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Hierarchical organization of the functional brain identified using floating aggregation of functional signals

机译:使用功能性信号的浮动聚集来识别功能性大脑的层次结构

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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.
机译:提出了一种新方法,以基于静止状态fMRI数据的分层组织,将大脑皮层分成多个尺度的功能均质区域。皮质顶点是根据顶点间功能相似性度量通过称为浮动聚合的过程在多个空间尺度上从精细到粗糙逐渐聚类的。浮动聚合同时考虑了区域间功能的相似性和区域内功能同质性度量在生成的每个拆分层次上的一致性。此聚合过程不需要指定分割区域的数量,并且可以基于层次结构各个层次上的整体区域同质性变化来帮助确定大脑分割的适当空间尺度。在静止状态fMRI数据集上的实验结果表明,所提出的方法不仅可以获得比最新技术更好的功能均质性测量方法,而且还能在多个位置识别出大脑的分层功能组织,从而获得大脑碎片化结果。空间尺度。

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