Browsing by Author "Saykin, Andrew J."

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    [(11)C]PiB PET in Gerstmann-Sträussler-Scheinker disease
    (e-Century Publishing Corporation, 2016) Deters, Kacie D.; Risacher, Shannon L.; Yoder, Karmen K.; Oblak, Adrian L.; Unverzagt, Frederick W.; Murrell, Jill R.; Epperson, Francine; Tallman, Eileen F.; Quaid, Kimberly A.; Farlow, Martin R.; Saykin, Andrew J.; Ghetti, Bernardino; Department of Pathology & Laboratory Medicine, IU School of Medicine
    Gerstmann-Sträussler-Scheinker Disease (GSS) is a familial neurodegenerative disorder characterized clinically by ataxia, parkinsonism, and dementia, and neuropathologically by deposition of diffuse and amyloid plaques composed of prion protein (PrP). The purpose of this study was to evaluate if [(11)C]Pittsburgh Compound B (PiB) positron emission tomography (PET) is capable of detecting PrP-amyloid in PRNP gene carriers. Six individuals at risk for GSS and eight controls underwent [(11)C]PiB PET scans using standard methods. Approximately one year after the initial scan, each of the three asymptomatic carriers (two with PRNP P102L mutation, one with PRNP F198S mutation) underwent a second [(11)C]PiB PET scan. Three P102L carriers, one F198S carrier, and one non-carrier of the F198S mutation were cognitively normal, while one F198S carrier was cognitively impaired during the course of this study. No [(11)C]PiB uptake was observed in any subject at baseline or at follow-up. Neuropathologic study of the symptomatic individual revealed PrP-immunopositive plaques and tau-immunopositive neurofibrillary tangles in cerebral cortex, subcortical nuclei, and brainstem. PrP deposits were also numerous in the cerebellar cortex. This is the first study to investigate the ability of [(11)C]PiB PET to bind to PrP-amyloid in GSS F198S subjects. This finding suggests that [(11)C]PiB PET is not suitable for in vivo assessment of PrP-amyloid plaques in patients with GSS.
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    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 Medicine
    The 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.
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    A cross‐sectional study of α‐synuclein seed amplification assay in Alzheimer's disease neuroimaging initiative: Prevalence and associations with Alzheimer's disease biomarkers and cognitive function
    (Wiley, 2024) Tosun, Duygu; Hausle, Zachary; Iwaki, Hirotaka; Thropp, Pamela; Lamoureux, Jennifer; Lee, Edward B.; MacLeod, Karen; McEvoy, Sean; Nalls, Michael; Perrin, Richard J.; Saykin, Andrew J.; Shaw, Leslie M.; Singleton, Andrew B.; Lebovitz, Russ; Weiner, Michael W.; Blauwendraat, Cornelis; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: Alzheimer's disease (AD) pathology is defined by β-amyloid (Aβ) plaques and neurofibrillary tau, but Lewy bodies (LBs; 𝛼-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed. Methods: A validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aβ and tau biomarkers, risk-factors, genetics, and cognitive trajectories. Results: SAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aβ burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive. Discussion: SAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD's nature and progression. Highlights: SAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aβ burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.
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    A DAT1 gene and APOE ε4 interaction is associated with apathy and structural brain changes in Alzheimer’s Disease
    (Wiley, 2025-01-09) Malik, Rubina; Beaton, Derek; Saykin, Andrew J.; Nho, Kwangsik; Finger, Elizabeth; Radiology and Imaging Sciences, School of Medicine
    Background: Apathy in patients with Alzheimer’s disease (AD) is associated with significant morbidity. We examined whether interactions between genetic variants related to neurotransmitter systems and regional brain atrophy are associated with apathy in patients with mild cognitive impairment (MCI) and AD. Method: For 1162 participants in the Alzheimer’s Disease Neuroimaging Initiative, including those with AD, MCI and cognitively normal individuals, a partial least squares correspondence analysis (PLS‐CA) modeled interactions between single nucleotide polymorphisms (SNPs), structural whole‐brain imaging variables, and apathy. Result: An interaction between apathy, the possession of an APOE (apolipoprotein E) ε4 allele combined with minor homozygosity for the DAT1 (dopamine transporter 1) gene, and brain atrophy. Conclusion: The results point to an association of a dopaminergic genetic marker and apathy in AD and may inform future design of clinical trials of apathy, as well as new treatment targets.
