Browsing by Author "Huang, Heng"

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    A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis
    (Springer Nature, 2013) Huang, Heng; Yan, Jingwen; Nie, Feiping; Huang, Jin; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model.
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    Addressing overfitting bias due to sample overlap in polygenic risk scoring
    (Wiley, 2025) Jeong, Seokho; Shivakumar, Manu; Jung, Sang-Hyuk; Won, Hong-Hee; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Young Jin; Kim, Bong-Jo; Lee, Seunggeun; Kim, Dokyoon; Radiology and Imaging Sciences, School of Medicine
    Introduction: Numerous studies on Alzheimer's disease polygenic risk scores (PRSs) overlook sample overlap between International Genomics of Alzheimer's Project (IGAP) and target datasets like Alzheimer's Disease Neuroimaging Initiative (ADNI). Methods: To address this, we developed overlap-adjusted PRS (OA PRS) and tested it on simulated data to assess biases from different scenarios by varying training, testing, and overlap proportions. OA PRS was used to adjust for sample bias in simulations; then, we applied OA PRS to IGAP and ADNI datasets and validated through visual diagnosis. Results: OA PRS effectively adjusted for sample overlap in all simulation scenarios, as well as for IGAP and ADNI. The original IGAP PRS showed an inflated area under the receiver operating characteristic (AUROC: 0.915) on overlapping samples. OA PRS reduced the AUROC to 0.726, closely aligning with the AUROC of non-overlapping samples (0.712). Further, visual diagnostics confirmed the effectiveness of our adjustments. Discussion: With OA PRS, we were able to adjust the IGAP summary-based PRS for the overlapped ADNI samples, allowing the dataset to be fully used without the risk of overfitting. Highlights: Sample overlap between large Alzheimer's disease (AD) cohorts poses overfitting bias when using AD polygenic risk scores (PRSs). This study highlighted the effectiveness of overlap-adjusted PRS (OA -PRS) in mitigating overfitting and improving the accuracy of PRS estimations. New PRSs based on adjusted effect sizes showed increased power in association with clinical features.
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    An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification
    (Frontiers Media, 2023-10-26) Suh, Erica H.; Lee, Garam; Jung, Sang-Hyuk; Wen, Zixuan; Bao, Jingxuan; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.
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    Distance-weighted Sinkhorn loss for Alzheimer's disease classification
    (Elsevier, 2024-02-12) Wang, Zexuan; Zhan, Qipeng; Tong, Boning; Yang, Shu; Hou, Bojian; Huang, Heng; Saykin, Andrew J.; Thompson, Paul M.; Davatzikos, Christos; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.
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    From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs
    (Oxford University Press, 2012) Wang, Hua; Nie, Feiping; Huang, Heng; Yan, Jingwen; Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Motivation: Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic. Results: Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.
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    GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics
    (2015) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.
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    Human connectome module pattern detection using a new multi-graph MinMax cut model
    (Springer, 2014) Wang, De; Wang, Yang; Nie, Feiping; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.
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    Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut.
    (Springer, 2015-10) Gao, Hongchang; Cai, Chengtao; Yan, Jingwen; Yan, Lin; Cortes, Joaquin Goni; Wang, Yang; Nie, Feiping; West, John; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.
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    Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
    (Oxford University Press, 2012) Wang, Hua; Nie, Feiping; Huang, Heng; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units. Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease. Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/.
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    IMAGING GENOMICS
    (2018) Huang, Heng; Shen, L. I.; Thompson, Paul M.; Huang, Kun; Huang, Junzhou; Yang, Lin; Radiology and Imaging Sciences, School of Medicine