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Lin H, Jiang Q, Yang Y, Huang Q, Zhang Y, Zhang Z, Zhu Y, Lu J, Wang J, Wang M, Men J, Yang Y, Zhang H, Guan Y, Ge J, Lu J, Jiang J, Zuo C. Harmonizing Aβ deposition threshold for 18F-florbetaben PET imaging: Addressing discrepancies and calibration between PET/CT and PET/MRI. Eur J Nucl Med Mol Imaging 2025:10.1007/s00259-025-07279-y. [PMID: 40266306 DOI: 10.1007/s00259-025-07279-y] [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: 02/08/2025] [Accepted: 04/08/2025] [Indexed: 04/24/2025]
Abstract
PURPOSE Discrepancies between PET/CT and PET/MRI scanners can affect the determination of amyloid beta (Aβ) deposition thresholds in patients with cognitive impairment. This study aimed to identify these differences and propose a calibration method to standardize Aβ quantification across imaging modalities. METHODS A total of 133 patients with cognitive impairment underwent Aβ PET imaging and were divided into four groups: a head-to-head PET/CT and PET/MRI cohort (group A, n = 6), an independent PET/CT cohort (group B, n = 48), an independent PET/MRI cohort (group C, n = 79), and another independent PET/MRI cohort (group D, n = 10). Standardized uptake value ratios (SUVR) of global cortical target (CTXsuvr) and centiloid (CL) values were compared within group A and between groups B and C. A whole cerebellum (WC)-referenced SUVR method was used to calibrate CL values in group C, with verification in group D. RESULTS CTXsuvr values were significantly higher in PET/MRI than in PET/CT in both group A (P < 0.05) and group C versus group B (P < 0.001). Aβ-negative/positive cases showed mean ± variance of CTXsuvr as 1.023 ± 0.104/1.479 ± 0.203 in group B and 1.146 ± 0.100/1.743 ± 0.254 in group C, with cutoffs of 1.140 (CL = 20) and 1.401 (CL = 60), respectively. WC-referenced calibration adjusted PET/MRI cutoff to 1.132 (CL = 19) in group C, aligning it with PET/CT thresholds and validated in group D. CONCLUSION WC-referenced SUVR calibration effectively mitigates differences in Aβ thresholds between PET/CT and PET/MRI, enhancing Aβ quantification standardization in multi-modal imaging.
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Affiliation(s)
- Huamei Lin
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Quanling Jiang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yunhao Yang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qi Huang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ying Zhang
- Institute of Biomedical Engineering, School of Medicine, Shanghai University, Shanghai, China
| | - Zhengwei Zhang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuhua Zhu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Wang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Jianwei Men
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Yufeng Yang
- Beijing Sinotau International Pharmaceutical Technology Co., Ltd, Beijing, China
| | - Huiwei Zhang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
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Carbonell F, Zijdenbos AP, Hempel E, Hajós M, Bedell BJ. A novel method for harmonization of PET image spatial resolution without phantoms. EJNMMI Phys 2025; 12:23. [PMID: 40082316 PMCID: PMC11906943 DOI: 10.1186/s40658-025-00740-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 02/28/2025] [Indexed: 03/16/2025] Open
Abstract
BACKGROUND Estimation of the spatial resolution in real images is extremely important in several fields, including crystallography, optics, microscopy, and tomography. In human PET imaging, estimating spatial resolution typically involves the acquisition of images from a physical phantom, typically a Hoffman phantom, which poses a logistical burden, especially in large multi-center studies. Indeed, phantom images may not always be readily available, and this method requires constant monitoring of scanner updates or replacements, scanning protocol changes, and image reconstruction guidelines to establish a equivalence with scans acquired from human subjects. METHODS We propose a new computational approach that allows estimation of spatial resolution directly from human subject PET images. The proposed technique is based on the generalization of the logarithmic intensity plots in the 2D Fourier domain to the 3D case. The spatial resolution of the image is obtained through the estimated coefficients of a multiple linear regression problem having the logarithm of the squared norm of the Fourier transform as dependent variable and the squared 3D frequencies as multiple predictors. RESULTS The proposed approach was applied to a cohort of subjects consisting of [18F]florbetapir amyloid PET images and matching phantoms from a Phase II clinical trial, and a second cohort including β-amyloid, FDG, and tau PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The resulting in-plane and axial resolution estimators varied between 3.5 mm and 8.5 mm for both PET and matching phantom images. They also yielded less than one voxel size across-subjects variability in groups of images sharing the same PET scanner model and reconstruction parameters. For human PET images, we also proved that the spatial resolution estimators showed: (1) a very high reproducibility, as measured by intraclass correlation coefficients (ICC > 0.985), (2) a strong cross-tracer linear correlations, and (3) a high within-subject longitudinal consistency, as measured by the maximum difference value between pairs of visits from the same subject. CONCLUSIONS Our novel approach does not only eliminate the need for surrogate phantom data, but also provides a general framework that can be applied to a wide range of tracers and other imaging modalities, such as SPECT. CLINICAL TRIAL DATA Cognito Therapeutics' OVERTURE clinical trial (NCT03556280, 2021-08-24), https://clinicaltrials.gov/study/NCT03556280 .
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Affiliation(s)
- Felix Carbonell
- Biospective Inc, 1255 Peel Street, Suite 560, Montreal, QC, H3B 2T9, Canada.
| | - Alex P Zijdenbos
- Biospective Inc, 1255 Peel Street, Suite 560, Montreal, QC, H3B 2T9, Canada
| | - Evan Hempel
- Cognito Therapeutics, 1218 Massachusetts Ave., Suite 200, Cambridge, MA, 02138, USA
| | - Mihály Hajós
- Cognito Therapeutics, 1218 Massachusetts Ave., Suite 200, Cambridge, MA, 02138, USA
| | - Barry J Bedell
- Biospective Inc, 1255 Peel Street, Suite 560, Montreal, QC, H3B 2T9, Canada
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Guillén N, Contador J, Buongiorno M, Álvarez I, Culell N, Alcolea D, Lleó A, Fortea J, Piñol-Ripoll G, Carnes-Vendrell A, Lourdes Ispierto M, Vilas D, Puig-Pijoan A, Fernández-Lebrero A, Balasa M, Sánchez-Valle R, Lladó A. Agreement of cerebrospinal fluid biomarkers and amyloid-PET in a multicenter study. Eur Arch Psychiatry Clin Neurosci 2025; 275:257-266. [PMID: 37898567 PMCID: PMC11799063 DOI: 10.1007/s00406-023-01701-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 10/02/2023] [Indexed: 10/30/2023]
Abstract
Core Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers have shown incomplete agreement with amyloid-positron emission tomography (PET). Our goal was to analyze the agreement between AD CSF biomarkers and amyloid-PET in a multicenter study. Retrospective multicenter study (5 centers). Participants who underwent both CSF biomarkers and amyloid-PET scan within 18 months were included. Clinical diagnoses were made according to latest diagnostic criteria by the attending clinicians. CSF Amyloid Beta1-42 (Aβ1-42, A), phosphorliated tau 181 (pTau181, T) and total tau (tTau, N) biomarkers were considered normal (-) or abnormal ( +) according to cutoffs of each center. Amyloid-PET was visually classified as positive/negative. Agreement between CSF biomarkers and amyloid-PET was analyzed by overall percent agreement (OPA). 236 participants were included (mean age 67.9 years (SD 9.1), MMSE score 24.5 (SD 4.1)). Diagnoses were mild cognitive impairment or dementia due to AD (49%), Lewy body dementia (22%), frontotemporal dementia (10%) and others (19%). Mean time between tests was 5.1 months (SD 4.1). OPA between single CSF biomarkers and amyloid-PET was 74% for Aβ1-42, 75% for pTau181, 73% for tTau. The use of biomarker ratios improved OPA: 87% for Aβ1-42/Aβ1-40 (n = 155), 88% for pTau181/Aβ1-42 (n = 94) and 82% for tTau/Aβ1-42 (n = 160). A + T + N + cases showed the highest agreement between CSF biomarkers and amyloid-PET (96%), followed by A-T-N- cases (89%). Aβ1-42/Aβ1-40 was a better marker of cerebral amyloid deposition, as identified by amyloid tracers, than Aβ1-42 alone. Combined biomarkers in CSF predicted amyloid-PET result better than single biomarkers.
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Affiliation(s)
- Núria Guillén
- Alzheimer's Disease and Other Cognitive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Carrer Villarroel, 170, 08036, Barcelona, Spain
| | - José Contador
- Alzheimer's Disease and Other Cognitive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Carrer Villarroel, 170, 08036, Barcelona, Spain
| | - Mariateresa Buongiorno
- Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Spain
- Fundació Docència i Recerca Mútua Terrassa, Terrassa, Spain
| | - Ignacio Álvarez
- Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Spain
- Fundació Docència i Recerca Mútua Terrassa, Terrassa, Spain
| | - Natalia Culell
- Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Spain
- Fundació Docència i Recerca Mútua Terrassa, Terrassa, Spain
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau-Biomedical Research Institute Sant Pau (IIB Sant Pau), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Madrid, Spain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau-Biomedical Research Institute Sant Pau (IIB Sant Pau), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Madrid, Spain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau-Biomedical Research Institute Sant Pau (IIB Sant Pau), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Madrid, Spain
| | - Gerard Piñol-Ripoll
- Clinical Neuroscience Research, Unitat Trastorns Cognitius, IRBLleida, Santa Maria University Hospital, Lleida, Spain
| | - Anna Carnes-Vendrell
- Clinical Neuroscience Research, Unitat Trastorns Cognitius, IRBLleida, Santa Maria University Hospital, Lleida, Spain
| | - María Lourdes Ispierto
- Neurodegenerative Diseases Unit, Neurology Service and Neurosciences Department, University Hospital Germans Trias i Pujol (HUGTP), Badalona, Spain
| | - Dolores Vilas
- Neurodegenerative Diseases Unit, Neurology Service and Neurosciences Department, University Hospital Germans Trias i Pujol (HUGTP), Badalona, Spain
| | - Albert Puig-Pijoan
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Aida Fernández-Lebrero
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Carrer Villarroel, 170, 08036, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Carrer Villarroel, 170, 08036, Barcelona, Spain
- Institute of Neurosciences, Department of Medicine, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Carrer Villarroel, 170, 08036, Barcelona, Spain.
- Institute of Neurosciences, Department of Medicine, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
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4
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Zeydan B, Johnson DR, Schwarz CG, Przybelski SA, Lesnick TG, Senjem ML, Kantarci OH, Min PH, Kemp BJ, Jack CR, Kantarci K, Lowe VJ. Visual assessments of 11 C-Pittsburgh compound-B PET vs. 18 F-flutemetamol PET across the age spectrum. Nucl Med Commun 2024; 45:1047-1054. [PMID: 39267525 PMCID: PMC11540735 DOI: 10.1097/mnm.0000000000001902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
OBJECTIVE Visual assessments of amyloid-β PET, used for Alzheimer's disease (AD) diagnosis and treatment evaluation, require a careful approach when different PET ligands are utilized. Because the gray matter (GM) and white matter (WM) ligand bindings vary with age, the objective was to investigate the agreement between visual reads of 11 C- and 18 F-PET scans. METHODS Cognitively unimpaired (CU) younger adults ( N = 30; 39.5 ± 6.0 years), CU older adults ( N = 30; 68.6 ± 5.9 years), and adults with AD ( N = 22; 67.0 ± 8.5 years) underwent brain MRI, 11 C-Pittsburgh compound-B (PiB)-PET, and 18 F-flutemetamol-PET. Amyloid-β deposition was assessed visually by two nuclear medicine specialists on 11 C-PiB-PET and 18 F-flutemetamol-PET, and quantitatively by PET centiloids. RESULTS Seventy-two 11 C-PiB-PET and 18 F-flutemetamol-PET visual reads were concordant. However, 1 18 F-flutemetamol-PET and 9 11 C-PiB-PET were discordant with quantitative values. In four additional cases, while 11 C-PiB-PET and 18 F-flutemetamol-PET visual reads were concordant, they were discordant with quantitative values. Disagreements in CU younger adults were only with 11 C-PiB-PET visual reads. The remaining disagreements were with CU older adults. CONCLUSION Age, GM/WM binding, amyloid-β load, and disease severity may affect visual assessments of PET ligands. Increase in WM binding with age causes a loss of contrast between GM and WM on 11 C-PiB-PET, particularly in CU younger adults, leading to false positivity. In CU older adults, increased WM signal may bleed more into cortical regions, hiding subtle cortical uptake, especially with 18 F-flutemetamol, whereas 11 C-PiB can detect true regional positivity. Understanding these differences will improve patient care and treatment evaluation in clinic and clinical trials.
