Structure-constrained combination-based nonlinear association analysis between incomplete multimodal imaging and genetic data for biomarker detection of neurodegenerative diseases.

Abstract

Multimodal imaging data are widely applied in imaging genetic studies to identify associations between imaging and genetic data for the biomarker detection of neurodegenerative diseases (NDs). However, the incomplete multimodal imaging data and complex relationships among imaging and genetic data make it difficult to effectively analyze associations between imaging and genetic data and accurately detect disease-related biomarkers. This study proposed a novel structure-constrained combination-based nonlinear association analysis method to exploit associations between incomplete multimodal imaging and genetic data for potential biomarker detection of NDs. Two types of structure constraints were used in imaging and genetic data. First, a parallel concatenated projection method with multiple constraints was adopted to handle missing data. Modality-shared and modality-specific information could be well captured to obtain latent imaging representations. A locality preserving constraint was applied to the imaging data for retaining structure information before and after projection. A connectivity penalty was also included to capture structure associations among latent imaging representations. Second, a group-induced graph self-expression constraint was incorporated into our method to exploit strong structure correlations among inter- and intra-group of genetic data. Finally, a nonlinear kernel-based method was used to explore the complex associations between latent imaging representations and genetic data for biomarker detection. A set of simulation data and two sets of real ND data, which were obtained from Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases, were applied to assess the effectiveness of our method. High accuracy of biomarker detection was achieved. Moreover, the identification of disease-related biomarkers was confirmed in previous studies. Therefore, our method may provide a novel way to gain insights into the pathological mechanism of NDs and early prediction of these diseases.