Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network.
Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network.
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DOI：
10.1038/s41598-022-10942-1
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发表时间：
2022-04-27
影响因子：
4.6
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中科院分区：
文献类型：
--
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动作观察网络（AON）是参与执行和观察给定动作的大脑区域网络。人们主要使用脑电图 (EEG) 和功能磁共振成像 (fMRI) 对人类 AON 进行研究，但由于缺乏生态上有效的神经影像学测量，动作观察和动作执行的共享神经相关性仍不清楚。在这项研究中，我们使用并发脑电图和功能性近红外光谱 (fNIRS) 在实时观察和执行范例中检查 AON。我们开发了结构化稀疏多集典型相关分析 (ssmCCA) 来执行 EEG-fNIRS 数据融合。 MCCA 是 CCA 对两组以上变量的推广，常用于医学多模态数据融合。然而，mCCA 在解释结果时存在多重共线性、高维度、单峰特征选择和空间信息丢失的问题。参与者数量有限（样本量小）是 mCCA 的另一个问题，这会导致模型过度拟合。在这里，我们对 mCCA 采用图引导（结构化）融合最小绝对收缩和选择算子（LASSO）惩罚来进行特征选择，将结构信息纳入变量（即大脑区域）中。受益于大脑血流动力学和电生理反应的同时记录，所提出的 ssmCCA 找到了每种模态的线性变换，使得它们的预测之间的相关性最大化。我们对 21 名右手参与者的分析表明，左下顶叶区域在动作执行和动作观察过程中都很活跃。我们的研究结果为 AON 的神经相关性提供了新的见解，这些相关性比每个单独的 EEG 或 fNIRS 分析的结果更加微调，并验证了使用 ssmCCA 来融合 EEG 和 fNIRS 数据集。
The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of action observation and action execution are still unclear due to lack of ecologically valid neuroimaging measures. In this study, we used concurrent EEG and functional Near Infrared Spectroscopy (fNIRS) to examine the AON during a live-action observation and execution paradigm. We developed structured sparse multiset canonical correlation analysis (ssmCCA) to perform EEG-fNIRS data fusion. MCCA is a generalization of CCA to more than two sets of variables and is commonly used in medical multimodal data fusion. However, mCCA suffers from multi-collinearity, high dimensionality, unimodal feature selection, and loss of spatial information in interpreting the results. A limited number of participants (small sample size) is another problem in mCCA, which leads to overfitted models. Here, we adopted graph-guided (structured) fused least absolute shrinkage and selection operator (LASSO) penalty to mCCA to conduct feature selection, incorporating structural information amongst the variables (i.e., brain regions). Benefitting from concurrent recordings of brain hemodynamic and electrophysiological responses, the proposed ssmCCA finds linear transforms of each modality such that the correlation between their projections is maximized. Our analysis of 21 right-handed participants indicated that the left inferior parietal region was active during both action execution and action observation. Our findings provide new insights into the neural correlates of AON which are more fine-tuned than the results from each individual EEG or fNIRS analysis and validate the use of ssmCCA to fuse EEG and fNIRS datasets.
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影响因子：
2.9
作者：
Bhat AN;Hoffman MD;Trost SL;Culotta ML;Eilbott J;Tsuzuki D;Pelphrey KA
通讯作者：
Pelphrey KA
影响因子：
5.7
作者：
Debnath R;Salo VC;Buzzell GA;Yoo KH;Fox NA
通讯作者：
Fox NA
影响因子：
2.1
作者：
Chen, Jun;Bushman, Frederic D.;Li, Hongzhe
通讯作者：
Li, Hongzhe
影响因子：
3.7
作者：
Debnath, Ranjan;Buzzell, George A.;Fox, Nathan A.
通讯作者：
Fox, Nathan A.
影响因子：
5.7
作者：
Caspers S;Zilles K;Laird AR;Eickhoff SB
通讯作者：
Eickhoff SB