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Affiliation(s)
- Burcu Zeydan
- Mayo Clinic, Department of Radiology
- Mayo Clinic, Department of Neurology
| | | | | | | | | | - Matthew L. Senjem
- Mayo Clinic, Department of Radiology
- Mayo Clinic, Department of Information Technology
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5
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Leuzy A, Raket LL, Villemagne VL, Klein G, Tonietto M, Olafson E, Baker S, Saad ZS, Bullich S, Lopresti B, Bohorquez SS, Boada M, Betthauser TJ, Charil A, Collins EC, Collins JA, Cullen N, Gunn RN, Higuchi M, Hostetler E, Hutchison RM, Iaccarino L, Insel PS, Irizarry MC, Jack CR, Jagust WJ, Johnson KA, Johnson SC, Karten Y, Marquié M, Mathotaarachchi S, Mintun MA, Ossenkoppele R, Pappas I, Petersen RC, Rabinovici GD, Rosa‐Neto P, Schwarz CG, Smith R, Stephens AW, Whittington A, Carrillo MC, Pontecorvo MJ, Haeberlein SB, Dunn B, Kolb HC, Sivakumaran S, Rowe CC, Hansson O, Doré V. Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model. Alzheimers Dement 2024; 20:5833-5848. [PMID: 39041435 PMCID: PMC11497758 DOI: 10.1002/alz.13908] [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: 11/07/2023] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification. HIGHLIGHTS Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.
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Shekari M, Vállez García D, Collij LE, Altomare D, Heeman F, Pemberton H, Roé Vellvé N, Bullich S, Buckley C, Stephens A, Farrar G, Frisoni G, Klunk WE, Barkhof F, Gispert JD, ADNI and the AMYPAD consortium. Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology. Alzheimers Dement 2024; 20:5102-5113. [PMID: 38961808 PMCID: PMC11350134 DOI: 10.1002/alz.13883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. HIGHLIGHTS We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.
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Grants
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- 115952 Innovative Medicines Initiative 2 Joint Undertaking
- GE Healthcare
- Transition Therapeutics
- F. Hoffmann-La Roche Ltd
- Eli Lilly and Company
- Eisai Inc.
- W81XWH-12-2-0012 Department of Defense
- EuroImmun
- Biogen
- Alzheimer's Drug Discovery Foundation
- Servier
- Lumosity
- Bristol-Myers Squibb Company
- U01 AG024904 NIA NIH HHS
- Piramal Imaging
- Takeda Pharmaceutical Company
- Alzheimer's Association
- Genentech, Inc.
- Araclon Biotech
- U01 AG024904 NIH HHS
- Meso Scale Diagnostics, LLC
- Novartis Pharmaceuticals Corporation
- CereSpir, Inc.
- BioClinica, Inc.
- Johnson & Johnson Pharmaceutical Research & Development LLC
- AbbVie
- Cogstate
- Merck & Co., Inc.
- NIBIB NIH HHS
- European Union's Horizon 2020
- Pfizer Inc.
- CIHR
- Elan Pharmaceuticals, Inc.
- IXICO Ltd.
- NeuroRx Research
- Neurotrack Technologies
- Fujirebio
- Lundbeck
- National Institutes of Health
- Department of Defense
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- BioClinica, Inc.
- Biogen
- Bristol‐Myers Squibb Company
- Eli Lilly and Company
- F. Hoffmann‐La Roche Ltd
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- Lundbeck
- Merck & Co., Inc.
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Servier
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
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Affiliation(s)
- Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - David Vállez García
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Clinical Memory Research UnitClinical Sciences MalmöLund UniversityMalmöSweden
| | - Daniele Altomare
- Memory CenterDepartment of Rehabilitation and GeriatricsUniversity Hospitals and University of GenevaGenèveSwitzerland
| | - Fiona Heeman
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Wallenberg Centre for Molecular and Translational MedicineUniversity of Gothenburg, The University of GothenburgGothenburgSweden
- Department of Psychiatry and NeurochemistryUniversity of GothenburgSahlgrenska University HospitalGothenburgSweden
| | - Hugh Pemberton
- GE Healthcare Life SciencesAmershamUK
- Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | | | | | | | | | | | - Giovanni Frisoni
- Memory CenterDepartment of Rehabilitation and GeriatricsUniversity Hospitals and University of GenevaGenèveSwitzerland
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER‐BBN)MadridSpain
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Munro CE, Farrell M, Hanseeuw B, Rentz DM, Buckley R, Properzi M, Yuan Z, Vannini P, Amariglio RE, Quiroz YT, Blacker D, Sperling RA, Johnson KA, Marshall GA, Gatchel JR. Change in Depressive Symptoms and Longitudinal Regional Amyloid Accumulation in Unimpaired Older Adults. JAMA Netw Open 2024; 7:e2427248. [PMID: 39207757 PMCID: PMC11362871 DOI: 10.1001/jamanetworkopen.2024.27248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/13/2024] [Indexed: 09/04/2024] Open
Abstract
Importance Depressive symptoms in older adults may be a harbinger of Alzheimer disease (AD), even in preclinical stages. It is unclear whether worsening depressive symptoms are manifestations of regional distributions of core AD pathology (amyloid) and whether cognitive changes affect this relationship. Objective To evaluate whether increasing depressive symptoms are associated with amyloid accumulation in brain regions important for emotional regulation and whether those associations vary by cognitive performance. Design, Setting, and Participants Participants from the Harvard Aging Brain Study, a longitudinal cohort study, underwent annual assessments of depressive symptoms and cognition alongside cortical amyloid positron emission tomography (PET) imaging at baseline and every 2 to 3 years thereafter (mean [SD] follow-up, 8.6 [2.2] years). Data collection was conducted from September 2010 to October 2022 in a convenience sample of community-dwelling older adults who were cognitively unimpaired with, at most, mild baseline depression. Data were analyzed from October 2022 to December 2023. Main Outcomes and Measures Depression (Geriatric Depression Scale [GDS]-30-item), cognition (Preclinical Alzheimer Cognitive Composite-5 [PACC]), and a continuous measure of cerebral amyloid (Pittsburgh compound B [PiB] PET) examined in a priori-defined regions (medial orbitofrontal cortex [mOFC], lateral orbitofrontal cortex, middle frontal cortex [MFC], superior frontal cortex, anterior cingulate cortex, isthmus cingulate cortex [IC], posterior cingulate cortex, and amygdala). Associations between longitudinal GDS scores, regional amyloid slopes, and PACC slopes were assessed using linear mixed-effects models. Results In this sample of 154 individuals (94 [61%] female; mean [SD] age, 72.6 [6.4] years; mean (SD) education, 15.9 [3.1] years), increasing PiB slopes in the bilateral mOFC, IC, and MFC were associated with increasing GDS scores (mOFC: β = 11.07 [95% CI, 5.26-16.87]; t = 3.74 [SE, 2.96]; P = .004; IC: β = 12.83 [95% CI, 5.68-19.98]; t = 3.51 [SE, 3.65]; P = .004; MFC: β = 9.22 [95% CI, 2.25-16.20]; t = 2.59 [SE, 3.56]; P = .03). Even with PACC slope as an additional covariate, associations remained significant in these regions. Conclusions and Relevance In this cohort study of cognitively unimpaired older adults with, at most, mild baseline depressive symptoms, greater depressive symptoms over time were associated with amyloid accumulation in regions associated with emotional control. Furthermore, these associations persisted in most regions independent of cognitive changes. These results shed light on the neurobiology of depressive symptoms in older individuals and underscore the importance of monitoring for elevated mood symptoms early in AD.
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Affiliation(s)
- Catherine E. Munro
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Michelle Farrell
- Massachusetts General Hospital, Harvard Medical School, Boston
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts
| | - Bernard Hanseeuw
- Massachusetts General Hospital, Harvard Medical School, Boston
- Institute of Neuroscience, Université Catholique de Louvain/Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Dorene M. Rentz
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Rachel Buckley
- Massachusetts General Hospital, Harvard Medical School, Boston
| | | | - Ziwen Yuan
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Patrizia Vannini
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Rebecca E. Amariglio
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Yakeel T. Quiroz
- Massachusetts General Hospital, Harvard Medical School, Boston
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | - Deborah Blacker
- Massachusetts General Hospital, Harvard Medical School, Boston
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Reisa A. Sperling
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Keith A. Johnson
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Gad A. Marshall
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Massachusetts General Hospital, Harvard Medical School, Boston
| | - Jennifer R. Gatchel
- Massachusetts General Hospital, Harvard Medical School, Boston
- McLean Hospital, Belmont, Massachusetts
- Baylor College of Medicine, Houston, Texas
- Michael E. Debakey Department of Veterans Affairs Medical Center, Houston, Texas
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8
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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses. J Nucl Med 2024; 65:781-787. [PMID: 38575189 PMCID: PMC11064829 DOI: 10.2967/jnumed.123.266188] [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: 07/03/2023] [Revised: 02/13/2024] [Indexed: 04/06/2024] Open
Abstract
Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
| | - Christopher C Rowe
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine E Davey
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia;
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
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9
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Albala B, Appelmans E, Burress R, De Santi S, Devins T, Klein G, Logovinsky V, Novak GP, Ribeiro K, Schmidt ME, Schwarz AJ, Scott D, Shcherbinin S, Siemers E, Travaglia A, Weber CJ, White L, Wolf‐Rodda J, Vasanthakumar A. The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partners Scientific Board (PPSB). Alzheimers Dement 2024; 20:695-708. [PMID: 37774088 PMCID: PMC10843521 DOI: 10.1002/alz.13483] [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: 06/16/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 10/01/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non-profit organizations that have until recently been managed by the Foundation for the National Institutes of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer's disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond. HIGHLIGHTS: The Private Partners Scientific board (PPSB) continues to play a key role in enabling several Alzheimer's Disease Neuroimaging Initiative (ADNI) activities. PPSB working groups have led landscape assessments to provide valuable feedback on new technologies, platforms, and methods that may be taken up by ADNI in current or future iterations.
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Affiliation(s)
- Bruce Albala
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Program in Public HealthIrvine and Department of NeurologyUCI School of MedicineUniversity of California856 Health Sciences QuadIrvineCalifornia92697‐3957USA
| | - Eline Appelmans
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
| | - Ramona Burress
- Janssen Research & Development, LLCTitusvilleNew JerseyUSA
- Present address:
Takeda95, Hayden AvenueLexingtonMassachusetts02421USA
| | - Susan De Santi
- Eisai Inc.NutleyNew JerseyUSA
- Life Molecular ImagingBerlinGermany
- Present address:
Eisai Inc.NutleyNew JerseyUSA
| | - Theresa Devins
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Cognition Therapeutics2500 Westchester AvenuePurchaseNew York10577USA
| | | | - Veronika Logovinsky
- Eisai Inc.NutleyNew JerseyUSA
- Present address:
Lundbeck6 Parkway NDeerfieldIllinois60015USA
| | | | | | | | | | | | | | | | - Alessio Travaglia
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
| | | | - Leah White
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
- Present address:
Veranex5420 Wade Park Blvd Suite 204RaleighNorth Carolina27607USA
| | - Julie Wolf‐Rodda
- Foundation for the National Institutes of HealthNorth BethesdaMarylandUSA
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10
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Bollack A, Pemberton HG, Collij LE, Markiewicz P, Cash DM, Farrar G, Barkhof F. Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification. Alzheimers Dement 2023; 19:5232-5252. [PMID: 37303269 DOI: 10.1002/alz.13158] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2023]
Abstract
Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.
