Parmar Harshit, Nutter Brian, Mitra Sunanda, Long Rodney, Antani Sameer
Dept. of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, USA.
National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Proc IEEE Southwest Symp Image Anal Interpret. 2024 Mar;2024:1-4. doi: 10.1109/ssiai59505.2024.10508652. Epub 2024 Apr 29.
Resting state functional Magnetic Resonance Imaging (rs-fMRI) is used to obtain spontaneous activation within the human brain in the absence of specific tasks. Analysis of the rs-fMRI data required spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Commonly used parcellation schemes have a tradeoff between intra-cluster functional similarity and alignment with anatomical regions. In this article, we present a clustering scheme for rs-fMRI data that obtains spatially and functionally homogenous clusters. Results show that the proposed multistage approach can identify various brain networks. Moreover, the functional homogeneity of the clusters is shown to be better than those found with functional atlas and simple k-means clusters. The spatial homogeneity is shown to be better than Independent Component Analysis (ICA), and simple k-means clusters.
静息态功能磁共振成像（rs-fMRI）用于在没有特定任务的情况下获取人类大脑内的自发激活。rs-fMRI数据的分析需要基于潜在的时间波动对全脑进行空间和功能上的均匀分割。常用的分割方案在簇内功能相似性与与解剖区域的对齐之间存在权衡。在本文中，我们提出了一种用于rs-fMRI数据的聚类方案，该方案可获得空间和功能上均匀的簇。结果表明，所提出的多阶段方法可以识别各种脑网络。此外，这些簇的功能同质性优于使用功能图谱和简单k均值聚类所得到的结果。空间同质性优于独立成分分析（ICA）和简单k均值聚类。
