Exploring alterations in white matter fiber tracts of Parkinson's disease patients via automated fiber quantification method
10.3969/j.issn.1002-1671.2025.10.003
- VernacularTitle:基于自动纤维定量方法探讨帕金森病患者脑白质纤维束的改变
- Author:
Ru TONG
1
;
Sai WANG
;
Hongze LÜ
;
Kun QIN
;
Yuxi WANG
;
Pengyu ZHU
;
Wen CHEN
Author Information
1. 湖北医药学院附属太和医院医学影像中心,湖北 十堰 442000
- Publication Type:Journal Article
- Keywords:
Parkinson's disease;
automated fiber quantification;
alba
- From:
Journal of Practical Radiology
2025;41(10):1604-1608
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.