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    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 Health
    Brain 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.
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    A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores
    (BMC, 2023-06-22) Kang, Moonil; Ang, Ting Fang Alvin; Devine, Sherral A.; Sherva, Richard; Mukherjee, Shubhabrata; Trittschuh, Emily H.; Gibbons, Laura E.; Scollard, Phoebe; Lee, Michael; Choi, Seo-Eun; Klinedinst, Brandon; Nakano, Connie; Dumitrescu, Logan C.; Durant, Alaina; Hohman, Timothy J.; Cuccaro, Michael L.; Saykin, Andrew J.; Kukull, Walter A.; Bennett, David A.; Wang, Li-San; Mayeux, Richard P.; Haines, Jonathan L.; Pericak-Vance, Margaret A.; Schellenberg, Gerard D.; Crane, Paul K.; Au, Rhoda; Lunetta, Kathryn L.; Mez, Jesse B.; Farrer, Lindsay A.; Radiology and Imaging Sciences, School of Medicine
    Background: More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes. Methods: We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software. Results: Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD. Conclusion: Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.
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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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    A protein panel in cerebrospinal fluid for diagnostic and predictive assessment of alzheimer’s disease
    (American Association for the Advancement of Science, 2023) Haque, Rafi; Watson, Caroline M.; Liu, Jiaqi; Carter, E. Kathleen; Duong, Duc M.; Lah, James J.; Wingo, Aliza P.; Roberts, Blaine R.; Johnson, Erik C. B.; Saykin, Andrew J.; Shaw, Leslie M.; Seyfried, Nicholas T.; Wingo, Thomas S.; Levey, Allan I.; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a neurodegenerative disease with heterogenous pathophysiological changes that develop years before the onset of clinical symptoms. These preclinical changes have generated considerable interest in identifying markers for the pathophysiological mechanisms linked to AD and AD-related disorders (ADRD). On the basis of our prior work integrating cerebrospinal fluid (CSF) and brain proteome networks, we developed a reliable and high-throughput mass spectrometry-selected reaction monitoring assay that targets 48 key proteins altered in CSF. To test the diagnostic utility of these proteins and compare them with existing AD biomarkers, CSF collected at baseline visits was assayed from 706 participants recruited from the Alzheimer's Disease Neuroimaging Initiative. We found that the targeted CSF panel of 48 proteins (CSF 48 panel) performed at least as well as existing AD CSF biomarkers (Aβ42, tTau, and pTau181) for predicting clinical diagnosis, FDG PET, hippocampal volume, and measures of cognitive and dementia severity. In addition, for each of those outcomes, the CSF 48 panel plus the existing AD CSF biomarkers significantly improved diagnostic performance. Furthermore, the CSF 48 panel plus existing AD CSF biomarkers significantly improved predictions for changes in FDG PET, hippocampal volume, and measures of cognitive decline and dementia severity compared with either measure alone. A potential reason for these improvements is that the CSF 48 panel reflects a range of altered biology observed in AD/ADRD. In conclusion, we show that the CSF 48 panel complements existing AD CSF biomarkers to improve diagnosis and predict future cognitive decline and dementia severity.
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    A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer’s disease risk and rates of disease progression
    (Wiley, 2023) Bae, Jinhyeong; Logan, Paige E.; Acri, Dominic J.; Bharthur, Apoorva; Nho, Kwangsik; Saykin, Andrew J.; Risacher, Shannon L.; Nudelman, Kelly; Polsinelli, Angelina J.; Pentchev, Valentin; Kim, Jungsu; Hammers, Dustin B.; Apostolova, Liana G.; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Background: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. Methods: We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. Results: Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. Discussion: The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
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    Aberrant GAP43 Gene Expression Is Alzheimer Disease Pathology-Specific
    (Wiley, 2023) Pyun, Jung-Min; Park, Young Ho; Wang, Jiebiao; Bice, Paula J.; Bennett, David A.; Kim, Sang Yun; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine