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Beer S, Elmenhorst D, Bischof GN, Ramirez A, Bauer A, Drzezga A. Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden. Neurobiol Aging 2024; 143:19-29. [PMID: 39208715 DOI: 10.1016/j.neurobiolaging.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 08/05/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Aquaporin-4 (AQP4) is hypothesized to be a component of the glymphatic system, a pathway for removing brain interstitial solutes like amyloid-β (Aβ). Evidence exists that genetic variation of AQP4 impacts Aβ clearance, clinical outcome in Alzheimer's disease as well as sleep measures. We examined whether a risk score calculated from several AQP4 single-nucleotide polymorphisms (SNPs) is related to Aβ neuropathology in older cognitively unimpaired white individuals. We used a machine learning approach and explainable artificial intelligence to extract information on synergistic effects of AQP4 SNPs on brain amyloid burden from the ADNI cohort. From this information, we formulated a sex-specific AQP4 SNP-based risk score and evaluated it using data from the screening process of the A4 study. We found in both cohorts significant associations of the risk score with brain amyloid burden. The results support the hypothesis of an involvement of the glymphatic system, and particularly AQP4, in brain amyloid aggregation pathology. They suggest also that different AQP4 SNPs exert a synergistic effect on the build-up of brain amyloid burden.
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Affiliation(s)
- Simone Beer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Germany.
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Germany; Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Gerard N Bischof
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Germany; Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, Bonn, Germany; Department of Psychiatry and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, United States
| | - Andreas Bauer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Germany
| | - Alexander Drzezga
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Germany; Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
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2
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Schindler SE, Petersen KK, Saef B, Tosun D, Shaw LM, Zetterberg H, Dage JL, Ferber K, Triana-Baltzer G, Du-Cuny L, Li Y, Coomaraswamy J, Baratta M, Mordashova Y, Saad ZS, Raunig DL, Ashton NJ, Meyers EA, Rubel CE, Rosenbaugh EG, Bannon AW, Potter WZ. Head-to-head comparison of leading blood tests for Alzheimer's disease pathology. Alzheimers Dement 2024. [PMID: 39394841 DOI: 10.1002/alz.14315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 09/10/2024] [Accepted: 09/11/2024] [Indexed: 10/14/2024]
Abstract
INTRODUCTION Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. METHODS Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. RESULTS Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. DISCUSSION This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes. HIGHLIGHTS Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low.
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Affiliation(s)
- Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Kellen K Petersen
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Benjamin Saef
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kyle Ferber
- Biogen, Biomarkers Group, Cambridge, Massachusetts, USA
| | - Gallen Triana-Baltzer
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, California, USA
| | - Lei Du-Cuny
- AbbVie, Ludwigshafen am Rhein, Rheinland-Pfalz, Germany
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Michael Baratta
- Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | | | - Ziad S Saad
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, California, USA
| | - David L Raunig
- Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Nicholas J Ashton
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | | | | | - Erin G Rosenbaugh
- The Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
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Hou B, Wen Z, Bao J, Zhang R, Tong B, Yang S, Wen J, Cui Y, Moore JH, Saykin AJ, Huang H, Thompson PM, Ritchie MD, Davatzikos C, Shen L. Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery. Med Image Anal 2024; 97:103231. [PMID: 38941858 PMCID: PMC11365808 DOI: 10.1016/j.media.2024.103231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 05/04/2024] [Accepted: 06/03/2024] [Indexed: 06/30/2024]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.
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Affiliation(s)
- Bojian Hou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Richard Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Boning Tong
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junhao Wen
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90007, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
| | - Paul M Thompson
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90007, USA
| | - Marylyn D Ritchie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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Oh S, Kim S, Lee JE, Park BY, Hye Won J, Park H. Multimodal analysis of disease onset in Alzheimer's disease using Connectome, Molecular, and genetics data. Neuroimage Clin 2024; 43:103660. [PMID: 39197213 PMCID: PMC11393605 DOI: 10.1016/j.nicl.2024.103660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 08/23/2024] [Accepted: 08/23/2024] [Indexed: 09/01/2024]
Abstract
Alzheimer's disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer's disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.
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Affiliation(s)
- Sewook Oh
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sunghun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Jong-Eun Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Ji Hye Won
- Department of Computer Engineering, Pukyong National University, Busan, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
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Li Y, Chen G, Wang G, Zhou Z, An S, Dai S, Jin Y, Zhang C, Zhang M, Yu F. Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD. Front Neurol 2024; 15:1444795. [PMID: 39211812 PMCID: PMC11358067 DOI: 10.3389/fneur.2024.1444795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 07/25/2024] [Indexed: 09/04/2024] Open
Abstract
Background Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. Methods In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). Results On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models. Conclusion We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.
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Affiliation(s)
- Yuxia Li
- Department of Neurology, Tangshan Central Hospital, Hebei, China
| | - Guanqun Chen
- Department of Neurology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Guoxin Wang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zhiyi Zhou
- JD Health International Inc., Beijing, China
| | - Shan An
- JD Health International Inc., Beijing, China
| | - Shipeng Dai
- College of Science, Northeastern University, Shenyang, China
| | - Yuxin Jin
- JD Health International Inc., Beijing, China
| | - Chao Zhang
- JD Health International Inc., Beijing, China
| | - Mingkai Zhang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Feng Yu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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Toccaceli Blasi M, Alfano AR, Salzillo M, Buscarnera S, Raparelli V, Cesari M, Bruno G, Canevelli M. Sex-specific clinical and neurobiological correlates of fatigue in older adults. GeroScience 2024:10.1007/s11357-024-01259-0. [PMID: 39129050 DOI: 10.1007/s11357-024-01259-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 06/14/2024] [Indexed: 08/13/2024] Open
Abstract
Fatigue is a common and distressful symptom in older people and has been associated with adverse health outcomes. Nevertheless, its sex-specific pathophysiological underpinnings and clinical correlates have been scarcely investigated. We aimed to comprehensively explore the clinical and neurobiological determinants of fatigue in cognitively unimpaired older adults. A sex-stratified analysis was conducted to look for differences in the clinical expression of fatigue among women and men. Data on cognitively normal individuals were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 study. Fatigue was defined based on self-report at baseline. For each participant, information on sociodemographics, comorbidities, mood, cognitive performance, frailty, and biomarkers of brain pathology was collected. Logistic regression models, stratified by sex, were conducted to explore the factors associated with fatigue. Among the 291 participants selected, 44 subjects (15.1% of the total sample) self-reported fatigue at baseline. Subjects reporting fatigue were more likely women, had higher frailty degrees, and more severe depressive symptoms than those without fatigue. Moreover, they tended to have lower MRI hippocampus volumes. Among women, those reporting fatigue exhibited higher frailty levels, worse depression, and lower MRI hippocampus volumes relative to those without fatigue. Higher frailty degrees were also observed in men reporting vs. non-reporting fatigue. In the adjusted logistic regression model, more severe depression (OR 1.64, 95% CI 1.18-2.28; p < 0.01) and lower MRI hippocampus volumes (OR 0.41, 95% CI 0.19-0.90; p = 0.03) resulted independently associated with fatigue in women, while higher frailty degrees (OR 3.10, 95% CI 1.27-7.54 per 0.1 increase in a 39-item Frailty index; p = 0.01) in men. Fatigue is a complex symptom with a sex-specific pattern of clinical and neurobiological correlates. A better understanding of the underlying mechanisms of these associations is warranted to develop sex-informed approaches for personalized treatments.
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Affiliation(s)
| | - Alba Rosa Alfano
- Department of Internal Medicine and Medical Specialties, UOC Geriatrics, "Sapienza" University, Rome, Italy
| | - Martina Salzillo
- Department of Human Neuroscience, "Sapienza" University, Rome, Italy
| | - Simona Buscarnera
- Department of Human Neuroscience, "Sapienza" University, Rome, Italy
| | - Valeria Raparelli
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Matteo Cesari
- Ageing and Health Unit, Department of Maternal, Newborn, Child, Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland
| | - Giuseppe Bruno
- Department of Human Neuroscience, "Sapienza" University, Rome, Italy
- Santa Lucia Foundation IRCCS, Rome, Italy
| | - Marco Canevelli
- Department of Human Neuroscience, "Sapienza" University, Rome, Italy
- National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
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Tosun D, Hausle Z, Iwaki H, Thropp P, Lamoureux J, Lee EB, MacLeod K, McEvoy S, Nalls M, Perrin RJ, Saykin AJ, Shaw LM, Singleton AB, Lebovitz R, Weiner MW, Blauwendraat C. 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. Alzheimers Dement 2024; 20:5114-5131. [PMID: 38770829 PMCID: PMC11350010 DOI: 10.1002/alz.13858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 05/22/2024]
Abstract
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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Affiliation(s)
- Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Zachary Hausle
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Hirotaka Iwaki
- Center for Alzheimer's and Related DementiasNational Institute on Aging and National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
| | - Pamela Thropp
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | | | - Edward B. Lee
- Department of Pathology and Laboratory MedicinePerelman School of Medicine University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | | | - Michael Nalls
- Center for Alzheimer's and Related DementiasNational Institute on Aging and National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMarylandUSA
- DataTecnica LLCWashingtonDistrict of ColumbiaUSA
- Laboratory of NeurogeneticsNational Institute on AgingNational Institutes of HealthBethesdaMarylandUSA
| | - Richard J. Perrin
- Department of Pathology & ImmunologyWashington University in St. LouisSt. LouisMissouriUSA
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Andrew J. Saykin
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicinePerelman School of Medicine University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew B. Singleton
- Center for Alzheimer's and Related DementiasNational Institute on Aging and National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingNational Institutes of HealthBethesdaMarylandUSA
| | | | - Michael W. Weiner
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Cornelis Blauwendraat
- Center for Alzheimer's and Related DementiasNational Institute on Aging and National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingNational Institutes of HealthBethesdaMarylandUSA
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Han SW, Lee SH, Kim JH, Lee JJ, Park YH, Kim S, Nho K, Sohn JH. Association of liver function markers and apolipoprotein E ε4 with pathogenesis and cognitive decline in Alzheimer's disease. Front Aging Neurosci 2024; 16:1411466. [PMID: 39114318 PMCID: PMC11303325 DOI: 10.3389/fnagi.2024.1411466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
Background Alzheimer's disease (AD) is a complex neurodegenerative disorder influenced by various factors, including liver function, which may impact the clearance of amyloid-β (Aβ) in the brain. This study aimed to explore how the apolipoprotein E (APOE) ε4 allele affects the relationship of liver function markers with AD pathology and cognition. Methods We analyzed data from two independent cohorts, including 732 participants from the Hallym University Medical Center and 483 from the Alzheimer's Disease Neuroimaging Initiative, each group consisting of individuals with and without the APOE ε4 allele. Cross-sectional analyses evaluated the associations of liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT], alkaline phosphatase, total bilirubin, and albumin) with AD diagnosis, amyloid positron emission tomography (PET) burden, and cerebrospinal fluid biomarkers for AD (Aβ42, total tau, and phosphorylated tau181) at baseline. Longitudinally, we investigated the associations between these liver enzymes and changes in cognitive performance over the course of a year. Logistic and linear regression models were used to analyze these associations and mediation analyses were conducted to assess whether age and amyloid PET burden mediated these associations. Results Only in the APOE ε4 carrier group, a high AST to ALT ratio and low ALT levels were significantly associated with AD diagnosis, increased amyloid PET burden, and faster longitudinal decline in cognitive function in both cohorts. In particular, the AST to ALT ratio was associated with cerebrospinal fluid Aβ42 levels exclusively in the APOE ε4 carrier group in the Alzheimer's Disease Neuroimaging Initiative cohort but not with phosphorylated tau181 or total tau levels. Moreover, mediation analyses from both cohorts revealed that in the APOE ε4 carriers group, age did not mediate the associations between liver enzymes and AD diagnosis or amyloid PET burden. However, amyloid PET burden partially mediated the association between liver enzymes and AD diagnosis exclusively in the APOE ε4 carriers group. Conclusion This study provides valuable insights into the significant association of the APOE ε4 allele with liver enzymes and their potential role in Aβ-related pathogenesis and cognition in AD. Further research is required to elucidate the underlying mechanisms and potential therapeutic implications of these findings.
