MOTIVATION: Neurodegenerative disorders influence millions of people worldwide, and uncovering the pathogenesis is of urgent need. Many efforts have been made to detect or predict neurodegenerative disorders, while exploring the pathogenesis has been ignored from a systemic perspective. RESULTS: To handle this issue, we propose a novel and powerful method, referred to as Pathogenesis-aware Mutual-Assistance Classification and Regression Optimization (Pa-MACRO). First, Pa-MACRO incorporates a mutual-assistance bidirectional mapping technique with a joint-embedding fine-grained interpretability module. This can extract the intrinsic factors and their interactions of multimodal pathogenesis. Second, our method can simultaneously classify an at-risk individual and predict the severity triggered by neurodegenerative disorders. Furthermore, to address the small sample size issue and the high-dimensional issue, we meticulously incorporate a semi-supervised cooperative learning method to integrate unlabeled data and extend it to a chromosome-wide setting in the spirit of divide-and-conquer. The Alzheimer's Disease Neuroimaging Initiative (ADNI) database was used to evaluate Pa-MACRO. Without bells and whistles, Pa-MACRO establishes new state-of-the-art results in various settings while maintaining superior interpretability, verifying its power and versatility in revealing the pathogenesis of neurodegenerative disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/ZJ-Techie/Pa-MACRO.

Multiomics co-learning is a powerful analytical paradigm that has benefited biomedical studies substantially. However, due to the diverse information and complex relationships of multiomics data, naive multi-view learning methods usually run into spurious correlations and biased signatures irrelevant to the diseases of interest. Therefore, the learned representations and cross-omics associations cannot translate into clinical knowledge for disease prediction. This issue becomes particularly severe when clinical data are limited and scarce. To handle this issue, we propose a novel and powerful scheme, referred to as the Causality-driven Trustworthy Multi-View maPping approach (Cad-TMVP). Specifically, we design a fined multi-directional mapping module to extract co-expression patterns across different modalities and capture fine-grained interpretability factors. We also meticulously design dynamic mechanisms to facilitate adaptive loss-term reweighting and trustworthy integration of multiple modalities. Cad-TMVP enhances downstream tasks by developing a cooperative learning module that simultaneously performs automated diagnosis and result interpretation. Furthermore, we develop an efficient search strategy and support computation to reduce the high computational burden, making our approach practicable. We conduct extensive experiments on different types of multiomics data. The proposed method establishes new state-of-the-art results in various settings while maintaining excellent interpretability. Thus, it sets a potentially newparadigm in trustworthy multi-modal learning and verifies its flexibility and versatility in real biomedical applications.

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), but not all MCI subjects develop into AD finally. Therefore, distinguishing progressive MCI (pMCI) subjects from stable MCI (sMCI) subjects is an area of intense interest, which may provide targeted treatments for at-risk individuals. On this account, building an MCI conversion prediction model at the early stage is particularly important. The neuroimaging data, especially multi-modal ones, has proven to be a great alternative in predicting MCIs' conversion. In addition, genetic variations such as Single Nucleotide Polymorphism (SNP) can also imply the conversion risk of an individual. The neuroimaging data represents the current status, while SNPs convey the inherited risk of an individual. In this paper, we propose a deep representative fusion method that combines multi-modal baseline neuroimaging data and genetic variations. It can predict the progressive status of MCIs over the following two years, three years and four years, respectively. Experimental results from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that the proposed method has better prediction capability than comparison methods. Moreover, findings show that the stability of the default mode network (DMN) and ventral attention network (VAN) are correlated with the MCI conversion and the learned imaging representations are related to minimental state examination (MMSE) scores which are associated with AD progression.

Integrating and analyzing multiple omics datasets, such as genomics, environmental influences, and imaging endophenotypes, has yielded an abundance of candidate biomarkers. However, translating such findings into beneficial clinical knowledge for disease prediction remains challenging. This becomes even more challenging when studying interpretable high-order feature interactions such as gene-environment interaction (G$\times $E) to understand the etiology. To fill this gap, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a fresh and powerful scheme, referred to as mutual-assistance causal biomarker discovery and stable disease prediction approach (MA-CBxDP). Specifically, we design an interpretable bi-directional mapping framework, integrated with a causal feature interaction module, to extract co-expression patterns across different modalities and identify trustworthy biomarkers including G$\times $E. A cooperative prediction module is further incorporated to ensure accurate diagnosis and identification of causal effects for pathogenesis. Importantly, biomarker discovery and disease prediction can mutually reinforce each other, helping to provide novel insights into chronic diseases. Furthermore, in light of the large computational burden incurred by the high-dimensional interactions, we devise a rapid strategy and extend it to a more practical but challenging chromosome-wide setting. We conduct extensive experiments on two databases under three tasks, i.e. multimodal correlation, disease diagnosis, and trait prediction. MA-CBxDP establishes new state-of-the-art results in predicting clinical scores and disease status classification, while maintaining exceptional interpretability, verifying its flexibility and versatility in practical applications.

