Study On Prediction Of Neurodegenerative Diseases Based On Incomplete Datarative Diseases

Neurodegenerative diseases(NDs),such as Alzheimer’s Disease(AD)and Parkinson’s Disease(PD),are a kind of chronic progressive diseases.Moreover,early prediction of NDs can be used to improve the diagnostic and therapeutic efficiency,which is of great clinical significance.With the development of imaging and genetic technologies,increasing multimodal imaging and genetic data can provide complementary information for NDs prediction from multiple perspectives.Since NDs are progressive diseases,disease progression information inherent in the longitudinal data from follow-ups can also provide a basis for NDs prediction.Besides,deep learning methods for disease prediction based on longitudinal and multimodal data can provide assistance for clinical diagnosis and therapy.However,missing modality issues in multimodal data and missing follow-up issues in longitudinal data hinder the effective application of multimodal and longitudinal data.On the premise of retaining data with missing information,it is critical to ensure that all available data can be reasonably applied for improvement of prediction performance.In this thesis,missing data problem exist in imaging data are first explored to achieve accurate disease prediction accuracy,and then,genetic data are included to further improve performance of disease prediction.Neuroimaging data can reflect varied information of brain structure and function,which are the most commonly used data type in deep learning methods for NDs prediction.Thus,we firstly explore methods of NDs prediction based on neuroimaging data.In clinical practice,previous studies focused on using single time point data for NDs prediction,especially data at the baseline visit(BL).Therefore,NDs prediction should be achieved without the intervention of longitudinal data in practical use.In this thesis,a multi-view imputation and cross-attention network is proposed to integrate missing data imputation and disease prediction in a unified framework,and accurate prediction of AD can be achieved by using BL data.First,a multi-view imputation method combined with adversarial learning is utilized to handle various types of missing data with small errors.Second,two cross-attention blocks are introduced to explore potential associations in longitudinal and multimodal data from different views to assist in prediction.With proper training,the disease progression information in longitudinal data and the complementary information in multimodal data can be exploited to improve the prediction performance at BL.Genetic data are closely related to the occurrence and progression of NDs.Therefore,neuroimaging and genetic data can be combined,generally denoted as imaging genetics,by using a deep learning method for NDs prediction.However,previous methods firstly fused multimodal data and then correlated them with genetic data,which failed to explore common and complementary information among multimodal imaging data and construct complex relationships between imaging and genetic data.In this thesis,a deep multimodality-disentangled association analysis network is proposed to solve missing data issue and aforementioned problems.First,a multimodality-disentangled module is introduced to disentangle the imaging representations with nonlinear projection.Moreover,the imaging representations of different modalities are divided into modality-common and modality-specific parts.Second,the genetic data are mapped to the disentangled imaging representations in a non-linear manner.Meanwhile,the associations between the imaging and genetic data are built,and mask vectors are synchronously generated to assist subsequent prediction of AD and PD.Moreover,the mapped genetic representations can be used to replace the imaging representations of the missing modality to handle missing data problem.The performance of the proposed methods are evaluated on independent testing sets,and the experimental results show that the proposed methods outperform several competitive methods,which verifies the effectiveness of the proposed methods.Moreover,NDs related biomarkers are detected via the proposed methods to ensure the interpretability of the proposed methods.Therefore,the proposed methods can provide potential tools to give an insight for the pathological mechanisms of NDs.