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Mao A, Flassbeck S, Marchetto E, Masurkar AV, Rusinek H, Assländer J. Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305860. [PMID: 38699343 PMCID: PMC11065014 DOI: 10.1101/2024.04.15.24305860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Magnetization transfer MRI is sensitive to semi-solid macromolecules, including amyloid beta, and has previously been used to discriminate Alzheimer's disease (AD) patients from controls. Here, we fit an unconstrained 2-pool quantitative MT (qMT) model, i.e., without constraints on the longitudinal relaxation rateR 1 s of semi-solids, and investigate the sensitivity of the estimated parameters to amyloid accumulation in preclinical subjects. We scanned 15 cognitively normal volunteers, of which 9 were amyloid positive by [18F]Florbetaben PET. A 12 min hybrid-state qMT scan with an effective resolution of 1.24 mm isotropic and whole-brain coverage was acquired to estimate the unconstrained 2-pool qMT parameters. Group comparisons and correlations with Florbetaben PET standardized uptake value ratios were analyzed at the lobar level. We find that the exchange rate and semi-solid pool'sR 1 s were sensitive to the amyloid concentration, while morphometric measures of cortical thickness derived from structural MRI were not. Changes in the exchange rate are consistent with previous reports in clinical AD, while changes inR 1 s have not been reported previously as its value is typically constrained in the literature. Our results demonstrate that qMT MRI may be a promising surrogate marker of amyloid beta without the need for contrast agents or radiotracers.
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Affiliation(s)
- Andrew Mao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, USA
| | - Sebastian Flassbeck
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Elisa Marchetto
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Arjun V. Masurkar
- Alzheimer’s Disease Research Center, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Henry Rusinek
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Alzheimer’s Disease Research Center, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Jakob Assländer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Lecy EE, Min HK, Apgar CJ, Maltais DD, Lundt ES, Albertson SM, Senjem ML, Schwarz CG, Botha H, Graff-Radford J, Jones DT, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR, Lee J, Lowe VJ. Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study. J Nucl Med 2024; 65:1122-1128. [PMID: 38782458 DOI: 10.2967/jnumed.123.267150] [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: 11/27/2023] [Revised: 04/29/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.
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Affiliation(s)
- Emily E Lecy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Christopher J Apgar
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | | | - Emily S Lundt
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Sabrina M Albertson
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | | | - Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
- Department of Biomedical Engineering, College of Medicine, Hanyang University, Seoul, South Korea
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
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Kim HH, Kwon MJ, Jo S, Park JE, Kim JW, Kim JH, Kim SE, Kim KW, Han JW. Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals. Sci Rep 2024; 14:10083. [PMID: 38698190 PMCID: PMC11066072 DOI: 10.1038/s41598-024-60843-8] [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: 11/28/2023] [Accepted: 04/28/2024] [Indexed: 05/05/2024] Open
Abstract
Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.
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Affiliation(s)
- Hak Hyeon Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea
| | - Min Jeong Kwon
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Sungman Jo
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji Eun Park
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Ji Won Kim
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam-si, Gyeonggi-do, South Korea
| | - Sang Eun Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam-si, Gyeonggi-do, Korea
- Center for Nanomolecular Imaging and Innovative Drug Development, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea.
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Therriault J, Schindler SE, Salvadó G, Pascoal TA, Benedet AL, Ashton NJ, Karikari TK, Apostolova L, Murray ME, Verberk I, Vogel JW, La Joie R, Gauthier S, Teunissen C, Rabinovici GD, Zetterberg H, Bateman RJ, Scheltens P, Blennow K, Sperling R, Hansson O, Jack CR, Rosa-Neto P. Biomarker-based staging of Alzheimer disease: rationale and clinical applications. Nat Rev Neurol 2024; 20:232-244. [PMID: 38429551 DOI: 10.1038/s41582-024-00942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andréa Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Liana Apostolova
- Department of Neurology, University of Indiana School of Medicine, Indianapolis, IN, USA
| | | | - Inge Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Charlotte Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Reisa Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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5
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Hojjati SH, Chiang GC, Butler TA, de Leon M, Gupta A, Li Y, Sabuncu MR, Feiz F, Nayak S, Shteingart J, Ozoria S, Gholipour Picha S, Stern Y, Luchsinger JA, Devanand DP, Razlighi QR. Remote Associations Between Tau and Cortical Amyloid-β Are Stage-Dependent. J Alzheimers Dis 2024; 98:1467-1482. [PMID: 38552116 DOI: 10.3233/jad-231362] [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: 04/20/2024]
Abstract
Background Histopathologic studies of Alzheimer's disease (AD) suggest that extracellular amyloid-β (Aβ) plaques promote the spread of neurofibrillary tau tangles. However, these two proteinopathies initiate in spatially distinct brain regions, so how they interact during AD progression is unclear. Objective In this study, we utilized Aβ and tau positron emission tomography (PET) scans from 572 older subjects (476 healthy controls (HC), 14 with mild cognitive impairment (MCI), 82 with mild AD), at varying stages of the disease, to investigate to what degree tau is associated with cortical Aβ deposition. Methods Using multiple linear regression models and a pseudo-longitudinal ordering technique, we investigated remote tau-Aβ associations in four pathologic phases of AD progression based on tau spread: 1) no-tau, 2) pre-acceleration, 3) acceleration, and 4) post-acceleration. Results No significant tau-Aβ association was detected in the no-tau phase. In the pre-acceleration phase, the earliest stage of tau deposition, associations emerged between regional tau in medial temporal lobe (MTL) (i.e., entorhinal cortex, parahippocampal gyrus) and cortical Aβ in lateral temporal lobe regions. The strongest tau-Aβ associations were found in the acceleration phase, in which tau in MTL regions was strongly associated with cortical Aβ (i.e., temporal and frontal lobes regions). Strikingly, in the post-acceleration phase, including 96% of symptomatic subjects, tau-Aβ associations were no longer significant. Conclusions The results indicate that associations between tau and Aβ are stage-dependent, which could have important implications for understanding the interplay between these two proteinopathies during the progressive stages of AD.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Gloria C Chiang
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tracy A Butler
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mony de Leon
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Li
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Farnia Feiz
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Siddharth Nayak
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Jacob Shteingart
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Sindy Ozoria
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Saman Gholipour Picha
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Yaakov Stern
- Departments of Neurology, Psychiatry, GH Sergievsky Center, The Taub Institute for the Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - José A Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Davangere P Devanand
- Division of Geriatric Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Qolamreza R Razlighi
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
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6
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Jovalekic A, Roé-Vellvé N, Koglin N, Quintana ML, Nelson A, Diemling M, Lilja J, Gómez-González JP, Doré V, Bourgeat P, Whittington A, Gunn R, Stephens AW, Bullich S. Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods. Eur J Nucl Med Mol Imaging 2023; 50:3276-3289. [PMID: 37300571 PMCID: PMC10542295 DOI: 10.1007/s00259-023-06279-0] [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: 01/27/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aβ deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification. METHODS This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aβ load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled. RESULTS The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods. CONCLUSION This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
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7
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Therriault J, Lussier FZ, Tissot C, Chamoun M, Stevenson J, Rahmouni N, Pallen V, Bezgin G, Servaes S, Kunach P, Wang Y, Fernandez‐Arias J, Vermeiren M, Pascoal TA, Massarweh G, Vitali P, Soucy J, Saha‐Chaudhuri P, Gauthie S, Rosa‐Neto P. Amyloid beta plaque accumulation with longitudinal [18F]AZD4694 PET. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12391. [PMID: 37644990 PMCID: PMC10461075 DOI: 10.1002/dad2.12391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 11/01/2022] [Accepted: 11/29/2022] [Indexed: 08/31/2023]
Abstract
Introduction [18F]AZD4694 is an amyloid beta (Aβ) imaging agent used in several observational studies and clinical trials. However, no studies have yet published data on longitudinal Aβ accumulation measured with [18F]AZD4694. Methods We assessed 146 individuals who were evaluated with [18F]AZD4694 at baseline and 2-year follow-up. We calculated annual rates of [18F]AZD4694 change for clinically defined and biomarker-defined groups. Results Cognitively unimpaired (CU) older adults displayed subtle [18F]AZD4694 standardized uptake value ratio (SUVR) accumulation over the follow-up period. In contrast, Aβ positive CU older adults displayed higher annual [18F]AZD4694 SUVR increases. [18F]AZD4694 SUVR accumulation in Aβ positive mild cognitive impairment (MCI) and dementia was modest across the neocortex. Discussion Larger increases in [18F]AZD4694 SUVR were observed in CU individuals who had abnormal amyloid positron emission tomography levels at baseline. [18F]AZD4694 can be used to monitor Aβ levels in therapeutic trials as well as clinical settings, particularly prior to initiating anti-amyloid therapies.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Firoza Z. Lussier
