Hua Lin, Huang Canpeng, Zeng Xinglin, Gao Fei, Yuan Zhen
Faculty of Health Sciences, University of Macau, Macau SAR 999078, PR China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, PR China.
Faculty of Health Sciences, University of Macau, Macau SAR 999078, PR China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, PR China; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, United States.
Neuroimage. 2025 Feb 1;306:121012. doi: 10.1016/j.neuroimage.2025.121012. Epub 2025 Jan 8.
Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.
帕金森病（PD）前驱期个体表现出显著的异质性，可根据临床症状、病理机制和脑网络模式分为不同亚型。然而，关于前驱期PD的有效分型研究较少，这阻碍了PD的早期诊断。因此，我们旨在利用基于脑影像组学的网络识别前驱期PD的亚型，并研究与各亚型临床表现相关的独特模式。为正常对照（NC；N = 110）、前驱期PD患者（N = 262）和PD患者（N = 108）构建了基于个体脑影像组学的网络。采用基于影像组学网络的数据驱动聚类方法，将前驱期PD患者聚类为高/低风险亚型。然后，评估这两个亚型之间临床表现、解剖结构改变和基因表达的分离模式。聚类结果表明，一个前驱期PD亚型与NCs模式（N-P；N = 159）非常相似，而另一个与PD模式（P-P；N = 103）相似。在多个临床测量、形态学变化的神经影像学以及突触传递的基因富集方面，各亚型之间存在显著差异。基于脑连接组识别前驱期PD亚型并全面了解此阶段的异质性，可为早期准确的PD诊断和有效的神经保护干预提供依据。
