Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
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发表时间：
2021
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通讯作者：
Amirreza Farnoosh;S. Ostadabbas
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作者：
Amirreza Farnoosh;S. Ostadabbas
因子分析方法已广泛应用于神经影像学，将高维成像数据转换为低维，理想的可解释表示。然而，大多数这些方法忽略了神经过程的高度非线性和复杂的时间动态时，分解其成像数据。在本文中，我们提出了深度马尔可夫因子分析（DMFA），这是一种生成模型，它在低维时间嵌入链中使用马尔可夫属性以及空间归纳假设，所有这些都通过神经网络相关，以捕获功能磁共振成像（fMRI）数据中的时间动态，并分别处理它们的高空间维度。通过离散潜增强，DMFA能够将fMRI数据聚类在其低维时间嵌入中，考虑到受试者和认知状态的可变性，因此，能够验证各种fMRI驱动的神经科学假设。在合成和真实fMRI数据上的实验结果表明，DMFA在揭示这些高维成像数据中的可解释簇和捕获非线性时间依赖性方面的能力。
Factor analysis methods have been widely used in neuroimaging to transfer high dimensional imaging data into low dimensional, ideally interpretable representations. However, most of these methods overlook the highly nonlinear and complex temporal dynamics of neural processes when factorizing their imaging data. In this paper, we present deep Markov factor analysis (DMFA), a generative model that employs Markov property in a chain of low dimensional temporal embeddings together with spatial inductive assumptions, all related through neural networks, to capture temporal dynamics in functional magnetic resonance imaging (fMRI) data, and tackle their high spatial dimensionality, respectively. Augmented with a discrete latent, DMFA is able to cluster fMRI data in its low dimensional temporal embedding with regard to subject and cognitive state variability, therefore, enables validation of a variety of fMRI-driven neuroscientific hypotheses. Experimental results on both synthetic and real fMRI data demonstrate the capacity of DMFA in revealing interpretable clusters and capturing nonlinear temporal dependencies in these high dimensional imaging data.