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Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI

机译:基于功能连通性的静止神经功能磁共振成像多级独立成分分析识别基底节和丘脑

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摘要

This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting-state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second-level ICA restrictively to the basal ganglia and the thalamus area to identify discrete functional subunits of those regions. As a result, the basal ganglia and the thalamus were parcelled into 31 functional subdivisions according to their temporal activity patterns. The extracted parcels showed functional network connectivity between hemispheres, between subdivisions of the basal ganglia and thalamus, and between the extracted subdivisions and cerebral functional components. Grossly, these findings correspond to cortico-striato-thalamo-cortical circuits in the brain. This study also showed the utility of multilevel ICA of resting state fMRI in brain network research.
机译:这项研究旨在确定基底神经节和丘脑的亚基,并研究健康受试者中这些解剖分离的细分与大脑皮层之间的功能连接。为此,我们介绍了静止状态功能磁共振成像(fMRI)的多级独立成分分析(ICA)。在将ICA应用于全脑灰质之后,我们将第二级ICA限制性地应用于基底神经节和丘脑区域,以识别这些区域的离散功能性亚基。结果,根据基底神经节和丘脑的暂时活动模式将其分为31个功能细分。提取的包裹显示了半球之间,基底神经节和丘脑的各个细分之间以及提取的各个细分与脑功能组件之间的功能网络连通性。总的来说,这些发现对应于大脑中的皮质-纹状体-丘脑-皮质回路。这项研究还显示了静止状态功能磁共振成像的多层次ICA在脑网络研究中的实用性。

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