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Affiliation(s)
- Sang-Won Han
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Sang-Hwa Lee
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jong Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jae-Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
- Institute of New Frontier Research Team, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jong-Hee Sohn
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
- Institute of New Frontier Research Team, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea
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9
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Bossa MN, Nakshathri AG, Berenguer AD, Sahli H. Generative AI unlocks PET insights: brain amyloid dynamics and quantification. Front Aging Neurosci 2024; 16:1410844. [PMID: 38952479 PMCID: PMC11215072 DOI: 10.3389/fnagi.2024.1410844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Introduction Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics. Methods Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution. Results We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression. Discussion Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.
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Affiliation(s)
- Matías Nicolás Bossa
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Akshaya Ganesh Nakshathri
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Abel Díaz Berenguer
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Hichem Sahli
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium
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10
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Hou B, Mondragón A, Tarzanagh DA, Zhou Z, Saykin AJ, Moore JH, Ritchie MD, Long Q, Shen L. PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:211-220. [PMID: 38827072 PMCID: PMC11141835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.
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Affiliation(s)
- Bojian Hou
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | | | | | - Qi Long
- University of Pennsylvania, Philadelphia, PA
| | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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11
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Qiu T, Liu Z, Rheault F, Legarreta JH, Valcourt Caron A, St‐Onge F, Strikwerda‐Brown C, Metz A, Dadar M, Soucy J, Pichet Binette A, Spreng RN, Descoteaux M, Villeneuve S. Structural white matter properties and cognitive resilience to tau pathology. Alzheimers Dement 2024; 20:3364-3377. [PMID: 38561254 PMCID: PMC11095478 DOI: 10.1002/alz.13776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/11/2024] [Accepted: 02/07/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION We assessed whether macro- and/or micro-structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. METHODS We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid-β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. RESULTS Higher global efficiency of the limbic network, as well as free-water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. DISCUSSION White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. HIGHLIGHTS Aβ and tau were associated with longitudinal memory change over ∼7.5 years. White matter properties attenuated the impact of tau pathology on memory change. Health/risk factors were associated with white matter properties.
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Affiliation(s)
- Ting Qiu
- Douglas Mental Health University InstituteMontrealCanada
| | - Zhen‐Qi Liu
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | - François Rheault
- Medical Imaging and NeuroInformatics LabUniversité de SherbrookeSherbrookeCanada
| | - Jon Haitz Legarreta
- Department of RadiologyBrigham and Women's HospitalMass General Brigham/Harvard Medical SchoolBostonMassachusettsUSA
| | - Alex Valcourt Caron
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | | | - Cherie Strikwerda‐Brown
- Douglas Mental Health University InstituteMontrealCanada
- School of Psychological ScienceThe University of Western AustraliaPerthAustralia
| | - Amelie Metz
- Douglas Mental Health University InstituteMontrealCanada
| | - Mahsa Dadar
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Jean‐Paul Soucy
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | | | - R. Nathan Spreng
- Douglas Mental Health University InstituteMontrealCanada
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | - Sylvia Villeneuve
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
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12
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Bischof GN, Jaeger E, Giehl K, Jessen F, Onur OA, O'Bryant S, Kara E, Weiss PH, Drzezga A. Cortical Tau Aggregation Patterns associated with Apraxia in Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.09.24305535. [PMID: 38645131 PMCID: PMC11030486 DOI: 10.1101/2024.04.09.24305535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Objectives Apraxia is a core feature of Alzheimer's disease, but the pathomechanism of this characteristic symptom is not well understood. Here, we systematically investigated apraxia profiles in a well-defined group of patients with Alzheimer's disease (AD; N=32) who additionally underwent PET imaging with the second-generation tau PET tracer [18F]PI-2620. We hypothesized that specific patterns of tau pathology might be related to apraxic deficits. Methods Patients (N=32) with a biomarker-confirmed diagnosis of Alzheimer's disease were recruited in addition to a sample cognitively unimpaired controls (CU 1 ; N=41). Both groups underwent in-depth neuropsychological assessment of apraxia (Dementia Apraxia Screening Test; DATE and the Cologne Apraxia Screening; KAS). In addition, static PET imaging with [18F]PI-2620 was performed to assess tau pathology in the AD patients. To specifically investigate the association of apraxia with regional tau-pathology, we compared the PET-data from this group with an independent sample of amyloid-negative cognitively intact participants (CU 2; N=54) by generation of z-score-deviation maps as well as voxel- based multiple regression analyses. Results We identified significant clusters of tau-aggregation in praxis-related regions (e.g., supramarginal gyrus, angular gyrus, temporal, parietal and occipital regions) that were associated with apraxia. These regions were similar between the two apraxia assessments. No correlations between tau-tracer uptake in primary motor cortical or subcortical brain regions and apraxia were observed. Conclusions These results suggest that tau deposition in specific cortical brain regions may induce local neuronal dysfunction leading to a dose-dependent functional decline in praxis performance.
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Rasi R, Guvenis A. Predicting amyloid positivity from FDG-PET images using radiomics: A parsimonious model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108098. [PMID: 38442621 DOI: 10.1016/j.cmpb.2024.108098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/30/2024] [Accepted: 02/20/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVE Amyloid plaques are one of the physical hallmarks of Alzheimer's disease. The objective of this study is to predict amyloid positivity non-invasively from FDG-PET images using a radiomics approach. METHODS We obtained FDG-PET images of 301 individuals from various groups, including control normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD), from the ADNI database. Following the utilization of the CSF Aβ1-42 (192) and Standardized Uptake Value Ratio (SUVR) (1.11) thresholds derived from Florbetapir scans, the subjects were categorized into two categories: those with a positive amyloid status (n = 185) and those with a negative amyloid status (n = 116). The process of segmenting the entire brain into 95 classes using the DKT-atlas was utilized. Following that, we obtained 120 characteristics for each of the 95 regions of interest (ROIs). We employed eight feature selection methods to analyze the features. Additionally, we utilized eight different classifiers on the 20 most significant features extracted from each feature selection method. Finally, in order to improve interpretability, we selected the most important features and ROIs. RESULT We found that the GNB classifier and the LASSO feature selection method had the best performance with an average accuracy of (AUC=0.924) while using 18 features on 15 ROIs. We were then able to reduce the model to three regions (Hippocampus, inferior parietal, and isthmus cingulate) and three gray-level based features (AUC=0.853). CONCLUSION The FDG-PET images which serve to study metabolic activity can be used to predict amyloid positivity without the use of invasive methods or another PET tracer and study. The proposed method has superior prediction accuracy with respect to similar studies reported in the literature using other imaging modalities. Only three brain regions had a high impact on amyloid positivity results.
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Affiliation(s)
- Ramin Rasi
- Institute of Biomedical Engineering Boğaziçi University, Türkiye.
| | - Albert Guvenis
- Institute of Biomedical Engineering Boğaziçi University, Türkiye.
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14
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Wang Z, Zhan Q, Tong B, Yang S, Hou B, Huang H, Saykin AJ, Thompson PM, Davatzikos C, Shen L. Distance-weighted Sinkhorn loss for Alzheimer's disease classification. iScience 2024; 27:109212. [PMID: 38433927 PMCID: PMC10906516 DOI: 10.1016/j.isci.2024.109212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
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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Affiliation(s)
- Zexuan Wang
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Qipeng Zhan
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Boning Tong
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Shu Yang
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Bojian Hou
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Heng Huang
- University of Maryland, College Park, 8125 Paint Branch Drive, College Park, MD 20742, USA
| | - Andrew J. Saykin
- Indiana University, 355 West 16th Street, Indianapolis, IN 46202, USA
| | - Paul M. Thompson
- University of Southern California, 4676 Admiralty Way, Marina Del Rey, CA 90292, USA
| | - Christos Davatzikos
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - Li Shen
- University of Pennsylvania, B301 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
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15
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Chumin EJ, Burton CP, Silvola R, Miner EW, Persohn SC, Veronese M, Territo PR. Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease. Alzheimers Dement 2024; 20:1538-1549. [PMID: 38032015 PMCID: PMC10984484 DOI: 10.1002/alz.13538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage. METHODS We performed 18 F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis. RESULTS The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model. DISCUSSION The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.
