利用功能信号的浮动聚合识别功能性大脑的等级组织

Hongming Li, Yong Fan
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引用次数: 3

摘要

提出了一种基于静息状态fMRI数据的分层组织方法,将大脑皮层在多个尺度上划分为功能均匀的区域。根据顶点间的功能相似性度量,通过浮动聚集的方法,在多个空间尺度上由细到粗逐步聚类皮质顶点。浮动聚集既考虑了区域间的功能相似性,也考虑了区域内的功能同质性措施在每一级的一致性。这种聚集过程不需要指定分割的区域数量,并且可以根据整个区域在层次结构中的均匀性变化来帮助确定大脑分割的适当空间尺度。在静息状态fMRI数据集上的实验结果表明,该方法不仅可以获得比现有技术更好的功能同质性测量的脑包裹结果,而且可以在多个空间尺度上识别大脑的分层功能组织。
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Hierarchical organization of the functional brain identified using floating aggregation of functional signals
A novel method is proposed to parcellate the cerebral cortex into functionally homogenous regions at multiple scales with a hierarchical organization based on resting-state fMRI data. The cortical vertices are clustered according to inter-vertex functional similarity measures progressively at multiple spatial scales from fine to coarse by a procedure referred to as floating aggregation. The floating aggregation takes into consideration both the inter-regional functional similarity and the consistency of intra-regional functional homogeneity measures at every level of the resulting parcellation hierarchy. This aggregation procedure does not require to specify the number of regions for the parcellation, and could help identify proper spatial scales for the brain parcellation based on the overall region homogeneity changes across levels of the hierarchy. The experimental results on a resting-state fMRI dataset have demonstrated that the proposed method could not only obtain brain parcellation results with better functional homogeneity measures than state-of-the-art techniques, but also identify a hierarchical functional organization of the brain at multiple spatial scales.
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