Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis.
Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis.
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DOI：
10.1002/brb3.2103
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发表时间：
2021-05
影响因子：
3.1
通讯作者：
Liu W
中科院分区：
文献类型：
--
作者：
Zhang X;Cao X;Xue C;Zheng J;Zhang S;Huang Q;Liu W
目前帕金森病（PD）合并抑郁症（DPD）的诊断在很大程度上依赖于临床评估。然而，这种方法在检测PD伴抑郁方面可能缺乏准确性。将功能连通性和活动与临床评分相结合的放射学方法有可能实现PD和DPD的准确和鉴别诊断。在这项研究中，我们旨在采用放射组学方法提取DPD、无抑郁PD （NDPD）和健康对照（HC）之间功能连接和活动的大规模特征。我们从所有受试者中提取了5种类型的6,557个特征，包括临床特征、静息状态功能连通性（RSFC）、低频波动幅度（ALFF）、区域均匀性（ReHo）和体素镜像同伦连通性（VMHC）。在训练集的基础上，采用Lasso、随机森林和支持向量机（SVM）进行特征选择和降维，比较了不同方法在测试集中的预测性能。结果显示，DPD与HC组共选择了19个特征，NDPD与HC组共选择了34个特征，DPD与NDPD组共保留了17个特征。在测试集中，Lasso预测区分DPD与HC、NDPD与HC、DPD与NDPD的准确率分别为0.95、0.96、0.85。随机森林区分DPD与HC、NDPD与HC、DPD与NDPD的准确率分别为0.90、0.82、0.90，支持向量机区分DPD与HC、NDPD与HC、DPD与NDPD的准确率分别为1.86、0.65。通过识别异常的功能连接和活动作为潜在的生物标志物，放射组学方法促进了对DPD的更深层次的理解，并为DPD的病理生理学提供了新的见解，以支持具有高预测准确性的临床诊断。通过识别异常的功能连接和活动作为潜在的生物标志物，放射组学方法促进了对DPD的更深层次的理解，并为DPD的病理生理学提供了新的见解，以支持具有高预测准确性的临床诊断。
The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD. In this study, we aimed to employ the radiomic approach to extract large‐scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting‐state functional connectivity (RSFC), amplitude of low‐frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel‐mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared. The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy. By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomics approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.
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作者：
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