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Affiliation(s)
- Evgeny J. Chumin
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Indiana University Network Science Institute, Indiana UniversityBloomingtonIndianaUSA
| | - Charles P. Burton
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rebecca Silvola
- Department of MedicineDivision of Clinical PharmacologyIndiana University School of MedicineIndianapolisIndianaUSA
- Eli Lilly and CompanyIndianapolisIndianaUSA
| | - Ethan W. Miner
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Scott C. Persohn
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - Mattia Veronese
- Department of Information EngineeringUniversity of PaduaPaduaItaly
- Department of NeuroimagingKing's College LondonLondonUK
| | - Paul R. Territo
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
- Department of MedicineDivision of Clinical PharmacologyIndiana University School of MedicineIndianapolisIndianaUSA
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16
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Rubinski A, Dewenter A, Zheng L, Franzmeier N, Stephenson H, Deming Y, Duering M, Gesierich B, Denecke J, Pham AV, Bendlin B, Ewers M. Florbetapir PET-assessed demyelination is associated with faster tau accumulation in an APOE ε4-dependent manner. Eur J Nucl Med Mol Imaging 2024; 51:1035-1049. [PMID: 38049659 PMCID: PMC10881623 DOI: 10.1007/s00259-023-06530-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023]
Abstract
PURPOSE The main objectives were to test whether (1) a decrease in myelin is associated with enhanced rate of fibrillar tau accumulation and cognitive decline in Alzheimer's disease, and (2) whether apolipoprotein E (APOE) ε4 genotype is associated with worse myelin decrease and thus tau accumulation. METHODS To address our objectives, we repurposed florbetapir-PET as a marker of myelin in the white matter (WM) based on previous validation studies showing that beta-amyloid (Aβ) PET tracers bind to WM myelin. We assessed 43 Aβ-biomarker negative (Aβ-) cognitively normal participants and 108 Aβ+ participants within the AD spectrum with florbetapir-PET at baseline and longitudinal flortaucipir-PET as a measure of fibrillar tau (tau-PET) over ~ 2 years. In linear regression analyses, we tested florbetapir-PET in the whole WM and major fiber tracts as predictors of tau-PET accumulation in a priori defined regions of interest (ROIs) and fiber-tract projection areas. In mediation analyses we tested whether tau-PET accumulation mediates the effect of florbetapir-PET in the whole WM on cognition. Finally, we assessed the role of myelin alteration on the association between APOE and tau-PET accumulation. RESULTS Lower florbetapir-PET in the whole WM or at a given fiber tract was predictive of faster tau-PET accumulation in Braak stages or the connected grey matter areas in Aβ+ participants. Faster tau-PET accumulation in higher cortical brain areas mediated the association between a decrease in florbetapir-PET in the WM and a faster rate of decline in global cognition and episodic memory. APOE ε4 genotype was associated with a worse decrease in the whole WM florbetapir-PET and thus enhanced tau-PET accumulation. CONCLUSION Myelin alterations are associated in an APOE ε4 dependent manner with faster tau progression and cognitive decline, and may therefore play a role in the etiology of AD.
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Affiliation(s)
- Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Henry Stephenson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Yuetiva Deming
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Benno Gesierich
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
| | - An-Vi Pham
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Barbara Bendlin
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilian-University Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
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17
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Zheng L, Rubinski A, Denecke J, Luan Y, Smith R, Strandberg O, Stomrud E, Ossenkoppele R, Svaldi DO, Higgins IA, Shcherbinin S, Pontecorvo MJ, Hansson O, Franzmeier N, Ewers M. Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease. Ann Neurol 2024; 95:274-287. [PMID: 37837382 DOI: 10.1002/ana.26818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/07/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.
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Affiliation(s)
- Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | | | | | | | - Michael J Pontecorvo
- Eli Lilly and Company, Indianapolis, IN, USA
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
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18
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Nowell J, Raza S, Livingston NR, Sivanathan S, Gentleman S, Edison P. Do Tau Deposition and Glucose Metabolism Dissociate in Alzheimer's Disease Trajectory? J Alzheimers Dis 2024; 101:987-999. [PMID: 39302365 DOI: 10.3233/jad-240434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Background Tau aggregation demonstrates close associations with hypometabolism in Alzheimer's disease (AD), although differing pathophysiological processes may underlie their development. Objective To establish whether tau deposition and glucose metabolism have different trajectories in AD progression and evaluate the utility of global measures of these pathological hallmarks in predicting cognitive deficits. Methods 279 participants with amyloid-β (Aβ) status, and T1-weighted MRI scans, were selected from the Alzheimer's Disease Neuroimaging Initiative (http://adni.loni.usc.edu). We created the standard uptake value ratio images using Statistical Parametric Mapping 12 for [18F]AV1451-PET (tau) and [18F]FDG-PET (glucose metabolism) scans. Voxel-wise group and single-subject level SPM analysis evaluated the relationship between global [18F]FDG-PET and [18F]AV1451-PET depending on the Aβ status. Linear models assessed whether tau deposition or glucose metabolism better predicted clinical progression. Results There was a dissociation between global cerebral glucose hypometabolism and global tau load in amyloid-positive AD and amyloid-negative mild cognitive impairment (MCI) (p > 0.05). Global hypometabolism was only associated with global cortical tau in amyloid-positive MCI. Voxel-level single subject tau load better predicted neuropsychological performance, Alzheimer's disease assessment scale-cognitive (ADAS-Cog) 13 score, and one-year change compared with regional and global hypometabolism. Conclusions A dissociation between tau pathology and glucose metabolism at a global level in AD could imply that other pathological processes influence glucose metabolism. Furthermore, as tau is a better predictor of clinical progression, these processes may have independent trajectories and require independent consideration in the context of therapeutic interventions.
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Affiliation(s)
- Joseph Nowell
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
| | - Sanara Raza
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
| | - Nicholas R Livingston
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
| | - Shayndhan Sivanathan
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
| | - Steve Gentleman
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
| | - Paul Edison
- Department of Brain Sciences, Division of Neurology, Faculty of Medicine, Imperial College London, London, UK
- School of Medicine, Cardiff University, Cardiff, Wales, UK
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Erickson CM, Karlawish J, Grill JD, Harkins K, Landau SM, Rivera-Mindt MG, Okonkwo O, Petersen RC, Aisen PS, Weiner MW, Largent EA. A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants. J Prev Alzheimers Dis 2024; 11:294-302. [PMID: 38374735 PMCID: PMC10883638 DOI: 10.14283/jpad.2024.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples. METHODS The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included. RESULTS Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators. CONCLUSION The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.
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Affiliation(s)
- C M Erickson
- Emily Largent JD, PhD, RN, 423 Guardian Drive Philadelphia, PA 19104, USA,
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Ryoo HG, Choi H, Shi K, Rominger A, Lee DY, Lee DS. Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters. Eur J Nucl Med Mol Imaging 2024; 51:443-454. [PMID: 37735259 DOI: 10.1007/s00259-023-06440-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD. METHODS A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes. RESULTS Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%). CONCLUSION We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.
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Affiliation(s)
- Hyun Gee Ryoo
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Avelar-Pereira B, Phillips CM, Hosseini SMH. Convergence of Accelerated Brain Volume Decline in Normal Aging and Alzheimer's Disease Pathology. J Alzheimers Dis 2024; 101:249-258. [PMID: 39177595 DOI: 10.3233/jad-231458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Background Age represents the largest risk factor for Alzheimer's disease (AD) but is typically treated as a covariate. Still, there are similarities between brain regions affected in AD and those showing accelerated decline in normal aging, suggesting that the distinction between the two might fall on a spectrum. Objective Our goal was to identify regions showing accelerated atrophy across the brain and investigate whether these overlapped with regions involved in AD or where related to amyloid. Methods We used a longitudinal sample of 137 healthy older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent magnetic resonance imaging (MRI). In addition, a total of 79 participants also had longitudinal positron emission tomography (PET) data. We computed linear-mixed effects models for brain regions declining faster than the average to investigate variability in the rate of change. Results 23 regions displayed a 0.5 standard deviation (SD) above average decline over 2 years. Of these, 52% overlapped with regions showing similar decline in a matched AD sample. Beyond this, the left precuneus, right superior frontal, transverse temporal, and superior temporal sulcus showed accelerated decline. Lastly, atrophy in the precuneus was associated with increased amyloid load. Conclusions Accelerated decline in normal aging might contribute to the detection of early signs of AD among healthy individuals.
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Affiliation(s)
- Bárbara Avelar-Pereira
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Curran Michael Phillips
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
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22
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Shirzadi Z, Schultz SA, Yau WYW, Joseph-Mathurin N, Fitzpatrick CD, Levin R, Kantarci K, Preboske GM, Jack CR, Farlow MR, Hassenstab J, Jucker M, Morris JC, Xiong C, Karch CM, Levey AI, Gordon BA, Schofield PR, Salloway SP, Perrin RJ, McDade E, Levin J, Cruchaga C, Allegri RF, Fox NC, Goate A, Day GS, Koeppe R, Chui HC, Berman S, Mori H, Sanchez-Valle R, Lee JH, Rosa-Neto P, Ruthirakuhan M, Wu CY, Swardfager W, Benzinger TLS, Sohrabi HR, Martins RN, Bateman RJ, Johnson KA, Sperling RA, Greenberg SM, Schultz AP, Chhatwal JP. Etiology of White Matter Hyperintensities in Autosomal Dominant and Sporadic Alzheimer Disease. JAMA Neurol 2023; 80:1353-1363. [PMID: 37843849 PMCID: PMC10580156 DOI: 10.1001/jamaneurol.2023.3618] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 06/26/2023] [Indexed: 10/17/2023]
Abstract
Importance Increased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors. Objective To estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes. Design, Setting, and Participants This cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers-the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer's Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019). Main Outcome and Measures The main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH. Results Data from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = -3.1 [P = .002]; ADNI: t = -5.6 [P < .001]; HABS: t = -2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001). Conclusions and Relevance The findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.
