Identifying Network Correlates of Brain States Using Tensor Decompositions of Whole-Brain Dynamic Functional Connectivity

Nora Leonardi, D. Ville
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引用次数: 26

Abstract

Network organization is fundamental to the human brain and alterations of this organization by brain states and neurological diseases is an active field of research. Many studies investigate functional networks by considering temporal correlations between the fMRI signal of distinct brain regions over long periods of time. Here, we propose to use the higher-order singular value decomposition (HOSVD), a tensor decomposition, to extract whole-brain network signatures from group-level dynamic functional connectivity data. HOSVD is a data-driven multivariate method that fits the data to a 3-way model, i.e., connectivity x time x subjects. We apply the proposed method to fMRI data with alternating epochs of resting and watching of movie excerpts, where we captured dynamic functional connectivity by sliding window correlations. By regressing the connectivity maps' time courses with the experimental paradigm, we find a characteristic connectivity pattern for the difference between the brain states. Using leave-one-subject-out cross-validation, we then show that the combination of connectivity patterns generalizes to unseen subjects as it predicts the paradigm. The proposed technique can be used as feature extraction for connectivity-based decoding and holds promise for the study of dynamic brain networks.
利用全脑动态功能连接的张量分解识别脑状态的网络关联
网络组织是人类大脑的基础,大脑状态和神经系统疾病对这种组织的改变是一个活跃的研究领域。许多研究通过考虑长时间内不同脑区fMRI信号之间的时间相关性来研究功能网络。本文提出利用张量分解中的高阶奇异值分解(HOSVD)从群级动态功能连接数据中提取全脑网络特征。HOSVD是一种数据驱动的多变量方法,它将数据拟合到三向模型中,即连通性x时间x受试者。我们将提出的方法应用于休息和观看电影片段交替的fMRI数据,其中我们通过滑动窗口相关性捕获动态功能连接。通过实验范式对连接图的时间轨迹进行回归,我们发现了脑状态差异的特征连接模式。使用留一个主体的交叉验证,我们然后显示连接模式的组合推广到看不见的主题,因为它预测范式。该技术可用于基于连接的解码的特征提取,并为动态脑网络的研究提供了前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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