The Experts below are selected from a list of 9918 Experts worldwide ranked by ideXlab platform
Li Shen - One of the best experts on this subject based on the ideXlab platform.
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Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics
IEEE ACM transactions on computational biology and bioinformatics, 2021Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) 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 analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method 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. MTSCCA enforces sparsity at the group level via the ${\mathrm G}_{2,1}$ G 2 , 1 -norm, and jointly selects features across multiple tasks for SNPs and QTs via the $\ell _{2,1}$ l 2 , 1 -norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide Imaging Genetics.
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deep network based feature selection for Imaging Genetics application to identifying biomarkers for parkinson s disease
International Symposium on Biomedical Imaging, 2020Co-Authors: Mansu Kim, Hyunjin Park, Ji Hye Won, Jisu Hong, Junmo Kwon, Li ShenAbstract:Imaging Genetics is a methodology for discovering associations between Imaging and genetic variables. Many studies adopted sparse models such as sparse canonical correlation analysis (SCCA) for Imaging Genetics. These methods are limited to modeling the linear Imaging Genetics relationship and cannot capture the non-linear high-level relationship between the explored variables. Deep learning approaches are underexplored in Imaging Genetics, compared to their great successes in many other biomedical domains such as image segmentation and disease classification. In this work, we proposed a deep learning model to select genetic features that can explain the Imaging features well. Our empirical study on simulated and real datasets demonstrated that our method outperformed the widely used SCCA method and was able to select important genetic features in a robust fashion. These promising results indicate our deep learning model has the potential to reveal new biomarkers to improve mechanistic understanding of the studied brain disorders.
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a dirty multi task learning method for multi modal brain Imaging Genetics
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Fang Liu, Andrew J. Saykin, Kefei Liu, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple Imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze Imaging Genetics problems with multiple modalities of brain Imaging quantitative traits (QTs) involved. The proposed method can not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroImaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain Imaging Genetics.
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Diagnosis Status Guided Brain Imaging Genetics Via Integrated Regression And Sparse Canonical Correlation Analysis
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Co-Authors: Lei Du, Andrew J. Saykin, Shannon L Risacher, Li ShenAbstract:Brain Imaging Genetics use the Imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in Imaging Genetics. The regression only selects relevant features for pre-chctors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for Imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markets and Imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroImaging data. This demonstrates that our method is a promising bi-multivariate tool for brain Imaging Genetics.
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fast multi task scca learning with feature selection for multi modal brain Imaging Genetics
PMC, 2019Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract: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) method-s are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few 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 $\ell _{2,1}$-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.
Vince D. Calhoun - One of the best experts on this subject based on the ideXlab platform.
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sparse deep neural networks on Imaging Genetics for schizophrenia case control classification
Human Brain Mapping, 2021Co-Authors: Jiayu Chen, Ole A Andreassen, Vince D. Calhoun, Jessica A Turner, Theo G M Van Erp, Lei Wang, Ingrid AgartzAbstract:Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L0 -norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools.
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the Genetics bids extension easing the search for genetic data associated with human brain Imaging
GigaScience, 2020Co-Authors: Clara Moreau, Vince D. Calhoun, Thomas E. Nichols, Jessica A Turner, Martineau Jeanlouis, Ross W Blair, Christopher J Markiewicz, Cyril PernetAbstract:Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain Imaging Genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the Genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain Imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale Imaging repositories, facilitating data aggregation across studies.
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sparse deep neural networks on Imaging Genetics for schizophrenia case control classification
medRxiv, 2020Co-Authors: Jiayu Chen, Ole A Andreassen, Vince D. Calhoun, Jessica A Turner, Theo G M Van Erp, Lei Wang, Ingrid Agartz, Lars T WestlyeAbstract:Machine learning approaches hold potential for deconstructing complex psychiatric traits and yielding biomarkers which have a large potential for clinical application. Particularly, the advancement in deep learning methods has promoted them as highly promising tools for this purpose due to their capability to handle high-dimensional data and automatically extract high-level latent features. However, current proposed approaches for psychiatric classification or prediction using biological data do not allow direct interpretation of original features, which hinders insights into the biological underpinnings and development of biomarkers. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L0-norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort (N = 1,684) with brain structural MRI (gray matter volume (GMV)) and genetic (single nucleotide polymorphism (SNP)) data for discrimination of patients with SZ vs. controls. A total of 634 individuals served as training samples, and the resulting classification model was evaluated for generalizability on three independent data sets collected at different sites with different scanning protocols (n = 635, 255 and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. The performance of the proposed approach was compared with that yielded by an independent component analysis + support vector machine (ICA+SVM) framework. Empirical experiments demonstrated that sparse DNN slightly outperformed ICA+SVM and more effectively fused GMV and SNP features for SZ discrimination. With combined GMV and SNP features, sparse DNN yielded an average classification error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification when a high sparsity was enforced, and parietal regions were further included with a lower sparsity setting, which strongly echoed previous literature. This is the first attempt to apply an interpretable sparse DNN model to Imaging and genetic features for SZ classification with generalizability assessed in a large and multi-study cohort. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities (e.g. functional and diffusion images) and traits (e.g. continuous scores) which ultimately may result in clinically useful tools.
