5 research outputs found
Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics
Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies
Beyond Scalar Treatment: A Causal Analysis of Hippocampal Atrophy on Behavioral Deficits in Alzheimer's Studies
Alzheimer's disease is a progressive form of dementia that results in
problems with memory, thinking and behavior. It often starts with abnormal
aggregation and deposition of beta-amyloid and tau, followed by neuronal damage
such as atrophy of the hippocampi, and finally leads to behavioral deficits.
Despite significant progress in finding biomarkers associated with behavioral
deficits, the underlying causal mechanism remains largely unknown. Here we
investigate whether and how hippocampal atrophy contributes to behavioral
deficits based on a large-scale observational study conducted by the
Alzheimer's Disease Neuroimaging Initiative (ADNI). As a key novelty, we use 2D
representations of the hippocampi, which allows us to better understand atrophy
associated with different subregions. It, however, introduces methodological
challenges as existing causal inference methods are not well suited for
exploiting structural information embedded in the 2D exposures. Moreover, our
data contain more than 6 million clinical and genetic covariates, necessitating
appropriate confounder selection methods. We hence develop a novel two-step
causal inference approach tailored for our ADNI data application. Analysis
results suggest that atrophy of CA1 and subiculum subregions may cause more
severe behavioral deficits compared to CA2 and CA3 subregions. We further
evaluate our method using simulations and provide theoretical guarantees
Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method
Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics
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Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics
Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G 2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1 -norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies