"},{"name":"Shanghai Clinical Medical Center of Neurosurgery  Shanghai China"},{"name":"National Center for Neurological Disorders  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanjuan","family":"He","sequence":"additional","affiliation":[{"name":"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 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