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Iaccarino L, Burnham SC, Tunali I, Wang J, Navitsky M, Arora AK, Pontecorvo MJ. A practical overview of the use of amyloid-PET Centiloid values in clinical trials and research. Neuroimage Clin 2025; 46:103765. [PMID: 40101674 PMCID: PMC11960669 DOI: 10.1016/j.nicl.2025.103765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 02/25/2025] [Accepted: 03/04/2025] [Indexed: 03/20/2025]
Abstract
The density of brain amyloid-beta neuritic plaque accumulation, a marker of Alzheimer's disease (AD), can be visualized and quantified using amyloid-positron emission tomography (PET). Amyloid-PET data can be obtained using different tracers and methodologies; therefore, comparison across studies can be difficult. The introduction of Centiloids in 2015 allowed for the transformation of amyloid-PET quantitative data to a common scale, enhancing comparability across studies and potentially enabling pooled analysis. Since then, Centiloid values have been used increasingly in research and clinical trials for multiple purposes, being tested and validated with a variety of clinical, biomarker and pathological standards of truth. In clinical trials, Centiloid values have been used for patient selection, to confirm the presence of AD pathology, as well as for treatment monitoring, especially in trials of disease-modifying treatments such as amyloid-targeting therapies. Building on their widespread adoption, Centiloid values are increasingly being integrated into commercially available software solutions for quantifying amyloid-PET, paving the way for real-world applications at the community level. This article addresses frequently asked questions about Centiloid definition, implementation, interpretation, and caveats, and also summarizes the available literature on published thresholds, ultimately supporting wider access and informed use of Centiloid values in Alzheimer's disease research.
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Affiliation(s)
- Leonardo Iaccarino
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA; Eli Lilly Italia S.p.A., Sesto Fiorentino FI 50019, Italy.
| | - Samantha C Burnham
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Ilke Tunali
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Jian Wang
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Michael Navitsky
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Anupa K Arora
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
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Gillman A, Bourgeat P, Cox T, Villemagne VL, Fripp J, Huang K, Williams R, Shishegar R, O'Keefe G, Li S, Krishnadas N, Feizpour A, Bozinovski S, Rowe CC, Doré V. Digital detector PET/CT increases Centiloid measures of amyloid in Alzheimer's disease: A head-to-head comparison of cameras. J Alzheimers Dis 2025; 103:1257-1268. [PMID: 39865687 DOI: 10.1177/13872877241313063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2025]
Abstract
BACKGROUND The introduction of therapeutics for Alzheimer's disease has led to increased interest in precisely quantifying amyloid-β (Aβ) burden for diagnosis, treatment monitoring, and further clinical research. Recent positron emission tomography (PET) hardware innovations including digital detectors have led to superior resolution and sensitivity, improving quantitative accuracy. However, the effect of PET scanner on Centiloid remains relatively unexplored and is assumed to be minimized by harmonizing PET resolutions. OBJECTIVE To quantify the differences in Centiloid between scanners in a paired cohort. METHODS 36 participants from the Australian Imaging, Biomarker and Lifestyle study (AIBL) cohort were scanned within a year on two scanners. Each participant underwent 18F-NAV4694 imaging on two of the three scanners investigated, the Siemens Vision, the Siemens mCT and the Philips Gemini. We compared Aβ Centiloid quantification between scanners and assessed the effectiveness of post-reconstruction PET resolution harmonization. We further compared the scanner differences in target sub-regions and with different reference regions to assess spatial variability. RESULTS Centiloid from the Vision camera was found to be significantly higher compared to the Gemini and mCT; the difference was greater at high-Centiloid levels. Post-reconstruction resolution harmonization only accounted for and corrected ∼20% of the Centiloid (CL) difference between scanners. We further demonstrated that residual differences have effects that vary spatially between different subregions of the Centiloid mask. CONCLUSIONS We have demonstrated that the type of PET scanner that a participant is scanned on affects Centiloid quantification, even when scanner resolution is harmonized. We conclude by highlighting the need for further investigation into harmonization techniques that consider scanner differences.
