Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order) relationship ROIs, overlooking the complex high-order interconnectedness of the brain as a network. To address this limitation, brain multiplexes have been introduced to model the relationship between at least two different brain networks. However, this inhibits their application to datasets with single brain networks such as functional networks. To fill this gap, we propose the first work on predicting brain multiplexes from a source network to investigate gender differences. Recently, generative adversarial networks (GANs) submerged the field of medical data synthesis. However, although conventional GANs work well on images, they cannot handle brain networks due to their non-Euclidean topological structure. Differently, in this paper, we tap into the nascent field of geometric-GANs (G-GAN) to design a deep multiplex prediction architecture comprising (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections and (ii) a conditional discriminator which classifies predicted target intra-layers by conditioning on the multiplex source intra-layers. Such architecture simultaneously learns the latent source network representation and the deep non-linear mapping from the source to target multiplex intra-layers. Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and identifies both low and high-order gender-specific multiplex connections.

中文翻译：

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来自单个网络的对抗性脑多路复用预测，用于高阶连接性别特定脑图

源自磁共振成像 (MRI) 的大脑连接网络以非侵入方式量化两个大脑感兴趣区域 (ROI) 之间的功能、结构和形态关系，并深入了解与性别相关的连接差异。然而，据我们所知，关于大脑连接性性别差异的研究仅限于研究成对（即低阶）关系的投资回报率，忽视了大脑作为网络的复杂高阶互连性。为了解决这个限制，已经引入了大脑多路复用来模拟至少两个不同大脑网络之间的关系。然而，这阻碍了它们在具有单脑网络（例如功能网络）的数据集上的应用。为了填补这个空白，我们提出了第一项关于从源网络预测大脑多路复用以调查性别差异的工作。最近，生成对抗网络（GAN）淹没了医学数据合成领域。然而，尽管传统的 GAN 在图像上运行良好，但由于其非欧式拓扑结构，它们无法处理大脑网络。不同的是，在本文中，我们利用几何 GAN (G-GAN) 的新兴领域来设计一个深度多重预测架构，包括 (i) 一个几何源到目标网络转换器，模仿具有跳跃连接的 U-Net 架构和（ ii) 一个条件鉴别器，它通过调节多重源内层来对预测的目标内层进行分类。这种架构同时学习潜在的源网络表示和从源到目标复用内层的深度非线性映射。我们在大型数据集上的实验表明，与源网络相比，预测的多路复用显着提高了性别分类的准确性，并识别了低阶和高阶的性别特异性多重连接。