An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.

Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer’s Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala

Background: The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer’s disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. Objective: To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. Methods: Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. Results: Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOE ɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOE ɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. Conclusion: These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.

Hyper-graph based Adaptive Sparse Multi-view Canonical Correlation Analysis with Application to Neuroimaging Genetics Study of Alzheimer’s Disease

Neuroimaging genetics has gained more and more attention on account of detecting the linkage between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants (i.e., Single Nucleotide Polymorphism (SNP) in Alzheimer’s disease (AD)). To overcome the problem of sparse multi-view canonical correlation (SMCCA) ‘unfair combination of pairwise convariance’, introducing adaptive weights when combining pairwise covariances, a novel formulation of SMCCA, named adaptive SMCCA. In this paper, we integrate multi-modal genomic data from postmortem AD brain and proposed a hyper-graph based sparse multi-view canonical correlation analysis (HGSMCCA) method to extract the most correlated multi-modal biomarkers. Specifically, we utilized the adaptive sparse multi-view canonical correlation analysis (AdsSMCCA) framework, consider the benefit of hyper-graph-based regularization term into consideration that will contribute to the selection of more discriminative biomarkers. We propose a hyper-graph optimization strategy based on the adaptive SMCCA model, and we apply it to neuroimaging genetics data. All these results demonstrate the capability of HGSMCCA in identifying diagnostically genotype-phenotype patterns.

Ten years of Enhancing Neuro-Imaging Genetics through Meta-Analysis: An overview from the ENIGMA Genetics Working Group

Here we review the motivation for creating the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings and future directions of the Genetics Working Group. A major goal of the working group is tackling the reproducibility crisis affecting ‘candidate gene’ and genome-wide association analyses in neuroimaging. To address this, we developed harmonised analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We showed common and distinct genetic loci to be associated with different brain structures, as well as genetic correlations with psychiatric and neurological diseases.

Sex disparities in psychiatric and neurodegenerative disorders: Insights from large-scale neuroimaging

Sex differences are found in the incidence and expression of psychiatric and neurodegenerative conditions, and many studies suggest these differences are influenced by innate biological differences between males and females and risk factors that interact with these differences. However, few studies have used neuroimaging to examine brain signatures of disease separately by sex, and many studies of sex differences have been based on small samples and their findings have not been replicated in larger cohorts. Large-scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analyses (ENIGMA) consortium, the UK Biobank, Human Connectome Project, and others offer an unprecedented source of power to address important questions about the role of sex as a risk or protective factor for suboptimal brain health, as well as sex-specific neuroimaging phenotypes in brain-related illnesses. Here we review the existing neuroimaging literature on sex differences in the human brain in healthy adults and those with the most common and debilitating psychiatric and age-related neurodegenerative conditions. Finally, we discuss key gaps in this literature and opportunities of large-scale collaborative efforts to identify, characterize, and explain how biological sex influences the humanbrain in health and disease.