A clinically interpretable model for Parkinson's disease: insights from complementary MRI modalities.
4 affiliations
- 1Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
- 2Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
- 3Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
- 4Center for Brain Imaging Science and Technology, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University School of Physics, Hangzhou 310027, China.
Abstract
Accurate identification of Parkinson's disease may remain challenging in certain clinical scenarios, highlighting the need for a clinical decision-support model based on complementary MRI modalities. The case-control study included 462 participants, split into training [247 Parkinson's disease, 122 normal controls (NC)] and testing (60 Parkinson's disease, 33 NC) cohorts to construct and validate a clinical decision-support model. We employed least absolute shrinkage and selection operator and logistic regression analysis to develop the model utilizing quantitative susceptibility mapping, arterial spin labelling and T1-weighted images. The model was assessed for calibration, discrimination and clinical utility, with individual predicted scores correlated to Parkinson's disease clinical symptoms. The model's performance was also evaluated independently in multiple system atrophy, progressive supranuclear palsy and essential tremor, with DeLong test comparisons. The model was trained in the training cohort (Parkinson's disease: 61.39 ± 8.06 years old, 134 males; NC: 61.29 ± 7.56 years old, 54 males) with an area under the ROC curve (AUC) of 0.842 [95% confidence interval (CI) 0.798-0.883] and 0.771 (95% CI 0.657-0.870) in the testing cohort, whose clinical utility was confirmed. The predicted scores were positively correlated with Unified Parkinson's Disease Rating Scale II (P = 0.034) and III (P = 0.015), while negatively correlated with the Purdue Pegboard total (P = 0.010) and assembly scores (P = 0.007). When discriminating each disease group from NC, the model's performance to identify multiple system atrophy and progressive supranuclear palsy with AUC values of 0.881 and 0.895 was superior than essential tremor with an AUC of 0.709 before false discovery rate correction (multiple system atrophy-NC versus essential tremor-NC: P = 0.041; progressive supranuclear palsy-NC versus essential tremor-NC: P = 0.010). The model based on complementary MRI modalities was an objective clinical decision-support measure for Parkinson's disease with potential applications in other parkinsonisms (multiple system atrophy, progressive supranuclear palsy).
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BibTeX
@article{zheng2026clinically,
title = {{A clinically interpretable model for Parkinson's disease: insights from complementary MRI modalities.}},
author = {Qianshi Zheng and Jiaqi Wen and Weijin Yuan and Jianmei Qin and Xiaojie Duanmu and Haoting Wu and Zihao Zhu and Tao Guo and Chenqing Wu and Cheng Zhou and Jingjing Wu and Jingwen Chen and Yuelin Fang and Bingting Zhu and Yaping Yan and Jun Tian and Baorong Zhang and Hongjian He and Minming Zhang and Xiaojun Guan and Xiaojun Xu},
journal = {Brain communications},
year = {2026},
month = oct,
doi = {10.1093/braincomms/fcag383},
url = {https://europepmc.org/article/MED/42909910},
note = {CC BY},
}
RIS
TY - JOUR
TI - A clinically interpretable model for Parkinson's disease: insights from complementary MRI modalities.
AU - Qianshi Zheng
AU - Jiaqi Wen
AU - Weijin Yuan
AU - Jianmei Qin
AU - Xiaojie Duanmu
AU - Haoting Wu
AU - Zihao Zhu
AU - Tao Guo
AU - Chenqing Wu
AU - Cheng Zhou
AU - Jingjing Wu
AU - Jingwen Chen
AU - Yuelin Fang
AU - Bingting Zhu
AU - Yaping Yan
AU - Jun Tian
AU - Baorong Zhang
AU - Hongjian He
AU - Minming Zhang
AU - Xiaojun Guan
AU - Xiaojun Xu
PY - 2026
DA - 2026/10/13
JO - Brain communications
DO - 10.1093/braincomms/fcag383
UR - https://europepmc.org/article/MED/42909910
AB - Accurate identification of Parkinson's disease may remain challenging in certain clinical scenarios, highlighting the need for a clinical decision-support model based on complementary MRI modalities. The case-control study included 462 participants, split into training [247 Parkinson's disease, 122 normal controls (NC)] and testing (60 Parkinson's disease, 33 NC) cohorts to construct and validate a clinical decision-support model. We employed least absolute shrinkage and selection operator and logistic regression analysis to develop the model utilizing quantitative susceptibility mapping, arterial spin labelling and T1-weighted images. The model was assessed for calibration, discrimination and clinical utility, with individual predicted scores correlated to Parkinson's disease clinical symptoms. The model's performance was also evaluated independently in multiple system atrophy, progressive supranuclear palsy and essential tremor, with DeLong test comparisons. The model was trained in the training cohort (Parkinson's disease: 61.39 ± 8.06 years old, 134 males; NC: 61.29 ± 7.56 years old, 54 males) with an area under the ROC curve (AUC) of 0.842 [95% confidence interval (CI) 0.798-0.883] and 0.771 (95% CI 0.657-0.870) in the testing cohort, whose clinical utility was confirmed. The predicted scores were positively correlated with Unified Parkinson's Disease Rating Scale II (P = 0.034) and III (P = 0.015), while negatively correlated with the Purdue Pegboard total (P = 0.010) and assembly scores (P = 0.007). When discriminating each disease group from NC, the model's performance to identify multiple system atrophy and progressive supranuclear palsy with AUC values of 0.881 and 0.895 was superior than essential tremor with an AUC of 0.709 before false discovery rate correction (multiple system atrophy-NC versus essential tremor-NC: P = 0.041; progressive supranuclear palsy-NC versus essential tremor-NC: P = 0.010). The model based on complementary MRI modalities was an objective clinical decision-support measure for Parkinson's disease with potential applications in other parkinsonisms (multiple system atrophy, progressive supranuclear palsy).
SN - 2632-1297
ER -