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Yue Y, Zhang X, Lv W, Lai HY, Shen T. Interplay between the glymphatic system and neurotoxic proteins in Parkinson's disease and related disorders: current knowledge and future directions. Neural Regen Res 2024; 19:1973-1980. [PMID: 38227524 DOI: 10.4103/1673-5374.390970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/26/2023] [Indexed: 01/17/2024] Open
Abstract
Parkinson's disease is a common neurodegenerative disorder that is associated with abnormal aggregation and accumulation of neurotoxic proteins, including α-synuclein, amyloid-β, and tau, in addition to the impaired elimination of these neurotoxic protein. Atypical parkinsonism, which has the same clinical presentation and neuropathology as Parkinson's disease, expands the disease landscape within the continuum of Parkinson's disease and related disorders. The glymphatic system is a waste clearance system in the brain, which is responsible for eliminating the neurotoxic proteins from the interstitial fluid. Impairment of the glymphatic system has been proposed as a significant contributor to the development and progression of neurodegenerative disease, as it exacerbates the aggregation of neurotoxic proteins and deteriorates neuronal damage. Therefore, impairment of the glymphatic system could be considered as the final common pathway to neurodegeneration. Previous evidence has provided initial insights into the potential effect of the impaired glymphatic system on Parkinson's disease and related disorders; however, many unanswered questions remain. This review aims to provide a comprehensive summary of the growing literature on the glymphatic system in Parkinson's disease and related disorders. The focus of this review is on identifying the manifestations and mechanisms of interplay between the glymphatic system and neurotoxic proteins, including loss of polarization of aquaporin-4 in astrocytic endfeet, sleep and circadian rhythms, neuroinflammation, astrogliosis, and gliosis. This review further delves into the underlying pathophysiology of the glymphatic system in Parkinson's disease and related disorders, and the potential implications of targeting the glymphatic system as a novel and promising therapeutic strategy.
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Affiliation(s)
- Yumei Yue
- Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Xiaodan Zhang
- Department of Emergency Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wen Lv
- Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hsin-Yi Lai
- Department of Neurology of the Second Affiliated Hospital and School of Brain Science and Brain Medicine, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Ting Shen
- Department of Neurology of the Second Affiliated Hospital and School of Brain Science and Brain Medicine, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
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Younes K, Smith V, Johns E, Carlson ML, Winer J, He Z, Henderson VW, Greicius MD, Young CB, Mormino EC. Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer's disease. Brain Behav Immun 2024; 119:807-817. [PMID: 38710339 DOI: 10.1016/j.bbi.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/31/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024] Open
Abstract
Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.
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Affiliation(s)
- Kyan Younes
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA.
| | - Viktorija Smith
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Emily Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Mackenzie L Carlson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Joseph Winer
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Victor W Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Department of Epidemiology and Population Health, Stanford University, USA
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Christina B Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Wu Tsai Neuroscience Institute, Stanford, CA, USA
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Wagatsuma K, Miwa K, Yamao T, Kamitaka Y, Akamatsu G, Nakajima K, Miyaji N, Ishibashi K, Ishii K. Development of a novel phantom for tau PET imaging. Phys Med 2024; 123:103399. [PMID: 38852366 DOI: 10.1016/j.ejmp.2024.103399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 06/02/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024] Open
Abstract
PURPOSE The cortical uptake of tau positron emission tomography (PET) tracers corresponds to the Braak stage and reflects the distribution and progression of tau neurofibrillary tangles. The present study aimed to develop and validate the basic performance of a novel tau PET phantom, as well as to establish standard test procedures and analytical methods. METHODS The tau PET phantom consisted of a brain simulation section simulated medial temporal lobe region and resolution and uniformity sections. The brain simulation section and hot rods and uniformity section contained 4 and 2 kBq/mL of 18F, respectively and images were acquired three times for 20 min with a PET/CT scanner. The resolution section was visually assessed with two sets of hot and cold rods. Recovery coefficients (RCs) as a quantitative value and coefficient of variation (CV) as image noise were determined based on the brain simulation and the uniformity section, respectively. RESULTS Preparation of activity in the phantom was repeatable among three measurements. The quality of images in the brain simulation and uniformity section with the rods was good. The 5- or 6-mm rods were detected separately. The mean RCs calculated based on the VOI template were between 0.75 and 0.83. The CV at the center slice of uniformity section was 5.54%. CONCLUSIONS We developed a novel tau PET phantom to assess quantitative value, image noise, and detectability and resolution from brain simulation section, uniformity section, and rods, respectively. This phantom will contribute to the standardization and harmonization of tau PET imaging.
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Affiliation(s)
- Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa 252-0373, Japan; Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan.
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima 960-8516, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima 960-8516, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan
| | - Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Kanta Nakajima
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa 252-0373, Japan
| | - Noriaki Miyaji
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima 960-8516, Japan
| | - Kenji Ishibashi
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan
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Naude J, Wang M, Leon R, Smith E, Ismail Z. Tau-PET in early cortical Alzheimer brain regions in relation to mild behavioral impairment in older adults with either normal cognition or mild cognitive impairment. Neurobiol Aging 2024; 138:19-27. [PMID: 38490074 DOI: 10.1016/j.neurobiolaging.2024.02.006] [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: 07/13/2023] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 03/17/2024]
Abstract
Mild Behavioral Impairment (MBI) leverages later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high-risk group for incident dementia. Phosphorylated tau (p-tau) is a hallmark biological manifestation of Alzheimer disease (AD). We investigated associations between MBI and tau accumulation in early-stage AD cortical regions. In 442 Alzheimer's Disease Neuroimaging Initiative participants with normal cognition or mild cognitive impairment, MBI status was determined alongside corresponding p-tau and Aβ. Two meta-regions of interest were generated to represent Braak I and III neuropathological stages. Multivariable linear regression modelled the association between MBI as independent variable and tau tracer uptake as dependent variable. Among Aβ positive individuals, MBI was associated with tau uptake in Braak I (β=0.45(0.15), p<.01) and Braak III (β=0.24(0.07), p<.01) regions. In Aβ negative individuals, MBI was not associated with tau in the Braak I region (p=0.11) with a negative association in Braak III (p=.01). These findings suggest MBI may be a sequela of neurodegeneration, and can be implemented as a cost-effective framework to help improve screening efficiency for AD.
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Affiliation(s)
- James Naude
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Meng Wang
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Rebeca Leon
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Eric Smith
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Zahinoor Ismail
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
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5
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Karlsson L, Vogel J, Arvidsson I, Åström K, Strandberg O, Seidlitz J, Bethlehem RAI, Stomrud E, Ossenkoppele R, Ashton NJ, Zetterberg H, Blennow K, Palmqvist S, Smith R, Janelidze S, La Joie R, Rabinovici GD, Pichet Binette A, Mattsson-Carlgren N, Hansson O. A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.31.24308264. [PMID: 38853877 PMCID: PMC11160861 DOI: 10.1101/2024.05.31.24308264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET outcomes from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded highly accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study reveals current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.
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6
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Lecy EE, Min HK, Apgar CJ, Maltais DD, Lundt ES, Albertson SM, Senjem ML, Schwarz CG, Botha H, Graff-Radford J, Jones DT, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR, Lee J, Lowe VJ. Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study. J Nucl Med 2024:jnumed.123.267150. [PMID: 38782458 DOI: 10.2967/jnumed.123.267150] [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: 11/27/2023] [Revised: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.
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Affiliation(s)
- Emily E Lecy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Christopher J Apgar
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | | | - Emily S Lundt
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Sabrina M Albertson
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota; and
| | | | | | - Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
- Department of Biomedical Engineering, College of Medicine, Hanyang University, Seoul, South Korea
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota;
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7
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Wuestefeld A, Binette AP, van Westen D, Strandberg O, Stomrud E, Mattsson-Carlgren N, Janelidze S, Smith R, Palmqvist S, Baumeister H, Berron D, Yushkevich PA, Hansson O, Spotorno N, Wisse LEM. Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.594976. [PMID: 38826333 PMCID: PMC11142072 DOI: 10.1101/2024.05.21.594976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. Methods BioFINDER-2 participants with memory impairment, abnormal amyloid-β status and tau-PET were included. Forty-one EOAD individuals aged ≤65 years and, as comparison, late-onset AD (LOAD, ≥70 years, n=154) and Aβ-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. Results AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. Conclusions We found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
- Image and Function, Skåne University Hospital, 22242 Lund Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Department of Neurology, Skåne University Hospital, 22242 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184 Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
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8
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Alexandersen CG, Goriely A, Bick C. Neuronal activity induces symmetry breaking in neurodegenerative disease spreading. J Math Biol 2024; 89:3. [PMID: 38740613 DOI: 10.1007/s00285-024-02103-x] [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: 09/29/2023] [Revised: 03/01/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimer's disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feedback loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimer's disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.
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Affiliation(s)
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK.
| | - Christian Bick
- Mathematical Institute, University of Oxford, Oxford, UK
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience - Systems and Network Neuroscience, Amsterdam, The Netherlands
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9
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Pichet Binette A, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Disease staging of Alzheimer's disease using a CSF-based biomarker model. NATURE AGING 2024; 4:694-708. [PMID: 38514824 PMCID: PMC11108782 DOI: 10.1038/s43587-024-00599-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: 05/03/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0-5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
| | - Kanta Horie
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai, Inc., Nutley, NJ, USA
| | - Nicolas R Barthélemy
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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Karlsson L, Vogel J, Arvidsson I, Åström K, Janelidze S, Blennow K, Palmqvist S, Stomrud E, Mattsson-Carlgren N, Hansson O. Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases. Nat Commun 2024; 15:3676. [PMID: 38693142 PMCID: PMC11063138 DOI: 10.1038/s41467-024-47971-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.
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Affiliation(s)
- Linda Karlsson
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden.
| | - Jacob Vogel
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
- Department of Clinical Sciences, Clinical Memory Research Unit, SciLifeLab, Lund University, Lund, Sweden
| | - Ida Arvidsson
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Kalle Åström
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastian Palmqvist
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Erik Stomrud
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Department of Clinical Sciences in Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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11
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Wisch JK, McKay NS, Boerwinkle AH, Kennedy J, Flores S, Handen BL, Christian BT, Head E, Mapstone M, Rafii MS, O'Bryant SE, Price JC, Laymon CM, Krinsky-McHale SJ, Lai F, Rosas HD, Hartley SL, Zaman S, Lott IT, Tudorascu D, Zammit M, Brickman AM, Lee JH, Bird TD, Cohen A, Chrem P, Daniels A, Chhatwal JP, Cruchaga C, Ibanez L, Jucker M, Karch CM, Day GS, Lee JH, Levin J, Llibre-Guerra J, Li Y, Lopera F, Roh JH, Ringman JM, Supnet-Bell C, van Dyck CH, Xiong C, Wang G, Morris JC, McDade E, Bateman RJ, Benzinger TLS, Gordon BA, Ances BM. Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study. Lancet Neurol 2024; 23:500-510. [PMID: 38631766 DOI: 10.1016/s1474-4422(24)00084-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/01/2024] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease. METHODS In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (18F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid. FINDINGS We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associated with increases in amyloid for people with Down syndrome compared with autosomal-dominant Alzheimer's disease. INTERPRETATION Although the general progression of amyloid followed by tau is similar for people Down syndrome and people with autosomal-dominant Alzheimer's disease, we found subtle differences in the spatial distribution, timing, and magnitude of the tau burden between these two cohorts. These differences might have important implications; differences in the temporal pattern of tau accumulation might influence the timing of drug administration in clinical trials, whereas differences in the spatial pattern and magnitude of tau burden might affect disease progression. FUNDING None.
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Affiliation(s)
- Julie K Wisch
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA.
| | - Nicole S McKay
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Anna H Boerwinkle
- McGovern Medical School, University of Texas in Houston, Houston, TX, USA
| | - James Kennedy
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Benjamin L Handen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bradley T Christian
- Department of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Elizabeth Head
- Department of Pathology, Gillespie Neuroscience Research Facility, University of California, Irvine, CA, USA
| | - Mark Mapstone
- Department of Neurology, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Michael S Rafii
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Sid E O'Bryant
- Institute for Translational Research Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Julie C Price
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Charles M Laymon
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sharon J Krinsky-McHale
- Department of Psychology, New York State Institute for Basic Research in Developmental Disabilities, New York, NY, USA
| | - Florence Lai
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - H Diana Rosas
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sigan L Hartley
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, University of Cambridge, Cambridge, UK
| | - Ira T Lott
- Department of Pediatrics, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Zammit
- Department of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Adam M Brickman
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Joseph H Lee
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA; Department of Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Thomas D Bird
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Annie Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Patricio Chrem
- Centro de Memoria y Envejecimiento, Buenos Aires, Argentina
| | - Alisha Daniels
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA; Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Mathias Jucker
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Celeste M Karch
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asian Medical Center, Seoul, South Korea
| | - Johannes Levin
- Department of Neurology, LMU University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases, site Munich, Munich, Germany; Munich Cluster for Systems Neurology, Munich, Germany
| | - Jorge Llibre-Guerra
- Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jee Hoon Roh
- Departments of Physiology and Neurology, Korea University College of Medicine, Seoul, South Korea
| | - John M Ringman
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | | | | | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Guoqiao Wang
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | | | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
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12
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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. [PMID: 38683905 DOI: 10.1002/alz.13769] [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: 10/10/2023] [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 Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Abdalla Bani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Sung Min Ha
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Diana A Hobbs
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis, Saint Louis, Missouri, USA
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13
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Bao YW, Wang ZJ, Guo LL, Bai GJ, Feng Y, Zhao GD. Expression of regional brain amyloid-β deposition with [18F]Flutemetamol in Centiloid scale -a multi-site study. Neuroradiology 2024:10.1007/s00234-024-03364-5. [PMID: 38676749 DOI: 10.1007/s00234-024-03364-5] [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: 12/12/2023] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
PURPOSE The Centiloid project helps calibrate the quantitative amyloid-β (Aβ) load into a unified Centiloid (CL) scale that allows data comparison across multi-site. How the smaller regional amyloid converted into CL has not been attempted. We first aimed to express regional Aβ deposition in CL using [18F]Flutemetamol and evaluate regional Aβ deposition in CL with that in standardized uptake value ratio (SUVr). Second, we aimed to determine the presence or absence of focal Aβ deposition by measuring regional CL in equivocal cases showing negative global CL. METHODS Following the Centiloid project pipeline, Level-1 replication, Level-2 calibration, and quality control were completed to generate corresponding Centiloid conversion equations to convert SUVr into Centiloid at regional levels. In equivocal cases, the regional CL was compared with visual inspection to evaluate regional Aβ positivity. RESULTS 14 out of 16 regional conversions from [18F]Flutemetamol SUVr to Centiloid successfully passed the quality control, showing good reliability and relative variance, especially precuneus/posterior cingulate and prefrontal regions with good stability for Centiloid scaling. The absence of focal Aβ deposition could be detected by measuring regional CL, showing a high agreement rate with visual inspection. The regional Aβ positivity in the bilateral anterior cingulate cortex was most prevalent in equivocal cases. CONCLUSION The expression of regional brain Aβ deposition in CL with [18F]Flutemetamol has been attempted in this study. Equivocal cases had focal Aβ deposition that can be detected by measuring regional CL.
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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, 1 Huanghe West Road, Huaiyin District, 223300, Huai'an, Jiangsu, China.
| | - Zuo-Jun Wang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Li-Li Guo
- Department of Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, 1 Huanghe West Road, Huaiyin District, 223300, Huai'an, Jiangsu, China
| | - Gen-Ji Bai
- Department of Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, 1 Huanghe West Road, Huaiyin District, 223300, Huai'an, Jiangsu, China
| | - Yun Feng
- Department of Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, 1 Huanghe West Road, Huaiyin District, 223300, Huai'an, Jiangsu, China
| | - Guo-Dong Zhao
- Department of General Surgery, Lianshui County People's Hospital, 223400, Huai'an, Jiang Su, China
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14
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Weinstein G, Kojis DJ, Ghosh S, Beiser AS, Seshadri S. Association of Neurotrophic Factors at Midlife With In Vivo Measures of β-Amyloid and Tau Burden 15 Years Later in Dementia-Free Adults. Neurology 2024; 102:e209198. [PMID: 38471064 PMCID: PMC11033983 DOI: 10.1212/wnl.0000000000209198] [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: 08/28/2023] [Accepted: 12/13/2023] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Neurotrophic factors (NTFs) play an important role in Alzheimer disease (AD) pathophysiology. Brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) are important NTFs. However, a direct link of BDNF and VEGF circulating levels with in vivo measures of amyloid-β (Aβ) and tau burden remains to be elucidated. We explored the relationship of BDNF and VEGF serum levels with future brain Aβ and tau pathology in a cohort of cognitively healthy, predominantly middle-aged adults and tested for possible effect modifications by sex and menopausal status. METHODS This cross-sectional analysis was conducted using data from the Framingham Heart Study (FHS), a community-based cohort study. The study sample included cognitively healthy participants from the FHS Offspring and Third-generation cohorts. BDNF and VEGF were measured in the third-generation cohort during examination cycles 2 (2005-2008) and 1 (2002-2005), respectively, and in the offspring cohort during examination cycle 7 (1998-2001). Participants underwent 11C-Pittsburgh compound B amyloid and 18F-Flortaucipir tau-PET imaging (2015-2021). Linear regression models were used to assess the relationship of serum BDNF and VEGF levels with regional tau and global Aβ, adjusting for potential confounders. Interactions with sex and menopausal status were additionally tested. RESULTS The sample included 414 individuals (mean age = 41 ± 9 years; 51% female). Continuous measures of BDNF and VEGF were associated with tau signal in the rhinal region after adjustment for potential confounders (β = -0.15 ± 0.06, p = 0.018 and β = -0.19 ± 0.09, p = 0.043, respectively). High BDNF (≥32,450 pg/mL) and VEGF (≥488 pg/mL) levels were significantly related to lower rhinal tau (β = -0.27 ± 0.11, p = 0.016 and β = -0.40 ± 0.14, p = 0.004, respectively) and inferior temporal tau (β = -0.24 ± 0.11, p = 0.028 and β = -0.26 ± 0.13, p = 0.049, respectively). The BDNF-rhinal tau association was observed only among male individuals. Overall, BDNF and VEGF were not associated with global amyloid; however, high VEGF levels were associated with lower amyloid burden in postmenopausal women (β = -1.96 ± 0.70, p = 0.013, per 1 pg/mL). DISCUSSION This study demonstrates a robust association between BDNF and VEGF serum levels with in vivo measures of tau almost 2 decades later. These findings add to mounting evidence from preclinical studies suggesting a role of NTFs as valuable blood biomarkers for AD risk prediction.
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Affiliation(s)
- Galit Weinstein
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Daniel J Kojis
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Saptaparni Ghosh
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Alexa S Beiser
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Sudha Seshadri
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
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15
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Fonseca CS, Baker SL, Dobyns L, Janabi M, Jagust WJ, Harrison TM. Tau accumulation and atrophy predict amyloid independent cognitive decline in aging. Alzheimers Dement 2024; 20:2526-2537. [PMID: 38334195 PMCID: PMC11032527 DOI: 10.1002/alz.13654] [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: 08/24/2023] [Revised: 11/15/2023] [Accepted: 11/30/2023] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Amyloid beta (Aβ) and tau pathology are cross-sectionally associated with atrophy and cognitive decline in aging and Alzheimer's disease (AD). METHODS We investigated relationships between concurrent longitudinal measures of Aβ (Pittsburgh compound B [PiB] positron emission tomography [PET]), tau (flortaucipir [FTP] PET), atrophy (structural magnetic resonance imaging), episodic memory (EM), and non-memory (NM) in 78 cognitively healthy older adults (OA). RESULTS Entorhinal FTP change was correlated with EM decline regardless of Aβ, but meta-temporal FTP and global PiB change were only associated with EM and NM decline in Aβ+ OA. Voxel-wise analyses revealed significant associations between temporal lobe FTP change and EM decline in all groups. PiB and FTP change were not associated with structural change, suggesting a functional or microstructural mechanism linking these measures to cognitive decline. DISCUSSION Our results show that longitudinal Aβ is linked to cognitive decline only in the presence of elevated Aβ, but longitudinal temporal lobe tau is associated with memory decline regardless of Aβ status. HIGHLIGHTS Entorhinal tau change was associated with memory decline in older adults (OA), regardless of amyloid beta (Aβ). Greater meta-region of interest (ROI) tau change correlated with memory decline in Aβ+ OA. Voxel-wise temporal tau change correlated with memory decline, regardless of Aβ. Meta-ROI tau and global amyloid change correlated with non-memory change in Aβ+ OA. Tau and amyloid accumulation were not associated with structural change in OA.
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Affiliation(s)
- Corrina S. Fonseca
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | | | - Lindsey Dobyns
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Mustafa Janabi
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - William J. Jagust
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Theresa M. Harrison
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
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16
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Doering S, McCullough A, Gordon BA, Chen CD, McKay N, Hobbs D, Keefe S, Flores S, Scott J, Smith H, Jarman S, Jackson K, Hornbeck RC, Ances BM, Xiong C, Aschenbrenner AJ, Hassenstab J, Cruchaga C, Daniels A, Bateman RJ, Morris JC, Benzinger TLS. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging. EBioMedicine 2024; 103:105080. [PMID: 38552342 PMCID: PMC10995809 DOI: 10.1016/j.ebiom.2024.105080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. METHODS 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. FINDINGS We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83). INTERPRETATION Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design. FUNDING This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.
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Affiliation(s)
- Stephanie Doering
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Austin McCullough
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Charles D Chen
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Nicole McKay
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Diana Hobbs
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sarah Keefe
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Shaney Flores
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jalen Scott
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Hunter Smith
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Stephen Jarman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Kelley Jackson
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Beau M Ances
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Chengjie Xiong
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | | | - Jason Hassenstab
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Carlos Cruchaga
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Alisha Daniels
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Randall J Bateman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
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17
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Sun Z, Naismith SL, Meikle S, Calamante F. A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease. Hum Brain Mapp 2024; 45:e26659. [PMID: 38491564 PMCID: PMC10943179 DOI: 10.1002/hbm.26659] [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: 11/14/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.
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Affiliation(s)
- Zhuopin Sun
- School of Biomedical EngineeringThe University of SydneySydneyNew South WalesAustralia
| | - Sharon L. Naismith
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Faculty of Science, School of PsychologyThe University of SydneySydneyNew South WalesAustralia
- Charles Perkins CenterThe University of SydneySydneyNew South WalesAustralia
| | - Steven Meikle
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Sydney ImagingThe University of SydneySydneyNew South WalesAustralia
- School of Health SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Fernando Calamante
- School of Biomedical EngineeringThe University of SydneySydneyNew South WalesAustralia
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
- Sydney ImagingThe University of SydneySydneyNew South WalesAustralia
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18
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Lee J, Burkett BJ, Min HK, Senjem ML, Dicks E, Corriveau-Lecavalier N, Mester CT, Wiste HJ, Lundt ES, Murray ME, Nguyen AT, Reichard RR, Botha H, Graff-Radford J, Barnard LR, Gunter JL, Schwarz CG, Kantarci K, Knopman DS, Boeve BF, Lowe VJ, Petersen RC, Jack CR, Jones DT. Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain 2024; 147:980-995. [PMID: 37804318 PMCID: PMC10907092 DOI: 10.1093/brain/awad346] [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: 02/20/2023] [Revised: 08/30/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023] Open
Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
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Affiliation(s)
- Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Brian J Burkett
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Carly T Mester
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Emily S Lundt
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross R Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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19
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Coomans EM, van Westen D, Binette AP, Strandberg O, Spotorno N, Serrano GE, Beach TG, Palmqvist S, Stomrud E, Ossenkoppele R, Hansson O. Interactions between vascular burden and amyloid-β pathology on trajectories of tau accumulation. Brain 2024; 147:949-960. [PMID: 37721482 PMCID: PMC10907085 DOI: 10.1093/brain/awad317] [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: 05/28/2023] [Revised: 08/02/2023] [Accepted: 09/07/2023] [Indexed: 09/19/2023] Open
Abstract
Cerebrovascular pathology often co-exists with Alzheimer's disease pathology and can contribute to Alzheimer's disease-related clinical progression. However, the degree to which vascular burden contributes to Alzheimer's disease pathological progression is still unclear. This study aimed to investigate interactions between vascular burden and amyloid-β pathology on both baseline tau tangle load and longitudinal tau accumulation. We included 1229 participants from the Swedish BioFINDER-2 Study, including cognitively unimpaired and impaired participants with and without biomarker-confirmed amyloid-β pathology. All underwent baseline tau-PET (18F-RO948), and a subset (n = 677) underwent longitudinal tau-PET after 2.5 ± 1.0 years. Tau-PET uptake was computed for a temporal meta-region-of-interest. We focused on four main vascular imaging features and risk factors: microbleeds; white matter lesion volume; stroke-related events (infarcts, lacunes and haemorrhages); and the Framingham Heart Study Cardiovascular Disease risk score. To validate our in vivo results, we examined 1610 autopsy cases from an Arizona-based neuropathology cohort on three main vascular pathological features: cerebral amyloid angiopathy; white matter rarefaction; and infarcts. For the in vivo cohort, primary analyses included age-, sex- and APOE ɛ4-corrected linear mixed models between tau-PET (outcome) and interactions between time, amyloid-β and each vascular feature (predictors). For the neuropathology cohort, age-, sex- and APOE ɛ4-corrected linear models between tau tangle density (outcome) and an interaction between plaque density and each vascular feature (predictors) were performed. In cognitively unimpaired individuals, we observed a significant interaction between microbleeds and amyloid-β pathology on greater baseline tau load (β = 0.68, P < 0.001) and longitudinal tau accumulation (β = 0.11, P < 0.001). For white matter lesion volume, we did not observe a significant independent interaction effect with amyloid-β on tau after accounting for microbleeds. In cognitively unimpaired individuals, we further found that stroke-related events showed a significant negative interaction with amyloid-β on longitudinal tau (β = -0.08, P < 0.001). In cognitively impaired individuals, there were no significant interaction effects between cerebrovascular and amyloid-β pathology at all. In the neuropathology dataset, the in vivo observed interaction effects between cerebral amyloid angiopathy and plaque density (β = 0.38, P < 0.001) and between infarcts and plaque density (β = -0.11, P = 0.005) on tau tangle density were replicated. To conclude, we demonstrated that cerebrovascular pathology-in the presence of amyloid-β pathology-modifies tau accumulation in early stages of Alzheimer's disease. More specifically, the co-occurrence of microbleeds and amyloid-β pathology was associated with greater accumulation of tau aggregates during early disease stages. This opens the possibility that interventions targeting microbleeds may attenuate the rate of tau accumulation in Alzheimer's disease.
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Affiliation(s)
- Emma M Coomans
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081HV Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081HV Amsterdam, The Netherlands
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081HV Amsterdam, The Netherlands
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
- Amsterdam Neuroscience, Neurodegeneration, 1071HV Amsterdam, The Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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20
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Parekh P, Badachhape AA, Tanifum EA, Annapragada AV, Ghaghada KB. Advances in nanoprobes for molecular MRI of Alzheimer's disease. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2024; 16:e1946. [PMID: 38426638 PMCID: PMC10983770 DOI: 10.1002/wnan.1946] [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: 06/21/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
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Affiliation(s)
- Parag Parekh
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Andrew A. Badachhape
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Eric A. Tanifum
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ananth V. Annapragada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ketan B. Ghaghada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
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21
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Dubbelman MA, Tomassen J, van der Landen SM, Bakker E, Kamps S, van Unnik AAJM, van de Glind MCABJ, van der Vlies AE, Koene T, Leeuwis AE, Barkhof F, van Harten AC, Teunissen C, van de Giessen E, Lemstra AW, Pijnenburg YAL, Ponds RWH, Sikkes SAM. Visual associative learning to detect early episodic memory deficits and distinguish Alzheimer's disease from other types of dementia. J Int Neuropsychol Soc 2024:1-10. [PMID: 38389489 DOI: 10.1017/s1355617724000079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
OBJECTIVE We investigated how well a visual associative learning task discriminates Alzheimer's disease (AD) dementia from other types of dementia and how it relates to AD pathology. METHODS 3,599 patients (63.9 ± 8.9 years old, 41% female) from the Amsterdam Dementia Cohort completed two sets of the Visual Association Test (VAT) in a single test session and underwent magnetic resonance imaging. We performed receiver operating curve analysis to investigate the VAT's discriminatory ability between AD dementia and other diagnoses and compared it to that of other episodic memory tests. We tested associations between VAT performance and medial temporal lobe atrophy (MTA), and amyloid status (n = 2,769, 77%). RESULTS Patients with AD dementia performed worse on the VAT than all other patients. The VAT discriminated well between AD and other types of dementia (area under the curve range 0.70-0.86), better than other episodic memory tests. Six-hundred forty patients (17.8%) learned all associations on VAT-A, but not on VAT-B, and they were more likely to have higher MTA scores (odds ratios range 1.63 (MTA 0.5) through 5.13 for MTA ≥ 3, all p < .001) and to be amyloid positive (odds ratio = 3.38, 95%CI = [2.71, 4.22], p < .001) than patients who learned all associations on both sets. CONCLUSIONS Performance on the VAT, especially on a second set administered immediately after the first, discriminates AD from other types of dementia and is associated with MTA and amyloid positivity. The VAT might be a useful, simple tool to assess early episodic memory deficits in the presence of AD pathology.
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Affiliation(s)
- Mark A Dubbelman
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Sophie M van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Els Bakker
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzie Kamps
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Annemartijn A J M van Unnik
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Marie-Christine A B J van de Glind
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Annelies E van der Vlies
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Ted Koene
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Anna E Leeuwis
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Old Age Psychiatry, GGZ inGeest, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Charlotte Teunissen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Rudolf W H Ponds
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sietske A M Sikkes
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Faculty of Behavioral and Movement Sciences, Clinical Developmental Psychology and Clinical Neuropsychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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22
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Naude J, Wang M, Leon R, Smith E, Ismail Z. Tau-PET in early cortical Alzheimer brain regions in relation to mild behavioral impairment in older adults with either normal cognition or mild cognitive impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.11.24302665. [PMID: 38405711 PMCID: PMC10888987 DOI: 10.1101/2024.02.11.24302665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Mild Behavioral Impairment (MBI) leverages later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high-risk group for incident dementia. Phosphorylated tau (p-tau) is a hallmark biological manifestation of Alzheimer disease (AD). We investigated associations between MBI and tau accumulation in early-stage AD cortical regions. In 442 Alzheimer's Disease Neuroimaging Initiative participants with normal cognition or mild cognitive impairment, MBI status was determined alongside corresponding p-tau and Aβ. Two meta-regions of interest were generated to represent Braak I and III neuropathological stages. Multivariable linear regression modelled the association between MBI as independent variable and tau tracer uptake as dependent variable. Among Aβ positive individuals, MBI was associated with tau uptake in Braak I (β =0.45(0.15), p<.01) and Braak III (β =0.24(0.07), p<.01) regions. In Aβ negative individuals, MBI was not associated with tau in the Braak I region (p=.11) with a negative association in Braak III (p=.01). These findings suggest MBI may be a sequela of neurodegeneration, and can be implemented as a cost-effective framework to help improve screening efficiency for AD.
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Affiliation(s)
- James Naude
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Meng Wang
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Rebeca Leon
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Eric Smith
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Zahinoor Ismail
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
- Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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23
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Chen X, Toueg TN, Harrison TM, Baker SL, Jagust WJ. Regional Tau Deposition Reflects Different Pathways of Subsequent Neurodegeneration and Memory Decline in Cognitively Normal Older Adults. Ann Neurol 2024; 95:249-259. [PMID: 37789559 PMCID: PMC10843500 DOI: 10.1002/ana.26813] [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: 05/20/2023] [Revised: 09/12/2023] [Accepted: 09/27/2023] [Indexed: 10/05/2023]
Abstract
OBJECTIVE Tau pathology is recognized as a primary contributor to neurodegeneration and clinical symptoms in Alzheimer's disease (AD). This study aims to localize the early tau pathology in cognitively normal older people that is predictive of subsequent neurodegeneration and memory decline, and delineate factors underlying tau-related memory decline in individuals with and without β-amyloid (Aβ). METHODS A total of 138 cognitively normal older individuals from the Berkeley Aging Cohort Study underwent 11 C-Pittsburgh Compound-B (PiB) positron emission tomography (PET) to determine Aβ positivity and 18 F-Flortaucipir (FTP) PET to measure tau deposition, with prospective cognitive assessments and structural magnetic resonance imaging. Voxel-wise FTP analyses examined associations between baseline tau deposition and longitudinal memory decline, longitudinal hippocampal atrophy, and longitudinal cortical thinning in AD signature regions. We also examined whether hippocampal atrophy and cortical thinning mediate tau effects on future memory decline. RESULTS We found Aβ-dependent tau associations with memory decline in the entorhinal and temporoparietal regions, Aβ-independent tau associations with hippocampal atrophy within the medial temporal lobe (MTL), and that widespread tau was associated with mean cortical thinning in AD signature regions. Tau-related memory decline was mediated by hippocampal atrophy in Aβ- individuals and by mean cortical thinning in Aβ+ individuals. INTERPRETATION Our results suggest that tau may affect memory through different mechanisms in normal aging and AD. Early tau deposition independent of Aβ predicts subsequent hippocampal atrophy that may lead to memory deficits in normal older individuals, whereas elevated cortical tau deposition is associated with cortical thinning that may lead to more severe memory decline in AD. ANN NEUROL 2024;95:249-259.
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Affiliation(s)
- Xi Chen
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tyler N Toueg
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Suzanne L Baker
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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24
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Cogswell PM, Lundt ES, Therneau TM, Wiste HJ, Graff‐Radford J, Algeciras‐Schimnich A, Lowe VJ, Mielke MM, Schwarz CG, Senjem ML, Gunter JL, Knopman DS, Vemuri P, Petersen RC, Jack Jr CR. Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET. Alzheimers Dement 2024; 20:1225-1238. [PMID: 37963289 PMCID: PMC10916944 DOI: 10.1002/alz.13539] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/02/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023]
Abstract
INTRODUCTION The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co-evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p-tau217, p-tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS Individual timing of plasma p-tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p-tau, and tau PET temporal meta-regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION Plasma p-tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p-tau and Aβ PET was the strongest. HIGHLIGHTS Plasma p-tau progression was more strongly associated with Aβ than tau PET. Progression on plasma p-tau was associated with Aβ PET and GFAP progression. P-tau181 and p-tau217 become abnormal after Aβ PET and before tau PET. GFAP became abnormal first, before plasma p-tau and Aβ PET.
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Affiliation(s)
| | - Emily S. Lundt
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Terry M. Therneau
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Heather J. Wiste
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
| | - Michelle M. Mielke
- Department of Epidemiology and PreventionWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
- Department of Information TechnologyMayo ClinicRochesterMinnesotaUSA
| | | | | | | | - Ronald C. Petersen
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
- Department of NeurologyMayo ClinicRochesterMinnesotaUSA
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25
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Creekmore BC, Watanabe R, Lee EB. Neurodegenerative Disease Tauopathies. ANNUAL REVIEW OF PATHOLOGY 2024; 19:345-370. [PMID: 37832941 PMCID: PMC11009985 DOI: 10.1146/annurev-pathmechdis-051222-120750] [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] [Indexed: 10/15/2023]
Abstract
Tauopathies are a diverse group of progressive and fatal neurodegenerative diseases characterized by aberrant tau inclusions in the central nervous system. Tau protein forms pathologic fibrillar aggregates that are typically closely associated with neuronal cell death, leading to varied clinical phenotypes including dementia, movement disorders, and motor neuron disease. In this review, we describe the clinicopathologic features of tauopathies and highlight recent advances in understanding the mechanisms that lead to spread of pathologic aggregates through interconnected neuronal pathways. The cell-to-cell propagation of tauopathy is then linked to posttranslational modifications, tau fibril structural variants, and the breakdown of cellular protein quality control.
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Affiliation(s)
- Benjamin C Creekmore
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Ryohei Watanabe
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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26
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Schulz MA, Bzdok D, Haufe S, Haynes JD, Ritter K. Performance reserves in brain-imaging-based phenotype prediction. Cell Rep 2024; 43:113597. [PMID: 38159275 DOI: 10.1016/j.celrep.2023.113597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 07/03/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024] Open
Abstract
This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases from 1,000 to 1 M participants. However, despite this increase, the data suggest that prediction accuracy remains worryingly low and far from fully exploiting the predictive potential of brain imaging data. Additionally, we find that integrating multiple imaging modalities boosts prediction accuracy, often equivalent to doubling the sample size. Interestingly, the most informative imaging modality often varied with increasing sample size, emphasizing the need to consider multiple modalities. Despite significant performance reserves for phenotype prediction, achieving substantial improvements may necessitate prohibitively large sample sizes, thus casting doubt on the practical or clinical utility of machine learning in some areas of neuroimaging.
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Affiliation(s)
- Marc-Andre Schulz
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Stefan Haufe
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Technische Universität Berlin, Berlin, Germany; Physikalisch-Technische Bundesanstalt, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Berlin, Germany; Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Neurology, Berlin Center for Advanced Neuroimaging, Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
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27
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Yoo HS, Kim HK, Lee JH, Chun JH, Lee HS, Grothe MJ, Teipel S, Cavedo E, Vergallo A, Hampel H, Ryu YH, Cho H, Lyoo CH. Association of Basal Forebrain Volume with Amyloid, Tau, and Cognition in Alzheimer's Disease. J Alzheimers Dis 2024; 99:145-159. [PMID: 38640150 DOI: 10.3233/jad-230975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
Background Degeneration of cholinergic basal forebrain (BF) neurons characterizes Alzheimer's disease (AD). However, what role the BF plays in the dynamics of AD pathophysiology has not been investigated precisely. Objective To investigate the baseline and longitudinal roles of BF along with core neuropathologies in AD. Methods In this retrospective cohort study, we enrolled 113 subjects (38 amyloid [Aβ]-negative cognitively unimpaired, 6 Aβ-positive cognitively unimpaired, 39 with prodromal AD, and 30 with AD dementia) who performed brain MRI for BF volume and cortical thickness, 18F-florbetaben PET for Aβ, 18F-flortaucipir PET for tau, and detailed cognitive testing longitudinally. We investigated the baseline and longitudinal association of BF volume with Aβ and tau standardized uptake value ratio and cognition. Results Cross-sectionally, lower BF volume was not independently associated with higher cortical Aβ, but it was associated with tau burden. Tau burden in the orbitofrontal, insular, lateral temporal, inferior temporo-occipital, and anterior cingulate cortices were associated with progressive BF atrophy. Lower BF volume was associated with faster Aβ accumulation, mainly in the prefrontal, anterior temporal, cingulate, and medial occipital cortices. BF volume was associated with progressive decline in language and memory functions regardless of baseline Aβ and tau burden. Conclusions Tau deposition affected progressive BF atrophy, which in turn accelerated amyloid deposition, leading to a vicious cycle. Also, lower baseline BF volume independently predicted deterioration in cognitive function.
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Affiliation(s)
- Han Soo Yoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Han-Kyeol Kim
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Jae-Hoon Lee
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Joong-Hyun Chun
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hye Sun Lee
- Department of Biostatistics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Michel J Grothe
- Reina Sofia Alzheimer Center, CIEN Foundation-ISCIII, Madrid, Spain
| | - Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)-Rostock/Greifswald, Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Germany
| | - Enrica Cavedo
- Sorbonne University Alzheimer Precision Medicine, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Andrea Vergallo
- Sorbonne University Alzheimer Precision Medicine, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Harald Hampel
- Sorbonne University Alzheimer Precision Medicine, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - 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
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Mattsson P, Cselényi Z, Forsberg Morén A, Freund-Levi Y, Wahlund LO, Halldin C, Farde L. High Contrast PET Imaging of Subcortical and Allocortical Amyloid-β in Early Alzheimer's Disease Using [11C]AZD2184. J Alzheimers Dis 2024; 98:1391-1401. [PMID: 38552111 DOI: 10.3233/jad-231013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Background Deposits of amyloid-β (Aβ) appear early in Alzheimer's disease (AD). Objective The aim of the present study was to compare the presence of cortical and subcortical Aβ in early AD using positron emission tomography (PET). Methods Eight cognitively unimpaired (CU) subjects, 8 with mild cognitive impairment (MCI) and 8 with mild AD were examined with PET and [11C]AZD2184. A data driven cut-point for Aβ positivity was defined by Gaussian mixture model of isocortex binding potential (BPND) values. Results Sixteen subjects (3 CU, 5 MCI and 8 AD) were Aβ-positive. BPND was lower in subcortical and allocortical regions compared to isocortex. Fifteen of the 16 Aβ-positive subjects displayed Aβ binding in striatum, 14 in thalamus and 10 in allocortical regions. Conclusions Aβ deposits appear to be widespread in early AD. It cannot be excluded that deposits appear simultaneously throughout the whole brain which has implications for improved diagnostics and disease monitoring.
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Affiliation(s)
- Patrik Mattsson
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Zsolt Cselényi
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden
| | - Anton Forsberg Morén
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Yvonne Freund-Levi
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
- School of Medicine, Örebro University, Örebro, Sweden
- Department of Geriatrics, Örebro University Hospital, Örebro and Södertälje Hospital, Södertälje, Sweden
| | - Lars-Olof Wahlund
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Christer Halldin
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Lars Farde
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
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29
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Lee S, Kim E, Moon CE, Park C, Lim JW, Baek M, Shin MK, Ki J, Cho H, Ji YW, Haam S. Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer's disease from tear fluid. Nat Commun 2023; 14:8153. [PMID: 38071202 PMCID: PMC10710446 DOI: 10.1038/s41467-023-43995-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Accurate diagnosis of Alzheimer's disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320-1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD.
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Affiliation(s)
- Sojeong Lee
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Eunjung Kim
- Division of Bioengineering, Incheon National University, Incheon, 22012, Republic of Korea
- Department of Bioengineering & Nano-bioengineering, Research Center for Bio Materials and Process Development, Incheon National University, Incheon, 22012, Republic of Korea
| | - Chae-Eun Moon
- Department of Ophthalmology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, 16995, Republic of Korea
| | - Chaewon Park
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jong-Woo Lim
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Minseok Baek
- Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, 26426, Republic of Korea
| | - Moo-Kwang Shin
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jisun Ki
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea.
| | - Yong Woo Ji
- Department of Ophthalmology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, 16995, Republic of Korea.
| | - Seungjoo Haam
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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30
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Yoon SH, Kim HK, Lee JH, Chun JH, Sohn YH, Lee PH, Ryu YH, Cho H, Yoo HS, Lyoo CH. Association of Sleep Disturbances With Brain Amyloid and Tau Burden, Cortical Atrophy, and Cognitive Dysfunction Across the AD Continuum. Neurology 2023; 101:e2162-e2171. [PMID: 37813585 PMCID: PMC10663023 DOI: 10.1212/wnl.0000000000207917] [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: 03/14/2023] [Accepted: 08/24/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Patients with Alzheimer disease (AD) frequently suffer from various sleep disturbances. However, how sleep disturbance is associated with AD and its progression remains poorly investigated. We investigated the association of total sleep time with brain amyloid and tau burden, cortical atrophy, cognitive dysfunction, and their longitudinal changes in the AD spectrum. METHODS In this retrospective cohort study, we enrolled participants on the AD spectrum who were positive on 18F-florbetaben (FBB) PET. All participants underwent the Pittsburgh Sleep Quality Index, brain MRI, FBB PET, 18F-flortaucipir (FTP) PET, and detailed neuropsychological testing. In addition, a subset of participants completed follow-up assessments. We analyzed the association of total sleep time with the baseline and longitudinal FBB-standardized uptake value ratio (SUVR), FTP-SUVR, cortical thickness, and cognitive domain composite scores. RESULTS We examined 138 participants on the AD spectrum (15 with preclinical AD, 62 with prodromal AD, and 61 with AD dementia; mean age 73.4 ± 8.0 years; female 58.7%). Total sleep time was longer in the AD dementia group (7.4 ± 1.6 hours) compared with the preclinical (6.5 ± 1.4 hours; p = 0.026) and prodromal groups (6.6 ± 1.4 hours; p = 0.001), whereas other sleep parameters did not differ between groups. Longer total sleep time was not associated with amyloid accumulation but rather with tau accumulation, especially in the amygdala, hippocampus, basal forebrain, insular, cingulate, occipital, inferior temporal cortices, and precuneus. Longer total sleep time predicted faster tau accumulation in Braak regions V-VI (β = 0.016, p = 0.007) and disease progression to mild cognitive impairment or dementia (hazard ratio = 1.554, p = 0.024). Longer total sleep time was also associated with memory deficit (β = -0.19, p = 0.008). DISCUSSION Prolonged total sleep time was associated with tau accumulation in sleep-related cortical and subcortical areas as well as memory dysfunction. It also predicted faster disease progression with tau accumulation. Our study highlights the clinical importance of assessing total sleep time as a marker for disease severity and prognosis in the AD spectrum.
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Affiliation(s)
- So Hoon Yoon
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Han-Kyeol Kim
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Hoon Lee
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Joong-Hyun Chun
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young H Sohn
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Phil Hyu Lee
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Hoon Ryu
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hanna Cho
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Han Soo Yoo
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Chul Hyoung Lyoo
- From the Department of Neurology (S.H.Y.), International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon; Departments of Neurology (H.-K.K., H.C., H.S.Y., C.H.L.) and Nuclear Medicine (J.-H.L., Y.H.R.), Gangnam Severance Hospital; Departments of Nuclear Medicine (J.-H.C.) and Neurology (Y.H.S., P.H.L.), Yonsei University College of Medicine, Seoul, Republic of Korea
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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32
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Younes K, Smith V, Johns E, Carlson ML, Winer J, He Z, Henderson VW, Greicius MD, Young CB, Mormino EC. Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer`s disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23296836. [PMID: 37986964 PMCID: PMC10659470 DOI: 10.1101/2023.11.10.23296836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Understanding psychiatric symptoms in Alzheimer`s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is unknown. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory, and composite scores for memory, executive function, and language; using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 858 individuals (age=73.9±7.7 years, 434(50%) females) were included, comprising 438 cognitively unimpaired (CU) (53.4%) and 420 impaired (CI) participants (48.9%). In the full cohort analysis, right temporal tau was associated with worse behavior (B(SE)=7.19 (2.9), p-value=0.01) and left temporal tau was associated with worse language (B(SE)=1.4(0.2), p-value<0.0001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, four patterns of tau PET uptake were observed: anterior temporal, typical AD, typical AD with frontal involvement, and posterior. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Behavioral and socioemotional measures are needed to understand right-sided asymmetry in AD.
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Affiliation(s)
- Kyan Younes
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Viktorija Smith
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Emily Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Mackenzie L. Carlson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Joseph Winer
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California
| | - Victor W. Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Department of Epidemiology and Population Health, Stanford University
| | - Michael D. Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Christina B. Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Wu Tsai Neuroscience Institute, Stanford, CA, USA
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33
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Pansuwan T, Quaegebeur A, Kaalund SS, Hidari E, Briggs M, Rowe JB, Rittman T. Accurate digital quantification of tau pathology in progressive supranuclear palsy. Acta Neuropathol Commun 2023; 11:178. [PMID: 37946288 PMCID: PMC10634011 DOI: 10.1186/s40478-023-01674-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: 08/16/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
The development of novel treatments for Progressive Supranuclear Palsy (PSP) is hindered by a knowledge gap of the impact of neurodegenerative neuropathology on brain structure and function. The current standard practice for measuring postmortem tau histology is semi-quantitative assessment, which is prone to inter-rater variability, time-consuming and difficult to scale. We developed and optimized a tau aggregate type-specific quantification pipeline for cortical and subcortical regions, in human brain donors with PSP. We quantified 4 tau objects ('neurofibrillary tangles', 'coiled bodies', 'tufted astrocytes', and 'tau fragments') using a probabilistic random forest machine learning classifier. The tau pipeline achieved high classification performance (F1-score > 0.90), comparable to neuropathologist inter-rater reliability in the held-out test set. Using 240 AT8 slides from 32 postmortem brains, the tau burden was correlated against the PSP pathology staging scheme using Spearman's rank correlation. We assessed whether clinical severity (PSP rating scale, PSPRS) score reflects neuropathological severity inferred from PSP stage and tau burden using Bayesian linear mixed regression. Tufted astrocyte density in cortical regions and coiled body density in subcortical regions showed the highest correlation to PSP stage (r = 0.62 and r = 0.38, respectively). Using traditional manual staging, only PSP patients in stage 6, not earlier stages, had significantly higher clinical severity than stage 2. Cortical tau density and neurofibrillary tangle density in subcortical regions correlated with clinical severity. Overall, our data indicate the potential for highly accurate digital tau aggregate type-specific quantification for neurodegenerative tauopathies; and the importance of studying tau aggregate type-specific burden in different brain regions as opposed to overall tau, to gain insights into the pathogenesis and progression of tauopathies.
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Affiliation(s)
- Tanrada Pansuwan
- Department of Clinical Neurosciences, Cambridge University Centre for Parkinson-Plus, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK.
| | - Annelies Quaegebeur
- Department of Clinical Neurosciences, Cambridge University Centre for Parkinson-Plus, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Sanne S Kaalund
- Centre for Neuroscience and Stereology, Bispebjerg University Hospital, Copenhagen, Denmark
| | - Eric Hidari
- Department of Clinical Neurosciences, Cambridge University Centre for Parkinson-Plus, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK
| | - Mayen Briggs
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, Cambridge University Centre for Parkinson-Plus, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, Cambridge University Centre for Parkinson-Plus, University of Cambridge, Herchel Smith Building, Robinson Way, Cambridge, CB2 0SZ, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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34
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Coomans EM, de Koning LA, Rikken RM, Verfaillie SCJ, Visser D, den Braber A, Tomassen J, van de Beek M, Collij LE, Lemstra AW, Windhorst AD, Barkhof F, Golla SSV, Visser PJ, Scheltens P, van der Flier WM, Ossenkoppele R, van Berckel BNM, van de Giessen E. Performance of a [ 18F]Flortaucipir PET Visual Read Method Across the Alzheimer Disease Continuum and in Dementia With Lewy Bodies. Neurology 2023; 101:e1850-e1862. [PMID: 37748892 PMCID: PMC10663007 DOI: 10.1212/wnl.0000000000207794] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/24/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Recently, the US Food and Drug Administration approved the tau-binding radiotracer [18F]flortaucipir and an accompanying visual read method to support the diagnostic process in cognitively impaired patients assessed for Alzheimer disease (AD). Studies evaluating this visual read method are limited. In this study, we evaluated the performance of the visual read method in participants along the AD continuum and dementia with Lewy bodies (DLB) by determining its reliability, accordance with semiquantitative analyses, and associations with clinically relevant variables. METHODS We included participants who underwent tau-PET at Amsterdam University Medical Center. A subset underwent follow-up tau-PET. Two trained nuclear medicine physicians visually assessed all scans. Inter-reader agreement was calculated using Cohen κ. To examine the concordance of visual read tau positivity with semiquantification, we defined standardized uptake value ratio (SUVr) positivity using different threshold approaches. To evaluate the prognostic value of tau-PET visual read, we performed linear mixed models with longitudinal Mini-Mental State Examination (MMSE). RESULTS We included 263 participants (mean age 68.5 years, 45.6% female), including 147 cognitively unimpaired (CU) participants, 97 amyloid-positive participants with mild cognitive impairment or AD dementia (AD), and 19 participants with DLB. The visual read inter-reader agreement was excellent (κ = 0.95, CI 0.91-0.99). None of the amyloid-negative CU participants (0/92 [0%]) and 1 amyloid-negative participant with DLB (1/12 [8.3%]) were tau-positive. Among amyloid-positive participants, 13 CU participants (13/52 [25.0%]), 85 with AD (85/97 [87.6%]), and 3 with DLB (3/7 [42.9%]) were tau-positive. Two-year follow-up visual read status was identical to baseline. Tau-PET visual read corresponded strongly to SUVr status, with up to 90.4% concordance. Visual read tau positivity was associated with a decline on the MMSE in CU participants (β = -0.52, CI -0.74 to -0.30, p < 0.001) and participants with AD (β = -0.30, CI -0.58 to -0.02, p = 0.04). DISCUSSION The excellent inter-reader agreement, strong correspondence with SUVr, and longitudinal stability indicate that the visual read method is reliable and robust, supporting clinical application. Furthermore, visual read tau positivity was associated with prospective cognitive decline, highlighting its additional prognostic potential. Future studies in unselected cohorts are needed for a better generalizability to the clinical population. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that [18F]flortaucipir visual read accurately distinguishes patients with low tau-tracer binding from those with high tau-tracer binding and is associated with amyloid positivity and cognitive decline.
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Affiliation(s)
- Emma M Coomans
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden.
| | - Lotte A de Koning
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Roos M Rikken
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Sander C J Verfaillie
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Denise Visser
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Anouk den Braber
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Jori Tomassen
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Marleen van de Beek
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Lyduine E Collij
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Afina W Lemstra
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Albert D Windhorst
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Frederik Barkhof
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Sandeep S V Golla
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Pieter Jelle Visser
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Philip Scheltens
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Wiesje M van der Flier
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Rik Ossenkoppele
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Bart N M van Berckel
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Elsmarieke van de Giessen
- From the Radiology & Nuclear Medicine (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Brain Imaging (E.M.C., L.A.d.K., R.M.R., S.C.J.V., D.V., L.E.C., A.D.W., F.B., S.S.V.G., B.N.M.v.B., E.v.d.G.), Amsterdam Neuroscience; Medical Psychology (S.C.J.V.), Amsterdam UMC location University of Amsterdam; Alzheimer Center Amsterdam (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc; Neurodegeneration (A.d.B., J.T., M.v.d.B., A.W.L., P.J.V., P.S., W.M.v.d.F., R.O.), Amsterdam Neuroscience; Department of Biological Psychology (A.d.B.), Vrije Universiteit Amsterdam, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, United Kingdom; 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; Department of Epidemiology & Data Science (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
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Oeckl P, Janelidze S, Halbgebauer S, Stomrud E, Palmqvist S, Otto M, Hansson O. Higher plasma β-synuclein indicates early synaptic degeneration in Alzheimer's disease. Alzheimers Dement 2023; 19:5095-5102. [PMID: 37186338 DOI: 10.1002/alz.13103] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 05/17/2023]
Abstract
INTRODUCTION β-Synuclein is an emerging synaptic blood biomarker for Alzheimer's disease (AD) but differences in β-synuclein levels in preclinical AD and its association with amyloid and tau pathology have not yet been studied. METHODS We measured plasma β-synuclein levels in cognitively unimpaired individuals with positive Aβ-PET (i.e., preclinical AD, N = 48) or negative Aβ-PET (N = 61), Aβ-positive patients with mild cognitive impairment (MCI, N = 36), and Aβ-positive AD dementia (N = 85). Amyloid (A) and tau (T) pathology were assessed by [18 F]flutemetamol and [18 F]RO948 PET. RESULTS Plasma β-synuclein levels were higher in preclinical AD and even higher in MCI and AD dementia. Stratification according to amyloid/tau pathology revealed higher β-synuclein in A+ T- and A+ T+ subjects compared with A- T- . Plasma β-synuclein levels were related to tau and Aβ pathology and associated with temporal cortical thinning and cognitive impairment. DISCUSSION Our data indicate that plasma β-synuclein might track synaptic dysfunction, even during the preclinical stages of AD. HIGHLIGHTS Plasma β-synuclein is already higher in preclinical AD. Plasma β-synuclein is higher in MCI and AD dementia than in preclinical AD. Aβ- and tau-PET SUVRs are associated with plasma β-synuclein levels. Plasma β-synuclein is already higher in tau-PET negative subjects. Plasma β-synuclein is related to temporal cortical atrophy and cognitive impairment.
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Affiliation(s)
- Patrick Oeckl
- German Center for Neurodegenerative Diseases e.V. (DZNE), Ulm, Germany
- Department of Neurology, Ulm University Hospital, Ulm, Germany
| | - Shorena Janelidze
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Steffen Halbgebauer
- German Center for Neurodegenerative Diseases e.V. (DZNE), Ulm, Germany
- Department of Neurology, Ulm University Hospital, Ulm, Germany
| | - Erik Stomrud
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Sebastian Palmqvist
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Markus Otto
- Department of Neurology, Ulm University Hospital, Ulm, Germany
- University Clinic and Polyclinic for Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Oskar Hansson
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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36
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Ahmed M, Chen J, Arani A, Senjem ML, Cogswell PM, Jack CR, Liu C. The diamagnetic component map from quantitative susceptibility mapping (QSM) source separation reveals pathological alteration in Alzheimer's disease-driven neurodegeneration. Neuroimage 2023; 280:120357. [PMID: 37661080 DOI: 10.1016/j.neuroimage.2023.120357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 08/13/2023] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
A sensitive and accurate imaging technique capable of tracking the disease progression of Alzheimer's Disease (AD) driven amnestic dementia would be beneficial. A currently available method for pathology detection in AD with high accuracy is Positron Emission Tomography (PET) imaging, despite certain limitations such as low spatial resolution, off-targeting error, and radiation exposure. Non-invasive MRI scanning with quantitative magnetic susceptibility measurements can be used as a complementary tool. To date, quantitative susceptibility mapping (QSM) has widely been used in tracking deep gray matter iron accumulation in AD. The present work proposes that by compartmentalizing quantitative susceptibility into paramagnetic and diamagnetic components, more holistic information about AD pathogenesis can be acquired. Particularly, diamagnetic component susceptibility (DCS) can be a powerful indicator for tracking protein accumulation in the gray matter (GM), demyelination in the white matter (WM), and relevant changes in the cerebrospinal fluid (CSF). In the current work, voxel-wise group analysis of the WM and the CSF regions show significantly lower |DCS| (the absolute value of DCS) value for amnestic dementia patients compared to healthy controls. Additionally, |DCS| and τ PET standardized uptake value ratio (SUVr) were found to be associated in several GM regions typically affected by τ deposition in AD. Therefore, we propose that the separated diamagnetic susceptibility can be used to track pathological neurodegeneration in different tissue types and regions of the brain. With the initial evidence, we believe the usage of compartmentalized susceptibility demonstrates substantive potential as an MRI-based technique for tracking AD-driven neurodegeneration.
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Affiliation(s)
- Maruf Ahmed
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Jingjia Chen
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.
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37
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Ali DG, Bahrani AA, El Khouli RH, Gold BT, Jiang Y, Zachariou V, Wilcock DM, Jicha GA. White matter hyperintensities influence distal cortical β-amyloid accumulation in default mode network pathways. Brain Behav 2023; 13:e3209. [PMID: 37534614 PMCID: PMC10570488 DOI: 10.1002/brb3.3209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND AND PURPOSE Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults. METHODS Regional standard uptake value ratios (SUVr) from Aβ-PET and white matter hyperintensity (WMH) volumes from three-dimensional magnetic resonance imaging FLAIR images were analyzed across a sample of 72 clinically unimpaired (mini-mental state examination ≥26), older adults (mean age 74.96 and standard deviation 8.13) from the Alzheimer's Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aβ-PET SUVr in the connected cortical DMN regions was analyzed using linear regression models adjusted for age, sex, ApoE, and total brain volumes. RESULTS The regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus were associated with increased regional SUVr in the inferior parietal lobule (p = .011). CONCLUSION The findings suggest that the relation between Aβ in parietal cortex is associated with SVD in downstream white matter (WM) pathways in preclinical AD. The biological relationships and interplay between Aβ and WM microstructure alterations that precede overt WMH development across the continuum of AD progression warrant further study.
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Affiliation(s)
- Doaa G. Ali
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Ahmed A. Bahrani
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Riham H. El Khouli
- Department of Radiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Brian T. Gold
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Yang Jiang
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Valentinos Zachariou
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Donna M. Wilcock
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Gregory A. Jicha
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
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38
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Quattrini G, Ferrari C, Pievani M, Geviti A, Ribaldi F, Scheffler M, Frisoni GB, Garibotto V, Marizzoni M. Unsupervised [ 18F]Flortaucipir cutoffs for tau positivity and staging in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2023; 50:3265-3275. [PMID: 37272955 PMCID: PMC10542510 DOI: 10.1007/s00259-023-06280-7] [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: 03/01/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
PURPOSE Several [18F]Flortaucipir cutoffs have been proposed for tau PET positivity (T+) in Alzheimer's disease (AD), but none were data-driven. The aim of this study was to establish and validate unsupervised T+ cutoffs by applying Gaussian mixture models (GMM). METHODS Amyloid negative (A-) cognitively normal (CN) and amyloid positive (A+) AD-related dementia (ADRD) subjects from ADNI (n=269) were included. ADNI (n=475) and Geneva Memory Clinic (GMC) cohorts (n=98) were used for validation. GMM-based cutoffs were extracted for the temporal meta-ROI, and validated against previously published cutoffs and visual rating. RESULTS GMM-based cutoffs classified less subjects as T+, mainly in the A- CN (<3.4% vs >28.5%) and A+ CN (<14.5% vs >42.9%) groups and showed higher agreement with visual rating (ICC=0.91 vs ICC<0.62) than published cutoffs. CONCLUSION We provided reliable data-driven [18F]Flortaucipir cutoffs for in vivo T+ detection in AD. These cutoffs might be useful to select participants in clinical and research studies.
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Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | - Clarissa Ferrari
- FONDAZIONE POLIAMBULANZA ISTITUTO OSPEDALIERO via Bissolati, 57, 25124, Brescia, Italy
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Andrea Geviti
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Federica Ribaldi
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre, Faculty of Medicine, University of Geneva, 1205, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, 1205, Geneva, Switzerland
- Centre for Biomedical Imaging (CIBM), 1205, Geneva, Switzerland
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
- Biological Psychiatric Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
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39
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Wagatsuma K, Miwa K, Akamatsu G, Yamao T, Kamitaka Y, Sakurai M, Fujita N, Hanaoka K, Matsuda H, Ishii K. Toward standardization of tau PET imaging corresponding to various tau PET tracers: a multicenter phantom study. Ann Nucl Med 2023; 37:494-503. [PMID: 37243882 DOI: 10.1007/s12149-023-01847-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Tau positron emission tomography (PET) imaging is a recently developed non-invasive tool that can detect the density and extension of tau neurofibrillary tangles. Tau PET tracers have been validated to harmonize and accelerate their development and implementation in clinical practice. Whereas standard protocols including injected dose, uptake time, and duration have been determined for tau PET tracers, reconstruction parameters have not been standardized. The present study conducted phantom experiments based on tau pathology to standardize quantitative tau PET imaging parameters and optimize reconstruction conditions of PET scanners at four Japanese sites according to the results of phantom experiments. METHODS The activity of 4.0 and 2.0 kBq/mL for Hoffman 3D brain and cylindrical phantoms, respectively, was estimated from published studies of brain activity using [18F]flortaucipir, [18F]THK5351, and [18F]MK6240. We developed an original tau-specific volume of interest template for the brain based on pathophysiological tau distribution in the brain defined as Braak stages. We acquired brain and cylindrical phantom images using four PET scanners. Iteration numbers were determined as contrast and recover coefficients (RCs) in gray (GM) and white (WM) matter, and the magnitude of the Gaussian filter was determined from image noise. RESULTS Contrast and RC converged at ≥ 4 iterations, the error rates of RC for GM and WM were < 15% and 1%, respectively, and noise was < 10% in Gaussian filters of 2-4 mm in images acquired using the four scanners. Optimizing the reconstruction conditions for phantom tau PET images acquired by each scanner improved contrast and image noise. CONCLUSIONS The phantom activity was comprehensive for first- and second-generation tau PET tracers. The mid-range activity that we determined could be applied to later tau PET tracers. We propose an analytical tau-specific VOI template based on tau pathophysiological changes in patients with AD to standardize tau PET imaging. Phantom images reconstructed under the optimized conditions for tau PET imaging achieved excellent image quality and quantitative accuracy.
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Affiliation(s)
- Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-Ku, Sagamihara, Kanagawa, 252-0373, Japan.
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan.
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima City, Fukushima, 960-1295, Japan
| | - Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima City, Fukushima, 960-1295, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan
| | - Minoru Sakurai
- Clinical Imaging Center for Healthcare, Nippon Medical School, 1-12-15, Sendagi, Bunkyo-Ku, Tokyo, 113-0022, Japan
| | - Naotoshi Fujita
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikarigaoka, Fukushima City, Fukushima, 960-1295, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-115, Yatsuyamada, Koriyama, 963-8052, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan
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40
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Erden Melikoğlu S, Köktürk Dalcalı B, Aydoğan S. The Relationship of Intensive Care Nurses' Attitudes Towards Organ Donation With Their Attitudes Towards Euthanasia and Moral Sensitivity. OMEGA-JOURNAL OF DEATH AND DYING 2023:302228231199882. [PMID: 37650678 DOI: 10.1177/00302228231199882] [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: 09/01/2023]
Abstract
Determination of the relationship between nurses' attitudes towards issues, such as end-of-life care that is specific to intensive care, euthanasia, and organ donation and their moral sensitivity levels is one of the important points for working out ethical problems encountered in intensive care units and increasing the quality of care. This study was conducted to determine the relationship between the attitudes of intensive care nurses towards organ donation, euthanasia, and terminal patients and their moral sensitivity. The study was completed with 175 nurses who agreed to participate in the study. Informed consent of the participants was obtained. While nurses' attitudes towards euthanasia, death, and caring for the dying patient did not correlate with their moral sensitivities, their attitudes towards organ donation did.
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Affiliation(s)
- Seçil Erden Melikoğlu
- Department of Fundamentals of Nursing, Florence Nightingale Faculty of Nursing, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | | | - Semine Aydoğan
- Anesthesiology and Resuscitation Department, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpaşa, Istanbul, Turkey
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41
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Horie K, Salvadó G, Barthélemy NR, Janelidze S, Li Y, He Y, Saef B, Chen CD, Jiang H, Strandberg O, Pichet Binette A, Palmqvist S, Sato C, Sachdev P, Koyama A, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Mattsson-Carlgren N, Stomrud E, Ossenkoppele R, Schindler SE, Hansson O, Bateman RJ. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer's disease. Nat Med 2023; 29:1954-1963. [PMID: 37443334 PMCID: PMC10427417 DOI: 10.1038/s41591-023-02443-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Aggregated insoluble tau is one of two defining features of Alzheimer's disease. Because clinical symptoms are strongly correlated with tau aggregates, drug development and clinical diagnosis need cost-effective and accessible specific fluid biomarkers of tau aggregates; however, recent studies suggest that the fluid biomarkers currently available cannot specifically track tau aggregates. We show that the microtubule-binding region (MTBR) of tau containing the residue 243 (MTBR-tau243) is a new cerebrospinal fluid (CSF) biomarker specific for insoluble tau aggregates and compared it to multiple other phosphorylated tau measures (p-tau181, p-tau205, p-tau217 and p-tau231) in two independent cohorts (BioFINDER-2, n = 448; and Knight Alzheimer Disease Research Center, n = 219). MTBR-tau243 was most strongly associated with tau-positron emission tomography (PET) and cognition, whereas showing the lowest association with amyloid-PET. In combination with p-tau205, MTBR-tau243 explained most of the total variance in tau-PET burden (0.58 ≤ R2 ≤ 0.75) and the performance in predicting cognitive measures (0.34 ≤ R2 ≤ 0.48) approached that of tau-PET (0.44 ≤ R2 ≤ 0.52). MTBR-tau243 levels longitudinally increased with insoluble tau aggregates, unlike CSF p-tau species. CSF MTBR-tau243 is a specific biomarker of tau aggregate pathology, which may be utilized in interventional trials and in the diagnosis of patients. Based on these findings, we propose to revise the A/T/(N) criteria to include MTBR-tau243 as representing insoluble tau aggregates ('T').
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Grants
- P30 AG066444 NIA NIH HHS
- R01 AG070941 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P01 AG026276 NIA NIH HHS
- P30 NS048056 NINDS NIH HHS
- S10 OD025214 NIH HHS
- The Tracy Family SILQ Center established by the Tracy Family, Richard Frimel and Gary Werths, GHR Foundation, David Payne, and the Willman Family brought together by The Foundation for Barnes-Jewish Hospital.
- Eisai industry grant
- The European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie action grant agreement No 101061836, from Greta och Johan Kocks research grants and, travel grants from the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- The Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Cure Alzheimer’s fund, the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279)
- The Knight ADRC developmental project
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Affiliation(s)
- Kanta Horie
- 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
- Eisai Inc., Nutley, NJ, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - 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
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- 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
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hong Jiang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Chihiro Sato
- 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
| | | | | | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, 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
| | - David M Holtzman
- 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
| | - 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
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - 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
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| | - 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.
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42
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Zhang T, Zeng Q, Li K, Liu X, Fu Y, Qiu T, Huang P, Luo X, Liu Z, Peng G. Distinct resting-state functional connectivity patterns of Anterior Insula affected by smoking in mild cognitive impairment. Brain Imaging Behav 2023; 17:386-394. [PMID: 37243752 PMCID: PMC10435406 DOI: 10.1007/s11682-023-00766-6] [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] [Accepted: 03/20/2023] [Indexed: 05/29/2023]
Abstract
Smoking is a modifiable risk factor for Alzheimer's disease (AD). The insula plays a vital role in both smoking and cognition. However, the smoking effects on insula-related networks in cognitively normal controls (CN) and mild cognitive impairment (MCI) patients remain unknown. We identified 129 CN (85 non-smokers and 44 smokers) and 83 MCI (54 non-smokers and 29 smokers). Each underwent neuropsychological assessment and MRI (structural and resting-state functional). Seed-based functional analyses in the anterior and posterior insula were performed to calculate the functional connectivity (FC) with voxels in the whole brain. Mixed-effect analyses were performed to explore the interactive effects on smoking and cognitive status. Associations between FC and neuropsychological scales were assessed. Mixed-effect analyses revealed the FC differences between the right anterior insula (RAI) with the left middle temporal gyrus (LMTG) and that with the right inferior parietal lobule (RIPL) (p < 0.01, cluster level < 0.05, two-tailed, gaussian random field correction). The FC of RAI in both LMTG and RIPL sees a significant decrease in MCI smokers (p < 0.01). Smoking affects insula FC differently between MCI and CN, and could decrease the insula FC in MCI patients. Our study provides evidence of neural mechanisms between smoking and AD.
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Affiliation(s)
- Tianyi Zhang
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
| | - Qingze Zeng
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanv Fu
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, Linyi People’s Hospital, Linyi, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
| | - for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- Department of Neurology, The 1st Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qing-Chun Road, Shang- Cheng District, Hangzhou, 310002 China
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Linyi People’s Hospital, Linyi, China
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43
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Binette AP, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TL, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Novel CSF tau biomarkers can be used for disease staging of sporadic Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.14.23292650. [PMID: 37503281 PMCID: PMC10370223 DOI: 10.1101/2023.07.14.23292650] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic work-up of dementia in clinical practice and the design of clinical trials. Here, we created a staging model using the Subtype and Stage Inference (SuStaIn) algorithm by evaluating cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau biomarkers in 426 participants from BioFINDER-2, that represent the entire spectrum of AD. The model composition and main analyses were replicated in 222 participants from the Knight ADRC cohort. SuStaIn revealed in the two cohorts that the data was best explained by a single biomarker sequence (one subtype), and that five CSF biomarkers (ordered: Aβ42/40, tau phosphorylation occupancies at the residues 217 and 205 [pT217/T217 and pT205/T205], microtubule-binding region of tau containing the residue 243 [MTBR-tau243], and total tau) were sufficient to create an accurate disease staging model. Increasing CSF stages (0-5) were associated with increased abnormality in other AD-related biomarkers, such as Aβ- and tau-PET, and aligned with different phases of longitudinal biomarker changes consistent with current models of AD progression. Higher CSF stages at baseline were associated with higher hazard ratio of clinical decline. Our findings indicate that a common pathophysiologic molecular pathway develops across all AD patients, and that a single CSF collection is sufficient to reliably indicate the presence of both AD pathologies and the degree and stage of disease progression.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Eisai Inc., Nutley, NJ, United States
| | - Nicolas R. Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jacob W. Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D. Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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Doher N, Davoudi V, Magaki S, Townley RA, Haeri M, Vinters HV. Illustrated Neuropathologic Diagnosis of Alzheimer's Disease. Neurol Int 2023; 15:857-867. [PMID: 37489360 PMCID: PMC10366902 DOI: 10.3390/neurolint15030054] [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: 03/14/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 07/26/2023] Open
Abstract
As of 2022, the prevalence of Alzheimer's disease (AD) among individuals aged 65 and older is estimated to be 6.2 million in the United States. This figure is predicted to grow to 13.8 million by 2060. An accurate assessment of neuropathologic changes represents a critical step in understanding the underlying mechanisms in AD. The current method for assessing postmortem Alzheimer's disease neuropathologic change follows version 11 of the National Alzheimer's Coordinating Center (NACC) coding guidebook. Ambiguity regarding steps in the ABC scoring method can lead to increased time or inaccuracy in staging AD. We present a concise overview of how this postmortem diagnosis is made and relate it to the evolving understanding of antemortem AD biomarkers.
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Affiliation(s)
- Nicholas Doher
- Department of Neurology, University of Florida, Gainesville, FL 32611, USA
| | - Vahid Davoudi
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Shino Magaki
- Department of Pathology and Laboratory Medicine, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Ryan A Townley
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
- The University of Kansas Alzheimer's Disease Research Center, University of Kansas Medical Center, Fairway City, KS 66205, USA
| | - Mohammad Haeri
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
- The University of Kansas Alzheimer's Disease Research Center, University of Kansas Medical Center, Fairway City, KS 66205, USA
| | - Harry V Vinters
- Department of Pathology and Laboratory Medicine, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
- Brain Research Institute, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
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45
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Lamontagne-Kam D, Ulfat AK, Hervé V, Vu TM, Brouillette J. Implication of tau propagation on neurodegeneration in Alzheimer's disease. Front Neurosci 2023; 17:1219299. [PMID: 37483337 PMCID: PMC10360202 DOI: 10.3389/fnins.2023.1219299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/07/2023] [Indexed: 07/25/2023] Open
Abstract
Propagation of tau fibrils correlate closely with neurodegeneration and memory deficits seen during the progression of Alzheimer's disease (AD). Although it is not well-established what drives or attenuates tau spreading, new studies on human brain using positron emission tomography (PET) have shed light on how tau phosphorylation, genetic factors, and the initial epicenter of tau accumulation influence tau accumulation and propagation throughout the brain. Here, we review the latest PET studies performed across the entire AD continuum looking at the impact of amyloid load on tau pathology. We also explore the effects of structural, functional, and proximity connectivity on tau spreading in a stereotypical manner in the brain of AD patients. Since tau propagation can be quite heterogenous between individuals, we then consider how the speed and pattern of propagation are influenced by the starting localization of tau accumulation in connected brain regions. We provide an overview of some genetic variants that were shown to accelerate or slow down tau spreading. Finally, we discuss how phosphorylation of certain tau epitopes affect the spreading of tau fibrils. Since tau pathology is an early event in AD pathogenesis and is one of the best predictors of neurodegeneration and memory impairments, understanding the process by which tau spread from one brain region to another could pave the way to novel therapeutic avenues that are efficient during the early stages of the disease, before neurodegeneration induces permanent brain damage and severe memory loss.
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Xu X, Ruan W, Liu F, Liu Q, Gai Y, Su Y, Liang Z, Sun X, Lan X. Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI: An AT(N) Framework-Based Study. Clin Nucl Med 2023; 48:474-482. [PMID: 37075301 DOI: 10.1097/rlu.0000000000004663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
PURPOSE Early-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework-based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD. METHODS Patients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18 F-FDG and 18 F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated. RESULTS One hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex ( P = 0.515) and education ( P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD ( P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13). CONCLUSIONS Multiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.
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Affiliation(s)
| | | | | | | | | | - Ying Su
- Departments of Neurology, Union Hospital, Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhihou Liang
- Departments of Neurology, Union Hospital, Medical College, Huazhong University of Science and Technology, Wuhan, China
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Cogswell PM, Lundt ES, Therneau TM, Mester CT, Wiste HJ, Graff-Radford J, Schwarz CG, Senjem ML, Gunter JL, Reid RI, Przybelski SA, Knopman DS, Vemuri P, Petersen RC, Jack CR. Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers. Nat Commun 2023; 14:3097. [PMID: 37248223 PMCID: PMC10226977 DOI: 10.1038/s41467-023-38878-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
| | - Emily S Lundt
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Carly T Mester
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | | | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
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48
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O'Connor A, Cash DM, Poole T, Markiewicz PJ, Fraser MR, Malone IB, Jiao J, Weston PSJ, Flores S, Hornbeck R, McDade E, Schöll M, Gordon BA, Bateman RJ, Benzinger TLS, Fox NC. Tau accumulation in autosomal dominant Alzheimer's disease: a longitudinal [ 18F]flortaucipir study. Alzheimers Res Ther 2023; 15:99. [PMID: 37231491 PMCID: PMC10210376 DOI: 10.1186/s13195-023-01234-5] [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: 08/20/2022] [Accepted: 04/19/2023] [Indexed: 05/27/2023]
Abstract
Cortical tau accumulation is a key pathological event that partly defines Alzheimer's disease (AD) onset and is associated with cognitive decline and future disease progression. However, an improved understanding of the timing and pattern of early tau deposition in AD and how this may be tracked in vivo is needed. Data from 59 participants involved in two longitudinal cohort studies of autosomal dominant AD (ADAD) were used to investigate whether tau PET can detect and track presymptomatic change; seven participants were symptomatic, and 52 were asymptomatic but at a 50% risk of carrying a pathogenic mutation. All had baseline flortaucipir (FTP) PET, MRI and clinical assessments; 26 individuals had more than one FTP PET scan. Standardised uptake value ratios (SUVRs) in prespecified regions of interest (ROIs) were obtained using inferior cerebellar grey matter as the reference region. We compared the changes in FTP SUVRs between presymptomatic carriers, symptomatic carriers and non-carriers, adjusting for age, sex and study site. We also investigated the relationship between regional FTP SUVRs and estimated years to/from symptom onset (EYO). Compared to both non-carriers and presymptomatic carriers, FTP SUVRs were significantly higher in symptomatic carriers in all ROIs tested (p < 0.001). There were no significant regional differences between presymptomatic carriers and non-carriers in FTP SUVRs, or their rates of change (p > 0.05), although increased FTP signal uptake was seen posteriorly in some individuals around the time of expected symptom onset. When we examined the relationship of FTP SUVR with respect to EYO, the earliest significant regional difference between mutation carriers and non-carriers was detected within the precuneus prior to estimated symptom onset in some cases. This study supports preliminary studies suggesting that presymptomatic tau tracer uptake is rare in ADAD. In cases where early uptake was seen, there was often a predilection for posterior regions (the precuneus and post-cingulate) as opposed to the medial temporal lobe, highlighting the importance of examining in vivo tau uptake beyond the confines of traditional Braak staging.
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Affiliation(s)
- Antoinette O'Connor
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK. Antoinette.o'
- UK Dementia Research Institute at UCL, London, UK. Antoinette.o'
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Maggie R Fraser
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Jieqing Jiao
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Philip S J Weston
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Shaney Flores
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael Schöll
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
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49
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Park SW, Yeo NY, Kim Y, Byeon G, Jang JW. Deep learning application for the classification of Alzheimer's disease using 18F-flortaucipir (AV-1451) tau positron emission tomography. Sci Rep 2023; 13:8096. [PMID: 37208383 PMCID: PMC10198973 DOI: 10.1038/s41598-023-35389-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: 12/13/2022] [Accepted: 05/17/2023] [Indexed: 05/21/2023] Open
Abstract
The positron emission tomography (PET) with 18F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of 18F-flortaucipir-PET images and multimodal data integration in the differentiation of CU from MCI or AD through DL. We used cross-sectional data (18F-flortaucipir-PET images, demographic and neuropsychological score) from the ADNI. All data for subjects (138 CU, 75 MCI, 63 AD) were acquired at baseline. The 2D convolutional neural network (CNN)-long short-term memory (LSTM) and 3D CNN were conducted. Multimodal learning was conducted by adding the clinical data with imaging data. Transfer learning was performed for classification between CU and MCI. The AUC for AD classification from CU was 0.964 and 0.947 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.947, and 0.976 in multimodal learning. The AUC for MCI classification from CU had 0.840 and 0.923 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.845, and 0.850 in multimodal learning. The 18F-flortaucipir PET is effective for the classification of AD stage. Furthermore, the effect of combination images with clinical data increased the performance of AD classification.
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Affiliation(s)
- Sang Won Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea
| | - Na Young Yeo
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gihwan Byeon
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea.
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea.
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea.
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.
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50
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Jack CR, Wiste HJ, Algeciras-Schimnich A, Figdore DJ, Schwarz CG, Lowe VJ, Ramanan VK, Vemuri P, Mielke MM, Knopman DS, Graff-Radford J, Boeve BF, Kantarci K, Cogswell PM, Senjem ML, Gunter JL, Therneau TM, Petersen RC. Predicting amyloid PET and tau PET stages with plasma biomarkers. Brain 2023; 146:2029-2044. [PMID: 36789483 PMCID: PMC10151195 DOI: 10.1093/brain/awad042] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/20/2022] [Accepted: 01/21/2023] [Indexed: 02/16/2023] Open
Abstract
Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Dan J Figdore
- Department of Laboratory Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
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