论文标题
在自闭症和其他任务中的大型混合现场fMRI数据集中的合奏深度学习
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
论文作者
论文摘要
MRI分类的深度学习模型面临两个反复出现的问题：它们通常受到低样本量的限制，并以其自身的复杂性（“黑匣子问题”）所抽象。在本文中，我们培训具有最大的多源功能性MRI（fMRI）连接数据集的卷积神经网络（CNN），由43,858个数据点组成。我们将此模型应用于自闭症（ASD）与通常开发的（TD）控件的横截面比较，该控制证明很难通过推论统计来表征。为了将这些发现进行上下文，我们还执行性别和任务与休息的分类。我们采用课堂平衡来建立训练套件，在合奏模型中培训了3 $ \ times $ 300修改的CNN，以将ASD vs vs vs vs vs td，Gender和Task vs vs Rest的AUROCS总体AUROC分类为fMRI连接矩阵，总体AUROC为0.6774、0.7680和0.9222。此外，我们旨在使用两种可视化方法在此上下文中解决黑匣子问题。首先，班级激活图显示了大脑的功能连接我们模型在执行分类时的重点。其次，通过分析隐藏层的最大激活，我们还能够探讨该模型如何组织一个大型且混合中心的数据集，发现它将其隐藏层的特定区域专用于处理不同的数据协变量（取决于独立变量分析），以及其他领域，以及其他领域以从不同来源中混合数据。我们的研究发现，将ASD与TD控制区分开的深度学习模型广泛地集中在时间和小脑连接上，特别关注右尾状核和中心沟。
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism (ASD) vs typically developing (TD) controls that has proved difficult to characterise with inferential statistics. To contextualise these findings, we additionally perform classifications of gender and task vs rest. Employing class-balancing to build a training set, we trained 3$\times$300 modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD vs TD, gender, and task vs rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-centre dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus.
