Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire CB2 0SZ, UK.
Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire CB2 0SZ, UK.
Neuroimage. 2021 Nov 1;241:118409. doi: 10.1016/j.neuroimage.2021.118409. Epub 2021 Jul 20.
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 sex. 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.
使用卷积神经网络 (CNN) 对全脑功能连接 MRI 数据进行分类已经显示出了前景，但这些模型的复杂性阻碍了我们理解哪些脑活动方面有助于分类。虽然已经开发了用于解释 CNN 的可视化技术，但由于对抽象输入数据进行编码的方法固有的偏差，以及深度学习模型的自然变化，这些技术的准确性受到了影响。我们在一组 CNN 中引入了随机编码方法，通过性别对功能连接体进行分类。我们将我们的方法应用于 UK BioBank 的静息态和任务数据，使用两种可视化技术来测量三个涉及任务和静息状态的脑网络及其相互作用的显着性。为了回归混杂因素，如头部运动、年龄和颅内体积，我们引入了一种多元平衡算法，以确保我们数据中这些协变量在类之间的分布相等。我们最终达到了 0.8459 的 AUROC。我们发现，与任务数据相比，静息态数据的分类更准确，三个网络中，内部显着性网络在静息态数据的分类中起着最重要的作用，而与中央执行网络的连接在任务数据中起着最重要的作用。
