Zhang Li, Pang Mengqian, Liu Xiaoyun, Hao Xiaoke, Wang Meiling, Xie Chunming, Zhang Zhijun, Yuan Yonggui, Zhang Daoqiang
College of Computer Science and Technology, Nanjing Forestry University, Nanjing, China.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Front Psychiatry. 2023 Mar 2;14:1139451. doi: 10.3389/fpsyt.2023.1139451. eCollection 2023.
Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.
抑郁症（重度抑郁症，MDD）是一种常见且严重的医学疾病。据估计，全球有5%的成年人患有抑郁症。近年来，影像遗传学受到越来越多的关注，并成为发现基因变异（如单核苷酸多态性，SNPs）与多模态脑成像数据之间关联的有力策略。然而，临床医生进行的大多数现有MDD影像遗传学研究通常采用简单的统计分析方法，且仅考虑单模态脑成像，这在对MDD机制理解的深入发现方面存在局限性。因此，必须利用强大而高效的技术来充分探索基因变异与多模态脑成像之间的关联。在本研究中，我们开发了一种新颖的影像遗传学关联框架，以挖掘遗传风险变异与多阶段诊断状态之间的多模态表型网络。具体而言，多模态表型网络由来自结构磁共振成像（sMRI）和静息态功能磁共振成像（rs-fMRI）的体素节点特征和连接边特征组成。此后，采用基于多任务学习策略的关联模型来充分探索MDD风险单核苷酸多态性与多模态表型网络之间的关系。引入多阶段诊断状态以进一步挖掘不同受试者多种模态之间的关系。我们从两家医院收集了多模态脑成像数据和基因型数据。实验结果不仅证明了我们提出的方法的有效性，还从多模态表型网络的节点和边特征中识别出一些一致且稳定的感兴趣脑区（ROIs）生物标志物。此外，还发现了四个与MDD相关的新的潜在风险单核苷酸多态性。
