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Gérard T, Colmant L, Malotaux V, Salman Y, Huyghe L, Quenon L, Dricot L, Ivanoiu A, Lhommel R, Hanseeuw B. The spatial extent of tauopathy on [ 18F]MK-6240 tau PET shows stronger association with cognitive performances than the standard uptake value ratio in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2024; 51:1662-1674. [PMID: 38228971 PMCID: PMC11043108 DOI: 10.1007/s00259-024-06603-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/04/2024] [Indexed: 01/18/2024]
Abstract
PURPOSE [18F]MK-6240, a second-generation tau PET tracer, is increasingly used for the detection and the quantification of in vivo cerebral tauopathy in Alzheimer's disease (AD). Given that neurological symptoms are better explained by the topography rather than by the nature of brain lesions, our study aimed to evaluate whether cognitive impairment would be more closely associated with the spatial extent than with the intensity of tau-PET signal, as measured by the standard uptake value ratio (SUVr). METHODS [18F]MK6240 tau-PET data from 82 participants in the AD spectrum were quantified in three different brain regions (Braak ≤ 2, Braak ≤ 4, and Braak ≤ 6) using SUVr and the extent of tauopathy (EOT, percentage of voxels with SUVr ≥ 1.3). PET data were first compared between diagnostic categories, and ROC curves were computed to evaluate sensitivity and specificity. PET data were then correlated to cognitive performances and cerebrospinal fluid (CSF) tau values. RESULTS The EOT in the Braak ≤ 2 region provided the highest diagnostic accuracies, distinguishing between amyloid-negative and positive clinically unimpaired individuals (threshold = 9%, sensitivity = 79%, specificity = 82%) as well as between prodromal AD and preclinical AD (threshold = 38%, sensitivity = 81%, specificity = 93%). The EOT better correlated with cognition than SUVr (∆R2 + 0.08-0.09) with the best correlation observed for EOT in the Braak ≤ 4 region (R2 = 0.64). Cognitive performances were more closely associated with PET metrics than with CSF values. CONCLUSIONS Quantifying [18F]MK-6240 tau PET in terms of EOT rather than SUVr significantly increases the correlation with cognitive performances. Quantification in the mesiotemporal lobe is the most useful to diagnose preclinical AD or prodromal AD.
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Affiliation(s)
- Thomas Gérard
- Nuclear Medicine Department, Cliniques Universitaires Saint Luc, Brussels, Belgium.
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium.
| | - Lise Colmant
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Vincent Malotaux
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Yasmine Salman
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Lara Huyghe
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Lisa Quenon
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Laurence Dricot
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Adrian Ivanoiu
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Renaud Lhommel
- Nuclear Medicine Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Bernard Hanseeuw
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Avenue Pasteur, 6, 1300, Wavre, Belgium
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Machado Reyes D, Chao H, Hahn J, Shen L, Yan P. Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer. J Pers Med 2024; 14:421. [PMID: 38673048 PMCID: PMC11051083 DOI: 10.3390/jpm14040421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.
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Affiliation(s)
- Diego Machado Reyes
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Hanqing Chao
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Pingkun Yan
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
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Joseph‐Mathurin N, Feldman RL, Lu R, Shirzadi Z, Toomer C, Saint Clair JR, Ma Y, McKay NS, Strain JF, Kilgore C, Friedrichsen KA, Chen CD, Gordon BA, Chen G, Hornbeck RC, Massoumzadeh P, McCullough AA, Wang Q, Li Y, Wang G, Keefe SJ, Schultz SA, Cruchaga C, Preboske GM, Jack CR, Llibre‐Guerra JJ, Allegri RF, Ances BM, Berman SB, Brooks WS, Cash DM, Day GS, Fox NC, Fulham M, Ghetti B, Johnson KA, Jucker M, Klunk WE, la Fougère C, Levin J, Niimi Y, Oh H, Perrin RJ, Reischl G, Ringman JM, Saykin AJ, Schofield PR, Su Y, Supnet‐Bell C, Vöglein J, Yakushev I, Brickman AM, Morris JC, McDade E, Xiong C, Bateman RJ, Chhatwal JP, Benzinger TLS. Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage. Alzheimers Dement 2024; 20:2680-2697. [PMID: 38380882 PMCID: PMC11032566 DOI: 10.1002/alz.13729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 02/22/2024]
Abstract
INTRODUCTION Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. METHODS Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. HIGHLIGHTS Mutation position influences Aβ burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aβ burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.
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Scotton WJ, Shand C, Todd EG, Bocchetta M, Cash DM, VandeVrede L, Heuer HW, Young AL, Oxtoby N, Alexander DC, Rowe JB, Morris HR, Boxer AL, Rohrer JD, Wijeratne PA. Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies. medRxiv 2024:2024.03.14.24304298. [PMID: 38562801 PMCID: PMC10984071 DOI: 10.1101/2024.03.14.24304298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Objective To identify imaging subtypes of the cortico-basal syndrome (CBS) based solely on a data-driven assessment of MRI atrophy patterns, and investigate whether these subtypes provide information on the underlying pathology. Methods We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 CBS cases (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline T1-weighted MRI scans and validated using follow-up MRI. We compared the clinical phenotypes of each subtype and investigated whether there were differences in associated pathology between the subtypes. Results SuStaIn identified two subtypes with distinct sequences of atrophy progression; four-repeat-tauopathy confirmed cases were most commonly assigned to the Subcortical subtype (83% of CBS-PSP and 75% of CBS-CBD), while CBS-AD was most commonly assigned to the Fronto-parieto-occipital subtype (81% of CBS-AD). Subtype assignment was stable at follow-up (98% of cases), and individuals consistently progressed to higher stages (100% stayed at the same stage or progressed), supporting the model's ability to stage progression. Interpretation By jointly modelling disease stage and subtype, we provide data-driven evidence for at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy in CBS that are associated with different underlying pathologies. In the absence of sensitive and specific biomarkers, accurately subtyping and staging individuals with CBS at baseline has important implications for screening on entry into clinical trials, as well as for tracking disease progression.
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Affiliation(s)
- W J Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - C Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - E G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - M Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - D M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - L VandeVrede
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA, USA
| | - H W Heuer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA, USA
| | - A L Young
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - N Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - D C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - J B Rowe
- Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge UK
| | - H R Morris
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- Movement Disorders Centre, University College London Queen Square Institute of Neurology, London, UK
| | - A L Boxer
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - J D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - P A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Informatics, University of Sussex, Brighton, BN1 9RH, UK
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Ishibashi K. Clinical application of MAO-B PET using 18F-THK5351 in neurological disorders. Geriatr Gerontol Int 2024; 24 Suppl 1:31-43. [PMID: 37973072 DOI: 10.1111/ggi.14729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
Monoamine oxidase B (MAO-B) is an enzyme localized to the outer mitochondrial membrane and highly concentrated in astrocytes. Temporal changes in regional MAO-B levels can be used as an index of astrocytic proliferation, known as activated astrocytes or astrogliosis. MAO-B is a marker to evaluate the degree of astrogliosis. Therefore, MAO-B positron emission tomography (PET) is a powerful imaging technique for visualizing and quantifying ongoing astrogliosis through the estimate of regional MAO-B levels. Each neurodegenerative disorder generally has a characteristic distribution pattern of astrogliosis secondary to neuronal loss and pathological protein aggregation. Therefore, by imaging astrogliosis, MAO-B PET can be used as a neurodegeneration marker for identifying degenerative lesions. Any inflammation in the brain usually accompanies astrogliosis starting from an acute phase to a chronic phase. Therefore, by imaging astrogliosis, MAO-B PET can be used as a neuroinflammation marker for identifying inflammatory lesions. MAO-B levels are high in gliomas originating from astrocytes but low in lymphoid tumors. Therefore, MAO-B PET can be used as a brain tumor marker for identifying astrocytic gliomas by imaging MAO-B levels and distinguishing between astrocytic and lymphoid tumors. This review summarizes the clinical application of MAO-B PET using 18F-THK5351 as markers for neurodegeneration, neuroinflammation, and brain tumors in neurological disorders. Because we assume that MAO-B PET is clinically applied to an individual patient, we focus on visual inspection of MAO-B images at the individual patient level. Geriatr Gerontol Int 2024; 24: 31-43.
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Affiliation(s)
- Kenji Ishibashi
- Diagnostic Neuroimaging Research, Research Team for Neuroimaging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
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Liu L, Sun S, Kang W, Wu S, Lin L. A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis. Rev Neurosci 2024; 35:121-139. [PMID: 37419866 DOI: 10.1515/revneuro-2023-0033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/18/2023] [Indexed: 07/09/2023]
Abstract
Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.
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Affiliation(s)
- Lingyu Liu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Wenjie Kang
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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Kim J, Kim J, Park YH, Yoo H, Kim JP, Jang H, Park H, Seo SW. Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment. Commun Biol 2024; 7:198. [PMID: 38368479 PMCID: PMC10874406 DOI: 10.1038/s42003-024-05787-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 01/03/2024] [Indexed: 02/19/2024] Open
Abstract
Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.
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Affiliation(s)
- Jinhee Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Departments of Neurology, Severance Hospital, Yonsei University School of Medicine, Seoul, Korea
| | - Jonghoon Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Yu-Hyun Park
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Korea
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea
| | - Heejin Yoo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Korea
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Korea
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Korea
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Research Institute for Future Medicine, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Korea.
- Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea.
- Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea.
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Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
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Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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Zheng L, Rubinski A, Denecke J, Luan Y, Smith R, Strandberg O, Stomrud E, Ossenkoppele R, Svaldi DO, Higgins IA, Shcherbinin S, Pontecorvo MJ, Hansson O, Franzmeier N, Ewers M. Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease. Ann Neurol 2024; 95:274-287. [PMID: 37837382 DOI: 10.1002/ana.26818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/07/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.
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Affiliation(s)
- Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- 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
| | - 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
| | | | | | | | - Michael J Pontecorvo
- Eli Lilly and Company, Indianapolis, IN, USA
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
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10
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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11
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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12
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Ding H, Wang B, Hamel AP, Karjadi C, Ang TFA, Au R, Lin H. Exploring cognitive progression subtypes in the Framingham Heart Study. Alzheimers Dement (Amst) 2024; 16:e12574. [PMID: 38515438 PMCID: PMC10955221 DOI: 10.1002/dad2.12574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. METHODS Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. RESULTS Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. DISCUSSION This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.
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Affiliation(s)
- Huitong Ding
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Biqi Wang
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Alexander P. Hamel
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Cody Karjadi
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Ting F. A. Ang
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Rhoda Au
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Departments of Neurology and MedicineBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Gherardini L, Zajdel A, Pini L, Crimi A. Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models. Cereb Cortex 2023; 33:11471-11485. [PMID: 37833822 PMCID: PMC10724880 DOI: 10.1093/cercor/bhad380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/23/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.
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Affiliation(s)
- Luca Gherardini
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Aleksandra Zajdel
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Lorenzo Pini
- Padua Neuroscience Center, University of Padua, Via 8 Febbraio, 2, Padua 35122, Italy
| | - Alessandro Crimi
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
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15
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Aksman LM, Oxtoby NP, Scelsi MA, Wijeratne PA, Young AL, Alves IL, Collij LE, Vogel JW, Barkhof F, Alexander DC, Altmann A. A data-driven study of Alzheimer's disease related amyloid and tau pathology progression. Brain 2023; 146:4935-4948. [PMID: 37433038 PMCID: PMC10690020 DOI: 10.1093/brain/awad232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 06/12/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies.
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Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | | | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Brain Research Center, Amsterdam 1081 GN, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
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16
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Doherty T, Yao Z, Khleifat AAL, Tantiangco H, Tamburin S, Albertyn C, Thakur L, Llewellyn DJ, Oxtoby NP, Lourida I, Ranson JM, Duce JA. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement 2023; 19:5922-5933. [PMID: 37587767 DOI: 10.1002/alz.13428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/26/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
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Affiliation(s)
- Thomas Doherty
- Eisai Europe Ltd, Hatfield, UK
- University of Westminster, London, UK
| | | | - Ahmad A L Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Stefano Tamburin
- University of Verona, Department of Neurosciences, Biomedicine & Movement Sciences, Verona, Italy
| | - Chris Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | - James A Duce
- The ALBORADA Drug Discovery Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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17
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Yada Y, Naoki H. Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates. NPJ Syst Biol Appl 2023; 9:59. [PMID: 37993458 PMCID: PMC10665362 DOI: 10.1038/s41540-023-00321-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/06/2023] [Indexed: 11/24/2023] Open
Abstract
The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer's disease (AD), however, obtaining such paired data is laborious and often avoided. Accumulation of amyloid-beta (Aβ) in the brain precedes neurodegeneration in AD, and the quantitative accumulation level may reflect disease progression in the very early phase. Nevertheless, the direct observation of Aβ is rarely paired with the observation of other biomarker candidates. To this end, we established a method that quantitatively predicts Aβ accumulation from biomarker candidates by integrating the mostly unpaired observations via a few-shot learning approach. When applied to 5xFAD mouse behavioral data, the proposed method predicted the accumulation level that conformed to the observed amount of Aβ in the samples with paired data. The results suggest that the proposed model can contribute to discovering Aβ predictability-based biomarkers.
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Affiliation(s)
- Yuichiro Yada
- Laboratory of Data-driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8526, Japan.
| | - Honda Naoki
- Laboratory of Data-driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8526, Japan.
- Kansei-Brain Informatics Group, Center for Brain, Mind and Kansei Sciences Research (BMK Center), Hiroshima University, Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
- Laboratory of Theoretical Biology, Graduate School of Biostudies, Kyoto University, Yoshidakonoecho, Sakyo, Kyoto, 606-8315, Japan.
- Theoretical Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, 444-8787, Japan.
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18
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Hong J, Lu J, Liu F, Wang M, Li X, Clement C, Lopes L, Brendel M, Rominger A, Yen TC, Guan Y, Tian M, Wang J, Zuo C, Shi K. Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [ 18F]Florzolotau PET imaging and subtype/stage inference algorithm. EBioMedicine 2023; 97:104835. [PMID: 37839135 PMCID: PMC10590768 DOI: 10.1016/j.ebiom.2023.104835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking. METHODS We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging. FINDINGS We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1. INTERPRETATION We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP. FUNDING This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198).
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Affiliation(s)
- Jimin Hong
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengtao Liu
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Xinyi Li
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Christoph Clement
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Leonor Lopes
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Matthias Brendel
- Department of Nuclear Medicine, University of Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | | | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mei Tian
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China; International Human Phenome Institutes (Shanghai), Shanghai, China
| | - Jian Wang
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
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He K, Li B, Huang L, Zhao J, Hua F, Wang T, Li J, Wang J, Huang Q, Chen K, Xu S, Ren S, Cai H, Jiang D, Hu J, Han X, Guan Y, Chen K, Guo Q, Xie F. Positive rate and quantification of amyloid pathology with [ 18F]florbetapir in the urban Chinese population. Eur Radiol 2023:10.1007/s00330-023-10366-z. [PMID: 37889270 DOI: 10.1007/s00330-023-10366-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 09/10/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVES Amyloid deposition is considered the initial pathology in Alzheimer's disease (AD). Personalized management requires investigation of amyloid pathology and the risk factors for both amyloid pathology and cognitive decline in the Chinese population. We aimed to investigate amyloid positivity and deposition in AD patients, as well as factors related to amyloid pathology in Chinese cities. METHODS This cross-sectional multicenter study was conducted in Shanghai and Zhengzhou, China. All participants were recruited from urban communities and memory clinics. Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET). We used partial least squares (PLS) models to investigate how related factors contributed to amyloid deposition and cognitive decline. RESULTS In total, 1026 participants were included: 768 participants from the community-based cohort (COMC) and 258 participants from the clinic-based cohort (CLIC). The overall amyloid-positive rates in individuals with clinically diagnosed AD, mild cognitive impairment (MCI), and normal cognition (NC) were 85.8%, 44.5%, and 26.9%, respectively. The global amyloid deposition standardized uptake value ratios (SUVr) (reference: cerebellar crus) were 1.44 ± 0.24, 1.30 ± 0.22, and 1.24 ± 0.14, respectively. CLIC status, apolipoprotein E (ApoE) ε4, and older age were strongly associated with amyloid pathology by PLS modeling. CONCLUSION The overall amyloid-positive rates accompanying AD, MCI, and NC in the Chinese population were similar to those in published cohorts of other populations. ApoE ε4 and CLIC status were risk factors for amyloid pathology across the AD continuum. Education was a risk factor for amyloid pathology in MCI. Female sex and age were risk factors for amyloid pathology in NC. CLINICAL RELEVANCE STATEMENT This study provides new details about amyloid pathology in the Chinese population. Factors related to amyloid deposition and cognitive decline can help to assess patients' AD risk. KEY POINTS • We studied amyloid pathology and related risk factors in the Chinese population. •·The overall amyloid-positive rates in individuals with clinically diagnosed AD, MCI, and NC were 85.8%, 44.5%, and 26.9%, respectively. • These overall amyloid-positive rates were in close agreement with the corresponding prevalence for other populations.
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Affiliation(s)
- Kun He
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Binyin Li
- Department of Neurology and Institute of Neurology, School of Medicine Affiliated Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, 200020, China
| | - Lin Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University, Shanghai, 200120, China
| | - Fengchun Hua
- Department of Nuclear Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Centre, Shanghai Jiaotong University School of Medicine, Shanghai, 200122, China
| | - Junpeng Li
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jie Wang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Qi Huang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Shasha Xu
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Shuhua Ren
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Huawei Cai
- Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Donglang Jiang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jingchao Hu
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
- School of Nursing, Shanghai Jiaotong University, Shanghai, 200025, China
| | - Xingmin Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Kewei Chen
- Banner Alzheimer Institute, Arizona State University, University of Arizona and Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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Peretti DE, Ribaldi F, Scheffler M, Chicherio C, Frisoni GB, Garibotto V. Prognostic value of imaging-based ATN profiles in a memory clinic cohort. Eur J Nucl Med Mol Imaging 2023; 50:3313-3323. [PMID: 37358619 PMCID: PMC10542279 DOI: 10.1007/s00259-023-06311-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/17/2023] [Indexed: 06/27/2023]
Abstract
PURPOSE The ATN model represents a research framework used to classify subjects based on the presence or absence of Alzheimer's disease (AD) pathology through biomarkers for amyloid (A), tau (T), and neurodegeneration (N). The aim of this study was to assess the relationship between ATN profiles defined through imaging and cognitive decline in a memory clinic cohort. METHODS One hundred-eight patients from the memory clinic of Geneva University Hospitals underwent complete clinical and neuropsychological evaluation at baseline and 23 ± 5 months after inclusion, magnetic resonance imaging, amyloid and tau PET scans. ATN profiles were divided into four groups: normal, AD pathological change (AD-PC: A + T-N-, A + T-N +), AD pathology (AD-P: A + T + N-, A + T + N +), and suspected non-AD pathology (SNAP: A-T + N-, A-T-N + , A-T + N +). RESULTS Mini-Mental State Examination (MMSE) scores were significantly different among groups, both at baseline and follow-up, with the normal group having higher average MMSE scores than the other groups. MMSE scores changed significantly after 2 years only in AD-PC and AD-P groups. AD-P profile classification also had the largest number of decliners at follow-up (55%) and the steepest global cognitive decline compared to the normal group. Cox regression showed that participants within the AD-P group had a higher risk of cognitive decline (HR = 6.15, CI = 2.59-14.59), followed by AD-PC (HR = 3.16, CI = 1.17-8.52). CONCLUSION Of the different group classifications, AD-P was found to have the most significant effect on cognitive decline over a period of 2 years, highlighting the value of both amyloid and tau PET molecular imaging as prognostic imaging biomarkers in clinical practice.
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Affiliation(s)
- Débora E Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Chicherio
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
- Centre for Interdisciplinary Study of Gerontology and Vulnerability (CIGEV), University of Geneva, Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Centre for Biomedical Imaging, University of Geneva, Geneva, Switzerland
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21
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Abstract
Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.
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Affiliation(s)
- Sven Haller
- From the Centre d'Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Geneva, Switzerland (S.H.); Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.); Faculty of Medicine of the University of Geneva, Geneva, Switzerland (S.H.); Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (S.H.); Tanta University, Faculty of Medicine, Tanta, Egypt (S.H.); Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology (H.R.J., F.B.), and Centre for Medical Image Computing, Institute of Healthcare Engineering (F.B.), University College London, London, England; Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, London, England (H.R.J.); Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.W.V.); and Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, the Netherlands (F.B.)
| | - Hans Rolf Jäger
- From the Centre d'Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Geneva, Switzerland (S.H.); Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.); Faculty of Medicine of the University of Geneva, Geneva, Switzerland (S.H.); Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (S.H.); Tanta University, Faculty of Medicine, Tanta, Egypt (S.H.); Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology (H.R.J., F.B.), and Centre for Medical Image Computing, Institute of Healthcare Engineering (F.B.), University College London, London, England; Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, London, England (H.R.J.); Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.W.V.); and Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, the Netherlands (F.B.)