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Affiliation(s)
- Ariane Bollack
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Hugh G Pemberton
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
- GE Healthcare, Amersham, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Pawel Markiewicz
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - David M Cash
- UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at University College London, London, UK
| | | | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
- UCL Queen Square Institute of Neurology, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
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11
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Gogola A, Lopresti BJ, Tudorascu D, Snitz B, Minhas D, Doré V, Ikonomovic MD, Shaaban CE, Matan C, Bourgeat P, Mason NS, Aizenstein H, Mathis CA, Klunk WE, Rowe CC, Lopez OL, Cohen AD, Villemagne VL. Biostatistical Estimation of Tau Threshold Hallmarks (BETTH) Algorithm for Human Tau PET Imaging Studies. J Nucl Med 2023; 64:1798-1805. [PMID: 37709531 PMCID: PMC10626371 DOI: 10.2967/jnumed.123.265941] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/03/2023] [Indexed: 09/16/2023] Open
Abstract
A methodology for determining tau PET thresholds is needed to confidently detect early tau deposition. We compared multiple threshold-determining methods in participants who underwent either 18F-flortaucipir or 18F-MK-6240 PET scans. Methods: 18F-flortaucipir (n = 798) and 18F-MK-6240 (n = 216) scans were processed and sampled to obtain regional SUV ratios. Subsamples of the cohorts were based on participant diagnosis, age, amyloid-β status (positive or negative), and neurodegeneration status (positive or negative), creating older-adult (age ≥ 55 y) cognitively unimpaired (amyloid-β-negative, neurodegeneration-negative) and cognitively impaired (mild cognitive impairment/Alzheimer disease, amyloid-β-positive, neurodegeneration-positive) groups, and then were further subsampled via matching to reduce significant differences in diagnostic prevalence, age, and Mini-Mental State Examination score. We used the biostatistical estimation of tau threshold hallmarks (BETTH) algorithm to determine sensitivity and specificity in 6 composite regions. Results: Parametric double receiver operating characteristic analysis yielded the greatest joint sensitivity in 5 of the 6 regions, whereas hierarchic clustering, gaussian mixture modeling, and k-means clustering all yielded perfect joint specificity (2.00) in all regions. Conclusion: When 18F-flortaucipir and 18F-MK-6240 are used, Alzheimer disease-related tau status is best assessed using 2 thresholds, a sensitivity one based on parametric double receiver operating characteristic analysis and a specificity one based on gaussian mixture modeling, delimiting an uncertainty zone indicating participants who may require further evaluation.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania;
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Beth Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vincent Doré
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; and
| | - C Elizabeth Shaaban
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Pierrick Bourgeat
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
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12
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Bollack A, Markiewicz PJ, Wink AM, Prosser L, Lilja J, Bourgeat P, Schott JM, Coath W, Collij LE, Pemberton HG, Farrar G, Barkhof F, Cash DM. Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies. Neuroimage 2023; 280:120313. [PMID: 37595816 DOI: 10.1016/j.neuroimage.2023.120313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/29/2023] [Accepted: 08/04/2023] [Indexed: 08/20/2023] Open
Abstract
PURPOSE Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. METHODS Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BPND). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CLNMF), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BPND and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation. RESULTS All metrics showed good reliability. Aβ load, Aβ index and CLNMF were strong associated with the BPND. The associations with CL suggest that cross-sectional measures of CLNMF, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CLNMF and Aβ load compared to the CL. CONCLUSION Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CLNMF can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CLNMF and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.
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Affiliation(s)
- Ariane Bollack
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK.
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Alle Meije Wink
- Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - Lloyd Prosser
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | | | | | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Lyduine E Collij
- Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Hugh G Pemberton
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; GE HealthCare, Amersham, UK; Queen Square Institute of Neurology, University College London, UK
| | | | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands; Queen Square Institute of Neurology, University College London, UK
| | - David M Cash
- Queen Square Institute of Neurology, University College London, UK; UK Dementia Research Institute at University College London, London, UK
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13
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Tian M, Zuo C, Civelek AC, Carrio I, Watanabe Y, Kang KW, Murakami K, Garibotto V, Prior JO, Barthel H, Guan Y, Lu J, Zhou R, Jin C, Wu S, Zhang X, Zhong Y, Zhang H, Molecular Imaging-Based Precision Medicine Task Group of A3 (China-Japan-Korea) Foresight Program. International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:375-389. [PMID: 37589025 PMCID: PMC10425321 DOI: 10.1007/s43657-022-00068-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/19/2022] [Accepted: 07/22/2022] [Indexed: 08/18/2023]
Abstract
Alzheimer's disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD.
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Affiliation(s)
- Mei Tian
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Ali Cahid Civelek
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
| | - Ignasi Carrio
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
| | - Yasuyoshi Watanabe
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
| | - Koji Murakami
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
| | - Valentina Garibotto
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
| | - John O. Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Shuang Wu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
| | - Molecular Imaging-Based Precision Medicine Task Group of A3 (China-Japan-Korea) Foresight Program
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
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14
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Fu JF, Lois C, Sanchez J, Becker JA, Rubinstein ZB, Thibault E, Salvatore AN, Sari H, Farrell ME, Guehl NJ, Normandin MD, Fakhri GE, Johnson KA, Price JC. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [ 18F]MK-6240 PET uptake. J Cereb Blood Flow Metab 2023; 43:581-594. [PMID: 36420769 PMCID: PMC10063833 DOI: 10.1177/0271678x221142139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/17/2022] [Accepted: 11/06/2022] [Indexed: 11/25/2022]
Abstract
[18F]MK-6240 meningeal/extracerebral off-target binding may impact tau quantification. We examined the kinetics and longitudinal changes of extracerebral and reference regions. [18F]MK-6240 PET was performed in 24 cognitively-normal and eight cognitively-impaired subjects, with arterial samples in 13 subjects. Follow-up scans at 6.1 ± 0.5 (n = 25) and 13.3 ± 0.9 (n = 16) months were acquired. Extracerebral and reference region (cerebellar gray matter (CerGM)-based, cerebral white matter (WM), pons) uptake were evaluated using standardized uptake values (SUV90-110), spectral analysis, and distribution volume. Longitudinal changes in SUV90-110 were examined. The impact of reference region on target region outcomes, partial volume correction (PVC) and regional erosion were evaluated. Eroded WM and pons showed lower variability, lower extracerebral contamination, and lower longitudinal changes than CerGM-based regions. CerGM-based regions resulted larger cross-sectional effect sizes for group differentiation. Extracerebral signal was high in 50% of subjects and exhibited irreversible kinetics and nonsignificant longitudinal changes over one-year but was highly variable at subject-level. PVC resulted in higher variability in reference region uptake and longitudinal changes. Our results suggest that eroded CerGM may be preferred for cross-sectional, whilst eroded WM or pons may be preferred for longitudinal [18F]MK-6240 studies. For CerGM, erosion was necessary (preferred over PVC) to address the heterogenous nature of extracerebral signal.
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Affiliation(s)
- Jessie Fanglu Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Cristina Lois
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Justin Sanchez
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - J Alex Becker
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Zoe B Rubinstein
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Emma Thibault
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew N Salvatore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hasan Sari
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | | | - Nicolas J Guehl
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Marc D Normandin
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Georges El Fakhri
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Keith A Johnson
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Julie C Price
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
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15
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Mao X, Shan W, Fox W, Yu J. Subtraction technique on 18F-fluoro-2-deoxy-d-glucose positron emission tomography ( 18F-FDG-PET) images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2169989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Xuewei Mao
- Shandong Key Laboratory of Industrial Control Technology, School of Automation, Qingdao University, Qingdao, People’s Republic of China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing, People’s Republic of China
- Beijing Institute for Brain Disorders, Beijing, People’s Republic of China
| | - Wilson Fox
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jinpeng Yu
- Shandong Key Laboratory of Industrial Control Technology, School of Automation, Qingdao University, Qingdao, People’s Republic of China
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16
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Smith AM, Obuchowski NA, Foster NL, Klein G, Mozley PD, Lammertsma AA, Wahl RL, Sunderland JJ, Vanderheyden JL, Benzinger TLS, Kinahan PE, Wong DF, Perlman ES, Minoshima S, Matthews D. The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification. J Nucl Med 2023; 64:294-303. [PMID: 36137760 PMCID: PMC9902844 DOI: 10.2967/jnumed.122.264031] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 02/04/2023] Open
Abstract
A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.
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Affiliation(s)
- Anne M Smith
- Siemens Medical Solutions USA, Inc., Knoxville, Tennessee;
| | | | - Norman L Foster
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - P David Mozley
- Weill Medical College of Cornell University, New York, New York
| | - Adriaan A Lammertsma
- Amsterdam Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - John J Sunderland
- Division of Nuclear Medicine, Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - Paul E Kinahan
- Department of Radiology, School of Medicine, University of Washington, Seattle, Washington
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; and
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17
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Nojima H, Ito S, Kushida A, Abe A, Motsuchi W, Verbel D, Vandijck M, Jannes G, Vandenbroucke I, Aoyagi K. Clinical utility of cerebrospinal fluid biomarkers measured by LUMIPULSE ® system. Ann Clin Transl Neurol 2022; 9:1898-1909. [PMID: 36321325 PMCID: PMC9735374 DOI: 10.1002/acn3.51681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are well-established in research settings, but their use in routine clinical practice remains a largely unexploited potential. Here, we examined the relationship between CSF biomarkers, measured by a fully automated immunoassay platform, and brain β-amyloid (Aβ) deposition status confirmed by amyloid positron emission tomography (PET). METHODS One hundred ninety-nine CSF samples from clinically diagnosed AD patients enrolled in a clinical study and who underwent amyloid PET were used for the measurement of CSF biomarkers Aβ 1-40 (Aβ40), Aβ 1-42 (Aβ42), total tau (t-Tau), and phosphorylated tau-181 (p-Tau181) using the LUMIPULSE system. These biomarkers and their combinations were compared to amyloid PET classification (negative or positive) using visual read assessments. Several combinations were also analyzed with a multivariable logistic regression model. RESULTS Aβ42, t-Tau, and p-Tau181, and the ratios of Aβ42 with other biomarkers had a good diagnostic agreement with amyloid PET imaging. The multivariable logistic regression analysis showed that amyloid PET status was associated with Aβ40 and Aβ42, but other factors, such as MMSE, sex, t-Tau, and p-Tau181, did not significantly add information to the model. CONCLUSIONS CSF biomarkers measured with the LUMIPULSE system showed good agreement with amyloid PET imaging. The ratio of Aβ42 with the other analyzed biomarkers showed a higher correlation with amyloid PET than Aβ42 alone, suggesting that the combinations of biomarkers could be useful in the diagnostic assessment in clinical research and potentially in routine clinical practice.
