BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; High Institute of Applied Sciences and Technologies of Sousse (ISSATSO), University of Sousse, Tunisia.
BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
Med Image Anal. 2021 Jan;67:101843. doi: 10.1016/j.media.2020.101843. Epub 2020 Oct 13.
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) relationships across ROIs, overlooking the complex high-order interconnectedness of the brain as a network. A few recent works on neurological disorders addressed this limitation by introducing the brain multiplex which is composed of a source network intra-layer, a target intra-layer, and a convolutional interlayer capturing the high-level relationship between both intra-layers. However, brain multiplexes are built from at least two different brain networks hindering their application to connectomic datasets with single brain networks (e.g., functional networks). To fill this gap, we propose Adversarial Brain Multiplex Translator (ABMT), the first work for predicting brain multiplexes from a source network using geometric adversarial learning to investigate gender differences in the human brain. Our framework comprises: (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections, (ii) a conditional discriminator which distinguishes between predicted and ground truth target intra-layers, and finally (iii) a multi-layer perceptron (MLP) classifier which supervises the prediction of the target multiplex using the subject class label (e.g., gender). Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and unprecedentedly identify both low and high-order gender-specific brain multiplex connections. Our ABMT source code is available on GitHub at https://github.com/basiralab/ABMT.
脑连接网络，源自磁共振成像（MRI），可无创地量化两个感兴趣脑区（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，关于脑连接性别差异的研究仅限于调查 ROI 之间的成对（即低阶）关系，而忽略了大脑作为网络的复杂高阶相互连接。一些关于神经障碍的最新研究通过引入脑复用解决了这一限制，脑复用由源网络内层、目标内层和卷积内层组成，可捕获两个内层之间的高级关系。然而，脑复用是由至少两个不同的脑网络构建的，这阻碍了它们在具有单个脑网络（例如功能网络）的连接组学数据集中的应用。为了填补这一空白，我们提出了对抗性脑复用翻译器（ABMT），这是第一个使用几何对抗学习从源网络预测脑复用的工作，以研究人类大脑中的性别差异。我们的框架包括：（i）几何源到目标网络翻译器，模拟具有跳过连接的 U-Net 架构，（ii）条件鉴别器，用于区分预测的和真实的目标内层，以及最后（iii）多层感知器（MLP）分类器，使用主体类别标签（例如性别）监督目标复用的预测。我们在大型数据集上的实验表明，与源网络相比，预测的复用显著提高了性别分类准确性，并前所未有地识别出了低阶和高阶特定于性别的脑复用连接。我们的 ABMT 源代码可在 GitHub 上获得：https://github.com/basiralab/ABMT。
