A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure
A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure
复制标题
解释大脑结构群体水平变异性的生成模型方法
DOI：
10.1007/978-3-030-59722-1_25
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Dyer, Eva L.
中科院分区：
文献类型：
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作者：
Liu, Ran;Subakan, Cem;Balwani, Aishwarya H.;Whitesell, Jennifer;Harris, Julie;Koyejo, Sanmi;Dyer, Eva L.
了解神经结构在个体之间的差异对于表征疾病、学习和衰老对大脑的影响至关重要。然而，理清导致个体差异的不同因素仍然是一个突出的挑战。在本文中，我们引入了一种深度生成建模方法来寻找许多个体之间的不同变异模式。我们的方法首先使用来自 1,700 多个小鼠大脑的 25m 分辨率的自发荧光图像集合来训练变分自动编码器。然后，我们利用学到的因素并通过一种新颖的双向技术验证模型的表达能力，该技术对网络的高维输入以及其瓶颈中的低维潜在变量进行结构化扰动。我们的结果表明，通过将生成模型框架与结构化扰动相结合，可以探测生成模型的潜在空间，以深入了解深层网络中形成的大脑结构的表示。
Understanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. Our approach starts with training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25m resolution. We then tap into the learned factors and validate the model’s expressiveness, via a novel bi-directional technique that makes structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space of the generative model to provide insights into the representations of brain structure formed in deep networks.