1
|
Takamiya A, Vande Casteele T, Bouckaert F, Van Cauwenberge MG, Laroy M, De Winter FL, Dupont P, Van den Stock J, Koole M, Van Laere K, Emsell L, Vandenbulcke M. Accelerated aging of white matter in late-life depression: evidence from 18F-flutemetamol PET imaging. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2025:S2451-9022(25)00126-0. [PMID: 40204237 DOI: 10.1016/j.bpsc.2025.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2024] [Revised: 03/13/2025] [Accepted: 03/15/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND Late-life depression (LLD) is associated with white matter (WM) alterations. Current evidence indicates amyloid PET tracers as sensitive and reliable markers for evaluating normal-appearing WM (NAWM) on magnetic resonance imaging (MRI), showing an association between lower uptake and Alzheimer's disease pathology and higher uptake with age-related changes. Utilizing this novel and reliable technique, we aimed to distinguish two hypothetical models for neurobiology of LLD: the pathological neurodegenerative model and the accelerated aging model. METHODS In this monocentric cross-sectional study, a total of 103 participants, including 61 patients with LLD (age 73.8±7.0 years, 41 female) and 42 healthy controls (age 72.5±7.6 years, 28 female), underwent PET imaging with 18F-flutemetamol, MRI, and clinical assessment. T2-weighted fluid-attenuated inversion recovery (FLAIR) images were segmented into WM hyperintensities (WMH) and NAWM. RESULTS 18F-flutemetamol standardized uptake value ratio (SUVR) in WMH was significantly lower than that in NAWM (t=7.8, df=102, p<0.001). Compared to healthy controls, patients with LLD exhibited higher 18F-flutemetamol SUVR in both NAWM (p<0.001, Cohen's d=0.91) and WMH (p=0.005, d=0.56), even after controlling for age and 18F-flutemetamol SUVR in cortical gray matter. CONCLUSIONS Our result of elevated 18F-flutemetamol uptake in NAWM does not align with the pathological neurodegenerative aging pattern observed in Alzheimer's disease but is in line with patterns of age-related changes. This distinction is crucial for the development of future targeted treatments.
Collapse
Affiliation(s)
- Akihiro Takamiya
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan.
| | - Thomas Vande Casteele
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium
| | - Filip Bouckaert
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; University Psychiatric Center KU Leuven, Department of Geriatric Psychiatry, Leuven, Belgium
| | | | - Maarten Laroy
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium
| | - François-Laurent De Winter
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; University Psychiatric Center KU Leuven, Department of Geriatric Psychiatry, Leuven, Belgium
| | - Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, Leuven, Belgium
| | - Jan Van den Stock
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; University Psychiatric Center KU Leuven, Department of Geriatric Psychiatry, Leuven, Belgium
| | - Michel Koole
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Nuclear Medicine, Leuven, Belgium
| | - Koen Van Laere
- KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Nuclear Medicine, Leuven, Belgium; Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Louise Emsell
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; University Psychiatric Center KU Leuven, Department of Geriatric Psychiatry, Leuven, Belgium; KU Leuven, Leuven Brain Institute, Department of Imaging and Pathology, Translational MRI, Leuven, Belgium
| | - Mathieu Vandenbulcke
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium; University Psychiatric Center KU Leuven, Department of Geriatric Psychiatry, Leuven, Belgium
| |
Collapse
|
2
|
Kim JW, Byun MS, Yi D, Jung JH, Kong N, Chang YY, Jung G, Ahn H, Lee JY, Kang KM, Sohn CH, Lee YS, Kim YK, Lee DY. Daily fluid intake and brain amyloid deposition: A cohort study. J Alzheimers Dis 2025; 104:138-149. [PMID: 39980438 PMCID: PMC11934770 DOI: 10.1177/13872877251314176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2025]
Abstract
BackgroundLittle information is yet available for the association between daily water intake, a modifiable lifestyle factor, and Alzheimer's disease (AD) pathology and cerebrovascular injury in the living human brain.ObjectiveOur aim was to explore the correlation between daily fluid intake and in vivo AD pathologies (i.e., amyloid-β (Aβ) and tau) and cerebrovascular injury.Methods287 cognitively normal (CN) older adults completed extensive clinical assessments, daily fluid intake evaluations, and multimodal brain imaging at both the initial baseline and the subsequent 2-year follow-up.ResultsLow daily fluid intake was significantly associated with a higher level or a more rapid increase of Aβ deposition, especially in apolipoprotein E4 negative individuals. Meanwhile, low daily fluid intake was cross-sectionally related with cerebrovascular injury.ConclusionsOur findings suggest that high daily fluid intake is associated with decreased brain amyloid deposition, indicating that sufficient daily fluid intake may be helpful for prevention of AD.
Collapse
Affiliation(s)
- Jee Wook Kim
- Department of Neuropsychiatry, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Gyeonggi, Republic of Korea
- Department of Psychiatry, Hallym University College of Medicine, Chuncheon, Gangwon, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Joon Hyung Jung
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Nayeong Kong
- Department of Psychiatry, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
| | - Yoon Young Chang
- Department of Psychiatry, Inje University Sanggye Paik Hospital, Seoul, Republic of Korea
| | - Gijung Jung
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Hyejin Ahn
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun-Sang Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| |
Collapse
|
3
|
Zeydan B, Johnson DR, Schwarz CG, Przybelski SA, Lesnick TG, Senjem ML, Kantarci OH, Min PH, Kemp BJ, Jack CR, Kantarci K, Lowe VJ. Visual assessments of 11 C-Pittsburgh compound-B PET vs. 18 F-flutemetamol PET across the age spectrum. Nucl Med Commun 2024; 45:1047-1054. [PMID: 39267525 PMCID: PMC11540735 DOI: 10.1097/mnm.0000000000001902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
OBJECTIVE Visual assessments of amyloid-β PET, used for Alzheimer's disease (AD) diagnosis and treatment evaluation, require a careful approach when different PET ligands are utilized. Because the gray matter (GM) and white matter (WM) ligand bindings vary with age, the objective was to investigate the agreement between visual reads of 11 C- and 18 F-PET scans. METHODS Cognitively unimpaired (CU) younger adults ( N = 30; 39.5 ± 6.0 years), CU older adults ( N = 30; 68.6 ± 5.9 years), and adults with AD ( N = 22; 67.0 ± 8.5 years) underwent brain MRI, 11 C-Pittsburgh compound-B (PiB)-PET, and 18 F-flutemetamol-PET. Amyloid-β deposition was assessed visually by two nuclear medicine specialists on 11 C-PiB-PET and 18 F-flutemetamol-PET, and quantitatively by PET centiloids. RESULTS Seventy-two 11 C-PiB-PET and 18 F-flutemetamol-PET visual reads were concordant. However, 1 18 F-flutemetamol-PET and 9 11 C-PiB-PET were discordant with quantitative values. In four additional cases, while 11 C-PiB-PET and 18 F-flutemetamol-PET visual reads were concordant, they were discordant with quantitative values. Disagreements in CU younger adults were only with 11 C-PiB-PET visual reads. The remaining disagreements were with CU older adults. CONCLUSION Age, GM/WM binding, amyloid-β load, and disease severity may affect visual assessments of PET ligands. Increase in WM binding with age causes a loss of contrast between GM and WM on 11 C-PiB-PET, particularly in CU younger adults, leading to false positivity. In CU older adults, increased WM signal may bleed more into cortical regions, hiding subtle cortical uptake, especially with 18 F-flutemetamol, whereas 11 C-PiB can detect true regional positivity. Understanding these differences will improve patient care and treatment evaluation in clinic and clinical trials.
Collapse
Affiliation(s)
- Burcu Zeydan
- Mayo Clinic, Department of Radiology
- Mayo Clinic, Department of Neurology
| | | | | | | | | | - Matthew L. Senjem
- Mayo Clinic, Department of Radiology
- Mayo Clinic, Department of Information Technology
| | | | | | | | | | | | | |
Collapse
|
4
|
Byeon G, Byun MS, Yi D, Jung JH, Kong N, Chang Y, KEUM MUSUNG, Jung G, Ahn H, Lee JY, Kim YK, Kang KM, Sohn CH, Lee DY. Visual and Auditory Sensory Impairments Differentially Relate with Alzheimer's Pathology. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:610-623. [PMID: 39420608 PMCID: PMC11494423 DOI: 10.9758/cpn.24.1169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/18/2024] [Accepted: 06/25/2024] [Indexed: 10/19/2024]
Abstract
Objective We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods We primarily tried to examine whether each sensory impairment is related to Alzheimer's disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline. Neuroimaging scans including brain [11C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = -0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.
Collapse
Affiliation(s)
- Gihwan Byeon
- Department of Neuropsychiatry, Kangwon National University Hospital, Chuncheon, Korea
| | - Min Soo Byun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Joon Hyung Jung
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju, Korea
| | - Nayeong Kong
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Yoonyoung Chang
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - MUSUNG KEUM
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Gijung Jung
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Hyejin Ahn
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Jun-Young Lee
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Dong Young Lee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | | |
Collapse
|
5
|
Shekari M, Vállez García D, Collij LE, Altomare D, Heeman F, Pemberton H, Roé Vellvé N, Bullich S, Buckley C, Stephens A, Farrar G, Frisoni G, Klunk WE, Barkhof F, Gispert JD. Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology. Alzheimers Dement 2024; 20:5102-5113. [PMID: 38961808 PMCID: PMC11350134 DOI: 10.1002/alz.13883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. HIGHLIGHTS We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.
Collapse
Grants
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- 115952 Innovative Medicines Initiative 2 Joint Undertaking
- GE Healthcare
- Transition Therapeutics
- F. Hoffmann-La Roche Ltd
- Eli Lilly and Company
- Eisai Inc.
- W81XWH-12-2-0012 Department of Defense
- EuroImmun
- Biogen
- Alzheimer's Drug Discovery Foundation
- Servier
- Lumosity
- Bristol-Myers Squibb Company
- U01 AG024904 NIA NIH HHS
- Piramal Imaging
- Takeda Pharmaceutical Company
- Alzheimer's Association
- Genentech, Inc.
- Araclon Biotech
- U01 AG024904 NIH HHS
- Meso Scale Diagnostics, LLC
- Novartis Pharmaceuticals Corporation
- CereSpir, Inc.
- BioClinica, Inc.
- Johnson & Johnson Pharmaceutical Research & Development LLC
- AbbVie
- Cogstate
- Merck & Co., Inc.
- NIBIB NIH HHS
- European Union's Horizon 2020
- Pfizer Inc.
- CIHR
- Elan Pharmaceuticals, Inc.
- IXICO Ltd.
- NeuroRx Research
- Neurotrack Technologies
- Fujirebio
- Lundbeck
- National Institutes of Health
- Department of Defense
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- BioClinica, Inc.
- Biogen
- Bristol‐Myers Squibb Company
- Eli Lilly and Company
- F. Hoffmann‐La Roche Ltd
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- Lundbeck
- Merck & Co., Inc.
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Servier
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
Collapse
Affiliation(s)
- Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - David Vállez García
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Clinical Memory Research UnitClinical Sciences MalmöLund UniversityMalmöSweden
| | - Daniele Altomare
- Memory CenterDepartment of Rehabilitation and GeriatricsUniversity Hospitals and University of GenevaGenèveSwitzerland
| | - Fiona Heeman
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Wallenberg Centre for Molecular and Translational MedicineUniversity of Gothenburg, The University of GothenburgGothenburgSweden
- Department of Psychiatry and NeurochemistryUniversity of GothenburgSahlgrenska University HospitalGothenburgSweden
| | - Hugh Pemberton
- GE Healthcare Life SciencesAmershamUK
- Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | | | | | | | | | | | - Giovanni Frisoni
- Memory CenterDepartment of Rehabilitation and GeriatricsUniversity Hospitals and University of GenevaGenèveSwitzerland
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER‐BBN)MadridSpain
| | | |
Collapse
|
6
|
Lecy EE, Min HK, Apgar CJ, Maltais DD, Lundt ES, Albertson SM, Senjem ML, Schwarz CG, Botha H, Graff-Radford J, Jones DT, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR, Lee J, Lowe VJ. Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study. J Nucl Med 2024; 65:1122-1128. [PMID: 38782458 DOI: 10.2967/jnumed.123.267150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/29/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.
Collapse
Affiliation(s)
- Emily E Lecy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Christopher J Apgar
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | | | - Emily S Lundt
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Sabrina M Albertson
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | | | - Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
- Department of Biomedical Engineering, College of Medicine, Hanyang University, Seoul, South Korea
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
| |
Collapse
|
7
|
Lee S, Byun MS, Yi D, Ahn H, Jung G, Jung JH, Chang YY, Kim K, Choi H, Choi J, Lee JY, Kang KM, Sohn CH, Lee YS, Kim YK, Lee DY. Plasma Leptin and Alzheimer Protein Pathologies Among Older Adults. JAMA Netw Open 2024; 7:e249539. [PMID: 38700863 PMCID: PMC11069086 DOI: 10.1001/jamanetworkopen.2024.9539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/01/2024] [Indexed: 05/06/2024] Open
Abstract
Importance Many epidemiologic studies have suggested that low levels of plasma leptin, a major adipokine, are associated with increased risk of Alzheimer disease (AD) dementia and cognitive decline. Nevertheless, the mechanistic pathway linking plasma leptin and AD-related cognitive decline is not yet fully understood. Objective To examine the association of plasma leptin levels with in vivo AD pathologies, including amyloid-beta (Aβ) and tau deposition, through both cross-sectional and longitudinal approaches among cognitively unimpaired older adults. Design, Setting, and Participants This was a longitudinal cohort study from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer Disease. Data were collected from January 1, 2014, to December 31, 2020, and data were analyzed from July 11 to September 6, 2022. The study included a total of 208 cognitively unimpaired participants who underwent baseline positron emission tomography (PET) scans for brain Aβ deposition. For longitudinal analyses, 192 participants who completed both baseline and 2-year follow-up PET scans for brain Aβ deposition were included. Exposure Plasma leptin levels as assessed by enzyme-linked immunosorbent assay. Main Outcomes and Measures Baseline levels and longitudinal changes of global Aβ and AD-signature region tau deposition measured by PET scans. Results Among the 208 participants, the mean (SD) age was 66.0 (11.3) years, 114 were women (54.8%), and 37 were apolipoprotein E ε4 carriers (17.8%). Lower plasma leptin levels had a significant cross-sectional association with greater brain Aβ deposition (β = -0.04; 95% CI, -0.09 to 0.00; P = .046), while there was no significant association between plasma leptin levels and tau deposition (β = -0.02; 95% CI, -0.05 to 0.02; P = .41). In contrast, longitudinal analyses revealed that there was a significant association between lower baseline leptin levels and greater increase of tau deposition over 2 years (β = -0.06; 95% CI, -0.11 to -0.01; P = .03), whereas plasma leptin levels did not have a significant association with longitudinal change of Aβ deposition (β = 0.006; 95% CI, 0.00-0.02; P = .27). Conclusions and Relevance The present findings suggest that plasma leptin may be protective for the development or progression of AD pathology, including both Aβ and tau deposition.
Collapse
Affiliation(s)
- Seunghoon Lee
- Department of Psychiatry, Myongji Hospital, Hanyang University College of Medicine, Goyang, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Hyejin Ahn
- Interdisciplinary Program of Cognitive Science, Seoul National University College of Humanities, Seoul, Republic of Korea
| | - Gijung Jung
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Joon Hyung Jung
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Yoon Young Chang
- Department of Psychiatry, Inje University, Sanggye Paik Hospital, Seoul, Republic of Korea
| | - Kyungtae Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeji Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jeongmin Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun-Young Lee
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun-Sang Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program of Cognitive Science, Seoul National University College of Humanities, Seoul, Republic of Korea
| |
Collapse
|
8
|
Cha WJ, Yi D, Ahn H, Byun MS, Chang YY, Choi JM, Kim K, Choi H, Jung G, Kang KM, Sohn CH, Lee YS, Kim YK, Lee DY. Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities. Alzheimers Res Ther 2024; 16:50. [PMID: 38454444 PMCID: PMC10918927 DOI: 10.1186/s13195-024-01417-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Growing evidence suggests that not only cerebrovascular disease but also Alzheimer's disease (AD) pathological process itself cause cerebral white matter degeneration, resulting in white matter hyperintensities (WMHs). Some preclinical evidence also indicates that white matter degeneration may precede or affect the development of AD pathology. This study aimed to clarify the direction of influence between in vivo AD pathologies, particularly beta-amyloid (Aβ) and tau deposition, and WMHs through longitudinal approach. METHODS Total 282 older adults including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV-1451 PET for measuring tau deposition, and MRI scans with fluid-attenuated inversion recovery image for measuring WMH volume. The relationships between Aβ or tau deposition and WMH volume were examined using multiple linear regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and change of WMH volume over 2 years was used as dependent variable to examine the effect of AD pathology on increase of WMH volume. Additionally, we set baseline WMH volume as independent variable and longitudinal change of Aβ or tau deposition for 2 years as dependent variables to investigate whether WMH volume could precede AD pathologies. RESULTS Baseline Aβ deposition, but not tau deposition, had significant positive association with longitudinal change of WMH volume over 2 years. Baseline WMH volume was not related with any of longitudinal change of Aβ or tau deposition for 2 years. We also found a significant interaction effect between baseline Aβ deposition and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female, but not in male. CONCLUSIONS Our findings suggest that Aβ deposition accelerates cerebral WMHs, particularly in female, whereas white matter degeneration appears not influence on longitudinal Aβ increase. The results also did not support any direction of influence between tau deposition and WMHs.
Collapse
Affiliation(s)
- Woo-Jin Cha
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Hyejin Ahn
- Interdisciplinary program of cognitive science, Seoul National University College of Humanities, Seoul, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yoon Young Chang
- Department of Psychiatry, Inje University Sanggye Paik Hospital, Seoul, Republic of Korea
| | - Jung-Min Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kyungtae Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeji Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Gijung Jung
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun-Sang Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary program of cognitive science, Seoul National University College of Humanities, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
9
|
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: 14] [Impact Index Per Article: 7.0] [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.
Collapse
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
| |
Collapse
|
10
|
Pauwels EK, Boer GJ. Friends and Foes in Alzheimer's Disease. Med Princ Pract 2023; 32:313-322. [PMID: 37788649 PMCID: PMC10727688 DOI: 10.1159/000534400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/01/2023] [Indexed: 10/05/2023] Open
Abstract
Alzheimer's disease (AD) is a disabling neurodegenerative disease. The prognosis is poor, and currently there are no proven effective therapies. Most likely, the etiology is related to cerebral inflammatory processes that cause neuronal damage, resulting in dysfunction and apoptosis of nerve cells. Pathogens that evoke a neuroinflammatory response, collectively activate astrocytes and microglia, which contributes to the secretion of pro-inflammatory cytokines. This leads to the deposit of clustered fragments of beta-amyloid and misfolded tau proteins which do not elicit an adequate immune reaction. Apart from the function of astrocytes and microglia, molecular entities such as TREM2, SYK, C22, and C33 play a role in the physiopathology of AD. Furthermore, bacteria and viruses may trigger an overactive inflammatory response in the brain. Pathogens like Helicobacter pylori, Chlamydia pneumonia, and Porphyromonas gingivalis (known for low-grade infection in the oral cavity) can release gingipains, which are enzymes that can damage and destroy neurons. Chronic infection with Borrelia burgdorferi (the causative agent of Lyme disease) can co-localize with tau tangles and amyloid deposits. As for viral infections, herpes simplex virus 1, cytomegalovirus, and Epstein-Barr virus can play a role in the pathogenesis of AD. Present investigations have resulted in the development of antibodies that can clear the brain of beta-amyloid plaques. Trials with humanized aducanumab, lecanemab, and donanemab revealed limited success in AD patients. However, AD should be considered as a continuum in which the initial preclinical phase may take 10 or even 20 years. It is generally thought that this phase offers a window for efficacious treatment. Therefore, research is also focused on the identification of biomarkers for early AD detection. In this respect, the plasma measurement of neurofilament light chain in patients treated with hydromethylthionine mesylate may well open a new way to prevent the formation of tau tangles and represents the first treatment for AD at its roots.
Collapse
Affiliation(s)
- Ernest K.J. Pauwels
- Leiden University and Leiden University Medical Center, Leiden, The Netherlands
| | - Gerard J. Boer
- Netherlands Institute for Brain Research, Royal Academy of Arts and Sciences, Amsterdam, The Netherlands
| |
Collapse
|
11
|
Diaz-Galvan P, Przybelski SA, Lesnick TG, Schwarz CG, Senjem ML, Gunter JL, Jack CR, Min HKP, Jain M, Miyagawa T, Forsberg LK, Fields JA, Savica R, Graff-Radford J, Jones DT, Botha H, St Louis EK, Knopman DS, Ramanan VK, Ross O, Graff-Radford N, Day GS, Dickson DW, Ferman TJ, Petersen RC, Lowe VJ, Boeve BF, Kantarci K. β-Amyloid Load on PET Along the Continuum of Dementia With Lewy Bodies. Neurology 2023; 101:e178-e188. [PMID: 37202168 PMCID: PMC10351554 DOI: 10.1212/wnl.0000000000207393] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 03/23/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND AND OBJECTIVES β-Amyloid (Aβ) plaques can co-occur with Lewy-related pathology in patients with dementia with Lewy bodies (DLB), but Aβ load at prodromal stages of DLB still needs to be elucidated. We investigated Aβ load on PET throughout the DLB continuum, from an early prodromal stage of isolated REM sleep behavior disorder (iRBD) to a stage of mild cognitive impairment with Lewy bodies (MCI-LB), and finally DLB. METHODS We performed a cross-sectional study in patients with a diagnosis of iRBD, MCI-LB, or DLB from the Mayo Clinic Alzheimer Disease Research Center. Aβ levels were measured by Pittsburgh compound B (PiB) PET, and global cortical standardized uptake value ratio (SUVR) was calculated. Global cortical PiB SUVR values from each clinical group were compared with each other and with those of cognitively unimpaired (CU) individuals (n = 100) balanced on age and sex using analysis of covariance. We used multiple linear regression testing for interaction to study the influences of sex and APOE ε4 status on PiB SUVR along the DLB continuum. RESULTS Of the 162 patients, 16 had iRBD, 64 had MCI-LB, and 82 had DLB. Compared with CU individuals, global cortical PiB SUVR was higher in those with DLB (p < 0.001) and MCI-LB (p = 0.012). The DLB group included the highest proportion of Aβ-positive patients (60%), followed by MCI-LB (41%), iRBD (25%), and finally CU (19%). Global cortical PiB SUVR was higher in APOE ε4 carriers compared with that in APOE ε4 noncarriers in MCI-LB (p < 0.001) and DLB groups (p = 0.049). Women had higher PiB SUVR with older age compared with men across the DLB continuum (β estimate = 0.014, p = 0.02). DISCUSSION In this cross-sectional study, levels of Aβ load was higher further along the DLB continuum. Whereas Aβ levels were comparable with those in CU individuals in iRBD, a significant elevation in Aβ levels was observed in the predementia stage of MCI-LB and in DLB. Specifically, APOE ε4 carriers had higher Aβ levels than APOE ε4 noncarriers, and women tended to have higher Aβ levels than men as they got older. These findings have important implications in targeting patients within the DLB continuum for clinical trials of disease-modifying therapies.
Collapse
Affiliation(s)
- Patricia Diaz-Galvan
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Scott A Przybelski
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Timothy G Lesnick
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Christopher G Schwarz
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Matthew L Senjem
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Jeffrey L Gunter
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Clifford R Jack
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Hoon-Ki Paul Min
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Manoj Jain
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Toji Miyagawa
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Leah K Forsberg
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Julie A Fields
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Rodolfo Savica
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Jonathan Graff-Radford
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - David T Jones
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Hugo Botha
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Erik K St Louis
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - David S Knopman
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Vijay K Ramanan
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Owen Ross
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Neill Graff-Radford
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Gregory S Day
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Dennis W Dickson
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Tanis J Ferman
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Ronald C Petersen
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Val J Lowe
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Brad F Boeve
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL
| | - Kejal Kantarci
- From the Department of Radiology (P.D.-G., C.G.S., M.L.S., J.L.G., C.R.J., H.-K.P.M., V.J.L., K.K.), Department of Quantitative Health Sciences (S.A.P., T.G.L., R.C.P.), and Department of Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; Department of Radiology (M.J.), Mayo Clinic, Jacksonville, FL; Department of Neurology (T.M., L.K.F., R.S., J.G.-R., D.T.J., H.B., E.K.S.L., D.S.K., V.K.R., R.C.P., B.F.B.), Department of Psychiatry and Psychology (J.A.F., E.K.S.L.), and Center for Sleep Medicine (E.K.S.L.), Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN; Mayo Clinic Health System Southwest Wisconsin (E.K.S.L.), La Crosse; Department of Neuroscience (O.R.), Department of Neurology (N.G.-R., G.S.D.), Laboratory of Medicine and Pathology (D.W.D.), and Department of Psychiatry and Psychology (T.J.F.), Mayo Clinic, Jacksonville, FL.
| |
Collapse
|
12
|
Lee S, Byun MS, Yi D, Kim MJ, Jung JH, Kong N, Jung G, Ahn H, Lee JY, Kang KM, Sohn CH, Lee YS, Kim YK, Lee DY. Body mass index and two-year change of in vivo Alzheimer's disease pathologies in cognitively normal older adults. Alzheimers Res Ther 2023; 15:108. [PMID: 37312229 DOI: 10.1186/s13195-023-01259-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND Low body mass index (BMI) or underweight status in late life is associated with an increased risk of dementia or Alzheimer's disease (AD). However, the relationship between late-life BMI and prospective longitudinal changes of in-vivo AD pathology has not been investigated. METHODS This prospective longitudinal study was conducted as part of the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease (KBASE). A total of 194 cognitive normal older adults were included in the analysis. BMI at baseline was measured, and two-year changes in brain Aβ and tau deposition on PET imaging were used as the main outcomes. Linear mixed-effects (LME) models were used to examine the relationships between late-life BMI and longitudinal change in AD neuropathological biomarkers. RESULTS A lower BMI at baseline was significantly associated with a greater increase in tau deposition in AD-signature region over 2 years (β, -0.018; 95% CI, -0.028 to -0.004; p = .008), In contrast, BMI was not related to two-year changes in global Aβ deposition (β, 0.0002; 95% CI, -0.003 to 0.002, p = .671). An additional exploratory analysis for each sex showed lower baseline BMI was associated with greater increases in tau deposition in males (β, -0.027; 95% CI, -0.046 to -0.009; p = 0.007), but not in females. DISCUSSION The findings suggest that lower BMI in late-life may predict or contribute to the progression of tau pathology over the subsequent years in cognitively unimpaired older adults.
Collapse
Affiliation(s)
- Seunghoon Lee
- Department of Psychiatry, Myongji Hospital, Hanyang University College of Medicine, Goyang, 10475, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Min Jung Kim
- Department of Neuropsychiatry, Nowon Eulji University Hospital, Seoul, 01830, Republic of Korea
| | - Joon Hyung Jung
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Nayeong Kong
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Gijung Jung
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Hyejin Ahn
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Jun-Young Lee
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Yun-Sang Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, 07061, Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
| |
Collapse
|
13
|
Coath W, Modat M, Cardoso MJ, Markiewicz PJ, Lane CA, Parker TD, Keshavan A, Buchanan SM, Keuss SE, Harris MJ, Burgos N, Dickson J, Barnes A, Thomas DL, Beasley D, Malone IB, Wong A, Erlandsson K, Thomas BA, Schöll M, Ourselin S, Richards M, Fox NC, Schott JM, Cash DM. Operationalizing the centiloid scale for [ 18F]florbetapir PET studies on PET/MRI. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12434. [PMID: 37201176 PMCID: PMC10186069 DOI: 10.1002/dad2.12434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/03/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aβ PET positivity were converted. RESULTS The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSION Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.
Collapse
Affiliation(s)
- William Coath
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Marc Modat
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - M. Jorge Cardoso
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Pawel J. Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUCLLondonUK
| | | | - Thomas D. Parker
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Ashvini Keshavan
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Sarah M. Buchanan
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Sarah E. Keuss
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Matthew J. Harris
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau ‐ Paris Brain Institute ‐ ICM, Inserm, CNRS, AP‐HP, Hôpital Pitié Salpêtrière, InriaAramis project‐teamParisFrance
| | - John Dickson
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Anna Barnes
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - David L. Thomas
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Department of Brain Repair and RehabilitationUCL Queen Square Institute of NeurologyLondonUK
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Daniel Beasley
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Ian B. Malone
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLLondonUK
| | - Kjell Erlandsson
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Benjamin A. Thomas
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Michael Schöll
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Wallenberg Centre for Molecular and Translational MedicineUniversity of GothenburgMölndalSweden
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | | | - Nick C. Fox
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Dementia Research InstituteUCL Queen Square Institute of NeurologyLondonUK
| | | | - David M. Cash
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUCLLondonUK
- Dementia Research InstituteUCL Queen Square Institute of NeurologyLondonUK
| | | |
Collapse
|
14
|
Fu JF, Lois C, Sanchez J, Becker JA, Rubinstein ZB, Thibault E, Salvatore AN, Sari H, Farrell ME, Guehl NJ, Normandin MD, Fakhri GE, Johnson KA, Price JC. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [ 18F]MK-6240 PET uptake. J Cereb Blood Flow Metab 2023; 43:581-594. [PMID: 36420769 PMCID: PMC10063833 DOI: 10.1177/0271678x221142139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/17/2022] [Accepted: 11/06/2022] [Indexed: 11/25/2022]
Abstract
[18F]MK-6240 meningeal/extracerebral off-target binding may impact tau quantification. We examined the kinetics and longitudinal changes of extracerebral and reference regions. [18F]MK-6240 PET was performed in 24 cognitively-normal and eight cognitively-impaired subjects, with arterial samples in 13 subjects. Follow-up scans at 6.1 ± 0.5 (n = 25) and 13.3 ± 0.9 (n = 16) months were acquired. Extracerebral and reference region (cerebellar gray matter (CerGM)-based, cerebral white matter (WM), pons) uptake were evaluated using standardized uptake values (SUV90-110), spectral analysis, and distribution volume. Longitudinal changes in SUV90-110 were examined. The impact of reference region on target region outcomes, partial volume correction (PVC) and regional erosion were evaluated. Eroded WM and pons showed lower variability, lower extracerebral contamination, and lower longitudinal changes than CerGM-based regions. CerGM-based regions resulted larger cross-sectional effect sizes for group differentiation. Extracerebral signal was high in 50% of subjects and exhibited irreversible kinetics and nonsignificant longitudinal changes over one-year but was highly variable at subject-level. PVC resulted in higher variability in reference region uptake and longitudinal changes. Our results suggest that eroded CerGM may be preferred for cross-sectional, whilst eroded WM or pons may be preferred for longitudinal [18F]MK-6240 studies. For CerGM, erosion was necessary (preferred over PVC) to address the heterogenous nature of extracerebral signal.
Collapse
Affiliation(s)
- Jessie Fanglu Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Cristina Lois
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Justin Sanchez
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - J Alex Becker
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Zoe B Rubinstein
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Emma Thibault
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew N Salvatore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hasan Sari
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | | | - Nicolas J Guehl
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Marc D Normandin
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Georges El Fakhri
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Keith A Johnson
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Julie C Price
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| |
Collapse
|
15
|
Ottoy J, Ozzoude M, Zukotynski K, Kang MS, Adamo S, Scott C, Ramirez J, Swardfager W, Lam B, Bhan A, Mojiri P, Kiss A, Strother S, Bocti C, Borrie M, Chertkow H, Frayne R, Hsiung R, Laforce RJ, Noseworthy MD, Prato FS, Sahlas DJ, Smith EE, Kuo PH, Chad JA, Pasternak O, Sossi V, Thiel A, Soucy JP, Tardif JC, Black SE, Goubran M. Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease. J Cereb Blood Flow Metab 2023; 43:921-936. [PMID: 36695071 DOI: 10.1177/0271678x231152001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (Aβ-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered Aβ-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to Aβ-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in Aβ-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in Aβ-PET related more closely to higher cortical Aβ (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and Aβ-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.
Collapse
Affiliation(s)
- Julie Ottoy
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Katherine Zukotynski
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Medical Imaging, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Min Su Kang
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher Scott
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Walter Swardfager
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Benjamin Lam
- Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Aparna Bhan
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Parisa Mojiri
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Alex Kiss
- Department of Research Design and Biostatistics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Stephen Strother
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Christian Bocti
- Service de Neurologie, Département de Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michael Borrie
- Lawson Health Research Institute, Western University, London, ON, Canada
| | - Howard Chertkow
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Robin Hsiung
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Robert Jr Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Université Laval, Québec, QC, Canada
| | - Michael D Noseworthy
- Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Frank S Prato
- Lawson Health Research Institute, Western University, London, ON, Canada
| | | | - Eric E Smith
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Phillip H Kuo
- Department of Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Jordan A Chad
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Vesna Sossi
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Thiel
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Sandra E Black
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | | |
Collapse
|
16
|
Luckett ES, Schaeverbeke J, De Meyer S, Adamczuk K, Van Laere K, Dupont P, Vandenberghe R. Longitudinal changes in 18F-Flutemetamol amyloid load in cognitively intact APOE4 carriers versus noncarriers: Methodological considerations. Neuroimage Clin 2023; 37:103321. [PMID: 36621019 PMCID: PMC9850036 DOI: 10.1016/j.nicl.2023.103321] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
PURPOSE Measuring longitudinal changes in amyloid load in the asymptomatic stage of Alzheimer's disease is of high relevance for clinical research and progress towards more efficacious, timely treatments. Apolipoprotein E ε4 (APOE4) has a well-established effect on the rate of amyloid accumulation. Here we investigated which region of interest and which reference region perform best at detecting the effect of APOE4 on longitudinal amyloid load in individuals participating in the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK). METHODS Ninety cognitively intact F-PACK participants (baseline age: 68 (52-80) years, 46 males, 42 APOE4 carriers) received structural MRI and 18F-Flutemetamol PET scans at baseline and follow-up (6.2 (3.4-10.9) year interval). Standardised uptake value ratios (SUVRs) and Centiloids (CLs) were calculated in a composite cortical volume of interest (SUVRcomp/CL) and in the precuneus (SUVRprec), and amyloid rate of change derived: (follow-up amyloid load - baseline amyloid load) / time interval (years). Four reference regions were used to derive amyloid load: whole cerebellum, cerebellar grey matter, eroded subcortical white matter, and pons. RESULTS When using whole cerebellum or cerebellar grey matter as reference region, APOE4 carriers had a significantly higher SUVRcomp amyloid rate of change than non-carriers (pcorr = 0.004, t = 3.40 (CI 0.005-0.018); pcorr = 0.036, t = 2.66 (CI 0.003-0.018), respectively). Significance was not observed for eroded subcortical white matter or pons (pcorr = 0.144, t = 2.13 (CI 0.0003-0.008); pcorr = 0.116, t = 2.22 (CI 0.005-0.010), respectively). When using CLs as the amyloid measurement, and whole cerebellum, APOE4 carriers had a higher amyloid rate of change than non-carriers (pcorr = 0.012, t = 3.05 (CI 0.499-2.359)). Significance was not observed for the other reference regions. No significance was observed with any of the reference regions and amyloid rate of change in the precuneus (SUVRprec). CONCLUSION In this cognitively intact cohort, a composite neocortical volume of interest together with whole cerebellum or cerebellar grey matter as reference region are the methods of choice for detecting APOE4-dependent differences in amyloid rate of change.
Collapse
Affiliation(s)
- Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium; Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium
| | | | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium; Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium; Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium; Neurology Department, University Hospitals Leuven, Leuven, Belgium.
| |
Collapse
|
17
|
Jeon SY, Byun MS, Yi D, Jung G, Lee JY, Kim YK, Sohn CH, Kang KM, Lee YJ, Lee DY. Circadian rest-activity rhythm and longitudinal brain changes underlying late-life cognitive decline. Psychiatry Clin Neurosci 2022; 77:205-212. [PMID: 36527292 PMCID: PMC10360409 DOI: 10.1111/pcn.13521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
AIM The neurobiological substrates underlying the relationship of circadian rest-activity rhythm (RAR) alteration with accelerated late-life cognitive decline are not clearly understood. In the present study, the longitudinal relationship of objectively measured circadian RAR with in vivo Alzheimer disease (AD) pathologies and cerebrovascular injury was investigated in older adults without dementia. METHODS The present study included 129 participants without dementia who participated in the KBASE (Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease) cohort. All participants underwent actigraphy at baseline and two consecutive [11 C] Pittsburgh compound-B positron emission tomography (PET), [18 F] fluorodeoxyglucose-PET, magnetic resonance imaging, and Mini-Mental State Examination (MMSE) at baseline and at a 2-year follow-up assessment. The associations of circadian RAR with annualized change in neuroimaging measures including global amyloid-beta retention, AD-signature region cerebral glucose metabolism (AD-CM), and white matter hyperintensity volume were examined. RESULTS Delayed acrophase at baseline was significantly associated with greater annualized decline of AD-CM over a 2-year period, but not with that of other neuroimaging measures. In contrast, other circadian RAR parameters at baseline had no association with annualized change of any neuroimaging measures. Annualized decline of AD-CM was also significantly positively associated with the annual change in MMSE scores. Furthermore, a mediation analysis showed that greater reduction in AD-CM mediated the effect of delayed acrophase at baseline on faster decline of MMSE score. CONCLUSION The findings indicate that delayed acrophase in late life may cause or predict hypometabolism at AD-signature brain regions, which underlies cognitive decline in the near future.
Collapse
Affiliation(s)
- So Yeon Jeon
- Department of Psychiatry, Chungnam National University Hospital, Daejeon, Republic of Korea.,Department of Psychiatry, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Centre, Seoul National University, Seoul, South Korea
| | - Gijung Jung
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun-Young Lee
- Department of Neuropsychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University College of Medcine, Seoul, South Korea.,Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Institute of Human Behavioral Medicine, Medical Research Centre, Seoul National University, Seoul, South Korea
| | | |
Collapse
|
18
|
Carneiro CDG, Faria DDP, Coutinho AM, Ono CR, Duran FLDS, da Costa NA, Garcez AT, da Silveira PS, Forlenza OV, Brucki SMD, Nitrini R, Busatto G, Buchpiguel CA. Evaluation of 10-minute post-injection 11C-PiB PET and its correlation with 18F-FDG PET in older adults who are cognitively healthy, mildly impaired, or with probable Alzheimer's disease. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2022; 44:495-506. [PMID: 36420910 PMCID: PMC9561831 DOI: 10.47626/1516-4446-2021-2374] [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: 11/22/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Positron emission tomography (PET) allows in vivo evaluation of molecular targets in neurodegenerative diseases, such as Alzheimer's disease. Mild cognitive impairment is an intermediate stage between normal cognition and Alzheimer-type dementia. In vivo fibrillar amyloid-beta can be detected in PET using [11C]-labeled Pittsburgh compound B (11C-PiB). In contrast, [18F]fluoro-2-deoxy-d-glucose (18F-FDG) is a neurodegeneration biomarker used to evaluate cerebral glucose metabolism, indicating neuronal injury and synaptic dysfunction. In addition, early cerebral uptake of amyloid-PET tracers can determine regional cerebral blood flow. The present study compared early-phase 11C-PiB and 18F-FDG in older adults without cognitive impairment, amnestic mild cognitive impairment, and clinical diagnosis of probable Alzheimer's disease. METHODS We selected 90 older adults, clinically classified as healthy controls, with amnestic mild cognitive impairment, or with probable Alzheimer's disease, who underwent an 18F-FDG PET, early-phase 11C-PiB PET and magnetic resonance imaging. All participants were also classified as amyloid-positive or -negative in late-phase 11C-PiB. The data were analyzed using statistical parametric mapping. RESULTS We found that the probable Alzheimer's disease and amnestic mild cognitive impairment group had lower early-phase 11C-PiB uptake in limbic structures than 18F-FDG uptake. The images showed significant interactions between amyloid-beta status (negative or positive). However, early-phase 11C-PiB appears to provide different information from 18F-FDG about neurodegeneration. CONCLUSIONS Our study suggests that early-phase 11C-PiB uptake correlates with 18F-FDG, irrespective of the particular amyloid-beta status. In addition, we observed distinct regional distribution patterns between both biomarkers, reinforcing the need for more robust studies to investigate the real clinical value of early-phase amyloid-PET imaging.
Collapse
Affiliation(s)
- Camila de Godoi Carneiro
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil,Centro de Investigação Translacional em Oncologia, Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Daniele de Paula Faria
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil,Centro de Investigação Translacional em Oncologia, Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Artur Martins Coutinho
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Carla Rachel Ono
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Fábio Luís de Souza Duran
- Laboratório Neuro-Imagem em Psiquiatria (LIM 21), Departamento de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Naomi Antunes da Costa
- Laboratório Neuro-Imagem em Psiquiatria (LIM 21), Departamento de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Alexandre Teles Garcez
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil
| | - Paula Squarzoni da Silveira
- Laboratório Neuro-Imagem em Psiquiatria (LIM 21), Departamento de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Orestes Vicente Forlenza
- Laboratório de Neurociências (LIM 27), Departamento de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Sonia Maria Dozzi Brucki
- Departamento de Neurologia, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Ricardo Nitrini
- Departamento de Neurologia, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Geraldo Busatto
- Laboratório Neuro-Imagem em Psiquiatria (LIM 21), Departamento de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, USP, São Paulo, SP, Brazil
| | - Carlos Alberto Buchpiguel
- Laboratório de Medicina Nuclear (LIM 43), Departamento de Radiologia e Oncologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil,Correspondence: Carlos Alberto Buchpiguel, Universidade de São Paulo, Faculdade de Medicina da Universidade de São Paulo, Av. Dr. Arnaldo, 455, CEP 01255-090, São Paulo, SP, Brazil. E-mail:
| |
Collapse
|
19
|
Hong YJ, Kim CM, Lee JH, Sepulcre J. Correlations between APOE4 allele and regional amyloid and tau burdens in cognitively normal older individuals. Sci Rep 2022; 12:14307. [PMID: 35995824 PMCID: PMC9395408 DOI: 10.1038/s41598-022-18325-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/09/2022] [Indexed: 11/09/2022] Open
Abstract
The correlations between apolipoprotein epsilon 4 (APOE4) status and regional amyloid, tau, and cortical thickness in cognitively normal elderly are not fully understood. Our cross-sectional study aimed to compare regional amyloid/tau burden, and cortical thickness according to APOE4 carrier status and assess correlations between APOE4 and Alzheimer's disease (AD)-related biomarker burdens. We analyzed 185 cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Participants aged 55-90 with normal cognitive function were divided into amyloid ß-positive (Aß+) APOE4 carriers (group 1, n = 27), Aß+ APOE4 non-carriers (group 2, n = 29), and Aß- normal controls (group 0, n = 129). We compared amyloid depositions, tau depositions, and cortical thickness among the three groups and assessed correlations between APOE4 existence and imaging biomarkers adjusted for age and sex. The participants in group 2 were older than those in the other groups. The regional amyloid/tau standardized uptake value ratios (SUVRs) did not differ between groups 1 and 2, but the amyloid/tau SUVRs in most regions were numerically higher after adjusting for age difference. APOE4 allele had robust correlations with increased amyloid burden in the fronto-temporo-parietal cortical areas after adjustment for age and sex, but it had weaker and mixed correlations with the regional tau burden and did not have significant correlation with cortical thickness. We identified that the presence of APOE4 allele might be more highly associated with amyloid deposition than with other AD-related biomarkers such as tau or cortical thickness in cognitively normal elderly.
Collapse
Affiliation(s)
- Yun Jeong Hong
- Department of Neurology, Uijeongbu St. Mary's Hospital, Catholic University of Korea, Seoul, Korea
| | - Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 13th Street, Charlestown, MA, 02129, USA
| | - Jae Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 13th Street, Charlestown, MA, 02129, USA.
| |
Collapse
|
20
|
Zeydan B, Schwarz CG, Przybelski SA, Lesnick TG, Kremers WK, Senjem ML, Kantarci OH, Min PH, Kemp BJ, Jack CR, Kantarci K, Lowe VJ. Comparison of 11C-Pittsburgh Compound B and 18F-Flutemetamol White Matter Binding in PET. J Nucl Med 2022; 63:1239-1244. [PMID: 34916245 PMCID: PMC9364341 DOI: 10.2967/jnumed.121.263281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/30/2021] [Indexed: 02/03/2023] Open
Abstract
PET imaging with β-amyloid ligands is emerging as a molecular imaging technique targeting white matter integrity and demyelination. β-amyloid PET ligands such as 11C-Pittsburgh compound B (11C-PiB) have been considered for quantitative measurement of myelin content changes in multiple sclerosis, but 11C-PiB is not commercially available given its short half-life. A 18F PET ligand such as flutemetamol with a longer half-life may be an alternative, but its ability to differentiate white matter hyperintensities (WMH) from normal-appearing white matter (NAWM) and its relationship with age remains to be investigated. Methods: Cognitively unimpaired (CU) older and younger adults (n = 61) were recruited from the community responding to a study advertisement for β-amyloid PET. Participants prospectively underwent MRI, 11C-PiB, and 18F-flutemetamol PET scans. MRI fluid-attenuated inversion recovery images were segmented into WMH and NAWM and registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol PET images were also registered to the T1-weighted MRI. 11C-PiB and 18F-flutemetamol SUV ratios (SUVrs) from the WMH and NAWM were calculated using cerebellar crus uptake as a reference for both 11C-PiB and 18F-flutemetamol. Results: The median age was 38 y (range, 30-48 y) in younger adults and 67 y (range, 61-83 y) in older adults. WMH and NAWM SUVrs were higher with 18F-flutemetamol than with 11C-PiB in both older (P < 0.001) and younger (P < 0.001) CU adults. 11C-PiB and 18F-flutemetamol SUVrs were higher in older than in younger CU adults in both WMH (P < 0.001) and NAWM (P < 0.001). 11C-PiB and 18F-flutemetamol SUVrs were higher in NAWM than WMH in both older (P < 0.001) and younger (P < 0.001) CU adults. There was no apparent difference between 11C-PiB and 18F-flutemetamol SUVrs in differentiating WMH from NAWM in older and in younger adults. Conclusion:11C-PiB and 18F-flutemetamol show a similar topographic pattern of uptake in white matter with a similar association with age in WMH and NAWM. 11C-PiB and 18F-flutemetamol can also effectively distinguish between WMH and NAWM. However, given its longer half-life, commercial availability, and higher binding potential, 18F-flutemetamol can be an alternative to 11C-PiB in molecular imaging studies specifically targeting multiple sclerosis to evaluate white matter integrity.
Collapse
Affiliation(s)
- Burcu Zeydan
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota; and
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota
| | | | - Paul H Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Bradley J Kemp
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
| |
Collapse
|
21
|
Abrahamson EE, Kofler JK, Becker CR, Price JC, Newell KL, Ghetti B, Murrell JR, McLean CA, Lopez OL, Mathis CA, Klunk WE, Villemagne VL, Ikonomovic MD. 11C-PiB PET can underestimate brain amyloid-β burden when cotton wool plaques are numerous. Brain 2022; 145:2161-2176. [PMID: 34918018 PMCID: PMC9630719 DOI: 10.1093/brain/awab434] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/02/2021] [Accepted: 10/20/2021] [Indexed: 09/01/2023] Open
Abstract
Individuals with familial Alzheimer's disease due to PSEN1 mutations develop high cortical fibrillar amyloid-β load but often have lower cortical 11C-Pittsburgh compound B (PiB) retention than Individuals with sporadic Alzheimer's disease. We hypothesized this is influenced by limited interactions of Pittsburgh compound B with cotton wool plaques, an amyloid-β plaque type common in familial Alzheimer's disease but rare in sporadic Alzheimer's disease. Histological sections of frontal and temporal cortex, caudate nucleus and cerebellum were obtained from 14 cases with sporadic Alzheimer's disease, 12 cases with familial Alzheimer's disease due to PSEN1 mutations, two relatives of a PSEN1 mutation carrier but without genotype information and three non-Alzheimer's disease cases. Sections were processed immunohistochemically using amyloid-β-targeting antibodies and the fluorescent amyloid stains cyano-PiB and X-34. Plaque load was quantified by percentage area analysis. Frozen homogenates from the same brain regions from five sporadic Alzheimer's disease and three familial Alzheimer's disease cases were analysed for 3H-PiB in vitro binding and concentrations of amyloid-β1-40 and amyloid-β1-42. Nine sporadic Alzheimer's disease, three familial Alzheimer's disease and three non-Alzheimer's disease participants had 11C-PiB PET with standardized uptake value ratios calculated using the cerebellum as the reference region. Cotton wool plaques were present in the neocortex of all familial Alzheimer's disease cases and one sporadic Alzheimer's disease case, in the caudate nucleus from four familial Alzheimer's disease cases, but not in the cerebellum. Cotton wool plaques immunolabelled robustly with 4G8 and amyloid-β42 antibodies but weakly with amyloid-β40 and amyloid-βN3pE antibodies and had only background cyano-PiB fluorescence despite labelling with X-34. Relative to amyloid-β plaque load, cyano-Pittsburgh compound B plaque load was similar in sporadic Alzheimer's disease while in familial Alzheimer's disease it was lower in the neocortex and the caudate nucleus. In both regions, insoluble amyloid-β1-42 and amyloid-β1-40 concentrations were similar in familial Alzheimer's disease and sporadic Alzheimer's disease groups, while 3H-PiB binding was lower in the familial Alzheimer's disease than the sporadic Alzheimer's disease group. Higher amyloid-β1-42 concentration associated with higher 3H-PiB binding in sporadic Alzheimer's disease but not familial Alzheimer's disease. 11C-PiB retention correlated with region-matched post-mortem amyloid-β plaque load; however, familial Alzheimer's disease cases with abundant cotton wool plaques had lower 11C-PiB retention than sporadic Alzheimer's disease cases with similar amyloid-β plaque loads. PiB has limited ability to detect amyloid-β aggregates in cotton wool plaques and may underestimate total amyloid-β plaque burden in brain regions with abundant cotton wool plaques.
Collapse
Affiliation(s)
- Eric E Abrahamson
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Pittsburgh VA Healthcare System, Pittsburgh, PA, USA
| | - Julia K Kofler
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carl R Becker
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Julie C Price
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Massachusetts General Hospital, A. A. Martinos Center for Biomedical Imaging, Cambridge, MA, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, IN, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, IN, USA
| | - Jill R Murrell
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Catriona A McLean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Pittsburgh VA Healthcare System, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| |
Collapse
|
22
|
Pietroboni AM, Colombi A, Carandini T, Sacchi L, Fenoglio C, Marotta G, Arighi A, De Riz MA, Fumagalli GG, Castellani M, Bozzali M, Scarpini E, Galimberti D. Amyloid PET imaging and dementias: potential applications in detecting and quantifying early white matter damage. Alzheimers Res Ther 2022; 14:33. [PMID: 35151361 PMCID: PMC8841045 DOI: 10.1186/s13195-021-00933-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/04/2021] [Indexed: 11/11/2022]
Abstract
Purpose Positron emission tomography (PET) with amyloid tracers (amy-PET) allows the quantification of pathological amyloid deposition in the brain tissues, including the white matter (WM). Here, we evaluate amy-PET uptake in WM lesions (WML) and in the normal-appearing WM (NAWM) of patients with Alzheimer’s disease (AD) and non-AD type of dementia. Methods Thirty-three cognitively impaired subjects underwent brain magnetic resonance imaging (MRI), Aβ1-42 (Aβ) determination in the cerebrospinal fluid (CSF) and amy-PET. Twenty-three patients exhibiting concordant results in both CSF analysis and amy-PET for cortical amyloid deposition were recruited and divided into two groups, amyloid positive (A+) and negative (A−). WML quantification and brain volumes’ segmentation were performed. Standardized uptake values ratios (SUVR) were calculated in the grey matter (GM), NAWM and WML on amy-PET coregistered to MRI images. Results A+ compared to A− showed a higher WML load (p = 0.049) alongside higher SUVR in all brain tissues (p < 0.01). No correlations between CSF Aβ levels and WML and NAWM SUVR were found in A+, while, in A−, CSF Aβ levels were directly correlated to NAWM SUVR (p = 0.04). CSF Aβ concentration was the only predictor of NAWM SUVR (adj R2 = 0.91; p = 0.04) in A−. In A+ but not in A− direct correlations were identified between WM and GM SUVR (p < 0.01). Conclusions Our data provide evidence on the role of amy-PET in the assessment of microstructural WM injury in non-AD dementia, whereas amy-PET seems less suitable to assess WM damage in AD patients due to a plausible amyloid accrual therein.
Collapse
Affiliation(s)
- Anna M Pietroboni
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy. .,University of Milan, Milan, Italy. .,Dino Ferrari Center, Milan, Italy.
| | - Annalisa Colombi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Tiziana Carandini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Luca Sacchi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | | | - Giorgio Marotta
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy
| | - Andrea Arighi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Milena A De Riz
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Giorgio G Fumagalli
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Massimo Castellani
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy
| | - Marco Bozzali
- 'Rita Levi Montalcini' Department of Neuroscience, University of Torino, Turin, Italy.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Elio Scarpini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Daniela Galimberti
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| |
Collapse
|
23
|
Teipel SJ, Dyrba M, Vergallo A, Lista S, Habert MO, Potier MC, Lamari F, Dubois B, Hampel H, Grothe MJ. Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis. Front Aging Neurosci 2022; 13:748198. [PMID: 35002673 PMCID: PMC8729321 DOI: 10.3389/fnagi.2021.748198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/05/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis. Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study). Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data. Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.
Collapse
Affiliation(s)
- Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Andrea Vergallo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Simone Lista
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France.,Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'Hôpital, Paris, France.,Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Marie Odile Habert
- Laboratoire d'Imagerie Biomédicale, CNRS, INSERM, LIB, Sorbonne University, Paris, France.,Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.,Centre d'Acquisition et Traitement des Images (CATI platform), Paris, France
| | - Marie-Claude Potier
- ICM Institut du Cerveau et de la Moelle Épinière, CNRS UMR 7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France
| | - Foudil Lamari
- UF Biochimie des Maladies Neurométaboliques, Service de Biochimie Métabolique, Hôpital Pitié-Salpêtrière, Paris, France
| | - Bruno Dubois
- Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'Hôpital, Paris, France
| | - Harald Hampel
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'Hôpital, Paris, France
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| |
Collapse
|
24
|
Chen CD, Joseph-Mathurin N, Sinha N, Zhou A, Li Y, Friedrichsen K, McCullough A, Franklin EE, Hornbeck R, Gordon B, Sharma V, Cruchaga C, Goate A, Karch C, McDade E, Xiong C, Bateman RJ, Ghetti B, Ringman JM, Chhatwal J, Masters CL, McLean C, Lashley T, Su Y, Koeppe R, Jack C, Klunk WE, Morris JC, Perrin RJ, Cairns NJ, Benzinger TLS. Comparing amyloid-β plaque burden with antemortem PiB PET in autosomal dominant and late-onset Alzheimer disease. Acta Neuropathol 2021; 142:689-706. [PMID: 34319442 PMCID: PMC8815340 DOI: 10.1007/s00401-021-02342-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 12/31/2022]
Abstract
Pittsburgh compound B (PiB) radiotracer for positron emission tomography (PET) imaging can bind to different types of amyloid-β plaques and blood vessels (cerebral amyloid angiopathy). However, the relative contributions of different plaque subtypes (diffuse versus cored/compact) to in vivo PiB PET signal on a region-by-region basis are incompletely understood. Of particular interest is whether the same staging schemes for summarizing amyloid-β burden are appropriate for both late-onset and autosomal dominant forms of Alzheimer disease (LOAD and ADAD). Here, we compared antemortem PiB PET with follow-up postmortem estimation of amyloid-β burden using stereologic methods to estimate the relative area fraction of diffuse and cored/compact amyloid-β plaques across 16 brain regions in 15 individuals with ADAD and 14 individuals with LOAD. In ADAD, we found that PiB PET correlated with diffuse plaques in the frontal, parietal, temporal, and striatal regions commonly used to summarize amyloid-β burden in PiB PET, and correlated with both diffuse and cored/compact plaques in the occipital lobe and parahippocampal gyrus. In LOAD, we found that PiB PET correlated with both diffuse and cored/compact plaques in the anterior cingulate, frontal lobe (middle frontal gyrus), and parietal lobe, and showed additional correlations with diffuse plaque in the amygdala and occipital lobe, and with cored/compact plaque in the temporal lobe. Thus, commonly used PiB PET summary regions predominantly reflect diffuse plaque burden in ADAD and a mixture of diffuse and cored/compact plaque burden in LOAD. In direct comparisons of ADAD and LOAD, postmortem stereology identified much greater mean amyloid-β plaque burdens in ADAD versus LOAD across almost all brain regions studied. However, standard PiB PET did not recapitulate these stereologic findings, likely due to non-trivial amyloid-β plaque burdens in ADAD within the cerebellum and brainstem-commonly used reference regions in PiB PET. Our findings suggest that PiB PET summary regions correlate with amyloid-β plaque burden in both ADAD and LOAD; however, they might not be reliable in direct comparisons of regional amyloid-β plaque burden between the two forms of AD.
Collapse
Affiliation(s)
- Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Namita Sinha
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology, University of Manitoba, Shared Health, Winnipeg, MB, Canada
| | - Aihong Zhou
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Karl Friedrichsen
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Erin E Franklin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Vijay Sharma
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John M Ringman
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jasmeer Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Catriona McLean
- Department of Anatomic Pathology, Alfred Hospital, Melbourne, VIC, Australia
| | - Tammaryn Lashley
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, University College London, London, UK
| | - Yi Su
- Banner Alzheimer's Institute, Banner Health, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Banner Health, Phoenix, AZ, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nigel J Cairns
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
| |
Collapse
|
25
|
18F-florbetapir PET as a marker of myelin integrity across the Alzheimer's disease spectrum. Eur J Nucl Med Mol Imaging 2021; 49:1242-1253. [PMID: 34581847 PMCID: PMC8921113 DOI: 10.1007/s00259-021-05493-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 07/08/2021] [Indexed: 01/23/2023]
Abstract
Purpose Recent evidence suggests that PET imaging with amyloid-β (Aβ) tracers can be used to assess myelin integrity in cerebral white matter (WM). Alzheimer’s disease (AD) is characterized by myelin changes that are believed to occur early in the disease course. Nevertheless, the extent to which demyelination, as measured with Aβ PET, contributes to AD progression remains unexplored. Methods Participants with concurrent 18F-florbetapir (FBP) PET, MRI, and cerebrospinal fluid (CSF) examinations were included (241 cognitively normal, 347 Aβ-positive cognitively impaired, and 207 Aβ-negative cognitively impaired subjects). A subset of these participants had also available diffusion tensor imaging (DTI) images (n = 195). We investigated cross-sectional associations of FBP retention in the white matter (WM) with MRI-based markers of WM degeneration, AD clinical progression, and fluid biomarkers. In longitudinal analyses, we used linear mixed models to assess whether FBP retention in normal-appearing WM (NAWM) predicted progression of WM hyperintensity (WMH) burden and clinical decline. Results In AD-continuum individuals, FBP retention in NAWM was (1) higher compared with WMH regions, (2) associated with DTI-based measures of WM integrity, and (3) associated with longitudinal progression of WMH burden. FBP uptake in WM decreased across the AD continuum and with increasingly abnormal CSF biomarkers of AD. Furthermore, FBP retention in the WM was associated with large-calibre axon degeneration as reflected by abnormal plasma neurofilament light chain levels. Low FBP uptake in NAWM predicted clinical decline in preclinical and prodromal AD, independent of demographics, global cortical Aβ, and WMH burden. Most of these associations were also observed in Aβ-negative cognitively impaired individuals. Conclusion These results support the hypothesis that FBP retention in the WM is myelin-related. Demyelination levels progressed across the AD continuum and were associated with clinical progression at early stages, suggesting that this pathologic process might be a relevant degenerative feature in the disease course. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05493-y.
Collapse
|
26
|
Young CB, Landau SM, Harrison TM, Poston KL, Mormino EC. Influence of common reference regions on regional tau patterns in cross-sectional and longitudinal [ 18F]-AV-1451 PET data. Neuroimage 2021; 243:118553. [PMID: 34487825 PMCID: PMC8785682 DOI: 10.1016/j.neuroimage.2021.118553] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/06/2021] [Accepted: 09/02/2021] [Indexed: 10/30/2022] Open
Abstract
Tau PET has allowed for critical insights into in vivo patterns of tau accumulation and change in individuals early in the Alzheimer's disease (AD) continuum. A key methodological step in tau PET analyses is the selection of a reference region, but there is not yet consensus on the optimal region especially for longitudinal tau PET analyses. This study examines how reference region selection influences results related to disease stage at baseline and over time. Longitudinal flortaucipir ([18F]-AV1451) PET scans were examined using several common reference regions (e.g., eroded subcortical white matter, inferior cerebellar gray matter) in 62 clinically unimpaired amyloid negative (CU A-) individuals, 73 CU amyloid positive (CU A+) individuals, and 64 amyloid positive individuals with mild cognitive impairment (MCI A+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cross-sectionally, both reference regions resulted in robust group differences between CU A-, CU A+, and MCI A+ groups, along with significant associations with CSF phosphorylated tau (pTau-181). However, these results were more focally specific and akin to Braak Staging when using eroded white matter, whereas effects with inferior cerebellum were globally distributed across most cortical regions. Longitudinally, utilization of eroded white matter revealed significant accumulation greater than zero across more regions whereas change over time was diminished using inferior cerebellum. Interestingly, the inferior temporal target region seemed most robust to reference region selection with expected cross-sectional and longitudinal signal across both reference regions. With few exceptions, baseline tau did not significantly predict longitudinal change in tau in the same region regardless of reference region. In summary, reference region selection deserves further evaluation as this methodological step may lead to disparate findings. Inferior cerebellar gray matter may be more sensitive to cross-sectional flortaucipir differences, whereas eroded subcortical white matter may be more sensitive for longitudinal analyses examining regional patterns of change.
Collapse
Affiliation(s)
- Christina B Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA United States.
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA United States
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA United States
| | - Kathleen L Poston
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA United States
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA United States
| | | |
Collapse
|
27
|
Markiewicz PJ, Matthews JC, Ashburner J, Cash DM, Thomas DL, De Vita E, Barnes A, Cardoso MJ, Modat M, Brown R, Thielemans K, da Costa-Luis C, Lopes Alves I, Gispert JD, Schmidt ME, Marsden P, Hammers A, Ourselin S, Barkhof F. Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging. Neuroimage 2021; 232:117821. [PMID: 33588030 PMCID: PMC8204268 DOI: 10.1016/j.neuroimage.2021.117821] [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: 09/10/2020] [Revised: 12/25/2020] [Accepted: 01/21/2021] [Indexed: 10/29/2022] Open
Abstract
Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.
Collapse
Affiliation(s)
- Pawel J Markiewicz
- Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK. http://www.nmi.cs.ucl.ac.uk
| | - Julian C Matthews
- Division of Neuroscience & Experimental Psychology, University of Manchester, UK
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK
| | - David M Cash
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - David L Thomas
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, UK; Dementia Research Centre, Queen Square Institute of Neurology, University College London, UK
| | - Enrico De Vita
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Richard Brown
- Institute of Nuclear Medicine, University College London, London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
| | - Casper da Costa-Luis
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK; Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK
| | - Isadora Lopes Alves
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
| | - Juan Domingo Gispert
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | | | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing; Department of Medical Physics and Biomedical Engineering, University College London Gower Street WC1E 6BT, London, UK; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, Netherlands
| |
Collapse
|
28
|
Wang ML, Yu MM, Li WB, Li YH. Longitudinal Association between White Matter Hyperintensities and White Matter Beta-Amyloid Deposition in Cognitively Unimpaired Elderly. Curr Alzheimer Res 2021; 18:8-13. [PMID: 33761854 DOI: 10.2174/1567205018666210324125116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 01/29/2021] [Accepted: 03/15/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND White matter (WM) beta-amyloid uptake has been used as a reference region to calculate the cortical standard uptake value ratio (SUVr). However, white matter hyperintensities (WMH) may have an influence on WM beta-amyloid uptake. Our study aimed to investigate the associations between WMH and WM beta-amyloid deposition in cognitively unimpaired elderly. METHODS Data from 83 cognitively unimpaired individuals in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were analyzed. All participants had complete baseline and four-year follow-up information about WMH volume, WM 18F-AV-45 SUVr, and cognitive function, including ADNI-Memory (ADNI-Mem) and ADNI-Executive function (ADNI-EF) scores. Cross-sectional and longitudinal linear regression analyses were used to determine the associations between WMH and WM SUVr and cognitive measures. RESULTS Lower WM 18F-AV-45 SUVr at baseline was associated with younger age (β=0.01, P=0.037) and larger WMH volume (β=-0.049, P=0.048). The longitudinal analysis found an annual increase in WM 18F-AV-45 SUVr was associated with an annual decrease in WMH volume (β=-0.016, P=0.041). An annual decrease in the ADNI-Mem score was associated with an annual increase in WMH volume (β=-0.070, P=0.001), an annual decrease in WM 18F-AV-45 SUVr (β=0.559, P=0.030), and fewer years of education (β=0.011, P=0.044). There was no significant association between WM 18F-AV-45 SUVr and ADNI-EF (P>0.05). CONCLUSION Reduced beta-amyloid deposition in WM was associated with higher WMH load and memory decline in cognitively unimpaired elderly. WMH volume should be considered when WM 18F-AV-45 SUVr is used as a reference for evaluating cortical 18F-AV-45 SUVr.
Collapse
Affiliation(s)
- Ming-Liang Wang
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Meng-Meng Yu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Wen-Bin Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Yue-Hua Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| |
Collapse
|
29
|
Heeman F, Hendriks J, Lopes Alves I, Ossenkoppele R, Tolboom N, van Berckel BNM, Lammertsma AA, Yaqub M. [ 11C]PIB amyloid quantification: effect of reference region selection. EJNMMI Res 2020; 10:123. [PMID: 33074395 PMCID: PMC7572969 DOI: 10.1186/s13550-020-00714-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 09/24/2020] [Indexed: 11/24/2022] Open
Abstract
Background The standard reference region (RR) for amyloid-beta (Aβ) PET studies is the cerebellar grey matter (GMCB), while alternative RRs have mostly been utilized without prior validation against the gold standard. This study compared five commonly used RRs to gold standard plasma input-based quantification using the GMCB. Methods Thirteen subjects from a test–retest (TRT) study and 30 from a longitudinal study were retrospectively included (total: 17 Alzheimer’s disease, 13 mild cognitive impairment, 13 controls). Dynamic [11C]PiB PET (90 min) and T1-weighted MR scans were co-registered and time–activity curves were extracted for cortical target regions and the following RRs: GMCB, whole cerebellum (WCB), white matter brainstem/pons (WMBS), whole brainstem (WBS) and eroded subcortical white matter (WMES). A two-tissue reversible plasma input model (2T4k_Vb) with GMCB as RR, reference Logan and the simplified reference tissue model were used to derive distribution volume ratios (DVRs), and standardized uptake value (SUV) ratios were calculated for 40–60 min and 60–90 min intervals. Parameter variability was evaluated using TRT scans, and correlations and agreements with the gold standard (DVR from 2T4k_Vb with GMCB RR) were also assessed. Next, longitudinal changes in SUVs (both intervals) were assessed for each RR. Finally, the ability to discriminate between visually Aβ positive and Aβ negative scans was assessed. Results All RRs yielded stable TRT performance (max 5.1% variability), with WCB consistently showing lower variability. All approaches were able to discriminate between Aβ positive and Aβ negative scans, with highest effect sizes obtained for GMCB (range − 0.9 to − 0.7), followed by WCB (range − 0.8 to − 0.6). Furthermore, all approaches provided good correlations with the gold standard (r ≥ 0.78), while the highest bias (as assessed by the regression slope) was observed using WMES (range slope 0.52–0.67), followed by WBS (range slope 0.58–0.92) and WMBS (range slope 0.62–0.91). Finally, RR SUVs were stable across a period of 2.6 years for all except WBS and WMBS RRs (60–90 min interval). Conclusions GMCB and WCB are considered the best RRs for quantifying amyloid burden using [11C]PiB PET.
Collapse
Affiliation(s)
- Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - Janine Hendriks
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Neurology and Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.,Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Nelleke Tolboom
- Imaging Division, Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | | |
Collapse
|
30
|
Therriault J, Benedet AL, Pascoal TA, Savard M, Ashton NJ, Chamoun M, Tissot C, Lussier F, Kang MS, Bezgin G, Wang T, Fernandes-Arias J, Massarweh G, Vitali P, Zetterberg H, Blennow K, Saha-Chaudhuri P, Soucy JP, Gauthier S, Rosa-Neto P. Determining Amyloid-β Positivity Using 18F-AZD4694 PET Imaging. J Nucl Med 2020; 62:247-252. [PMID: 32737243 DOI: 10.2967/jnumed.120.245209] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
Amyloid-β deposition into plaques is a pathologic hallmark of Alzheimer disease appearing years before the onset of symptoms. Although cerebral amyloid-β deposition occurs on a continuum, dichotomization into positive and negative groups has advantages for diagnosis, clinical management, and population enrichment for clinical trials. 18F-AZD4694 (also known as 18F-NAV4694) is an amyloid-β imaging ligand with high affinity for amyloid-β plaques. Despite being used in multiple academic centers, no studies have assessed a quantitative cutoff for amyloid-β positivity using 18F-AZD4694 PET. Methods: We assessed 176 individuals [young adults (n = 22), cognitively unimpaired elderly (n = 89), and cognitively impaired (n = 65)] who underwent amyloid-β PET with 18F-AZD4694, lumbar puncture, structural MRI, and genotyping for APOEε4 18F-AZD4694 values were normalized using the cerebellar gray matter as a reference region. We compared 5 methods for deriving a quantitative threshold for 18F-AZD4694 PET positivity: comparison with young-control SUV ratios (SUVRs), receiver-operating-characteristic (ROC) curves based on clinical classification of cognitively unimpaired elderly versus Alzheimer disease dementia, ROC curves based on visual Aβ-positive/Aβ-negative classification, gaussian mixture modeling, and comparison with cerebrospinal fluid measures of amyloid-β, specifically the Aβ42/Aβ40 ratio. Results: We observed good convergence among the 4 methods: ROC curves based on visual classification (optimal cut point, 1.55 SUVR), ROC curves based on clinical classification (optimal cut point, 1.56 SUVR) gaussian mixture modeling (optimal cut point, 1.55 SUVR), and comparison with cerebrospinal fluid measures of amyloid-β (optimal cut point, 1.51 SUVR). Means and 2 SDs from young controls resulted in a lower threshold (1.33 SUVR) that did not agree with the other methods and labeled most elderly individuals as Aβ-positive. Conclusion: Good convergence was obtained among several methods for determining an optimal cutoff for 18F-AZD4694 PET positivity. Despite conceptual and analytic idiosyncrasies linked with dichotomization of continuous variables, an 18F-AZD4694 threshold of 1.55 SUVR had reliable discriminative accuracy. Although clinical use of amyloid PET is currently by visual inspection of scans, quantitative thresholds may be helpful to arbitrate disagreement among raters or in borderline cases.
Collapse
Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Cecile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Firoza Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Gleb Bezgin
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Tina Wang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Jaime Fernandes-Arias
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, Montreal, Quebec, Canada.,Department of Radiochemistry, McGill University, Montreal, Quebec, Canada; and
| | - Paolo Vitali
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada .,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, Montreal, Quebec, Canada
| |
Collapse
|
31
|
Tanaka T, Stephenson MC, Nai YH, Khor D, Saridin FN, Hilal S, Villaraza S, Gyanwali B, Ihara M, Vrooman H, Weekes AA, Totman JJ, Robins EG, Chen CP, Reilhac A. Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease. Eur J Nucl Med Mol Imaging 2019; 47:319-331. [PMID: 31863136 DOI: 10.1007/s00259-019-04642-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 11/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort. METHODS The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AβL which quantifies the global Aβ burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques. RESULTS Subject's motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AβL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AβL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment. CONCLUSION The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AβL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.
Collapse
Affiliation(s)
- Tomotaka Tanaka
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore. .,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore. .,Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan.
| | - Mary C Stephenson
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Ying-Hwey Nai
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Damian Khor
- Department of Diagnostic Imaging, National Cancer Institute of Singapore, 11 Hospital Drive, Singapore, 169610, Singapore
| | - Francis N Saridin
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Saima Hilal
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Steven Villaraza
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Bibek Gyanwali
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan
| | - Henri Vrooman
- Biomedical Imaging group Rotterdam, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Ashley A Weekes
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - John J Totman
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Edward G Robins
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.,Singapore Bioimaging Consortium, Agency for Science, A*Star,1Fusionopolis way, #20-10 Connexis North Tower, Singapore, 138632, Singapore
| | - Christopher P Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| |
Collapse
|
32
|
López-González FJ, Moscoso A, Efthimiou N, Fernández-Ferreiro A, Piñeiro-Fiel M, Archibald SJ, Aguiar P, Silva-Rodríguez J. Spill-in counts in the quantification of 18F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region. EJNMMI Phys 2019; 6:27. [PMID: 31858289 PMCID: PMC6923310 DOI: 10.1186/s40658-019-0258-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 10/25/2019] [Indexed: 12/17/2022] Open
Abstract
Background We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated. Methods We evaluated magnetic resonance imaging and 18F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVRCGM) and the whole cerebellum (SUVRWC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVRCGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation. Results WM-SUVRCGM followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVRCGM was linearly correlated to WM-SUVRCGM (r = 0.82, linear fit slope = 0.28). SUVRWC was linearly correlated to WM-SUVRCGM (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVRCGM (slopes of 0.06 and 0.07 for iY and RBV), while SUVRWC showed a negative correlation with SUVRCGM after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVRCGM, r = 0.18 for SUVRWC). Conclusions There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.
Collapse
Affiliation(s)
- Francisco Javier López-González
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain
| | - Alexis Moscoso
- Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
| | - Nikos Efthimiou
- PET Research Centre, Faculty of Health Sciences, University of Hull, Hull, UK
| | - Anxo Fernández-Ferreiro
- Pharmacy Department and Pharmacology Group, University Hospital (SERGAS) and Health Research Institute Santiago Compostela (IDIS), Santiago de Compostela, Galicia, Spain
| | - Manuel Piñeiro-Fiel
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain
| | - Stephen J Archibald
- PET Research Centre, Faculty of Health Sciences, University of Hull, Hull, UK
| | - Pablo Aguiar
- Molecular Imaging and Medical Physics Group, Radiology Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain. .,Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain.
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department and Molecular Imaging Research Group, University Hospital (SERGAS) and Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain.,R&D Department, Qubiotech Health Intelligence SL, A Coruña, Galicia, Spain
| | | |
Collapse
|
33
|
Kameyama M, Ishibash K, Wagatsuma K, Toyohara J, Ishii K. A pitfall of white matter reference regions used in [18F] florbetapir PET: a consideration of kinetics. Ann Nucl Med 2019; 33:848-854. [DOI: 10.1007/s12149-019-01397-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 08/17/2019] [Indexed: 12/16/2022]
|
34
|
Hanseeuw BJ, Betensky RA, Jacobs HIL, Schultz AP, Sepulcre J, Becker JA, Cosio DMO, Farrell M, Quiroz YT, Mormino EC, Buckley RF, Papp KV, Amariglio RA, Dewachter I, Ivanoiu A, Huijbers W, Hedden T, Marshall GA, Chhatwal JP, Rentz DM, Sperling RA, Johnson K. Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study. JAMA Neurol 2019; 76:915-924. [PMID: 31157827 PMCID: PMC6547132 DOI: 10.1001/jamaneurol.2019.1424] [Citation(s) in RCA: 548] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Importance Positron emission tomography (PET) imaging now allows in vivo visualization of both neuropathologic hallmarks of Alzheimer disease (AD): amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Observing their progressive accumulation in the brains of clinically normal older adults is critically important to understand the pathophysiologic cascade leading to AD and to inform the choice of outcome measures in prevention trials. Objective To assess the associations among Aβ, tau, and cognition, measured during different observation periods for 7 years. Design, Setting, and Participants Prospective cohort study conducted between 2010 and 2017 at the Harvard Aging Brain Study, Boston, Massachusetts. The study enrolled 279 clinically normal participants. An additional 90 individuals were approached but declined the study or did not meet the inclusion criteria. In this report, we analyzed data from 60 participants who had multiple Aβ and tau PET observations available on October 31, 2017. Main Outcomes and Measures A median of 3 Pittsburgh compound B-PET (Aβ, 2010-2017) and 2 flortaucipir-PET (tau, 2013-2017) images were collected. We used initial PET and slope data, assessing the rates of change in Aβ and tau, to measure cognitive changes. Cognition was evaluated annually using the Preclinical Alzheimer Cognitive Composite (2010-2017). Annual consensus meetings evaluated progression to mild cognitive impairment. Results Of the 60 participants, 35 were women (58%) and 25 were men (42%); median age at inclusion was 73 years (range, 65-85 years). Seventeen participants (28%) exhibited an initial high Aβ burden. An antecedent rise in Aβ was associated with subsequent changes in tau (1.07 flortaucipir standardized uptake value ratios [SUVr]/PiB-SUVr; 95% CI, 0.13-3.46; P = .02). Tau changes were associated with cognitive changes (-3.28 z scores/SUVR; 95% CI, -6.67 to -0.91; P = .001), covarying baseline Aβ and tau. Tau changes were greater in the participants who progressed to mild cognitive impairment (n = 6) than in those who did not (n = 11; 0.05 SUVr per year; 95% CI, 0.03-0.07; P = .001). A serial mediation model demonstrated that the association between initial Aβ and final cognition, measured 7 years later, was mediated by successive changes in Aβ and tau. Conclusions and Relevance We identified sequential changes in normal older adults, from Aβ to tau to cognition, after which the participants with high Aβ with greater tau increase met clinical criteria for mild cognitive impairment. These findings highlight the importance of repeated tau-PET observations to track disease progression and the importance of repeated amyloid-PET observations to detect the earliest AD pathologic changes.
Collapse
Affiliation(s)
- Bernard J Hanseeuw
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Rebecca A Betensky
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Heidi I L Jacobs
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Jorge Sepulcre
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston
| | - J Alex Becker
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston
| | - Danielle M Orozco Cosio
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston
| | - Michelle Farrell
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Yakeel T Quiroz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Elizabeth C Mormino
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Department of Neurology and Neurological Sciences, Stanford University, California
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,The Florey Institute, The University of Melbourne, Victoria, Australia; Melbourne School of Psychological Science, University of Melbourne, Victoria, Australia
| | - Kathryn V Papp
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Rebecca A Amariglio
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ilse Dewachter
- Dementia Research Group, BioMedical Research Institute, Hasselt University, Hasselt, Belgium.,Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.,Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Willem Huijbers
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands
| | - Trey Hedden
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston
| | - Gad A Marshall
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Dorene M Rentz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Keith Johnson
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
35
|
Bischof GN, Jacobs HIL. Subthreshold amyloid and its biological and clinical meaning: Long way ahead. Neurology 2019; 93:72-79. [PMID: 31167933 DOI: 10.1212/wnl.0000000000007747] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 03/26/2019] [Indexed: 01/22/2023] Open
Abstract
The development of in vivo imaging of the pathologic hallmark of Alzheimer disease (AD), β-amyloid (Aβ), altered the framing of its pathophysiology and formulation of inclusion criteria for clinical trials. Recent evidence suggests that in vivo measures of Aβ deposition below a threshold indicative of Aβ positivity carry critical information on future cognitive decline and accumulation of AD pathology, potentially already at a younger age. Here, we integrate the existing literature on histopathology of Aβ and its convergence and divergence with in vivo Aβ imaging. The evidence presented amounts to a reconceptualization, in which we advocate for a closer look into Aβ accumulation rates in earlier life, the factors that promote accumulation, comparative studies with different markers of Aβ, and longitudinal designs to elucidate when AD pathology rises and how it shifts from benign to malignant stages that ultimately define AD. These efforts open a new window of opportunity for disease-modifying interventions.
Collapse
Affiliation(s)
- Gérard N Bischof
- From the Multimodal Imaging Group (G.N.B.), Department of Nuclear Medicine, University Hospital Cologne, Germany; Cognitive Neuroscience (H.I.L.J.), Faculty of Psychology and Neuroscience, and School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Alzheimer Centre Limburg, Maastricht University, the Netherlands; and Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Massachusetts General Hospital, Boston.
| | - Heidi I L Jacobs
- From the Multimodal Imaging Group (G.N.B.), Department of Nuclear Medicine, University Hospital Cologne, Germany; Cognitive Neuroscience (H.I.L.J.), Faculty of Psychology and Neuroscience, and School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Alzheimer Centre Limburg, Maastricht University, the Netherlands; and Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Harvard Medical School, Massachusetts General Hospital, Boston
| |
Collapse
|
36
|
Zeydan B, Schwarz CG, Lowe VJ, Reid RI, Przybelski SA, Lesnick TG, Kremers WK, Senjem ML, Gunter JL, Min H, Vemuri P, Knopman DS, Petersen RC, Jack CR, Kantarci OH, Kantarci K. Investigation of white matter PiB uptake as a marker of white matter integrity. Ann Clin Transl Neurol 2019; 6:678-688. [PMID: 31019992 PMCID: PMC6469255 DOI: 10.1002/acn3.741] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/14/2019] [Accepted: 02/03/2019] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVE To investigate the associations of Pittsburgh compound-B (PiB) uptake in white matter hyperintensities (WMH) and normal appearing white matter (NAWM) with white matter (WM) integrity measured with DTI and cognitive function in cognitively unimpaired older adults. METHODS Cognitively unimpaired older adults from the population-based Mayo Clinic Study of Aging (n = 537, age 65-95) who underwent both PiB PET and DTI were included. The associations of WM PiB standard uptake value ratio (SUVr) with fractional anisotropy (FA) and mean diffusivity (MD) in the WMH and NAWM were tested after adjusting for age. The associations of PiB SUVr with cognitive function z-scores were tested after adjusting for age and global cortical PiB SUVr. RESULTS The WMH PiB SUVr was lower than NAWM PiB SUVr (P < 0.001). In the WMH, lower PiB SUVr correlated with lower FA (r = 0.21, P < 0.001), and higher MD (r = -0.31, P < 0.001). In the NAWM, lower PiB SUVr only correlated with higher MD (r = -0.10, P = 0.02). Both in the WMH and NAWM, lower PiB SUVr was associated with lower memory, language, and global cognitive function z-scores after adjusting for age and global cortical PiB SUVr. INTERPRETATION Reduced PiB uptake in the WMH is associated with a loss of WM integrity and cognitive function after accounting for the global cortical PiB uptake, suggesting that WM PiB uptake may be an early biomarker of WM integrity that precedes cognitive impairment in older adults. When using WM as a reference region in cross-sectional analysis of PiB SUVr, individual variability in WMH volume as well as age should be considered.
Collapse
Affiliation(s)
- Burcu Zeydan
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
| | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesota
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | | | | | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Jeffrey L. Gunter
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Hoon‐Ki Min
- Department of RadiologyMayo ClinicRochesterMinnesota
| | | | | | | | | | - Orhun H. Kantarci
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
| | | |
Collapse
|
37
|
Ottoy J, Niemantsverdriet E, Verhaeghe J, De Roeck E, Struyfs H, Somers C, Wyffels L, Ceyssens S, Van Mossevelde S, Van den Bossche T, Van Broeckhoven C, Ribbens A, Bjerke M, Stroobants S, Engelborghs S, Staelens S. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging. NEUROIMAGE-CLINICAL 2019; 22:101771. [PMID: 30927601 PMCID: PMC6444289 DOI: 10.1016/j.nicl.2019.101771] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/08/2019] [Accepted: 03/10/2019] [Indexed: 12/31/2022]
Abstract
Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included Aβ1–42, Aβ1–40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters. FDG-PET and MRI HV are the strongest predictors of cognitive decline and conversion to AD. Combination of visuospatial construction testing with FDG-PET or MRI HV present high predicting power of conversion. CSF and amyloid-PET seem less suitable markers of disease progression. Increased AV45-PET predicts short-term cognitive decline if SUVR is referenced to WM instead of CB.
Collapse
Affiliation(s)
- Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Charisse Somers
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Leonie Wyffels
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sara Van Mossevelde
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Tobi Van den Bossche
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sigrid Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium.
| |
Collapse
|
38
|
Su Y, Flores S, Wang G, Hornbeck RC, Speidel B, Joseph-Mathurin N, Vlassenko AG, Gordon BA, Koeppe RA, Klunk WE, Jack CR, Farlow MR, Salloway S, Snider BJ, Berman SB, Roberson ED, Brosch J, Jimenez-Velazques I, van Dyck CH, Galasko D, Yuan SH, Jayadev S, Honig LS, Gauthier S, Hsiung GYR, Masellis M, Brooks WS, Fulham M, Clarnette R, Masters CL, Wallon D, Hannequin D, Dubois B, Pariente J, Sanchez-Valle R, Mummery C, Ringman JM, Bottlaender M, Klein G, Milosavljevic-Ristic S, McDade E, Xiong C, Morris JC, Bateman RJ, Benzinger TLS. Comparison of Pittsburgh compound B and florbetapir in cross-sectional and longitudinal studies. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:180-190. [PMID: 30847382 PMCID: PMC6389727 DOI: 10.1016/j.dadm.2018.12.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.
Collapse
Affiliation(s)
- Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Shaney Flores
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Guoqiao Wang
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Benjamin Speidel
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Nelly Joseph-Mathurin
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrei G Vlassenko
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert A Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Martin R Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Barbara J Snider
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Erik D Roberson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jared Brosch
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | | | - Shauna H Yuan
- University of California-San Diego, San Diego, CA, USA
| | | | | | - Serge Gauthier
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
| | | | - Mario Masellis
- Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | | | - Michael Fulham
- University of Sydney and Royal Prince Alfred Hospital, Sydney, NSW, Australia
| | | | - Colin L Masters
- The University of Melbourne and the Florey Institute, Parkville, VIC, Australia
| | - David Wallon
- Inserm U1245, Department of Neurology and CNR-MAJ, Rouen, France.,Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | - Didier Hannequin
- Inserm U1245, Department of Neurology and CNR-MAJ, Rouen, France.,Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | - Bruno Dubois
- University Salpêtrière Hospital in Paris, Paris, France
| | | | | | | | - John M Ringman
- Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Eric McDade
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| |
Collapse
|
39
|
Landau SM. Optimizing Longitudinal Amyloid-β PET Measurement: The Challenges of Intensity Normalization. J Nucl Med 2018; 59:1581-1582. [PMID: 30213847 DOI: 10.2967/jnumed.118.212662] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 09/10/2018] [Indexed: 11/16/2022] Open
Affiliation(s)
- Susan M Landau
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California; and Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California
| |
Collapse
|