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Cécile Tissot
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Mira Chamoun
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Jenna Stevenson
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Nesrine Rahmouni
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Vanessa Pallen
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Gleb Bezgin
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Stijn Servaes
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Peter Kunach
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Yi‐Ting Wang
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Jaime Fernandez‐Arias
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Marie Vermeiren
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
| | - Tharick A. Pascoal
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Gassan Massarweh
- Department of RadiochemistryMcGill UniversityMontrealQuebecCanada
| | - Paolo Vitali
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Jean‐Paul Soucy
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Paramita Saha‐Chaudhuri
- Department of EpidemiologyBiostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Department of Mathematics & StatisticsUniversity of VermontBurlingtonVermontUSA
| | - Serge Gauthie
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
| | - Pedro Rosa‐Neto
- Translational Neuroimaging LaboratoryDouglas Mental Health InstituteMcGill University Research Centre for Studies in AgingMontrealQuebecCanada
- Department of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuebecCanada
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8
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Pemberton HG, Buckley C, Battle M, Bollack A, Patel V, Tomova P, Cooke D, Balhorn W, Hegedorn K, Lilja J, Brand C, Farrar G. Software compatibility analysis for quantitative measures of [ 18F]flutemetamol amyloid PET burden in mild cognitive impairment. EJNMMI Res 2023; 13:48. [PMID: 37225974 DOI: 10.1186/s13550-023-00994-3] [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: 09/26/2022] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
RATIONALE Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease pathogenesis. In clinical practice, trained readers will visually categorise positron emission tomography (PET) scans as either Aβ positive or negative. However, adjunct quantitative analysis is becoming more widely available, where regulatory approved software can currently generate metrics such as standardised uptake value ratios (SUVr) and individual Z-scores. Therefore, it is of direct value to the imaging community to assess the compatibility of commercially available software packages. In this collaborative project, the compatibility of amyloid PET quantification was investigated across four regulatory approved software packages. In doing so, the intention is to increase visibility and understanding of clinically relevant quantitative methods. METHODS Composite SUVr using the pons as the reference region was generated from [18F]flutemetamol (GE Healthcare) PET in a retrospective cohort of 80 amnestic mild cognitive impairment (aMCI) patients (40 each male/female; mean age = 73 years, SD = 8.52). Based on previous autopsy validation work, an Aβ positivity threshold of ≥ 0.6 SUVrpons was applied. Quantitative results from MIM Software's MIMneuro, Syntermed's NeuroQ, Hermes Medical Solutions' BRASS and GE Healthcare's CortexID were analysed using intraclass correlation coefficient (ICC), percentage agreement around the Aβ positivity threshold and kappa scores. RESULTS Using an Aβ positivity threshold of ≥ 0.6 SUVrpons, 95% agreement was achieved across the four software packages. Two patients were narrowly classed as Aβ negative by one software package but positive by the others, and two patients vice versa. All kappa scores around the same Aβ positivity threshold, both combined (Fleiss') and individual software pairings (Cohen's), were ≥ 0.9 signifying "almost perfect" inter-rater reliability. Excellent reliability was found between composite SUVr measurements for all four software packages, with an average measure ICC of 0.97 and 95% confidence interval of 0.957-0.979. Correlation coefficient analysis between the two software packages reporting composite z-scores was strong (r2 = 0.98). CONCLUSION Using an optimised cortical mask, regulatory approved software packages provided highly correlated and reliable quantification of [18F]flutemetamol amyloid PET with a ≥ 0.6 SUVrpons positivity threshold. In particular, this work could be of interest to physicians performing routine clinical imaging rather than researchers performing more bespoke image analysis. Similar analysis is encouraged using other reference regions as well as the Centiloid scale, when it has been implemented by more software packages.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | | | - Mark Battle
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Vrajesh Patel
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Petya Tomova
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | | | | | | | | | - Christine Brand
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Gill Farrar
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
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9
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Alchera N, Garibotto V, Tomczyk S, Treyer V, Hock C, Gietl AF, Lövblad KO, Scheffler M, Chincarini A, Frisoni GB, Ribaldi F. Patterns of amyloid accumulation in amyloid-negative cases. Neurobiol Aging 2023; 129:99-108. [PMID: 37279618 DOI: 10.1016/j.neurobiolaging.2023.05.006] [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: 10/25/2022] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 06/08/2023]
Abstract
Amyloid staging models showed that regional abnormality occurs before global positivity. Several studies assumed that the trajectory of amyloid spread is homogeneous, but clinical evidence suggests that it is highly heterogeneous. We tested whether different amyloid-β (Aβ) patterns exist by applying clustering on negative scans and investigating their demographics, clinical, cognitive, and biomarkers correlates, and cognitive trajectories. 151 individuals from Geneva and Zurich cohorts with T1-MRI, negative Aβ positron emission tomography (PET,centiloid<12) and clinical assessment were included. N=123 underwent tau PET, and N=65 follow-up neuropsychological assessment. We performed k-means clustering using 33 Aβ regional Standardized Uptake Vales ratio. Demographics, clinical, cognitive, and biomarkers differences were investigated. Longitudinal cognitive changes by baseline cluster status were estimated using a linear mixed model. The cluster analysis identified two clusters: temporal predominant (TP) and cingulate predominant (CP). TP tau deposition was higher than CP. A trend for a higher cognitive decline in TP compared to CP was observed. This study suggests the existence of two Aβ deposition patterns in the earliest phases of Aβ accumulation, differently prone to tau pathology and cognitive decline.
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Affiliation(s)
- Nicola Alchera
- Istituto Nazionale di Fisica Nucleare (INFN), Genova, University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland; Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Center for Biomedical imaging (CIBM), Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland; Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Neurimmune, Schlieren, Switzerland
| | - Anton F Gietl
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Department for Geriatric Psychiatry, University Hospital for Psychiatry Zurich, Zurich, Switzerland
| | - Karl-Olof Lövblad
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland; Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland; Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland.
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10
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Brugulat-Serrat A, Sánchez-Benavides G, Cacciaglia R, Salvadó G, Shekari M, Collij LE, Buckley C, van Berckel BNM, Perissinotti A, Niñerola-Baizán A, Milà-Alomà M, Vilor-Tejedor N, Operto G, Falcon C, Grau-Rivera O, Arenaza-Urquijo EM, Minguillón C, Fauria K, Molinuevo JL, Suárez-Calvet M, Gispert JD. APOE-ε4 modulates the association between regional amyloid deposition and cognitive performance in cognitively unimpaired middle-aged individuals. EJNMMI Res 2023; 13:18. [PMID: 36856866 PMCID: PMC9978048 DOI: 10.1186/s13550-023-00967-6] [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: 08/25/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
PURPOSE To determine whether the APOE-ε4 allele modulates the relationship between regional β-amyloid (Aβ) accumulation and cognitive change in middle-aged cognitively unimpaired (CU) participants. METHODS The 352 CU participants (mean aged 61.1 [4.7] years) included completed two cognitive assessments (average interval 3.34 years), underwent [18F]flutemetamol Aβ positron emission tomography (PET), T1w magnetic resonance imaging (MRI), as well as APOE genotyping. Global and regional Aβ PET positivity was assessed across five regions-of-interest by visual reading (VR) and regional Centiloids. Linear regression models were developed to examine the interaction between regional and global Aβ PET positivity and APOE-ε4 status on longitudinal cognitive change assessed with the Preclinical Alzheimer's Cognitive Composite (PACC), episodic memory, and executive function, after controlling for age, sex, education, cognitive baseline scores, and hippocampal volume. RESULTS In total, 57 participants (16.2%) were VR+ of whom 41 (71.9%) were APOE-ε4 carriers. No significant APOE-ε4*global Aβ PET interactions were associated with cognitive change for any cognitive test. However, APOE-ε4 carriers who were VR+ in temporal areas (n = 19 [9.81%], p = 0.04) and in the striatum (n = 8 [4.14%], p = 0.01) exhibited a higher decline in the PACC. The temporal areas findings were replicated when regional PET positivity was determined with Centiloid values. Regionally, VR+ in the striatum was associated with higher memory decline. As for executive function, interactions between APOE-ε4 and regional VR+ were found in temporal and parietal regions, and in the striatum. CONCLUSION CU APOE-ε4 carriers with a positive Aβ PET VR in regions known to accumulate amyloid at later stages of the Alzheimer's disease (AD) continuum exhibited a steeper cognitive decline. This work supports the contention that regional VR of Aβ PET might convey prognostic information about future cognitive decline in individuals at higher risk of developing AD. CLINICALTRIALS gov Identifier: NCT02485730. Registered 20 June 2015 https://clinicaltrials.gov/ct2/show/NCT02485730 and ClinicalTrials.gov Identifier:NCT02685969. Registered 19 February 2016 https://clinicaltrials.gov/ct2/show/NCT02685969 .
