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1. Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

   https://doi.org/10.1093/bioinformatics/btz320  

   Du, Lei ; Liu, Kefei ; Zhu, Lei ; Yao, Xiaohui ; Risacher, Shannon L. ; Guo, Lei ; Saykin, Andrew J. ; Shen, Li ; Alzheimer’s Disease Neuroimaging Initiative ( July 2019 , Bioinformatics)

   Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.

   We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression.

   The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA.

   Supplementary data are available at Bioinformatics online.

    

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2. Bayesian graphical models for modern biological applications

   https://doi.org/10.1007/s10260-021-00572-8  

   Ni, Yang ; Baladandayuthapani, Veerabhadran ; Vannucci, Marina ; Stingo, Francesco C. ( May 2021 , Statistical Methods & Applications)

   Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

    

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3. Task Balanced Multimodal Feature Selection to Predict the Progression of Alzheimer’s Disease

   https://doi.org/10.1109/BIBE50027.2020.00040  

   Brand, Lodewijk ; O'Callaghan, Braedon ; Sun, Anthony ; Wang, Hua ( October 2020 , 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE))

   null (Ed.)

   The social and financial costs associated with Alzheimer's disease (AD) result in significant burdens on our society. In order to understand the causes of this disease, public-private partnerships such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) release data into the scientific community. These data are organized into various modalities (genetic, brain-imaging, cognitive scores, diagnoses, etc.) for analysis. Many statistical learning approaches used in medical image analysis do not explicitly take advantage of this multimodal data structure. In this work we propose a novel objective function and optimization algorithm that is designed to handle multimodal information for the prediction and analysis of AD. Our approach relies on robust matrix-factorization and row-wise sparsity provided by the ℓ2,1- norm in order to integrate multimodal data provided by the ADNI. These techniques are jointly optimized with a classification task to guide the feature selection in our proposed Task Balanced Multimodal Feature Selection method. Our results, when compared against some widely used machine learning algorithms, show improved balanced accuracies, precision, and Matthew's correlation coefficients for identifying cognitive decline. In addition to the improved prediction performance, our method is able to identify brain and genetic biomarkers that are of interest to the clinical research community. Our experiments validate existing brain biomarkers and single nucleotide polymorphisms located on chromosome 11 and detail novel polymorphisms on chromosome 10 that, to the best of the authors' knowledge, have not previously been reported. We anticipate that our method will be of interest to the greater research community and have released our method's code online.11Code is provided at: https://github.com/minds-mines/TBMFSjl 

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4. Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease

   https://doi.org/10.1186/s12920-022-01323-8  

   Kim, Mansu ; Wu, Ruiming ; Yao, Xiaohui ; Saykin, Andrew J. ; Moore, Jason H. ; Shen, Li ( December 2022 , BMC Medical Genomics)

   Abstract Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 $$\%$$ % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer’s Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes. 

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5. Local-aggregate Modeling for Big Data via Distributed Optimization: Applications to Neuroimaging

   https://doi.org/10.1111/biom.12355  

   Hu, Yue ; Allen, Genevera I. ( August 2015 , Biometrics)

   Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-subject neuroimaging data based on each subject's brain imaging scans. This is an ultra-high-dimensional problem that consists of a matrix of covariates (brain locations by time points) for each subject; few methods currently exist to fit supervised models directly to this tensor data. We propose a novel modeling and algorithmic strategy to apply generalized linear models (GLMs) to this massive tensor data in which one set of variables is associated with locations. Our method begins by fitting GLMs to each location separately, and then builds an ensemble by blending information across locations through regularization with what we term an aggregating penalty. Our so called, Local-Aggregate Model, can be fit in a completely distributed manner over the locations using an Alternating Direction Method of Multipliers (ADMM) strategy, and thus greatly reduces the computational burden. Furthermore, we propose to select the appropriate model through a novel sequence of faster algorithmic solutions that is similar to regularization paths. We will demonstrate both the computational and predictive modeling advantages of our methods via simulations and an EEG classification problem.

    

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