MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology Pub Date : 2025-02-27 DOI:10.1016/j.slast.2025.100260

Dacong Zhao , Jiang Guo , Guanghua Lu , Rui Jiang , Chao Tian , Xu Liang

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引用次数: 0

Abstract

Background

The underlying mechanism of Parkinson's disease (PD) is associated with the neurodegeneration of the dopaminergic neurons, and the cerebellum plays a significant role together in non-motor and motor functions in PD progression. Morphological changes in the cerebellum can greatly impact patients' clinical symptoms, especially motor control symptoms, and may also help distinguish patients from healthy subjects. This study aimed to explore the potential of cerebellar gray matter volume, related to motor control function, as a neuroimaging biomarker to classify patients with PD and healthy controls (HC) by using voxel-based morphometric (VBM) measurements and support vector machine (SVM) methods based on independent component analysis (ICA).

Methods

Cerebellar gray matter volume was measured using VBM in patients with PD (n = 27) and HC (n = 16) from the Neurocon dataset. ICA analysis was performed on the gray matter volume of all subregions, resulting in 7 independent components. These independent components were then utilized for correlation analysis with clinical scales and trained as input features for the SVM model. PD patients (n = 20) and HC (n = 20) from the TaoWu dataset were used as test data to validate our SVM model.

Results

Among patients with PD, 3 out of the 7 independent components showed a significant correlation with clinical scales. The SVM model achieved an accuracy of 86 % in classifying PD patients and HC, with a sensitivity of 72.2 %, specificity of 88 %, and F1 Score of 76.5 %. The accuracy of the SVM model verification analysis using the TaoWu dataset was 70 %, with a sensitivity of 62.5 %, a specificity of 100 %, and the F1 Score was 76.9 %.

Conclusions

The results suggest that abnormal cerebellar gray matter volume, which is highly correlated with motor control function in Parkinson's patients, may serve as a valuable neuroimaging biomarker capable of distinguishing Parkinson's patients from healthy individuals. We observed that the combination of the ICA method and the SVM method produced an improved classification model. This model may function as an early warning tool that enables clinicians to conduct preliminary identification and intervention for patients with PD.

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基于mri的小脑灰质体积鉴别帕金森病。

背景：帕金森病（PD）的潜在机制与多巴胺能神经元的神经退行性变有关，小脑在PD进展中的非运动功能和运动功能共同起着重要作用。小脑的形态学改变对患者的临床症状，尤其是运动控制症状有很大影响，也可能有助于区分患者与健康者。本研究旨在通过基于体素的形态测量（VBM）和基于独立分量分析（ICA）的支持向量机（SVM）方法，探讨与运动控制功能相关的小脑灰质体积作为PD和健康对照（HC）患者分类的神经影像学生物标志物的潜力。方法：使用VBM测量来自Neurocon数据集的PD （n = 27）和HC （n = 16）患者的小脑灰质体积。对所有分区的灰质体积进行ICA分析，得到7个独立分量。然后利用这些独立分量与临床量表进行相关性分析，并作为SVM模型的输入特征进行训练。以TaoWu数据集中的PD患者（n = 20）和HC患者（n = 20）作为检验数据，对我们的SVM模型进行验证。结果：在PD患者中，7个独立分量中有3个与临床量表有显著相关性。SVM模型对PD和HC的分类准确率为86%，敏感性为72.2%，特异性为88%，F1评分为76.5%。TaoWu数据集支持向量机模型验证分析准确率为70%，灵敏度为62.5%，特异性为100%，F1评分为76.9%。结论：小脑灰质体积异常与帕金森病患者的运动控制功能高度相关，可作为区分帕金森病患者与健康人的一种有价值的神经影像学生物标志物。我们观察到ICA方法和SVM方法的结合产生了一个改进的分类模型。该模型可以作为一种早期预警工具，使临床医生能够对PD患者进行初步的识别和干预。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SLAS Technology Computer Science-Computer Science Applications

CiteScore

6.30

自引率

7.40%

发文量

审稿时长

106 days

期刊介绍： SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.