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Harrison TM, Chadwick T, Pezzoli S, Lee J, Landau SM, Jagust WJ. Cognitive Trajectories and Alzheimer Disease Biomarkers: From Successful Cognitive Aging to Clinical Impairment. Ann Neurol 2024; 96:378-389. [PMID: 38747315 PMCID: PMC11236492 DOI: 10.1002/ana.26964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 07/11/2024]
Abstract
OBJECTIVE Cross-sectional definitions of successful cognitive aging have been widely utilized, but longitudinal measurements can identify people who do not decline. We performed this study to contrast maintenance with declining trajectories, including clinical conversion. METHODS We included baseline cognitively unimpaired Alzheimer's Disease Neuroimaging Initiative participants with 3 or more cognitive testing sessions (n = 539, follow-up 6.1 ± 3.5 years) and calculated slopes of an episodic memory composite (MEM) to classify them into two groups: maintainers (slope ≥ 0) and decliners (slope < 0). Within decliners, we examined a subgroup of individuals who became clinically impaired during follow-up. These groups were compared on baseline characteristics and cognitive performance, as well as both cross-sectional and longitudinal Alzheimer disease (AD) biomarker measures (beta-amyloid [Aβ], tau, and hippocampal volume). RESULTS Forty-one percent (n = 221) of the cohort were MEM maintainers, and 33% (n = 105) of decliners converted to clinical impairment during follow-up. Compared to those with superior baseline scores, maintainers had lower education and were more likely to be male. Maintainers and decliners did not differ on baseline MEM scores, but maintainers did have higher non-MEM cognitive scores. Maintainers had lower baseline global Aβ, lower tau pathology, and larger hippocampal volumes than decliners, even after removing converters. There were no differences in rates of change of any AD biomarkers between any cognitive trajectory groups except for a higher rate of hippocampal atrophy in clinical converters compared to maintainers. INTERPRETATION Using longitudinal data to define cognitive trajectory groups reduces education and sex bias and reveals the prognostic importance of early onset of accumulation of AD pathology. ANN NEUROL 2024;96:378-389.
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Affiliation(s)
- Theresa M Harrison
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
| | - Trevor Chadwick
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
| | - Stefania Pezzoli
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - JiaQie Lee
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
| | - Susan M Landau
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
| | - William J Jagust
- Neuroscience Department, University of California, Berkeley, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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2
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Leuzy A, Raket LL, Villemagne VL, Klein G, Tonietto M, Olafson E, Baker S, Saad ZS, Bullich S, Lopresti B, Bohorquez SS, Boada M, Betthauser TJ, Charil A, Collins EC, Collins JA, Cullen N, Gunn RN, Higuchi M, Hostetler E, Hutchison RM, Iaccarino L, Insel PS, Irizarry MC, Jack CR, Jagust WJ, Johnson KA, Johnson SC, Karten Y, Marquié M, Mathotaarachchi S, Mintun MA, Ossenkoppele R, Pappas I, Petersen RC, Rabinovici GD, Rosa-Neto P, Schwarz CG, Smith R, Stephens AW, Whittington A, Carrillo MC, Pontecorvo MJ, Haeberlein SB, Dunn B, Kolb HC, Sivakumaran S, Rowe CC, Hansson O, Doré V. Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model. Alzheimers Dement 2024. [PMID: 39041435 DOI: 10.1002/alz.13908] [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: 11/07/2023] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification. HIGHLIGHTS Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, Arizona, USA
- Enigma Biomedical Group, Knoxville, Tennessee, USA
| | - Lars Lau Raket
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
- Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Victor L Villemagne
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Florey Department of Neuroscience, University of Melbourne, Parkville, Victoria, Australia
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | | | | | - Emily Olafson
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California, USA
| | - Suzanne Baker
- Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Ziad S Saad
- Janssen Research & Development, Merryfield Row San Diego, California, USA
| | | | - Brian Lopresti
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | | | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Av. de Monforte de Lemos, Madrid, Spain
| | - Tobey J Betthauser
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medicine Division of Geriatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA
| | | | | | | | - Nicholas Cullen
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, Arizona, USA
| | - Roger N Gunn
- Invicro, Hammersmith Hospital, London, UK
- Brain Sciences, Imperial College London, Hammersmith Hospital, London, UK
| | - Makoto Higuchi
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Inage-ku, Chiba-shi, Chiba, Japan
| | | | | | | | - Philip S Insel
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California, USA
| | - Michael C Irizarry
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Clifford R Jack
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - William J Jagust
- University of California Berkeley, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Keith A Johnson
- Harvard Medical School, Department of Radiology, Boston, Minnesota, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Minnesota, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medicine Division of Geriatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Yashmin Karten
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, Arizona, USA
| | - Marta Marquié
- Department of Medicine Division of Geriatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA
| | | | | | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
- Amsterdam University Medical Center, Neuroscience Campus Amsterdam, Alzheimercenter, HZ Amsterdam, the Netherlands
| | - Ioannis Pappas
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
- Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | | | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Verdun, Quebec, Canada
- Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | | | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | | | | | | | | | | | - Billy Dunn
- Senior advisor to CPAD Consortium, Critical Path Institute, Tucson, Arizona, USA
| | | | - Sudhir Sivakumaran
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, Arizona, USA
| | - Christopher C Rowe
- Florey Department of Neuroscience, University of Melbourne, Parkville, Victoria, Australia
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
- The Australian Dementia Network (ADNeT), The University of Melbourne, Parkville, Victoria, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, CSIRO, Parkville, Victoria, Australia
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Wisch JK, Gordon BA, Barthélemy NR, Horie K, Henson RL, He Y, Flores S, Benzinger TLS, Morris JC, Bateman RJ, Ances BM, Schindler SE. Predicting continuous amyloid PET values with CSF tau phosphorylation occupancies. Alzheimers Dement 2024. [PMID: 39041391 DOI: 10.1002/alz.14132] [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: 04/24/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Cerebrospinal fluid (CSF) tau phosphorylation at multiple sites is associated with cortical amyloid and other pathologic changes in Alzheimer's disease. These relationships can be non-linear. We used an artificial neural network to assess the ability of 10 different CSF tau phosphorylation sites to predict continuous amyloid positron emission tomography (PET) values. METHODS CSF tau phosphorylation occupancies at 10 sites (including pT181/T181, pT217/T217, pT231/T231 and pT205/T205) were measured by mass spectrometry in 346 individuals (57 cognitively impaired, 289 cognitively unimpaired). We generated synthetic amyloid PET scans using biomarkers and evaluated their performance. RESULTS Concentration of CSF pT217/T217 had low predictive error (average error: 13%), but also a low predictive range (ceiling 63 Centiloids). CSF pT231/T231 has slightly higher error (average error: 19%) but predicted through a greater range (87 Centiloids). DISCUSSION Tradeoffs exist in biomarker selection. Some phosphorylation sites offer greater concordance with amyloid PET at lower levels, while others perform better over a greater range. HIGHLIGHTS Novel pTau isoforms can predict cortical amyloid burden. pT217/T217 accurately predicts cortical amyloid burden in low-amyloid individuals. Traditional CSF biomarkers correspond with higher levels of amyloid.
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Affiliation(s)
- Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Nicolas R Barthélemy
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- SILQ Center for Neurodegenerative Biology, St. Louis, Missouri, USA
| | - Kanta Horie
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- SILQ Center for Neurodegenerative Biology, St. Louis, Missouri, USA
| | - Rachel L Henson
- Hope Center, Washington University in Saint Louis, St. Louis, Missouri, USA
| | - Yingxin He
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- SILQ Center for Neurodegenerative Biology, St. Louis, Missouri, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
- SILQ Center for Neurodegenerative Biology, St. Louis, Missouri, USA
- Hope Center, Washington University in Saint Louis, St. Louis, Missouri, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri, USA
- Hope Center, Washington University in Saint Louis, St. Louis, Missouri, USA
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Gonzales MM, O'Donnell A, Ghosh S, Thibault E, Tanner J, Satizabal CL, Decarli CS, Fakhri GE, Johnson KA, Beiser AS, Seshadri S, Pase M. Associations of cerebral amyloid beta and tau with cognition from midlife. Alzheimers Dement 2024. [PMID: 39039896 DOI: 10.1002/alz.14060] [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: 12/11/2023] [Revised: 04/12/2024] [Accepted: 05/01/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Understanding early neuropathological changes and their associations with cognition may aid dementia prevention. This study investigated associations of cerebral amyloid and tau positron emission tomography (PET) retention with cognition in a predominately middle-aged community-based cohort and examined factors that may modify these relationships. METHODS 11C-Pittsburgh compound B amyloid and 18F-flortaucipir tau PET imaging were performed. Associations of amyloid and tau PET with cognition were evaluated using linear regression. Interactions with age, apolipoprotein E (APOE) ε4 status, and education were examined. RESULTS Amyloid and tau PET were not associated with cognition in the overall sample (N = 423; mean: 57 ± 10 years; 50% female). However, younger age (< 55 years) and APOE ε4 were significant effect modifiers, worsening cognition in the presence of higher amyloid and tau. DISCUSSION Higher levels of Aβ and tau may have a pernicious effect on cognition among APOE ε4 carriers and younger adults, suggesting a potential role for targeted early interventions. HIGHLIGHTS Risk and resilience factors influenced cognitive vulnerability due to Aβ and tau. Higher fusiform tau associated with poorer visuospatial skills in younger adults. APOE ε4 interacted with Aβ and tau to worsen cognition across multiple domains.
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Affiliation(s)
- Mitzi M Gonzales
- Department of Neurology, Cedars Sinai Medical Center, Los Angeles, California, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Adrienne O'Donnell
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Saptaparni Ghosh
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Emma Thibault
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy Tanner
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Neurology, University of California Davis, Sacramento, California, USA
| | - Charles S Decarli
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Center for Neuroscience, University of California Davis, Davis, California, USA
| | - Georges El Fakhri
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Yale School of Medicine, New Haven, United States
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexa S Beiser
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Matthew Pase
- The Framingham Heart Study, Framingham, Massachusetts, USA
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
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5
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Engels-Domínguez N, Riphagen JM, Van Egroo M, Koops EA, Smegal LF, Becker JA, Prokopiou PC, Bueichekú E, Kwong KK, Rentz DM, Salat DH, Sperling RA, Johnson KA, Jacobs HIL. Lower Locus Coeruleus Integrity Signals Elevated Entorhinal Tau and Clinical Progression in Asymptomatic Older Individuals. Ann Neurol 2024. [PMID: 39007398 DOI: 10.1002/ana.27022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/29/2024] [Accepted: 06/17/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE Elevated entorhinal cortex (EC) tau in low beta-amyloid individuals can predict accumulation of pathology and cognitive decline. We compared the accuracy of magnetic resonance imaging (MRI)-derived locus coeruleus integrity, neocortical beta-amyloid burden by positron emission tomography (PET), and hippocampal volume in identifying elevated entorhinal tau signal in asymptomatic individuals who are considered beta-amyloid PET-negative. METHODS We included 188 asymptomatic individuals (70.78 ± 11.51 years, 58% female) who underwent 3T-MRI of the locus coeruleus, Pittsburgh compound-B (PiB), and Flortaucipir (FTP) PET. Associations between elevated EC tau and neocortical PiB, hippocampal volume, or locus coeruleus integrity were evaluated and compared using logistic regression and receiver operating characteristic analyses in the PiB- sample with a clinical dementia rating (CDR) of 0. Associations with clinical progression (CDR-sum-of-boxes) over a time span of 6 years were evaluated with Cox proportional hazard models. RESULTS We identified 26 (21%) individuals with high EC FTP in the CDR = 0/PiB- sample. Locus coeruleus integrity was a significantly more sensitive and specific predictor of elevated EC FTP (area under the curve [AUC] = 85%) compared with PiB (AUC = 77%) or hippocampal volume (AUC = 76%). Based on the Youden-index, locus coeruleus integrity obtained a sensitivity of 77% and 85% specificity. Using the resulting locus coeruleus Youden cut-off, lower locus coeruleus integrity was associated with a two-fold increase in clinical progression, including mild cognitive impairment. INTERPRETATION Locus coeruleus integrity has promise as a low-cost, non-invasive screening instrument to detect early cortical tau deposition and associated clinical progression in asymptomatic, low beta-amyloid individuals. ANN NEUROL 2024.
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Affiliation(s)
- Nina Engels-Domínguez
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Joost M Riphagen
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maxime Van Egroo
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Elouise A Koops
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Lindsay F Smegal
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - J Alex Becker
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Prokopis C Prokopiou
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Kenneth K Kwong
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Dorene M Rentz
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - David H Salat
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Keith A Johnson
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Heidi I L Jacobs
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
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Bao YW, Wang ZJ, Shea YF, Chiu PKC, Kwan JS, Chan FHW, Mak HKF. Combined quantitative amyloid-β PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study. Acad Radiol 2024:S1076-6332(24)00426-4. [PMID: 39003227 DOI: 10.1016/j.acra.2024.06.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/18/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
RATIONALE AND OBJECTIVES Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD. MATERIAL AND METHODS We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification. RESULTS The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others. CONCLUSION Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.
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Affiliation(s)
- Yi-Wen Bao
- Department of Medical Imaging Center, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China (Y-W.B.)
| | - Zuo-Jun Wang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.)
| | - Yat-Fung Shea
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Patrick Ka-Chun Chiu
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Joseph Sk Kwan
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Felix Hon-Wai Chan
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.).
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7
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Yu X, Shi R, Zhou X, Zhang M, Cai Y, Jiang J, Han Y. Correlations between plasma markers and brain Aβ deposition across the AD continuum: Evidence from SILCODE. Alzheimers Dement 2024. [PMID: 38982860 DOI: 10.1002/alz.14084] [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: 04/10/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Previous studies have found that Alzheimer's disease (AD)-related plasma markers are associated with amyloid beta (Aβ) deposition, but the change of this association in different Aβ pathological stages remains unclear. METHODS Data were obtained from the SILCODE. According to the standardized uptake value ratio (SUVR) and Aβ stage classification, correlation analysis was performed among plasma biomarkers, and voxel/SUVR values in the regions of interest (ROI) and clinical scale information, respectively. Mediation analysis was used to study the possible pathways. RESULTS The proportion of cognitively normal (CN) and subjective cognitive decline (SCD) was the highest in stages A0 to 1, while in stages A2 to 4, the proportion of mild cognitive impairment (MCI) and AD increased. Plasma phosphorylated tau (p-tau)181 and glial fibrillary acidic protein (GFAP) levels were significantly lower in stage A0 compared to the later phases. Two pathways demonstrated fully mediated effects: positron emission tomography (PET) SUVR-plasma p-tau181-Mini-Mental State Examination (MMSE) and PET SUVR-plasma GFAP-MMSE. DISCUSSION This study demonstrated the role of plasma biomarkers in the early stage of AD, especially in SCD, from both the clinical diagnosis and Aβ stage dimensions. HIGHLIGHTS Plasma ptau181 and GFAP level serve as indicators of early Alzheimer's disease and the pathologic Aβ staging classification. A possible ceiling effect of GFAP was observed in the mid-to-late stages of the AD course. This study confirms the role of AD plasma markers in promoting Aβ deposition at an early stage, particularly in females with subjective cognitive decline(SCD). The overlapping brain regions of plasma p-tau181, GFAP, and neurofilament light for Aβ deposition in the brain in early AD were distributed across various regions, including the posterior cingulate gyrus, rectus gyrus, and inferior temporal gyrus.
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Affiliation(s)
- Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rong Shi
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Xia Zhou
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingkai Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- School of Biomedical Engineering, Hainan University, Haikou, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
- The Central Hospital of Karamay, Xinjiang, China
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8
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Farrell ME, Thibault EG, Becker JA, Price JC, Healy BC, Hanseeuw BJ, Buckley RF, Jacobs HIL, Schultz AP, Chen CD, Sperling RA, Johnson KA. Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease. Alzheimers Dement 2024. [PMID: 38988055 DOI: 10.1002/alz.14036] [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: 03/06/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 07/12/2024]
Abstract
INTRODUCTION Spatial extent-based measures of how far amyloid beta (Aβ) has spread throughout the neocortex may be more sensitive than traditional Aβ-positron emission tomography (PET) measures of Aβ level for detecting early Aβ deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aβ's association with tau proliferation and cognitive decline. METHODS Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aβ level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+. RESULTS EXT enabled earlier detection of Aβ deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aβ+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL. DISCUSSION These findings indicate EXT may be more sensitive to Aβ's role in preclinical AD than level and improve targeting of individuals for AD prevention trials. HIGHLIGHTS Aβ spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aβ EXT improved detection of Aβ below traditional PET thresholds. Early regional Aβ deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aβ EXT than Aβ level. Neocortical tau onset aligned with reaching widespread neocortical Aβ.
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Affiliation(s)
- Michelle E Farrell
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Emma G Thibault
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - J Alex Becker
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Julie C Price
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian C Healy
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bernard J Hanseeuw
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Bruxelles, Belgium
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Heidi I L Jacobs
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Charles D Chen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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9
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Mastenbroek SE, Sala A, Vállez García D, Shekari M, Salvadó G, Lorenzini L, Pieperhoff L, Wink AM, Lopes Alves I, Wolz R, Ritchie C, Boada M, Visser PJ, Bucci M, Farrar G, Hansson O, Nordberg AK, Ossenkoppele R, Barkhof F, Gispert JD, Rodriguez-Vieitez E, Collij LE. Continuous β-Amyloid CSF/PET Imbalance Model to Capture Alzheimer Disease Heterogeneity. Neurology 2024; 103:e209419. [PMID: 38862136 PMCID: PMC11244744 DOI: 10.1212/wnl.0000000000209419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/29/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Discordance between CSF and PET biomarkers of β-amyloid (Aβ) might reflect an imbalance between soluble and aggregated species, possibly reflecting disease heterogeneity. Previous studies generally used binary cutoffs to assess discrepancies in CSF/PET biomarkers, resulting in a loss of information on the extent of discordance. In this study, we (1) jointly modeled Aβ-CSF/PET data to derive a continuous measure of the imbalance between soluble and fibrillar pools of Aβ, (2) investigated factors contributing to this imbalance, and (3) examined associations with cognitive trajectories. METHODS Across 822 cognitively unimpaired (n = 261) and cognitively impaired (n = 561) Alzheimer's Disease Neuroimaging Initiative individuals (384 [46.7%] females, mean age 73.0 ± 7.4 years), we fitted baseline CSF-Aβ42 and global Aβ-PET to a hyperbolic regression model, deriving a participant-specific Aβ-aggregation score (standardized residuals); negative values represent more soluble relative to aggregated Aβ and positive values more aggregated relative to soluble Aβ. Using linear models, we investigated whether methodological factors, demographics, CSF biomarkers, and vascular burden contributed to Aβ-aggregation scores. With linear mixed models, we assessed whether Aβ-aggregation scores were predictive of cognitive functioning. Analyses were repeated in an early independent validation cohort of 383 Amyloid Imaging to Prevent Alzheimer's Disease Prognostic and Natural History Study individuals (224 [58.5%] females, mean age 65.2 ± 6.9 years). RESULTS The imbalance model could be fit (pseudo-R2 = 0.94) in both cohorts, across CSF kits and PET tracers. Although no associations were observed with the main methodological factors, lower Aβ-aggregation scores were associated with larger ventricular volume (β = 0.13, p < 0.001), male sex (β = -0.18, p = 0.019), and homozygous APOE-ε4 carriership (β = -0.56, p < 0.001), whereas higher scores were associated with increased uncorrected CSF p-tau (β = 0.17, p < 0.001) and t-tau (β = 0.16, p < 0.001), better baseline executive functioning (β = 0.12, p < 0.001), and slower global cognitive decline (β = 0.14, p = 0.006). In the validation cohort, we replicated the associations with APOE-ε4, CSF t-tau, and, although modestly, with cognition. DISCUSSION We propose a novel continuous model of Aβ CSF/PET biomarker imbalance, accurately describing heterogeneity in soluble vs aggregated Aβ pools in 2 independent cohorts across the full Aβ continuum. Aβ-aggregation scores were consistently associated with genetic and AD-associated CSF biomarkers, possibly reflecting disease heterogeneity beyond methodological influences.
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Affiliation(s)
- Sophie E Mastenbroek
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Arianna Sala
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - David Vállez García
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Mahnaz Shekari
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Gemma Salvadó
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Luigi Lorenzini
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Leonard Pieperhoff
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Alle Meije Wink
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Isadora Lopes Alves
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Robin Wolz
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Craig Ritchie
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Mercè Boada
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Pieter Jelle Visser
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Marco Bucci
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Gill Farrar
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Oskar Hansson
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Agneta K Nordberg
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Rik Ossenkoppele
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Juan Domingo Gispert
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Elena Rodriguez-Vieitez
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Lyduine E Collij
- From the Department of Radiology and Nuclear Medicine (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc; Amsterdam Neuroscience (S.E.M., D.V.G., L.L., L.P., A.M.W., F.B., L.E.C.), Brain Imaging, the Netherlands; Clinical Memory Research Unit (S.E.M., G.S., O.H., R.O., L.E.C.), Department of Clinical Sciences Malmö, Lund University; Division of Clinical Geriatrics (A.S., M. Bucci, A.K.N., E.R.-V.), Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Coma Science Group (A.S.), GIGA-Consciousness, University of Liège; Centre du Cerveau2 (A.S.), University Hospital of Liège, Belgium; Barcelonaβeta Brain Research Center (BBRC) (M.S., G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (M.S., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (M.S., J.D.G.), Instituto de Salud Carlos III, Madrid; Universitat Pompeu Fabra (M.S.), Barcelona, Spain; Brain Research Center (I.L.A.), Amsterdam, the Netherlands; IXICO (R.W.), London; Centre for Clinical Brain Sciences (C.R.), University of Edinburgh, United Kingdom; Ace Alzheimer Center Barcelona (M. Boada), Universitat Internacional de Catalunya, Spain; Networking Research Center on Neurodegenerative Diseases (CIBERNED) (M. Boada), Instituto de Salud Carlos III, Madrid, Spain; Alzheimer Center Amsterdam (P.J.V., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Amsterdam Neuroscience (P.J.V.), Neurodegeneration; Alzheimer Center Limburg (P.J.V.), School for Mental Health and Neuroscience, Maastricht University, the Netherlands; Division of Neurogeriatrics (P.J.V.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging (M. Bucci, A.K.N.), Karolinska University Hospital, Stockholm, Sweden; GE Healthcare (G.F.), Amersham, United Kingdom; Memory Clinic (O.H.), Skåne University Hospital, Malmö, Sweden; and Centre for Medical Image Computing (F.B.), and Queen Square Institute of Neurology, UCL, London, United Kingdom
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10
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Stricker NH, Christianson TJ, Pudumjee SB, Polsinelli AJ, Lundt ES, Frank RD, Kremers WK, Machulda MM, Fields JA, Jack CR, Knopman DS, Graff-Radford J, Vemuri P, Mielke MM, Petersen RC. Mayo Normative Studies: Amyloid and Neurodegeneration Negative Normative Data for the Auditory Verbal Learning Test and Sex-Specific Sensitivity to Mild Cognitive Impairment/Dementia. J Alzheimers Dis 2024:JAD240081. [PMID: 38995784 DOI: 10.3233/jad-240081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
Background Conventional normative samples include individuals with undetected Alzheimer's disease neuropathology, lowering test sensitivity for cognitive impairment. Objective We developed Mayo Normative Studies (MNS) norms limited to individuals without elevated amyloid or neurodegeneration (A-N-) for Rey's Auditory Verbal Learning Test (AVLT). We compared these MNS A-N- norms in female, male, and total samples to conventional MNS norms with varying levels of demographic adjustments. Methods The A-N- sample included 1,059 Mayo Clinic Study of Aging cognitively unimpaired (CU) participants living in Olmsted County, MN, who are predominantly non-Hispanic White. Using a regression-based approach correcting for age, sex, and education, we derived fully-adjusted T-score formulas for AVLT variables. We validated these A-N- norms in two independent samples of CU (n = 261) and mild cognitive impairment (MCI)/dementia participants (n = 392) > 55 years of age. Results Variability associated with age decreased by almost half in the A-N- norm sample relative to the conventional norm sample. Fully-adjusted MNS A-N- norms showed approximately 7- 9% higher sensitivity to MCI/dementia compared to fully-adjusted MNS conventional norms for trials 1- 5 total and sum of trials. Among women, sensitivity to MCI/dementia increased with each normative data refinement. In contrast, age-adjusted conventional MNS norms showed greatest sensitivity to MCI/dementia in men. Conclusions A-N- norms show some benefits over conventional normative approaches to MCI/dementia sensitivity, especially for women. We recommend using these MNS A-N- norms alongside MNS conventional norms. Future work is needed to determine if normative samples that are not well characterized clinically show greater benefit from biomarker-refined approaches.
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Affiliation(s)
- Nikki H Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Angelina J Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Emily S Lundt
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ryan D Frank
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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11
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Warmenhoven N, Salvadó G, Janelidze S, Mattsson-Carlgren N, Bali D, Dolado AO, Kolb H, Triana-Baltzer G, Barthélemy NR, Schindler SE, Aschenbrenner AJ, Raji CA, Benzinger TL, Morris JC, Ibanez L, Timsina J, Cruchaga C, Bateman RJ, Ashton N, Arslan B, Zetterberg H, Blennow K, Pichet Binette A, Hansson O. A Comprehensive Head-to-Head Comparison of Key Plasma Phosphorylated Tau 217 Biomarker Tests. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.02.24309629. [PMID: 39006421 PMCID: PMC11245081 DOI: 10.1101/2024.07.02.24309629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Plasma phosphorylated-tau 217 (p-tau217) is currently the most promising biomarkers for reliable detection of Alzheimer's disease (AD) pathology. Various p-tau217 assays have been developed, but their relative performance is unclear. We compared key plasma p-tau217 tests using cross-sectional and longitudinal measures of amyloid-β (Aβ)-PET, tau-PET, and cognition as outcomes, and benchmarked them against cerebrospinal fluid (CSF) biomarker tests. Samples from 998 individuals (mean[range] age 68.5[20.0-92.5], 53% female) from the Swedish BioFINDER-2 cohort were analyzed. Plasma p-tau217 was measured with mass spectrometry (MS) assays (the ratio between phosphorylated and non-phosphorylated [%p-tau217WashU]and ptau217WashU) as well as with immunoassays (p-tau217Lilly, p-tau217Janssen, p-tau217ALZpath). CSF biomarkers included p-tau217Lilly, and the FDA-approved p-tau181/Aβ42Elecsys and p-tau181Elecsys. All plasma p-tau217 tests exhibited high ability to detect abnormal Aβ-PET (AUC range: 0.91-0.96) and tau-PET (AUC range: 0.94-0.97). Plasma %p-tau217WashU had the highest performance, with significantly higher AUCs than all the immunoassays (P diff<0.007). For detecting Aβ-PET status, %p-tau217WashU had an accuracy of 0.93 (immunoassays: 0.83-0.88), sensitivity of 91% (immunoassays: 84-87%), and a specificity of 94% (immunoassays: 85-89%). Among immunoassays, p-tau217Lilly and plasma p-tau217ALZpath had higher AUCs than plasma p-tau217Janssen for Aβ-PET status (P diff<0.006), and p-tau217Lilly outperformed plasma p-tau217ALZpath for tau-PET status (P diff=0.025). Plasma %p-tau217WashU exhibited higher associations with all PET load outcomes compared to immunoassays; baseline Aβ-PET load (R2: 0.72; immunoassays: 0.47-0.58; Pdiff<0.001), baseline tau-PET load (R2: 0.51; immunoassays: 0.38-0.45; Pdiff<0.001), longitudinal Aβ-PET load (R2: 0.53; immunoassays: 0.31-0.38; Pdiff<0.001) and longitudinal tau-PET load (R2: 0.50; immunoassays: 0.35-0.43; Pdiff<0.014). Among immunoassays, plasma p-tau217Lilly was more strongly associated with Aβ-PET load than plasma p-tau217Janssen (P diff<0.020) and with tau-PET load than both plasma p-tau217Janssen and plasma p-tau217ALZpath (all P diff<0.010). Plasma %p-tau217 also correlated more strongly with baseline cognition (Mini-Mental State Examination[MMSE]) than all immunoassays (R2 %p-tau217WashU: 0.33; immunoassays: 0.27-0.30; P diff<0.024). The main results were replicated in an external cohort from Washington University in St Louis (n =219). Finally, p-tau217Nulisa showed similar performance to other immunoassays in subsets of both cohorts. In summary, both MS- and immunoassay-based p-tau217 tests generally perform well in identifying Aβ-PET, tau-PET, and cognitive abnormalities, but %p-tau217WashU performed significantly better than all the examined immunoassays. Plasma %p-tau217 may be considered as a stand-alone confirmatory test for AD pathology, while some immunoassays might be better suited as triage tests where positive results are confirmed with a second test.
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Affiliation(s)
- Noëlle Warmenhoven
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Divya Bali
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Anna Orduña Dolado
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hartmuth Kolb
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, CA, USA
| | - Gallen Triana-Baltzer
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, CA, USA
| | - Nicolas R. Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Cyrus A. Raji
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University, St. Louis, MO, USA
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University, St. Louis, MO, USA
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, USA
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University St. Louis, MO, USA
| | - Randall J. Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Burak Arslan
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund University, Lund, Sweden
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12
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Shekari M, Vállez García D, Collij LE, Altomare D, Heeman F, Pemberton H, Roé Vellvé N, Bullich S, Buckley C, Stephens A, Farrar G, Frisoni G, Klunk WE, Barkhof F, Gispert JD. Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology. Alzheimers Dement 2024. [PMID: 38961808 DOI: 10.1002/alz.13883] [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: 10/05/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. HIGHLIGHTS We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.
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Affiliation(s)
- Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - David Vállez García
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Clinical Memory Research Unit, Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Daniele Altomare
- Memory Center, Department of Rehabilitation and Geriatrics, University Hospitals and University of Geneva, Genève, Switzerland
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, The University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Hugh Pemberton
- GE Healthcare Life Sciences, Amersham, UK
- Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | | | | | | | | | | | - Giovanni Frisoni
- Memory Center, Department of Rehabilitation and Geriatrics, University Hospitals and University of Geneva, Genève, Switzerland
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
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13
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Johns E, Vossler HA, Young CB, Carlson ML, Winer JR, Younes K, Park J, Rathmann-Bloch J, Smith V, Harrison TM, Landau S, Henderson V, Wagner A, Sha SJ, Zeineh M, Zaharchuk G, Poston KL, Davidzon GA, Mormino EC. Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation. Alzheimers Dement 2024. [PMID: 38962867 DOI: 10.1002/alz.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Amyloid positron emission tomography (PET) acquisition timing impacts quantification. METHODS In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. RESULTS CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. DISCUSSION The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. HIGHLIGHTS Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.
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Affiliation(s)
- Emily Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Hillary A Vossler
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Christina B Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Mackenzie L Carlson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Joseph R Winer
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Kyan Younes
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Jennifer Park
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | | | - Viktorija Smith
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA
| | - Susan Landau
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA
| | - Victor Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
| | - Anthony Wagner
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA
- Stanford University, Wu Tsai Neuroscience Institute, Stanford, California, USA
| | - Sharon J Sha
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Stanford University, Wu Tsai Neuroscience Institute, Stanford, California, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Kathleen L Poston
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Stanford University, Wu Tsai Neuroscience Institute, Stanford, California, USA
| | - Guido A Davidzon
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Stanford University, Wu Tsai Neuroscience Institute, Stanford, California, USA
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Falcon C, Montesinos P, Václavů L, Kassinopoulos M, Minguillon C, Fauria K, Cascales-Lahoz D, Contador J, Fernández-Lebrero A, Navalpotro I, Puig-Pijoan A, Grau-Rivera O, Kollmorgen G, Quijano-Rubio C, Molinuevo JL, Zetterberg H, Blennow K, Suárez-Calvet M, Van Osch MJP, Sanchez-Gonzalez J, Gispert JD. Time-encoded ASL reveals lower cerebral blood flow in the early AD continuum. Alzheimers Dement 2024. [PMID: 38958557 DOI: 10.1002/alz.14059] [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: 12/21/2023] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 07/04/2024]
Abstract
INTRODUCTION Cerebral blood flow (CBF) is reduced in cognitively impaired (CI) Alzheimer's disease (AD) patients. We checked the sensitivity of time-encoded arterial spin labeling (te-ASL) in measuring CBF alterations in individuals with positive AD biomarkers and associations with relevant biomarkers in cognitively unimpaired (CU) individuals. METHODS We compared te-ASL with single-postlabel delay (PLD) ASL in measuring CBF in 59 adults across the AD continuum, classified as CU amyloid beta (Aβ) negative (-), CU Aβ positive (+), and CI Aβ+. We sought associations of CBF with biomarkers of AD, cerebrovascular disease, synaptic dysfunction, neurodegeneration, and cognition in CU participants. RESULTS te-ASL was more sensitive at detecting CBF reduction in the CU Aβ+ and CI Aβ+ groups. In CU participants, lower CBF was associated with altered biomarkers of Aβ, tau, synaptic dysfunction, and neurodegeneration. DISCUSSION CBF reduction occurs early in the AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF changes in AD. HIGHLIGHTS Lower CBF can be detected in CU subjects in the early AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF alterations in AD. CBF is linked to biomarkers of AD, synaptic dysfunction, and neurodegeneration.
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Affiliation(s)
- Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | | | - Lena Václavů
- Department of Radiology, C. J. Gorter MRI Center, Leiden University Medical Center, Leiden, Netherlands
| | - Michalis Kassinopoulos
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Diego Cascales-Lahoz
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - José Contador
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - Aida Fernández-Lebrero
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - Irene Navalpotro
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - Albert Puig-Pijoan
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | | | | | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- UK Dementia Research Institute at University College London (UCL), London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Neurologia, Hospital del Mar, Pg. Marítim de la Barceloneta, Barcelona, Spain
| | - Matthias J P Van Osch
- Department of Radiology, C. J. Gorter MRI Center, Leiden University Medical Center, Leiden, Netherlands
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
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Morris JC, Buckles VD. The role of the Alzheimer's Disease Neuroimaging Initiative in establishing the Dominantly Inherited Alzheimer Network. Alzheimers Dement 2024. [PMID: 38958563 DOI: 10.1002/alz.14106] [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: 04/29/2024] [Accepted: 06/06/2024] [Indexed: 07/04/2024]
Abstract
The Dominantly Inherited Alzheimer Network (DIAN) initially was funded by the National Institute on Aging (NIA) in 2008 and thus was able to adopt and incorporate the protocols developed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) that had been established by the NIA in 2004. The use of ADNI protocols for DIAN neuroimaging studies and assays of biological fluids for Alzheimer disease (AD) biomarkers permitted examination of the hypothesis that autosomal dominant AD (ADAD), studied by DIAN, and "sporadic" late-onset AD (LOAD), studied by ADNI, shared the same pathobiological construct. In a collaborative effort, the longitudinal DIAN and ADNI databases were compared and the findings supported the conclusion that ADAD and LOAD share a similar pathophysiology. The importance of the DIAN study thus is amplified by its relevance to LOAD, as characterized by the "parent" ADNI program.
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Affiliation(s)
- John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Virginia D Buckles
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
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Asken BM, DeSimone JC, Wang W, McFarland KN, Arias F, Levy S, Fiala J, Velez‐Uribe I, Mayrand RP, Sawada LO, Freytes C, Adeyosoye M, Barker WW, Crocco EA, DeKosky ST, Adjouadi M, Rosselli M, Marsiske M, Armstrong MJ, Smith GE, Cid RC, Loewenstein DA, Vaillancourt DE, Duara R. Plasma p-tau217 concordance with amyloid PET among ethnically diverse older adults. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12617. [PMID: 39021585 PMCID: PMC11253830 DOI: 10.1002/dad2.12617] [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: 04/18/2024] [Revised: 05/31/2024] [Accepted: 06/08/2024] [Indexed: 07/20/2024]
Abstract
INTRODUCTION Commercially available plasma p-tau217 biomarker tests are not well studied in ethnically diverse samples. METHODS We evaluated associations between ALZPath plasma p-tau217 and amyloid-beta positron emission tomography (Aβ-PET) in Hispanic/Latino (88% of Cuban or South American ancestry) and non-Hispanic/Latino older adults. One- and two-cutoff ranges were derived and evaluated to assess agreement with Aβ-PET. RESULTS A total of 239 participants underwent blood draw and Aβ-PET (age 70.8 ± 7.8, 55.2% female, education 15.6 ± 3.4 years, 48.9% Hispanic/Latino, 94.9% white). Plasma p-tau217 showed excellent discrimination of Aβ-PET positive and negative participants (visual read: AUC = 0.91 [0.87-0.95], p < 0.001; Centiloids quantification: AUC = 0.90 [0.86-0.94]). There was a greater percent agreement between low p-tau217 and negative Aβ-PET (95.8%) than high p-tau217 and positive Aβ-PET (86.3%). Analyses within ethnicity-specific subgroups suggested similar p-tau217 performance. DISCUSSION Plasma p-tau217 (ALZPath) relates to brain Aβ in Hispanic/Latino and non-Hispanic/Latino older adults. Independent validation and replication are necessary to establish reference ranges and inform appropriate contexts of use across ethno-racially diverse populations. HIGHLIGHTS Plasma p-tau217 (ALZPath) and Aβ-PET were measured in Hispanic/Latino and non-Hispanic/Latino older adults.Plasma p-tau217 accurately discriminated Aβ-PET positive and negative participants.Applying a two-cutoff "intermediate" plasma p-tau217 approach could reduce need for more invasive and costly testing.Plasma p-tau217 associations with Aβ-PET were strong within both Hispanic/Latino and non-Hispanic/Latino groups.
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17
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Holland N, Savulich G, Jones PS, Whiteside DJ, Street D, Swann P, Naessens M, Malpetti M, Hong YT, Fryer TD, Rittman T, Mulroy E, Aigbirhio FI, Bhatia KP, O'Brien JT, Rowe JB. Differential Synaptic Loss in β-Amyloid Positive Versus β-Amyloid Negative Corticobasal Syndrome. Mov Disord 2024; 39:1166-1178. [PMID: 38671545 DOI: 10.1002/mds.29814] [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: 01/08/2024] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND/OBJECTIVE The corticobasal syndrome (CBS) is a complex asymmetric movement disorder, with cognitive impairment. Although commonly associated with the primary 4-repeat-tauopathy of corticobasal degeneration, clinicopathological correlation is poor, and a significant proportion is due to Alzheimer's disease (AD). Synaptic loss is a pathological feature of many clinical and preclinical tauopathies. We therefore measured the degree of synaptic loss in patients with CBS and tested whether synaptic loss differed according to β-amyloid status. METHODS Twenty-five people with CBS, and 32 age-/sex-/education-matched healthy controls participated. Regional synaptic density was estimated by [11C]UCB-J non-displaceable binding potential (BPND), AD-tau pathology by [18F]AV-1451 BPND, and gray matter volume by T1-weighted magnetic resonance imaging. Participants with CBS had β-amyloid imaging with 11C-labeled Pittsburgh Compound-B ([11C]PiB) positron emission tomography. Symptom severity was assessed with the progressive supranuclear palsy-rating-scale, the cortical basal ganglia functional scale, and the revised Addenbrooke's Cognitive Examination. Regional differences in BPND and gray matter volume between groups were assessed by ANOVA. RESULTS Compared to controls, patients with CBS had higher [18F]AV-1451 uptake, gray matter volume loss, and reduced synaptic density. Synaptic loss was more severe and widespread in the β-amyloid negative group. Asymmetry of synaptic loss was in line with the clinically most affected side. DISCUSSION Distinct patterns of [11C]UCB-J and [18F]AV-1451 binding and gray matter volume loss, indicate differences in the pathogenic mechanisms of CBS according to whether it is associated with the presence of Alzheimer's disease or not. This highlights the potential for different therapeutic strategies in CBSs. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - George Savulich
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - P Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - David J Whiteside
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Duncan Street
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Peter Swann
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Michelle Naessens
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Young T Hong
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Eoin Mulroy
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Franklin I Aigbirhio
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Kailash P Bhatia
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John T O'Brien
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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18
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Wybitul M, Buchmann A, Langer N, Hock C, Treyer V, Gietl A. Trajectories of amyloid beta accumulation - Unveiling the relationship with APOE genotype and cognitive decline. Neurobiol Aging 2024; 139:44-53. [PMID: 38593527 DOI: 10.1016/j.neurobiolaging.2024.03.007] [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: 10/23/2023] [Revised: 03/18/2024] [Accepted: 03/24/2024] [Indexed: 04/11/2024]
Abstract
Amyloid beta (Aβ) follows a sigmoidal time function with varying accumulation rates. We studied how the position on this function, reflected by different Aβ accumulation phases, influences APOE ɛ4's association with Aβ and cognitive decline in 503 participants without dementia using Aβ-PET imaging over 5.3-years. First, Aβ load and accumulation were analyzed irrespective of phases using linear mixed regression. Generally, ɛ4 carriers displayed a higher Aβ load. Moreover, Aβ normal (Aβ-) ɛ4 carriers demonstrated higher accumulation. Next, we categorized accumulation phases as "decrease", "stable", or "increase" based on trajectory shapes. After excluding the Aβ-/decrease participants from the initial regression, the difference in accumulation attributable to genotype among Aβ- individuals was no longer significant. Further analysis revealed that in increase phases, Aβ accumulation was higher among noncarriers, indicating a genotype-related timeline shift. Finally, cognitive decline was analyzed across phases and was already evident in the Aβ-/increase phase. Our results encourage early interventions for ɛ4 carriers and imply that monitoring accumulating Aβ- individuals might help identify those at risk for cognitive decline.
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Affiliation(s)
- Maha Wybitul
- Institute for Regenerative Medicine, Faculty of Medicine, University of Zurich, Schlieren 8952, Switzerland; Department of Psychology, Faculty of Philosophy, University of Zurich, Zurich 8050, Switzerland
| | - Andreas Buchmann
- Institute for Regenerative Medicine, Faculty of Medicine, University of Zurich, Schlieren 8952, Switzerland
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich 8050, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, Faculty of Medicine, University of Zurich, Schlieren 8952, Switzerland; Neurimmune, Schlieren 8952, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, Faculty of Medicine, University of Zurich, Schlieren 8952, Switzerland; Department of Nuclear Medicine, University of Zurich, Zurich 8091, Switzerland.
| | - Anton Gietl
- Institute for Regenerative Medicine, Faculty of Medicine, University of Zurich, Schlieren 8952, Switzerland; University Hospital for Geriatric Psychiatry, Zurich 8008, Switzerland
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McKay NS, Millar PR, Nicosia J, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Cruchaga CC, Schindler SE, Morris JC, Hassenstab J. Pick a PACC: Comparing domain-specific and general cognitive composites in Alzheimer disease research. Neuropsychology 2024; 38:443-464. [PMID: 38602816 PMCID: PMC11176005 DOI: 10.1037/neu0000949] [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: 04/13/2024] Open
Abstract
OBJECTIVE We aimed to illustrate how complex cognitive data can be used to create domain-specific and general cognitive composites relevant to Alzheimer disease research. METHOD Using equipercentile equating, we combined data from the Charles F. and Joanne Knight Alzheimer Disease Research Center that spanned multiple iterations of the Uniform Data Set. Exploratory factor analyses revealed four domain-specific composites representing episodic memory, semantic memory, working memory, and attention/processing speed. The previously defined preclinical Alzheimer disease cognitive composite (PACC) and a novel alternative, the Knight-PACC, were also computed alongside a global composite comprising all available tests. These three composites allowed us to compare the usefulness of domain and general composites in the context of predicting common Alzheimer disease biomarkers. RESULTS General composites slightly outperformed domain-specific metrics in predicting imaging-derived amyloid, tau, and neurodegeneration burden. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the Alzheimer Disease Cooperative Study-PACC required two to three times as many participants. CONCLUSIONS Analyses of cognition with the Knight-PACC and our domain-specific composites offer researchers flexibility by providing validated outcome assessments that can equate across test versions to answer a wide range of questions regarding cognitive decline in normal aging and neurodegenerative disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Devanarayan V, Doherty T, Charil A, Sachdev P, Ye Y, Murali LK, Llano DA, Zhou J, Reyderman L, Hampel H, Kramer LD, Dhadda S, Irizarry MC. Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease. Alzheimers Dement 2024. [PMID: 38940656 DOI: 10.1002/alz.14073] [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: 04/08/2024] [Revised: 05/24/2024] [Accepted: 05/24/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND This study investigated the potential of phosphorylated plasma Tau217 ratio (pTau217R) and plasma amyloid beta (Aβ) 42/Aβ40 in predicting brain amyloid levels measured by positron emission tomography (PET) Centiloid (CL) for Alzheimer's disease (AD) staging and screening. METHODS Quantification of plasma pTau217R and Aβ42/Aβ40 employed immunoprecipitation-mass spectrometry. CL prediction models were developed on a cohort of 904 cognitively unimpaired, preclinical and early AD subjects and validated on two independent cohorts. RESULTS Models integrating pTau217R outperformed Aβ42/Aβ40 alone, predicting amyloid levels up to 89.1 CL. High area under the receiver operating characteristic curve (AUROC) values (89.3% to 94.7%) were observed across a broad CL range (15 to 90). Utilizing pTau217R-based models for low amyloid levels reduced PET scans by 70.5% to 78.6%. DISCUSSION pTau217R effectively predicts brain amyloid levels, surpassing cerebrospinal fluid Aβ42/Aβ40's range. Combining it with plasma Aβ42/Aβ40 enhances sensitivity for low amyloid detection, reducing unnecessary PET scans and expanding clinical utility. HIGHLIGHTS Phosphorylated plasma Tau217 ratio (pTau217R) effectively predicts amyloid-PET Centiloid (CL) across a broad spectrum. Integrating pTau217R with Aβ42/Aβ40 extends the CL prediction upper limit to 89.1 CL. Combined model predicts amyloid status with high accuracy, especially in cognitively unimpaired individuals. This model identifies subjects above or below various CL thresholds with high accuracy. pTau217R-based models significantly reduce PET scans by up to 78.6% for screening out individuals with no/low amyloid.
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Affiliation(s)
- Viswanath Devanarayan
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
- Department of Mathematics, Statistics and Computer Science, University of Illinois Chicago, Chicago, Illinois, USA
| | | | - Arnaud Charil
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Pallavi Sachdev
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Yuanqing Ye
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | | | - Daniel A Llano
- Carle Illinois College of Medicine, Urbana, Illinois, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, Urbana, Illinois, USA
| | - Jin Zhou
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Larisa Reyderman
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Harald Hampel
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Lynn D Kramer
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
| | - Shobha Dhadda
- Eisai Inc., Clinical Evidence Generation, Nutley, New Jersey, USA
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Krasny S, Yan C, Hartley SL, Handen BL, Wisch JK, Boehrwinkle AH, Ances BM, Rafii MS. Assessing amyloid PET positivity and cognitive function in Down syndrome to guide clinical trials targeting amyloid. Alzheimers Dement 2024. [PMID: 38940611 DOI: 10.1002/alz.14068] [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: 03/01/2024] [Revised: 04/26/2024] [Accepted: 05/21/2024] [Indexed: 06/29/2024]
Abstract
INTRODUCTION Trisomy 21, or Down syndrome (DS), predisposes individuals to early-onset Alzheimer's disease (AD). While monoclonal antibodies (mAbs) targeting amyloid are approved for older AD patients, their efficacy in DS remains unexplored. This study examines amyloid positron emission tomography (PET) positivity (A+), memory function, and clinical status across ages in DS to guide mAb trial designs. METHODS Cross-sectional data from the Alzheimer Biomarker Consortium-Down Syndrome (ABC-DS) was analyzed. PET amyloid beta in Centiloids classified amyloid status using various cutoffs. Episodic memory was assessed using the modified Cued Recall Test, and clinical status was determined through consensus processes. RESULTS Four hundred nine DS adults (mean age = 44.83 years) were evaluated. A+ rates increased with age, with mean amyloid load rising significantly. Memory decline and cognitive impairment are also correlated with age. DISCUSSION These findings emphasize the necessity of tailoring mAb trials for DS, considering age-related AD characteristics. HIGHLIGHTS There is rapid increase in prevalence of amyloid beta (Aβ) positron emission tomography (PET) positivity in Down syndrome (DS) after the age of 40 years. Aβ PET positivity thresholds have significant impact on prevalence rates in DS. There is a significant lag between Aβ PET positivity and clinical symptom onset in DS.
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Affiliation(s)
- Sophia Krasny
- Scripps Research Institute, La Jolla, California, USA
| | - Cynthia Yan
- Department of Neurology, Washington University, Saint Louis, Missouri, USA
| | - Sigan L Hartley
- Waisman Center, University of Wisconsin, Madison, Wisconsin, USA
| | - Ben L Handen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Julie K Wisch
- Department of Neurology, Washington University, Saint Louis, Missouri, USA
| | - Anna H Boehrwinkle
- Department of Neurology, Washington University, Saint Louis, Missouri, USA
| | - Beau M Ances
- Department of Neurology, Washington University, Saint Louis, Missouri, USA
| | - Michael S Rafii
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of University of Southern California, San Diego, California, USA
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Jadick MF, Robinson T, Farrell ME, Klinger H, Buckley RF, Marshall GA, Vannini P, Rentz DM, Johnson KA, Sperling RA, Amariglio RE. Associations Between Self and Study Partner Report of Cognitive Decline With Regional Tau in a Multicohort Study. Neurology 2024; 102:e209447. [PMID: 38810211 PMCID: PMC11226320 DOI: 10.1212/wnl.0000000000209447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/04/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Self-reported cognitive decline is an early behavioral manifestation of Alzheimer disease (AD) at the preclinical stage, often believed to precede concerns reported by a study partner. Previous work shows cross-sectional associations with β-amyloid (Aβ) status and self-reported and study partner-reported cognitive decline, but less is known about their associations with tau deposition, particularly among those with preclinical AD. METHODS This cross-sectional study included participants from the Anti-Amyloid Treatment in Asymptomatic AD/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration studies (N = 444) and the Harvard Aging Brain Study and affiliated studies (N = 231), which resulted in a cognitively unimpaired (CU) sample of individuals with both nonelevated (Aβ-) and elevated Aβ (Aβ+). All participants and study partners completed the Cognitive Function Index (CFI). Two regional tau composites were derived by averaging flortaucipir PET uptake in the medial temporal lobe (MTL) and neocortex (NEO). Global Aβ PET was measured in Centiloids (CLs) with Aβ+ >26 CL. We conducted multiple linear regression analyses to test associations between tau PET and CFI, covarying for amyloid, age, sex, education, and cohort. We also controlled for objective cognitive performance, measured using the Preclinical Alzheimer Cognitive Composite (PACC). RESULTS Across 675 CU participants (age = 72.3 ± 6.6 years, female = 59%, Aβ+ = 60%), greater tau was associated with greater self-CFI (MTL: β = 0.28 [0.12, 0.44], p < 0.001, and NEO: β = 0.26 [0.09, 0.42], p = 0.002) and study partner CFI (MTL: β = 0.28 [0.14, 0.41], p < 0.001, and NEO: β = 0.31 [0.17, 0.44], p < 0.001). Significant associations between both CFI measures and MTL/NEO tau PET were driven by Aβ+. Continuous Aβ showed an independent effect on CFI in addition to MTL and NEO tau for both self-CFI and study partner CFI. Self-CFI (β = 0.01 [0.001, 0.02], p = 0.03), study partner CFI (β = 0.01 [0.003, 0.02], p = 0.01), and the PACC (β = -0.02 [-0.03, -0.01], p < 0.001) were independently associated with MTL tau, but for NEO tau, PACC (β = -0.02 [-0.03, -0.01], p < 0.001) and study partner report (β = 0.01 [0.004, 0.02], p = 0.002) were associated, but not self-CFI (β = 0.01 [-0.001, 0.02], p = 0.10). DISCUSSION Both self-report and study partner report showed associations with tau in addition to Aβ. Additionally, self-report and study partner report were associated with tau above and beyond performance on a neuropsychological composite. Stratification analyses by Aβ status indicate that associations between self-reported and study partner-reported cognitive concerns with regional tau are driven by those at the preclinical stage of AD, suggesting that both are useful to collect on the early AD continuum.
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Affiliation(s)
- Michalina F Jadick
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Talia Robinson
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Michelle E Farrell
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Hannah Klinger
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Rachel F Buckley
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Gad A Marshall
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Patrizia Vannini
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Dorene M Rentz
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Keith A Johnson
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Reisa A Sperling
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Rebecca E Amariglio
- From the Department of Neurology (M.F.J., H.K., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), and Department of Radiology (M.F.J., K.A.J.), Massachusetts General Hospital, and Center for Alzheimer Research and Treatment (T.R., M.E.F., R.F.B., G.A.M., P.V., D.M.R., K.A.J., R.A.S., R.E.A.), Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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23
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Okuyama C, Higashi T, Ishizu K, Oishi N, Kusano K, Ito M, Kagawa S, Okina T, Suzuki N, Hasegawa H, Nagahama Y, Watanabe H, Ono M, Yamauchi H. New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map. Ann Nucl Med 2024:10.1007/s12149-024-01956-y. [PMID: 38907835 DOI: 10.1007/s12149-024-01956-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
OBJECTIVE This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses. METHODS Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects. RESULTS In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern. CONCLUSIONS Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment.
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Affiliation(s)
- Chio Okuyama
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan.
| | - Tatsuya Higashi
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan.
- Department of Molecular Imaging and Theranostics, National Institute of Quantum Science and Technology, Chiba, Japan.
| | - Koichi Ishizu
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kuninori Kusano
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Radiology, Shiga General Hospital, Moriyama, Japan
| | - Miki Ito
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Radiology, Shiga General Hospital, Moriyama, Japan
| | - Shinya Kagawa
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
| | - Tomoko Okina
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Norio Suzuki
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Hiroshi Hasegawa
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Yasuhiro Nagahama
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
- Department of Psychiatry and Neurology, Kawasaki Memorial Hospital, Kawasaki, Japan
| | - Hiroyuki Watanabe
- Department of Patho-Fundamental Bioanalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Masahiro Ono
- Department of Patho-Fundamental Bioanalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Hiroshi Yamauchi
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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24
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Brendel M, Guedj E, Yakushev I, Morbelli S, Höglinger GU, Tolboom N, Verger A, Albert NL, Cecchin D, Fernandez PA, Fraioli F, Traub-Weidinger T, Van Weehaeghe D, Barthel H. Neuroimaging biomarkers in the biological definition of Parkinson's disease and dementia with Lewy bodies - EANM position on current state, unmet needs and future perspectives. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06803-w. [PMID: 38907856 DOI: 10.1007/s00259-024-06803-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Affiliation(s)
- Matthias Brendel
- Department of Nuclear Medicine, LMU Hospital, Ludwig-Maximilians-University of Munich, Marchioninstraße 15, 81377, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
| | - Eric Guedj
- Département de Médecine Nucléaire, Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Hôpital de La Timone, CERIMED, Marseille, France
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute E Della Scienza Di Torino, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Günter U Höglinger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, LMU Hospital, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging, Platform, CHRU Nancy, Université de Lorraine, IADI, INSERM U1254, Allée du Morvan, 54500, Nancy, France
| | - Nathalie L Albert
- Department of Nuclear Medicine, LMU Hospital, Ludwig-Maximilians-University of Munich, Marchioninstraße 15, 81377, Munich, Germany
| | - Diego Cecchin
- Department of Medicine, Unit of Nuclear Medicine, University Hospital of Padova, Padua, Italy
| | - Pablo Aguiar Fernandez
- CIMUS, Universidade Santiago de Compostela & Nuclear Medicine Department, Univ. Hospital IDIS, Santiago de Compostela, Spain
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London (UCL), London, UK
| | - Tatjana Traub-Weidinger
- Department of Diagnostic and Therapeutic Nuclear Medicine, Clinic Donaustadt, Vienna Health Care Group, Vienna, Austria
| | - Donatienne Van Weehaeghe
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
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25
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Yamakuni R, Murakami T, Ukon N, Kakamu T, Toda W, Hattori K, Sekino H, Ishii S, Fukushima K, Matsuda H, Ugawa Y, Wakasugi N, Abe M, Ito H. Differential centiloid scale normalization techniques: comparison between hybrid PET/MRI and independently acquired MRI. Ann Nucl Med 2024:10.1007/s12149-024-01955-z. [PMID: 38902587 DOI: 10.1007/s12149-024-01955-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/11/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVE Centiloid (CL) scales play an important role in semiquantitative analyses of amyloid-β (Aβ) PET. CLs are derived from the standardized uptake value ratio (SUVR), which needs Aβ positron emission tomography (PET) normalization processing. There are two methods to collect the T1-weighted imaging (T1WI) for normalization: (i) anatomical standardization using simultaneously acquired T1WI (PET/MRI), usually adapted to PET images from PET/MRI scanners, and (ii) T1WI from a separate examination (PET + MRI), usually adapted to PET images from PET/CT scanners. This study aimed to elucidate the correlations and differences in CLs between when using the above two T1WI collection methods. METHODS Among patients who underwent Aβ PET/MRI (using 11C-Pittuberg compound B (11C-PiB) or 18F-flutemetamol (18F-FMM)) at our institution from 2015 to 2023, we selected 49 patients who also underwent other additional MRI examinations, including T1WI for anatomic standardization within 3 years. Thirty-one of them underwent 11C-PiB PET/MRI, and 18 participants underwent 18F-FMM PET/MRI. Twenty-five of them, additional MRI acquisition parameters were identical to simultaneous MRI during PET, and 24 participants were different. After normalization using PET/MRI or PET + MRI method each, SUVR was measured using the Global Alzheimer's Association Initiative Network cerebral cortical and striatum Volume of Interest templates (VOI) and whole cerebellum VOI. Subsequently, CLs were calculated using the previously established equations for each Aβ PET tracer. RESULTS Between PET/MRI and PET + MRI methods, CLs correlated linearly in 11C-PiB PET (y = 1.00x - 0.11, R2 = 0.999), 18F-FMM PET (y = 0.97x - 0.12, 0.997), identical additional MRI acquisition (y = 1.00x + 0.33, 0.999), different acquisition (y = 0.98x - 0.43, 0.997), and entire study group (y = 1.00x - 0.24, 0.999). Wilcoxon signed-rank test revealed no significant differences: 11C-PiB (p = 0.49), 18F-FMM (0.08), and whole PET (0.46). However, significant differences were identified in identical acquisition (p = 0.04) and different acquisition (p = 0.02). Bland-Altman analysis documented only a small bias between PET/MRI and PET + MRI in 11C-PiB PET, 18F-FMM PET, identical additional MRI acquisition, different acquisition, and whole PET (- 0.05, 0.67, - 0.30, 0.78, and 0.21, respectively). CONCLUSIONS Anatomical standardizations using PET/MRI and using PET + MRI can lead to almost equivalent CL. The CL values obtained using PET/MRI or PET + MRI normalization methods are consistent and comparable in clinical studies.
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Affiliation(s)
- Ryo Yamakuni
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.
| | - Takenobu Murakami
- Division of Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Tottori, Japan
| | - Naoyuki Ukon
- Advanced Clinical Research Center, Fukushima Medical University, Fukushima, Japan
| | - Takeyasu Kakamu
- Department of Hygiene and Preventive Medicine, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Wataru Toda
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Kasumi Hattori
- Department of Neurology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hirofumi Sekino
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan
| | - Shiro Ishii
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan
| | - Kenji Fukushima
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan
| | - Hiroshi Matsuda
- Department of Bio-Functional Imaging, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Noritaka Wakasugi
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Mitsunari Abe
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroshi Ito
- Department of Radiology and Nuclear Medicine, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan
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26
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat harmonization for reducing across-tracer biases in regional amyloid PET analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308952. [PMID: 38947044 PMCID: PMC11213066 DOI: 10.1101/2024.06.14.24308952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Background Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Intraclass correlation coefficient (ICC) and mean standardized absolute error (MsAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, the best ICC and MsAE were achieved after harmonizing with ComBat with no covariates for the global summary SUVR. ComBat with no covariates also performed the best in harmonizing regional SUVRs. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FPB-to-PiB proportions. Conclusions ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
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Nabizadeh F. Disruption in functional networks mediated tau spreading in Alzheimer's disease. Brain Commun 2024; 6:fcae198. [PMID: 38978728 PMCID: PMC11227975 DOI: 10.1093/braincomms/fcae198] [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: 01/18/2024] [Revised: 04/27/2024] [Accepted: 06/07/2024] [Indexed: 07/10/2024] Open
Abstract
Alzheimer's disease may be conceptualized as a 'disconnection syndrome', characterized by the breakdown of neural connectivity within the brain as a result of amyloid-beta plaques, tau neurofibrillary tangles and other factors leading to progressive degeneration and shrinkage of neurons, along with synaptic dysfunction. It has been suggested that misfolded tau proteins spread through functional connections (known as 'prion-like' properties of tau). However, the local effect of tau spreading on the synaptic function and communication between regions is not well understood. I aimed to investigate how the spreading of tau aggregates through connections can locally influence functional connectivity. In total, the imaging data of 211 participants including 117 amyloid-beta-negative non-demented and 94 amyloid-beta-positive non-demented participants were recruited from the Alzheimer's Disease Neuroimaging Initiative. Furthermore, normative resting-state functional MRI connectomes were used to model tau spreading through functional connections, and functional MRI of the included participants was used to determine the effect of tau spreading on functional connectivity. I found that lower functional connectivity to tau epicentres is associated with tau spreading through functional connections in both amyloid-beta-negative and amyloid-beta-positive participants. Also, amyloid-beta-PET in tau epicentres mediated the association of tau spreading and functional connectivity to epicentres suggesting a partial mediating effect of amyloid-beta deposition in tau epicentres on the local effect of tau spreading on functional connectivity. My findings provide strong support for the notion that tau spreading through connection is locally associated with disrupted functional connectivity between tau epicentre and non-epicentre regions independent of amyloid-beta pathology. Also, I defined several groups based on the relationship between tau spreading and functional disconnection, which provides quantitative assessment to investigate susceptibility or resilience to functional disconnection related to tau spreading. I showed that amyloid-beta, other copathologies and the apolipoprotein E epsilon 4 allele can be a leading factor towards vulnerability to tau relative functional disconnection.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran 441265421414, Iran
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28
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Lee S, Kim SE, Jang H, Kim JP, Sohn G, Park YH, Ham H, Gu Y, Park CJ, Kim HJ, Na DL, Kim K, Seo SW. Distinct effects of blood pressure parameters on Alzheimer's and vascular markers in 1,952 Asian individuals without dementia. Alzheimers Res Ther 2024; 16:125. [PMID: 38863019 PMCID: PMC11167921 DOI: 10.1186/s13195-024-01483-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Risk factors for cardiovascular disease, including elevated blood pressure, are known to increase risk of Alzheimer's disease. There has been increasing awareness of the relationship between long-term blood pressure (BP) patterns and their effects on the brain. We aimed to investigate the association of repeated BP measurements with Alzheimer's and vascular disease markers. METHODS We recruited 1,952 participants without dementia between August 2015 and February 2022. During serial clinic visits, we assessed both systolic BP (SBP) and diastolic BP (DBP), and visit-to-visit BP variability (BPV) was quantified from repeated measurements. In order to investigate the relationship of mean SBP (or DBP) with Alzheimer's and vascular markers and cognition, we performed multiple linear and logistic regression analyses after controlling for potential confounders (Model 1). Next, we investigated the relationship of with variation of SBP (or DBP) with the aforementioned variables by adding it into Model 1 (Model 2). In addition, mediation analyses were conducted to determine mediation effects of Alzheimer's and vascular makers on the relationship between BP parameters and cognitive impairment. RESULTS High Aβ uptake was associated with greater mean SBP (β = 1.049, 95% confidence interval 1.016-1.083). High vascular burden was positively associated with mean SBP (odds ratio = 1.293, 95% CI 1.015-1.647) and mean DBP (1.390, 1.098-1.757). High tau uptake was related to greater systolic BPV (0.094, 0.001-0.187) and diastolic BPV (0.096, 0.007-0.184). High Aβ uptake partially mediated the relationship between mean SBP and the Mini-Mental State Examination (MMSE) scores. Hippocampal atrophy mediated the relationship between diastolic BPV and MMSE scores. CONCLUSIONS Each BP parameter affects Alzheimer's and vascular disease markers differently, which in turn leads to cognitive impairment. Therefore, it is necessary to appropriately control specific BP parameters to prevent the development of dementia. Furthermore, a better understanding of pathways from specific BP parameters to cognitive impairments might enable us to select the managements targeting the specific BP parameters to prevent dementia effectively.
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Affiliation(s)
- Sungjoo Lee
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, 48108, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Gyeongmo Sohn
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, 48108, Republic of Korea
| | - Yu Hyun Park
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hongki Ham
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Yuna Gu
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Chae Jung Park
- Research Institute, National Cancer Center, Goyang, 10408, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea.
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea.
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29
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Groot C, Smith R, Collij LE, Mastenbroek SE, Stomrud E, Binette AP, Leuzy A, Palmqvist S, Mattsson-Carlgren N, Strandberg O, Cho H, Lyoo CH, Frisoni GB, Peretti DE, Garibotto V, La Joie R, Soleimani-Meigooni DN, Rabinovici G, Ossenkoppele R, Hansson O. Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment. JAMA Neurol 2024:2819811. [PMID: 38857029 PMCID: PMC11165418 DOI: 10.1001/jamaneurol.2024.1612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Importance An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. Objective To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. Design, Setting, and Participants This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures Tau PET, Aβ PET, and MRI. Main Outcomes and Measures Positive results on tau PET (temporal meta-region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. Results In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. Conclusions and Relevance In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.
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Affiliation(s)
- Colin Groot
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Lyduine E. Collij
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Sophie E. Mastenbroek
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Giovanni B. Frisoni
- Memory Clinic, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - Debora E. Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging, Geneva, Switzerland
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
| | - David N. Soleimani-Meigooni
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Gil Rabinovici
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
- Associate Editor, JAMA Neurology
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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30
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Cody KA, Langhough RE, Zammit MD, Clark L, Chin N, Christian BT, Betthauser TJ, Johnson SC. Characterizing brain tau and cognitive decline along the amyloid timeline in Alzheimer's disease. Brain 2024; 147:2144-2157. [PMID: 38667631 PMCID: PMC11146417 DOI: 10.1093/brain/awae116] [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: 10/30/2023] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/04/2024] Open
Abstract
Recent longitudinal PET imaging studies have established methods to estimate the age at which amyloid becomes abnormal at the level of the individual. Here we recontextualized amyloid levels into the temporal domain to better understand the downstream Alzheimer's disease processes of tau neurofibrillary tangle (NFT) accumulation and cognitive decline. This cohort study included a total of 601 individuals from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center that underwent amyloid and tau PET, longitudinal neuropsychological assessments and met clinical criteria for three clinical diagnosis groups: cognitively unimpaired (n = 537); mild cognitive impairment (n = 48); or dementia (n = 16). Cortical 11C-Pittsburgh compound B (PiB) distribution volume ratio (DVR) and sampled iterative local approximation were used to estimate amyloid positive (A+; global PiB DVR > 1.16 equivalent to 17.1 centiloids) onset age and years of A+ duration at tau PET (i.e. amyloid chronicity). Tau PET burden was quantified using 18F-MK-6240 standardized uptake value ratios (70-90 min, inferior cerebellar grey matter reference region). Whole-brain and region-specific approaches were used to examine tau PET binding along the amyloid timeline and across the Alzheimer's disease clinical continuum. Voxel-wise 18F-MK-6240 analyses revealed that with each decade of A+, the spatial extent of measurable tau spread (i.e. progressed) from regions associated with early to late NFT tau stages. Regional analyses indicated that tau burden in the entorhinal cortex was detectable, on average, within 10 years of A+ onset. Additionally, the entorhinal cortex was the region most sensitive to early amyloid pathology and clinical impairment in this predominantly preclinical sample. Among initially cognitively unimpaired (n = 472) individuals with longitudinal cognitive follow-up, mixed effects models showed significant linear and non-linear interactions of A+ duration and entorhinal tau on cognitive decline, suggesting a synergistic effect whereby greater A+ duration, together with a higher entorhinal tau burden, increases the likelihood of cognitive decline beyond their separable effects. Overall, the amyloid time framework enabled a spatiotemporal characterization of tau deposition patterns across the Alzheimer's disease continuum. This approach, which examined cross-sectional tau PET data along the amyloid timeline to make longitudinal disease course inferences, demonstrated that A+ duration explains a considerable amount of variability in the magnitude and topography of tau spread, which largely recapitulated NFT staging observed in human neuropathological studies. By anchoring disease progression to the onset of amyloid, this study provides a temporal disease context, which may help inform disease prognosis and timing windows for anti-amyloid therapies.
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Affiliation(s)
- Karly A Cody
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Rebecca E Langhough
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Matthew D Zammit
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Lindsay Clark
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Nathaniel Chin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bradley T Christian
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Tobey J Betthauser
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
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31
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Fan S, Ponisio MR, Xiao P, Ha SM, Chakrabarty S, Lee JJ, Flores S, LaMontagne P, Gordon B, Raji CA, Marcus DS, Nazeri A, Ances BM, Bateman RJ, Morris JC, Benzinger TLS, Sotiras A, Atzen S. AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning. Radiology 2024; 311:e231442. [PMID: 38860897 PMCID: PMC11211958 DOI: 10.1148/radiol.231442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 03/11/2024] [Accepted: 03/29/2024] [Indexed: 06/12/2024]
Abstract
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.
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Affiliation(s)
- Shuyang Fan
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Maria Rosana Ponisio
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Pan Xiao
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Sung Min Ha
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Satrajit Chakrabarty
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - John J. Lee
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Shaney Flores
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Pamela LaMontagne
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Brian Gordon
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Cyrus A. Raji
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Daniel S. Marcus
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Arash Nazeri
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Beau M. Ances
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Randall J. Bateman
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - John C. Morris
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Tammie L. S. Benzinger
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | - Aristeidis Sotiras
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
| | | | - Sarah Atzen
- From the Department of Bioengineering, Rice University, Houston, Tex
(S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S.
Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne
Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.),
Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for
Informatics, Data Science and Biostatistics (A.S.), Washington University School
of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS
Medical School, Singapore (S. Fan); Department of Electrical and Systems
Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain
Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre
for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ
Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
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Earnest T, Bani A, Ha SM, Hobbs DA, Kothapalli D, Yang B, Lee JJ, Benzinger TLS, Gordon BA, Sotiras A. Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease. Alzheimers Dement 2024; 20:4002-4019. [PMID: 38683905 PMCID: PMC11180875 DOI: 10.1002/alz.13769] [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: 10/10/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 05/02/2024]
Abstract
INTRODUCTION Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. RESULTS We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. HIGHLIGHTS NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.
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Affiliation(s)
- Tom Earnest
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Abdalla Bani
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Sung Min Ha
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Diana A. Hobbs
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Deydeep Kothapalli
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Braden Yang
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - John J. Lee
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Brian A. Gordon
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
- Institute for Informatics, Data Science & BiostatisticsWashington University School of Medicine in St LouisSaint LouisMissouriUSA
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Schneider C, Prokopiou PC, Papp KV, Engels‐Domínguez N, Hsieh S, Juneau TA, Schultz AP, Rentz DM, Sperling RA, Johnson KA, Jacobs HIL. Atrophy links lower novelty-related locus coeruleus connectivity to cognitive decline in preclinical AD. Alzheimers Dement 2024; 20:3958-3971. [PMID: 38676563 PMCID: PMC11180940 DOI: 10.1002/alz.13839] [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: 11/01/2023] [Revised: 02/29/2024] [Accepted: 03/08/2024] [Indexed: 04/29/2024]
Abstract
INTRODUCTION Animal research has shown that tau pathology in the locus coeruleus (LC) is associated with reduced norepinephrine signaling, lower projection density to the medial temporal lobe (MTL), atrophy, and cognitive impairment. We investigated the contribution of LC-MTL functional connectivity (FCLC-MTL) on cortical atrophy across Braak stage regions and its impact on cognition. METHODS We analyzed functional magnetic resonance imaging and amyloid beta (Aβ) positron emission tomography data from 128 cognitively normal participants, associating novelty-related FCLC-MTL with longitudinal atrophy and cognition with and without Aβ moderation. RESULTS Cross-sectionally, lower FCLC-MTL was associated with atrophy in Braak stage II regions. Longitudinally, atrophy in Braak stage 2 to 4 regions related to lower baseline FCLC-MTL at elevated levels of Aβ, but not to other regions. Atrophy in Braak stage 2 regions mediated the relation between FCLC-MTL and subsequent cognitive decline. DISCUSSION FCLC-MTL is implicated in Aβ-related cortical atrophy, suggesting that LC-MTL connectivity could confer neuroprotective effects in preclinical AD. HIGHLIGHTS Novelty-related functional magnetic resonance imaging (fMRI) LC-medial temporal lobe (MTL) connectivity links to longitudinal Aβ-dependent atrophy. This relationship extended to higher Braak stage regions with increasing Aβ burden. Longitudinal MTL atrophy mediated the LC-MTL connectivity-cognition relationship. Our findings mirror the animal data on MTL atrophy following NE signal dysfunction.
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Affiliation(s)
- Christoph Schneider
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Prokopis C. Prokopiou
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Kathryn V. Papp
- Harvard Medical SchoolBostonMassachusettsUSA
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalBostonMassachusettsUSA
| | - Nina Engels‐Domínguez
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Faculty of HealthMedicine and Life SciencesSchool for Mental Health and NeuroscienceAlzheimer Centre LimburgMaastricht University, MDMaastrichtThe Netherlands
| | - Stephanie Hsieh
- The Athinoula A. Martinos Center for Biomedical ImagingDepartment of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Truley A. Juneau
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Aaron P. Schultz
- The Athinoula A. Martinos Center for Biomedical ImagingDepartment of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Dorene M. Rentz
- Harvard Medical SchoolBostonMassachusettsUSA
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Reisa A. Sperling
- Harvard Medical SchoolBostonMassachusettsUSA
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Keith A. Johnson
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Heidi I. L. Jacobs
- Gordon Center for Medical ImagingDepartment of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
- Faculty of HealthMedicine and Life SciencesSchool for Mental Health and NeuroscienceAlzheimer Centre LimburgMaastricht University, MDMaastrichtThe Netherlands
- The Athinoula A. Martinos Center for Biomedical ImagingDepartment of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
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Tagmazian AA, Schwarz C, Lange C, Pitkänen E, Vuoksimaa E. ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images. Eur J Neurosci 2024; 59:3030-3044. [PMID: 38576196 DOI: 10.1111/ejn.16332] [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: 10/27/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = -0.64, rFBB = -0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination.
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Affiliation(s)
- Arina A Tagmazian
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Claudia Schwarz
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Rudolph MD, Sutphen CL, Register TC, Whitlow CT, Solingapuram Sai KK, Hughes TM, Bateman JR, Dage JL, Russ KA, Mielke MM, Craft S, Lockhart SN. Associations among plasma, MRI, and amyloid PET biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities in a community-dwelling cohort. Alzheimers Dement 2024; 20:4159-4173. [PMID: 38747525 PMCID: PMC11180870 DOI: 10.1002/alz.13835] [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: 11/08/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 06/05/2024]
Abstract
INTRODUCTION We evaluated associations between plasma and neuroimaging-derived biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities. METHODS We examined plasma biomarkers (neurofilament light chain, glial fibrillary acidic protein, amyloid beta [Aβ] 42/40, phosphorylated tau 181) and neuroimaging measures of amyloid deposition (Aβ-positron emission tomography [PET]), total brain volume, white matter hyperintensity volume, diffusion-weighted fractional anisotropy, and neurite orientation dispersion and density imaging free water. Participants were adjudicated as cognitively unimpaired (CU; N = 299), mild cognitive impairment (MCI; N = 192), or dementia (DEM; N = 65). Biomarkers were compared across groups stratified by diagnosis, sex, race, and APOE ε4 carrier status. General linear models examined plasma-imaging associations before and after adjusting for demographics (age, sex, race, education), APOE ε4 status, medications, diagnosis, and other factors (estimated glomerular filtration rate [eGFR], body mass index [BMI]). RESULTS Plasma biomarkers differed across diagnostic groups (DEM > MCI > CU), were altered in Aβ-PET-positive individuals, and were associated with poorer brain health and kidney function. DISCUSSION eGFR and BMI did not substantially impact associations between plasma and neuroimaging biomarkers. HIGHLIGHTS Plasma biomarkers differ across diagnostic groups (DEM > MCI > CU) and are altered in Aβ-PET-positive individuals. Altered plasma biomarker levels are associated with poorer brain health and kidney function. Plasma and neuroimaging biomarker associations are largely independent of comorbidities.
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Affiliation(s)
- Marc D. Rudolph
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Courtney L. Sutphen
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Thomas C. Register
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Christopher T. Whitlow
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Kiran K. Solingapuram Sai
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Timothy M. Hughes
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - James R. Bateman
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Jeffrey L. Dage
- Department Of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kristen A. Russ
- Department Of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Michelle M. Mielke
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Suzanne Craft
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Samuel N. Lockhart
- Department of Internal MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
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Pahl J, Prokopiou PC, Bueichekú E, Schultz AP, Papp KV, Farrell ME, Rentz DM, Sperling RA, Johnson KA, Jacobs HIL. Locus coeruleus integrity and left frontoparietal connectivity provide resilience against attentional decline in preclinical alzheimer's disease. Alzheimers Res Ther 2024; 16:119. [PMID: 38822365 PMCID: PMC11140954 DOI: 10.1186/s13195-024-01485-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/22/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Autopsy work reported that neuronal density in the locus coeruleus (LC) provides neural reserve against cognitive decline in dementia. Recent neuroimaging and pharmacological studies reported that left frontoparietal network functional connectivity (LFPN-FC) confers resilience against beta-amyloid (Aβ)-related cognitive decline in preclinical sporadic and autosomal dominant Alzheimer's disease (AD), as well as against LC-related cognitive changes. Given that the LFPN and the LC play important roles in attention, and attention deficits have been observed early in the disease process, we examined whether LFPN-FC and LC structural health attenuate attentional decline in the context of AD pathology. METHODS 142 participants from the Harvard Aging Brain Study who underwent resting-state functional MRI, LC structural imaging, PiB(Aβ)-PET, and up to 5 years of cognitive follow-ups were included (mean age = 74.5 ± 9.9 years, 89 women). Cross-sectional robust linear regression associated LC integrity (measured as the average of five continuous voxels with the highest intensities in the structural LC images) or LFPN-FC with Digit Symbol Substitution Test (DSST) performance at baseline. Longitudinal robust mixed effect analyses examined associations between DSST decline and (i) two-way interactions of baseline LC integrity (or LFPN-FC) and PiB or (ii) the three-way interaction of baseline LC integrity, LFPN-FC, and PiB. Baseline age, sex, and years of education were included as covariates. RESULTS At baseline, lower LFPN-FC, but not LC integrity, was related to worse DSST performance. Longitudinally, lower baseline LC integrity was associated with a faster DSST decline, especially at PiB > 10.38 CL. Lower baseline LFPN-FC was associated with a steeper decline on the DSST but independent of PiB. At elevated PiB levels (> 46 CL), higher baseline LFPN-FC was associated with an attenuated decline on the DSST, despite the presence of lower LC integrity. CONCLUSIONS Our findings demonstrate that the LC can provide resilience against Aβ-related attention decline. However, when Aβ accumulates and the LC's resources may be depleted, the functioning of cortical target regions of the LC, such as the LFPN-FC, can provide additional resilience to sustain attentional performance in preclinical AD. These results provide critical insights into the neural correlates contributing to individual variability at risk versus resilience against Aβ-related cognitive decline.
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Affiliation(s)
- Jennifer Pahl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Prokopis C Prokopiou
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn V Papp
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michelle E Farrell
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dorene M Rentz
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Heidi I L Jacobs
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Okazawa H, Nogami M, Ishida S, Makino A, Mori T, Kiyono Y, Ikawa M. PET/MRI multimodality imaging to evaluate changes in glymphatic system function and biomarkers of Alzheimer's disease. Sci Rep 2024; 14:12310. [PMID: 38811627 PMCID: PMC11137097 DOI: 10.1038/s41598-024-62806-5] [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: 01/25/2024] [Accepted: 05/20/2024] [Indexed: 05/31/2024] Open
Abstract
The glymphatic system is considered to play a pivotal role in the clearance of disease-causing proteins in neurodegenerative diseases. This study employed MR diffusion tensor imaging (DTI) to evaluate glymphatic system function and its correlation with brain amyloid accumulation levels measured using [11C]Pittsburgh compound-B (PiB) PET/MRI. Fifty-six patients with mild cognitive impairment and early Alzheimer's disease (AD: 70 ± 11 y) underwent [11C]PiB PET/MRI to assess amyloid deposition and were compared with 27 age-matched cognitively normal volunteers (CN: 69 ± 10y). All participants were evaluated for cognitive function using the Mini Mental State Examination (MMSE) before [11C]PiB PET/MRI. DTI images were acquired during the PET/MRI scan with several other MR sequences. The DTI analysis along the perivascular space index (DTI-ALPS index) was calculated to estimate the functional activity of the glymphatic system. Centiloid scale was applied to quantify amyloid deposition levels from [11C]PiB PET images. All patients in the AD group showed positive [11C]PiB accumulation, whereas all CN participants were negative. ALPS-index for all subjects linearly correlated with PiB centiloid, MMSE scores, and hippocampal volume. The correlation between the ALPS-index and PiB accumulation was more pronounced than with any other biomarkers. These findings suggest that glymphatic system dysfunction is a significant factor in the early stages of Alzheimer's disease.
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Affiliation(s)
- Hidehiko Okazawa
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan.
| | - Munenobu Nogami
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan
- Department of Radiology, Kobe University Hospital, Kobe, Japan
| | | | - Akira Makino
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan
| | - Tetsuya Mori
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan
| | - Yasushi Kiyono
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan
| | - Masamichi Ikawa
- Biomedical Imaging Research Center, University of Fukui, 23-3, Matsuoka-Shimaizuki, Eiheiji-cho, Fukui, 910-1193, Japan
- Department of Community Health Science, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
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Lao P, Edwards N, Flores-Aguilar L, Alshikho M, Rizvi B, Tudorascu D, Rosas HD, Yassa M, Christian BT, Mapstone M, Handen B, Zimmerman ME, Gutierrez J, Wilcock D, Head E, Brickman AM. Cerebrovascular disease emerges with age and Alzheimer's disease in adults with Down syndrome. Sci Rep 2024; 14:12334. [PMID: 38811657 PMCID: PMC11137035 DOI: 10.1038/s41598-024-61962-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/13/2024] [Indexed: 05/31/2024] Open
Abstract
Adults with Down syndrome have a genetic form of Alzheimer's disease (AD) and evidence of cerebrovascular disease across the AD continuum, despite few systemic vascular risk factors. The onset and progression of AD in Down syndrome is highly age-dependent, but it is unknown at what age cerebrovascular disease emerges and what factors influence its severity. In the Alzheimer's Biomarker Consortium-Down Syndrome study (ABC-DS; n = 242; age = 25-72), we estimated the age inflection point at which MRI-based white matter hyperintensities (WMH), enlarged perivascular spaces (PVS), microbleeds, and infarcts emerge in relation to demographic data, risk factors, amyloid and tau, and AD diagnosis. Enlarged PVS and infarcts appear to develop in the early 30s, while microbleeds, WMH, amyloid, and tau emerge in the mid to late 30s. Age-residualized WMH were higher in women, in individuals with dementia, and with lower body mass index. Participants with hypertension and APOE-ε4 had higher age-residualized PVS and microbleeds, respectively. Lifespan trajectories demonstrate a dramatic cerebrovascular profile in adults with Down syndrome that appears to evolve developmentally in parallel with AD pathophysiology approximately two decades prior to dementia symptoms.
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Affiliation(s)
- Patrick Lao
- Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, PS Box 16, New York, NY, 10032, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
| | - Natalie Edwards
- Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, PS Box 16, New York, NY, 10032, USA
| | - Lisi Flores-Aguilar
- Department of Pathology and Laboratory Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Mohamad Alshikho
- Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, PS Box 16, New York, NY, 10032, USA
| | - Batool Rizvi
- Department of Neurology, University of California, Irvine, Irvine, CA, 92697, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
| | - H Diana Rosas
- Department of Neurology, Massachusetts General Hospital, Harvard Medical Center, Boston, MA, 02114, USA
| | - Michael Yassa
- Department of Neurology, University of California, Irvine, Irvine, CA, 92697, USA
| | | | - Mark Mapstone
- Department of Neurology, University of California, Irvine, Irvine, CA, 92697, USA
| | - Benjamin Handen
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
| | | | - Jose Gutierrez
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
| | - Donna Wilcock
- Departments of Neurology and Anatomy, Cell Biology, and Physiology, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Elizabeth Head
- Department of Pathology and Laboratory Medicine, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Adam M Brickman
- Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, PS Box 16, New York, NY, 10032, USA.
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
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Iordan AD, Ploutz-Snyder R, Ghosh B, Rahman-Filipiak A, Koeppe R, Peltier S, Giordani B, Albin RL, Hampstead BM. Salience Network Segregation Mediates the Effect of Tau Pathology on Mild Behavioral Impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.26.24307943. [PMID: 38854100 PMCID: PMC11160832 DOI: 10.1101/2024.05.26.24307943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
INTRODUCTION A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the links between MBI, brain function, and Alzheimer's disease (AD) biomarkers are unclear. METHODS Using data from 128 participants with diagnosis of amnestic mild cognitive impairment and mild dementia - Alzheimer's type, we test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks. RESULTS We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI. DISCUSSION Our findings suggest an indirect role of tau pathology in MBI through brain network dysfunction and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations.
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Affiliation(s)
- Alexandru D. Iordan
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
| | - Robert Ploutz-Snyder
- Applied Biostatistics Laboratory, School of Nursing, University of Michigan, 426 N Ingalls St, Ann Arbor, MI 48109, USA
| | - Bidisha Ghosh
- Applied Biostatistics Laboratory, School of Nursing, University of Michigan, 426 N Ingalls St, Ann Arbor, MI 48109, USA
| | - Annalise Rahman-Filipiak
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
| | - Scott Peltier
- Functional MRI Laboratory, University of Michigan, 2360 Bonisteel Blvd, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel Blvd, Ann Arbor, MI 48109, USA
| | - Bruno Giordani
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
- Department of Neurology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
| | - Roger L. Albin
- Department of Neurology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
- Neurology Service & GRECC, VAAAHS, 2215 Fuller Rd, Ann Arbor, MI 48105, USA
| | - Benjamin M. Hampstead
- Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI), Department of Psychiatry, University of Michigan, 4251 Plymouth Rd., Ann Arbor, MI, 48105, USA
- VA Ann Arbor Healthcare System, Neuropsychology Section, Mental Health Service, 2215 Fuller Rd, Ann Arbor, MI 48105, USA
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Vanderlip CR, Stark CE. Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595638. [PMID: 38826456 PMCID: PMC11142177 DOI: 10.1101/2024.05.23.595638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimer's or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and Aβ/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical Aβ and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimer's detection and monitoring.
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Affiliation(s)
- Casey R. Vanderlip
- Department of Neurobiology and Behavior, 1424 Biological Sciences III Irvine, University of California Irvine, Irvine, CA, 92697 USA
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, 1424 Biological Sciences III Irvine, University of California Irvine, Irvine, CA, 92697 USA
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Niimi Y, Janelidze S, Sato K, Tomita N, Tsukamoto T, Kato T, Yoshiyama K, Kowa H, Iwata A, Ihara R, Suzuki K, Kasuga K, Ikeuchi T, Ishii K, Ito K, Nakamura A, Senda M, Day TA, Burnham SC, Iaccarino L, Pontecorvo MJ, Hansson O, Iwatsubo T. Combining plasma Aβ and p-tau217 improves detection of brain amyloid in non-demented elderly. Alzheimers Res Ther 2024; 16:115. [PMID: 38778353 PMCID: PMC11112892 DOI: 10.1186/s13195-024-01469-w] [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: 03/12/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Maximizing the efficiency to screen amyloid-positive individuals in asymptomatic and non-demented aged population using blood-based biomarkers is essential for future success of clinical trials in the early stage of Alzheimer's disease (AD). In this study, we elucidate the utility of combination of plasma amyloid-β (Aβ)-related biomarkers and tau phosphorylated at threonine 217 (p-tau217) to predict abnormal Aβ-positron emission tomography (PET) in the preclinical and prodromal AD. METHODS We designed the cross-sectional study including two ethnically distinct cohorts, the Japanese trial-ready cohort for preclinica and prodromal AD (J-TRC) and the Swedish BioFINDER study. J-TRC included 474 non-demented individuals (CDR 0: 331, CDR 0.5: 143). Participants underwent plasma Aβ and p-tau217 assessments, and Aβ-PET imaging. Findings in J-TRC were replicated in the BioFINDER cohort including 177 participants (cognitively unimpaired: 114, mild cognitive impairment: 63). In both cohorts, plasma Aβ(1-42) (Aβ42) and Aβ(1-40) (Aβ40) were measured using immunoprecipitation-MALDI TOF mass spectrometry (Shimadzu), and p-tau217 was measured with an immunoassay on the Meso Scale Discovery platform (Eli Lilly). RESULTS Aβ-PET was abnormal in 81 participants from J-TRC and 71 participants from BioFINDER. Plasma Aβ42/Aβ40 ratio and p-tau217 individually showed moderate to high accuracies when detecting abnormal Aβ-PET scans, which were improved by combining plasma biomarkers and by including age, sex and APOE genotype in the models. In J-TRC, the highest AUCs were observed for the models combining p-tau217/Aβ42 ratio, APOE, age, sex in the whole cohort (AUC = 0.936), combining p-tau217, Aβ42/Aβ40 ratio, APOE, age, sex in the CDR 0 group (AUC = 0.948), and combining p-tau217/Aβ42 ratio, APOE, age, sex in the CDR 0.5 group (AUC = 0.955), respectively. Each subgroup results were replicated in BioFINDER, where the highest AUCs were seen for models combining p-tau217, Aβ42/40 ratio, APOE, age, sex in cognitively unimpaired (AUC = 0.938), and p-tau217/Aβ42 ratio, APOE, age, sex in mild cognitive impairment (AUC = 0.914). CONCLUSIONS Combination of plasma Aβ-related biomarkers and p-tau217 exhibits high performance when predicting Aβ-PET positivity. Adding basic clinical information (i.e., age, sex, APOE ε genotype) improved the prediction in preclinical AD, but not in prodromal AD. Combination of Aβ-related biomarkers and p-tau217 could be highly useful for pre-screening of participants in clinical trials of preclinical and prodromal AD.
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Affiliation(s)
- Yoshiki Niimi
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Kenichiro Sato
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naoki Tomita
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Department of Geriatric Medicine and Neuroimaging, Tohoku University Hospital, Sendai, Japan
| | - Tadashi Tsukamoto
- Department of Neurology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takashi Kato
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Aichi, Japan
| | - Kenji Yoshiyama
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hisatomo Kowa
- Graduate School of Health Sciences, Kobe University, Hyogo, Japan
| | - Atsushi Iwata
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Ryoko Ihara
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Kazushi Suzuki
- Division of Neurology, Internal Medicine, National Defense Medical College, Saitama, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Kenji Ishii
- Integrated Research Initiative for Living Well With Dementia, Tokyo Metropolitan Institute for Geriatric and Gerontology, Tokyo, Japan
| | - Kengo Ito
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Aichi, Japan
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Aichi, Japan
| | - Michio Senda
- Department of Molecular Imaging Research, Kobe City Medical Center General Hospital, Hyogo, Japan
| | | | | | | | | | - Oskar Hansson
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
- Memory Clinic, Skåne University Hospital, Lund, Sweden.
| | - Takeshi Iwatsubo
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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Rhodius-Meester HFM, van Maurik IS, Collij LE, van Gils AM, Koikkalainen J, Tolonen A, Pijnenburg YAL, Berkhof J, Barkhof F, van de Giessen E, Lötjönen J, van der Flier WM. Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET. PLoS One 2024; 19:e0303111. [PMID: 38768188 PMCID: PMC11104589 DOI: 10.1371/journal.pone.0303111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.
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Affiliation(s)
- Hanneke F. M. Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aniek M. van Gils
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | | | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Elsmarieke van de Giessen
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
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Teipel S, Grazia A, Dyrba M, Grothe MJ, Pomara N. Basal forebrain volume and metabolism in carriers of the Colombian mutation for autosomal dominant Alzheimer's disease. Sci Rep 2024; 14:11268. [PMID: 38760448 PMCID: PMC11101449 DOI: 10.1038/s41598-024-60799-9] [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: 01/03/2024] [Accepted: 04/26/2024] [Indexed: 05/19/2024] Open
Abstract
We aimed to study atrophy and glucose metabolism of the cholinergic basal forebrain in non-demented mutation carriers for autosomal dominant Alzheimer's disease (ADAD). We determined the level of evidence for or against atrophy and impaired metabolism of the basal forebrain in 167 non-demented carriers of the Colombian PSEN1 E280A mutation and 75 age- and sex-matched non-mutation carriers of the same kindred using a Bayesian analysis framework. We analyzed baseline MRI, amyloid PET, and FDG-PET scans of the Alzheimer's Prevention Initiative ADAD Colombia Trial. We found moderate evidence against an association of carrier status with basal forebrain volume (Bayes factor (BF10) = 0.182). We found moderate evidence against a difference of basal forebrain metabolism (BF10 = 0.167). There was only inconclusive evidence for an association between basal forebrain volume and delayed memory and attention (BF10 = 0.884 and 0.184, respectively), and between basal forebrain volume and global amyloid load (BF10 = 2.1). Our results distinguish PSEN1 E280A mutation carriers from sporadic AD cases in which cholinergic involvement of the basal forebrain is already detectable in the preclinical and prodromal stages. This indicates an important difference between ADAD and sporadic AD in terms of pathogenesis and potential treatment targets.
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Affiliation(s)
- Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Gehlsheimer Str. 20, 18147, Rostock, Germany.
- Department of Psychosomatic Medicine, University Medicine Rostock, Gehlsheimer Str. 20, 18147, Rostock, Germany.
| | - Alice Grazia
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Gehlsheimer Str. 20, 18147, Rostock, Germany
| | - Martin Dyrba
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Gehlsheimer Str. 20, 18147, Rostock, Germany
| | - Michel J Grothe
- CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain
| | - Nunzio Pomara
- Geriatric Psychiatry Division, Nathan Kline Institute/Department of Psychiatry and Pathology, NYU Grossman School of Medicine, Orangeburg, NY, USA
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Mendes AJ, Ribaldi F, Lathuiliere A, Ashton NJ, Zetterberg H, Abramowicz M, Scheffler M, Assal F, Garibotto V, Blennow K, Frisoni GB. Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients. Alzheimers Res Ther 2024; 16:110. [PMID: 38755703 PMCID: PMC11097559 DOI: 10.1186/s13195-024-01478-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/07/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). METHODS Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. RESULTS Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. CONCLUSIONS Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.
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Affiliation(s)
- Augusto J Mendes
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland.
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
| | - Aurelien Lathuiliere
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer?s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Marc Abramowicz
- Genetic Medicine, Diagnostics Dept, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Frédéric Assal
- Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
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Jung W, Kim SE, Kim JP, Jang H, Park CJ, Kim HJ, Na DL, Seo SW, Suk HI. Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment. Front Aging Neurosci 2024; 16:1356745. [PMID: 38813529 PMCID: PMC11135285 DOI: 10.3389/fnagi.2024.1356745] [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: 12/16/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024] Open
Abstract
Objectives Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI. Methods We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network. Results The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (-). Conclusion The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.
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Affiliation(s)
- Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chae Jung Park
- National Cancer Center Research Institute, Goyang, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea
- Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
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Bueichekú E, Diez I, Kim CM, Becker JA, Koops EA, Kwong K, Papp KV, Salat DH, Bennett DA, Rentz DM, Sperling RA, Johnson KA, Sepulcre J, Jacobs HIL. Spatiotemporal patterns of locus coeruleus integrity predict cortical tau and cognition. NATURE AGING 2024; 4:625-637. [PMID: 38664576 PMCID: PMC11108787 DOI: 10.1038/s43587-024-00626-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Autopsy studies indicated that the locus coeruleus (LC) accumulates hyperphosphorylated tau before allocortical regions in Alzheimer's disease. By combining in vivo longitudinal magnetic resonance imaging measures of LC integrity, tau positron emission tomography imaging and cognition with autopsy data and transcriptomic information, we examined whether LC changes precede allocortical tau deposition and whether specific genetic features underlie LC's selective vulnerability to tau. We found that LC integrity changes preceded medial temporal lobe tau accumulation, and together these processes were associated with lower cognitive performance. Common gene expression profiles between LC-medial temporal lobe-limbic regions map to biological functions in protein transport regulation. These findings advance our understanding of the spatiotemporal patterns of initial tau spreading from the LC and LC's selective vulnerability to Alzheimer's disease pathology. LC integrity measures can be a promising indicator for identifying the time window when individuals are at risk of disease progression and underscore the importance of interventions mitigating initial tau spread.
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Affiliation(s)
- Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - John Alex Becker
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Kenneth Kwong
- Harvard Medical School, Boston, MA, USA
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Kathryn V Papp
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - David H Salat
- Harvard Medical School, Boston, MA, USA
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Dorene M Rentz
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Reisa A Sperling
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Keith A Johnson
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Radiology, Yale PET Center, Yale Medical School, Yale University, New Haven, CT, USA.
| | - Heidi I L Jacobs
- Harvard Medical School, Boston, MA, USA.
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands.
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Mathoux G, Boccalini C, Peretti DE, Arnone A, Ribaldi F, Scheffler M, Frisoni GB, Garibotto V. A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [ 18F]flortaucipir PET in a memory clinic cohort. Eur J Nucl Med Mol Imaging 2024; 51:1639-1650. [PMID: 38182839 DOI: 10.1007/s00259-023-06583-9] [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: 10/04/2023] [Accepted: 12/22/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE [18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer's disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population. METHODS We included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages. Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen's kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches. RESULTS We found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater's expertise (k>0.68, p<0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p<0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC>0.80) and amyloid-positive from negative subjects (AUC>0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p<0.01), independently from the classification method. CONCLUSION Our results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.
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Affiliation(s)
- Gregory Mathoux
- Diagnostic Department, Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
- Università degli Studi Milano-Bicocca, Milano, Italy
| | - Cecilia Boccalini
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
- Università Vita e Salute San Raffaele, Milano, Italy
| | - Debora E Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
| | - Annachiara Arnone
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
| | - Federica Ribaldi
- Department of Rehabilitation and Geriatrics, Memory Clinic, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- Department of Rehabilitation and Geriatrics, Memory Clinic, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Diagnostic Department, Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland.
- CIBM Center for Biomedical Imaging, Geneva, Switzerland.
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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses. J Nucl Med 2024; 65:781-787. [PMID: 38575189 PMCID: PMC11064829 DOI: 10.2967/jnumed.123.266188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/13/2024] [Indexed: 04/06/2024] Open
Abstract
Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
| | - Christopher C Rowe
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine E Davey
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia;
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
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Chen T, O'Gorman J, Castrillo‐Viguera C, Rajagovindan R, Curiale GG, Tian Y, Patel D, von Rosenstiel P, von Hehn C, Salloway S, Hock C, Nitsch RM, Haeberlein SB, Sandrock A, Singhal P. Results from the long-term extension of PRIME: A randomized Phase 1b trial of aducanumab. Alzheimers Dement 2024; 20:3406-3415. [PMID: 38567735 PMCID: PMC11095417 DOI: 10.1002/alz.13755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 01/22/2024] [Accepted: 01/29/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION Aducanumab selectively targets aggregated forms of amyloid beta (Aβ), a neuropathological hallmark of Alzheimer's disease (AD). METHODS PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. During the 12-month placebo-controlled period, participants with prodromal AD or mild AD dementia were randomized to receive aducanumab or placebo. At week 56, participants could enroll in a long-term extension (LTE), in which all participants received aducanumab. The primary endpoint was safety and tolerability. RESULTS Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event. Dose titration was associated with a decrease in the incidence of ARIA-E. Over 48 months, aducanumab decreased brain amyloid levels in a dose- and time-dependent manner. Exploratory endpoints suggested a continued benefit in the reduction of clinical decline over 48 months. DISCUSSION The safety profile of aducanumab remained unchanged in the LTE of PRIME. Amyloid plaque levels continued to decrease in participants treated with aducanumab. HIGHLIGHTS PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. We report cumulative safety and 48-month efficacy results from PRIME. Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event (AE); 61% of participants with ARIA-E were asymptomatic. Dose titration was associated with a decrease in the incidence of ARIA-E. Aducanumab decreased levels of amyloid beta (Aβ) in a dose- and time-dependent manner.
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Jagust WJ, Mattay VS, Krainak DM, Wang SJ, Weidner LD, Hofling AA, Koo H, Hsieh P, Kuo PH, Farrar G, Marzella L. Quantitative Brain Amyloid PET. J Nucl Med 2024; 65:670-678. [PMID: 38514082 PMCID: PMC11064834 DOI: 10.2967/jnumed.123.265766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
Since the development of amyloid tracers for PET imaging, there has been interest in quantifying amyloid burden in the brains of patients with Alzheimer disease. Quantitative amyloid PET imaging is poised to become a valuable approach in disease staging, theranostics, monitoring, and as an outcome measure for interventional studies. Yet, there are significant challenges and hurdles to overcome before it can be implemented into widespread clinical practice. On November 17, 2022, the U.S. Food and Drug Administration, Society of Nuclear Medicine and Molecular Imaging, and Medical Imaging and Technology Alliance cosponsored a public workshop comprising experts from academia, industry, and government agencies to discuss the role of quantitative brain amyloid PET imaging in staging, prognosis, and longitudinal assessment of Alzheimer disease. The workshop discussed a range of topics, including available radiopharmaceuticals for amyloid imaging; the methodology, metrics, and analytic validity of quantitative amyloid PET imaging; its use in disease staging, prognosis, and monitoring of progression; and challenges facing the field. This report provides a high-level summary of the presentations and the discussion.
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Affiliation(s)
| | - Venkata S Mattay
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland;
| | - Daniel M Krainak
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Sue-Jane Wang
- Division of Biometrics I, Office of Biostatistics, Office of Translational Sciences, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Lora D Weidner
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - A Alex Hofling
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Hayoung Koo
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | | | | | | | - Libero Marzella
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
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