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Affiliation(s)
- Zahra Shirzadi
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Stephanie A. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Wai-Ying W. Yau
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | | | - Colleen D. Fitzpatrick
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Raina Levin
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Jason Hassenstab
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Tübingen, Germany
| | - John C. Morris
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Chengjie Xiong
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Celeste M. Karch
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | | | - Brian A. Gordon
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | | | - Richard J. Perrin
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Eric McDade
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, German Center for Neurodegenerative Diseases, site Munich, Munich Cluster for Systems Neurology, Munich, Germany
| | - Carlos Cruchaga
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | | | - Nick C. Fox
- UK Dementia Research Institute, University College London, London, United Kingdom
| | - Alison Goate
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor
| | - Helena C. Chui
- Keck School of Medicine, University of Southern California, Los Angeles
| | - Sarah Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Hiroshi Mori
- Osaka Metropolitan University Medical School, Osaka, Nagaoka Sutoku University, Osaka City, Niigata, Japan
| | | | - Jae-Hong Lee
- Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Pedro Rosa-Neto
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Myuri Ruthirakuhan
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Che-Yuan Wu
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Walter Swardfager
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | | | - Hamid R. Sohrabi
- Centre for Healthy Ageing, School of Psychology, Health Future Institute, Murdoch University, Perth, Western Australia, Australia
| | - Ralph N. Martins
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Randall J. Bateman
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Keith A. Johnson
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Reisa A. Sperling
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Steven M. Greenberg
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Aaron P. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Jasmeer P. Chhatwal
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
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Zhang Z, Chan MY, Han L, Carreno CA, Winter-Nelson E, Wig GS. Dissociable Effects of Alzheimer's Disease-Related Cognitive Dysfunction and Aging on Functional Brain Network Segregation. J Neurosci 2023; 43:7879-7892. [PMID: 37714710 PMCID: PMC10648516 DOI: 10.1523/jneurosci.0579-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/17/2023] Open
Abstract
Alzheimer's disease (AD) is associated with changes in large-scale functional brain network organization. Individuals with AD exhibit less segregated resting-state brain networks compared with individuals without dementia. However, declines in brain network segregation are also evident as adult individuals grow older. Determining whether these observations reflect unique or overlapping alterations on the functional connectome of the brain is essential for understanding the impact of AD on network organization and incorporating measures of functional brain network organization toward AD characterization. Relationships between AD dementia severity and participant's age on resting-state brain system segregation were examined in 326 cognitively healthy and 275 cognitively impaired human individuals recruited through the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 601; age range, 55-96 years; 320 females). Greater dementia severity and increasing age were independently associated with lower brain system segregation. Further, dementia versus age relationships with brain network organization varied according to the processing roles of brain systems and types of network interactions. Aging was associated with alterations to association systems, primarily among within-system relationships. Conversely, dementia severity was associated with alterations that included both association systems and sensory-motor systems and was most prominent among cross-system interactions. Dementia-related network alterations were evident regardless of the presence of cortical amyloid burden, revealing that the measures of functional network organization are unique from this marker of AD-related pathology. Collectively, these observations demonstrate the specific and widespread alterations in the topological organization of large-scale brain networks that accompany AD and highlight functionally dissociable brain network vulnerabilities associated with AD-related cognitive dysfunction versus aging.SIGNIFICANCE STATEMENT Alzheimer's disease (AD)-associated cognitive dysfunction is hypothesized to be a consequence of brain network damage. It is unclear exactly how brain network alterations vary with dementia severity and whether they are distinct from alterations associated with aging. We evaluated functional brain network organization measured at rest among individuals who varied in age and dementia status. AD and aging exerted dissociable impacts on the brain's functional connectome. AD-associated brain network alterations were widespread and involved systems that subserve not only higher-order cognitive operations, but also sensory and motor operations. Notably, AD-related network alterations were independent of amyloid pathology. The research furthers our understanding of AD-related brain dysfunction and motivates refining existing frameworks of dementia characterization with measures of functional network organization.
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Affiliation(s)
- Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Claudia A Carreno
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas 75390
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24
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Nemes S, Logan PE, Manchella MK, Mundada NS, Joie RL, Polsinelli AJ, Hammers DB, Koeppe RA, Foroud TM, Nudelman KN, Eloyan A, Iaccarino L, Dorsant-Ardón V, Taurone A, Maryanne Thangarajah, Dage JL, Aisen P, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Womack KB, Wolk DA, Rabinovici GD, Carrillo MC, Dickerson BC, Apostolova LG. Sex and APOE ε4 carrier effects on atrophy, amyloid PET, and tau PET burden in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S49-S63. [PMID: 37496307 PMCID: PMC10811272 DOI: 10.1002/alz.13403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early-onset Alzheimer's disease (EOAD). METHODS We included baseline data from 77 cognitively normal (CN), 230 EOAD, and 70 EO non-Alzheimer's disease (EOnonAD) participants from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification. Voxel-wise multiple linear regressions yielded statistical brain maps of gray matter density, amyloid, and tau PET burden. RESULTS EOAD females had greater amyloid and tau PET burdens than males. EOAD female APOE ε4 non-carriers had greater amyloid PET burdens and greater gray matter atrophy than female ε4 carriers. EOnonAD female ε4 non-carriers also had greater gray matter atrophy than female ε4 carriers. DISCUSSION The effects of sex and APOE ε4 must be considered when studying these populations. HIGHLIGHTS Novel analysis examining the effects of biological sex and apolipoprotein E ε4 (APOE ε4) carrier status on neuroimaging biomarkers among early-onset Alzheimer's disease (EOAD), early-onset non-AD (EOnonAD), and cognitively normal (CN) participants. Female sex is associated with greater pathology burden in the EOAD cohort compared to male sex. The effect of APOE ε4 carrier status on pathology burden was the most impactful in females across all cohorts.
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Affiliation(s)
- Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Mohit K. Manchella
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Chemistry, University of Southern Indiana, Evansville, Indiana, 47712, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Renaud La Joie
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Robert A. Koeppe
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, 48105, USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kelly N. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Valérie Dorsant-Ardón
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Jeffery L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, 92121, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California, San Francisco, California, 94158, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195, USA
| | - Melissa E. Murray
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85315, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, 02115, USA
- Wein Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, 559095, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Sharon J. Sha
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown Universit, Washington, DC, 20007, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - David A. Wolk
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,19104, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
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25
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Swinford CG, Risacher SL, Vosmeier A, Deardorff R, Chumin EJ, Dzemidzic M, Wu YC, Gao S, McDonald BC, Yoder KK, Unverzagt FW, Wang S, Farlow MR, Brosch JR, Clark DG, Apostolova LG, Sims J, Wang DJ, Saykin AJ. Amyloid and tau pathology are associated with cerebral blood flow in a mixed sample of nondemented older adults with and without vascular risk factors for Alzheimer's disease. Neurobiol Aging 2023; 130:103-113. [PMID: 37499587 PMCID: PMC10529454 DOI: 10.1016/j.neurobiolaging.2023.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 05/30/2023] [Accepted: 06/21/2023] [Indexed: 07/29/2023]
Abstract
Identification of biomarkers for the early stages of Alzheimer's disease (AD) is an imperative step in developing effective treatments. Cerebral blood flow (CBF) is a potential early biomarker for AD; generally, older adults with AD have decreased CBF compared to normally aging peers. CBF deviates as the disease process and symptoms progress. However, further characterization of the relationships between CBF and AD risk factors and pathologies is still needed. We assessed the relationships between CBF quantified by arterial spin-labeled magnetic resonance imaging, hypertension, APOEε4, and tau and amyloid positron emission tomography in 77 older adults: cognitively normal, subjective cognitive decline, and mild cognitive impairment. Tau and amyloid aggregation were related to altered CBF, and some of these relationships were dependent on hypertension or APOEε4 status. Our findings suggest a complex relationship between risk factors, AD pathologies, and CBF that warrants future studies of CBF as a potential early biomarker for AD.
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Affiliation(s)
- Cecily G Swinford
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Aaron Vosmeier
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Evgeny J Chumin
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Mario Dzemidzic
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Karmen K Yoder
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sophia Wang
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jared R Brosch
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David G Clark
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA
| | - Justin Sims
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Danny J Wang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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26
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Kim H, Zhu X, Zhao Y, Bell SA, Gehrman PR, Cohen D, Devanand DP, Goldberg TE, Lee S. Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden. Mol Psychiatry 2023; 28:4399-4406. [PMID: 37596355 PMCID: PMC10842478 DOI: 10.1038/s41380-023-02214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/20/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using the linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β = 0.11, P = 0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
- Hyun Kim
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA.
| | - Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Anxiety, Mood, Eating, and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Yiming Zhao
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sophie A Bell
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Philip R Gehrman
- Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
- Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Daniel Cohen
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
| | - D P Devanand
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Terry E Goldberg
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
- Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
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27
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Sha J, Bao J, Liu K, Yang S, Wen Z, Wen J, Cui Y, Tong B, Moore JH, Saykin AJ, Davatzikos C, Long Q, Shen L. Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease. Methods 2023; 218:27-38. [PMID: 37507059 PMCID: PMC10528049 DOI: 10.1016/j.ymeth.2023.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/26/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
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Affiliation(s)
- Jiahang Sha
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Kefei Liu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215000, China.
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA; Stevens Neuroimaging and Informatics Institute, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA.
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Boning Tong
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
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28
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Zhou Z, Tong B, Tarzanagh DA, Hou B, Saykin AJ, Long Q, Shen L. Multi-Group Tensor Canonical Correlation Analysis. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2023; 2023:12. [PMID: 37876849 PMCID: PMC10593155 DOI: 10.1145/3584371.3612962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.
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Affiliation(s)
| | - Boning Tong
- University of Pennsylvania, Philadelphia, USA
| | | | - Bojian Hou
- University of Pennsylvania, Philadelphia, USA
| | | | - Qi Long
- University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- University of Pennsylvania, Philadelphia, USA
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29
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Quattrini G, Ferrari C, Pievani M, Geviti A, Ribaldi F, Scheffler M, Frisoni GB, Garibotto V, Marizzoni M. Unsupervised [ 18F]Flortaucipir cutoffs for tau positivity and staging in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2023; 50:3265-3275. [PMID: 37272955 PMCID: PMC10542510 DOI: 10.1007/s00259-023-06280-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
PURPOSE Several [18F]Flortaucipir cutoffs have been proposed for tau PET positivity (T+) in Alzheimer's disease (AD), but none were data-driven. The aim of this study was to establish and validate unsupervised T+ cutoffs by applying Gaussian mixture models (GMM). METHODS Amyloid negative (A-) cognitively normal (CN) and amyloid positive (A+) AD-related dementia (ADRD) subjects from ADNI (n=269) were included. ADNI (n=475) and Geneva Memory Clinic (GMC) cohorts (n=98) were used for validation. GMM-based cutoffs were extracted for the temporal meta-ROI, and validated against previously published cutoffs and visual rating. RESULTS GMM-based cutoffs classified less subjects as T+, mainly in the A- CN (<3.4% vs >28.5%) and A+ CN (<14.5% vs >42.9%) groups and showed higher agreement with visual rating (ICC=0.91 vs ICC<0.62) than published cutoffs. CONCLUSION We provided reliable data-driven [18F]Flortaucipir cutoffs for in vivo T+ detection in AD. These cutoffs might be useful to select participants in clinical and research studies.