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Affiliation(s)
- Hisashi Nojima
- FUJIREBIO Inc.2‐1‐1, Nishishinjuku, Shinjuku‐kuTokyo163‐0410Japan
| | - Satoshi Ito
- Eisai Co., Ltd. 4‐6‐10 KoishikawaBunkyo‐kuTokyo112‐8088Japan,Eisai Inc.200 Metro BoulevardNutleyNew Jersey07110USA
| | - Akira Kushida
- FUJIREBIO Inc.2‐1‐1, Nishishinjuku, Shinjuku‐kuTokyo163‐0410Japan
| | - Aki Abe
- FUJIREBIO Inc.2‐1‐1, Nishishinjuku, Shinjuku‐kuTokyo163‐0410Japan
| | - Wataru Motsuchi
- FUJIREBIO Inc.2‐1‐1, Nishishinjuku, Shinjuku‐kuTokyo163‐0410Japan
| | - David Verbel
- Eisai Inc.200 Metro BoulevardNutleyNew Jersey07110USA
| | - Manu Vandijck
- Fujirebio‐Europe N.V.Technologiepark 69052GhentBelgium
| | - Geert Jannes
- Fujirebio‐Europe N.V.Technologiepark 69052GhentBelgium
| | | | - Katsumi Aoyagi
- FUJIREBIO Inc.2‐1‐1, Nishishinjuku, Shinjuku‐kuTokyo163‐0410Japan
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18
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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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Buckley RF, O'Donnell A, McGrath ER, Jacobs HI, Lois C, Satizabal CL, Ghosh S, Rubinstein ZB, Murabito JM, Sperling RA, Johnson KA, Seshadri S, Beiser AS. Menopause Status Moderates Sex Differences in Tau Burden: A Framingham PET Study. Ann Neurol 2022; 92:11-22. [PMID: 35471588 PMCID: PMC9233144 DOI: 10.1002/ana.26382] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Women have a higher lifetime risk of Alzheimer's disease (AD) than men. Among cognitively normal (CN) older adults, women exhibit elevated tau positron emission tomography (PET) signal compared with men. We explored whether menopause exacerbates sex differences in tau deposition in middle-aged adults. METHODS 328 CN participants from the Framingham Study (mean age = 57 years (±10 years), 161 women, of whom, 104 were post-menopausal) underwent tau and β-amyloid (Aβ)-PET neuroimaging. We examined global Aβ-PET, and tau-PET signal in 5 regions identified a priori as demonstrating significant sex differences in older adults (in temporal, inferior parietal, middle frontal, and lateral occipital regions). We examined sex and menopause status-related differences in each region-of-interest, using linear regressions, as well as interactions with Aβ and APOEε4 genotype. RESULTS Women exhibited higher tau-PET signal (p < 0.002), and global Aβ-PET (p = 0.010), than men in inferior parietal, rostral middle frontal, and lateral occipital regions. Compared with age-matched men, post-menopausal women showed significantly higher tau-PET signal in parieto-occipital regions (p < 0.0001). By contrast, no differences in tau-PET signal existed between pre-menopausal women and men. Aβ-PET was not associated with menopausal status or age. Neither Aβ-PET nor APOEε4 status moderated sex or menopause associations with tau-PET. INTERPRETATION Clear divergence in tauopathy between the sexes are apparent approximately 20 years earlier than previously reported. Menopause status moderated sex differences in Aβ and tau-PET burden, with tau first appearing post-menopause. Sex and menopause differences consistently appeared in middle frontal and parieto-occipital regions but were not moderated by Aβ burden or APOEε4, suggesting that menopause-related tau vulnerability may be independent of AD-related pathways. ANN NEUROL 2022;92:11-22.
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Affiliation(s)
- Rachel F. Buckley
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
- Center for Alzheimer Research and Treatment, Department of NeurologyBrigham and Women's HospitalBostonMAUSA
- Melbourne School of Psychological Science and Florey InstitutesUniversity of MelbourneParkvilleVICAustralia
| | - Adrienne O'Donnell
- Department of BiostatisticsBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyFraminghamMAUSA
| | - Emer R. McGrath
- Framingham Heart StudyFraminghamMAUSA
- HRB Clinical Research FacilityNational University of Ireland GalwayGalwayIreland
| | - Heidi I.L. Jacobs
- Gordon Center for Medical Imaging, Department of RadiologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre LimburgMaastricht UniversityMaastrichtThe Netherlands
| | - Cristina Lois
- Gordon Center for Medical Imaging, Department of RadiologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Claudia L. Satizabal
- Framingham Heart StudyFraminghamMAUSA
- Glen Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTXUSA
- Department of NeurologyBoston University School of MedicineBostonMAUSA
| | | | - Zoe B. Rubinstein
- Gordon Center for Medical Imaging, Department of RadiologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | | | - Reisa A. Sperling
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
- Center for Alzheimer Research and Treatment, Department of NeurologyBrigham and Women's HospitalBostonMAUSA
| | - Keith A. Johnson
- Center for Alzheimer Research and Treatment, Department of NeurologyBrigham and Women's HospitalBostonMAUSA
- Gordon Center for Medical Imaging, Department of RadiologyMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Sudha Seshadri
- Framingham Heart StudyFraminghamMAUSA
- Glen Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTXUSA
- Department of NeurologyBoston University School of MedicineBostonMAUSA
| | - Alexandra S. Beiser
- Department of BiostatisticsBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyFraminghamMAUSA
- Department of NeurologyBoston University School of MedicineBostonMAUSA
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21
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Peira E, Poggiali D, Pardini M, Barthel H, Sabri O, Morbelli S, Cagnin A, Chincarini A, Cecchin D. A comparison of advanced semi-quantitative amyloid PET analysis methods. Eur J Nucl Med Mol Imaging 2022; 49:4097-4108. [PMID: 35652962 PMCID: PMC9525368 DOI: 10.1007/s00259-022-05846-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/18/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE To date, there is no consensus on how to semi-quantitatively assess brain amyloid PET. Some approaches use late acquisition alone (e.g., ELBA, based on radiomic features), others integrate the early scan (e.g., TDr, which targets the area of maximum perfusion) and structural imaging (e.g., WMR, that compares kinetic behaviour of white and grey matter, or SI based on the kinetic characteristics of the grey matter alone). In this study SUVr, ELBA, TDr, WMR, and SI were compared. The latter - the most complete one - provided the reference measure for amyloid burden allowing to assess the efficacy and feasibility in clinical setting of the other approaches. METHODS We used data from 85 patients (aged 44-87) who underwent dual time-point PET/MRI acquisitions. The correlations with SI were computed and the methods compared with the visual assessment. Assuming SUVr, ELBA, TDr, and WMR to be independent measures, we linearly combined them to obtain more robust indices. Finally, we investigated possible associations between each quantifier and age in amyloid-negative patients. RESULTS Each quantifier exhibited excellent agreement with visual assessment and strong correlation with SI (average AUC = 0.99, ρ = 0.91). Exceptions to this were observed for subcortical regions with ELBA and WMR (ρELBA = 0.44, ρWMR = 0.70). The linear combinations showed better performances than the individual methods. Significant associations were observed between TDr, WMR, SI, and age in amyloid-negative patients (p < 0.05). CONCLUSION Among the other methods, TDr came closest to the reference with less implementation complexity. Moreover, this study suggests that combining independent approaches gives better results than the individual procedure, so efforts should focus on multi-classifier systems for amyloid PET. Finally, the ability of techniques integrating blood perfusion to depict age-related variations in amyloid load in amyloid-negative subjects demonstrates the goodness of the estimate.
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Affiliation(s)
- Enrico Peira
- INFN - National Institute of Nuclear Physics, via Dodecaneso 33, 16146, Genoa, Italy.
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.
| | - Davide Poggiali
- PNC - Padua Neuroscience Center, University of Padua, Padua, Italy
| | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Nuclear Medicine Unit, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Annachiara Cagnin
- Neurology Unit, Department of Neurology, University Hospital of Padua, Padua, Italy
| | - Andrea Chincarini
- INFN - National Institute of Nuclear Physics, via Dodecaneso 33, 16146, Genoa, Italy
| | - Diego Cecchin
- PNC - Padua Neuroscience Center, University of Padua, Padua, Italy
- Nuclear Medicine Unit, Department of Medicine - DIMED, University Hospital of Padua, Padua, Italy
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22
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García Vicente A, Tello Galán M, Pena Pardo F, Amo-Salas M, Mondejar Marín B, Navarro Muñoz S, Rueda Medina I, Poblete García V, Marsal Alonso C, Soriano Castrejón Á. Aumento de la confianza en la interpretación del PET con 18F-Florbetaben: “machine learning” basado en la aproximación cuantitativa. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2021.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Teipel SJ, Dyrba M, Vergallo A, Lista S, Habert MO, Potier MC, Lamari F, Dubois B, Hampel H, Grothe MJ. Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis. Front Aging Neurosci 2022; 13:748198. [PMID: 35002673 PMCID: PMC8729321 DOI: 10.3389/fnagi.2021.748198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/05/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis. Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study). Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data. Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.
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Affiliation(s)
- Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Andrea Vergallo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Simone Lista
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France.,Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'Hôpital, Paris, France.,Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Marie Odile Habert
- Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, LIB, Sorbonne University, Paris, France.,Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.,Centre d'Acquisition et Traitement des Images (CATI platform), Paris, France
| | - Marie-Claude Potier
- ICM Institut du Cerveau et de la Moelle Épinière, CNRS UMR 7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France
| | - Foudil Lamari
- UF Biochimie des Maladies Neurométaboliques, Service de Biochimie Métabolique, Hôpital Pitié-Salpêtrière, Paris, France
| | - Bruno Dubois
- Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Harald Hampel
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
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Kim YK. Recent Updates on PET Imaging in Neurodegenerative Diseases. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:453-472. [PMID: 36238518 PMCID: PMC9514517 DOI: 10.3348/jksr.2022.0052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
양전자방출단층촬영(PET)을 이용한 단백질병리의 생체영상기술은 퇴행성 치매의 질병 기전을 이해하는데 필요한 정보를 제공할 뿐 아니라, 질병의 조기 발견과 치료법 개발에서 중요한 역할을 수행하고 있다. 베타아밀로이드와 타우 PET 영상은 인체 뇌병리에 기반한 알츠하이머병 연속체에 대한 진단 바이오마커로 확립되어 조기진단과 감별진단을 용이하게 하고, 질병 예후를 예측하고 있다. 또한, 치매치료제 개발에서 예후 및 대리 바이오마커로의 역할이 커지고 있다. 이 종설에서는 치매를 유발하는 알츠하이머병 및 기타 퇴행성 뇌질환에서 베타아밀로이드와 타우 단백질의 뇌축적을 영상화하는 PET의 최근 임상적 적용과 최근 동향을 살펴보고, 잠재적 유용성을 소개하고자 한다.
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Affiliation(s)
- Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
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25
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Weber CJ, Carrillo MC, Jagust W, Jack CR, Shaw LM, Trojanowski JQ, Saykin AJ, Beckett LA, Sur C, Rao NP, Mendez PC, Black SE, Li K, Iwatsubo T, Chang C, Sosa AL, Rowe CC, Perrin RJ, Morris JC, Healan AM, Hall SE, Weiner MW. The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI-3 updates and global perspectives. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12226. [PMID: 35005206 PMCID: PMC8719344 DOI: 10.1002/trc2.12226] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/05/2021] [Indexed: 11/06/2022]
Abstract
The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public-private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI-1, ADNI-GO, ADNI-2, and ADNI-3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW-ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW-ADNI researchers who shared updates from ADNI-3 and their vision for ADNI-4.