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Affiliation(s)
- Anna Brugulat-Serrat
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.512357.7Global Brain Health Institute, San Francisco, CA USA
| | - Gonzalo Sánchez-Benavides
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Raffaele Cacciaglia
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Gemma Salvadó
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.4514.40000 0001 0930 2361Department of Clinical Sciences, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain
| | - Lyduine E. Collij
- grid.12380.380000 0004 1754 9227Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands
| | - Christopher Buckley
- grid.83440.3b0000000121901201Center for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
| | - Bart N. M. van Berckel
- grid.12380.380000 0004 1754 9227Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands
| | - Andrés Perissinotti
- grid.410458.c0000 0000 9635 9413Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Aida Niñerola-Baizán
- grid.410458.c0000 0000 9635 9413Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Milà-Alomà
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain
| | - Natàlia Vilor-Tejedor
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain ,grid.473715.30000 0004 6475 7299Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Grégory Operto
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Falcon
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Oriol Grau-Rivera
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Eider M. Arenaza-Urquijo
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Minguillón
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Karine Fauria
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Molinuevo
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.424580.f0000 0004 0476 7612H. Lundbeck A/S, Copenhagen, Denmark
| | - Marc Suárez-Calvet
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005, Barcelona, Spain. .,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. .,Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
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11
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Sun Y, Zhao Y, Hu K, Wang M, Liu Y, Liu B. Distinct spatiotemporal subtypes of amyloid deposition are associated with diverging disease profiles in cognitively normal and mild cognitive impairment individuals. Transl Psychiatry 2023; 13:35. [PMID: 36732496 PMCID: PMC9895066 DOI: 10.1038/s41398-023-02328-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/04/2023] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
We aimed to investigate the relationship between spatiotemporal changes of amyloid deposition and Alzheimer's disease (AD) profiles in cognitively normal (CN) and those with mild cognitive impairment (MCI). Using a data-driven method and amyloid-PET data, we identified and validated two subtypes in two independent datasets (discovery dataset: N = 548, age = 72.4 ± 6.78, 49% female; validation dataset: N = 348, age = 74.9 ± 8.16, 47% female) from the Alzheimer's Disease Neuroimaging Initiative across a range of individuals who were CN or had MCI. The two subtypes showed distinct regional progression patterns and presented distinct genetic, clinical and biomarker characteristics. The cortex-priority subtype was more likely to show typical clinical syndromes of symptomatic AD and vice versa. Furthermore, the regional progression patterns were associated with clinical and biomarker profiles. In sum, our findings suggest that the spatiotemporal variants of amyloid depositions are in close association with disease trajectories; these findings may provide insight into the disease monitoring and enrollment of therapeutic trials in AD.
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Affiliation(s)
- Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuxin Zhao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ke Hu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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12
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Collij LE, Farrar G, Valléz García D, Bader I, Shekari M, Lorenzini L, Pemberton H, Altomare D, Pla S, Loor M, Markiewicz P, Yaqub M, Buckley C, Frisoni GB, Nordberg A, Payoux P, Stephens A, Gismondi R, Visser PJ, Ford L, Schmidt M, Birck C, Georges J, Mett A, Walker Z, Boada M, Drzezga A, Vandenberghe R, Hanseeuw B, Jessen F, Schöll M, Ritchie C, Lopes Alves I, Gispert JD, Barkhof F. The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact. Front Neurol 2023; 13:1063598. [PMID: 36761917 PMCID: PMC9907029 DOI: 10.3389/fneur.2022.1063598] [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: 10/07/2022] [Accepted: 12/08/2022] [Indexed: 01/22/2023] Open
Abstract
Background Amyloid-β (Aβ) accumulation is considered the earliest pathological change in Alzheimer's disease (AD). The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) consortium is a collaborative European framework across European Federation of Pharmaceutical Industries Associations (EFPIA), academic, and 'Small and Medium-sized enterprises' (SME) partners aiming to provide evidence on the clinical utility and cost-effectiveness of Positron Emission Tomography (PET) imaging in diagnostic work-up of AD and to support clinical trial design by developing optimal quantitative methodology in an early AD population. The AMYPAD studies In the Diagnostic and Patient Management Study (DPMS), 844 participants from eight centres across three clinical subgroups (245 subjective cognitive decline, 342 mild cognitive impairment, and 258 dementia) were included. The Prognostic and Natural History Study (PNHS) recruited pre-dementia subjects across 11 European parent cohorts (PCs). Approximately 1600 unique subjects with historical and prospective data were collected within this study. PET acquisition with [18F]flutemetamol or [18F]florbetaben radiotracers was performed and quantified using the Centiloid (CL) method. Results AMYPAD has significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Main contributions so far include the validation of the dual-time window acquisition protocol to derive the fully quantitative non-displaceable binding potential (BP ND ), assess the value of this metric in the context of clinical trials, improve PET-sensitivity to emerging Aβ burden and utilize its available regional information, establish the quantitative accuracy of the Centiloid method across tracers and support implementation of quantitative amyloid-PET measures in the clinical routine. Future steps The AMYPAD consortium has succeeded in recruiting and following a large number of prospective subjects and setting up a collaborative framework to integrate data across European PCs. Efforts are currently ongoing in collaboration with ARIDHIA and ADDI to harmonize, integrate, and curate all available clinical data from the PNHS PCs, which will become openly accessible to the wider scientific community.
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Affiliation(s)
- Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,*Correspondence: Lyduine E. Collij ✉
| | | | - David Valléz García
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Ilona Bader
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | | | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Hugh Pemberton
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Sandra Pla
- Synapse Research Management Partners, Barcelona, Spain
| | - Mery Loor
- Synapse Research Management Partners, Barcelona, Spain
| | - Pawel Markiewicz
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | | | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Pierre Payoux
- Department of Nuclear Medicine, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Andrew Stephens
- Life Molecular Imaging GmbH, Berlin, Baden-Württemberg, Germany
| | | | - Pieter Jelle Visser
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Lisa Ford
- Janssen Pharmaceutica NV, Beerse, Belgium
| | | | | | | | - Anja Mett
- GE Healthcare, Amersham, United Kingdom
| | - Zuzana Walker
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Mercé Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Alexander Drzezga
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Rik Vandenberghe
- Faculty of Medicine, University Hospitals Leuven, Leuven, Brussels, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Brussels, Belgium
| | - Frank Jessen
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | | | - Juan Domingo Gispert
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
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Collij LE, Salvadó G, de Wilde A, Altomare D, Shekari M, Gispert JD, Bullich S, Stephens A, Barkhof F, Scheltens P, Bouwman F, van der Flier WM. Quantification of [
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F]florbetaben amyloid‐PET imaging in a mixed memory clinic population: The ABIDE project. Alzheimers Dement 2022. [DOI: 10.1002/alz.12886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Lyduine E. Collij
- Department of Radiology and Nuclear Medicine Amsterdam University Medical Center Amsterdam Neuroscience Amsterdam The Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- Clinical Memory Research Unit Department of Clinical Sciences Lund University Malmö Sweden
| | - Arno de Wilde
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE) University of Geneva Geneva Switzerland
- Memory Center Geneva University Hospitals Geneva Switzerland
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain
- Pompeu Fabra University Barcelona Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales y Nanomedicina (CIBER‐BBN) Madrid Spain
| | | | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine Amsterdam University Medical Center Amsterdam Neuroscience Amsterdam The Netherlands
- Centre for Medical Image Computing and Queen Square Institute of Neurology UCL London UK
| | - Philip Scheltens
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Femke Bouwman
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Wiesje M. van der Flier
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
- Department of Epidemiology & Data Science Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
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Rogojin A, Gorbet DJ, Hawkins KM, Sergio LE. Differences in resting state functional connectivity underlie visuomotor performance declines in older adults with a genetic risk (APOE ε4) for Alzheimer’s disease. Front Aging Neurosci 2022; 14:1054523. [DOI: 10.3389/fnagi.2022.1054523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/15/2022] [Indexed: 12/04/2022] Open
Abstract
IntroductionNon-standard visuomotor integration requires the interaction of large networks in the brain. Previous findings have shown that non-standard visuomotor performance is impaired in individuals with specific dementia risk factors (family history of dementia and presence of the APOE ε4 allele) in advance of any cognitive impairments. These findings suggest that visuomotor impairments are associated with early dementia-related brain changes. The current study examined the underlying resting state functional connectivity (RSFC) associated with impaired non-standard visuomotor performance, as well as the impacts of dementia family history, sex, and APOE status.MethodsCognitively healthy older adults (n = 48) were tested on four visuomotor tasks where reach and gaze were increasingly spatially dissociated. Participants who had a family history of dementia or the APOE ε4 allele were considered to be at an increased risk for AD. To quantify RSFC within networks of interest, an EPI sequence sensitive to BOLD contrast was collected. The networks of interest were the default mode network (DMN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), and frontoparietal control network (FPN).ResultsIndividuals with the ε4 allele showed abnormalities in RSFC between posterior DMN nodes that predicted poorer non-standard visuomotor performance. Specifically, multiple linear regression analyses revealed lower RSFC between the precuneus/posterior cingulate cortex and the left inferior parietal lobule as well as the left parahippocampal cortex. Presence of the APOE ε4 allele also modified the relationship between mean DAN RSFC and visuomotor control, where lower mean RSFC in the DAN predicted worse non-standard visuomotor performance only in APOE ε4 carriers. There were otherwise no effects of family history, APOE ε4 status, or sex on the relationship between RSFC and visuomotor performance for any of the other resting networks.ConclusionThe preliminary findings provide insight into the impact of APOE ε4-related genetic risk on neural networks underlying complex visuomotor transformations, and demonstrate that the non-standard visuomotor task paradigm discussed in this study may be used as a non-invasive, easily accessible assessment tool for dementia risk.
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Ozlen H, Pichet Binette A, Köbe T, Meyer PF, Gonneaud J, St-Onge F, Provost K, Soucy JP, Rosa-Neto P, Breitner J, Poirier J, Villeneuve S. Spatial Extent of Amyloid-β Levels and Associations With Tau-PET and Cognition. JAMA Neurol 2022; 79:1025-1035. [PMID: 35994280 PMCID: PMC9396472 DOI: 10.1001/jamaneurol.2022.2442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/24/2022] [Indexed: 11/14/2022]
Abstract
Importance Preventive trials of anti-amyloid agents might preferably recruit persons showing earliest biologically relevant β-amyloid (Aβ) binding on positron emission tomography (PET). Objective To investigate the timing at which Aβ-PET binding starts showing associations with other markers of Alzheimer disease. Design, Setting, and Participants This longitudinal multicentric cohort study included 3 independent cohorts: Presymptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease (PREVENT-AD) (data collected from 2012-2020), Alzheimer Disease Neuroimaging Initiative (ADNI) (data collected from 2005-2019), and Harvard Aging Brain Study (HABS) (data collected from 2011-2019). In a 3-tiered categorization of Aβ-PET binding spatial extent, individuals were assigned as having widespread Aβ deposition if they showed positive signal throughout a designated set of brain regions prone to early Aβ accumulation. Those with binding in some but not all were categorized as having regional deposition, while those who failed to show any criterion Aβ signal were considered Aβ-negative. All participants who were cognitively unimpaired at their first Aβ PET scan. Main Outcomes and Measures Differences in cerebrospinal fluid (CSF), genetics, tau-PET burden, and cognitive decline. Results A total of 817 participants were included, including 129 from the PREVENT-AD cohort (mean [SD] age, 63.5 [4.7] years; 33 [26%] male; 126 [98%] White), 400 from ADNI (mean [SD] age, 73.6 [5.8] years; 190 [47%] male; 10 [5%] Hispanic, 338 [91%] White), and 288 from HABS (mean [SD] age, 73.7 [6.2] years; 117 [40%] male; 234 [81%] White). Compared with Aβ-negative persons, those with regional Aβ binding showed proportionately more APOE ε4 carriers (18 [64%] vs 22 [27%] in PREVENT-AD and 34 [31%] vs 38 [19%] in ADNI), reduced CSF Aβ1-42 levels (F = 24 and 71), and greater longitudinal Aβ-PET accumulation (significant β = 0.019 to 0.056). Participants with widespread amyloid binding further exhibited notable cognitive decline (significant β = -0.014 to -0.08), greater CSF phosphorylated tau181 (F = 5 and 27), and tau-PET binding (all F > 7.55). Using each cohort's specified dichotomous threshold for Aβ positivity or a visual read classification, most participants (56% to 100%, depending on classification method and cohort) with regional Aβ would have been classified Aβ-negative. Conclusions and Relevance Regional Aβ binding appears to be biologically relevant and participants at this stage remain relatively free from CSF phosphorylated tau181, tau-PET binding, and related cognitive decline, making them ideal targets for anti-amyloid agents. Most of these individuals would be classified as negative based on classical thresholds of Aβ positivity.
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Affiliation(s)
- Hazal Ozlen
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Alexa Pichet Binette
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Theresa Köbe
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Pierre-François Meyer
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Julie Gonneaud
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Frédéric St-Onge
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Karine Provost
- Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Jean-Paul Soucy
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - John Breitner
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Judes Poirier
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer's Disease (StoP-AD), Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
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Michalowska MM, Herholz K, Hinz R, Amadi C, McInnes L, Anton-Rodriguez JM, Karikari TK, Blennow K, Zetterberg H, Ashton NJ, Pendleton N, Carter SF. Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94. Mol Psychiatry 2022; 27:4335-4342. [PMID: 35858992 PMCID: PMC9718666 DOI: 10.1038/s41380-022-01685-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 02/07/2023]
Abstract
Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.
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Affiliation(s)
- Malgorzata M Michalowska
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Karl Herholz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Rainer Hinz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Chinenye Amadi
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Lynn McInnes
- Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Jose M Anton-Rodriguez
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Stephen F Carter
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Oblak AL, Cope ZA, Quinney SK, Pandey RS, Biesdorf C, Masters AR, Onos KD, Haynes L, Keezer KJ, Meyer JA, Peters JS, Persohn SA, Bedwell AA, Eldridge K, Speedy R, Little G, Williams S, Noarbe B, Obenaus A, Sasner M, Howell GR, Carter GW, Williams H, Lamb BT, Territo PR, Sukoff Rizzo SJ. Prophylactic evaluation of verubecestat on disease- and symptom-modifying effects in 5XFAD mice. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12317. [PMID: 35846156 PMCID: PMC9281365 DOI: 10.1002/trc2.12317] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/31/2022] [Accepted: 05/24/2022] [Indexed: 11/12/2022]
Abstract
Introduction Alzheimer's disease (AD) is the most common form of dementia. Beta-secretase (BACE) inhibitors have been proposed as potential therapeutic interventions; however, initiating treatment once disease has significantly progressed has failed to effectively stop or treat disease. Whether BACE inhibition may have efficacy when administered prophylactically in the early stages of AD has been under-investigated. The present studies aimed to evaluate prophylactic treatment of the BACE inhibitor verubecestat in an AD mouse model using the National Institute on Aging (NIA) resources of the Model Organism Development for Late-Onset Alzheimer's Disease (MODEL-AD) Preclinical Testing Core (PTC) Drug Screening Pipeline. Methods 5XFAD mice were administered verubecestat ad libitum in chow from 3 to 6 months of age, prior to the onset of significant disease pathology. Following treatment (6 months of age), in vivo imaging was conducted with 18F-florbetapir (AV-45/Amyvid) (18F-AV45) and 18-FDG (fluorodeoxyglucose)-PET (positron emission tomography)/MRI (magnetic resonance imaging), brain and plasma amyloid beta (Aβ) were measured, and the clinical and behavioral characteristics of the mice were assessed and correlated with the pharmacokinetic data. Results Prophylactic verubecestat treatment resulted in dose- and region-dependent attenuations of 18F-AV45 uptake in male and female 5XFAD mice. Plasma Aβ40 and Aβ42 were also dose-dependently attenuated with treatment. Across the dose range evaluated, side effects including coat color changes and motor alterations were reported, in the absence of cognitive improvement or changes in 18F-FDG uptake. Discussion Prophylactic treatment with verubecestat resulted in attenuated amyloid plaque deposition when treatment was initiated prior to significant pathology in 5XFAD mice. At the same dose range effective at attenuating Aβ levels, verubecestat produced side effects in the absence of improvements in cognitive function. Taken together these data demonstrate the rigorous translational approaches of the MODEL-AD PTC for interrogating potential therapeutics and provide insight into the limitations of verubecestat as a prophylactic intervention for early-stage AD.
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Affiliation(s)
| | - Zackary A. Cope
- University of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | | | - Ravi S. Pandey
- The Jackson Laboratory for Genomic MedicineFarmingtonConnecticutUSA
- The Jackson LaboratoryBar HarborMaineUSA
| | - Carla Biesdorf
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | | | | | - Jill A. Meyer
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | | | | | - Rachael Speedy
- Indiana University School of MedicineIndianapolisIndianaUSA
| | - Gabriela Little
- University of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | | | | | | | | | - Gareth R. Howell
- The Jackson Laboratory for Genomic MedicineFarmingtonConnecticutUSA
- University of CaliforniaIrvineCaliforniaUSA
| | - Gregory W. Carter
- The Jackson Laboratory for Genomic MedicineFarmingtonConnecticutUSA
- University of CaliforniaIrvineCaliforniaUSA
| | | | - Bruce T. Lamb
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | - Stacey J. Sukoff Rizzo
- University of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
- The Jackson LaboratoryBar HarborMaineUSA
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Staging of Alzheimer's disease: past, present, and future perspectives. Trends Mol Med 2022; 28:726-741. [PMID: 35717526 DOI: 10.1016/j.molmed.2022.05.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 01/01/2023]
Abstract
For many years Alzheimer's disease (AD) was associated with the dementia stage of the disease, the tail end of a pathophysiological process that lasts approximately two decades. Whereas early disease staging assessments focused on progressive deterioration of clinical functioning, brain imaging with positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarker studies highlighted the long preclinical phase of AD in which a cascade of detectable biological abnormalities precede cognitive decline. The recent proliferation of imaging and fluid biomarkers of AD pathophysiology provide an opportunity for the identification of several biological stages in the preclinical phase of AD. We discuss the use of clinical and biomarker information in past, present, and future staging of AD. We highlight potential applications of PET, CSF, and plasma biomarkers for staging AD severity in vivo.
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Patterns of Focal Amyloid Deposition Using 18F-Florbetaben PET in Patients with Cognitive Impairment. Diagnostics (Basel) 2022; 12:diagnostics12061357. [PMID: 35741166 PMCID: PMC9221882 DOI: 10.3390/diagnostics12061357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022] Open
Abstract
Accumulation of aggregated amyloid-β (Aβ) in the brain is considered the first pathological event within the pathogenesis of Alzheimer’s disease (AD). It is difficult to accurately identify the initial brain regions of Aβ accumulation due to the time-lag between the start of the pathophysiology and symptom onset. However, focal regional amyloid uptake on amyloid PET scans may provide insights into this. Hence, we aimed to evaluate the topographic distribution of amyloid deposition in patients with cognitive impairment and to identify the starting order of amyloid accumulation in the brain using conditional probability. We enrolled 58 patients composed of 9 normal cognition (NC), 32 mild cognitive impairment (MCI), and 17 dementia showing focal regional amyloid deposition corresponding to a brain amyloid plaque load (BAPL) score of 2 among those who visited the Memory Clinic of Asan Medical Center and underwent an 18F-florbetaben PET scan (March 2013 to April 2019). Regions of interest (ROI) included the frontal, parietal, lateral temporal, and occipital cortices, the posterior cingulate/precuneus, and the striatum. The most frequent occurrence of Aβ deposition was in the posterior cingulate/precuneus (n = 41, 68.3%). The second most frequent site was the lateral temporal cortex (n = 24, 40.0%), followed by the lateral parietal cortex (n = 21, 35.6%) and other lesions, such as the frontal and occipital cortices. The striatum was the least frequently affected. Our study found that the posterior cingulate/precuneus and the lateral temporal and parietal cortices may be the earliest areas to be affected by Aβ accumulation. Longitudinal follow-up of focal brain amyloid deposition may help elucidate the evolutionary pattern of Aβ accumulation in the brain of people with AD continuum.
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Collij LE, Salvadó G, Wottschel V, Mastenbroek SE, Schoenmakers P, Heeman F, Aksman L, Wink AM, Berckel BNM, van de Flier WM, Scheltens P, Visser PJ, Barkhof F, Haller S, Gispert JD, Lopes Alves I. Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis. Neurology 2022; 98:e1692-e1703. [PMID: 35292558 PMCID: PMC9071373 DOI: 10.1212/wnl.0000000000200148] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND AND OBJECTIVES β-amyloid (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression. RESULTS Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.
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Affiliation(s)
- Lyduine E Collij
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Gemma Salvadó
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Viktor Wottschel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sophie E Mastenbroek
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pierre Schoenmakers
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Fiona Heeman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Leon Aksman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Alle Meije Wink
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Bart N M Berckel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Wiesje M van de Flier
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Philip Scheltens
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pieter Jelle Visser
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sven Haller
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Juan Domingo Gispert
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Isadora Lopes Alves
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
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Teipel SJ, Dyrba M, Vergallo A, Lista S, Habert MO, Potier MC, Lamari F, Dubois B, Hampel H, Grothe MJ. Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis. Front Aging Neurosci 2022; 13:748198. [PMID: 35002673 PMCID: PMC8729321 DOI: 10.3389/fnagi.2021.748198] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/05/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis. Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study). Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data. Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.
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Affiliation(s)
- Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Andrea Vergallo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Simone Lista
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France.,Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'Hôpital, Paris, France.,Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Marie Odile Habert
- Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, LIB, Sorbonne University, Paris, France.,Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.,Centre d'Acquisition et Traitement des Images (CATI platform), Paris, France
| | - Marie-Claude Potier
- ICM Institut du Cerveau et de la Moelle Épinière, CNRS UMR 7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France
| | - Foudil Lamari
- UF Biochimie des Maladies Neurométaboliques, Service de Biochimie Métabolique, Hôpital Pitié-Salpêtrière, Paris, France
| | - Bruno Dubois
- Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Harald Hampel
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,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
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Weinstein G, O’Donnell A, Davis-Plourde K, Zelber-Sagi S, Ghosh S, DeCarli CS, Thibault EG, Sperling RA, Johnson KA, Beiser AS, Seshadri S. Non-Alcoholic Fatty Liver Disease, Liver Fibrosis, and Regional Amyloid-β and Tau Pathology in Middle-Aged Adults: The Framingham Study. J Alzheimers Dis 2022; 86:1371-1383. [PMID: 35213373 PMCID: PMC11323287 DOI: 10.3233/jad-215409] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Liver steatosis and fibrosis are emerging as risk factors for multiple extrahepatic health conditions; however, their relationship with Alzheimer's disease pathology is unclear. OBJECTIVE To examine whether non-alcoholic fatty liver disease (NAFLD) and FIB-4, a non-invasive index of advanced fibrosis, are associated with brain amyloid-β (Aβ) and tau pathology. METHODS The study sample included Framingham Study participants from the Offspring and Third generation cohorts who attended exams 9 (2011-2014) and 2 (2008-2011), respectively. Participants underwent 11C-Pittsburgh Compound-B amyloid and 18F-Flortaucipir tau positron emission tomography (PET) imaging and abdomen computed tomography, or had information on all components of the FIB-4 index. Linear regression models were used to assess the relationship of NAFLD and FIB-4 with regional tau and Aβ, adjusting for potential confounders and multiple comparisons. RESULTS Of the subsample with NAFLD information (N = 169; mean age 52±9 y; 57% males), 57 (34%) had NAFLD. Of the subsample with information on liver fibrosis (N = 177; mean age 50±10 y; 51% males), 34 (19%) had advanced fibrosis (FIB-4 > 1.3). Prevalent NAFLD was not associated with Aβ or tau PET. However, FIB-4 index was significantly associated with increased rhinal tau (β= 1.03±0.33, p = 0.002). Among individuals with prevalent NAFLD, FIB-4 was related to inferior temporal, parahippocampal gyrus, entorhinal and rhinal tau (β= 2.01±0.47, p < 0.001; β= 1.60±0.53, p = 0.007, and β= 1.59±0.47, p = 0.003 and β= 1.60±0.42, p = 0.001, respectively) and to Aβ deposition overall and in the inferior temporal and parahippocampal regions (β= 1.93±0.47, p < 0.001; β= 1.59±0.38, p < 0.001, and β= 1.52±0.54, p = 0.008, respectively). CONCLUSION This study suggests a possible association between liver fibrosis and early Alzheimer's disease pathology, independently of cardio-metabolic risk factors.
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Affiliation(s)
| | - Adrienne O’Donnell
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
| | - Kendra Davis-Plourde
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
| | - Shira Zelber-Sagi
- School of Public Health, University of Haifa, Haifa, Israel
- Liver Unit, Department of Gastroenterology, Tel-Aviv Medical Center, Tel-Aviv, Israel
| | - Saptaparni Ghosh
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles S. DeCarli
- Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California Davis, Davis, CA, USA
| | - Emma G. Thibault
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A. Sperling
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A. Johnson
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexa S. Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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Collij LE, Mastenbroek SE, Salvadó G, Wink AM, Visser PJ, Barkhof F, van Berckel BN, Lopes Alves I. Regional amyloid accumulation predicts memory decline in initially cognitively unimpaired individuals. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12216. [PMID: 34368416 PMCID: PMC8327468 DOI: 10.1002/dad2.12216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/04/2021] [Accepted: 06/04/2021] [Indexed: 01/02/2023]
Abstract
INTRODUCTION The value of quantitative longitudinal and regional amyloid beta (Aβ) measurements in predicting cognitive decline in initially cognitively unimpaired (CU) individuals remains to be determined. METHODS We selected 133 CU individuals with two or more [11C]Pittsburgh compound B ([11C]PiB) scans and neuropsychological data from Open Access Series of Imaging Studies (OASIS-3). Baseline and annualized distribution volume ratios were computed for a global composite and four regional clusters. The predictive value of Aβ measurements (baseline, slope, and interaction) on longitudinal cognitive performance was examined. RESULTS Global performance could only be predicted by Aβ burden in an early cluster (precuneus, lateral orbitofrontal, and insula) and the precuneus region of interest (ROI) by itself significantly improved the model. Precuneal Aβ burden was also predictive of immediate and delayed episodic memory performance. In Aβ subjects at baseline (N = 93), lateral orbitofrontal Aβ burden predicted working and semantic memory performance. DISCUSSION Quantifying longitudinal and regional changes in Aβ can improve the prediction of cognitive functioning in initially CU individuals.
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Affiliation(s)
- Lyduine E. Collij
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Sophie E. Mastenbroek
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
| | - Alle Meije Wink
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Pieter Jelle Visser
- Amsterdam UMCAlzheimer Center and department of NeurologyVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Frederik Barkhof
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
- Medical Physics and Biomedical EngineeringCentre for Medical Image ComputingUCLLondonUK
| | - Bart. N.M. van Berckel
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Isadora Lopes Alves
- Amsterdam UMCDepartment of Radiology and Nuclear MedicineVrije Universiteit AmsterdamAmsterdamNetherlands
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25
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Byun MS, Park SW, Lee JH, Yi D, Jeon SY, Choi HJ, Joung H, Ghim UH, Park UC, Kim YK, Shin SA, Yu HG, Lee DY. Association of Retinal Changes With Alzheimer Disease Neuroimaging Biomarkers in Cognitively Normal Individuals. JAMA Ophthalmol 2021; 139:548-556. [PMID: 33764406 DOI: 10.1001/jamaophthalmol.2021.0320] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Importance Retinal biomarkers reflecting in vivo brain Alzheimer disease (AD) pathologic abnormalities could be a useful tool for screening cognitively normal (CN) individuals at the preclinical stage of AD. Objectives To investigate the association of both functional and structural alterations of the retina with in vivo AD pathologic abnormalities in CN older adults and model a screening tool for detection of preclinical AD. Design, Setting, and Participants This cross-sectional study included a total of 49 CN individuals, and all assessment was done at the Seoul National University Hospital, Seoul, South Korea. All participants underwent complete ophthalmic examination, including swept-source optical coherence tomography (SS-OCT) and multifocal electroretinogram as well as amyloid-β (Aβ) positron emission tomography and magnetic resonance imaging. Data were collected from January 1, 2016, through October 31, 2017, and analyzed from February 1, 2018, through June 30, 2020. Main Outcomes and Measures For structural parameters of the retina, the thickness of the macula and layer-specific thicknesses, including peripapillary retinal nerve fiber layer and ganglion cell-inner plexiform layer measured by SS-OCT, were used for analysis. For functional parameters of the retina, implicit time and amplitude of rings 1 to 6 measured by multifocal electroretinogram were used. Results Of the 49 participants, 25 were women (51.0%); mean (SD) age was 70.6 (9.4) years. Compared with 33 CN individuals without Aβ deposition (Aβ-CN), the 16 participants with Aβ (Aβ+CN) showed reduced inner nasal macular thickness (mean [SD], 308.9 [18.4] vs 286.1 [22.5] μm; P = .007) and retinal nerve fiber layer thickness, particularly in the inferior quadrant (133.8 [17.9] vs 103.8 [43.5] μm; P = .003). In addition, the Aβ+CN group showed prolonged implicit time compared with the Aβ-CN group, particularly in ring 5 (41.3 [4.0] vs 38.2 [1.3] milliseconds; P = .002). AD-related neurodegeneration was correlated with the thickness of the ganglion cell-inner plexiform layer only (r = 0.41, P = .005). The model to differentiate the Aβ+CN vs Aβ-CN groups derived from the results showed 90% accuracy. Conclusions and Relevance The findings of this study showing both functional as well as structural changes of retina measured by multifocal electroretinogram and SS-OCT in preclinical AD suggest the potential use of retinal biomarkers as a tool for early detection of in vivo AD pathologic abnormalities in CN older adults.
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Affiliation(s)
- Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sung Wook Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Ophthalmology, Seoul National University Hospital, Seoul, Republic of Korea.,Retinal Degeneration Laboratory, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.,Seran Eye Center, Seoul, Republic of Korea
| | - Jun Ho Lee
- Department of Neuropsychiatry, National Center for Mental Health, Seoul, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - So Yeon Jeon
- Department of Neuropsychiatry, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Hyo Jung Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Haejung Joung
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Un Hyung Ghim
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Ophthalmology, Seoul National University Hospital, Seoul, Republic of Korea.,Retinal Degeneration Laboratory, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Un Chul Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Ophthalmology, Seoul National University Hospital, Seoul, Republic of Korea.,Retinal Degeneration Laboratory, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Seong A Shin
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Hyeong Gon Yu
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Ophthalmology, Seoul National University Hospital, Seoul, Republic of Korea.,Retinal Degeneration Laboratory, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Young Lee
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
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26
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Lopes Alves I, Heeman F, Collij LE, Salvadó G, Tolboom N, Vilor-Tejedor N, Markiewicz P, Yaqub M, Cash D, Mormino EC, Insel PS, Boellaard R, van Berckel BNM, Lammertsma AA, Barkhof F, Gispert JD. Strategies to reduce sample sizes in Alzheimer's disease primary and secondary prevention trials using longitudinal amyloid PET imaging. Alzheimers Res Ther 2021; 13:82. [PMID: 33875021 PMCID: PMC8056524 DOI: 10.1186/s13195-021-00819-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/26/2021] [Indexed: 01/07/2023]
Abstract
BACKGROUND Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials. METHODS Baseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only. RESULTS Although highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR). CONCLUSION Sample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.
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Affiliation(s)
- Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Nelleke Tolboom
- Imaging Division, Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Natàlia Vilor-Tejedor
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Pawel Markiewicz
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - David Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Philip S Insel
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain.
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27
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van Dyck CH, O'Dell RS, Mecca AP. Amyloid-Associated Depression-or Not? Biol Psychiatry 2021; 89:737-738. [PMID: 33766236 PMCID: PMC8396710 DOI: 10.1016/j.biopsych.2021.02.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 02/10/2021] [Accepted: 02/10/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Christopher H van Dyck
- Alzheimer's Disease Research Unit, Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut.
| | - Ryan S O'Dell
- Alzheimer's Disease Research Unit, Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Adam P Mecca
- Alzheimer's Disease Research Unit, Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
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28
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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29
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Collij LE, Ingala S, Top H, Wottschel V, Stickney KE, Tomassen J, Konijnenberg E, ten Kate M, Sudre C, Lopes Alves I, Yaqub MM, Wink AM, Van ‘t Ent D, Scheltens P, van Berckel BN, Visser PJ, Barkhof F, Braber AD. White matter microstructure disruption in early stage amyloid pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12124. [PMID: 33816751 PMCID: PMC8015832 DOI: 10.1002/dad2.12124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. METHODS We included 179 individuals from the European Medical Information Framework for AD (EMIF-AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract-level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. RESULTS Regression analyses showed a non-linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. DISCUSSION Early amyloid deposition is associated with changes in WM microstructure. The non-linear relationship might reflect multiple stages of axonal damage.
