Subject identification using edge-centric functional connectivity
Subject identification using edge-centric functional connectivity
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DOI：
10.1016/j.neuroimage.2021.118204
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发表时间：
2021-06-08
期刊：
影响因子：
5.7
通讯作者：
Betzel, Richard F.
中科院分区：
文献类型：
--
作者：
Jo, Youngheun;Faskowitz, Joshua;Betzel, Richard F.
群体水平的研究没有捕捉到网络组织的个体差异，而网络组织是理解塑造行为的神经基质和在临床条件下开发干预措施的重要前提。最近的研究采用了“指纹”分析功能连接，以确定受试者的特质特征。在这里，我们开发了一个互补的方法的基础上，以边缘为中心的模型的功能连接，重点是边缘的共同波动。我们首先表明，全脑边缘功能连接（eFC）是一个强大的基板，提高了跨不同的数据集和包裹节点FC（nFC）的可识别性。接下来，我们使用k均值聚类来表征对象在不同空间尺度上的可识别性，从单个节点到功能系统和集群级别。在整个空间尺度上，我们发现，异模态的大脑区域表现出一贯更大的可识别性比单峰，感觉运动，边缘系统的地区。最后，我们表明，可识别性可以进一步提高重建eFC使用其主成分的特定子集。总之，我们的研究结果突出了边缘为中心的网络模型捕捉有意义的特定主题的功能的实用性，并为未来的调查使用边缘为中心的模型的个体差异。
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.