As a popular deep learning method, generative adversarial networks (GAN) have achieved outstanding performance in multiple classifications and segmentation tasks. However, the application of GANs to fMRI data is relatively rare. In this work, we proposed a functional network connectivity (FNC) based GAN for classifying psychotic disorders from healthy controls (HCs), in which FNC matrices were calculated by correlation of time courses derived from non-artefactual fMRI independent components (ICs). The proposed GAN model consisted of one discriminator (real FNCs) and one generator (fake FNCs), each has four fully-connected layers. The generator was trained to match the discriminator in the intermediate layers while simultaneously a new objective loss was determined for the generator to improve the whole classification performance. In a case for classifying 269 major depressive disorder (MDD) patients from 286 HCs, an average accuracy of 70.1% was achieved in 10-fold cross-validation, with at least 6% higher compared to the other 6 popular classification approaches (54.5-64.2%). In another application to discriminating 558 schizophrenia patients from 542 HCs from 7 sites, the proposed GAN model achieved 80.7% accuracy in leave-one-site-out prediction, outperforming support vector machine (SVM) and deep neural net (DNN) by 3%-6%. More importantly, we are able to identify the most contributing FNC nodes and edges with the strategy of leave-one-FNC-out recursively. To the best of our knowledge, this is the first attempt to apply the GAN model on the FNC-based classification of mental disorders. Such a framework promises wide utility and great potential in neuroimaging biomarker identification.

中文翻译：

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基于功能网络连接（FNC）的生成对抗网络（GAN）及其在精神障碍分类中的应用。

生成对抗网络（GAN）作为一种流行的深度学习方法，在多种分类和细分任务中均取得了出色的表现。但是，GAN在fMRI数据中的应用相对较少。在这项工作中，我们提出了一种基于功能网络连接（FNC）的GAN，用于对来自健康对照（HCs）的精神病进行分类，其中FNC矩阵是通过从非人工fMRI独立组件（IC）导出的时间过程的相关性来计算的。拟议的GAN模型由一个鉴别器（实际FNC）和一个生成器（伪FNC）组成，每一个都有四个完全连接的层。训练生成器以匹配中间层中的鉴别器，同时为生成器确定新的客观损失以改善整体分类性能。从286个HC对269名重度抑郁症（MDD）患者进行分类的案例中，十倍交叉验证的平均准确率达到70.1％，比其他6种流行的分类方法高出至少6％（54.5- 64.2％）。在从7个地点区分558个精神分裂症患者和542个HC的另一个应用中，拟议的GAN模型在留一站式预测，优于支持向量机（SVM）和深度神经网络（DNN）的情况下达到了30.7％的准确性。 -6％。更重要的是，我们能够通过递归留一FNC的策略来识别出最有贡献的FNC节点和边缘。据我们所知，这是将GAN模型应用于基于FNC的精神障碍分类的首次尝试。