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Cohen AD, Villemagne VL. A brief history of Aβ imaging. Alzheimers Dement 2025; 21:e70291. [PMID: 40407091 PMCID: PMC12100503 DOI: 10.1002/alz.70291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2025] [Revised: 04/24/2025] [Accepted: 04/25/2025] [Indexed: 05/26/2025]
Abstract
β-Amyloid (Αβ) imaging revolutionized the in vivo assessment of Alzheimer's disease (AD) Αβ pathology and its changes over time, increasing our insights into Aβ deposition in the brain by providing highly accurate, reliable, and reproducible quantitative statements of regional and global Aβ burden in the brain, proving essential for the differential diagnosis, staging, and evaluation of disease-specific anti-Αβ therapeutic approaches. Longitudinal observations, coupled with different disease-specific biomarkers to assess potential downstream effects of Aβ, have confirmed that Αβ deposition in the brain starts decades before the onset of symptoms. Aβ imaging studies continue to refine our understanding of the role of Αβ deposition in AD, and its relation to other imaging and fluid biomarkers. HIGHLIGHTS: Αβ imaging revolutionized the in vivo assessment of Alzheimer's disease Αβ pathology. Αβ imaging has increased our insights into Aβ deposition in the brain by providing highly accurate, reliable, and reproducible quantitative statements of regional and global Αβ burden in the brain. Αβ imaging is essential for the differential diagnosis, staging, and evaluation of disease-specific anti-Αβ therapeutic approaches. Αβ imaging studies continue to refine our understanding of the role of Αβ deposition in Alzheimer's disease, and its relation to other imaging and fluid biomarkers.
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Affiliation(s)
- Ann D. Cohen
- Department of PsychiatryThe University of PittsburghPittsburghPennsylvaniaUSA
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Expert Panel on Neurological Imaging, Soderlund KA, Austin MJ, Ben-Haim S, Chu S, Ivanidze J, Joshi P, Kalnins A, Kennedy M, Kulshreshtha A, Kuo PH, Masdeu JC, Nikumbh T, Soares BP, Thaker AA, Wang LL, Yasar S, Shih RY. ACR Appropriateness Criteria® Dementia: 2024 Update. J Am Coll Radiol 2025; 22:S202-S233. [PMID: 40409878 DOI: 10.1016/j.jacr.2025.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2025] [Accepted: 02/24/2025] [Indexed: 05/25/2025]
Abstract
Dementia is defined by significant chronic or acquired impairment in a single domain or loss of two or more cognitive functions by brain disease or injury. It is a common chronic syndrome in adults and constitutes the fifth leading cause of death in patients >65 years of age. Multiple etiologies of dementia exist, most notably Alzheimer disease, frontotemporal dementia, and dementia with Lewy bodies, as well as other neurologic diseases such as vascular dementia and normal pressure hydrocephalus. In addition to aiding clinicians in selecting the most appropriate imaging test for patients suspected of one of these dementia syndromes, this document highlights the most appropriate initial imaging tests for patients with suspected mild cognitive impairment and rapidly progressive dementia, as well as the most appropriate pre- and posttreatment imaging tests for patients undergoing therapy with antiamyloid monoclonal antibodies. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
| | - Karl A Soderlund
- Panel Chair, Naval Medical Center Portsmouth, Portsmouth, Virginia.
| | | | - Sharona Ben-Haim
- University of California, San Diego, School of Medicine/UC San Diego Health, San Diego, California; American Association of Neurological Surgeons/Congress of Neurological Surgeons
| | - Sammy Chu
- University of Washington, Seattle, Washington, and University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Pallavi Joshi
- Banner Alzheimer's Institute, Phoenix, Arizona; American Psychiatric Association
| | | | - Maura Kennedy
- Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; American College of Emergency Physicians
| | - Ambar Kulshreshtha
- Emory University, Atlanta, Georgia; American Academy of Family Physicians
| | - Phillip H Kuo
- University of Arizona, Tucson, Arizona; Commission on Nuclear Medicine and Molecular Imaging
| | - Joseph C Masdeu
- Houston Methodist and Weill Cornell Medicine, Houston, Texas; American Academy of Neurology
| | - Tejas Nikumbh
- The Wright Center for Graduate Medical Education, Scranton, Pennsylvania; American College of Physicians
| | - Bruno P Soares
- Stanford University School of Medicine, Stanford, California
| | | | - Lily L Wang
- University of Cincinnati Medical Center, Cincinnati, Ohio
| | - Sevil Yasar
- Johns Hopkins University School of Medicine, Baltimore, Maryland; American Geriatrics Society
| | - Robert Y Shih
- Specialty Chair, Uniformed Services University, Bethesda, Maryland
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Pan Y, Li L, Cao N, Liao J, Chen H, Zhang M. Advanced nano delivery system for stem cell therapy for Alzheimer's disease. Biomaterials 2025; 314:122852. [PMID: 39357149 DOI: 10.1016/j.biomaterials.2024.122852] [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: 06/20/2024] [Revised: 09/10/2024] [Accepted: 09/26/2024] [Indexed: 10/04/2024]
Abstract
Alzheimer's Disease (AD) represents one of the most significant neurodegenerative challenges of our time, with its increasing prevalence and the lack of curative treatments underscoring an urgent need for innovative therapeutic strategies. Stem cells (SCs) therapy emerges as a promising frontier, offering potential mechanisms for neuroregeneration, neuroprotection, and disease modification in AD. This article provides a comprehensive overview of the current landscape and future directions of stem cell therapy in AD treatment, addressing key aspects such as stem cell migration, differentiation, paracrine effects, and mitochondrial translocation. Despite the promising therapeutic mechanisms of SCs, translating these findings into clinical applications faces substantial hurdles, including production scalability, quality control, ethical concerns, immunogenicity, and regulatory challenges. Furthermore, we delve into emerging trends in stem cell modification and application, highlighting the roles of genetic engineering, biomaterials, and advanced delivery systems. Potential solutions to overcome translational barriers are discussed, emphasizing the importance of interdisciplinary collaboration, regulatory harmonization, and adaptive clinical trial designs. The article concludes with reflections on the future of stem cell therapy in AD, balancing optimism with a pragmatic recognition of the challenges ahead. As we navigate these complexities, the ultimate goal remains to translate stem cell research into safe, effective, and accessible treatments for AD, heralding a new era in the fight against this devastating disease.
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Affiliation(s)
- Yilong Pan
- Department of Cardiology, Shengjing Hospital of China Medical University, Liaoning, 110004, China.
| | - Long Li
- Department of Neurosurgery, First Hospital of China Medical University, Liaoning, 110001, China.
| | - Ning Cao
- Army Medical University, Chongqing, 400000, China
| | - Jun Liao
- Institute of Systems Biomedicine, Beijing Key Laboratory of Tumor Systems Biology, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
| | - Huiyue Chen
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Liaoning, 110001, China.
| | - Meng Zhang
- Department of Emergency Medicine, Shengjing Hospital of China Medical University, Liaoning, 110004, China.
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Wang C, Ji D, Su X, Liu F, Zhang Y, Lu Q, Cai L, Wang Y, Qin W, Xing G, Liu P, Liu X, Liu M, Zhang N. Cerebral perfusion correlates with amyloid deposition in patients with mild cognitive impairment due to Alzheimer's disease. J Prev Alzheimers Dis 2025; 12:100031. [PMID: 39863326 DOI: 10.1016/j.tjpad.2024.100031] [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: 09/23/2024] [Revised: 11/19/2024] [Accepted: 12/02/2024] [Indexed: 01/27/2025]
Abstract
BACKGROUND Changes in cerebral blood flow (CBF) may contribute to the initial stages of the pathophysiological process in patients with Alzheimer's disease (AD). Hypoperfusion has been observed in several brain regions in patients with mild cognitive impairment (MCI). However, the clinical significance of CBF changes in the early stages of AD is currently unclear. OBJECTIVES The aim of this study was to investigate the characteristics, diagnostic value and cognitive correlation of cerebral perfusion measured with arterial spin labeling (ASL) magnetic resonance imaging (MRI) in patients with MCI due to AD. DESIGN, SETTING AND PARTICIPANTS A total of fifty-nine MCI patients and 49 cognitively unimpaired controls (CUCs) were recruited and underwent multimodal MRI scans, including pseudocontinuous ASL, and neurocognitive testing. MCI patients were dichotomously classified according to the presence of amyloid deposition on 11C-labelled Pittsburgh compound B (PiB) positron emission tomography (PET). MEASUREMENTS The differences in CBF and expression of the AD-related perfusion pattern (ADRP), established by spatial covariance analysis in our previous study, were compared between the PiB+ MCI group and the CUC group and between the PiB+ and PiB- MCI groups. The diagnostic accuracy and correlations with cognitive function scores for CBF and ADRP expression were further analyzed. RESULTS Hypoperfusion in the precuneus and posterior cingulate cortex (PCC) was more characteristic of patients with MCI due to AD than of those with non-AD-related MCI. The relative regional CBF value of the left precuneus best distinguished patients with MCI due to AD from CUCs and patients with MCI due to non-AD conditions. Cerebral perfusion, as indicated by either the relative regional CBF or the expression score of the ADRP, was strongly correlated with certain cognitive function scores. CONCLUSIONS Here, we show that changes in CBF in the precuneus/PCC are promising MRI biomarkers for the identification of an AD etiology in patients with MCI. ASL, a noninvasive and cost-effective tool, has broad application prospects in the screening and early diagnosis of AD.
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Affiliation(s)
- Caixia Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Baotou Central Hospital, Baotou 014040, PR China
| | - Deli Ji
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Chifeng Municipal Hospital, Chifeng 024000, PR China
| | - Xiao Su
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Inner Mongolia Medical University Affiliated Hospital, Hohhot 010000, PR China
| | - Fang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Yulin First Hospital, Yulin City, Shanxi Province 719000, China
| | - Yanxin Zhang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China
| | - Qingzheng Lu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China
| | - Li Cai
- PET/CT Diagnostic Department, Tianjin Medical University General Hospital, Tianjin 300052, PR China
| | - Ying Wang
- PET/CT Diagnostic Department, Tianjin Medical University General Hospital, Tianjin 300052, PR China
| | - Wen Qin
- Department of Radiology, Tianjin Key Lab of Functional Imaging and Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, PR China
| | - Gebeili Xing
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Inner Mongolia People's Hospital, Hohhot 010000, PR China
| | - Peng Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology and Interventional Neurology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao 266000, PR China
| | - Xin Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Affiliated Hospital of Hebei University, Baoding 071000, PR China
| | - Meili Liu
- Department of Neurology, Baotou Central Hospital, Baotou 014040, PR China
| | - Nan Zhang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China, 154 Anshan Road Tianjin 300052, PR China; Department of Neurology, Tianjin Medical University General Hospital Airport Site, Tianjin 300052, PR China.
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Mackay GA, Gall C, Jampana R, Sleith C, Lip GYH. Scottish Intercollegiate Guidelines Network Guidance on Dementia: The Investigation of Suspected Dementia (SIGN 168) with Focus on Biomarkers-Executive Summary. Thromb Haemost 2025; 125:12-20. [PMID: 38788775 DOI: 10.1055/a-2332-6426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
This is an executive summary of the recent guidance produced by the Scottish Intercollegiate Guidelines Network (SIGN) dementia guideline group with regards to the investigation of suspected dementia. This is a sub-section of the broader SIGN 168 guideline released in November 2023. The guideline group included clinicians with expertise in Old Age Psychiatry, Neurology, Radiology, and Nuclear Medicine supported by colleagues from the SIGN and Healthcare Improvement Scotland teams. There was representation from carers and support organizations with experience of dementia, to ensure the recommendations were appropriate from the perspective of the people being assessed for possible dementia and their carers. As the 2018 National Institute for Health and Clinical Excellence (NICE) dementia review included a review of the evidenced investigation of dementia, the SIGN guideline development group decided to focus on a review on the up-to-date evidence regarding the role of imaging and fluid biomarkers in the diagnosis of dementia. To give context to the consideration of more advanced diagnostic biomarker investigations, the guideline and this summary include the NICE guidance on the use of standard investigations as well as more specialist investigations. The evidence review supports consideration of the use of structural imaging, nuclear medicine imaging, and established Alzheimer's cerebrospinal fluid biomarkers (amyloid and tau) in the diagnosis of dementia. Although routine use of amyloid positron emission tomography imaging was not recommended, its potential use, under specialist direction, in patients with atypical or young-onset presentations of suspected Alzheimer's dementia was included as a clinical good practice point.
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Affiliation(s)
- Graham Andrew Mackay
- Department of Neurology, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, United Kingdom
| | - Claire Gall
- Department of Neurology, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Ravi Jampana
- Department of Neuroradiology, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Carolyn Sleith
- Healthcare Improvement Scotland, Edinburgh, United Kingdom
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
- Department of Clinical Medicine, Danish Center for Health Services Research, Aalborg University, Aalborg, Denmark
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Yang X, Wu M, Liang M, Zhang H, Li B, Mao C, Dong L, Wang Y, Xing H, Ren C, Huang Z, Wen Q, Ge Q, Yu Z, Feng F, Gao J, Huo L. Ultra-fast [ 18F]florbetapir PET imaging using the uMI Panorama PET/CT system. EJNMMI Phys 2024; 11:107. [PMID: 39738784 DOI: 10.1186/s40658-024-00712-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 12/16/2024] [Indexed: 01/02/2025] Open
Abstract
BACKGROUND There is a need for faster amyloid PET scans to reduce patients' discomfort, minimize movement artifacts, and increase throughput. The recently introduced uMI Panorama PET/CT system featuring enhanced spatial resolution and sub-200ps TOF offers the potential for shorter scan duration without sacrificing image quality or efficacy to detect Aβ deposition. The study aims to establish a faster acquisition protocol for [18F]florbetapir PET imaging using digital PET/CT scanner uMI Panorama, while ensuring adequate image quality and amyloid-β (Aβ) detectability comparable to the standard 10-minute scan. METHODS Thirty-eight participants (29 Aβ positive and 9 Aβ negative) from a prospective dementia cohort at Peking Union Medical University Hospital underwent routine [18F]florbetapir PET scans using the uMI Panorama PET/CT scanner and a T1-weighted brain MRI scan. List-mode PET data were reconstructed into durations of 10 min, 2 min, 1 min, 45 s, and 30 s (G10min, G2min, G1min, G45s, G30s). Two trained nuclear medicine physicians independently evaluated the image quality using a 5-point scale and provided binary diagnosis. Standardized uptake value ratios (SUVr) of the composite cortex (frontal, lateral parietal, lateral temporal, and cingulate cortices) were calculated to discriminate Aβ status and coefficient of variation assessed objective image quality. Comparisons of image quality and Aβ detectability between various fast scan groups and G10min group were conducted. RESULTS The subjective image quality evaluation and Aβ detectability results from the two physicians showed both good intra-reader and inter-reader agreements (Cohen's kappa coefficient: 0.759-1.000). The subjective and objective image qualities of the G2min scans were comparable to the G10min scans, whereas adequate image quality was achieved with the G1min and G45s scans (5-point score ≥ 3). Subjective visual diagnosis by two physicians yielded consistent accuracy for G10min, G2min, and G1min groups, but lower specificity for G45s and G30s groups. The objective detection of Aβ status by cortex SUVr across all scan durations maintained perfect discriminatory efficiency and relatively high effect size (Hedge's G: 2.48-2.54). CONCLUSIONS A 1-min ultra-fast scan is feasible for [18F]florbetapir PET imaging using uMI Panorama PET/CT, while maintaining adequate image quality and Aβ diagnostic efficiency. CLINICAL TRIAL REGISTRATION NCT05023564. Registered September 2022 https://clinicaltrials.gov/search?term=NCT05023564 .
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Affiliation(s)
- Xueqian Yang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Meiqi Wu
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Menglin Liang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Haiqiong Zhang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Bo Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yuan Wang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Haiqun Xing
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Chao Ren
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Zhenghai Huang
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Qingxiang Wen
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Qi Ge
- Central Research Institute, United Imaging Healthcare, Shanghai, 201807, China
| | - Zhengqing Yu
- Central Research Institute, United Imaging Healthcare, Shanghai, 201807, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
| | - Li Huo
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 1# Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
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Wagatsuma K, Sakata M, Miwa K, Hamano Y, Kawakami H, Kamitaka Y, Yamao T, Miyaji N, Ishibashi K, Tago T, Toyohara J, Ishii K. Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images. EJNMMI Phys 2024; 11:37. [PMID: 38647924 PMCID: PMC11035535 DOI: 10.1186/s40658-024-00641-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 04/12/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. METHODS Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR). RESULTS The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91-0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. CONCLUSION Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.
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Affiliation(s)
- Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan.
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.
| | - Muneyuki Sakata
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima, 960-8516, Japan
| | - Yumi Hamano
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan
| | - Hirofumi Kawakami
- GE HealthCare Japan, 4-7-127 Asahigaoka, Hino-shi, Tokyo, 191-8503, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima, 960-8516, Japan
| | - Noriaki Miyaji
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima, 960-8516, Japan
| | - Kenji Ishibashi
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Tetsuro Tago
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Jun Toyohara
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
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Chen K, Wang M, Wu J, Zuo C, Huang Y, Wang W, Zhao M, Zhang Y, Zhang X, Chen S, Liu W, Li M, Ge J, Ma X, Wang J, Zheng L, Guan Y, Dong Q, Cui M, Xie F, Zhao Q, Yu J. Incremental value of amyloid PET in a tertiary memory clinic setting in China. Alzheimers Dement 2024; 20:2516-2525. [PMID: 38329281 PMCID: PMC11032579 DOI: 10.1002/alz.13728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION The objective of this study is to investigate the incremental value of amyloid positron emission tomography (Aβ-PET) in a tertiary memory clinic setting in China. METHODS A total of 1073 patients were offered Aβ-PET using 18F-florbetapir. The neurologists determined a suspected etiology (Alzheimer's disease [AD] or non-AD) with a percentage estimate of their confidence and medication prescription both before and after receiving the Aβ-PET results. RESULTS After disclosure of the Aβ-PET results, etiological diagnoses changed in 19.3% of patients, and diagnostic confidence increased from 69.3% to 85.6%. Amyloid PET results led to a change of treatment plan in 36.5% of patients. Compared to the late-onset group, the early-onset group had a more frequent change in diagnoses and a higher increase in diagnostic confidence. DISCUSSION Aβ-PET has significant impacts on the changes of diagnoses and management in Chinese population. Early-onset cases are more likely to benefit from Aβ-PET than late-onset cases. HIGHLIGHTS Amyloid PET contributes to diagnostic changes and its confidence in Chinese patients. Amyloid PET leads to a change of treatment plans in Chinese patients. Early-onset cases are more likely to benefit from amyloid PET than late-onset cases.
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Affiliation(s)
- Ke‐Liang Chen
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Ming‐Yu Wang
- School of MedicineQingdao UniversityQingdaoShandongChina
- Departments of NeurologyWeifang People's HospitalWeifangShandongChina
| | - Jie Wu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Chuan‐Tao Zuo
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Yu‐Yuan Huang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Wei‐Yi Wang
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Meng Zhao
- Department of Neurologythe First Hospital of Jilin UniversityChangchunJilinChina
| | - Ya‐Ru Zhang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Xue Zhang
- Department of NeurologyQingdao shi zhongxin yiyuanQingdaoShandongChina
| | - Shu‐Fen Chen
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Wei‐Shi Liu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Meng‐Meng Li
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jing‐Jie Ge
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Xiao‐Xi Ma
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jie Wang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Yi‐Hui Guan
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Qiang Dong
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Mei Cui
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Fang Xie
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Qian‐Hua Zhao
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jin‐Tai Yu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
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9
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Mc Veigh M, Bellan LM. Microfluidic synthesis of radiotracers: recent developments and commercialization prospects. LAB ON A CHIP 2024; 24:1226-1243. [PMID: 38165824 DOI: 10.1039/d3lc00779k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Positron emission tomography (PET) is a powerful diagnostic tool that holds incredible potential for clinicians to track a wide variety of biological processes using specialized radiotracers. Currently, however, a single radiotracer accounts for over 95% of procedures, largely due to the cost of radiotracer synthesis. Microfluidic platforms provide a solution to this problem by enabling a dose-on-demand pipeline in which a single benchtop platform would synthesize a wide array of radiotracers. In this review, we will explore the field of microfluidic production of radiotracers from early research to current development. Furthermore, the benefits and drawbacks of different microfluidic reactor designs will be analyzed. Lastly, we will discuss the various engineering considerations that must be addressed to create a fully developed, commercially effective platform that can usher the field from research and development to commercialization.
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Affiliation(s)
- Mark Mc Veigh
- Interdisciplinary Materials Science Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Leon M Bellan
- Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
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10
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Bucci M, Bluma M, Savitcheva I, Ashton NJ, Chiotis K, Matton A, Kivipelto M, Di Molfetta G, Blennow K, Zetterberg H, Nordberg A. Profiling of plasma biomarkers in the context of memory assessment in a tertiary memory clinic. Transl Psychiatry 2023; 13:268. [PMID: 37491358 PMCID: PMC10368630 DOI: 10.1038/s41398-023-02558-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023] Open
Abstract
Plasma biomarkers have shown promising performance in research cohorts in discriminating between different stages of Alzheimer's disease (AD). Studies in clinical populations are necessary to provide insights on the clinical utility of plasma biomarkers before their implementation in real-world settings. Here we investigated plasma biomarkers (glial fibrillary acidic protein (GFAP), tau phosphorylated at 181 and 231 (pTau181, pTau231), amyloid β (Aβ) 42/40 ratio, neurofilament light) in 126 patients (age = 65 ± 8) who were admitted to the Clinic for Cognitive Disorders, at Karolinska University Hospital. After extensive clinical assessment (including CSF analysis), patients were classified as: mild cognitive impairment (MCI) (n = 75), AD (n = 25), non-AD dementia (n = 16), no dementia (n = 9). To refine the diagnosis, patients were examined with [18F]flutemetamol PET (Aβ-PET). Aβ-PET images were visually rated for positivity/negativity and quantified in Centiloid. Accordingly, 68 Aβ+ and 54 Aβ- patients were identified. Plasma biomarkers were measured using single molecule arrays (SIMOA). Receiver-operated curve (ROC) analyses were performed to detect Aβ-PET+ using the different biomarkers. In the whole cohort, the Aβ-PET centiloid values correlated positively with plasma GFAP, pTau231, pTau181, and negatively with Aβ42/40 ratio. While in the whole MCI group, only GFAP was associated with Aβ PET centiloid. In ROC analyses, among the standalone biomarkers, GFAP showed the highest area under the curve discriminating Aβ+ and Aβ- compared to other plasma biomarkers. The combination of plasma biomarkers via regression was the most predictive of Aβ-PET, especially in the MCI group (prior to PET, n = 75) (sensitivity = 100%, specificity = 82%, negative predictive value = 100%). In our cohort of memory clinic patients (mainly MCI), the combination of plasma biomarkers was sensitive in ruling out Aβ-PET negative individuals, thus suggesting a potential role as rule-out tool in clinical practice.
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Affiliation(s)
- Marco Bucci
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Marina Bluma
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Karolinska University, SE-14186, Stockholm, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
| | - Konstantinos Chiotis
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Anna Matton
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
| | - Miia Kivipelto
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden
| | - Guglielmo Di Molfetta
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, SE-43180, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, SE-43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, SE-43180, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1N 3BG, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Centre for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, SE-14183, Stockholm, Sweden.
- Theme Inflammation and Aging, Karolinska University Hospital, SE-14186, Stockholm, Sweden.
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11
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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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12
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Altomare D, Barkhof F, Caprioglio C, Collij LE, Scheltens P, Lopes Alves I, Bouwman F, Berkhof J, van Maurik IS, Garibotto V, Moro C, Delrieu J, Payoux P, Saint-Aubert L, Hitzel A, Molinuevo JL, Grau-Rivera O, Gispert JD, Drzezga A, Jessen F, Zeyen P, Nordberg A, Savitcheva I, Jelic V, Walker Z, Edison P, Demonet JF, Gismondi R, Farrar G, Stephens AW, Frisoni GB. Clinical Effect of Early vs Late Amyloid Positron Emission Tomography in Memory Clinic Patients: The AMYPAD-DPMS Randomized Clinical Trial. JAMA Neurol 2023:2804755. [PMID: 37155177 PMCID: PMC10167601 DOI: 10.1001/jamaneurol.2023.0997] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Importance Amyloid positron emission tomography (PET) allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer disease. However, this technique is currently not widely reimbursed because of the lack of appropriately designed studies demonstrating its clinical effect. Objective To assess the clinical effect of amyloid PET in memory clinic patients. Design, Setting, and Participants The AMYPAD-DPMS is a prospective randomized clinical trial in 8 European memory clinics. Participants were allocated (using a minimization method) to 3 study groups based on the performance of amyloid PET: arm 1, early in the diagnostic workup (within 1 month); arm 2, late in the diagnostic workup (after a mean [SD] 8 [2] months); or arm 3, if and when the managing physician chose. Participants were patients with subjective cognitive decline plus (SCD+; SCD plus clinical features increasing the likelihood of preclinical Alzheimer disease), mild cognitive impairment (MCI), or dementia; they were assessed at baseline and after 3 months. Recruitment took place between April 16, 2018, and October 30, 2020. Data analysis was performed from July 2022 to January 2023. Intervention Amyloid PET. Main Outcome and Measure The main outcome was the difference between arm 1 and arm 2 in the proportion of participants receiving an etiological diagnosis with a very high confidence (ie, ≥90% on a 50%-100% visual numeric scale) after 3 months. Results A total of 844 participants were screened, and 840 were enrolled (291 in arm 1, 271 in arm 2, 278 in arm 3). Baseline and 3-month visit data were available for 272 participants in arm 1 and 260 in arm 2 (median [IQR] age: 71 [65-77] and 71 [65-77] years; 150/272 male [55%] and 135/260 male [52%]; 122/272 female [45%] and 125/260 female [48%]; median [IQR] education: 12 [10-15] and 13 [10-16] years, respectively). After 3 months, 109 of 272 participants (40%) in arm 1 had a diagnosis with very high confidence vs 30 of 260 (11%) in arm 2 (P < .001). This was consistent across cognitive stages (SCD+: 25/84 [30%] vs 5/78 [6%]; P < .001; MCI: 45/108 [42%] vs 9/102 [9%]; P < .001; dementia: 39/80 [49%] vs 16/80 [20%]; P < .001). Conclusion and Relevance In this study, early amyloid PET allowed memory clinic patients to receive an etiological diagnosis with very high confidence after only 3 months compared with patients who had not undergone amyloid PET. These findings support the implementation of amyloid PET early in the diagnostic workup of memory clinic patients. Trial Registration EudraCT Number: 2017-002527-21.
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Affiliation(s)
- Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
- Institute of Neurology, Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Femke Bouwman
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Delrieu
- Gérontopôle, Department of Geriatrics, Toulouse University Hospital, Toulouse, France
- Maintain Aging Research Team, CERPOP, Inserm, Université Paul Sabatier, Toulouse, France
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Laure Saint-Aubert
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Anne Hitzel
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck, Copenhagen, Denmark
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Cologne, Germany
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
- Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Vesna Jelic
- Cognitive Disorders Clinic, Theme Inflammation and Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- St Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | | | | | | | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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13
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Caprioglio C, Ribaldi F, Visser LNC, Minguillon C, Collij LE, Grau-Rivera O, Zeyen P, Molinuevo JL, Gispert JD, Garibotto V, Moro C, Walker Z, Edison P, Demonet JF, Barkhof F, Scheltens P, Alves IL, Gismondi R, Farrar G, Stephens AW, Jessen F, Frisoni GB, Altomare D. Analysis of Psychological Symptoms Following Disclosure of Amyloid-Positron Emission Tomography Imaging Results to Adults With Subjective Cognitive Decline. JAMA Netw Open 2023; 6:e2250921. [PMID: 36637820 PMCID: PMC9857261 DOI: 10.1001/jamanetworkopen.2022.50921] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
IMPORTANCE Individuals who are amyloid-positive with subjective cognitive decline and clinical features increasing the likelihood of preclinical Alzheimer disease (SCD+) are at higher risk of developing dementia. Some individuals with SCD+ undergo amyloid-positron emission tomography (PET) as part of research studies and frequently wish to know their amyloid status; however, the disclosure of a positive amyloid-PET result might have psychological risks. OBJECTIVE To assess the psychological outcomes of the amyloid-PET result disclosure in individuals with SCD+ and explore which variables are associated with a safer disclosure in individuals who are amyloid positive. DESIGN, SETTING, AND PARTICIPANTS This prospective, multicenter study was conducted as part of The Amyloid Imaging to Prevent Alzheimer Disease Diagnostic and Patient Management Study (AMYPAD-DPMS) (recruitment period: from April 2018 to October 2020). The setting was 5 European memory clinics, and participants included patients with SCD+ who underwent amyloid-PET. Statistical analysis was performed from July to October 2022. EXPOSURES Disclosure of amyloid-PET result. MAIN OUTCOMES AND MEASURES Psychological outcomes were defined as (1) disclosure related distress, assessed using the Impact of Event Scale-Revised (IES-R; scores of at least 33 indicate probable presence of posttraumatic stress disorder [PTSD]); and (2) anxiety and depression, assessed using the Hospital Anxiety and Depression scale (HADS; scores of at least 15 indicate probable presence of severe mood disorder symptoms). RESULTS After disclosure, 27 patients with amyloid-positive SCD+ (median [IQR] age, 70 [66-74] years; gender: 14 men [52%]; median [IQR] education: 15 [13 to 17] years, median [IQR] Mini-Mental State Examination [MMSE] score, 29 [28 to 30]) had higher median (IQR) IES-R total score (10 [2 to 14] vs 0 [0 to 2]; P < .001), IES-R avoidance (0.00 [0.00 to 0.69] vs 0.00 [0.00 to 0.00]; P < .001), IES-R intrusions (0.50 [0.13 to 0.75] vs 0.00 [0.00 to 0.25]; P < .001), and IES-R hyperarousal (0.33 [0.00 to 0.67] vs 0.00 [0.00 to 0.00]; P < .001) scores than the 78 patients who were amyloid-negative (median [IQR], age, 67 [64 to 74] years, 45 men [58%], median [IQR] education: 15 [12 to 17] years, median [IQR] MMSE score: 29 [28 to 30]). There were no observed differences between amyloid-positive and amyloid-negative patients in the median (IQR) HADS Anxiety (-1.0 [-3.0 to 1.8] vs -2.0 [-4.8 to 1.0]; P = .06) and Depression (-1.0 [-2.0 to 0.0] vs -1.0 [-3.0 to 0.0]; P = .46) deltas (score after disclosure - scores at baseline). In patients with amyloid-positive SCD+, despite the small sample size, higher education was associated with lower disclosure-related distress (ρ = -0.43; P = .02) whereas the presence of study partner was associated with higher disclosure-related distress (W = 7.5; P = .03). No participants with amyloid-positive SCD+ showed probable presence of PTSD or severe anxiety or depression symptoms at follow-up. CONCLUSIONS AND RELEVANCE The disclosure of a positive amyloid-PET result to patients with SCD+ was associated with a bigger psychological change, yet such change did not reach the threshold for clinical concern.
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Affiliation(s)
- Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Leonie N. C. Visser
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm/Solna, Sweden
- Department of Medical Psychology, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Carolina Minguillon
- Barcelonaßeta 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 Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Oriol Grau-Rivera
- Barcelonaßeta 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 Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - José Luis Molinuevo
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck A/S, Denmark
| | - Juan Domingo Gispert
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- Margaret’s Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, United Kingdom
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | | | | | | | - Frank Jessen
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Germany
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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14
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Atay LO, Saka E, Akdemir UO, Yetim E, Balcı E, Arsava EM, Topcuoglu MA. Hybrid PET/MRI with Flutemetamol and FDG in Alzheimer's Disease Clinical Continuum. Curr Alzheimer Res 2023; 20:481-495. [PMID: 38050727 DOI: 10.2174/0115672050243131230925034334] [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: 12/28/2022] [Revised: 07/26/2023] [Accepted: 08/17/2023] [Indexed: 12/06/2023]
Abstract
AIMS We aimed to investigate the interaction between β -amyloid (Aβ) accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes in the Alzheimer's disease (AD) clinical continuum. BACKGROUND Utility of positron emission tomography (PET) / magnetic resonance imaging (MRI) hybrid imaging for diagnostic categorization of the AD clinical continuum including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD) has not been fully crystallized. OBJECTIVE To evaluate the interaction between Aβ accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes such as cortex thickness or cerebral white matter disease burden and to detect the discriminative yields of these imaging modalities in the AD clinical continuum. METHODS Fifty patients (20 women and 30 men; median age: 64 years) with clinical SCD (n=11), aMCI (n=17) and ADD (n=22) underwent PET/MRI with [18F]-fluoro-D-glucose (FDG) and [18F]- Flutemetamol in addition to cerebral blood flow (CBF) and quantitative structural imaging along with detailed cognitive assessment. RESULTS High Aβ deposition (increased temporal [18F]-Flutemetamol standardized uptake value ratio (SUVr) and centiloid score), low glucose metabolism (decreased temporal lobe and posterior cingulate [18F]-FDG SUVr), low parietal CBF and right hemispheric cortical thickness were independent predictors of low cognitive test performance. CONCLUSION Integrated use of structural, metabolic, molecular (Aβ) and perfusion (CBF) parameters contribute to the discrimination of SCD, aMCI, and ADD.
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Affiliation(s)
- Lutfiye Ozlem Atay
- Department of Nuclear Medicine, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Esen Saka
- Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Umit Ozgur Akdemir
- Department of Nuclear Medicine, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Ezgi Yetim
- Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Erdem Balcı
- Department of Nuclear Medicine, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Ethem Murat Arsava
- Department of Neurology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
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15
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Rauhala E, Johansson J, Karrasch M, Eskola O, Tolvanen T, Parkkola R, Virtanen KA, Rinne JO. Change in brain amyloid load and cognition in patients with amnestic mild cognitive impairment: a 3-year follow-up study. EJNMMI Res 2022; 12:55. [PMID: 36065070 PMCID: PMC9445147 DOI: 10.1186/s13550-022-00928-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/22/2022] [Indexed: 11/10/2022] Open
Abstract
Background Our aim was to investigate the discriminative value of 18F-Flutemetamol PET in longitudinal assessment of amyloid beta accumulation in amnestic mild cognitive impairment (aMCI) patients, in relation to longitudinal cognitive changes.
Methods We investigated the change in 18F-Flutemetamol uptake and cognitive impairment in aMCI patients over time up to 3 years which enabled us to investigate possible association between changes in brain amyloid load and cognition over time. Thirty-four patients with aMCI (mean age 73.4 years, SD 6.6) were examined with 18F-Flutemetamol PET scan, brain MRI and cognitive tests at baseline and after 3-year follow-up or earlier if the patient had converted to Alzheimer´s disease (AD). 18F-Flutemetamol data were analyzed both with automated region-of-interest analysis and voxel-based statistical parametric mapping. Results 18F-flutemetamol uptake increased during the follow-up, and the increase was significantly higher in patients who were amyloid positive at baseline as compared to the amyloid-negative ones. At follow-up, there was a significant association between 18F-Flutemetamol uptake and MMSE, logical memory I (immediate recall), logical memory II (delayed recall) and verbal fluency. An association was seen between the increase in 18F-Flutemetamol uptake and decline in MMSE and logical memory I scores. Conclusions In the early phase of aMCI, presence of amyloid pathology at baseline strongly predicted amyloid accumulation during follow-up, which was further paralleled by cognitive declines. Inversely, some of our patients remained amyloid negative also at the end of the study without significant change in 18F-Flutemetamol uptake or cognition. Future studies with longer follow-up are needed to distinguish whether the underlying pathophysiology of aMCI in such patients is other than AD.
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Affiliation(s)
- Elina Rauhala
- Clinical Neurosciences, Faculty of Medicine, Turku University Hospital, University of Turku and Neurocenter, Turku, Finland
| | - Jarkko Johansson
- Turku PET Centre, Turku University Hospital, Turku, Finland.,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Mira Karrasch
- Department of Psychology, Åbo Akademi University, Turku, Finland
| | - Olli Eskola
- Turku PET Centre, University of Turku, Turku, Finland
| | - Tuula Tolvanen
- Turku PET Centre, University of Turku, Turku, Finland.,Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | | | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, Turku, Finland. .,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
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16
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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: 89] [Impact Index Per Article: 29.7] [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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17
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Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort. Sci Rep 2022; 12:10385. [PMID: 35725910 PMCID: PMC9209498 DOI: 10.1038/s41598-022-14532-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/08/2022] [Indexed: 11/08/2022] Open
Abstract
To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%, p < 0.001) involving the following etiological subgroups: AD-related MCI (57%, p < 0.01) and non-specific MCI (100%, p < 0.0001); and syndromal dementia group (61%, p < 0.0001) consisting of non-specific dementia subgroup (100%, p < 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (p < 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.
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18
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Matsuda H, Yamao T, Shakado M, Shigemoto Y, Okita K, Sato N. Amyloid PET quantification using low-dose CT-guided anatomic standardization. EJNMMI Res 2021; 11:125. [PMID: 34905145 PMCID: PMC8671596 DOI: 10.1186/s13550-021-00867-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 11/26/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Centiloid (CL) scaling has become a standardized quantitative measure in amyloid PET because it facilitates the direct comparison of results across institutions, even when different analytical methods or tracers are used. Standard volumes of interest must be used to calculate the CL scale after the anatomic standardization of amyloid PET images using coregistered MRI; if the MRI is unavailable, the CL scale cannot be accurately calculated. This study sought to determine the substitutability of low-dose CT, which is used to correct PET attenuation in PET/CT equipment, by evaluating the measurement accuracy when low-dose CT is used as an alternative to MRI in the calculation of the CL scale. Amyloid PET images obtained using 18F-flutemetamol from 24 patients with possible or probable Alzheimer's disease were processed to calculate the CL scale using 3D T1-weighted MRI and low-dose CT of PET/CT. CLMRI and CLCT were, respectively, defined as the use of MRI and CT for anatomic standardization and compared. Regional differences in the CT-based and MRI-based standardized anatomic images were also investigated. TRIAL REGISTRATION Japan Registry of Clinical Trials, jRCTs031180321 (registered 18 March 2019, https://jrct.niph.go.jp/latest-detail/jRCTs031180321 ). RESULTS A Bland-Altman plot showed that CLCT was slightly but significantly underestimated (mean ± standard deviation, - 1.7 ± 2.4; p < 0.002) compared with CLMRI. The 95% limits of agreement ranged from - 2.8 to - 0.7. Pearson correlation analysis showed a highly significant correlation of r = 0.998 between CLCT and CLMRI (p < 0.001). The linear regression equation was CLMRI = 1.027 × CLCT + 0.762. In a Bland-Altman plot, Spearman correlation analysis did not identify a significant association between the difference in CLMRI versus CLCT and CL load (ρ = - 0.389, p = 0.060). This slight underestimation of CLCT may derive from slightly higher uptake when the cerebellum is used as a reference area in CT-based anatomically standardized PET images versus MRI-based images. CONCLUSIONS Low-dose CT of PET/CT can substitute for MRI in the anatomic standardization used to calculate the CL scale from amyloid PET, although a slight underestimation occurs.
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Affiliation(s)
- Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikariga-oka, Fukushima City, Fukushima, 960-1295, Japan. .,Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-61-2 Yatsuyamada, Koriyama, Fukushima, 963-8052, Japan. .,Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan. .,Department of Biofunctional Imaging, Fukushima Medical University, 6F(621), Shin-Otemachi Building, 2-2-1, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
| | - Tensho Yamao
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-61-2 Yatsuyamada, Koriyama, Fukushima, 963-8052, Japan.,Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6, Sakae, Fukushima, 960-8516, Japan
| | - Mitsuru Shakado
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
| | - Yoko Shigemoto
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-61-2 Yatsuyamada, Koriyama, Fukushima, 963-8052, Japan.,Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
| | - Kyoji Okita
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
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19
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Alongi P, Chiaravalloti A, Berti V, Vellani C, Trifirò G, Puccini G, Carli G, Chincarini A, Morbelli S, Perani D, Sestini S. Amyloid PET in the diagnostic workup of neurodegenerative disease. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00428-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Damian A, Portugal F, Niell N, Quagliata A, Bayardo K, Alonso O, Ferrando R. Clinical Impact of PET With 18F-FDG and 11C-PIB in Patients With Dementia in a Developing Country. Front Neurol 2021; 12:630958. [PMID: 34017300 PMCID: PMC8129494 DOI: 10.3389/fneur.2021.630958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/06/2021] [Indexed: 12/03/2022] Open
Abstract
Introduction: The objective of this study was to evaluate the clinical impact PET with 18F-FDG and 11C-PIB in patients with dementia in a developing country. Methodology: Retrospective study of the patients referred for the evaluation of dementia to the only PET center in Uruguay. A total of 248 patients were identified, from which 70 patients were included based on the availability of medical history and clinical follow-up. Main outcomes included change in diagnosis, diagnostic dilemma and AD treatment. We evaluated the association of clinical outcomes with PET concordance with baseline diagnosis, diagnostic dilemma, level of education, AD pathology/Non-AD pathology (AD/Non-AD), baseline diagnosis and 11C-PIB PET result. Results: Baseline clinical diagnosis was concordant with 18F-FDG and 11C-PIB PET results in 64.7 and 77.1% of the patients, respectively. Change in diagnosis after PET was identified in 30.0% of the patients and was associated with discordant 18F-FDG (p = 0.002) and 11C-PIB (p < 0.001) PET results, previous diagnostic dilemma (p = 0.005), low education (p = 0.027), Non-AD baseline diagnosis (p = 0.027), and negative 11C-PIB PET result (p < 0.001). Only the last variable remained significant in the multivariate analysis (adjusted p = 0.038). Diagnostic dilemma decreased after PET from 15.7 to 7.1% (p = 0.11) and was associated with Non-AD diagnosis (p = 0.002) and negative 11C-PIB PET result (p = 0.003). Change in AD treatment after PET occurred in 45.7% of the patients. Conclusion:18F-FDG and 11C-PIB PET had a significant clinical impact in terms of change in diagnosis and treatment in patients with dementia in a developing country, similar to that reported in high-income countries.
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Affiliation(s)
- Andres Damian
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Fabiola Portugal
- Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Nicolas Niell
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Adriana Quagliata
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | - Karina Bayardo
- Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Omar Alonso
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
| | - Rodolfo Ferrando
- Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.,Centro de Medicina Nuclear e Imagenología Molecular, Hospital de Clínicas, Universidad de la República (UdelaR), Montevideo, Uruguay
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21
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Bucci M, Savitcheva I, Farrar G, Salvadó G, Collij L, Doré V, Gispert JD, Gunn R, Hanseeuw B, Hansson O, Shekari M, Lhommel R, Molinuevo JL, Rowe C, Sur C, Whittington A, Buckley C, Nordberg A. A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [ 18F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 2021; 48:2183-2199. [PMID: 33844055 PMCID: PMC8175298 DOI: 10.1007/s00259-021-05311-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 03/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. METHODS A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD). RESULTS Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. CONCLUSIONS Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.
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Affiliation(s)
- Marco Bucci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Amersham, UK
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Vincent Doré
- Austin Health, University of Melbourne, Melbourne, Australia.,Health and Biosecurity, CSIRO, Parkville, Australia
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), 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 Bioingenieriá, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Roger Gunn
- Invicro, London, UK.,Division of Brain Sciences, Department of Medicine, Imperial College, London, UK
| | - Bernard Hanseeuw
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmo, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Renaud Lhommel
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), 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 de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Christopher Rowe
- Austin Health, University of Melbourne, Melbourne, Australia.,Department of Medicine, The University of Melbourne, Melbourne, Australia
| | | | | | | | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. .,Department of Aging, Karolinska University Hospital, Stockholm, Sweden.
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22
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2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2121-2139. [PMID: 33674895 PMCID: PMC8175301 DOI: 10.1007/s00259-021-05258-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/11/2021] [Indexed: 12/15/2022]
Abstract
Purpose In the last decade, the research community has focused on defining reliable biomarkers for the early detection of Alzheimer’s disease (AD) pathology. In 2017, the Geneva AD Biomarker Roadmap Initiative adapted a framework for the systematic validation of oncological biomarkers to cerebrospinal fluid (CSF) AD biomarkers—encompassing the 42 amino-acid isoform of amyloid-β (Aβ42), phosphorylated-tau (P-tau), and Total-tau (T-tau)—with the aim to accelerate their development and clinical implementation. The aim of this work is to update the current validation status of CSF AD biomarkers based on the Biomarker Roadmap methodology. Methods A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of CSF AD biomarkers was assessed based on the Biomarker Roadmap methodology before the meeting and presented and discussed during the workshop. Results By comparison to the previous 2017 Geneva Roadmap meeting, the primary advances in CSF AD biomarkers have been in the area of a unified protocol for CSF sampling, handling and storage, the introduction of certified reference methods and materials for Aβ42, and the introduction of fully automated assays. Additional advances have occurred in the form of defining thresholds for biomarker positivity and assessing the impact of covariates on their discriminatory ability. Conclusions Though much has been achieved for phases one through three, much work remains in phases four (real world performance) and five (assessment of impact/cost). To a large degree, this will depend on the availability of disease-modifying treatments for AD, given these will make accurate and generally available diagnostic tools key to initiate therapy. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05258-7.
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Abstract
Alzheimer's disease (AD) is the most common cause of dementia and accounts for approximately 50% to 80% of all cases of dementia. The diagnosis of probable AD is based on clinical criteria and overlapping clinical features pose a challenge to accurate diagnosis. However, neuroimaging has been included as a biomarker in various published criteria for the diagnosis of probable AD, in the absence of a confirmatory diagnostic test during life. Advances in neuroimaging techniques and their inclusion in diagnostic and research criteria for the diagnosis of AD includes the use of positron emission tomography (PET) imaging as a biomarker in various therapeutic and prognostic studies in AD. The development and application of a range of PET tracers will allow more detailed assessment of people with AD and will improve diagnostic specificity and targeted therapy of AD. The aim of this review is to summarize current evidence on PET imaging using the non-specific tracer [18F]fluorodeoxyglucose and specific tracers that target amyloid and tau pathology in people with AD.
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Affiliation(s)
- Shailendra Mohan Tripathi
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, UK
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24
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Outcomes of clinical utility in amyloid-PET studies: state of art and future perspectives. Eur J Nucl Med Mol Imaging 2021; 48:2157-2168. [PMID: 33594474 PMCID: PMC8175294 DOI: 10.1007/s00259-020-05187-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/28/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To review how outcomes of clinical utility are operationalized in current amyloid-PET validation studies, to prepare for formal assessment of clinical utility of amyloid-PET-based diagnosis. METHODS Systematic review of amyloid-PET research studies published up to April 2020 that included outcomes of clinical utility. We extracted and analyzed (a) outcome categories, (b) their definition, and (c) their methods of assessment. RESULTS Thirty-two studies were eligible. (a) Outcome categories were clinician-centered (found in 25/32 studies, 78%), patient-/caregiver-centered (in 9/32 studies, 28%), and health economics-centered (5/32, 16%). (b) Definition: Outcomes were mainly defined by clinical researchers; only the ABIDE study expressly included stakeholders in group discussions. Clinician-centered outcomes mainly consisted of incremental diagnostic value (25/32, 78%) and change in patient management (17/32, 53%); patient-/caregiver-centered outcomes considered distress after amyloid-pet-based diagnosis disclosure (8/32, 25%), including quantified burden of procedure for patients' outcomes (n = 8) (1/8, 12.5%), impact of disclosure of results (6/8, 75%), and psychological implications of biomarker-based diagnosis (75%); and health economics outcomes focused on costs to achieve a high-confidence etiological diagnosis (5/32, 16%) and impact on quality of life (1/32, 3%). (c) Assessment: all outcome categories were operationalized inconsistently across studies, employing 26 different tools without formal rationale for selection. CONCLUSION Current studies validating amyloid-PET already assessed outcomes for clinical utility, although non-clinician-based outcomes were inconsistent. A wider participation of stakeholders may help produce a more thorough and systematic definition and assessment of outcomes of clinical utility and help collect evidence informing decisions on reimbursement of amyloid-PET.
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25
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Doré V, Krishnadas N, Bourgeat P, Huang K, Li S, Burnham S, Masters CL, Fripp J, Villemagne VL, Rowe CC. Relationship between amyloid and tau levels and its impact on tau spreading. Eur J Nucl Med Mol Imaging 2021; 48:2225-2232. [PMID: 33495928 PMCID: PMC8175299 DOI: 10.1007/s00259-021-05191-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/02/2021] [Indexed: 12/04/2022]
Abstract
Purpose Previous studies have shown that Aβ-amyloid (Aβ) likely promotes tau to spread beyond the medial temporal lobe. However, the Aβ levels necessary for tau to spread in the neocortex is still unclear. Methods Four hundred sixty-six participants underwent tau imaging with [18F]MK6420 and Aβ imaging with [18F]NAV4694. Aβ scans were quantified on the Centiloid (CL) scale with a cut-off of 25 CL for abnormal levels of Aβ (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus, and parahippocampus). Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A- subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Results The plots of prevalence of T+ show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aβ level between 10 and 40 CL reaching 23% in Me, 15% in Te, and 11% in R. Between 40 and 70 CL, the prevalence of T+ subjects per CL increased fourfold faster and at 70 CL was 64% in Me, 51% in Te, and 37% in R. In cognitively unimpaired, there were no T+ in R below 50 CL. The highest prevalence of T+ were found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aβ below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aβ levels are required before abnormal neocortical tau becomes detectable. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05191-9.
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Affiliation(s)
- Vincent Doré
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia.
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia.
| | - Natasha Krishnadas
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Pierrick Bourgeat
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | - Kun Huang
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Shenpeng Li
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Samantha Burnham
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jurgen Fripp
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
- The Australian Dementia Network (ADNeT), Melbourne, Australia
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26
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Bao YW, Chau ACM, Chiu PKC, Shea YF, Kwan JSK, Chan FHW, Mak HKF. Heterogeneity of Amyloid Binding in Cognitively Impaired Patients Consecutively Recruited from a Memory Clinic: Evaluating the Utility of Quantitative 18F-Flutemetamol PET-CT in Discrimination of Mild Cognitive Impairment from Alzheimer's Disease and Other Dementias. J Alzheimers Dis 2021; 79:819-832. [PMID: 33361593 PMCID: PMC7902948 DOI: 10.3233/jad-200890] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker. OBJECTIVE 18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer's disease (AD), and 2) MCI from other non-AD dementias (OD). METHODS 109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare). RESULTS The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively. CONCLUSION 18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.
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Affiliation(s)
- Yi-Wen Bao
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Anson C M Chau
- Department of Medical Imaging, The University of Hong Kong (Shenzhen) Teaching Hospital , The University of Hong Kong, Hong Kong SAR, China
| | - Patrick Ka-Chun Chiu
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Yat Fung Shea
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Joseph S K Kwan
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Felix Hon Wai Chan
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
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27
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Matsuda H, Ito K, Ishii K, Shimosegawa E, Okazawa H, Mishina M, Mizumura S, Ishii K, Okita K, Shigemoto Y, Kato T, Takenaka A, Kaida H, Hanaoka K, Matsunaga K, Hatazawa J, Ikawa M, Tsujikawa T, Morooka M, Ishibashi K, Kameyama M, Yamao T, Miwa K, Ogawa M, Sato N. Quantitative Evaluation of 18F-Flutemetamol PET in Patients With Cognitive Impairment and Suspected Alzheimer's Disease: A Multicenter Study. Front Neurol 2021; 11:578753. [PMID: 33519667 PMCID: PMC7838486 DOI: 10.3389/fneur.2020.578753] [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: 07/01/2020] [Accepted: 11/30/2020] [Indexed: 11/23/2022] Open
Abstract
Background: In clinical practice, equivocal findings are inevitable in visual interpretation of whether amyloid positron emission tomography (PET) is positive or negative. It is therefore necessary to establish a more objective quantitative evaluation method for determining the indication for disease-modifying drugs currently under development. Aims: We aimed to determine cutoffs for positivity in quantitative analysis of 18F-flutemetamol PET in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also evaluated the clinical efficacy of amyloid PET in the diagnosis of AD. This study was registered in the Japan Registry of Clinical Trials (jRCTs, 031180321). Methods: Ninety-three patients suspected of having AD underwent 18F-flutemetamol PET in seven institutions. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by two board-certified nuclear medicine physicians. If the two readers obtained different interpretations, the visual rating was rerun until they reached consensus. The PET images were quantitatively analyzed using the standardized uptake value ratio (SUVR) and standardized Centiloid (CL) scale with the whole cerebellum as a reference area. Results: Visual interpretation obtained 61 positive and 32 negative PET scans. Receiver operating characteristic analysis determined the best agreement of quantitative assessments and visual interpretation of PET scans to have an area under curve of 0.982 at an SUVR of 1.13 and a CL of 16. Using these cutoff values, there was high agreement between the two approaches (kappa = 0.88). Five discordant cases had SUVR and CL values ranging from 1.00 to 1.22 and from 1 to 26, respectively. In these discordant cases, either diffuse or mildly focal elevation of cortical activity confused visual interpretation. The amyloid PET outcome significantly altered the diagnosis of AD (χ2 = 51.3, p < 0.0001). PET imaging elevated the proportions of the very high likelihood category from 20.4 to 46.2% and the very low likelihood category from 0 to 22.6%. Conclusion: Quantitative analysis of amyloid PET using 18F-flutemetamol can objectively evaluate amyloid positivity using the determined cutoffs for SUVR and CL. Moreover, amyloid PET may have added value over the standard diagnostic workup in dementia patients with cognitive impairment and suspected AD.
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Affiliation(s)
- Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan.,Department of Radiology, National Center of Neurology and Psychiatry, Kodaira, Japan.,Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, Koriyama, Japan
| | - Kengo Ito
- Innovation Center for Clinical Research, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunari Ishii
- Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan.,Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, Osakasayama, Japan
| | - Eku Shimosegawa
- Department of Molecular Imaging in Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Hidehiko Okazawa
- Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
| | - Masahiro Mishina
- Department of Neuro-Pathophysiological Imaging, Graduate School of Medicine, Nippon Medical School, Kawasaki, Japan
| | - Sunao Mizumura
- Department of Radiology, Medical Centre Omori, Toho University, Tokyo, Japan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Kyoji Okita
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, Kodaira, Japan.,Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, Koriyama, Japan
| | - Takashi Kato
- Department of Radiology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Akinori Takenaka
- Department of Radiology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Hayato Kaida
- Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan.,Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, Osakasayama, Japan
| | - Kohei Hanaoka
- Joint Research Division for the Quantum Cancer Therapy, Research Center for Nuclear Physics, Osaka University, Osaka, Japan
| | - Keiko Matsunaga
- Department of Molecular Imaging in Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Jun Hatazawa
- Joint Research Division for the Quantum Cancer Therapy, Research Center for Nuclear Physics, Osaka University, Osaka, Japan
| | - Masamichi Ikawa
- Department of Neurology, Faculty of Medical Sciences, Fukui, Japan
| | - Tetsuya Tsujikawa
- Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
| | - Miyako Morooka
- Department of Radiology, Medical Centre Omori, Toho University, Tokyo, Japan
| | - Kenji Ishibashi
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Masashi Kameyama
- Department of Radiology, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan
| | - Tensho Yamao
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan.,Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, Koriyama, Japan.,Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Fukushima, Japan
| | - Kenta Miwa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan.,Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Fukushima, Japan
| | - Masayo Ogawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Kodaira, Japan
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28
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Drzezga A, Bischof GN, Giehl K, van Eimeren T. PET and SPECT Imaging of Neurodegenerative Diseases. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00085-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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29
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Smailovic U, Koenig T, Savitcheva I, Chiotis K, Nordberg A, Blennow K, Winblad B, Jelic V. Regional Disconnection in Alzheimer Dementia and Amyloid-Positive Mild Cognitive Impairment: Association Between EEG Functional Connectivity and Brain Glucose Metabolism. Brain Connect 2020; 10:555-565. [PMID: 33073602 PMCID: PMC7757561 DOI: 10.1089/brain.2020.0785] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Introduction: The disconnection hypothesis of Alzheimer's disease (AD) is supported by growing neuroimaging and neurophysiological evidence of altered brain functional connectivity in cognitively impaired individuals. Brain functional modalities such as [18F]fluorodeoxyglucose positron-emission tomography ([18F]FDG-PET) and electroencephalography (EEG) measure different aspects of synaptic functioning, and can contribute to understanding brain connectivity disruptions in AD. Aim: This study investigated the relationship between cortical glucose metabolism and topographical EEG measures of brain functional connectivity in subjects along AD continuum. Methods: Patients diagnosed with mild cognitive impairment (MCI) and AD (n = 67), and stratified into amyloid-positive (n = 32) and negative (n = 10) groups according to cerebrospinal fluid Aβ42/40 ratio, were assessed with [18F]FDG-PET and resting-state EEG recordings. EEG-based neuroimaging analysis involved standardized low-resolution electromagnetic tomography (sLORETA), which estimates functional connectivity from cortical sources of electrical activity in a 3D head model. Results: Glucose hypometabolism in temporoparietal lobes was significantly associated with altered EEG functional connectivity of the same regions of interest in clinically diagnosed MCI and AD patients and in patients with biomarker-verified AD pathology. The correlative pattern of disrupted connectivity in temporoparietal lobes, as detected by EEG sLORETA analysis, included decreased instantaneous linear connectivity in fast frequencies and increased lagged linear connectivity in slow frequencies in relation to the activity of remaining cortex. Conclusions: Topographical EEG measures of functional connectivity detect regional dysfunction of AD-vulnerable brain areas as evidenced by association and spatial overlap with the cortical glucose hypometabolism in MCI and AD patients.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Chiotis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Huddinge, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry and Sahlgrenska University Hospital, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Bengt Winblad
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Geriatrics, Karolinska University Hospital, Huddinge, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Huddinge, Sweden
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30
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Reimand J, Groot C, Teunissen CE, Windhorst AD, Boellaard R, Barkhof F, Nazarenko S, van der Flier WM, van Berckel BNM, Scheltens P, Ossenkoppele R, Bouwman F. Why Is Amyloid-β PET Requested After Performing CSF Biomarkers? J Alzheimers Dis 2020; 73:559-569. [PMID: 31796674 PMCID: PMC7081099 DOI: 10.3233/jad-190836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Amyloid-β positron emission tomography (PET) and cerebrospinal fluid (CSF) Aβ42 are considered interchangeable for clinical diagnosis of Alzheimer's disease. OBJECTIVE To explore the clinical reasoning for requesting additional amyloid-β PET after performing CSF biomarkers. METHODS We retrospectively identified 72 memory clinic patients who underwent amyloid-β PET after CSF biomarkers analysis for clinical diagnostic evaluation between 2011 and 2019. We performed patient chart reviews to identify factors which led to additional amyloid-β PET. Additionally, we assessed accordance with appropriate-use-criteria (AUC) for amyloid-β PET. RESULTS Mean patient age was 62.0 (SD = 8.1) and mean Mini-Mental State Exam score was 23.6 (SD = 3.8). CSF analysis conflicting with the clinical diagnosis was the most frequent reason for requesting an amyloid-β PET scan (n = 53, 74%), followed by incongruent MRI (n = 16, 22%), unusual clinical presentation (n = 11, 15%) and young age (n = 8, 11%). An amyloid-β PET scan was rarely (n = 5, 7%) requested in patients with a CSF Aβ+/tau+ status. Fifteen (47%) patients with a post-PET diagnosis of AD had a predominantly non-amnestic presentation. In n = 11 (15%) cases, the reason that the clinician requested amyloid-β was not covered by AUC. This happened most often (n = 7) when previous CSF analysis did not support current clinical diagnosis, which led to requesting amyloid-β PET. CONCLUSION In this single-center study, the main reason for requesting an amyloid-β PET scan after performing CSF biomarkers was the occurrence of a mismatch between the primary clinical diagnosis and CSF Aβ/tau results.
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Affiliation(s)
- Juhan Reimand
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia.,Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia
| | - Colin Groot
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, United Kingdom
| | - Sergei Nazarenko
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Rik Ossenkoppele
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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31
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Chételat G, Arbizu J, Barthel H, Garibotto V, Law I, Morbelli S, van de Giessen E, Agosta F, Barkhof F, Brooks DJ, Carrillo MC, Dubois B, Fjell AM, Frisoni GB, Hansson O, Herholz K, Hutton BF, Jack CR, Lammertsma AA, Landau SM, Minoshima S, Nobili F, Nordberg A, Ossenkoppele R, Oyen WJG, Perani D, Rabinovici GD, Scheltens P, Villemagne VL, Zetterberg H, Drzezga A. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol 2020; 19:951-962. [PMID: 33098804 DOI: 10.1016/s1474-4422(20)30314-8] [Citation(s) in RCA: 284] [Impact Index Per Article: 56.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 07/22/2020] [Accepted: 08/06/2020] [Indexed: 12/14/2022]
Abstract
Various biomarkers are available to support the diagnosis of neurodegenerative diseases in clinical and research settings. Among the molecular imaging biomarkers, amyloid-PET, which assesses brain amyloid deposition, and 18F-fluorodeoxyglucose (18F-FDG) PET, which assesses glucose metabolism, provide valuable and complementary information. However, uncertainty remains regarding the optimal timepoint, combination, and an order in which these PET biomarkers should be used in diagnostic evaluations because conclusive evidence is missing. Following an expert panel discussion, we reached an agreement on the specific use of the individual biomarkers, based on available evidence and clinical expertise. We propose a diagnostic algorithm with optimal timepoints for these PET biomarkers, also taking into account evidence from other biomarkers, for early and differential diagnosis of neurodegenerative diseases that can lead to dementia. We propose three main diagnostic pathways with distinct biomarker sequences, in which amyloid-PET and 18F-FDG-PET are placed at different positions in the order of diagnostic evaluations, depending on clinical presentation. We hope that this algorithm can support diagnostic decision making in specialist clinical settings with access to these biomarkers and might stimulate further research towards optimal diagnostic strategies.
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Affiliation(s)
- Gaël Chételat
- Normandie Université, Université de Caen, Institut National de la Santé et de la Recherche Médicale, Unité 1237, Groupement d'Intérêt Public Cyceron, Caen, France.
| | - Javier Arbizu
- Department of Nuclear Medicine, University of Navarra, Clinica Universidad de Navarra, Pamplona, Spain
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and NIMTlab, Geneva University, Geneva, Switzerland
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Silvia Morbelli
- Nuclear Medicine Unit, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genova, Italy
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - David J Brooks
- Institute of Neuroscience, Newcastle University, Newcastle, UK; Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | | | - Bruno Dubois
- Centre des Maladies Cognitives et Comportementales, University Hospital of Pitié Salpêtrière, Assistance Publique-Hôpitaux de Paris, Sorbonne-Université, Paris, France
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway, Oslo; Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Giovanni B Frisoni
- Memory Clinic, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Karl Herholz
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, UK
| | | | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Flavio Nobili
- UO Clinica Neurologica, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Child and Mother Health, University of Genoa, Genova, Italy
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Rik Ossenkoppele
- Department of Neurology, Alzheimer Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wim J G Oyen
- Humanitas University and Humanitas Clinical and Research Center, Department of Nuclear Medicine, Milan, Italy; Rijnstate, Department of Radiology and Nuclear Medicine, Arnhem, Netherlands; Radboud UMC, Department of Radiology and Nuclear Medicine, Nijmegen, Netherlands
| | - Daniela Perani
- Vita-Salute San Raffaele University, Nuclear Medicine Unit, San Raffaele Hospital, Division of Neuroscience San Raffaele Scientific Institute, Milan, Italy
| | - Gil D Rabinovici
- Departments of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC, Australia; School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, Institute of Neurology, University College London, London, UK; 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; UK Dementia Research Institute at University College London, London, UK
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany; German Center for Neurodegenerative Diseases, Bonn-Cologne, Germany; Institute of Neuroscience and Medicine, Molecular Organization of the Brain, Forschungszentrum Jülich, Germany
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Rowley PA, Samsonov AA, Betthauser TJ, Pirasteh A, Johnson SC, Eisenmenger LB. Amyloid and Tau PET Imaging of Alzheimer Disease and Other Neurodegenerative Conditions. Semin Ultrasound CT MR 2020; 41:572-583. [PMID: 33308496 DOI: 10.1053/j.sult.2020.08.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although diagnosing the syndrome of dementia is largely a clinical endeavor, neuroimaging plays an increasingly important role in accurately determining the underlying etiology, which extends beyond its traditional role in excluding other causes of altered cognition. New neuroimaging methods not only facilitate the diagnosis of the most common neurodegenerative conditions (particularly Alzheimer Disease [AD]) after symptom onset, but also show diagnostic promise even in the very early or presymptomatic phases of disease. Positron emission tomography (PET) is increasingly recognized as a key clinical tool for differentiating normal age-related changes in brain metabolism (using 18F-fluorodeoxyglucose [FDG]) from those seen in the earliest stages of specific forms of dementia. However, FDG PET only demonstrates nonspecific changes in altered parenchymal glucose uptake and not the specific etiologic proteinopathy causing the abnormal glucose uptake. A growing class of radiotracers targeting specific protein aggregates for amyloid-β (Aβ) and tau are changing the way AD is diagnosed, as these radiotracers directly label the underlying disease pathology. As these pathology-specific radiotracers are currently making their way to the clinic, it is important for the clinical neuroradiologist to understand the underlying patterns of Aβ and tau deposition in the context of AD (across its clinical continuum) and in other causes of dementia, as well as understand the implications of current research.
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Affiliation(s)
- Paul A Rowley
- Department of Radiology, University of Wisconsin, Madison, WI
| | | | | | - Ali Pirasteh
- Department of Radiology, University of Wisconsin, Madison, WI
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Delta-secretase cleavage of Tau mediates its pathology and propagation in Alzheimer's disease. Exp Mol Med 2020; 52:1275-1287. [PMID: 32859953 PMCID: PMC8080617 DOI: 10.1038/s12276-020-00494-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 01/08/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with age as a major risk factor. AD is the most common dementia with abnormal structures, including extracellular senile plaques and intraneuronal neurofibrillary tangles, as key neuropathologic hallmarks. The early feature of AD pathology is degeneration of the locus coeruleus (LC), which is the main source of norepinephrine (NE) supplying various cortical and subcortical areas that are affected in AD. The spread of Tau deposits is first initiated in the LC and is transported in a stepwise manner from the entorhinal cortex to the hippocampus and then to associative regions of the neocortex as the disease progresses. Most recently, we reported that the NE metabolite DOPEGAL activates delta-secretase (AEP, asparagine endopeptidase) and triggers pathological Tau aggregation in the LC, providing molecular insight into why LC neurons are selectively vulnerable to developing early Tau pathology and degenerating later in the disease and how δ-secretase mediates the spread of Tau pathology to the rest of the brain. This review summarizes our current understanding of the crucial role of δ-secretase in driving and spreading AD pathologies by cleaving multiple critical players, including APP and Tau, supporting that blockade of δ-secretase may provide an innovative disease-modifying therapeutic strategy for treating AD. The identification of an enzyme that plays a critical role in the progression of Alzheimer’s disease (AD) could lead to novel therapeutic interventions. In the earliest stage of AD, the build-up of Tau protein aggregates causes degeneration of a site in the brainstem. These abnormal Tau accumulations then spread to other parts of the brain. Recent research suggests that an enzyme called delta-secretase cleaves Tau and other key molecules, making Tau more prone to forming aggregates and thus facilitating disease progression. Keqiang Ye and co-workers at Emory University School of Medicine in Atlanta, USA, reviewed current understanding of the role of delta-secretase in AD pathology. Studies show that delta-secretase expression levels are high in aged mice and AD brains. Inhibiting delta-secretase could therefore limit neurodegeneration and alleviate cognitive deficits in patients.
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Perini G, Rodriguez-Vieitez E, Kadir A, Sala A, Savitcheva I, Nordberg A. Clinical impact of 18F-FDG-PET among memory clinic patients with uncertain diagnosis. Eur J Nucl Med Mol Imaging 2020; 48:612-622. [PMID: 32734458 PMCID: PMC7835147 DOI: 10.1007/s00259-020-04969-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 07/22/2020] [Indexed: 12/14/2022]
Abstract
Purpose To assess the clinical impact and incremental diagnostic value of 18F-fluorodeoxyglucose (FDG-PET) among memory clinic patients with uncertain diagnosis. Methods The study population consisted of 277 patients who, despite extensive baseline cognitive assessment, MRI, and CSF analyses, had an uncertain diagnosis of mild cognitive impairment (MCI) (n = 177) or dementia (n = 100). After baseline diagnosis, each patient underwent an FDG-PET, followed by a post-FDG-PET diagnosis formulation. We evaluated (i) the change in diagnosis (baseline vs. post-FDG-PET), (ii) the change in diagnostic accuracy when comparing each baseline and post-FDG-PET diagnosis to a long-term follow-up (3.6 ± 1.8 years) diagnosis used as reference, and (iii) comparative FDG-PET performance testing in MCI and dementia conditions. Results FDG-PET led to a change in diagnosis in 86 of 277 (31%) patients, in particular in 57 of 177 (32%) MCI and in 29 of 100 (29%) dementia patients. Diagnostic change was greater than two-fold in the sub-sample of cases with dementia “of unclear etiology” (change in diagnosis in 20 of 32 (63%) patients). In the dementia group, after results of FDG-PET, diagnostic accuracy improved from 77 to 90% in Alzheimer’s disease (AD) and from 85 to 94% in frontotemporal lobar degeneration (FTLD) patients (p < 0.01). FDG-PET performed better in dementia than in MCI (positive likelihood ratios >5 and < 5, respectively). Conclusion Within a selected clinical population, FDG-PET has a significant clinical impact, both in early and differential diagnosis of uncertain dementia. FDG-PET provides significant incremental value to detect AD and FTLD over a clinical diagnosis of uncertain dementia. Electronic supplementary material The online version of this article (10.1007/s00259-020-04969-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Giulia Perini
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, 141 52, Stockholm, Sweden.,Center for Cognitive and Behavioral Disorders, IRCCS Mondino Foundation and Dept of Brain and Behavior, University of Pavia, 27100, Pavia, Italy
| | - Elena Rodriguez-Vieitez
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, 141 52, Stockholm, Sweden
| | - Ahmadul Kadir
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, 141 52, Stockholm, Sweden.,Theme Aging, The Aging Brain Unit, Karolinska University Hospital, 141 86, Stockholm, Sweden
| | - Arianna Sala
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, 141 52, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine Imaging, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, 141 52, Stockholm, Sweden. .,Theme Aging, The Aging Brain Unit, Karolinska University Hospital, 141 86, Stockholm, Sweden.
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Abstract
The gold standard for diagnosis of cardiac amyloidosis (CA) is endomyocardial biopsy showing Congo red staining followed by mass spectroscopy, but the diagnosis can also be made with high certainty by demonstration of typical cardiac imaging features along with amyloid on biopsy of another involved organ. The use of cardiac imaging techniques to detect amyloid deposits may frequently obviate the need for invasive methods in order to ascertain the presence, and potentially the type, of amyloid deposition. PURPOSE OF REVIEW: We aim to review the evidence behind the development of novel positron emission tomography (PET) radiotracers for demonstrating cardiac amyloid deposition and potentially distinguishing between light-chain (AL) or transthyretin (ATTR) cardiac amyloidosis. RECENT FINDINGS: Multiple recent studies have shown that thioflavin-analogue tracers such as18F-florbetapir, 18F-florbetaben, 18F-flutemetamol, and 11C-labeled Pittsburg Compound-B (PiB) may be able to fulfill the unmet need of elucidating the presence of amyloid deposition in the heart. Because they bind to the beta-pleated motif of the amyloid fibril due to their similarity to the thioflavin structure, they could potentially be used to image CA (Table 1). The use of PET amyloid radiotracers shows promise; however, further data is needed to define their overall accuracy and additive value to the care of patients with suspected systemic and/or cardiac amyloidosis.
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Affiliation(s)
- Cesia Gallegos
- Section of Cardiovascular Medicine, Yale University School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, CT, 06520-8017, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Yale University School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, CT, 06520-8017, USA.
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Hagberg G, Ihle-Hansen H, Fure B, Thommessen B, Ihle-Hansen H, Øksengård AR, Beyer MK, Wyller TB, Müller EG, Pendlebury ST, Selnes P. No evidence for amyloid pathology as a key mediator of neurodegeneration post-stroke - a seven-year follow-up study. BMC Neurol 2020; 20:174. [PMID: 32384876 PMCID: PMC7206753 DOI: 10.1186/s12883-020-01753-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 04/29/2020] [Indexed: 12/24/2022] Open
Abstract
Background Cognitive impairment (CI) with mixed vascular and neurodegenerative pathologies after stroke is common. The role of amyloid pathology in post-stroke CI is unclear. We hypothesize that amyloid deposition, measured with Flutemetamol (18F-Flut) positron emission tomography (PET), is common in seven-year stroke survivors diagnosed with CI and, further, that quantitatively assessed 18F-Flut-PET uptake after 7 years correlates with amyloid-β peptide (Aβ42) levels in cerebrospinal fluid (CSF) at 1 year, and with measures of neurodegeneration and cognition at 7 years post-stroke. Methods 208 patients with first-ever stroke or transient Ischemic Attack (TIA) without pre-existing CI were included during 2007 and 2008. At one- and seven-years post-stroke, cognitive status was assessed, and categorized into dementia, mild cognitive impairment or normal. Etiologic sub-classification was based on magnetic resonance imaging (MRI) findings, CSF biomarkers and clinical cognitive profile. At 7 years, patients were offered 18F-Flut-PET, and amyloid-positivity was assessed visually and semi-quantitatively. The associations between 18F-Flut-PET standardized uptake value ratios (SUVr) and measures of neurodegeneration (medial temporal lobe atrophy (MTLA), global cortical atrophy (GCA)) and cognition (Mini-Mental State Exam (MMSE), Trail-making test A (TMT-A)) and CSF Aβ42 levels were assessed using linear regression. Results In total, 111 patients completed 7-year follow-up, and 26 patients agreed to PET imaging, of whom 13 had CSF biomarkers from 1 year. Thirteen out of 26 patients were diagnosed with CI 7 years post-stroke, but only one had visually assessed amyloid positivity. CSF Aβ42 levels at 1 year, MTA grade, GCA scale, MMSE score or TMT-A at 7 years did not correlate with 18F-Flut-PET SUVr in this cohort. Conclusions Amyloid binding was not common in 7-year stroke survivors diagnosed with CI. Quantitatively assessed, cortical amyloid deposition did not correlate with other measures related to neurodegeneration or cognition. Therefore, amyloid pathology may not be a key mediator of neurodegeneration 7 years post-stroke. Trial registration Clinicaltrials.gov (NCT00506818). July 23, 2007. Inclusion from February 2007, randomization and intervention from May 2007 and trial registration in July 2007.
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Affiliation(s)
- Guri Hagberg
- Bærum Hospital, Vestre Viken Hospital Trust, N-3004, Drammen, Norway. .,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Hege Ihle-Hansen
- Bærum Hospital, Vestre Viken Hospital Trust, N-3004, Drammen, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Brynjar Fure
- Department of Neurology, Department of Internal Medicine, Central Hospital Karlstad and Faculty of Medicine, Örebro University, Örebro, Sweden
| | - Bente Thommessen
- Department of Neurology, Akershus University Hospital, Oslo, Norway
| | - Håkon Ihle-Hansen
- Bærum Hospital, Vestre Viken Hospital Trust, N-3004, Drammen, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Mona K Beyer
- Division of Radiology, Nuclear Medicine Oslo University Hospital, Oslo, Norway
| | - Torgeir B Wyller
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Ebba Gløersen Müller
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Sarah T Pendlebury
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Per Selnes
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Neurology, Akershus University Hospital, Oslo, Norway
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Uzuegbunam BC, Librizzi D, Hooshyar Yousefi B. PET Radiopharmaceuticals for Alzheimer's Disease and Parkinson's Disease Diagnosis, the Current and Future Landscape. Molecules 2020; 25:E977. [PMID: 32098280 PMCID: PMC7070523 DOI: 10.3390/molecules25040977] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/17/2020] [Accepted: 02/17/2020] [Indexed: 02/06/2023] Open
Abstract
Ironically, population aging which is considered a public health success has been accompanied by a myriad of new health challenges, which include neurodegenerative disorders (NDDs), the incidence of which increases proportionally to age. Among them, Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common, with the misfolding and the aggregation of proteins being common and causal in the pathogenesis of both diseases. AD is characterized by the presence of hyperphosphorylated τ protein (tau), which is the main component of neurofibrillary tangles (NFTs), and senile plaques the main component of which is β-amyloid peptide aggregates (Aβ). The neuropathological hallmark of PD is α-synuclein aggregates (α-syn), which are present as insoluble fibrils, the primary structural component of Lewy body (LB) and neurites (LN). An increasing number of non-invasive PET examinations have been used for AD, to monitor the pathological progress (hallmarks) of disease. Notwithstanding, still the need for the development of novel detection tools for other proteinopathies still remains. This review, although not exhaustively, looks at the timeline of the development of existing tracers used in the imaging of Aβ and important moments that led to the development of these tracers.
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Affiliation(s)
| | - Damiano Librizzi
- Department of Nuclear Medicine, Philipps-University of Marburg, 35043 Marburg, Germany;
| | - Behrooz Hooshyar Yousefi
- Nuclear Medicine Department, and Neuroimaging Center, Technical University of Munich, 81675 Munich, Germany;
- Department of Nuclear Medicine, Philipps-University of Marburg, 35043 Marburg, Germany;
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Jack CR, Wiste HJ, Botha H, Weigand SD, Therneau TM, Knopman DS, Graff-Radford J, Jones DT, Ferman TJ, Boeve BF, Kantarci K, Lowe VJ, Vemuri P, Mielke MM, Fields JA, Machulda MM, Schwarz CG, Senjem ML, Gunter JL, Petersen RC. The bivariate distribution of amyloid-β and tau: relationship with established neurocognitive clinical syndromes. Brain 2019; 142:3230-3242. [PMID: 31501889 PMCID: PMC6763736 DOI: 10.1093/brain/awz268] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 06/26/2019] [Accepted: 07/07/2019] [Indexed: 12/14/2022] Open
Abstract
Large phenotypically diverse research cohorts with both amyloid and tau PET have only recently come into existence. Our objective was to determine relationships between the bivariate distribution of amyloid-β and tau on PET and established clinical syndromes that are relevant to cognitive ageing and dementia. All individuals in this study were enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive ageing, or the Mayo Alzheimer Disease Research Center, a longitudinal study of individuals recruited from clinical practice. We studied 1343 participants who had amyloid PET and tau PET from 2 April 2015 to 3 May 2019, and met criteria for membership in one of five clinical diagnostic groups: cognitively unimpaired, mild cognitive impairment, frontotemporal dementia, probable dementia with Lewy bodies, and Alzheimer clinical syndrome. We examined these clinical groups in relation to the bivariate distribution of amyloid and tau PET values. Individuals were grouped into amyloid (A)/tau (T) quadrants based on previously established abnormality cut points of standardized uptake value ratio 1.48 (A) and 1.33 (T). Individual participants largely fell into one of three amyloid/tau quadrants: low amyloid and low tau (A-T-), high amyloid and low tau (A+T-), or high amyloid and high tau (A+T+). Seventy per cent of cognitively unimpaired and 74% of FTD participants fell into the A-T- quadrant. Participants with mild cognitive impairment spanned the A-T- (42%), A+T- (28%), and A+T+ (27%) quadrants. Probable dementia with Lewy body participants spanned the A-T- (38%) and A+T- (44%) quadrants. Most (89%) participants with Alzheimer clinical syndrome fell into the A+T+ quadrant. These data support several conclusions. First, among 1343 participants, abnormal tau PET rarely occurred in the absence of abnormal amyloid PET, but the reverse was common. Thus, with rare exceptions, amyloidosis appears to be required for high levels of 3R/4R tau deposition. Second, abnormal amyloid PET is compatible with normal cognition but highly abnormal tau PET is not. These two conclusions support a dynamic biomarker model in which Alzheimer's disease is characterized first by the appearance of amyloidosis and later by tauopathy, with tauopathy being the proteinopathy associated with clinical symptoms. Third, bivariate amyloid and tau PET relationships differed across clinical groups and thus have a role for clarifying the aetiologies underlying neurocognitive clinical syndromes.
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Affiliation(s)
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Terry M Therneau
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | | | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Tanis J Ferman
- Department of Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
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Matsuda H, Shigemoto Y, Sato N. Neuroimaging of Alzheimer's disease: focus on amyloid and tau PET. Jpn J Radiol 2019; 37:735-749. [PMID: 31493197 DOI: 10.1007/s11604-019-00867-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 08/28/2019] [Indexed: 12/14/2022]
Abstract
Although the diagnosis of dementia is still largely a clinical one, based on history and disease course, neuroimaging has dramatically increased our ability to accurately diagnose it. Neuroimaging modalities now play a wider role in dementia beyond their traditional role of excluding neurosurgical lesions and are recommended in most clinical guidelines for dementia. In addition, new neuroimaging methods facilitate the diagnosis of most neurodegenerative conditions after symptom onset and show diagnostic promise even in the very early or presymptomatic phases of some diseases. In the case of Alzheimer's disease (AD), extracellular amyloid-β (Aβ) aggregates and intracellular tau neurofibrillary tangles are the two neuropathological hallmarks of the disease. Recent molecular imaging techniques using amyloid and tau PET ligands have led to preclinical diagnosis and improved differential diagnosis as well as narrowed subject selection and treatment monitoring in clinical trials aimed at delaying or preventing the symptomatic phase of AD. This review discusses the recent progress in amyloid and tau PET imaging and the key findings achieved by the use of this molecular imaging modality related to the respective roles of Aβ and tau in AD, as well as its specific limitations.
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Affiliation(s)
- Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan.
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi, Kodaira, Tokyo, 187-8551, Japan
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Ramusino MC, Garibotto V, Bacchin R, Altomare D, Dodich A, Assal F, Mendes A, Costa A, Tinazzi M, Morbelli SD, Bauckneht M, Picco A, Dottorini ME, Tranfaglia C, Farotti L, Salvadori N, Moretti D, Savelli G, Tarallo A, Nobili F, Parapini M, Cavaliere C, Salvatore E, Salvatore M, Boccardi M, Frisoni GB. Incremental value of amyloid-PET versus CSF in the diagnosis of Alzheimer's disease. Eur J Nucl Med Mol Imaging 2019; 47:270-280. [PMID: 31388720 DOI: 10.1007/s00259-019-04466-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 07/26/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE To compare the incremental diagnostic value of amyloid-PET and CSF (Aβ42, tau, and phospho-tau) in AD diagnosis in patients with mild cognitive impairment (MCI) or mild dementia, in order to improve the definition of diagnostic algorithm. METHODS Two independent dementia experts provided etiological diagnosis and relative diagnostic confidence in 71 patients on 3 rounds, based on (1) clinical, neuropsychological, and structural MRI information alone; (2) adding one biomarker (CSF amyloid and tau levels or amyloid-PET with a balanced randomized design); and (3) adding the other biomarker. RESULTS Among patients with a pre-biomarker diagnosis of AD, negative PET induced significantly more diagnostic changes than amyloid-negative CSF at both rounds 2 (CSF 67%, PET 100%, P = 0.028) and 3 (CSF 0%; PET 78%, P < 0.001); PET induced a diagnostic confidence increase significantly higher than CSF on both rounds 2 and 3. CONCLUSIONS Amyloid-PET should be prioritized over CSF biomarkers in the diagnostic workup of patients investigated for suspected AD, as it provides greater changes in diagnosis and diagnostic confidence. TRIAL REGISTRATION EudraCT no.: 2014-005389-31.
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Affiliation(s)
- Matteo Cotta Ramusino
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland. .,Center for Cognitive and Behavioral Disorders, IRCCS Mondino Foundation and Dept of Brain and Behavior, University of Pavia, 27100, Pavia, Italy.
| | - Valentina Garibotto
- NIMTlab, Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, CH1205, Geneva, Switzerland.,Division of Nuclear Medicine, Geneva University Hospitals, CH1205, Geneva, Switzerland
| | - Ruggero Bacchin
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland.,Dept of Neurosciences, Biomedicine and Movement Sciences, Section of Neurology, University of Verona, 34134, Verona, Italy
| | - Daniele Altomare
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Alessandra Dodich
- NIMTlab, Neuroimaging and Innovative Molecular Tracers Laboratory, University of Geneva, CH1205, Geneva, Switzerland
| | - Frederic Assal
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Aline Mendes
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | - Alfredo Costa
- Center for Cognitive and Behavioral Disorders, IRCCS Mondino Foundation and Dept of Brain and Behavior, University of Pavia, 27100, Pavia, Italy
| | - Michele Tinazzi
- Dept of Neurosciences, Biomedicine and Movement Sciences, Section of Neurology, University of Verona, 34134, Verona, Italy
| | - Silvia D Morbelli
- Nuclear Medicine, Dept of Health Sciences (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, 16132, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine, Dept of Health Sciences (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, 16132, Genoa, Italy
| | - Agnese Picco
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa, 16126, Genoa, Italy
| | - Massimo E Dottorini
- Nuclear Medicine Division, "S. Maria della Misericordia" Hospital, 06129, Perugia, Italy
| | - Cristina Tranfaglia
- Nuclear Medicine Division, "S. Maria della Misericordia" Hospital, 06129, Perugia, Italy
| | - Lucia Farotti
- Center for Memory Disturbances, Laboratory of Clinical Neurochemistry, University of Perugia, 06123, Perugia, Italy
| | - Nicola Salvadori
- Center for Memory Disturbances, Laboratory of Clinical Neurochemistry, University of Perugia, 06123, Perugia, Italy
| | - Davide Moretti
- Alzheimer's Disease Operative Unit, IRCCS S, Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Giordano Savelli
- Nuclear Medicine Division, Fondazione Poliambulanza Istituto Ospedaliero, 25124, Brescia, Italy
| | - Anna Tarallo
- LANE-Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Flavio Nobili
- Clinical Neurology, Dept. of Neuroscience (DINOGMI), University of Genoa, 16126, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, 16132, Genoa, Italy
| | - Maura Parapini
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
| | | | | | | | - Marina Boccardi
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland.,LANE-Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE -Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Chemin du Petit Bel-Air 2, Bâtiment Voirons, CH1225, Geneva, Switzerland
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