论文标题
深度学习中图的随机编码允许对英国生物库的静止状态和任务功能性脑网络中的性别分类进行复杂的分析
Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank
论文作者
论文摘要
与卷积神经网络（CNN）的全脑功能连通性数据分类MRI数据已显示出希望，但是这些模型的复杂性阻碍了人们对大脑活动的哪些方面有助于分类的理解。尽管已经开发出可视化技术来解释CNN，但编码抽象输入数据的方法固有的偏差以及深度学习模型的自然差异，从而降低了这些技术的准确性。我们在CNN集合中引入了一种随机编码方法，以通过性别对功能连接进行分类。我们使用两种可视化技术将方法应用于来自英国生物库的休息状态和任务数据，以测量与任务和休息状态有关的三个大脑网络及其相互作用的显着性。为了回归诸如头部运动，年龄和颅内体积之类的混杂因素，我们引入了多元平衡算法，以确保数据中类之间此类协变量的平等分布。我们达到了最终的AUROC为0.8459。我们发现，静止状态数据比任务数据更准确地分类，而内部显着性网络在静止状态数据的分类以及与中央执行网络的连接中扮演着三个网络的最重要作用。
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by gender. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.