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canonical correlation analysis of Imaging Genetics data based on statistical independence and structural sparsity
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Yipu Zhang, Peng Peng, Vince D. Calhoun, Yu-ping WangAbstract:Current developments of neuroImaging and Genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real Imaging Genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.
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Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation
IEEE transactions on medical imaging, 2017Co-Authors: Jian Fang, Vince D. Calhoun, Pascal Zille, Dongdong Lin, Hong-wen Deng, Yu-ping WangAbstract:Recent advances in Imaging Genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper, we present a novel method to detect conditional associations between Imaging Genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, and then use multiple testing to detect significant group level associations (e.g., regions of interest-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large volume of Imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with G-PDC than distance correlation, Pearson’s correlation, and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The MATLAB code is available at https://sites.google.com/site/jianfang86/gPDC .
David C Glahn - One of the best experts on this subject based on the ideXlab platform.
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Imaging Genetics from neanderthals to modern human s psychiatric disorders
European Neuropsychopharmacology, 2019Co-Authors: Barbara Franke, Sarah E Medland, David C GlahnAbstract:Overall Abstract The structure of our brain is intricately linked to its functioning and malfunctioning. Indeed, alterations in brain structure are observed in all psychiatric disorders, but such findings are still often based on suboptimal sample sizes. Within the ENIGMA consortium, we bring together scientists from all parts of the world interested in Imaging and Imaging Genetics. This has allowed us to integrate data on MRI-assessed brain gray and white matter across health and disease reaching unprecedented sample sizes. Pooling of data from multiple cohorts provides more accurate effect size estimates on brain structure alteration in psychiatric disorders, contributes new information on trajectories of disease-related brain volume alterations across the lifespan, and clarifies commonalities and differences between different psychiatric disorders. It is often not clear, whether observed brain structure alterations are a consequence of living with the disorder or are more likely linked to disease-causal processes. Genetics research may shed light on this. The Imaging Genetics community in ENIGMA has identified novel genetic variants linked to subcortical brain volumes and intracranial volume (ICV) in the largest sample sizes available, currently involving more than 35,000 participants (recent publications: Hibar et al., Nature 2015; Adams et al., Nature Neuroscience 2016; Hibar et al., Nature Communications 2017). During the symposium, we will also present new results from the study of global and regional measures of cortical thickness and surface area. Combining ENIGMA data with those from large consortia on the Genetics of psychiatric disorders, the Psychiatric Genomics Consortium (PGC) and iPSYCH, we can identify potential etiologic links between brain structure and disease risk based on molecular genetic overlap. Surprisingly, a first study involving data on over 88,000 individuals did not provide evidence for overlap between schizophrenia risk variants and those for subcortical brain volume or ICV (Franke et al., Nature Neuroscience 2016). However, new data on ADHD, to be presented in the symposium, do show significant genetic overlap. Evolutionary aspects of brain performance and the involvement of Genetics in this represent a research area of growing interest. Are psychiatric disorders the price we pay for the evolutionary recent expansion of our brain? This symposium will shed some light on the role of genetic loci linked to brain morphology changes from Neanderthals to modern humans and/or those having undergone accelerated evolution in hominids in influencing brain functioning and disease.
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impact of family structure and common environment on heritability estimation for neuroImaging Genetics studies using sequential oligogenic linkage analysis routines
Journal of medical imaging, 2014Co-Authors: Mary Ellen I Koran, Paul M Thompson, Thomas E. Nichols, David C Glahn, Peter Kochunov, Neda Jahanshad, John Blangero, Tricia A Thorntonwells, Bennett A LandmanAbstract:Imaging Genetics is an emerging methodological field that combines genetic information with medical Imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an Imaging Genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) Imaging Genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated Imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an Imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.
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on study design in neuroImaging heritability analyses
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2014Co-Authors: Mary Ellen I Koran, Paul M Thompson, Thomas E. Nichols, David C Glahn, Peter Kochunov, Neda Jahanshad, John Blangero, Tricia A Thorntonwells, Bennett A LandmanAbstract:Imaging Genetics is an emerging methodology that combines genetic information with Imaging-derived metrics to understand how genetic factors impact observable structural, functional, and quantitative phenotypes. Many of the most well-known genetic studies are based on Genome-Wide Association Studies (GWAS), which use large populations of related or unrelated individuals to associate traits and disorders with individual genetic factors. Merging Imaging and Genetics may potentially lead to improved power of association in GWAS because Imaging traits may be more sensitive phenotypes, being closer to underlying genetic mechanisms, and their quantitative nature inherently increases power. We are developing SOLAR-ECLIPSE (SE) Imaging Genetics software which is capable of performing genetic analyses with both large-scale quantitative trait data and family structures of variable complexity. This program can estimate the contribution of genetic commonality among related subjects to a given phenotype, and essentially answer the question of whether or not the phenotype is heritable. This central factor of interest, heritability, offers bounds on the direct genetic influence over observed phenotypes. In order for a trait to be a good phenotype for GWAS, it must be heritable: at least some proportion of its variance must be due to genetic influences. A variety of family structures are commonly used for estimating heritability, yet the variability and biases for each as a function of the sample size are unknown. Herein, we investigate the ability of SOLAR to accurately estimate heritability models based on Imaging data simulated using Monte Carlo methods implemented in R. We characterize the bias and the variability of heritability estimates from SOLAR as a function of sample size and pedigree structure (including twins, nuclear families, and nuclear families with grandparents).
