The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

中文翻译：

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通过多对象fMRI数据的张量分量分析发现对自然刺激的隐藏脑网络响应

对自然主义刺激过程中脑网络相互作用的研究有助于更深入地了解人脑功能。功能磁共振成像（fMRI）数据的受试者间相关（ISC）分析是一种广泛使用的方法，可以测量对受试者之间一致的自然刺激的神经反应。但是，ISC中相互依存的相关值人为地增加了自由度，这阻碍了对个体差异的研究。此外，现有的ISC模型主要关注对象之间的相似性，但无法区分对不同刺激特征的神经反应。为了估计由自然主义刺激引起的大规模脑网络，我们提出了一种新颖的分析框架，以纯数据驱动的方式表征受试者之间共享的时空模式。在该框架中，对于所有参与者，从从给定分割中的所有脑区域提取的时间序列构造三阶张量，其张量的模式对应于空间分布，时间序列和参与者。然后，张量分量分析（TCA）将揭示空间和时间共享的分量，即自然刺激的诱发网络，它们的活动时间过程以及每个分量的主题负荷。为了提高TCA估计的可重复性，提出了一种新的谱聚类方法-张量谱聚类，并将其应用于评价TCA算法的稳定性。我们通过仿真和传统功能磁共振成像研究设计在运动任务期间收集的真实功能磁共振成像数据来证明所提出框架的有效性。