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Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | - Clarissa Ferrari
- FONDAZIONE POLIAMBULANZA ISTITUTO OSPEDALIERO via Bissolati, 57, 25124, Brescia, Italy
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Andrea Geviti
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Federica Ribaldi
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre, Faculty of Medicine, University of Geneva, 1205, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, 1205, Geneva, Switzerland
- Centre for Biomedical Imaging (CIBM), 1205, Geneva, Switzerland
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
- Biological Psychiatric Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
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30
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McKay NS, Gordon BA, Hornbeck RC, Dincer A, Flores S, Keefe SJ, Joseph-Mathurin N, Jack CR, Koeppe R, Millar PR, Ances BM, Chen CD, Daniels A, Hobbs DA, Jackson K, Koudelis D, Massoumzadeh P, McCullough A, Nickels ML, Rahmani F, Swisher L, Wang Q, Allegri RF, Berman SB, Brickman AM, Brooks WS, Cash DM, Chhatwal JP, Day GS, Farlow MR, la Fougère C, Fox NC, Fulham M, Ghetti B, Graff-Radford N, Ikeuchi T, Klunk W, Lee JH, Levin J, Martins R, Masters CL, McConathy J, Mori H, Noble JM, Reischl G, Rowe C, Salloway S, Sanchez-Valle R, Schofield PR, Shimada H, Shoji M, Su Y, Suzuki K, Vöglein J, Yakushev I, Cruchaga C, Hassenstab J, Karch C, McDade E, Perrin RJ, Xiong C, Morris JC, Bateman RJ, Benzinger TLS. Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN). Nat Neurosci 2023; 26:1449-1460. [PMID: 37429916 PMCID: PMC10400428 DOI: 10.1038/s41593-023-01359-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.
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Affiliation(s)
| | | | | | - Aylin Dincer
- Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah J Keefe
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | - Beau M Ances
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Diana A Hobbs
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | | | | | - Laura Swisher
- Washington University in St. Louis, St. Louis, MO, USA
| | - Qing Wang
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Adam M Brickman
- Columbia University Irving Medical Center, New York, NY, USA
| | - William S Brooks
- Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - David M Cash
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Jasmeer P Chhatwal
- Massachusetts General and Brigham & Women's Hospitals, Harvard Medical School, Boston, MA, USA
| | | | | | - Christian la Fougère
- Department of Radiology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Nick C Fox
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Michael Fulham
- Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | | | | | | | | | | | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Ralph Martins
- Edith Cowan University, Joondalup, Western Australia, Australia
| | | | | | | | - James M Noble
- Columbia University Irving Medical Center, New York, NY, USA
| | - Gerald Reischl
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | | | | | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | | | | | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Igor Yakushev
- School of Medicine, Technical University of Munich, Munich, Germany
| | | | | | - Celeste Karch
- Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
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31
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Erickson P, Simrén J, Brum WS, Ennis GE, Kollmorgen G, Suridjan I, Langhough R, Jonaitis EM, Van Hulle CA, Betthauser TJ, Carlsson CM, Asthana S, Ashton NJ, Johnson SC, Shaw LM, Blennow K, Andreasson U, Bendlin BB, Zetterberg H. Prevalence and Clinical Implications of a β-Amyloid-Negative, Tau-Positive Cerebrospinal Fluid Biomarker Profile in Alzheimer Disease. JAMA Neurol 2023; 80:2807607. [PMID: 37523162 PMCID: PMC10391361 DOI: 10.1001/jamaneurol.2023.2338] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/05/2023] [Indexed: 08/01/2023]
Abstract
Importance Knowledge is lacking on the prevalence and prognosis of individuals with a β-amyloid-negative, tau-positive (A-T+) cerebrospinal fluid (CSF) biomarker profile. Objective To estimate the prevalence of a CSF A-T+ biomarker profile and investigate its clinical implications. Design, Setting, and Participants This was a retrospective cohort study of the cross-sectional multicenter University of Gothenburg (UGOT) cohort (November 2019-January 2021), the longitudinal multicenter Alzheimer Disease Neuroimaging Initiative (ADNI) cohort (individuals with mild cognitive impairment [MCI] and no cognitive impairment; September 2005-May 2022), and 2 Wisconsin cohorts, Wisconsin Alzheimer Disease Research Center and Wisconsin Registry for Alzheimer Prevention (WISC; individuals without cognitive impairment; February 2007-November 2020). This was a multicenter study, with data collected from referral centers in clinical routine (UGOT) and research settings (ADNI and WISC). Eligible individuals had 1 lumbar puncture (all cohorts), 2 or more cognitive assessments (ADNI and WISC), and imaging (ADNI only) performed on 2 separate occasions. Data were analyzed on August 2022 to April 2023. Exposures Baseline CSF Aβ42/40 and phosphorylated tau (p-tau)181; cognitive tests (ADNI: modified preclinical Alzheimer cognitive composite [mPACC]; WISC: modified 3-test PACC [PACC-3]). Exposures in the ADNI cohort included [18F]-florbetapir amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose PET (FDG-PET), and cross-sectional tau-PET (ADNI: [18F]-flortaucipir, WISC: [18F]-MK6240). Main Outcomes and Measures Primary outcomes were the prevalence of CSF AT biomarker profiles and continuous longitudinal global cognitive outcome and imaging biomarker trajectories in A-T+ vs A-T- groups. Secondary outcomes included cross-sectional tau-PET. Results A total of 7679 individuals (mean [SD] age, 71.0 [8.4] years; 4101 male [53%]) were included in the UGOT cohort, 970 individuals (mean [SD] age, 73 [7.0] years; 526 male [54%]) were included in the ADNI cohort, and 519 individuals (mean [SD] age, 60 [7.3] years; 346 female [67%]) were included in the WISC cohort. The prevalence of an A-T+ profile in the UGOT cohort was 4.1% (95% CI, 3.7%-4.6%), being less common than the other patterns. Longitudinally, no significant differences in rates of worsening were observed between A-T+ and A-T- profiles for cognition or imaging biomarkers. Cross-sectionally, A-T+ had similar tau-PET uptake to individuals with an A-T- biomarker profile. Conclusion and Relevance Results suggest that the CSF A-T+ biomarker profile was found in approximately 5% of lumbar punctures and was not associated with a higher rate of cognitive decline or biomarker signs of disease progression compared with biomarker-negative individuals.
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Affiliation(s)
- Pontus Erickson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joel Simrén
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Wagner S. Brum
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gilda E. Ennis
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | | | | | - Rebecca Langhough
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - Erin M. Jonaitis
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - Carol A. Van Hulle
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - Tobey J. Betthauser
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - Cynthia M. Carlsson
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison
- Geriatric Research Education and Clinical Center of the Wm. S. Middleton Memorial Veterans Hospital, Madison, Wisconsin
| | - Sanjay Asthana
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison
- Geriatric Research Education and Clinical Center of the Wm. S. Middleton Memorial Veterans Hospital, Madison, Wisconsin
| | - Nicholas J. Ashton
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, King’s College London, London, England
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, England
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Sterling C. Johnson
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ulf Andreasson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Barbara B. Bendlin
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison
- Institute of Neurology, Department of Neurodegenerative Disease, University College London, London, England
- UK Dementia Research Institute, University College London, London, England
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
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Jeong SH, Cha J, Jung JH, Yun M, Sohn YH, Chung SJ, Lee PH. Occipital Amyloid Deposition Is Associated with Rapid Cognitive Decline in the Alzheimer's Disease Continuum. J Alzheimers Dis 2023:JAD230187. [PMID: 37355901 DOI: 10.3233/jad-230187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND Clinical significance of additional occipital amyloid-β (Aβ) plaques in Alzheimer's disease (AD) remains unclear. OBJECTIVE In this study, we investigated the effect of regional Aβ deposition on cognition in patients on the AD continuum, especially in the occipital region. METHODS We retrospectively reviewed the medical record of 208 patients with AD across the cognitive continuum (non-dementia and dementia). Multivariable linear regression analyses were performed to determine the effect of regional Aβ deposition on cognitive function. A linear mixed model was used to assess the effect of regional deposition on longitudinal changes in Mini-Mental State Examination (MMSE) scores. Additionally, the patients were dichotomized according to the occipital-to-global Aβ deposition ratio (ratio ≤1, Aβ-OCC- group; ratio >1, Aβ-OCC+ group), and the same statistical analyses were applied for between-group comparisons. RESULTS Regional Aβ burden itself was not associated with baseline cognitive function. In terms of Aβ-OCC group effect, the Aβ-OCC+ group exhibited a poorer cognitive performance on language function compared to the Aβ-OCC- group. High Aβ retention in each region was associated with a rapid decline in MMSE scores, only in the dementia subgroup. Additionally, Aβ-OCC+ individuals exhibited a faster annual decline in MMSE scores than Aβ-OCC- individuals in the non-dementia subgroup (β= -0.77, standard error [SE] = 0.31, p = 0.013). CONCLUSION The present study demonstrated that additional occipital Aβ deposition was associated with poor baseline language function and rapid cognitive deterioration in patients on the AD continuum.
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Affiliation(s)
- Seong Ho Jeong
- Department of Neurology, Inje University Sanggye Paik Hospital, Seoul, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Ho Jung
- Department of Neurology, Inje University Busan Paik Hospital, Busan, South Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
- YONSEI BEYOND LAB, Yongin, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
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Tong B, Risacher SL, Bao J, Feng Y, Wang X, Ritchie MD, Moore JH, Urbanowicz R, Saykin AJ, Shen L. Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:525-533. [PMID: 37350880 PMCID: PMC10283108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.
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Affiliation(s)
- Boning Tong
- University of Pennsylvania, Philadelphia, PA
| | | | | | - Yanbo Feng
- University of Pennsylvania, Philadelphia, PA
| | - Xinkai Wang
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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34
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Wang X, Feng Y, Tong B, Bao J, Ritchie MD, Saykin AJ, Moore JH, Urbanowicz R, Shen L. Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:544-553. [PMID: 37350896 PMCID: PMC10283099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.