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Affiliation(s)
| | | | - William Jagust
- School of Public Health and Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | | | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory MedicinePerelman School of MedicineInstitute on AgingPerelman School of MedicineAlzheimer's Disease Core Center, Perelman School of MedicineUdall Parkinson's Research CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterDepartment of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Cyrille Sur
- Merck Research LaboratoriesMerckKenilworthNew JerseyUSA
| | - Naren P. Rao
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBengaluruKarnatakaIndia
| | | | - Sandra E. Black
- Department of Medicine (Neurology)Hurvitz Brain Sciences ProgramCanadian Partnership for Stroke Recovery, and LC Campbell Cognitive Neurology Research UnitHurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreUniversity of TorontoTorontoCanada
| | - Kuncheng Li
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Takeshi Iwatsubo
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Chiung‐Chih Chang
- Department of General Neurology and Institute for Translational Research in BiomedicineKaohsiung Chang Gung Memorial HospitalChang Gung University College of MedicineKaohsiungTaiwan
| | - Ana Luisa Sosa
- National Institute of Neurology and Neurosurgery of MexicoMexico CityMexico
| | - Christopher C. Rowe
- Department of Molecular Imaging and TherapyAustin Health and Florey Department of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Richard J. Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of Pathology and ImmunologyDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | - John C. Morris
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesDepartment of RadiologyDepartment of MedicineDepartment of PsychiatryDepartment of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
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26
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Levin F, Jelistratova I, Betthauser TJ, Okonkwo O, Johnson SC, Teipel SJ, Grothe MJ. In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer's disease. Alzheimers Res Ther 2021; 13:178. [PMID: 34674764 PMCID: PMC8532333 DOI: 10.1186/s13195-021-00918-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/11/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND We investigated regional amyloid staging characteristics in 11C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer's Prevention. METHODS We analyzed partial volume effect-corrected 11C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.9-76.8 years). Regional amyloid positivity was established using region-specific thresholds. We used four stages from the frequency-based staging of amyloid positivity to characterize individual amyloid deposition. Longitudinal PET data was used to assess the temporal progression of stages and to evaluate the emergence of regional amyloid positivity in participants who were amyloid-negative at baseline. We also assessed the effect of amyloid stage on longitudinal cognitive trajectories. RESULTS The staging model suggested progressive accumulation of amyloid from associative to primary neocortex and gradually involving subcortical regions. Longitudinal PET measurements supported the cross-sectionally estimated amyloid progression. In mixed-effects longitudinal analysis of cognitive follow-up data obtained over an average period of 6.5 years following the baseline PET measurement, amyloid stage II showed a faster decline in executive function, and advanced amyloid stages (III and IV) showed a faster decline across multiple cognitive domains compared to stage 0. CONCLUSIONS Overall, the 11C-PiB-PET-based staging model was generally consistent with previously derived models from 18F-labeled amyloid PET scans and a longitudinal course of amyloid accumulation. Differences in longitudinal cognitive decline support the potential clinical utility of in vivo amyloid staging for risk stratification of the preclinical phase of AD even in middle-aged to older individuals at risk for AD.
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Affiliation(s)
- Fedor Levin
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Irina Jelistratova
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Tobey J Betthauser
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Ozioma Okonkwo
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, s/n, 41013, Seville, Spain.
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Peira E, Grazzini M, Bauckneht M, Sensi F, Bosco P, Arnaldi D, Morbelli S, Chincarini A, Pardini M, Nobili F. Probing the Role of a Regional Quantitative Assessment of Amyloid PET. J Alzheimers Dis 2021; 80:383-396. [PMID: 33554908 DOI: 10.3233/jad-201156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND In clinical practice, the amy-PET is globally inspected to provide a binary outcome, but the role of a regional assessment has not been fully investigated yet. OBJECTIVE To deepen the role of regional amyloid burden and its implication on clinical-neuropsychological features. MATERIALS Amy-PET and a complete neuropsychological assessment (Trail Making Test, Rey Auditory Verbal Learning Test, semantic verbal fluency, Symbol Digit, Stroop, visuoconstruction) were available in 109 patients with clinical suspicion of Alzheimer's disease. By averaging the standardized uptake value ratio and ELBA, a regional quantification was calculated for each scan. Patients were grouped according to their overall amyloid load: correlation maps, based on regional quantification, were calculated and compared. A regression analysis between neuropsychological assessment and the regional amyloid-β (Aβ) load was carried out. RESULTS Significant differences were observed between the correlation maps of patients at increasing levels of Aβ and the overall dataset. The Aβ uptake of the subcortical gray matter resulted not related to other brain regions independently of the global Aβ level. A significant association of semantic verbal fluency was observed with ratios of cortical and subcortical distribution of Aβ which represent a coarse measure of differences in regional distribution of Aβ. CONCLUSION Our observations confirmed the different susceptibility to Aβ accumulation among brain regions. The association between cognition and Aβ distribution deserves further investigations: it is possibly due to a direct local effect or it represents a proxy marker of a more aggressive disease subtype. Regional Aβ assessment represents an available resource on amy-PET scan with possibly clinical and prognostic implications.
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Affiliation(s)
- Enrico Peira
- INFN, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Grazzini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Bauckneht
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | | | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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28
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Schwarz C, Lange C, Benson GS, Horn N, Wurdack K, Lukas M, Buchert R, Wirth M, Flöel A. Severity of Subjective Cognitive Complaints and Worries in Older Adults Are Associated With Cerebral Amyloid-β Load. Front Aging Neurosci 2021; 13:675583. [PMID: 34408640 PMCID: PMC8365025 DOI: 10.3389/fnagi.2021.675583] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/22/2021] [Indexed: 01/19/2023] Open
Abstract
Subjective cognitive decline (SCD) is considered an early risk stage for dementia due to Alzheimer's disease (AD) and the development of pathological brain changes, such as the aggregation of amyloid-beta (amyloid-β) plaques. This study evaluates the association between specific features of SCD and cerebral amyloid-β load measured by positron emission tomography (PET) with 18F-florbetaben in 40 cognitively normal older individuals. Global amyloid-β, as well as regional amyloid-β load for the frontal, temporal, parietal, and cingulate cortex, was quantified. Specific features of SCD, such as subjective cognitive complaints and worry, were assessed using the 39-item Everyday Cognition Scales and the 16-item Penn State Worry Questionnaire. Spearman's rank partial correlation analyses, adjusted for age and apolipoprotein E ε4 status, were conducted to test the associations between specific features of SCD and cerebral amyloid-β load. The severity of subjective cognitive complaints in everyday memory and organization was positively correlated with amyloid-β load in the frontal cortex. In addition, the severity of subjective cognitive complaints in everyday planning was positively correlated with amyloid-β load in the parietal cortex. Higher levels of worry were associated with higher amyloid-β load in the frontal cortex. After correction of the PET data for partial volume effects, these associations were reduced to trend level. In conclusion, the severity of subjective cognitive complaints and the level of trait worry were positively associated with cortical amyloid-β burden, particularly in the frontal and parietal cortex. Further studies are required to elucidate the direction of these associations in order to develop strategies to prevent amyloid deposition and cognitive decline.
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Affiliation(s)
- Claudia Schwarz
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gloria S Benson
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nora Horn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Katharina Wurdack
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mathias Lukas
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Siemens Healthcare GmbH, Berlin, Germany
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases (DZNE) Site: Dresden, Dresden, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases (DZNE) Site: Greifswald, Greifswald, Germany
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29
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The Evaluation of Tau Deposition with [ 18F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer's Disease. Mol Imaging 2021; 2021:6640054. [PMID: 34381315 PMCID: PMC8328488 DOI: 10.1155/2021/6640054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/18/2021] [Accepted: 06/22/2021] [Indexed: 11/18/2022] Open
Abstract
Background Some studies have reported the effectiveness of [18F]PI-2620 as an effective tau-binding radiotracer; however, few reports have applied semiquantitative analysis to the tracer. Therefore, this study's aim was to perform a semiquantitative analysis of [18F]PI-2620 in individuals with normal cognition and patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Methods Twenty-six cognitively normal (CN) subjects, 7 patients with AD, and 36 patients with MCI were enrolled. A dynamic positron emission tomography (PET) scan was performed 30–75 min postinjection. PET and T1-weighted magnetic resonance imaging scans were coregistered. The standardized uptake value ratio (SUVr) was used for semiquantitative analysis. The P-Mod software was applied to create volumes of interest. The ANOVA and post hoc Tukey HSD were used for statistical analysis. Results In the AD group, the occipital lobe had a significantly higher mean SUVr (1.46 ± 0.57) than in the CN and MCI groups. Compared with the CN group, the AD group showed significantly higher mean SUVr in the fusiform gyrus (1.06 ± 0.09 vs. 1.49 ± 0.86), inferior temporal (1.07 ± 0.07 vs. 1.46 ± 0.08), parietal lobe, lingual gyrus, and precuneus regions. Similarly, the AD group demonstrated a higher mean SUVr than the MCI group in the precuneus, lingual, inferior temporal, fusiform, supramarginal, orbitofrontal, and superior temporal regions. The remaining observed regions, including the striatum, basal ganglia, thalamus, and white matter, showed a low SUVr across all groups with no statistically significant differences. Conclusion A significantly higher mean SUVr of [18F]PI-2620 was observed in the AD group; a significant area of the brain in the AD group demonstrated tau protein deposit in concordance with Braak Stages III–V, providing useful information to differentiate AD from CN and MCI. Moreover, the low SUVr in the deep striatum and thalamus could be useful for excluding primary tauopathies.
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30
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Royse SK, Minhas DS, Lopresti BJ, Murphy A, Ward T, Koeppe RA, Bullich S, DeSanti S, Jagust WJ, Landau SM. Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach. ALZHEIMERS RESEARCH & THERAPY 2021; 13:99. [PMID: 33971965 PMCID: PMC8111744 DOI: 10.1186/s13195-021-00836-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 11/16/2022]
Abstract
Background Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one β-amyloid (Aβ) radiotracer in positron emission tomography (PET). Variability related to these factors contributes to disagreement and lack of replicability in research and clinical trials. To address these problems and promote Aβ PET harmonization, we used [18F]florbetaben (FBB) and [18F]florbetapir (FBP) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to derive (1) standardized Centiloid (CL) transformations and (2) internally consistent positivity thresholds based on separate young control samples. Methods We analyzed Aβ PET data using a native-space, automated image processing pipeline that is used for PET quantification in many large, multisite AD studies and trials and made available to the research community. With this pipeline, we derived SUVR-to-CL transformations using the Global Alzheimer’s Association Interactive Network data; we used reference regions for cross-sectional (whole cerebellum) and longitudinal (subcortical white matter, brain stem, whole cerebellum) analyses. Finally, we developed a FBB positivity threshold using an independent young control sample (N=62) with methods parallel to our existing FBP positivity threshold and validated the FBB threshold using a data-driven approach in ADNI participants (N=295). Results The FBB threshold based on the young sample (1.08; 18 CL) was consistent with that of the data-driven approach (1.10; 21 CL), and the existing FBP threshold converted to CL with the derived transformation (1.11; 20 CL). The following equations can be used to convert whole cerebellum- (cross-sectional) and composite- (longitudinal) normalized FBB and FBP data quantified with the native-space pipeline to CL units: [18F]FBB: CLwhole cerebellum = 157.15 × SUVRFBB − 151.87; threshold=1.08, 18 CL [18F]FBP: CLwhole cerebellum = 188.22 × SUVRFBP − 189.16; threshold=1.11, 20 CL [18F]FBB: CLcomposite = 244.20 × SUVRFBB − 170.80 [18F]FBP: CLcomposite = 300.66 × SUVRFBP − 208.84 Conclusions FBB and FBP positivity thresholds derived from independent young control samples and quantified using an automated, native-space approach result in similar CL values. These findings are applicable to thousands of available and anticipated outcomes analyzed using this pipeline and shared with the scientific community. This work demonstrates the feasibility of harmonized PET acquisition and analysis in multisite PET studies and internal consistency of positivity thresholds in standardized units. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00836-1.
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Affiliation(s)
- Sarah K Royse
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Davneet S Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alice Murphy
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Tyler Ward
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Robert A Koeppe
- Division of Nuclear Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
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31
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Cho SH, Choe YS, Kim YJ, Kim HJ, Jang H, Kim Y, Kim SE, Kim SJ, Kim JP, Jung YH, Kim BC, Lockhart SN, Farrar G, Na DL, Moon SH, Seo SW. Head-to-Head Comparison of 18F-Florbetaben and 18F-Flutemetamol in the Cortical and Striatal Regions. J Alzheimers Dis 2021; 76:281-290. [PMID: 32474468 DOI: 10.3233/jad-200079] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) amyloid PET have been developed and approved for clinical use. It is important to understand the distinct features of these ligands to compare and correctly interpret the results of different amyloid PET studies. OBJECTIVE We performed a head-to-head comparison of FBB and FMM to compare with regard to imaging characteristics, including dynamic range of retention, and differences in quantitative measurements between the two ligands in cortical, striatal, and white matter (WM) regions. METHODS Paired FBB and FMM PET images were acquired in 107 participants. Correlations of FBB and FMM amyloid deposition in the cortex, striatum, and WM were investigated and compared in different reference regions (cerebellar gray matter (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons). RESULTS The cortical SUVR (R2 = 0.97) and striatal SUVR (R2 = 0.95) demonstrated an excellent linear correlation between FBB and FMM using a WC as reference region. There was no difference in the cortical SUVR ratio between the two ligands (p = 0.90), but the striatal SUVR ratio was higher in FMM than in FBB (p < 0.001). Also, the effect size of differences in striatal SUVR seemed to be higher with FMM (2.61) than with FBB (2.34). These trends were similarly observed according to four different reference regions (CG, WC, WC + B, and pons). CONCLUSION Our findings suggest that FMM might be better than FBB to detect amyloid burden in the striatum, although both ligands are comparable for imaging AD pathology in vivo.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Si Eun Kim
- Departments of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Samuel N Lockhart
- Internal Medicine - Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea.,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
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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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33
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García Vicente AM, Tello Galán MJ, Pena Pardo FJ, Amo-Salas M, Mondejar Marín B, Navarro Muñoz S, Rueda Medina I, Poblete García VM, Marsal Alonso C, Soriano Castrejón Á. Increasing the confidence of 18F-Florbetaben PET interpretations: Machine learning quantitative approximation. Rev Esp Med Nucl Imagen Mol 2021; 41:153-163. [DOI: 10.1016/j.remnie.2021.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 01/27/2021] [Indexed: 11/28/2022]
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34
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Villemagne VL, Lopresti BJ, Doré V, Tudorascu D, Ikonomovic MD, Burnham S, Minhas D, Pascoal TA, Mason NS, Snitz B, Aizenstein H, Mathis CA, Lopez O, Rowe CC, Klunk WE, Cohen AD. What Is T+? A Gordian Knot of Tracers, Thresholds, and Topographies. J Nucl Med 2020; 62:614-619. [PMID: 33384320 DOI: 10.2967/jnumed.120.245423] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/16/2020] [Indexed: 11/16/2022] Open
Abstract
In this review we examine, in the context of the amyloid, tau, and neurodegeneration framework, the available evidence and potential alternatives on how to establish tau positivity (T+) for multiple tau-imaging tracers in order to reach a consensus on normal and abnormal tau imaging values that can be universally implemented in clinical research and therapeutic trials.
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Affiliation(s)
- Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania .,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,School of Medical and Health Sciences, Edith Cowan University, Perth, Washington, Australia
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Vincent Doré
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia.,CSIRO Health and Biosecurity, Melbourne, Victoria, Australia
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Milos D Ikonomovic
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; and
| | - Samantha Burnham
- CSIRO Health and Biosecurity, Melbourne, Victoria, Australia.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital and Massachusetts General Hospital, Boston, Massachusetts
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Beth Snitz
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Oscar Lopez
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Christopher C Rowe
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; and
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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35
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Leuzy A, Lilja J, Buckley CJ, Ossenkoppele R, Palmqvist S, Battle M, Farrar G, Thal DR, Janelidze S, Stomrud E, Strandberg O, Smith R, Hansson O. Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load. Neurology 2020; 95:e2834-e2844. [PMID: 33077542 PMCID: PMC7734735 DOI: 10.1212/wnl.0000000000011031] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 08/03/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate a novel β-amyloid (Aβ)-PET-based quantitative measure (Aβ accumulation index [Aβ index]), including the assessment of its ability to discriminate between participants based on Aβ status using visual read, CSF Aβ42/Aβ40, and post-mortem neuritic plaque burden as standards of truth. METHODS One thousand one hundred twenty-one participants (with and without cognitive impairment) were scanned with Aβ-PET: Swedish BioFINDER, n = 392, [18F]flutemetamol; Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 692, [18F]florbetapir; and a phase 3 end-of-life study, n = 100, [18F]flutemetamol. The relationships between Aβ index and standardized uptake values ratios (SUVR) from Aβ-PET were assessed. The diagnostic performances of Aβ index and SUVR were compared with visual reads, CSF Aβ42/Aβ40, and Aβ histopathology used as reference standards. RESULTS Strong associations were observed between Aβ index and SUVR (R 2: BioFINDER 0.951, ADNI 0.943, end-of-life, 0.916). Both measures performed equally well in differentiating Aβ-positive from Aβ-negative participants, with areas under the curve (AUCs) of 0.979 to 0.991 to detect abnormal visual reads, AUCs of 0.961 to 0.966 to detect abnormal CSF Aβ42/Aβ40, and AUCs of 0.820 to 0.823 to detect abnormal Aβ histopathology. Both measures also showed a similar distribution across postmortem-based Aβ phases (based on anti-Aβ 4G8 antibodies). Compared to models using visual read alone, the addition of the Aβ index resulted in a significant increase in AUC and a decrease in Akaike information criterion to detect abnormal Aβ histopathology. CONCLUSION The proposed Aβ index showed a tight association to SUVR and carries an advantage over the latter in that it does not require the definition of regions of interest or the use of MRI. Aβ index may thus prove simpler to implement in clinical settings and may also facilitate the comparison of findings using different Aβ-PET tracers. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that the Aβ accumulation index accurately differentiates Aβ-positive from Aβ-negative participants compared to Aβ-PET visual reads, CSF Aβ42/Aβ40, and Aβ histopathology.
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Affiliation(s)
- Antoine Leuzy
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium.
| | - Johan Lilja
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Christopher J Buckley
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Rik Ossenkoppele
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Sebastian Palmqvist
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Mark Battle
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Gill Farrar
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Dietmar R Thal
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Shorena Janelidze
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Erik Stomrud
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Olof Strandberg
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Ruben Smith
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Oskar Hansson
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
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Haller S, Montandon ML, Lilja J, Rodriguez C, Garibotto V, Herrmann FR, Giannakopoulos P. PET amyloid in normal aging: direct comparison of visual and automatic processing methods. Sci Rep 2020; 10:16665. [PMID: 33028945 PMCID: PMC7542434 DOI: 10.1038/s41598-020-73673-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/21/2020] [Indexed: 12/20/2022] Open
Abstract
Assessment of amyloid deposits is a critical step for the identification of Alzheimer disease (AD) signature in asymptomatic elders. Whether the different amyloid processing methods impacts on the quality of clinico-radiological correlations is still unclear. We directly compared in 155 elderly controls with extensive neuropsychological testing at baseline and 4.5 years follow-up three approaches: (i) operator-dependent standard visual reading, (ii) operator-independent automatic SUVR with four different reference regions, and (iii) novel operator and region of reference-independent automatic Aβ-index. The coefficient of variance was used to examine inter-individual variability for each processing method. Using visually-established amyloid positivity as the gold standard, the area under the receiver operating characteristic curve (ROC) was computed. Linear regression models were used to assess the association between changes in continuous cognitive score and amyloid uptake values. In SUVR analyses, the coefficient of variance varied from 1.718 to 1.762 according to the area of reference and was of − 3.045 for the Aβ-index method. Compared to the visual rating, Aβ-index method showed the largest area under the ROC curve [0.9568 (95% CI 0.9252, 0.98833)]. The best cut-off score was of − 0.3359 with sensitivity and specificity values of 0.97 and 0.83, respectively. Only the Aß-index was related to more severe decrement of cognitive performances [regression coefficient: 9.103 (95% CI 1.148, 17.058)]. The Aβ-index is considered as preferred option in asymptomatic elders, since it is operator-independent, avoids the selection of reference area, is closer to established visual scoring and correlates with the evolution of cognitive performances.
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Affiliation(s)
- Sven Haller
- CIRD Centre d'imagerie Rive Droite, Geneva, Switzerland. .,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden. .,Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Johan Lilja
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Hermes Medical Solutions, Stockholm, Sweden
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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37
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Toledo JB, Habes M, Sotiras A, Bjerke M, Fan Y, Weiner MW, Shaw LM, Davatzikos C, Trojanowski JQ. APOE Effect on Amyloid-β PET Spatial Distribution, Deposition Rate, and Cut-Points. J Alzheimers Dis 2020; 69:783-793. [PMID: 31127775 DOI: 10.3233/jad-181282] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
There are conflicting results regarding how APOE genotype, the strongest genetic risk factor for Alzheimer's disease (AD), influences spatial and longitudinal amyloid-β (Aβ) deposition and its impact on the selection of biomarker cut-points. In our study, we sought to determine the impact of APOE genotype on cross-sectional and longitudinal florbetapir positron emission tomography (PET) amyloid measures and its impact in classification of patients and interpretation of clinical cohort results. We included 1,019 and 1,072 Alzheimer's Disease Neuroimaging Initiative participants with cerebrospinal fluid Aβ1 - 42 and florbetapir PET values, respectively. 623 of these subjects had a second florbetapir PET scans two years after the baseline visit. We evaluated the effect of APOE genotype on Aβ distribution pattern, pathological biomarker cut-points, cross-sectional clinical associations with Aβ load, and longitudinal Aβ deposition rate measured using florbetapir PET scans. 1) APOEɛ4 genotype influences brain amyloid deposition pattern; 2) APOEɛ4 genotype does not modify Aβ biomarker cut-points estimated using unsupervised mixture modeling methods if white matter and brainstem references are used (but not when cerebellum is used as a reference); 3) findings of large differences in Aβ biomarker value differences based on APOE genotype are due to increased probability of having AD neuropathology and are most significant in mild cognitive impairment subjects; and 4) APOE genotype and age (but not gender) were associated with increased Aβ deposition rate. APOEɛ4 carrier status affects rate and location of brain Aβ deposition but does not affect choice of biomarker cut-points if adequate references are selected for florbetapir PET processing.
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Affiliation(s)
- Jon B Toledo
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurology, Houston Methodist Hospital, Houston, TX, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Maria Bjerke
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael W Weiner
- Department of Radiology, Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco, San Francisco, CA, USA
| | - Leslie M Shaw
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Collij LE, Heeman F, Salvadó G, Ingala S, Altomare D, de Wilde A, Konijnenberg E, van Buchem M, Yaqub M, Markiewicz P, Golla SSV, Wottschel V, Wink AM, Visser PJ, Teunissen CE, Lammertsma AA, Scheltens P, van der Flier WM, Boellaard R, van Berckel BNM, Molinuevo JL, Gispert JD, Schmidt ME, Barkhof F, Lopes Alves I. Multitracer model for staging cortical amyloid deposition using PET imaging. Neurology 2020; 95:e1538-e1553. [PMID: 32675080 PMCID: PMC7713745 DOI: 10.1212/wnl.0000000000010256] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 03/20/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]stage1 2.00, HRstage2 3.53, HRstage3 4.55, HRstage4 9.91, p < 0.001; OASIS: n = 469, HRstage4 4.80, p < 0.001). CONCLUSION The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.
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Affiliation(s)
- Lyduine E Collij
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Fiona Heeman
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Gemma Salvadó
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Silvia Ingala
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Daniele Altomare
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Arno de Wilde
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Elles Konijnenberg
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Marieke van Buchem
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Maqsood Yaqub
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pawel Markiewicz
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Sandeep S V Golla
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Viktor Wottschel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Alle Meije Wink
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pieter Jelle Visser
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Charlotte E Teunissen
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Adriaan A Lammertsma
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Philip Scheltens
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Wiesje M van der Flier
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Ronald Boellaard
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Bart N M van Berckel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - José Luis Molinuevo
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Juan Domingo Gispert
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Mark E Schmidt
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Frederik Barkhof
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Isadora Lopes Alves
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium.
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Cho SH, Choe YS, Park S, Kim YJ, Kim HJ, Jang H, Kim SJ, Kim JP, Jung YH, Kim BC, Na DL, Moon SH, Seo SW. Appropriate reference region selection of 18F-florbetaben and 18F-flutemetamol beta-amyloid PET expressed in Centiloid. Sci Rep 2020; 10:14950. [PMID: 32917930 PMCID: PMC7486392 DOI: 10.1038/s41598-020-70978-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/27/2020] [Indexed: 12/01/2022] Open
Abstract
The Centiloid (CL) is a method for standardizing amyloid beta (Aβ) quantification through different ligands and methods. To find the most appropriate reference region to reduce the variance in the Aβ CL unit between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM), we conducted head-to-head comparisons from 56 participants using the direct comparison of FBB-FMM CL (dcCL) method with four reference regions: cerebellar gray (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons. The FBB and FMM dcCL units were highly correlated in four reference regions: WC (R2 = 0.97), WC + B (R2 = 0.98), CG (R2 = 0.92), and pons (R2 = 0.98). WC showed the largest effect size in both FBB and FMM. Comparison of the variance of the dcCL values within the young control group showed that with FBB, WC + B had the smallest variance and with FMM, the WC had the smallest variance. Additionally, WC + B showed the smallest absolute difference between FBB and FMM, followed by the WC, pons, and CG. We found that it would be reasonable to use the WC or WC + B as the reference region when converting FBB and FMM SUVRs into dcCL, which can increase the accuracy of standardizing FBB and FMM PET results.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. .,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea. .,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea.
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Grey zone amyloid burden affects memory function: the SCIENCe project. Eur J Nucl Med Mol Imaging 2020; 48:747-756. [PMID: 32888039 PMCID: PMC8036199 DOI: 10.1007/s00259-020-05012-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022]
Abstract
Purpose To determine thresholds for amyloid beta pathology and evaluate associations with longitudinal memory performance with the aim to identify a grey zone of early amyloid beta accumulation and investigate its clinical relevance. Methods We included 162 cognitively normal participants with subjective cognitive decline from the SCIENCe cohort (64 ± 8 years, 38% F, MMSE 29 ± 1). Each underwent a dynamic [18F] florbetapir PET scan, a T1-weighted MRI scan and longitudinal memory assessments (RAVLT delayed recall, n = 655 examinations). PET scans were visually assessed as amyloid positive/negative. Additionally, we calculated the mean binding potential (BPND) and standardized uptake value ratio (SUVr50–70) for an a priori defined composite region of interest. We determined six amyloid positivity thresholds using various data-driven methods (resulting thresholds: BPND 0.19/0.23/0.29; SUVr 1.28/1.34/1.43). We used Cohen’s kappa to analyse concordance between thresholds and visual assessment. Next, we used quantiles to divide the sample into two to five subgroups of equal numbers (median, tertiles, quartiles, quintiles), and operationalized a grey zone as the range between the thresholds (0.19–0.29 BPND/1.28–1.43 SUVr). We used linear mixed models to determine associations between thresholds and memory slope. Results As determined by visual assessment, 24% of 162 individuals were amyloid positive. Concordance with visual assessment was comparable but slightly higher for BPND thresholds (range kappa 0.65–0.70 versus 0.60–0.63). All thresholds predicted memory decline (range beta − 0.29 to − 0.21, all p < 0.05). Analyses in subgroups showed memory slopes gradually became steeper with higher amyloid load (all p for trend < 0.05). Participants with a low amyloid burden benefited from a practice effect (i.e. increase in memory), whilst high amyloid burden was associated with memory decline. Memory slopes of individuals in the grey zone were intermediate. Conclusion We provide evidence that not only high but also grey zone amyloid burden subtly impacts memory function. Therefore, in case a binary classification is required, we suggest using a relatively low threshold which includes grey zone amyloid pathology.
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Giovacchini G, Giovannini E, Borsò E, Lazzeri P, Duce V, Ferrando O, Foppiano F, Ciarmiello A. Impact of Tracer Retention Levels on Visual Analysis of Cerebral [ 18F]- Florbetaben Pet Images. Curr Radiopharm 2020; 14:70-77. [PMID: 32727344 DOI: 10.2174/1874471013666200729155717] [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: 02/25/2019] [Revised: 06/15/2020] [Accepted: 06/19/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND To compare visual and semi-quantitative analysis of brain [18F]Florbetaben PET images in Mild Cognitive Impairment (MCI) patients and relate this finding to the degree of ß-amyloid burden. METHODS A sample of 71 amnestic MCI patients (age 74 ± 7.3 years, Mini Mental State Examination 24.2 ± 5.3) underwent cerebral [18F]Florbetaben PET/CT. Images were visually scored as positive or negative independently by three certified readers blinded to clinical and neuropsychological assessment. Amyloid positivity was also assessed by semiquantitative approach by means of a previously published threshold (SUVr ≥ 1.3). Fleiss kappa coefficient was used to compare visual analysis (after consensus among readers) and semi-quantitative analysis. Statistical significance was taken at P<0.05. RESULTS After the consensus reading, 43/71 (60.6%) patients were considered positive. Cases that were interpreted as visually positive had higher SUVr than visually negative patients (1.48 ± 0.19 vs 1.11 ± 0.09) (P<0.05). Agreement between visual analysis and semi-quantitative analysis was excellent (k=0.86, P<0.05). Disagreement occurred in 7/71 patients (9.9%) (6 false positives and 1 false negative). Agreement between the two analyses was 90.0% (18/20) for SUVr < 1.1, 83% (24/29) for SUVr between 1.1 and 1.5, and 100% (22/22) for SUVr > 1.5 indicating lowest agreement for the group with intermediate amyloid burden. CONCLUSION Inter-rater agreement of visual analysis of amyloid PET images is high. Agreement between visual analysis and SUVr semi-quantitative analysis decreases in the range of 1.1<SUVr <=1.5, where the clinical scenario is more challenging.
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Affiliation(s)
- Giampiero Giovacchini
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
| | - Elisabetta Giovannini
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
| | - Elisa Borsò
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
| | - Patrizia Lazzeri
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
| | - Valerio Duce
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
| | | | | | - Andrea Ciarmiello
- Nuclear Medicine Unit S. Andrea Hospital Via Vittorio Veneto, 197 19124 La Spezia, Italy
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Jelistratova I, Teipel SJ, Grothe MJ. Longitudinal validity of PET-based staging of regional amyloid deposition. Hum Brain Mapp 2020; 41:4219-4231. [PMID: 32648624 PMCID: PMC7502828 DOI: 10.1002/hbm.25121] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Abstract
Positron emission tomography (PET)-based staging of regional amyloid deposition has recently emerged as a promising tool for sensitive detection and stratification of pathology progression in Alzheimer's Disease (AD). Here we present an updated methodological framework for PET-based amyloid staging using region-specific amyloid-positivity thresholds and assess its longitudinal validity using serial PET acquisitions. We defined region-specific thresholds of amyloid-positivity based on Florbetapir-PET data of 13 young healthy individuals (age ≤ 45y), applied these thresholds to Florbetapir-PET data of 179 cognitively normal older individuals to estimate a regional amyloid staging model, and tested this model in a larger sample of patients with mild cognitive impairment (N = 403) and AD dementia (N = 85). 2-year follow-up Florbetapir-PET scans from a subset of this sample (N = 436) were used to assess the longitudinal validity of the cross-sectional model based on individual stage transitions and data-driven longitudinal trajectory modeling. Results show a remarkable congruence between cross-sectionally estimated and longitudinally modeled trajectories of amyloid accumulation, beginning in anterior temporal areas, followed by frontal and medial parietal areas, the remaining associative neocortex, and finally primary sensory-motor areas and subcortical regions. Over 98% of individual amyloid deposition profiles and longitudinal stage transitions adhered to this staging scheme of regional pathology progression, which was further supported by corresponding changes in cerebrospinal fluid biomarkers. In conclusion, we provide a methodological refinement and longitudinal validation of PET-based staging of regional amyloid accumulation, which may help improving early detection and in-vivo stratification of pathologic disease progression in AD.
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Affiliation(s)
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Department of Psychosomatic MedicineUniversity of RostockRostockGermany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de SevillaHospital Universitario Virgen del Rocío/CSIC/Universidad de SevillaSevilleSpain
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Mullane K, Williams M. Alzheimer’s disease beyond amyloid: Can the repetitive failures of amyloid-targeted therapeutics inform future approaches to dementia drug discovery? Biochem Pharmacol 2020; 177:113945. [DOI: 10.1016/j.bcp.2020.113945] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
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Chotipanich C, Jantarato A, Kunawudhi A, Kongthai S, Promteangtrong C. 11C-Pittsburgh compound B and 18F-THK 5351 positron emission tomography brain imaging in cognitively normal individuals. World J Nucl Med 2020; 20:133-138. [PMID: 34321964 PMCID: PMC8286010 DOI: 10.4103/wjnm.wjnm_57_20] [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: 04/21/2020] [Revised: 04/29/2020] [Accepted: 05/01/2020] [Indexed: 11/07/2022] Open
Abstract
Abnormal beta-amyloid plaques and tau protein accumulation are the core pathologic features of Alzheimer's disease. However, the accumulation of these proteins is also common in cognitively normal elderly people. Therefore, this study is aimed to evaluate the amyloid and tau accumulation in the cognitively normal population. A preliminary prospective study was conducted on 24 cognitively normal individuals who underwent Pittsburgh compound B (11C-PiB) and 18F-THK 5351 positron emission tomography (PET)/computed tomography scans. The standardized uptake value ratio (SUVR) was used for quantitative analysis of the two tracers and comparisons between two age groups: ≤60 years and >60 years. Co-registration was applied between the dynamic acquisition PET and T1-weighted magnetic resonance imaging to delineate various cortical regions. P-mod software with the automated anatomical labeling-merged atlas was employed to generate automatic volumes of interest for different brain regions. The posterior cingulate versus precuneus SUVRs of PiB uptake was 1.40 ± 0.07 and 1.38 ± 0.22 versus 1.17 ± 0.07 and 1.14 ± 0.18 in those aged ≤60 years and >60 years, respectively, whereas the SUVRs of THK5351 retention at brain stem versus inferior temporal SUVRs were 1.84 ± 0.06 and 1.91 ± 0.18 versus 1.37 ± 0.04 and 1.48 ± 0.21 in the age groups of ≤ 60 years and >60 years, respectively (P = 0.20). Our findings allow the determination of the preliminary optimal cutoff points for SUVRs in amyloid and tau PET studies. Ultimately, these values can be applied to normal databases in clinical use to improve quantitative analysis.