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Affiliation(s)
- Lyduine E. Collij
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Silvia Ingala
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Herwin Top
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Viktor Wottschel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Jori Tomassen
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Mara ten Kate
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Carole Sudre
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Isadora Lopes Alves
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Maqsood M. Yaqub
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Alle Meije Wink
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Dennis Van ‘t Ent
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
| | - Philip Scheltens
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Bart N.M. van Berckel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Pieter Jelle Visser
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience (MHeNS), Alzheimer Centrum LimburgMaastricht UniversityMaastrichtThe Netherlands
- Department of NeurobiologyCare Sciences Division of NeurogeriatricsKarolinska InstitutetStockholmSweden
| | - Frederik Barkhof
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Anouk Den Braber
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
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30
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Bullich S, Roé-Vellvé N, Marquié M, Landau SM, Barthel H, Villemagne VL, Sanabria Á, Tartari JP, Sotolongo-Grau O, Doré V, Koglin N, Müller A, Perrotin A, Jovalekic A, De Santi S, Tárraga L, Stephens AW, Rowe CC, Sabri O, Seibyl JP, Boada M. Early detection of amyloid load using 18F-florbetaben PET. ALZHEIMERS RESEARCH & THERAPY 2021; 13:67. [PMID: 33773598 PMCID: PMC8005243 DOI: 10.1186/s13195-021-00807-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/10/2021] [Indexed: 03/26/2023]
Abstract
BACKGROUND A low amount and extent of Aβ deposition at early stages of Alzheimer's disease (AD) may limit the use of previously developed pathology-proven composite SUVR cutoffs. This study aims to characterize the population with earliest abnormal Aβ accumulation using 18F-florbetaben PET. Quantitative thresholds for the early (SUVRearly) and established (SUVRestab) Aβ deposition were developed, and the topography of early Aβ deposition was assessed. Subsequently, Aβ accumulation over time, progression from mild cognitive impairment (MCI) to AD dementia, and tau deposition were assessed in subjects with early and established Aβ deposition. METHODS The study population consisted of 686 subjects (n = 287 (cognitively normal healthy controls), n = 166 (subjects with subjective cognitive decline (SCD)), n = 129 (subjects with MCI), and n = 101 (subjects with AD dementia)). Three categories in the Aβ-deposition continuum were defined based on the developed SUVR cutoffs: Aβ-negative subjects, subjects with early Aβ deposition ("gray zone"), and subjects with established Aβ pathology. RESULTS SUVR using the whole cerebellum as the reference region and centiloid (CL) cutoffs for early and established amyloid pathology were 1.10 (13.5 CL) and 1.24 (35.7 CL), respectively. Cingulate cortices and precuneus, frontal, and inferior lateral temporal cortices were the regions showing the initial pathological tracer retention. Subjects in the "gray zone" or with established Aβ pathology accumulated more amyloid over time than Aβ-negative subjects. After a 4-year clinical follow-up, none of the Aβ-negative or the gray zone subjects progressed to AD dementia while 91% of the MCI subjects with established Aβ pathology progressed. Tau deposition was infrequent in those subjects without established Aβ pathology. CONCLUSIONS This study supports the utility of using two cutoffs for amyloid PET abnormality defining a "gray zone": a lower cutoff of 13.5 CL indicating emerging Aβ pathology and a higher cutoff of 35.7 CL where amyloid burden levels correspond to established neuropathology findings. These cutoffs define a subset of subjects characterized by pre-AD dementia levels of amyloid burden that precede other biomarkers such as tau deposition or clinical symptoms and accelerated amyloid accumulation. The determination of different amyloid loads, particularly low amyloid levels, is useful in determining who will eventually progress to dementia. Quantitation of amyloid provides a sensitive measure in these low-load cases and may help to identify a group of subjects most likely to benefit from intervention. TRIAL REGISTRATION Data used in this manuscript belong to clinical trials registered in ClinicalTrials.gov ( NCT00928304 , NCT00750282 , NCT01138111 , NCT02854033 ) and EudraCT (2014-000798-38).
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Affiliation(s)
- Santiago Bullich
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany.
| | - Núria Roé-Vellvé
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany
| | - Marta Marquié
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley and Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Departments of Medicine and Molecular Imaging, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | - Ángela Sanabria
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Juan Pablo Tartari
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Oscar Sotolongo-Grau
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - Vincent Doré
- Departments of Medicine and Molecular Imaging, University of Melbourne, Austin Health, Melbourne, Victoria, Australia.,The Australian e-Health Research Centre, Health and Biosecurity, CSIRO, Melbourne, Victoria, Australia
| | - Norman Koglin
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany
| | - Andre Müller
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany
| | - Audrey Perrotin
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany
| | | | | | - Lluís Tárraga
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Andrew W Stephens
- Life Molecular Imaging GmbH, Tegeler Str. 6-7, 13353, Berlin, Germany
| | - Christopher C Rowe
- Departments of Medicine and Molecular Imaging, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | | | - Mercè Boada
- Fundació ACE Institut Català de Neurociències Aplicades, Research Center and Memory Unit - Universitat Internacional de Catalunya (UIC), Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
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Collij LE, Salvadó G, Shekari M, Lopes Alves I, Reimand J, Wink AM, Zwan M, Niñerola-Baizán A, Perissinotti A, Scheltens P, Ikonomovic MD, Smith APL, Farrar G, Molinuevo JL, Barkhof F, Buckley CJ, van Berckel BNM, Gispert JD. Visual assessment of [ 18F]flutemetamol PET images can detect early amyloid pathology and grade its extent. Eur J Nucl Med Mol Imaging 2021; 48:2169-2182. [PMID: 33615397 PMCID: PMC8175297 DOI: 10.1007/s00259-020-05174-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/20/2020] [Indexed: 11/08/2022]
Abstract
Purpose To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR. Methods [18F]Flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0–5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden’s index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [18F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density. Results VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERADSOT-based classification (i.e., any region mCERADSOT > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density. Conclusion VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05174-2.
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Affiliation(s)
- Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Juhan Reimand
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia.,Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Marissa Zwan
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Andrés Perissinotti
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Philip Scheltens
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
| | | | | | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Universitat Pompeu Fabra, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
| | | | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands. .,Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1117, 1108 HV, Amsterdam, The Netherlands.
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. .,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. .,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. .,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. .,Alzheimer Prevention Program, BarcelonaBeta Brain Research Center (BBRC), C/ Wellington, 30, 08005, Barcelona, Spain.
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32
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Collij LE, Heeman F, Salvadó G, Ingala S, Altomare D, de Wilde A, Konijnenberg E, van Buchem M, Yaqub M, Markiewicz P, Golla SSV, Wottschel V, Wink AM, Visser PJ, Teunissen CE, Lammertsma AA, Scheltens P, van der Flier WM, Boellaard R, van Berckel BNM, Molinuevo JL, Gispert JD, Schmidt ME, Barkhof F, Lopes Alves I. Multitracer model for staging cortical amyloid deposition using PET imaging. Neurology 2020; 95:e1538-e1553. [PMID: 32675080 PMCID: PMC7713745 DOI: 10.1212/wnl.0000000000010256] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 03/20/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]stage1 2.00, HRstage2 3.53, HRstage3 4.55, HRstage4 9.91, p < 0.001; OASIS: n = 469, HRstage4 4.80, p < 0.001). CONCLUSION The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.
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Affiliation(s)
- Lyduine E Collij
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Fiona Heeman
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Gemma Salvadó
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Silvia Ingala
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Daniele Altomare
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Arno de Wilde
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Elles Konijnenberg
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Marieke van Buchem
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Maqsood Yaqub
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pawel Markiewicz
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Sandeep S V Golla
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Viktor Wottschel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Alle Meije Wink
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pieter Jelle Visser
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Charlotte E Teunissen
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Adriaan A Lammertsma
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Philip Scheltens
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Wiesje M van der Flier
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Ronald Boellaard
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Bart N M van Berckel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - José Luis Molinuevo
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Juan Domingo Gispert
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Mark E Schmidt
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Frederik Barkhof
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Isadora Lopes Alves
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium.
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Lauretani F, Ruffini L, Scarlattei M, Maggio M. Relationship between comprehensive geriatric assessment and amyloid PET in older persons with MCI. BMC Geriatr 2020; 20:337. [PMID: 32907545 PMCID: PMC7487621 DOI: 10.1186/s12877-020-01746-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 08/30/2020] [Indexed: 01/23/2023] Open
Abstract
Background The association between amyloid deposition and cognitive, behavioral and physical performance in mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) has been poorly investigated, especially in older persons. Methods We studied the in vivo correlation between the amyloid deposition at Positron Emission Tomography (amyloid-PET) and the presence of memory loss, reduced executive function, neuropsychiatric symptoms and physical performance in older persons with MCI. Amyloid-PET was performed with 18F-flutemetamol and quantitatively analyzed. Results We evaluated 48 subjects, 21 men and 27 women. We performed in each patient a comprehensive geriatric assessment (CGA) including Mini Mental State Examination (MMSE), Clock Drawing Test (CDT), Activity Daily Living (ADL), Instrumental Activity of Daily Living (IADL), Neuropsychiatric inventory (NPI) questionnaire, 15 Geriatric Depression Scale (GDS), Short Physical Performance Battery (SPPB) and Hand Grip strength. Then, each patient underwent amyloid-PET. Mean age of the enrolled subjects was 74.6 ± 7.8 years. All of these subjects showed preserved cognitive function at MMSE > 24, while 29 of 48 subjects (61.0%) had altered CDT. Mean NPI score was 6.9 ± 5.9. The mean value of SPPB score was 9.0 ± 2.6, while the average muscle strength of the upper extremities measured by hand grip was 25.6 ± 7.7 Kg. CT/MRI images showed cortical atrophic changes in 26 of the 48 examined subjects (54.0%), while cerebrovascular modifications were present in 31 subjects (64.5%). Pathological burden of amyloid deposits was detected in 25 of 48 (52.0%) patients with a mean value of global z-score of 2.8 (subjects defined as MCI due to AD). After stratifying subjects in subclasses of clinical alterations, more probability of pathological amyloid deposition was found in subjects with impaired CDT and higher NPI score (O.R. = 3.45 [1.01–11.2], p = 0.04), with both impaired CDT and low physical performance (O.R. = 5.80 [1.04–32.2], p = 0.04), with altered CDT and high NPI score (O.R. = 7.98 [1.38–46.0], p = 0.02), and finally in those subjects with altered CDT, high NPI and low physical performance (O.R. = 5.80 [1.05–32.2], p = 0.04). Conclusion Our findings support the recent hypothesis that amyloid deposition could be associated with multiple cerebral dysfunction, mainly affecting executive, behavioral and motor abilities.
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Affiliation(s)
- Fulvio Lauretani
- Department of Medicine and Surgery, University of Parma, Via Gramsci 14, 43100, Parma, Italy. .,Cognitive and Motoric Center, Medicine and Geriatric-Rehabilitation Department of Parma, University-Hospital of Parma, 43126, Parma, Italy.
| | - Livia Ruffini
- Nuclear Medicine Unit, University Hospital of Parma, Parma, Italy
| | - Maura Scarlattei
- Nuclear Medicine Unit, University Hospital of Parma, Parma, Italy
| | - Marcello Maggio
- Department of Medicine and Surgery, University of Parma, Via Gramsci 14, 43100, Parma, Italy.,Cognitive and Motoric Center, Medicine and Geriatric-Rehabilitation Department of Parma, University-Hospital of Parma, 43126, Parma, Italy
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Therriault J, Benedet AL, Pascoal TA, Savard M, Ashton NJ, Chamoun M, Tissot C, Lussier F, Kang MS, Bezgin G, Wang T, Fernandes-Arias J, Massarweh G, Vitali P, Zetterberg H, Blennow K, Saha-Chaudhuri P, Soucy JP, Gauthier S, Rosa-Neto P. Determining Amyloid-β Positivity Using 18F-AZD4694 PET Imaging. J Nucl Med 2020; 62:247-252. [PMID: 32737243 DOI: 10.2967/jnumed.120.245209] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
Amyloid-β deposition into plaques is a pathologic hallmark of Alzheimer disease appearing years before the onset of symptoms. Although cerebral amyloid-β deposition occurs on a continuum, dichotomization into positive and negative groups has advantages for diagnosis, clinical management, and population enrichment for clinical trials. 18F-AZD4694 (also known as 18F-NAV4694) is an amyloid-β imaging ligand with high affinity for amyloid-β plaques. Despite being used in multiple academic centers, no studies have assessed a quantitative cutoff for amyloid-β positivity using 18F-AZD4694 PET. Methods: We assessed 176 individuals [young adults (n = 22), cognitively unimpaired elderly (n = 89), and cognitively impaired (n = 65)] who underwent amyloid-β PET with 18F-AZD4694, lumbar puncture, structural MRI, and genotyping for APOEε4 18F-AZD4694 values were normalized using the cerebellar gray matter as a reference region. We compared 5 methods for deriving a quantitative threshold for 18F-AZD4694 PET positivity: comparison with young-control SUV ratios (SUVRs), receiver-operating-characteristic (ROC) curves based on clinical classification of cognitively unimpaired elderly versus Alzheimer disease dementia, ROC curves based on visual Aβ-positive/Aβ-negative classification, gaussian mixture modeling, and comparison with cerebrospinal fluid measures of amyloid-β, specifically the Aβ42/Aβ40 ratio. Results: We observed good convergence among the 4 methods: ROC curves based on visual classification (optimal cut point, 1.55 SUVR), ROC curves based on clinical classification (optimal cut point, 1.56 SUVR) gaussian mixture modeling (optimal cut point, 1.55 SUVR), and comparison with cerebrospinal fluid measures of amyloid-β (optimal cut point, 1.51 SUVR). Means and 2 SDs from young controls resulted in a lower threshold (1.33 SUVR) that did not agree with the other methods and labeled most elderly individuals as Aβ-positive. Conclusion: Good convergence was obtained among several methods for determining an optimal cutoff for 18F-AZD4694 PET positivity. Despite conceptual and analytic idiosyncrasies linked with dichotomization of continuous variables, an 18F-AZD4694 threshold of 1.55 SUVR had reliable discriminative accuracy. Although clinical use of amyloid PET is currently by visual inspection of scans, quantitative thresholds may be helpful to arbitrate disagreement among raters or in borderline cases.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Cecile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Firoza Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Gleb Bezgin
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Tina Wang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Jaime Fernandes-Arias
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, Montreal, Quebec, Canada.,Department of Radiochemistry, McGill University, Montreal, Quebec, Canada; and
| | - Paolo Vitali
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada .,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
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35
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Noor A, Hayne DJ, Lim S, Van Zuylekom JK, Cullinane C, Roselt PD, McLean CA, White JM, Donnelly PS. Copper Bis(thiosemicarbazonato)-stilbenyl Complexes That Bind to Amyloid-β Plaques. Inorg Chem 2020; 59:11658-11669. [PMID: 32799487 DOI: 10.1021/acs.inorgchem.0c01520] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Alzheimer's disease is characterized by the presence of extracellular amyloid-β plaques. Positron emission tomography (PET) imaging with tracers radiolabeled with positron-emitting radionuclides that bind to amyloid-β plaques can assist in the diagnosis of Alzheimer's disease. With the goal of designing new imaging agents radiolabeled with positron-emitting copper-64 radionuclides that bind to amyloid-β plaques, a family of bis(thiosemicarbazone) ligands with appended substituted stilbenyl functional groups has been prepared. The ligands form charge-neutral and stable complexes with copper(II). The new ligands can be radiolabeled with copper-64 at room temperature. Two lead complexes were demonstrated to bind to amyloid-β plaques present in post-mortem brain tissue from subjects with clinically diagnosed Alzheimer's disease and crossed the blood-brain barrier in mice. The work presented here provides strategies to prepare compounds with radionuclides of copper that can be used for targeted brain PET imaging.
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Affiliation(s)
| | | | | | | | | | | | - Catriona A McLean
- Department of Anatomical Pathology, The Alfred Hospital, Victoria 3181, Australia
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36
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Hanseeuw BJ, Malotaux V, Dricot L, Quenon L, Sznajer Y, Cerman J, Woodard JL, Buckley C, Farrar G, Ivanoiu A, Lhommel R. Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: an [ 18F] flutemetamol amyloid PET study. Eur J Nucl Med Mol Imaging 2020; 48:302-310. [PMID: 32601802 PMCID: PMC7835306 DOI: 10.1007/s00259-020-04942-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate cerebral amyloid-β(Aβ) pathology in older adults with cognitive complaints, visual assessment of PET images is approved as the routine method for image interpretation. In research studies however, Aβ-PET semi-quantitative measures are associated with greater risk of progression to dementia; but until recently, these measures lacked standardization. Therefore, the Centiloid scale, providing standardized Aβ-PET semi-quantitation, was recently validated. We aimed to determine the predictive values of visual assessments and Centiloids in non-demented patients, using long-term progression to dementia as our standard of truth. METHODS One hundred sixty non-demented participants (age, 54-86) were enrolled in a monocentric [18F] flutemetamol Aβ-PET study. Flutemetamol images were interpreted visually following the manufacturers recommendations. SUVr values were converted to the Centiloid scale using the GAAIN guidelines. Ninety-eight persons were followed until dementia diagnosis or were clinically stable for a median of 6 years (min = 4.0; max = 8.0). Twenty-five patients with short follow-up (median = 2.0 years; min = 0.8; max = 3.9) and 37 patients with no follow-up were excluded. We computed ROC curves predicting subsequent dementia using baseline PET data and calculated negative (NPV) and positive (PPV) predictive values. RESULTS In the 98 participants with long follow-up, Centiloid = 26 provided the highest overall predictive value = 87% (NPV = 85%, PPV = 88%). Visual assessment corresponded to Centiloid = 40, which predicted dementia with an overall predictive value = 86% (NPV = 81%, PPV = 92%). Inclusion of the 25 patients who only had a 2-year follow-up decreased the PPV = 67% (NPV = 88%), reflecting the many positive cases that did not progress to dementia after short follow-ups. CONCLUSION A Centiloid threshold = 26 optimally predicts progression to dementia 6 years after PET. Visual assessment provides similar predictive value, with higher specificity and lower sensitivity. TRIAL REGISTRATION Eudra-CT number: 2011-001756-12.
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Affiliation(s)
- Bernard J Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium. .,Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium. .,Gordon Center for Medical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Vincent Malotaux
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Laurence Dricot
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Lisa Quenon
- Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - Yves Sznajer
- Genetics Department, Saint-Luc University Hospital, Brussels, Belgium
| | - Jiri Cerman
- Department of Neurology, Charles University, 2nd Faculty of Medicine and Motol University Hospital, Prague, Czech Republic
| | - John L Woodard
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | | | - Adrian Ivanoiu
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Neurology Department, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - Renaud Lhommel
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Nuclear Medicine Department, Saint-Luc University Hospital, Brussels, Belgium.,Institute of Experimental and Clinical Research, Université Catholique de Louvain, Brussels, Belgium
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