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Affiliation(s)
- Xinkai Wang
- University of Pennsylvania, Philadelphia, PA
| | - Yanbo Feng
- University of Pennsylvania, Philadelphia, PA
| | - Boning Tong
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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35
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Morrison C, Dadar M, Kamal F, Collins DL. Differences in AD-related pathology profiles across APOE groups. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.25.23289108. [PMID: 37162910 PMCID: PMC10168520 DOI: 10.1101/2023.04.25.23289108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND The apolipoprotein (APOE) e4 allele is a known risk factor for Alzheimer's disease (AD), while the e2 allele is thought to be protective against AD. Few studies have examined the relationship between brain pathologies, atrophy, and white matter hyperintensities (WMHs) and APOE status in those with the e2e4 genotype and results are inconsistent for those with an e2 allele. METHODS We analyzed Alzheimer's Disease Neuroimaging participants that had APOE genotyping and at least one of the following metrics: regional WMH load, ventricle size, hippocampal (HC) and entorhinal cortex (EC) volume, amyloid level (i.e., AV-45), and phosphorylated tau (pTau). Participants were divided into one of four APOE allele profiles (E4=e4e4 or e3e4; E2=e2e2 or e2e3; E3=e3e3; or E24=e2e4, Fig.1). Linear mixed models examined the relationship between APOE profiles and each pathology (i.e., regional WMHs, ventricle size, hippocampal and entorhinal cortex volume, amyloid level, and phosphorylated tau measures). while controlling for age, sex, education, and diagnostic status at baseline and over time. RESULTS APOE ε4 is associated with increased pathology while ε2 positivity is associated with reduced baseline and lower accumulation of pathologies and rates of neurodegeneration. APOE ε2ε4 is similar to ε4 (increased neurodegeneration) but with a slower rate of change. CONCLUSIONS The strong associations observed between APOE and pathology in this study show the importance of how genetic factors influence structural brain changes. These findings suggest that ε2ε4 genotype is related to increased declines associated with the ε4 as opposed to the protective effects of the ε2. These findings have important implications for initiating treatments and interventions. Given that people who have the ε2ε4 genotype can expect to have increased atrophy, they must be included (alongside those with an ε4 profile) in targeted interventions to reduce brain changes that occur with AD.
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36
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Groechel RC, Tripodis Y, Alosco ML, Mez J, Qiu WQ, Mercier G, Goldstein L, Budson AE, Kowall N, Killiany RJ. Annualized changes in rate of amyloid deposition and neurodegeneration are greater in participants who become amyloid positive than those who remain amyloid negative. Neurobiol Aging 2023; 127:33-42. [PMID: 37043881 DOI: 10.1016/j.neurobiolaging.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
This study longitudinally examined participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent a conversion in amyloid-beta (Aβ) status in comparison to a group of ADNI participants who did not show a change in amyloid status over the same follow-up period. Participants included 136 ADNI dementia-free participants with 2 florbetapir positron emission tomography (PET) scans. Of these participants, 68 showed amyloid conversion as measured on florbetapir PET, and the other 68 did not. Amyloid converters and non-converters were chosen to have representative demographic data (age, education, sex, diagnostic status, and race). The amyloid converter group showed increased prevalence of APOE ε4 (p < 0.001), greater annualized percent volume loss in selected magnetic resonance imaging (MRI) regions (p < 0.05), lower cerebrospinal fluid Aβ1-42 (p < 0.001), and greater amyloid retention (as measured by standard uptake value ratios) on florbetapir PET scans (p < 0.001) in comparison to the non-converter group. These results provide compelling evidence that important neuropathological changes are occurring alongside amyloid conversion.
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37
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Izmailova ES, Maguire RP, McCarthy TJ, Müller MLTM, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clin Transl Sci 2023; 16:383-397. [PMID: 36382716 PMCID: PMC10014695 DOI: 10.1111/cts.13461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 11/03/2022] [Indexed: 11/17/2022] Open
Abstract
The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.
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38
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Kim H, Zhu X, Zhao Y, Bell S, Gehrman P, Cohen D, Devanand D, Goldberg T, Lee S. Resting-State Functional Connectivity Changes in Older Adults with Sleep Disturbance and the Role of Amyloid Burden. RESEARCH SQUARE 2023:rs.3.rs-2547880. [PMID: 36798352 PMCID: PMC9934741 DOI: 10.21203/rs.3.rs-2547880/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β=0.11, P=0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
| | - Xi Zhu
- Columbia University Medical Center
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Ali DG, Abner EL, Bahrani AA, El Khouli R, Gold BT, Jiang Y, Wilcock DM, Jicha GA. Amyloid-PET and White Matter Hyperintensities Have Independent Effects on Baseline Cognitive Function and Synergistic Effects on Longitudinal Executive Function. Brain Sci 2023; 13:218. [PMID: 36831761 PMCID: PMC9953773 DOI: 10.3390/brainsci13020218] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
Co-occurrence of beta amyloid (Aβ) and white matter hyperintensities (WMHs) increase the risk of dementia and both are considered biomarkers of preclinical dementia. Moderation and mediation modeling were used to define the interplay between global and regional Aβ and WMHs measures in relation to executive function (EF) and memory composite scores outcomes at baseline and after approximately 2 years across a sample of 714 clinically normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI 2). The moderation regression analysis showed additive effects of Aβ and WMHs over baseline memory and EF scores (p = 0.401 and 0.061, respectively) and synergistic effects over follow-up EF (p < 0.05). Through mediation analysis, the data presented demonstrate that WMHs effects, mediated by global and regional amyloid burden, are responsible for baseline cognitive performance deficits in memory and EF. These findings suggest that Aβ and WMHs contribute to baseline cognition independently while WMHs volumes exert effects on baseline cognitive performance directly and through influences on Aβ accumulation.
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Affiliation(s)
- Doaa G. Ali
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
| | - Erin L. Abner
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, USA
| | - Ahmed A. Bahrani
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
| | - Riham El Khouli
- Department of Radiology, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Brian T. Gold
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Yang Jiang
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
| | - Donna M. Wilcock
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
| | - Gregory A. Jicha
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY 40506, USA
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40
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Daamen M, Scheef L, Li S, Grothe MJ, Gaertner FC, Buchert R, Buerger K, Dobisch L, Drzezga A, Essler M, Ewers M, Fliessbach K, Herrera Melendez AL, Hetzer S, Janowitz D, Kilimann I, Krause BJ, Lange C, Laske C, Munk MH, Peters O, Priller J, Ramirez A, Reimold M, Rominger A, Rostamzadeh A, Roeske S, Roy N, Scheffler K, Schneider A, Spottke A, Spruth EJ, Teipel SJ, Wagner M, Düzel E, Jessen F, Boecker H. Cortical Amyloid Burden Relates to Basal Forebrain Volume in Subjective Cognitive Decline. J Alzheimers Dis 2023; 95:1013-1028. [PMID: 37638433 DOI: 10.3233/jad-230141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
BACKGROUND Atrophy of cholinergic basal forebrain (BF) nuclei is a frequent finding in magnetic resonance imaging (MRI) volumetry studies that examined patients with prodromal or clinical Alzheimer's disease (AD), but less clear for individuals in earlier stages of the clinical AD continuum. OBJECTIVE To examine BF volume reductions in subjective cognitive decline (SCD) participants with AD pathologic changes. METHODS The present study compared MRI-based BF volume measurements in age- and sex-matched samples of N = 24 amyloid-positive and N = 24 amyloid-negative SCD individuals, based on binary visual ratings of Florbetaben positron emission tomography (PET) measurements. Additionally, we assessed associations of BF volume with cortical amyloid burden, based on semiquantitative Centiloid (CL) analyses. RESULTS Group differences approached significance for BF total volume (p = 0.061) and the Ch4 subregion (p = 0.059) only, showing the expected relative volume reductions for the amyloid-positive subgroup. There were also significant inverse correlations between BF volumes and CL values, which again were most robust for BF total volume and the Ch4 subregion. CONCLUSIONS The results are consistent with the hypothesis that amyloid-positive SCD individuals, which are considered to represent a transitional stage on the clinical AD continuum, already show incipient alterations of BF integrity. The negative association with a continuous measure of cortical amyloid burden also suggests that this may reflect an incremental process. Yet, further research is needed to evaluate whether BF changes already emerge at "grey zone" levels of amyloid accumulation, before amyloidosis is reliably detected by PET visual readings.
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Affiliation(s)
- Marcel Daamen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Lukas Scheef
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- RheinAhrCampus, University of Applied Sciences Koblenz, Remagen, Germany
| | - Shumei Li
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| | | | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilian University Munich, Munich, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Alexander Drzezga
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Jülich, Germany
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Michael Ewers
- Institute for Clinical Radiology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Ana Lucia Herrera Melendez
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stefan Hetzer
- Berlin Center of Advanced Neuroimaging, Charité -Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilian University Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Bernd Joachim Krause
- Department of Nuclear Medicine, Rostock University Medical Centre, Rostock, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Oliver Peters
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
- University of Edinburgh and UK Dementia Research Institute, Edinburgh, UK
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Department of Psychiatry and Psychotherapy, Division of Neurogenetics and Molecular Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Medical Faculty, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
| | - Matthias Reimold
- Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard-Karls-University, Tübingen, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Ludwig-Maximilian-University Munich, Munich, Germany
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ayda Rostamzadeh
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Henning Boecker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
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41
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Morys F, Potvin O, Zeighami Y, Vogel J, Lamontagne-Caron R, Duchesne S, Dagher A. Obesity-Associated Neurodegeneration Pattern Mimics Alzheimer's Disease in an Observational Cohort Study. J Alzheimers Dis 2023; 91:1059-1071. [PMID: 36565111 PMCID: PMC9912737 DOI: 10.3233/jad-220535] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Excess weight in adulthood leads to health complications such as diabetes, hypertension, or dyslipidemia. Recently, excess weight has also been related to brain atrophy and cognitive decline. Reports show that obesity is linked with Alzheimer's disease (AD)-related changes, such as cerebrovascular damage or amyloid-β accumulation. However, to date no research has conducted a direct comparison between brain atrophy patterns in AD and obesity. OBJECTIVE Here, we compared patterns of brain atrophy and amyloid-β/tau protein accumulation in obesity and AD using a sample of over 1,300 individuals from four groups: AD patients, healthy controls, obese otherwise healthy individuals, and lean individuals. METHODS We age- and sex-matched all groups to the AD-patients group and created cortical thickness maps of AD and obesity. This was done by comparing AD patients with healthy controls, and obese individuals with lean individuals. We then compared the AD and obesity maps using correlation analyses and permutation-based tests that account for spatial autocorrelation. Similarly, we compared obesity brain maps with amyloid-β and tau protein maps from other studies. RESULTS Obesity maps were highly correlated with AD maps but were not correlated with amyloid-β/tau protein maps. This effect was not accounted for by the presence of obesity in the AD group. CONCLUSION Our research confirms that obesity-related grey matter atrophy resembles that of AD. Excess weight management could lead to improved health outcomes, slow down cognitive decline in aging, and lower the risk for AD.
