多视图低秩子空间的图结构学习多站点自闭症诊断方法

Graph Structure Learning Method for Multi-site Autism Diagnosis Based on Multi-view Low-Rank Subspace

自闭症谱系障碍(Autism spectrum disorder,ASD)是一种最常见且具有遗传性的神经发育障碍疾病,具有社交沟通缺陷等多种症状。准确识别生物标记物对ASD的早期干预起到至关重要的作用。现有大量方法利用了多站点影像数据来增加样本量,从而提高了方法诊断的准确性,但是多站点间由于成像装置、成像参数和数据处理流程存在的差异造成的数据异质性影响往往被忽略。为了解决上述问题,本文提出了一种基于多视图低秩子空间的图结构学习多站点自闭症诊断方法(MVLL-GSL)。首先构建具有不同拓扑结构信息的多视图脑网络,然后分别将视图中不同类的样本分别投影到各自的低秩子空间,从而降低数据异质性的影响,最后使用图结构学习和多任务图嵌入学习相结合,并融入先验子网络和多视图一致性正则化约束,旨在从多视图低秩子空间中获得更具判别性和一致性的特征。使用自闭症公开数据库ABIDE(Autism brain imaging data exchange)对提出的方法进行验证。实验结果表明,MVLL-GSL方法提高了ASD的诊断性能,并解释了不同先验子网络与ASD发病机制的关联性。

Autism spectrum disorder(ASD)stands as one of the most prevalent and genetically inherited neurodevelopmental disorders,characterized by a multitude of clinical symptoms,notably featuring social communication deficits.Effective identification of biomarkers holds paramount significance in facilitating early interventions for ASD.Many current methods leverage multi-site imaging data to augment sample size,thereby enhancing diagnostic accuracy.However,the heterogeneity of data across multiple sites,resulting from variations in imaging devices,imaging parameters,and data processing workflows,is frequently overlooked.To overcome the above problem,this paper proposes a graph structure learning method for multi-site autism diagnosis based on multi-view low-rank subspace(MVLL-GSL).Firstly,the multiple views of brain network are constructed for each sample,encompassing diverse topological information.Subsequently,samples from different classes are projected into their respective low-rank subspaces to mitigate the impact of data heterogeneity.Finally,the integration of graph structure learning with multi-task graph embedding learning,incorporating prior subnetworks and multi-view consistency regularization constraints,aims to extract more discriminative and coherent features from multi-view lowrank subspaces.The autism public ABIDE(Autism brain imaging data exchange)database is used to verify the proposed method.Experimental results show that the MVLL-GSL method improves the performance of ASD disgnosis and explains the association of different prior sub-networks with ASD pathogenesis.