Phlypo Ronald, Thirion Bertrand, Varoquaux Gaël
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):185-92. doi: 10.1007/978-3-319-10443-0_24.
The estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing olume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping. In this work, we propose a novel learning scheme for functional connectivity based on sparse Gaussian graphical models that aims at minimizing the bias induced by the regularization used in the estimation, by carefully separating the estimation of the model support from the coefficients. Moreover, our strategy makes it possible to include new data with a limited computational cost. We illustrate the physiological relevance of he learned prior, that can be identified as a functional connectivity atlas, based on an experiment on 46 subjects of the Human Connectome Dataset.
从功能神经影像数据估计功能连接结构是理解各种脑部疾病机制和构建相关生物标志物的重要一步。然而，此类推断必须应对低信噪比和数据稀缺的问题。不过，随着我们可获取的公开可用神经影像数据量稳步增长，改进连接组映射中涉及的估计程序成为可能。在这项工作中，我们提出了一种基于稀疏高斯图形模型的功能连接新学习方案，旨在通过仔细分离模型支持的估计和系数，最小化估计中使用正则化所引起的偏差。此外，我们的策略能够以有限的计算成本纳入新数据。基于对人类连接组数据集46名受试者的实验，我们说明了所学习先验的生理相关性，该先验可被识别为功能连接图谱。
