Browsing by Subject "Alzheimer's disease"

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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 deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure
    (Springer Nature, 2021-12-03) Yang, Zhijian; Nasrallah, Ilya M.; Shou, Haochang; Wen, Junhao; Doshi, Jimit; Habes, Mohamad; Erus, Guray; Abdulkadir, Ahmed; Resnick, Susan M.; Albert, Marilyn S.; Maruff, Paul; Fripp, Jurgen; Morris, John C.; Wolk, David A.; Davatzikos, Christos; iSTAGING Consortium; Baltimore Longitudinal Study of Aging (BLSA); Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.
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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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    Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease
    (Springer Nature, 2021-09-09) Gonneaud, Julie; Baria, Alex T.; Binette, Alexa Pichet; Gordon, Brian A.; Chhatwal, Jasmeer P.; Cruchaga, Carlos; Jucker, Mathias; Levin, Johannes; Salloway, Stephen; Farlow, Martin; Gauthier, Serge; Benzinger, Tammie L.S.; Morris, John C.; Bateman, Randall J.; Breitner, John C.S.; Poirier, Judes; Vachon-Presseau, Etienne; Villeneuve, Sylvia; Neurology, School of Medicine
    Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
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    Accelerating diversity in Alzheimer's disease research by partnering with a community advisory board
    (Wiley, 2023-05-28) Pena-Garcia, Alex; Richards, Ralph; Richards, Mollie; Campbell, Christopher; Mosley, Hank; Asper, Joseph; Eliacin, Johanne; Polsinelli, Angelina; Apostolova, Liana; Hendrie, Hugh; Tackett, Andrew; Elliott, Caprice; Van Heiden, Sarah; Gao, Sujuan; Saykin, Andrew; Wang, Sophia; Medicine, School of Medicine
    Introduction: Community advisory boards (CABs) and researcher partnerships present a promising opportunity to accelerate enrollment of underrepresented groups (URGs). We outline the framework for how the CAB and researchers at the Indiana Alzheimer's Disease Research Center (IADRC) partnered to accelerate URG participation in AD neuroimaging research. Methods: CAB and the IADRC researchers partnered to increase the CAB's impact on URG study enrollment through community and research interactions. Community interactions included the CAB collaboratively building a network of URG focused community organizations and collaborating with those URG-focused organizations to host IADRC outreach and recruitment events. Research interactions included direct impact (CAB members referring themselves or close contacts as participants) and strategic impact, mainly by the CAB working with researchers to develop and refine URG focused outreach and recruitment strategies for IADRC and affiliated studies to increase URG representation. We created a database infrastructure to measure how these interactions impacted URG study enrollment. Results: Out of the 354 URG research referrals made to the IADRC between October 2019 and December 2022, 267 referrals were directly referred by the CAB (N = 36) or from community events in which CAB members organized and/or volunteered at (N = 231). Out of these 267 referrals, 34 were enrolled in IADRC and 2 were enrolled in Indiana University Longitudinal Early Onset AD Study (IU LEADS). Of note, both studies require the prospective participants to be willing to do MRI and PET scans. As of December 2022, 30 out of the 34 enrolled participants have received a consensus diagnosis; the majority were cognitively normal (64.7%), with the remainder having mild cognitive impairment (17.6%) or early-stage AD (2.9%). Discussion: The IADRC CAB-researcher partnership had a measurable impact on the enrollment of African American/Black adults in AD neuroimaging studies. Future studies will need to test whether this conceptual model works for other sites and for other URGs.
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    AD Informer Set: Chemical tools to facilitate Alzheimer's disease drug discovery
    (Wiley, 2022-04-20) Potjewyd, Frances M.; Annor-Gyamfi, Joel K.; Aubé, Jeffrey; Chu, Shaoyou; Conlon, Ivie L.; Frankowski, Kevin J.; Guduru, Shiva K.R.; Hardy, Brian P.; Hopkins, Megan D.; Kinoshita, Chizuru; Kireev, Dmitri B.; Mason, Emily R.; Moerk, Charles T.; Nwogbo, Felix; Pearce, Kenneth H.; Richardson, Timothy I.; Rogers, David A.; Soni, Disha M.; Stashko, Michael; Wang, Xiaodong; Wells, Carrow; Willson, Timothy M.; Frye, Stephen V.; Young, Jessica E.; Axtman, Alison D.; Medicine, School of Medicine
    Introduction: The portfolio of novel targets to treat Alzheimer's disease (AD) has been enriched by the Accelerating Medicines Partnership Program for Alzheimer's Disease (AMP AD) program. Methods: Publicly available resources, such as literature and databases, enabled a data-driven effort to identify existing small molecule modulators for many protein products expressed by the genes nominated by AMP AD and suitable positive control compounds to be included in the set. Compounds contained within the set were manually selected and annotated with associated published, predicted, and/or experimental data. Results: We built an annotated set of 171 small molecule modulators targeting 98 unique proteins that have been nominated by AMP AD consortium members as novel targets for the treatment of AD. The majority of compounds included in the set are inhibitors. These small molecules vary in their quality and should be considered chemical tools that can be used in efforts to validate therapeutic hypotheses, but which will require further optimization. A physical copy of the AD Informer Set can be requested on the Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) website. Discussion: Small molecules that enable target validation are important tools for the translation of novel hypotheses into viable therapeutic strategies for AD.
