帕金森病
纹状体
聚类分析
计算机科学
神经影像学
人工智能
静息状态功能磁共振成像
心理学
主成分分析
医学
模式识别(心理学)
神经科学
疾病
病理
多巴胺
作者
Yu Li,Aiping Liu,Taomian Mi,Runyu Yang,Piu Chan,Martin J. McKeown,Xun Chen,Feng Wu
标识
DOI:10.1109/jbhi.2021.3083879
摘要
Recent fMRI connectivity-based parcellation (CBP) methods have been developed to obtain homogeneous and functionally coherent brain parcels. However, most of these studies utilize traditional clustering methods that neglect hidden nonlinear features. To enhance parcellation performance, here we propose a deep embedded connectivity-based parcellation (DECBP) framework and apply it to determine functional subdivisions of the striatum in public resting state fMRI data sets. This framework integrates fMRI connectivity features into deep embedded clustering (DEC), a deep neural network based on a stacked autoencoder. Compared to three prevalent clustering methods and their combinations with principal component analysis (PCA), the DECBP exhibited a significantly higher similarity between scans, individuals, and groups, indicating enhanced reproducibility. The generated reliable parcellations were also largely consistent with other public atlases. We further explored the functional subunits in the striatum in a data set from 23 Parkinson's disease (PD) subjects and 27 age-matched healthy controls (HC). All putaminal subregions of PD demonstrated lower interhemispheric connectivity than those of HC, which might reflect imbalance in the pathological progression of PD. Such hypo-connectivity was also observed between putaminal subregions and other brain regions, reflecting neuroimaging manifestations of the altered cortico-striato-thalamo-cortical circuit. These observed weaker couplings were associated with PD severity and duration. Our results support the utilization of the DECBP framework and suggest that abnormal connectivity in putaminal subregions may be a potential indicator of PD.
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