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Affiliation(s)
- Filip Morys
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | | | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, McGill University, Québec, Canada
| | - Jacob Vogel
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | | | - Simon Duchesne
- CERVO Brain Research Centre, Québec, Canada
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Laval University, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montréal, Canada
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Katzdobler S, Nitschmann A, Barthel H, Bischof G, Beyer L, Marek K, Song M, Wagemann O, Palleis C, Weidinger E, Nack A, Fietzek U, Kurz C, Häckert J, Stapf T, Ferschmann C, Scheifele M, Eckenweber F, Biechele G, Franzmeier N, Dewenter A, Schönecker S, Saur D, Schroeter ML, Rumpf JJ, Rullmann M, Schildan A, Patt M, Stephens AW, van Eimeren T, Neumaier B, Drzezga A, Danek A, Classen J, Bürger K, Janowitz D, Rauchmann BS, Stöcklein S, Perneczky R, Schöberl F, Zwergal A, Höglinger GU, Bartenstein P, Villemagne V, Seibyl J, Sabri O, Levin J, Brendel M. Additive value of [ 18F]PI-2620 perfusion imaging in progressive supranuclear palsy and corticobasal syndrome. Eur J Nucl Med Mol Imaging 2023; 50:423-434. [PMID: 36102964 PMCID: PMC9816230 DOI: 10.1007/s00259-022-05964-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 09/01/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE Early after [18F]PI-2620 PET tracer administration, perfusion imaging has potential for regional assessment of neuronal injury in neurodegenerative diseases. This is while standard late-phase [18F]PI-2620 tau-PET is able to discriminate the 4-repeat tauopathies progressive supranuclear palsy and corticobasal syndrome (4RTs) from disease controls and healthy controls. Here, we investigated whether early-phase [18F]PI-2620 PET has an additive value for biomarker based evaluation of 4RTs. METHODS Seventy-eight patients with 4RTs (71 ± 7 years, 39 female), 79 patients with other neurodegenerative diseases (67 ± 12 years, 35 female) and twelve age-matched controls (69 ± 8 years, 8 female) underwent dynamic (0-60 min) [18F]PI-2620 PET imaging. Regional perfusion (0.5-2.5 min p.i.) and tau load (20-40 min p.i.) were measured in 246 predefined brain regions [standardized-uptake-value ratios (SUVr), cerebellar reference]. Regional SUVr were compared between 4RTs and controls by an ANOVA including false-discovery-rate (FDR, p < 0.01) correction. Hypoperfusion in resulting 4RT target regions was evaluated at the patient level in all patients (mean value - 2SD threshold). Additionally, perfusion and tau pattern expression levels were explored regarding their potential discriminatory value of 4RTs against other neurodegenerative disorders, including validation in an independent external dataset (n = 37), and correlated with clinical severity in 4RTs (PSP rating scale, MoCA, activities of daily living). RESULTS Patients with 4RTs had significant hypoperfusion in 21/246 brain regions, most dominant in thalamus, caudate nucleus, and anterior cingulate cortex, fitting to the topology of the 4RT disease spectrum. However, single region hypoperfusion was not specific regarding the discrimination of patients with 4RTs against patients with other neurodegenerative diseases. In contrast, perfusion pattern expression showed promise for discrimination of patients with 4RTs from other neurodegenerative diseases (AUC: 0.850). Discrimination by the combined perfusion-tau pattern expression (AUC: 0.903) exceeded that of the sole tau pattern expression (AUC: 0.864) and the discriminatory power of the combined perfusion-tau pattern expression was replicated in the external dataset (AUC: 0.917). Perfusion but not tau pattern expression was associated with PSP rating scale (R = 0.402; p = 0.0012) and activities of daily living (R = - 0.431; p = 0.0005). CONCLUSION [18F]PI-2620 perfusion imaging mirrors known topology of regional hypoperfusion in 4RTs. Single region hypoperfusion is not specific for 4RTs, but perfusion pattern expression may provide an additive value for the discrimination of 4RTs from other neurodegenerative diseases and correlates closer with clinical severity than tau pattern expression.
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Affiliation(s)
- Sabrina Katzdobler
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Alexander Nitschmann
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Henryk Barthel
- grid.411339.d0000 0000 8517 9062Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Gerard Bischof
- grid.411097.a0000 0000 8852 305XDepartment of Nuclear Medicine, University Hospital Cologne, Cologne, Germany ,Molecular Organization of the Brain, Institute for Neuroscience and Medicine, INM-2), Jülich, Germany
| | - Leonie Beyer
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Ken Marek
- grid.452597.8InviCRO, LLC, Boston, MA USA ,grid.452597.8Molecular Neuroimaging, A Division of inviCRO, New Haven, CT USA
| | - Mengmeng Song
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Olivia Wagemann
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Carla Palleis
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Endy Weidinger
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Anne Nack
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Urban Fietzek
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Carolin Kurz
- grid.411095.80000 0004 0477 2585Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Jan Häckert
- grid.411095.80000 0004 0477 2585Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany ,grid.7307.30000 0001 2108 9006Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, BKH Augsburg, Augsburg, Germany
| | - Theresa Stapf
- grid.411095.80000 0004 0477 2585Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Christian Ferschmann
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Maximilian Scheifele
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Florian Eckenweber
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Gloria Biechele
- grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Nicolai Franzmeier
- grid.411095.80000 0004 0477 2585Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Anna Dewenter
- grid.411095.80000 0004 0477 2585Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Sonja Schönecker
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Dorothee Saur
- grid.9647.c0000 0004 7669 9786Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Matthias L. Schroeter
- grid.9647.c0000 0004 7669 9786Clinic for Cognitive Neurology, University of Leipzig, Leipzig, Germany ,grid.9647.c0000 0004 7669 9786LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jost-Julian Rumpf
- grid.9647.c0000 0004 7669 9786Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Michael Rullmann
- grid.411339.d0000 0000 8517 9062Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Andreas Schildan
- grid.411339.d0000 0000 8517 9062Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Marianne Patt
- grid.411339.d0000 0000 8517 9062Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | | | - Thilo van Eimeren
- grid.411097.a0000 0000 8852 305XDepartment of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Bernd Neumaier
- grid.411097.a0000 0000 8852 305XDepartment of Nuclear Medicine, University Hospital Cologne, Cologne, Germany ,grid.8385.60000 0001 2297 375XInstitute for Neuroscience and Medicine (INM-3), Cognitive Neuroscience, Research Centre Juelich, Juelich, Germany
| | - Alexander Drzezga
- grid.411097.a0000 0000 8852 305XDepartment of Nuclear Medicine, University Hospital Cologne, Cologne, Germany ,Molecular Organization of the Brain, Institute for Neuroscience and Medicine, INM-2), Jülich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Adrian Danek
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Joseph Classen
- grid.9647.c0000 0004 7669 9786Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Katharina Bürger
- grid.411095.80000 0004 0477 2585Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- grid.411095.80000 0004 0477 2585Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- grid.7307.30000 0001 2108 9006Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, BKH Augsburg, Augsburg, Germany ,grid.411095.80000 0004 0477 2585Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Sophia Stöcklein
- grid.411095.80000 0004 0477 2585Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Robert Perneczky
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ,grid.7307.30000 0001 2108 9006Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, BKH Augsburg, Augsburg, Germany ,grid.7445.20000 0001 2113 8111Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College, London, UK
| | - Florian Schöberl
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Andreas Zwergal
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Günter U. Höglinger
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ,grid.10423.340000 0000 9529 9877Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Peter Bartenstein
- grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ,grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
| | - Victor Villemagne
- grid.410678.c0000 0000 9374 3516Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Medicine, Austin Health, The University of Melbourne, Melbourne, VIC Australia ,grid.21925.3d0000 0004 1936 9000Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA USA
| | - John Seibyl
- grid.452597.8InviCRO, LLC, Boston, MA USA ,grid.452597.8Molecular Neuroimaging, A Division of inviCRO, New Haven, CT USA
| | - Osama Sabri
- grid.411339.d0000 0000 8517 9062Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Johannes Levin
- grid.411095.80000 0004 0477 2585Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Matthias Brendel
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ,grid.411095.80000 0004 0477 2585Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany
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Phuah CL, Chen Y, Strain JF, Yechoor N, Laurido-Soto OJ, Ances BM, Lee JM. Association of Data-Driven White Matter Hyperintensity Spatial Signatures With Distinct Cerebral Small Vessel Disease Etiologies. Neurology 2022; 99:e2535-e2547. [PMID: 36123127 PMCID: PMC9754646 DOI: 10.1212/wnl.0000000000201186] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies. METHODS We performed a cross-sectional study on participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-β PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment. RESULTS We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into 5 unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline. DISCUSSION Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathologic process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.
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Affiliation(s)
- Chia-Ling Phuah
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Yasheng Chen
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jeremy F Strain
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Nirupama Yechoor
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Osvaldo J Laurido-Soto
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Beau M Ances
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jin-Moo Lee
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO.
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Pala D, Lee B, Ning X, Kim D, Shen L. Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer's Disease. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2667-2673. [PMID: 36824222 PMCID: PMC9942815 DOI: 10.1109/bibm55620.2022.9995405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Alzheimer's disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.
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Affiliation(s)
- Daniele Pala
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
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Shah J, Gao F, Li B, Ghisays V, Luo J, Chen Y, Lee W, Zhou Y, Benzinger TL, Reiman EM, Chen K, Su Y, Wu T. Deep residual inception encoder-decoder network for amyloid PET harmonization. Alzheimers Dement 2022; 18:2448-2457. [PMID: 35142053 PMCID: PMC9360199 DOI: 10.1002/alz.12564] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 12/02/2021] [Accepted: 12/05/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. METHOD A Residual Inception Encoder-Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound-B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10-fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects. RESULTS Significantly stronger between-tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel-wise measurements in the training cohort and the external testing cohort. DISCUSSION We proposed and validated a novel encoder-decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.