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cortical thickness or grey matter volume the importance of selecting the phenotype for Imaging Genetics studies
NeuroImage, 2010Co-Authors: Anderson M Winkler, David C Glahn, Peter Kochunov, John Blangero, Laura Almasy, Karl Zilles, Peter T Fox, Ravindranath DuggiralaAbstract:Choosing the appropriate neuroImaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroImaging methods provide numerous potential phenotypes, their role for Imaging Genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for Imaging genetic studies.
Andrew J. Saykin - One of the best experts on this subject based on the ideXlab platform.
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Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics
IEEE ACM transactions on computational biology and bioinformatics, 2021Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) 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 analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method 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. MTSCCA enforces sparsity at the group level via the ${\mathrm G}_{2,1}$ G 2 , 1 -norm, and jointly selects features across multiple tasks for SNPs and QTs via the $\ell _{2,1}$ l 2 , 1 -norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide Imaging Genetics.
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a dirty multi task learning method for multi modal brain Imaging Genetics
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Fang Liu, Andrew J. Saykin, Kefei Liu, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple Imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze Imaging Genetics problems with multiple modalities of brain Imaging quantitative traits (QTs) involved. The proposed method can not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroImaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain Imaging Genetics.
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Diagnosis Status Guided Brain Imaging Genetics Via Integrated Regression And Sparse Canonical Correlation Analysis
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Co-Authors: Lei Du, Andrew J. Saykin, Shannon L Risacher, Li ShenAbstract:Brain Imaging Genetics use the Imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in Imaging Genetics. The regression only selects relevant features for pre-chctors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for Imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markets and Imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroImaging data. This demonstrates that our method is a promising bi-multivariate tool for brain Imaging Genetics.
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fast multi task scca learning with feature selection for multi modal brain Imaging Genetics
PMC, 2019Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract: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) method-s are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few 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 $\ell _{2,1}$-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.
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a longitudinal Imaging Genetics study of neuroanatomical asymmetry in alzheimer s disease
Biological Psychiatry, 2018Co-Authors: Christian Wachinger, Andrew J. Saykin, Kwangsik Nho, Martin Reuter, Anna RieckmannAbstract:BACKGROUND: Contralateral brain structures represent a unique, within-patient reference element for disease, and asymmetries can provide a personalized measure of the accumulation of past disease p ...
Shannon L Risacher - One of the best experts on this subject based on the ideXlab platform.
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Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics
IEEE ACM transactions on computational biology and bioinformatics, 2021Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) 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 analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method 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. MTSCCA enforces sparsity at the group level via the ${\mathrm G}_{2,1}$ G 2 , 1 -norm, and jointly selects features across multiple tasks for SNPs and QTs via the $\ell _{2,1}$ l 2 , 1 -norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide Imaging Genetics.
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a dirty multi task learning method for multi modal brain Imaging Genetics
Medical Image Computing and Computer-Assisted Intervention, 2019Co-Authors: Fang Liu, Andrew J. Saykin, Kefei Liu, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract:Brain Imaging Genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple Imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze Imaging Genetics problems with multiple modalities of brain Imaging quantitative traits (QTs) involved. The proposed method can not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroImaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain Imaging Genetics.
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Diagnosis Status Guided Brain Imaging Genetics Via Integrated Regression And Sparse Canonical Correlation Analysis
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Co-Authors: Lei Du, Andrew J. Saykin, Shannon L Risacher, Li ShenAbstract:Brain Imaging Genetics use the Imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in Imaging Genetics. The regression only selects relevant features for pre-chctors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for Imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markets and Imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroImaging data. This demonstrates that our method is a promising bi-multivariate tool for brain Imaging Genetics.
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fast multi task scca learning with feature selection for multi modal brain Imaging Genetics
PMC, 2019Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Li ShenAbstract: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) method-s are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few 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 $\ell _{2,1}$-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.
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a novel scca approach via truncated l1 norm and truncated group lasso for brain Imaging Genetics
Bioinformatics, 2018Co-Authors: Kefei Liu, Andrew J. Saykin, Xiaohui Yao, Shannon L Risacher, Junwei Han, Lei Guo, Jingwen Yan, Tuo Zhang, Li ShenAbstract:MOTIVATION Brain Imaging Genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroImaging quantitative traits (QTs) is one major task in Imaging Genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the l1-norm or its variants to induce sparsity. The l0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. RESULTS In this paper, we propose the truncated l1-norm penalized SCCA to improve the performance and effectiveness of the l1-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ. It can avoid the time intensive parameter tuning if given a reasonable small τ. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting Imaging genetic associations. AVAILABILITY AND IMPLEMENTATION The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