MOTIVATION: Discovering genetic variations underpinning brain disorders is important to understand their pathogenesis. Indirect associations or spurious causal relationships pose a threat to the reliability of biomarker discovery for brain disorders, potentially misleading or incurring bias in subsequent decision-making. Unfortunately, the stringent selection of reliable biomarker candidates for brain disorders remains a predominantly unexplored challenge. RESULTS: In this article, to fill this gap, we propose a fresh and powerful scheme, referred to as the Causality-aware Genotype intermediate Phenotype Correlation Approach (Ca-GPCA). Specifically, we design a bidirectional association learning framework, integrated with a parallel causal variable decorrelation module and sparse variable regularizer module, to identify trustworthy causal biomarkers. A disease diagnosis module is further incorporated to ensure accurate diagnosis and identification of causal effects for pathogenesis. Additionally, considering the large computational burden incurred by high-dimensional genotype-phenotype covariances, we develop a fast and efficient strategy to reduce the runtime and prompt practical availability and applicability. Extensive experimental results on four simulation data and real neuroimaging genetic data clearly show that Ca-GPCA outperforms state-of-the-art methods with excellent built-in interpretability. This can provide novel and reliable insights into the underlying pathogenic mechanisms of brain disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/ZJ-Techie/Ca-GPCA.

Brain imaging genetics offers insights into the genetic basis of brain structure and function by exploring the relationships between genetic variations and neuroimaging features, with canonical correlation association learning as a vital and effective tool. However, imaging large cohorts affected by specific brain diseases entails significant costs. To tackle this challenge, we introduced a novel bi-multivariate sparse canonical correlation association method based on summary statistics from large GWAS (S-SCCA). S-SCCA leverages effect sizes obtained from these datasets to identify genetic variants associated with complex traits, including those influenced by pleiotropy, while simultaneously identifying imaging factors related to the disease under study. Moreover, we have implemented a rapid optimization strategy to circumvent computational burdens while identifying disease-associated risk factors within genetic variations across the entire chromosome. We assessed S-SCCA against conventional SCCA using a neuroimaging genetic dataset from the Alzheimer’s Disease Neuroimaging Initiative. Results showed that S-SCCA demonstrated comparable or superior modeling performance and feature selection capabilities. Furthermore, we applied S-SCCA to two summary statistics datasets from two large GWAS, where original imaging and genetic data were inaccessible. S-SCCA replicated the genetic loci identified by GWAS and additional meaningful variants. Additionally, it revealed bi-multivariate relationships between imaging QTs and SNPs, indicating its powerful modeling capability. These findings highlight the promise of S-SCCA as a practical bi-multivariate learning technique in brain imaging genetics, circumventing the need for sensitive individual-level imaging and genetic data, thereby enhancing its potential for broader applicability and accessibility in biomedical studies.

Understanding neurological manifestations and their genetic architectures are important for exploring the etiology and pathology of brain disorders. Multimodal neuroimaging data carry complementary information and are known to exhibit shared and specific characteristics from different perspectives. Hence, exploring modality-shared and modality-specific imaging features as well as their genetic underpinnings is a challenging but beneficial task. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose a fresh and straightforward insight, referred as Multimodality-Disentangled Phenotype-Genotype Correlation approach (MDPGC). Specifically, we design a unified framework for exploring the multimodality-disentangled characteristics of image-based phenotypes, and further detect genetic variants associated with the disorder using modality-shared and modality-specific biomarkers as intermediate phenotypes. Extensive experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset reveal that our method attains superior correlation coefficients compared to state-of-the-art methods, and at the same time provided excellent interpretability. In addition, the subsequent analysis demonstrates that MDPGC successfully identifies different types of characteristics of imaging phenotypes and reveals relevant genetic variations. These findings not only contribute to AD diagnosis but also help better understand the pathological and pathogenic mechanisms of brain disorders.

Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers' comprehensive understanding of Alzheimer's disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype-protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose $\textbf{M}$ulti-$\textbf{T}$ask $\textbf{G}$enotype-$\textbf{P}$rotein $\textbf{I}$nteraction and $\textbf{C}$orrelation disentangling method ($\textbf{MT-GPIC}$) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT-GPIC, which is guaranteed to converge. Experimental results on the Alzheimer's Disease Neuroimaging Initiative data set show that MT-GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD.

Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ$\ell _{{2},{1}}$-norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.

Discovering critical genetic biomarkers of Alzheimer’s disease (AD) by detecting the complex associations between genotypes (i.e. single nucleotide polymorphism, SNP) and phenotypes (i.e. quantitative trait, QT) is a long-standing and beneficial task for the diagnosis and the follow-up treatment of patients. The function of genes and their relationships with phenotypes are extremely complex. A lot of imaging genetic methods have been designed to uncover the association between brain imaging QTs and SNPs. However, most of them are focused on the effect of a single SNP, which may have limited ability due to the oligogenic or polygenic characteristic of AD. In this paper, we propose a deep reconstruction bidirectional association with feature selection (DRBA-FS) method to explore the multi-SNPmulti-QT associations. In this method, the co-effect of multiple AD-related genetic variations is identified and aggregated, and their high-level genetic associations to brain imaging QTs are jointly modeled. Experiment results on real neuroimaging genetic data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that the identified biomarkers are all related to AD. Interestingly, our method can learn the joint effect of multiple AD-related genetic variations across the genome, and thus has significant potential in understanding the genetic mechanism of AD.

Brain imaging genetics investigates the complex relationships between genetic variations and brain imaging quantitative traits (QTs). However, existing approaches primarily focus on the main effects of genetic variations, potentially neglecting the crucial role of epistasis that explains the missing heritability of brain disorders. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we present Multi-Task feature interaction-aware Sparse Canonical Correlation Analysis (MTfiSCCA) to identify disease-related main effect and epistasis of risk genetic factors on multimodal neuroimaging phenotypes simultaneously. To ensure stability and interpretation, we use innovative sparsity-inducing penalties to identify biomarkers that make significant contributions. Additionally, we develop an efficient optimization algorithm to solve the proposed method, which converges to a local optimum. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) dataset show that our MTfiSCCA method achieves higher canonical correlation coefficients (CCC) and better feature selection subsets such as disease-related biomarkers compared to the state-of-the-art methods. Furthermore, MTfiSCCA reveals interpretable epistasis among genetic variations implicated in AD, offering novel insights into the underlying pathogenic mechanisms of brain disorders such as Alzheimer’s disease (AD).

Alzheimer’s disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.

BACKGROUND: With the development of noninvasive imaging technology, collecting different imaging measurements of the same brain has become more and more easy. These multimodal imaging data carry complementary information of the same brain, with both specific and shared information being intertwined. Within these multimodal data, it is essential to discriminate the specific information from the shared information since it is of benefit to comprehensively characterize brain diseases. While most existing methods are unqualified, in this paper, we propose a parameter decomposition based sparse multi-view canonical correlation analysis (PDSMCCA) method. PDSMCCA could identify both modality-shared and -specific information of multimodal data, leading to an in-depth understanding of complex pathology of brain disease. RESULTS: Compared with the SMCCA method, our method obtains higher correlation coefficients and better canonical weights on both synthetic data and real neuroimaging data. This indicates that, coupled with modality-shared and -specific feature selection, PDSMCCA improves the multi-view association identification and shows meaningful feature selection capability with desirable interpretation. CONCLUSIONS: The novel PDSMCCA confirms that the parameter decomposition is a suitable strategy to identify both modality-shared and -specific imaging features. The multimodal association and the diverse information of multimodal imaging data enable us to better understand the brain disease such as Alzheimer's disease.

Brain imaging genetics develops rapidly, aiming to identify bi-multivariate associations between genetic loci and neuroimaging quantitative traits (QTs). The multi-task Sparse Canonical Correlation Analysis (MTSCCA) is a popular and effective technique in this area since it obtains superior results than those single-task based SCCA methods. Unfortunately, the most existing MTSCCA methods are either unsupervised or incapable of identifying the shared and specific patterns of multimodal neuroimaging QTs simultaneously. In this paper, we propose a novel diagnosis guided MTSCCA to identify the association between genetic and imaging phenotypic markers. Our method has three merits. First, it follows the same modeling paradigm of previous MTSCCA. This enables it to incorporate multimodal imaging QTs jointly, thereby facilitating a more comprehensive identification of genetic factors. Second, our method utilizes the parameter decomposition which could identify both modality-shared and -specific imaging QTs, and further uncovers their genetic mechanisms. Third, we also employed a new network constraint which could find out potentially meaningful brain imaging networks. Compared with conventional SCCA methods including both single-task and multi-task ones, the proposed method has improved or comparable correlation coefficients, and obtains a clean imaging pattern of good meaning. In addition, these results on the Alzheimer’s disease neuroimaging initiative (ADNI) cohort show that our method selects meaningful biomarkers, indicating that it could offer a significant addition to brain imaging genetic studies.

Neuroimaging techniques have shown increasing power to understand the neuropathology of brain disorders. Multimodal brain imaging data carry distinct but complementary information and thus could depict brain disorders comprehensively. To deepen our understanding, it is essential to investigate the intrinsic associations among multiple modalities. To date, the pairwise correlations between imaging data captured by different imaging modalities have been well studied, leaving formidable challenges to identify high-order associations. In this paper, we first propose a new sparse tensor canonical correlation analysis (STCCA) with feature selection to analyze the complex high-order relationships among multimodal brain imaging data. In addition, we find that methods for identifying pairwise associations and high-order associations have complementary advantages, providing a sound reason to fuse them. Therefore, we further propose an improved STCCA (STCCA+) which integrates STCCA and sparse multiple CCA (SMCCA) to fully uncover associations among multiple imaging modalities. The proposed STCCA+detects equivalent association levels among multimodal imaging data compared to SMCCA. Most importantly, both STCCA and STCCA+yield modality-consistent imaging markers and modality-specific ones, assuring a better and meaningful feature selection capability. Finally, the identified imaging markers and their high-order correlations could form a comprehensive indication of brain disorders, showing their promise in high-order multimodal brain imaging analysis.