Browsing by Author "Shen, Li"
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Item 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception(Elsevier, 2016-06-01) Weiner, Michael W.; Veitch, Dallas P.; Aisen, Paul S.; Beckett, Laurel A.; Cairns, Nigel J.; Cedarbaum, Jesse; Green, Robert C.; Harvey, Danielle; Jack, Clifford R.; Jagust, William; Luthman, Johan; Morris, John C.; Petersen, Ronald C.; Saykin, Andrew J.; Shaw, Leslie; Shen, Li; Schwarz, Adam; Toga, Arthur W.; Trojanowski, John Q.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineThe Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.Item A genetically informed brain atlas for enhancing brain imaging genomics(Springer Nature, 2025-04-14) Bao, Jingxuan; Wen, Junhao; Chang, Changgee; Mu, Shizhuo; Chen, Jiong; Shivakumar, Manu; Cui, Yuhan; Erus, Guray; Yang, Zhijian; Yang, Shu; Wen, Zixuan; The Alzheimer’s Disease Neuroimaging Initiative; Zhao, Yize; Kim, Dokyoon; Duong-Tran, Duy; Saykin, Andrew J.; Zhao, Bingxin; Davatzikos, Christos; Long, Qi; Shen, Li; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBrain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.Item 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 MedicineThe 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.Item 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 MedicineIntroduction: 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.Item 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 MedicineIntroduction: 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.Item Analysis of Copy Number Variation in Alzheimer’s Disease in a Cohort of Clinically Characterized and Neuropathologically Verified Individuals(Public Library of Science, 2012) Swaminathan, Shanker; Huentelman, Matthew J.; Corneveaux, Jason J.; Myers, Amanda J.; Faber, Kelley M.; Foroud, Tatiana; Mayeux, Richard; Shen, Li; Kim, Sungeun; Turk, Mari; Hardy, John; Reiman, Eric M.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); NIA-LOAD/NCRAD Family Study Group; Radiology and Imaging Sciences, School of MedicineCopy number variations (CNVs) are genomic regions that have added (duplications) or deleted (deletions) genetic material. They may overlap genes affecting their function and have been shown to be associated with disease. We previously investigated the role of CNVs in late-onset Alzheimer's disease (AD) and mild cognitive impairment using Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study participants, and identified a number of genes overlapped by CNV calls. To confirm the findings and identify other potential candidate regions, we analyzed array data from a unique cohort of 1617 Caucasian participants (1022 AD cases and 595 controls) who were clinically characterized and whose diagnosis was neuropathologically verified. All DNA samples were extracted from brain tissue. CNV calls were generated and subjected to quality control (QC). 728 cases and 438 controls who passed all QC measures were included in case/control association analyses including candidate gene and genome-wide approaches. Rates of deletions and duplications did not significantly differ between cases and controls. Case-control association identified a number of previously reported regions (CHRFAM7A, RELN and DOPEY2) as well as a new gene (HLA-DRA). Meta-analysis of CHRFAM7A indicated a significant association of the gene with AD and/or MCI risk (P = 0.006, odds ratio = 3.986 (95% confidence interval 1.490-10.667)). A novel APP gene duplication was observed in one case sample. Further investigation of the identified genes in independent and larger samples is warranted.Item Analysis of the Inverse Association between Cancer and Alzheimer’s Disease: Results from the Alzheimer’s Disease Neuroimaging Initiative Cohort(Office of the Vice Chancellor for Research, 2014-04-11) Nudelman, Kelly N. H.; Risacher, Shannon L.; West, John D.; Nho, Kwangsik; Ramanan, Vijay K.; McDonald, Brenna C.; Shen, Li; Foroud, Tatiana M.; Schneider, Bryan P.; Saykin, Andrew J.Although a number of studies support a reciprocal inverse association between diagnoses of cancer and Alzheimer’s disease (AD), to date there has not been any systemic investigation of the neurobiological impact of or genetic risk factors underlying this effect. To facilitate this goal, this study aimed to replicate the inverse association of cancer and AD using data from the NIA Alzheimer’s Disease Neuroimaging Initiative, which includes age-matched cases and controls with information on cancer history, AD progression, neuroimaging, and genomic data. Subjects included individuals with AD (n=234), mild cognitive impairment (MCI, n=542), and healthy controls (HC, n=293). After controlling for sex, education, race/ethnicity, smoking, and