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Affiliation(s)
- Jay Shah
- ASU‐Mayo Center for Innovative ImagingArizona State University699 S. Mill Ave.TempeArizona85287USA
- School of Computing and Augmented IntelligenceArizona State University699 S. Mill Ave.TempeArizona85287USA
| | - Fei Gao
- ASU‐Mayo Center for Innovative ImagingArizona State University699 S. Mill Ave.TempeArizona85287USA
- School of Computing and Augmented IntelligenceArizona State University699 S. Mill Ave.TempeArizona85287USA
| | - Baoxin Li
- ASU‐Mayo Center for Innovative ImagingArizona State University699 S. Mill Ave.TempeArizona85287USA
- School of Computing and Augmented IntelligenceArizona State University699 S. Mill Ave.TempeArizona85287USA
| | - Valentina Ghisays
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Ji Luo
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Yinghua Chen
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Wendy Lee
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Yuxiang Zhou
- Department of RadiologyMayo Clinic at Arizona5777 E Mayo BlvdPhoenixArizona85054USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St. Louis510 South Kingshighway BoulevardSt. LouisMissouri63110USA
| | - Eric M. Reiman
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Kewei Chen
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Yi Su
- ASU‐Mayo Center for Innovative ImagingArizona State University699 S. Mill Ave.TempeArizona85287USA
- School of Computing and Augmented IntelligenceArizona State University699 S. Mill Ave.TempeArizona85287USA
- Banner Alzheimer's Institute901 E. Willetta StreetPhoenixArizona85006USA
| | - Teresa Wu
- ASU‐Mayo Center for Innovative ImagingArizona State University699 S. Mill Ave.TempeArizona85287USA
- School of Computing and Augmented IntelligenceArizona State University699 S. Mill Ave.TempeArizona85287USA
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46
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Moscoso A, Karikari TK, Grothe MJ, Ashton NJ, Lantero-Rodriguez J, Snellman A, Zetterberg H, Blennow K, Schöll M. CSF biomarkers and plasma p-tau181 as predictors of longitudinal tau accumulation: Implications for clinical trial design. Alzheimers Dement 2022; 18:2614-2626. [PMID: 35226405 DOI: 10.1002/alz.12570] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/11/2021] [Accepted: 12/12/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Clinical trials targeting tau in Alzheimer's disease (AD) need to recruit individuals at risk of tau accumulation. Here, we studied cerebrospinal fluid (CSF) biomarkers and plasma phosphorylated tau (p-tau)181 as predictors of tau accumulation on positron emission tomography (PET) to evaluate implications for trial designs. METHODS We included older individuals who had serial tau-PET scans, baseline amyloid beta (Aβ)-PET, and baseline CSF biomarkers (n = 163) or plasma p-tau181 (n = 74). We studied fluid biomarker associations with tau accumulation and estimated trial sample sizes and screening failure reductions by implementing these markers into participant selection for trials. RESULTS P-tau181 in CSF and plasma predicted tau accumulation (r > 0.36, P < .001), even in AD-continuum individuals with normal baseline tau-PET (A+T-; r > 0.37, P < .05). Recruitment based on CSF biomarkers yielded comparable sample sizes to Aβ-PET. Prescreening with plasma p-tau181 reduced up to ≈50% of screening failures. DISCUSSION Clinical trials testing tau-targeting therapies may benefit from using fluid biomarkers to recruit individuals at risk of tau aggregation.
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Affiliation(s)
- Alexis Moscoso
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michel J Grothe
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,Unidad de Trastornos del Movimiento, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,King's College London, Institute of Psychiatry, Psychology & Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK.,NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK
| | - Juan Lantero-Rodriguez
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anniina Snellman
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Turku PET Centre, University of Turku, Turku, Finland
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.,UK Dementia Research Institute at University College London, London, UK.,Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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47
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Betthauser TJ, Bilgel M, Koscik RL, Jedynak BM, An Y, Kellett KA, Moghekar A, Jonaitis EM, Stone CK, Engelman CD, Asthana S, Christian BT, Wong DF, Albert M, Resnick SM, Johnson SC. Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts. Brain 2022; 145:4065-4079. [PMID: 35856240 PMCID: PMC9679170 DOI: 10.1093/brain/awac213] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/24/2022] [Accepted: 05/20/2022] [Indexed: 01/25/2023] Open
Abstract
Alzheimer's disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease.
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Affiliation(s)
- Tobey J Betthauser
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging,
National Institutes of Health, Baltimore, MD,
USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine
and Public Health, Madison, WI, USA
| | - Bruno M Jedynak
- Department of Mathematics and Statistics, Portland State
University, Portland, OR, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging,
National Institutes of Health, Baltimore, MD,
USA
| | - Kristina A Kellett
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
| | - Abhay Moghekar
- Laboratory of Behavioral Neuroscience, National Institute on Aging,
National Institutes of Health, Baltimore, MD,
USA
| | - Erin M Jonaitis
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine
and Public Health, Madison, WI, USA
| | - Charles K Stone
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
| | - Corinne D Engelman
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine
and Public Health, Madison, WI, USA
- Department of Population Health Sciences, University of Wisconsin-Madison
School of Medicine and Public Health, Madison,
WI, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine
and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton
Veterans Hospital, Madison, WI, USA
| | - Bradley T Christian
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of
Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison School of
Medicine and Public Health, Madison, WI, USA
| | - Dean F Wong
- Department of Radiology, Mallinckrodt Institute of Radiology, Neurology,
Psychiatry and Neuroscience, Washington University School of Medicine,
St. Louis, MO, USA
| | - Marilyn Albert
- Department of Neurology, Division of Cognitive Neuroscience, Johns Hopkins
University School of Medicine, Baltimore, MD,
USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging,
National Institutes of Health, Baltimore, MD,
USA
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin
School of Medicine and Public Health, Madison,
WI, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine
and Public Health, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine
and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton
Veterans Hospital, Madison, WI, USA
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48
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Karaman BK, Mormino EC, Sabuncu MR. Machine learning based multi-modal prediction of future decline toward Alzheimer's disease: An empirical study. PLoS One 2022; 17:e0277322. [PMID: 36383528 PMCID: PMC9668188 DOI: 10.1371/journal.pone.0277322] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects' future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model's prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.
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Affiliation(s)
- Batuhan K. Karaman
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, United States of America
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States of America
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States of America
| | - Mert R. Sabuncu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, United States of America
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States of America
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49
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Akamatsu G, Takahashi M, Tashima H, Iwao Y, Yoshida E, Wakizaka H, Kumagai M, Yamashita T, Yamaya T. Performance evaluation of VRAIN: a brain-dedicated PET with a hemispherical detector arrangement. Phys Med Biol 2022; 67. [PMID: 36317319 DOI: 10.1088/1361-6560/ac9e87] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/28/2022] [Indexed: 11/07/2022]
Abstract
Objective.For PET imaging systems, a smaller detector ring enables less intrinsic spatial resolution loss due to the photon non-collinearity effect as well as better balance between production cost and sensitivity, and a hemispherical detector arrangement is more appropriate for brain imaging than a conventional cylindrical arrangement. Therefore, we have developed a brain-dedicated PET system with a hemispherical detector arrangement, which has been commercialized in Japan under the product name of VRAINTM. In this study, we evaluated imaging performance of VRAIN.Approach.The VRAIN used 54 detectors to form the main hemispherical unit and an additional half-ring behind the neck. Each detector was composed of a 12 × 12 array of lutetium fine silicate crystals (4.1 × 4.1 × 10 mm3) and a 12 × 12 array of silicon photomultipliers (4 × 4 mm2active area) with the one-to-one coupling. We evaluated the physical performance of VRAIN according to the NEMA NU 2-2018 standards. Some measurements were modified so as to fit the hemispherical geometry. In addition, we performed18F-FDG imaging in a healthy volunteer.Main results.In the phantom study, the VRAIN showed high resolution for separating 2.2 mm rods, 229 ps TOF resolution and 19% scatter fraction. With the TOF gain for a 20 cm diameter object (an assumed head diameter), the peak noise-equivalent count rate was 144 kcps at 9.8 kBq ml-1and the sensitivity was 25 kcps MBq-1. Overall, the VRAIN provided excellent image quality in phantom and human studies. In the human FDG images, small brain nuclei and gray matter structures were clearly visualized with high contrast and low noise.Significance.We demonstrated the excellent imaging performance of VRAIN, which supported the advantages of the hemispherical detector arrangement.
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Affiliation(s)
- Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Miwako Takahashi
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Hideaki Tashima
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Yuma Iwao
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Eiji Yoshida
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | - Hidekatsu Wakizaka
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
| | | | | | - Taiga Yamaya
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), Chiba, Japan
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50
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Morris JC, Weiner M, Xiong C, Beckett L, Coble D, Saito N, Aisen PS, Allegri R, Benzinger TLS, Berman SB, Cairns NJ, Carrillo MC, Chui HC, Chhatwal JP, Cruchaga C, Fagan AM, Farlow M, Fox NC, Ghetti B, Goate AM, Gordon BA, Graff-Radford N, Day GS, Hassenstab J, Ikeuchi T, Jack CR, Jagust WJ, Jucker M, Levin J, Massoumzadeh P, Masters CL, Martins R, McDade E, Mori H, Noble JM, Petersen RC, Ringman JM, Salloway S, Saykin AJ, Schofield PR, Shaw LM, Toga AW, Trojanowski JQ, Vöglein J, Weninger S, Bateman RJ, Buckles VD. Autosomal dominant and sporadic late onset Alzheimer's disease share a common in vivo pathophysiology. Brain 2022; 145:3594-3607. [PMID: 35580594 PMCID: PMC9989348 DOI: 10.1093/brain/awac181] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/12/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.
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Affiliation(s)
- John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael Weiner
- Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Laurel Beckett
- Department of Public Health Sciences, School of Medicine, University of California; Davis, Davis, CA, USA
| | - Dean Coble
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Naomi Saito
- Department of Public Health Sciences, School of Medicine, University of California; Davis, Davis, CA, USA
| | - Paul S Aisen
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Neuropsychology and Neuropsychiatry, Institute for Neurological Research (FLENI), Buenos Aires, Argentina
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nigel J Cairns
- College of Medicine and Health and the Living Systems Institute, University of Exeter, Exeter, UK
| | | | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Martin Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease and UK Dementia Research Institute, UCL Institute of Neurology, London, UK
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer’s Disease, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | | | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Mathias Jucker
- Cell Biology of Neurological Diseases Group, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Johannes Levin
- DZNE Munich, Munich Cluster of Systems Neurology (SyNergy) and Ludwig-Maximilians-Universität, Munich, Germany
| | - Parinaz Massoumzadeh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Colin L Masters
- Florey Institute, University of Melbourne, Melbourne, Australia
| | - Ralph Martins
- Sir James McCusker Alzheimer’s Disease Research Unit, Edith Cowan University, Nedlands, Australia
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hiroshi Mori
- Department of Neuroscience, Osaka City University Medical School, Osaka City, Japan
| | - James M Noble
- Department of Neurology, Taub Institute for Research on Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | | | - John M Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephen Salloway
- Department of Neurology, Butler Hospital and Alpert Medical School of Brown University, Providence, RI, 02906, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Peter R Schofield
- Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE) and Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | | | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Virginia D Buckles
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
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