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Affiliation(s)
- Ashley Gillman
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Pierrick Bourgeat
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Timothy Cox
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Kun Huang
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
| | - Rob Williams
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
| | - Rosita Shishegar
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Graeme O'Keefe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
| | - Shenpeng Li
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Natasha Krishnadas
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Azadeh Feizpour
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Svetlana Bozinovski
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Vincent Doré
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
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Farrar G, Weber CJ, Rabinovici GD. Expert opinion on Centiloid thresholds suitable for initiating anti-amyloid therapy. Summary of discussion at the 2024 spring Alzheimer's Association Research Roundtable. J Prev Alzheimers Dis 2025; 12:100008. [PMID: 39800462 DOI: 10.1016/j.tjpad.2024.100008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 08/28/2024] [Accepted: 08/28/2024] [Indexed: 05/02/2025]
Abstract
A 24-30 Centiloid (CL) threshold was collectively considered by a group of global dementia experts as a practical and implementable cut-off for anti-amyloid therapy intervention, in Alzheimer's disease patients who have been diagnosed at the mild cognitive impairment or mild dementia stage of their disease. Though additional validation is needed, knowledge of this threshold would be valuable to those involved in diagnosing and treating patients in the new AD care pathways, as well as entry into clinical trials. Therapy monitoring to determine future treatment response and assess amyloid clearance can be accomplished with amyloid PET with some technical details still to be elucidated.
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Affiliation(s)
- Gill Farrar
- GE HealthCare, Little Chalfont, Buckinghamshire, UK
| | | | - Gil D Rabinovici
- Memory & Aging Center, Departments of Neurology, Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
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Collij LE, Bollack A, La Joie R, Shekari M, Bullich S, Roé‐Vellvé N, Koglin N, Jovalekic A, Garciá DV, Drzezga A, Garibotto V, Stephens AW, Battle M, Buckley C, Barkhof F, Farrar G, Gispert JD, AMYPAD consortium. Centiloid recommendations for clinical context-of-use from the AMYPAD consortium. Alzheimers Dement 2024; 20:9037-9048. [PMID: 39564918 PMCID: PMC11667534 DOI: 10.1002/alz.14336] [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: 07/16/2024] [Revised: 09/10/2024] [Accepted: 09/23/2024] [Indexed: 11/21/2024]
Abstract
Amyloid-PET quantification through the tracer-independent Centiloid (CL) scale has emerged as an essential tool for the accurate measurement of amyloid-β (Aβ) pathology in Alzheimer's disease (AD) patients. The AMYPAD consortium set out to integrate existing literature and recent work from the consortium to provide clinical context-of-use recommendations for the CL scale. Compared to histopathology, visual reads, and cerebrospinal fluid, CL quantification accurately reflects the amount of AD pathology. With high certainty, a CL value below 10 excludes the presence of Aβ pathology, while a value above 30 corresponds well with pathological amounts. Values falling in between these two cutoffs ("intermediate range") are related to an increased risk of disease progression. Together, CL quantification is a valuable adjunct to visual assessments of amyloid-PET images. An abnormal amyloid biomarker assessment is a key criterion to determine eligibility for anti-amyloid disease-modifying therapies, and amyloid-PET quantification can add further value by precisely monitoring amyloid clearance, and hence guiding patient management decisions. HIGHLIGHTS: Centiloid (CL) quantification robustly reflects of the amount of Aβ pathology. CL < 10/CL > 30 reflects Aβ-negativity/positivity thresholds with high certainty. CL quantification is a valuable adjunct to visual assessments of amyloid-PET. CL quantification can support trial design and treatment management. CL quantification could support the identification of early or emerging Aβ pathology.
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Affiliation(s)
- Lyduine E. Collij
- Department of Radiology & Nuclear Medicine, Amsterdam UMCVrije UniversiteitAmsterdamThe Netherlands
- Brain ImagingAmsterdam NeuroscienceAmsterdamThe Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of MedicineLund UniversityMalmöSweden
| | - Ariane Bollack
- GE HealthcareChalfont St GilesBuckinghamshireUK
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Renaud La Joie
- Memory and Aging Center, Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research CenterPasqual Maragall FoundationWellingtonBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | | | | | | | | | - David Valléz Garciá
- Department of Radiology & Nuclear Medicine, Amsterdam UMCVrije UniversiteitAmsterdamThe Netherlands
- Brain ImagingAmsterdam NeuroscienceAmsterdamThe Netherlands
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital CologneUniversity of CologneCologneGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Institute of Neuroscience and Medicine (INM‐2), Molecular Organization of the Brain, ForschungszentrumJülichGermany
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular ImagingUniversity Hospitals of GenevaGenevaSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- CIBM Center for Biomedical ImagingLausanneZwitserland
| | | | - Mark Battle
- GE HealthcareChalfont St GilesBuckinghamshireUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMCVrije UniversiteitAmsterdamThe Netherlands
- Brain ImagingAmsterdam NeuroscienceAmsterdamThe Netherlands
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Gill Farrar
- GE HealthcareChalfont St GilesBuckinghamshireUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research CenterPasqual Maragall FoundationWellingtonBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER‐BBN)MadridSpain
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Huang B, Sawicki S, Habiger C, Mattis PJ, Gordon ML, Franceschi AM, Giliberto L. Memories and mimics: unveiling the potential of FDG-PET in guiding therapeutic approaches for neurodegenerative cognitive disorders. Front Neurol 2024; 15:1428036. [PMID: 39628892 PMCID: PMC11612009 DOI: 10.3389/fneur.2024.1428036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 10/22/2024] [Indexed: 12/06/2024] Open
Abstract
Fluorodeoxyglucose F18 (FDG) positron emission tomography (PET) imaging can help clinicians pursue the differential diagnosis of various neurodegenerative diseases. It has become an invaluable diagnostic tool in routine clinical practice in conjunction with computed tomography (CT) imaging, magnetic resonance imaging (MRI), and biomarker studies. We present a single-institution case series and systematic literature review, showing how FDG-PET imaging has helped physicians diagnose neurodegenerative diseases and their mimickers and how patient care was amended. A single institution analysis and comprehensive literature search were completed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. These medical subjects' headings (MeSH) terms were used: "FDG-PET" AND "dementia" OR "Alzheimer's" OR "neurodegeneration" OR "frontotemporal dementia" OR "atypical parkinsonian syndrome" OR "primary progressive aphasia" OR "lewy body dementia." The inclusion criteria included studies with uncertain diagnoses of neurocognitive disease resolved with FDG-PET, PET/MRI, or PET/CT hybrid imaging. A literature search resulted in 3,976 articles. After considering inclusion and exclusion criteria, 14 case reports and 1 case series were selected, representing 19 patients. The average age of patients was 70.8 years (range: 54-83 years). Five of the 19 patients were females. Dementia with Lewy bodies (DLB) had the highest propensity for being misidentified as another neurodegenerative disease, followed by Alzheimer's disease (AD) and frontotemporal dementia (FTD). Without accurate molecular imaging, neurodegenerative diseases may be missed or misdiagnosed. Our single-institution case series and literature review demonstrate how FDG-PET brain imaging can be used to correct and clarify preexisting clinical diagnoses of neurodegenerative disease.
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Affiliation(s)
- Brendan Huang
- Department of Neurology, Northwell, New Hyde Park, NY, United States
| | - Sara Sawicki
- Department of Neurology, Northwell, New Hyde Park, NY, United States
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Carolyn Habiger
- Department of Neurology, Northwell, New Hyde Park, NY, United States
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Paul J. Mattis
- Department of Psychiatry, Northwell, New Hyde Park, NY, United States
| | - Marc L. Gordon
- Departments of Neurology and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
- Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Ana M. Franceschi
- Department of Radiology, Northwell, New Hyde Park, NY, United States
| | - Luca Giliberto
- Department of Neurology, Northwell, New Hyde Park, NY, United States
- Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
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Borne L, Thienel R, Lupton MK, Guo C, Mosley P, Behler A, Giorgio J, Adam R, Ceslis A, Bourgeat P, Fazlollahi A, Maruff P, Rowe CC, Masters CL, Fripp J, Robinson GA, Breakspear M. The interplay of age, gender and amyloid on brain and cognition in mid-life and older adults. Sci Rep 2024; 14:27207. [PMID: 39516511 PMCID: PMC11549469 DOI: 10.1038/s41598-024-78308-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 10/30/2024] [Indexed: 11/16/2024] Open
Abstract
Deficits in memory are seen as a canonical sign of aging and a prodrome to dementia in older adults. However, our understanding of age-related cognition and brain morphology occurring throughout a broader spectrum of adulthood remains limited. We quantified the relationship between cognitive function and brain morphology (sulcal width, SW) using three cross-sectional observational datasets (PISA, AIBL, ADNI) from mid-life to older adulthood, assessing the influence of age, sex, amyloid (Aβ) and genetic risk for dementia. The data comprised cognitive, genetic and neuroimaging measures of a total of 1570 non-clinical mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). Among non-clinical adults, we found robust modes of co-variation between regional SW and multidomain cognitive function that differed between the mid-life and older age range. These cortical and cognitive profiles derived from healthy cohorts predicted out-of-sample AD and MCI. Furthermore, Aβ-deposition and educational attainment levels were associated with cognition but not SW. These findings underscoring the complex interplay between factors influencing cognition and brain structure from mid-life onwards, providing valuable insights for future research into neurodegeneration and the development of future screening algorithms.
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Affiliation(s)
- Léonie Borne
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, NSW, Australia
| | - Renate Thienel
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia.
| | | | | | - Philip Mosley
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Anna Behler
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, NSW, Australia
| | - Joseph Giorgio
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, NSW, Australia
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA
| | - Robert Adam
- UQ Centre for Clinical Research (UQCCR), University of Queensland, Brisbane, QLD, Australia
| | - Amelia Ceslis
- Queensland Brain Institute & School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | | | | | - Paul Maruff
- Florey Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher C Rowe
- Florey Institute, University of Melbourne, Melbourne, VIC, Australia
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Colin L Masters
- Florey Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Gail A Robinson
- Queensland Brain Institute & School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, NSW, Australia
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses. Hum Brain Mapp 2024; 45:e70068. [PMID: 39540665 PMCID: PMC11561838 DOI: 10.1002/hbm.70068] [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: 06/22/2024] [Revised: 09/30/2024] [Accepted: 10/22/2024] [Indexed: 11/16/2024] Open
Abstract
Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. LouisSt. LouisMissouriUSA
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat harmonization for reducing across-tracer differences in regional amyloid PET analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308952. [PMID: 38947044 PMCID: PMC11213066 DOI: 10.1101/2024.06.14.24308952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Introduction Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. Conclusion ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
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Johns E, Vossler HA, Young CB, Carlson ML, Winer JR, Younes K, Park J, Rathmann‐Bloch J, Smith V, Harrison TM, Landau S, Henderson V, Wagner A, Sha SJ, Zeineh M, Zaharchuk G, Poston KL, Davidzon GA, Mormino EC. Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation. Alzheimers Dement 2024; 20:5299-5310. [PMID: 38962867 PMCID: PMC11350032 DOI: 10.1002/alz.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Amyloid positron emission tomography (PET) acquisition timing impacts quantification. METHODS In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. RESULTS CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. DISCUSSION The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. HIGHLIGHTS Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.
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Affiliation(s)
- Emily Johns
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Hillary A. Vossler
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Christina B. Young
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Mackenzie L. Carlson
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Joseph R. Winer
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Kyan Younes
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Jennifer Park
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | | | - Viktorija Smith
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Theresa M. Harrison
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Victor Henderson
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Department of Epidemiology and Population HealthStanford UniversityStanfordCaliforniaUSA
| | - Anthony Wagner
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Sharon J. Sha
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Michael Zeineh
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Greg Zaharchuk
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Kathleen L. Poston
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Guido A. Davidzon
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
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10
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Xia Y, Dore V, Fripp J, Bourgeat P, Laws SM, Fowler CJ, Rainey-Smith SR, Martins RN, Rowe C, Masters CL, Coulson EJ, Maruff P. Association of Basal Forebrain Atrophy With Cognitive Decline in Early Alzheimer Disease. Neurology 2024; 103:e209626. [PMID: 38885444 PMCID: PMC11254448 DOI: 10.1212/wnl.0000000000209626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 05/09/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES In early Alzheimer disease (AD), β-amyloid (Aβ) deposition is associated with volume loss in the basal forebrain (BF) and cognitive decline. However, the extent to which Aβ-related BF atrophy manifests as cognitive decline is not understood. This study sought to characterize the relationship between BF atrophy and the decline in memory and attention in patients with early AD. METHODS Participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study who completed Aβ-PET imaging and repeated MRI and cognitive assessments were included. At baseline, participants were classified based on their clinical dementia stage and Aβ status, yielding groups that were cognitively unimpaired (CU) Aβ-, CU Aβ+, and mild cognitive impairment (MCI) Aβ+. Linear mixed-effects models were used to assess changes in volumetric measures of BF subregions and the hippocampus and changes in AIBL memory and attention composite scores for each group compared with CU Aβ- participants. Associations between Aβ burden, brain atrophy, and cognitive decline were evaluated and explored further using mediation analyses. RESULTS The cohort included 476 participants (72.6 ± 5.9 years, 55.0% female) with longitudinal data from a median follow-up period of 6.1 years. Compared with the CU Aβ- group (n = 308), both CU Aβ+ (n = 107) and MCI Aβ+ (n = 61) adults showed faster decline in BF and hippocampal volumes and in memory and attention (Cohen d = 0.73-1.74). Rates of atrophy in BF subregions and the hippocampus correlated with cognitive decline, and each individually mediated the impact of Aβ burden on memory and attention decline. When all mediators were considered simultaneously, hippocampal atrophy primarily influenced the effect of Aβ burden on memory decline (β [SE] = -0.139 [0.032], proportion mediated [PM] = 28.0%) while the atrophy of the posterior nucleus basalis of Meynert in the BF (β [SE] = -0.068 [0.029], PM = 13.1%) and hippocampus (β [SE] = -0.121 [0.033], PM = 23.4%) distinctively influenced Aβ-related attention decline. DISCUSSION These findings highlight the significant role of BF atrophy in the complex pathway linking Aβ to cognitive impairment in early stages of AD. Volumetric assessment of BF subregions could be essential in elucidating the relationships between the brain structure and behavior in AD.
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Affiliation(s)
- Ying Xia
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Vincent Dore
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Jurgen Fripp
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Pierrick Bourgeat
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Simon M Laws
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Christopher J Fowler
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Stephanie R Rainey-Smith
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Ralph N Martins
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Christopher Rowe
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Colin L Masters
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Elizabeth J Coulson
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Paul Maruff
- From the The Australian e-Health Research Centre (Y.X., V.D., J.F., P.B.), CSIRO Health and Biosecurity, Brisbane; Department of Nuclear Medicine and Centre for PET (V.D., C.R.), Austin Health, Melbourne; Centre for Precision Health (S.M.L.), Edith Cowan University; Collaborative Genomics and Translation Group (S.M.L.), School of Medical and Health Sciences, Edith Cowan University, Joondalup; Curtin Medical School (S.M.L.), Curtin University, Bentley; The Florey Institute of Neuroscience and Mental Health (C.J.F., C.R., C.L.M., P.M.), The University of Melbourne; Centre for Healthy Ageing (S.R.R.-S.), Health Futures Institute, Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-S., R.N.M.), Sarich Neuroscience Research Institute, Nedlands; School of Psychological Science (S.R.R.-S.), University of Western Australia, Crawley; School of Medical and Health Sciences (S.R.R.-S., R.N.M.), Edith Cowan University, Joondalup; Department of Biomedical Sciences (R.N.M.), Macquarie University, Sydney; Queensland Brain Institute (E.J.C.), and School of Biomedical Sciences (E.J.C.), The University of Queensland, Brisbane; and Cogstate Ltd. (P.M.), Melbourne, Australia
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11
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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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12
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Bollack A, Collij LE, García DV, Shekari M, Altomare D, Payoux P, Dubois B, Grau‐Rivera O, Boada M, Marquié M, Nordberg A, Walker Z, Scheltens P, Schöll M, Wolz R, Schott JM, Gismondi R, Stephens A, Buckley C, Frisoni GB, Hanseeuw B, Visser PJ, Vandenberghe R, Drzezga A, Yaqub M, Boellaard R, Gispert JD, Markiewicz P, Cash DM, Farrar G, Barkhof F, AMYPAD consortium. Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study. Alzheimers Dement 2024; 20:3429-3441. [PMID: 38574374 PMCID: PMC11095430 DOI: 10.1002/alz.13761] [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: 10/09/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 04/06/2024]
Abstract
INTRODUCTION To support clinical trial designs focused on early interventions, our study determined reliable early amyloid-β (Aβ) accumulation based on Centiloids (CL) in pre-dementia populations. METHODS A total of 1032 participants from the Amyloid Imaging to Prevent Alzheimer's Disease-Prognostic and Natural History Study (AMYPAD-PNHS) and Insight46 who underwent [18F]flutemetamol, [18F]florbetaben or [18F]florbetapir amyloid-PET were included. A normative strategy was used to define reliable accumulation by estimating the 95th percentile of longitudinal measurements in sub-populations (NPNHS = 101/750, NInsight46 = 35/382) expected to remain stable over time. The baseline CL threshold that optimally predicts future accumulation was investigated using precision-recall analyses. Accumulation rates were examined using linear mixed-effect models. RESULTS Reliable accumulation in the PNHS was estimated to occur at >3.0 CL/year. Baseline CL of 16 [12,19] best predicted future Aβ-accumulators. Rates of amyloid accumulation were tracer-independent, lower for APOE ε4 non-carriers, and for subjects with higher levels of education. DISCUSSION Our results support a 12-20 CL window for inclusion into early secondary prevention studies. Reliable accumulation definition warrants further investigations.
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Affiliation(s)
- Ariane Bollack
- Centre for Medical Image Computing (CMIC)Department of Medical Physics and BioengineeringUniversity College LondonLondonLondonUK
| | - Lyduine E. Collij
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Amsterdam Neuroscience, Brain ImagingVU University AmsterdamAmsterdamThe Netherlands
| | - David Vállez García
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Instituto de investigaciones médicas Hospital del Mar (IMIM)BarcelonaSpain
| | - Daniele Altomare
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Pierre Payoux
- Department of Nuclear MedicineImaging PoleToulouse University HospitalToulouseFrance
- Toulouse NeuroImaging CenterUniversité de ToulouseInsermUPSCHU PurpanPavillon BaudotPlace du Docteur Joseph BaylacToulouseFrance
| | - Bruno Dubois
- Department of NeurologySalpêtrière HospitalAP‐HPSorbonne UniversityParisFrance
| | - Oriol Grau‐Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona – Universitat Internacional de CatalunyaBarcelonaSpain
- CIBERNEDNetwork Center for Biomedical Research in Neurodegenerative DiseasesNational Institute of Health Carlos IIIMadridSpain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona – Universitat Internacional de CatalunyaBarcelonaSpain
- CIBERNEDNetwork Center for Biomedical Research in Neurodegenerative DiseasesNational Institute of Health Carlos IIIMadridSpain
| | - Agneta Nordberg
- Department of NeurobiologyCare Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska InstitutetStockholmSweden
- Theme Inflammation and Aging, Karolinska University Hospital, Karolinska InstitutetStockholmSweden
| | - Zuzana Walker
- Division of PsychiatryUniversity College LondonLondonUK
- Essex Partnership University NHS Foundation Trust, The LodgeWickfordUK
| | - Philip Scheltens
- Alzheimer Center and Department of NeurologyAmsterdam Neuroscience, VU University Medical Center, Alzheimercentrum AmsterdamAmsterdamThe Netherlands
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, The University of GothenburgGothenburgSweden
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University HospitalGothenburgSweden
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
| | | | - Jonathan M. Schott
- Dementia Research Centre, UCL Queen Square Institute of NeurologyLondonUK
| | | | | | | | - Giovanni B. Frisoni
- Neurology UnitDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Bernard Hanseeuw
- Department of NeurologyInstitute of Neuroscience, Université Catholique de Louvain, Cliniques Universitaires Saint‐LucBrusselsBelgium
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- WELBIO DepartmentWEL Research InstituteWavreBelgium
| | - Pieter Jelle Visser
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
- Department of NeurobiologyCare Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska InstitutetStockholmSweden
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht UniversityMaastrichtThe Netherlands
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, LBI – KU Leuven Brain InstituteLeuvenBelgium
| | - Alexander Drzezga
- Department of Nuclear MedicineUniversity Hospital Cologne, Universitätsklinikums KölnKölnGermany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine, INM‐2), Forschungszentrum Jülich GmbHJülichGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Maqsood Yaqub
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
- Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos IIIMadridSpain
| | - Pawel Markiewicz
- Centre for Medical Image Computing (CMIC)Department of Medical Physics and BioengineeringUniversity College LondonLondonLondonUK
- Computer Science and Informatics, School of Engineering, London South Bank UniversityLondonUK
| | - David M. Cash
- Queen Square Institute of Neurology, University College LondonLondonUK
- UK Dementia Research Institute at University College LondonLondonUK
| | | | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC)Department of Medical Physics and BioengineeringUniversity College LondonLondonLondonUK
- Department of Radiology and Nuclear MedicineAmsterdam UMCAmsterdamThe Netherlands
- Queen Square Institute of Neurology, University College LondonLondonUK
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13
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Xia Y, Maruff P, Doré V, Bourgeat P, Laws SM, Fowler C, Rainey-Smith SR, Martins RN, Villemagne VL, Rowe CC, Masters CL, Coulson EJ, Fripp J. Longitudinal trajectories of basal forebrain volume in normal aging and Alzheimer's disease. Neurobiol Aging 2023; 132:120-130. [PMID: 37801885 DOI: 10.1016/j.neurobiolaging.2023.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 08/03/2023] [Accepted: 09/07/2023] [Indexed: 10/08/2023]
Abstract
Dysfunction of the cholinergic basal forebrain (BF) system and amyloid-β (Aβ) deposition are early pathological features in Alzheimer's disease (AD). However, their association in early AD is not well-established. This study investigated the nature and magnitude of volume loss in the BF, over an extended period, in 516 older adults who completed Aβ-PET and serial magnetic resonance imaging scans. Individuals were grouped at baseline according to the presence of cognitive impairment (CU, CI) and Aβ status (Aβ-, Aβ+). Longitudinal volumetric changes in the BF and hippocampus were assessed across groups. The results indicated that high Aβ levels correlated with faster volume loss in the BF and hippocampus, and the effect of Aβ varied within BF subregions. Compared to CU Aβ+ individuals, Aβ-related loss among CI Aβ+ adults was much greater in the predominantly cholinergic subregion of Ch4p, whereas no difference was observed for the Ch1/Ch2 region. The findings support early and substantial vulnerability of the BF and further reveal distinctive degeneration of BF subregions during early AD.
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Affiliation(s)
- Ying Xia
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia.
| | - Paul Maruff
- Cogstate Ltd, Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Vincent Doré
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Melbourne, Victoria, Australia
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Simon M Laws
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia; Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
| | - Christopher Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Stephanie R Rainey-Smith
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia; Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia; School of Psychological Science, University of Western Australia, Crawley, Western Australia, Australia; School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Ralph N Martins
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia; School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Victor L Villemagne
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher C Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Elizabeth J Coulson
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
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14
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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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15
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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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16
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Jovalekic A, Roé-Vellvé N, Koglin N, Quintana ML, Nelson A, Diemling M, Lilja J, Gómez-González JP, Doré V, Bourgeat P, Whittington A, Gunn R, Stephens AW, Bullich S. Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods. Eur J Nucl Med Mol Imaging 2023; 50:3276-3289. [PMID: 37300571 PMCID: PMC10542295 DOI: 10.1007/s00259-023-06279-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aβ deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification. METHODS This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aβ load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled. RESULTS The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods. CONCLUSION This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
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17
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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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18
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Bourgeat P, Doré V, Rowe CC, Benzinger T, Tosun D, Goyal MS, LaMontagne P, Jin L, Weiner MW, Masters CL, Fripp J, Villemagne VL, for the Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group. A universal neocortical mask for Centiloid quantification. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12457. [PMID: 37492802 PMCID: PMC10363815 DOI: 10.1002/dad2.12457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/22/2023] [Accepted: 06/21/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation. METHODS Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI). RESULTS In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline. DISCUSSION The universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers. HIGHLIGHTS This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.
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Affiliation(s)
- Pierrick Bourgeat
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
| | - Vincent Doré
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
| | - Christopher C. Rowe
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Tammie Benzinger
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Duygu Tosun
- San Francisco Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Manu S. Goyal
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Pamela LaMontagne
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Michael W. Weiner
- San Francisco Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Jurgen Fripp
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
| | - Victor L. Villemagne
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- Department of PsychiatryThe University of PittsburghPittsburghPennsylvaniaUSA
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19
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Bilgel M. Probabilistic estimation for across-batch compatibility enhancement for amyloid PET. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12436. [PMID: 37424963 PMCID: PMC10323321 DOI: 10.1002/dad2.12436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/20/2023] [Accepted: 04/10/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION It is necessary to accurately account for systematic differences due to variability in scanners, radiotracers, and acquisition protocols in multisite studies combining amyloid imaging data. METHODS We propose Probabilistic Estimation for Across-batch Compatibility Enhancement (PEACE), a fully Bayesian multimodal extension of the widely used ComBat harmonization model, and we apply it to harmonize regional amyloid positron emission tomography data from two scanners. RESULTS Simulations show that PEACE recovers true harmonized values better than ComBat, even for unimodal data. PEACE harmonization of multiscanner regional amyloid imaging data yields results that agree better with longitudinal data compared to ComBat, without removing the known biological effects of age or apolipoprotein E genotype. DISCUSSION PEACE outperforms ComBat in both unimodal and bimodal contexts, is applicable to multisite amyloid imaging data, and holds promise for the harmonization of other neuroimaging data over ComBat. HIGHLIGHTS We introduce PEACE, a fully Bayesian multimodal extension of ComBat harmonization.Simulations show that PEACE recovers true harmonized values better than ComBat.PEACE accurately harmonizes multiscanner regional amyloid imaging data.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingBaltimoreMarylandUSA
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20
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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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21
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Bourgeat P, Doré V, Burnham SC, Benzinger T, Tosun D, Li S, Goyal M, LaMontagne P, Jin L, Rowe CC, Weiner MW, Morris JC, Masters CL, Fripp J, Villemagne VL. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3. Neuroimage 2022; 262:119527. [PMID: 35917917 PMCID: PMC9550562 DOI: 10.1016/j.neuroimage.2022.119527] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/11/2022] [Accepted: 07/28/2022] [Indexed: 10/31/2022] Open
Abstract
INTRODUCTION The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. METHODS All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. RESULTS The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. CONCLUSIONS FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.
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Affiliation(s)
| | - Vincent Doré
- CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
| | | | | | - Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Shenpeng Li
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Manu Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, USA
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22
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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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23
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Xia Y, Eeles E, Fripp J, Pinsker D, Thomas P, Latter M, Doré V, Fazlollahi A, Bourgeat P, Villemagne VL, Coulson EJ, Rose S. Reduced cortical cholinergic innervation measured using [ 18F]-FEOBV PET imaging correlates with cognitive decline in mild cognitive impairment. Neuroimage Clin 2022; 34:102992. [PMID: 35344804 PMCID: PMC8958543 DOI: 10.1016/j.nicl.2022.102992] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 03/06/2022] [Accepted: 03/22/2022] [Indexed: 12/12/2022]
Abstract
Topographic FEOBV binding correlates with domain-specific cognitive performance. Global and regional reductions in cholinergic innervation are observed in MCI. Global FEOBV SUVR is associated with basal forebrain and hippocampal volumes. Our results provide proof of concept for FEOBV PET to assess cholinergic terminal integrity.
Dysfunction of the cholinergic basal forebrain (BF) neurotransmitter system, including cholinergic axon denervation of the cortex, plays an important role in cognitive decline and dementia. A validated method to directly quantify cortical cholinergic terminal integrity enables exploration of the involvement of this system in diverse cognitive profiles associated with dementia, particularly at a prodromal stage. In this study, we used the radiotracer [18F]-fluoroethoxybenzovesamicol (FEOBV) as a direct measure of cholinergic terminal integrity and investigated its value for the assessment of cholinergic denervation in the cortex and associated cognitive deficits. Eighteen participants (8 with mild cognitive impairment (MCI) and 10 cognitively unimpaired controls) underwent neuropsychological assessment and brain imaging using FEOBV and [18F]-florbetaben for amyloid-β imaging. The MCI group showed a significant global reduction of FEOBV retention in the cortex and in the parietal and occipital cortices specifically compared to the control group. The global cortical FEOBV retention of all participants positively correlated with the BF, hippocampus and grey matter volumes, but no association was found between the global FEOBV retention and amyloid-β status. Topographic profiles from voxel-wise analysis of FEOBV images revealed significant positive correlations with the cognitive domains associated with the underlying cortical areas. Overlapping profiles of decreased FEOBV were identified in correlation with impairment in executive function, attention and language, which covered the anterior cingulate gyrus, olfactory cortex, calcarine cortex, middle temporal gyrus and caudate nucleus. However, the absence of cortical atrophy in these areas suggested that reduced cholinergic terminal integrity in the cortex is an important factor underlying the observed cognitive decline in early dementia. Our results provide support for the utility and validity of FEOBV PET for quantitative assessment of region-specific cholinergic terminal integrity that could potentially be used for early detection of cholinergic dysfunction in dementia following further validation in larger cohorts.
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Affiliation(s)
- Ying Xia
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia.
| | - Eamonn Eeles
- Internal Medicine Service, The Prince Charles Hospital, Brisbane, QLD, Australia; School of Medicine, Northside Clinical School, The Prince Charles Hospital, Brisbane, QLD, Australia; Dementia & Neuro Mental Health Research Unit, UQCCR, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Donna Pinsker
- Internal Medicine Service, The Prince Charles Hospital, Brisbane, QLD, Australia; School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Paul Thomas
- Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Melissa Latter
- Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Vincent Doré
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia; Austin Health, Melbourne, VIC, Australia
| | - Amir Fazlollahi
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Victor L Villemagne
- Austin Health, Melbourne, VIC, Australia; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth J Coulson
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Stephen Rose
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
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24
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Schwarz CG, Therneau TM, Weigand SD, Gunter JL, Lowe VJ, Przybelski SA, Senjem ML, Botha H, Vemuri P, Kantarci K, Boeve BF, Whitwell JL, Josephs KA, Petersen RC, Knopman DS, Jack CR. Selecting software pipelines for change in flortaucipir SUVR: Balancing repeatability and group separation. Neuroimage 2021; 238:118259. [PMID: 34118395 PMCID: PMC8407434 DOI: 10.1016/j.neuroimage.2021.118259] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/26/2021] [Accepted: 06/08/2021] [Indexed: 12/11/2022] Open
Abstract
Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520-526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.
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Affiliation(s)
- Christopher G Schwarz
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA.
| | - Terry M Therneau
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Jennifer L Whitwell
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester 55905, MN, USA
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