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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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    Advancements in APOE and dementia research: Highlights from the 2023 AAIC Advancements: APOE conference
    (Wiley, 2024) Kloske, Courtney M.; Belloy, Michael E.; Blue, Elizabeth E.; Bowman, Gregory R.; Carrillo, Maria C.; Chen, Xiaoying; Chiba-Falek, Ornit; Davis, Albert A.; Di Paolo, Gilbert; Garretti, Francesca; Gate, David; Golden, Lesley R.; Heinecke, Jay W.; Herz, Joachim; Huang, Yadong; Iadecola, Costantino; Johnson, Lance A.; Kanekiyo, Takahisa; Karch, Celeste M.; Khvorova, Anastasia; Koppes-den Hertog, Sascha J.; Lamb, Bruce T.; Lawler, Paige E.; Le Guen, Yann; Litvinchuk, Alexandra; Liu, Chia-Chen; Mahinrad, Simin; Marcora, Edoardo; Marino, Claudia; Michaelson, Danny M.; Miller, Justin J.; Morganti, Josh M.; Narayan, Priyanka S.; Naslavsky, Michel S.; Oosthoek, Marlies; Ramachandran, Kapil V.; Ramakrishnan, Abhirami; Raulin, Ana-Caroline; Robert, Aiko; Saleh, Rasha N. M.; Sexton, Claire; Shah, Nilomi; Shue, Francis; Sible, Isabel J.; Soranno, Andrea; Strickland, Michael R.; Tcw, Julia; Thierry, Manon; Tsai, Li-Huei; Tuckey, Ryan A.; Ulrich, Jason D.; van der Kant, Rik; Wang, Na; Wellington, Cheryl L.; Weninger, Stacie C.; Yassine, Hussein N.; Zhao, Na; Bu, Guojun; Goate, Alison M.; Holtzman, David M.; Neurology, School of Medicine
    Introduction: The apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimer's disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid-beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex-dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research. Methods: In 2023, the Alzheimer's Association convened multidisciplinary researchers at the "AAIC Advancements: APOE" conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE-targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function. Results: This manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field. Discussion: Understanding apoE's multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine. Highlights: APOE is a central player in the pathogenesis of Alzheimer's disease. APOE exerts a numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.
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    Advancements in Immunity and Dementia Research: Highlights from the 2023 AAIC Advancements: Immunity Conference
    (Wiley, 2025) Kloske, Courtney M.; Mahinrad, Simin; Barnum, Christopher J.; Batista, Andre F.; Bradshaw, Elizabeth M.; Butts, Brittany; Carrillo, Maria C.; Chakrabarty, Paramita; Chen, Xiaoying; Craft, Suzanne; Da Mesquita, Sandro; Dabin, Luke C.; Devanand, Davangere; Duran-Laforet, Violeta; Elyaman, Wassim; Evans, Elizabeth E.; Fitzgerald-Bocarsly, Patricia; Foley, Kate E.; Harms, Ashley S.; Heneka, Michael T.; Hong, Soyon; Huang, Yu-Wen A.; Jackvony, Stephanie; Lai, Laijun; Le Guen, Yann; Lemere, Cynthia A.; Liddelow, Shane A.; Martin-Peña, Alfonso; Orr, Anna G.; Quintana, Francisco J.; Ramey, Grace D.; Rexach, Jessica E.; Rizzo, Stacey J. S.; Sexton, Claire; Tang, Alice S.; Torrellas, Jose G.; Tsai, Andy P.; van Olst, Lynn; Walker, Keenan A.; Wharton, Whitney; Tansey, Malú Gámez; Wilcock, Donna M.; Medical and Molecular Genetics, School of Medicine
    The immune system is a key player in the onset and progression of neurodegenerative disorders. While brain resident immune cell-mediated neuroinflammation and peripheral immune cell (eg, T cell) infiltration into the brain have been shown to significantly contribute to Alzheimer's disease (AD) pathology, the nature and extent of immune responses in the brain in the context of AD and related dementias (ADRD) remain unclear. Furthermore, the roles of the peripheral immune system in driving ADRD pathology remain incompletely elucidated. In March of 2023, the Alzheimer's Association convened the Alzheimer's Association International Conference (AAIC), Advancements: Immunity, to discuss the roles of the immune system in ADRD. A wide range of topics were discussed, such as animal models that replicate human pathology, immune-related biomarkers and clinical trials, and lessons from other fields describing immune responses in neurodegeneration. This manuscript presents highlights from the conference and outlines avenues for future research on the roles of immunity in neurodegenerative disorders. HIGHLIGHTS: The immune system plays a central role in the pathogenesis of Alzheimer's disease. The immune system exerts numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The 2023 AAIC, Advancements: Immunity, encouraged discussions and collaborations on understanding the role of the immune system.