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Affiliation(s)
- Chanisa Chotipanich
- National Cyclotron and PET Centre, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Attapon Jantarato
- National Cyclotron and PET Centre, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Anchisa Kunawudhi
- National Cyclotron and PET Centre, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Supaporn Kongthai
- National Cyclotron and PET Centre, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand
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Lopes Alves I, Collij LE, Altomare D, Frisoni GB, Saint‐Aubert L, Payoux P, Kivipelto M, Jessen F, Drzezga A, Leeuwis A, Wink AM, Visser PJ, van Berckel BN, Scheltens P, Gray KR, Wolz R, Stephens A, Gismondi R, Buckely C, Gispert JD, Schmidt M, Ford L, Ritchie C, Farrar G, Barkhof F, Molinuevo JL, the AMYPAD Consortium. Quantitative amyloid PET in Alzheimer's disease: the AMYPAD prognostic and natural history study. Alzheimers Dement 2020; 16:750-758. [PMID: 32281303 PMCID: PMC7984341 DOI: 10.1002/alz.12069] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/12/2019] [Accepted: 12/23/2019] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) Prognostic and Natural History Study (PNHS) aims at understanding the role of amyloid imaging in the earliest stages of Alzheimer's disease (AD). AMYPAD PNHS adds (semi-)quantitative amyloid PET imaging to several European parent cohorts (PCs) to predict AD-related progression as well as address methodological challenges in amyloid PET. METHODS AMYPAD PNHS is an open-label, prospective, multi-center, cohort study recruiting from multiple PCs. Around 2000 participants will undergo baseline amyloid positron emission tomography (PET), half of whom will be invited for a follow-up PET 12 at least 12 months later. RESULTS Primary include several amyloid PET measurements (Centiloid, SUVr, BPND , R1 ), and secondary are their changes from baseline, relationship to other amyloid markers (cerebrospinal fluid and visual assessment), and predictive value of AD-related decline. EXPECTED IMPACT Determining the role of amyloid PET for the understanding of this complex disease and potentially improving secondary prevention trials.
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Affiliation(s)
- Isadora Lopes Alves
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
- Memory ClinicUniversity Hospital of GenevaGenevaSwitzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
- Memory ClinicUniversity Hospital of GenevaGenevaSwitzerland
| | - Laure Saint‐Aubert
- Department of Nuclear MedicineImaging PoleToulouse, University HospitalToulouseFrance
- Toulouse NeuroImaging CenterUniversité de Toulouse, Inserm, UPSToulouseFrance
| | - Pierre Payoux
- Department of Nuclear MedicineImaging PoleToulouse, University HospitalToulouseFrance
- Toulouse NeuroImaging CenterUniversité de Toulouse, Inserm, UPSToulouseFrance
| | - Miia Kivipelto
- Department of Geriatric MedicineKarolinska University Hospital HuddingeStockholmSweden
| | - Frank Jessen
- Department of Nuclear MedicineUniversity of CologneCologneGermany
| | | | - Annebet Leeuwis
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | - Bart N.M. van Berckel
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Philip Scheltens
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | | | | | | | | | | | - Juan Domingo Gispert
- Barcelona β Brain Research CenterBarcelonaSpain
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y Nanomedicina (CIBER‐BBN)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | | | - Lisa Ford
- Janssen Pharmaceutica RNDTitusvilleNew JerseyUSA
| | - Craig Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Gill Farrar
- GE HealthcareLife SciencesAmershamUnited Kingdom
| | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Centre for Medical Image ComputingMedical Physics and Biomedical Engineering, UCLLondonUnited Kingdom
| | - José Luis Molinuevo
- Barcelona β Brain Research CenterBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - the AMYPAD Consortium
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
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Parker TD, Cash DM, Lane CA, Lu K, Malone IB, Nicholas JM, James S, Keshavan A, Murray‐Smith H, Wong A, Buchanan SM, Keuss SE, Sudre CH, Thomas DL, Crutch SJ, Fox NC, Richards M, Schott JM. Amyloid β influences the relationship between cortical thickness and vascular load. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12022. [PMID: 32313829 PMCID: PMC7163924 DOI: 10.1002/dad2.12022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 11/30/2019] [Accepted: 01/02/2020] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Cortical thickness has been proposed as a biomarker of Alzheimer's disease (AD)- related neurodegeneration, but the nature of its relationship with amyloid beta (Aβ) deposition and white matter hyperintensity volume (WMHV) in cognitively normal adults is unclear. METHODS We investigated the influences of Aβ status (negative/positive) and WMHV on cortical thickness in 408 cognitively normal adults aged 69.2 to 71.9 years who underwent 18F-Florbetapir positron emission tomography (PET) and structural magnetic resonance imaging (MRI). Two previously defined Alzheimer's disease (AD) cortical signature regions and the major cortical lobes were selected as regions of interest (ROIs) for cortical thickness. RESULTS Higher WMHV, but not Aβ status, predicted lower cortical thickness across all participants, in all ROIs. Conversely, when Aβ-positive participants were considered alone, higher WMHV predicted higher cortical thickness in a temporal AD-signature region. DISCUSSION WMHV may differentially influence cortical thickness depending on the presence or absence of Aβ, potentially reflecting different pathological mechanisms.
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Affiliation(s)
- Thomas D. Parker
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - David M. Cash
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Christopher A. Lane
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Kirsty Lu
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Ian B. Malone
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Jennifer M. Nicholas
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
| | | | - Ashvini Keshavan
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Heidi Murray‐Smith
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLLondonUK
| | - Sarah M. Buchanan
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Sarah E. Keuss
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Carole H. Sudre
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUCLLondonUK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Queen Square Institute of NeurologyUCLLondonUK
- Neuroradiological Academic Unit, Department of Brain Repair and RehabilitationUCL Queen Square Institute of NeurologyLondonUK
| | - Sebastian J. Crutch
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | - Nick C. Fox
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | | | - Jonathan M. Schott
- Department of Neurodegenerative DiseaseThe Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
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A kinetics-based approach to amyloid PET semi-quantification. Eur J Nucl Med Mol Imaging 2020; 47:2175-2185. [PMID: 31982991 DOI: 10.1007/s00259-020-04689-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 01/07/2020] [Indexed: 10/25/2022]
Abstract
PURPOSE To develop and validate a semi-quantification method (time-delayed ratio, TDr) applied to amyloid PET scans, based on tracer kinetics information. METHODS The TDr method requires two static scans per subject: one early (~ 0-10 min after the injection) and one late (typically 50-70 min or 90-100 min after the injection, depending on the tracer). High perfusion regions are delineated on the early scan and applied onto the late scan. A SUVr-like ratio is calculated between the average intensities in the high perfusion regions and the late scan hotspot. TDr was applied to a naturalistic multicenter dataset of 143 subjects acquired with [18F]florbetapir. TDr values are compared to visual evaluation, cortical-cerebellar SUVr, and to the geometrical semi-quantification method ELBA. All three methods are gauged versus the heterogeneity of the dataset. RESULTS TDr shows excellent agreement with respect to the binary visual assessment (AUC = 0.99) and significantly correlates with both validated semi-quantification methods, reaching a Pearson correlation coefficient of 0.86 with respect to ELBA. CONCLUSIONS TDr is an alternative approach to previously validated ones (SUVr and ELBA). It requires minimal image processing; it is independent on predefined regions of interest and does not require MR registration. Besides, it takes advantage on the availability of early scans which are becoming common practice while imposing a negligible added patient discomfort.
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Chotipanich C, Nivorn M, Kunawudhi A, Promteangtrong C, Boonkawin N, Jantarato A. Evaluation of Imaging Windows for Tau PET Imaging Using 18F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer's Disease Patients. Mol Imaging 2020; 19:1536012120947582. [PMID: 32862780 PMCID: PMC7466905 DOI: 10.1177/1536012120947582] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The study aimed to evaluate the appropriate uptake-timing in cognitively normal individuals, mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients, using 18F-PI 2620 dynamic PET acquisition. METHODS Thirty-four MCI patients, 6 AD patients, and 24 cognitively normal individuals were enrolled in this study. A dynamic 18F-PI 2620 PET study was conducted at 30-75 minutes post-injection in these groups. Co-registration was applied between the dynamic acquisition PET and T1-weighted MRI to delineate various cortical regions. The standardized uptake value ratio (SUVR) was used for quantitative analysis. P-mod software with the Automated Anatomical Labeling (AAL)-merged atlas was employed to generate automatic volumes of interest for 11 brain regions. RESULTS The curves in most brain regions presented an average SUVR stability at 30-40 minutes post-injection in each group. The appropriate uptake-timing interval of 18F-PI 2620 was 30-75 minutes post injection for AD group and 30-40 minutes post injection for both cognitively normal individuals and MCI groups. CONCLUSION Short uptake time around 30-40 minutes post-injection would be more comfortable and convenient for all patients, especially in those with dementia who were unable to stay motionless for long periods of scanning time in the scanner.
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Affiliation(s)
| | - Monchaya Nivorn
- National Cyclotron and PET Centre, Chulabhorn Hospital, Bangkok, Thailand
| | - Anchisa Kunawudhi
- National Cyclotron and PET Centre, Chulabhorn Hospital, Bangkok, Thailand
| | | | | | - Attapon Jantarato
- National Cyclotron and PET Centre, Chulabhorn Hospital, Bangkok, Thailand
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López-González FJ, Moscoso A, Efthimiou N, Fernández-Ferreiro A, Piñeiro-Fiel M, Archibald SJ, Aguiar P, Silva-Rodríguez J. Spill-in counts in the quantification of 18F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region. EJNMMI Phys 2019; 6:27. [PMID: 31858289 PMCID: PMC6923310 DOI: 10.1186/s40658-019-0258-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 10/25/2019] [Indexed: 12/17/2022] Open
Abstract
Background We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated. Methods We evaluated magnetic resonance imaging and 18F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVRCGM) and the whole cerebellum (SUVRWC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVRCGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation. Results WM-SUVRCGM followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVRCGM was linearly correlated to WM-SUVRCGM (r = 0.82, linear fit slope = 0.28). SUVRWC was linearly correlated to WM-SUVRCGM (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVRCGM (slopes of 0.06 and 0.07 for iY and RBV), while SUVRWC showed a negative correlation with SUVRCGM after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVRCGM, r = 0.18 for SUVRWC). Conclusions There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.
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Affiliation(s)
- Francisco Javier López-González
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain
| | - Alexis Moscoso
- Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
| | - Nikos Efthimiou
- PET Research Centre, Faculty of Health Sciences, University of Hull, Hull, UK
| | - Anxo Fernández-Ferreiro
- Pharmacy Department and Pharmacology Group, University Hospital (SERGAS) and Health Research Institute Santiago Compostela (IDIS), Santiago de Compostela, Galicia, Spain
| | - Manuel Piñeiro-Fiel
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain
| | - Stephen J Archibald
- PET Research Centre, Faculty of Health Sciences, University of Hull, Hull, UK
| | - Pablo Aguiar
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain. .,Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain.
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain.,R&D Department, Qubiotech Health Intelligence SL, A Coruña, Galicia, Spain
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Fantoni E, Collij L, Lopes Alves I, Buckley C, Farrar G. The Spatial-Temporal Ordering of Amyloid Pathology and Opportunities for PET Imaging. J Nucl Med 2019; 61:166-171. [PMID: 31836683 DOI: 10.2967/jnumed.119.235879] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 12/03/2019] [Indexed: 12/24/2022] Open
Abstract
Although clinical routine focuses on dichotomous and visual interpretation of amyloid PET, regional image assessment in research settings may yield additional opportunities. Understanding the regional-temporal evolution of amyloid pathology may enable earlier identification of subjects in the Alzheimer Disease pathologic continuum, as well as a finer-grained assessment of pathology beyond traditional dichotomous measures. This review summarizes current research in the detection of regional amyloid deposition patterns and its potential for staging amyloid pathology. Pathology studies, cross-sectional and longitudinal PET-only studies, and comparative PET and autopsy studies are included. Despite certain differences, cortical deposition generally precedes striatal pathology, and in PET-only studies, medial cortical regions are seen to accumulate amyloid earlier than lateral regions. Based on regional amyloid PET, multiple studies have developed and implemented models for staging amyloid pathology that could improve subject selection into secondary prevention trials and visual assessment in clinical routine.
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Affiliation(s)
- Enrico Fantoni
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St. Giles, Buckinghamshire, United Kingdom; and
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Christopher Buckley
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St. Giles, Buckinghamshire, United Kingdom; and
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