Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments
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发表时间：
2021-06
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通讯作者：
Yu Takagi;L. Hunt;Ryu Ohata;H. Imamizu;J. Hirayama
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作者：
Yu Takagi;L. Hunt;Ryu Ohata;H. Imamizu;J. Hirayama
神经元群体之间的多区域相互作用是我们日常生活中大脑处理丰富感觉信息的基础。最近的神经科学和神经影像学研究越来越多地使用自然主义刺激和实验设计来识别大脑中的这种现实感觉计算。然而，用于具有降维的跨区域相互作用分析的现有方法（诸如降秩回归和典型相关分析）在自然主义设置中具有有限的适用性和可解释性，因为它们通常不适当地将神经相互作用“分层”成与不同类型的任务参数或刺激特征（例如，视频或音频）。在本文中，我们开发了一种新的跨区域交互分析方法，该方法使用丰富的任务或刺激参数来揭示不同神经群体共享的信息类型。所提出的神经分层共享成分分析结合了现有的降维方法与一个实用的神经网络实现的功能方差分析与潜变量，从而有效地分层连续和多模态刺激的非线性效应。我们还提出了一个简化的替代线性效应和单峰刺激的假设下。为了证明我们的方法，我们分析了两个参与者观看电影和舞蹈动作的自然视频的人类神经成像数据集。结果表明，我们的方法提供了新的见解，在大脑中的多区域的相互作用，在自然的感觉输入，这是不能被传统技术捕获。
Multi-regional interaction among neuronal populations underlies the brain's processing of rich sensory information in our daily lives. Recent neuroscience and neuroimaging studies have increasingly used naturalistic stimuli and experimental design to identify such realistic sensory computation in the brain. However, existing methods for cross-areal interaction analysis with dimensionality reduction, such as reduced-rank regression and canonical correlation analysis, have limited applicability and interpretability in naturalistic settings because they usually do not appropriately 'demix' neural interactions into those associated with different types of task parameters or stimulus features (e.g., visual or audio). In this paper, we develop a new method for cross-areal interaction analysis that uses the rich task or stimulus parameters to reveal how and what types of information are shared by different neural populations. The proposed neural demixed shared component analysis combines existing dimensionality reduction methods with a practical neural network implementation of functional analysis of variance with latent variables, thereby efficiently demixing nonlinear effects of continuous and multimodal stimuli. We also propose a simplifying alternative under the assumptions of linear effects and unimodal stimuli. To demonstrate our methods, we analyzed two human neuroimaging datasets of participants watching naturalistic videos of movies and dance movements. The results demonstrate that our methods provide new insights into multi-regional interaction in the brain during naturalistic sensory inputs, which cannot be captured by conventional techniques.