College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China.
Med Image Anal. 2023 Oct;89:102883. doi: 10.1016/j.media.2023.102883. Epub 2023 Jun 30.
Recent studies show that multi-modal data fusion techniques combining information from diverse sources are helpful to diagnose and predict complex brain disorders. However, most existing diagnosis methods have only simply employed a feature combination strategy for multiple imaging and genetic data, ignoring the imaging phenotypes associated with the risk gene information. To this end, we present a hypergraph-regularized multimodal learning by graph diffusion (HMGD) for joint association learning and outcome prediction. Specifically, we first present a graph diffusion method for enhancing similarity measures among subjects given from multi-modality phenotypes, which fully uses multiple input similarity graphs and integrates them into a unified graph with valuable geometric structures among different imaging phenotypes. Then, we employ the unified graph to represent the high-order similarity relationships among subjects, and enforce a hypergraph-regularized term to incorporate both inter- and cross-modality information for selecting the imaging phenotypes associated with the risk single nucleotide polymorphism (SNP). Finally, a multi-kernel support vector machine (MK-SVM) is adopted to fuse such phenotypic features selected from different modalities for the final diagnosis and prediction. The proposed approach is experimentally explored on brain imaging genetic data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Relevant results present that the proposed approach is superior to several competing algorithms, and realizes strong associations and discovers significant consistent and robust ROIs across different imaging phenotypes associated with the genetic risk biomarkers to guide disease interpretation and prediction.
最近的研究表明，结合来自不同来源的信息的多模态数据融合技术有助于诊断和预测复杂的大脑疾病。然而，大多数现有的诊断方法仅简单地采用了一种特征组合策略，用于多种成像和遗传数据，而忽略了与风险基因信息相关的成像表型。为此，我们提出了一种超图正则化多模态学习通过图扩散（HMGD），用于联合关联学习和结果预测。具体来说，我们首先提出了一种图扩散方法，用于增强来自多模态表型的主体之间的相似性度量，该方法充分利用了多个输入相似性图，并将它们集成到一个具有不同成像表型之间有价值的几何结构的统一图中。然后，我们利用统一的图来表示主体之间的高阶相似关系，并施加超图正则化项，以结合跨模态信息，选择与风险单核苷酸多态性（SNP）相关的成像表型。最后，采用多核支持向量机（MK-SVM）融合来自不同模态的不同模态选择的表型特征，用于最终的诊断和预测。我们在阿尔茨海默病神经影像学倡议（ADNI）数据集的脑影像遗传数据上进行了实验探索。相关结果表明，所提出的方法优于几种竞争算法，实现了强关联，并发现了与遗传风险生物标志物相关的不同成像表型之间一致且稳健的具有统计学意义的 ROI，以指导疾病解释和预测。