apolipoprotein E (APOE) e2/3/4 allele groups, cancer history was protective against baseline AD diagnosis (p=0.042), and was associated with later age of AD onset (p=0.001). Cancer history appears to result in a cumulative protective effect; individuals with more than one cancer had a later age of AD onset compared to those with only one cancer (p=0.001). Finally, a protective effect of AD was also observed in individuals who developed incident cancer after enrolling (post-baseline visit); 20 individuals with MCI and 9 HC developed cancer, while no AD patients had subsequent cancer diagnoses (p=0.013). This supports previous research on the inverse association of cancer and AD, and importantly provides novel evidence that this effect appears to be independent of APOE, the major known genetic risk factor for AD. Future analyses will investigate the neurobiological and genetic basis of this effect.Item APOE and BCHE as modulators of cerebral amyloid deposition: a florbetapir PET genome-wide association study(Springer Nature, 2014) Ramanan, Vijay K.; Risacher, Shannon L.; Nho, Kwangsik; Kim, Sungeun; Swaminathan, Shanker; Shen, Li; Foroud, Tatiana M.; Hakonarson, Hakon; Huentelman, Matthew J.; Aisen, Paul S.; Petersen, Ronald C.; Green, Robert C.; Jack, Clifford R.; Koeppe, Robert A.; Jagust, William J.; Weiner, Michael W.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineDeposition of amyloid-β (Aβ) in the cerebral cortex is thought to be a pivotal event in Alzheimer's disease (AD) pathogenesis with a significant genetic contribution. Molecular imaging can provide an early noninvasive phenotype, but small samples have prohibited genome-wide association studies (GWAS) of cortical Aβ load until now. We employed florbetapir ((18)F) positron emission tomography (PET) imaging to assess brain Aβ levels in vivo for 555 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). More than six million common genetic variants were tested for association to quantitative global cortical Aβ load controlling for age, gender and diagnosis. Independent genome-wide significant associations were identified on chromosome 19 within APOE (apolipoprotein E) (rs429358, P=5.5 × 10(-14)) and on chromosome 3 upstream of BCHE (butyrylcholinesterase) (rs509208, P=2.7 × 10(-8)) in a region previously associated with serum BCHE activity. Together, these loci explained 15% of the variance in cortical Aβ levels in this sample (APOE 10.7%, BCHE 4.3%). Suggestive associations were identified within ITGA6, near EFNA5, EDIL3, ITGA1, PIK3R1, NFIB and ARID1B, and between NUAK1 and C12orf75. These results confirm the association of APOE with Aβ deposition and represent the largest known effect of BCHE on an AD-related phenotype. BCHE has been found in senile plaques and this new association of genetic variation at the BCHE locus with Aβ burden in humans may have implications for potential disease-modifying effects of BCHE-modulating agents in the AD spectrum.Item APOE effect on Alzheimer's disease biomarkers in older adults with significant memory concern(Elsevier, 2015-12) Risacher, Shannon L.; Kim, Sungeun; Nho, Kwangsik; Foroud, Tatiana; Shen, Li; Peterson, Ronald C.; Jack Jr, Clifford R.; Beckett, Laurel A.; Aisen, Paul S.; Koeppe, Robert A.; Jagust, William J.; Shaw, Leslie M.; Trojanowski, John Q.; Department of Radiology and Imaging Sciences, IU School of MedicineINTRODUCTION: This study assessed apolipoprotein E (APOE) ε4 carrier status effects on Alzheimer's disease imaging and cerebrospinal fluid (CSF) biomarkers in cognitively normal older adults with significant memory concerns (SMC). METHODS: Cognitively normal, SMC, and early mild cognitive impairment participants from Alzheimer's Disease Neuroimaging Initiative were divided by APOE ε4 carrier status. Diagnostic and APOE effects were evaluated with emphasis on SMC. Additional analyses in SMC evaluated the effect of the interaction between APOE and [(18)F]Florbetapir amyloid positivity on CSF biomarkers. RESULTS: SMC ε4+ showed greater amyloid deposition than SMC ε4-, but no hypometabolism or medial temporal lobe (MTL) atrophy. SMC ε4+ showed lower amyloid beta 1-42 and higher tau/p-tau than ε4-, which was most abnormal in APOE ε4+ and cerebral amyloid positive SMC. DISCUSSION: SMC APOE ε4+ show abnormal changes in amyloid and tau biomarkers, but no hypometabolism or MTL neurodegeneration, reflecting the at-risk nature of the SMC group and the importance of APOE in mediating this risk.Item Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method(IEEE, 2020) Du, Lei; Liu, Fang; Liu, Kefei; Yao, Xiaohui; Risacher, Shannon L.; Han, Junwei; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of MedicineBrain 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.