| | - Meike W Vernooij
- From the Centre d'Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Geneva, Switzerland (S.H.); Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.); Faculty of Medicine of the University of Geneva, Geneva, Switzerland (S.H.); Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (S.H.); Tanta University, Faculty of Medicine, Tanta, Egypt (S.H.); Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology (H.R.J., F.B.), and Centre for Medical Image Computing, Institute of Healthcare Engineering (F.B.), University College London, London, England; Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, London, England (H.R.J.); Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.W.V.); and Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, the Netherlands (F.B.)
| | - Frederik Barkhof
- From the Centre d'Imagerie Médicale de Cornavin, Place de Cornavin 18, 1201 Geneva, Switzerland (S.H.); Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.); Faculty of Medicine of the University of Geneva, Geneva, Switzerland (S.H.); Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (S.H.); Tanta University, Faculty of Medicine, Tanta, Egypt (S.H.); Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology (H.R.J., F.B.), and Centre for Medical Image Computing, Institute of Healthcare Engineering (F.B.), University College London, London, England; Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, London, England (H.R.J.); Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.W.V.); and Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, the Netherlands (F.B.)
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22
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Zhou C, Wang L, Cheng W, Lv J, Guan X, Guo T, Wu J, Zhang W, Gao T, Liu X, Bai X, Wu H, Cao Z, Gu L, Chen J, Wen J, Huang P, Xu X, Zhang B, Feng J, Zhang M. Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:111. [PMID: 37443179 PMCID: PMC10344958 DOI: 10.1038/s41531-023-00556-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.
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Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - JinChao Lv
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China.
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Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis. Neurology 2022; 101:52. [PMID: 36180245 DOI: 10.1212/WNL.0000000000201144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/06/2022] [Indexed: 11/15/2022] Open
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Jeong SH, Cha J, Jung JH, Yun M, Sohn YH, Chung SJ, Lee PH. Occipital Amyloid Deposition Is Associated with Rapid Cognitive Decline in the Alzheimer's Disease Continuum. J Alzheimers Dis 2023:JAD230187. [PMID: 37355901 DOI: 10.3233/jad-230187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND Clinical significance of additional occipital amyloid-β (Aβ) plaques in Alzheimer's disease (AD) remains unclear. OBJECTIVE In this study, we investigated the effect of regional Aβ deposition on cognition in patients on the AD continuum, especially in the occipital region. METHODS We retrospectively reviewed the medical record of 208 patients with AD across the cognitive continuum (non-dementia and dementia). Multivariable linear regression analyses were performed to determine the effect of regional Aβ deposition on cognitive function. A linear mixed model was used to assess the effect of regional deposition on longitudinal changes in Mini-Mental State Examination (MMSE) scores. Additionally, the patients were dichotomized according to the occipital-to-global Aβ deposition ratio (ratio ≤1, Aβ-OCC- group; ratio >1, Aβ-OCC+ group), and the same statistical analyses were applied for between-group comparisons. RESULTS Regional Aβ burden itself was not associated with baseline cognitive function. In terms of Aβ-OCC group effect, the Aβ-OCC+ group exhibited a poorer cognitive performance on language function compared to the Aβ-OCC- group. High Aβ retention in each region was associated with a rapid decline in MMSE scores, only in the dementia subgroup. Additionally, Aβ-OCC+ individuals exhibited a faster annual decline in MMSE scores than Aβ-OCC- individuals in the non-dementia subgroup (β= -0.77, standard error [SE] = 0.31, p = 0.013). CONCLUSION The present study demonstrated that additional occipital Aβ deposition was associated with poor baseline language function and rapid cognitive deterioration in patients on the AD continuum.
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Affiliation(s)
- Seong Ho Jeong
- Department of Neurology, Inje University Sanggye Paik Hospital, Seoul, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Ho Jung
- Department of Neurology, Inje University Busan Paik Hospital, Busan, South Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
- YONSEI BEYOND LAB, Yongin, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
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25
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Abstract
Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Audrey P Fan
- Department of Biomedical Engineering and Department of Neurology, University of California, Davis, 1590 Drew Avenue, Davis, CA 95618, USA
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26
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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27
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Lu J, Zhang Z, Wu P, Liang X, Zhang H, Hong J, Clement C, Yen TC, Ding S, Wang M, Xiao Z, Rominger A, Shi K, Guan Y, Zuo C, Zhao Q. The heterogeneity of asymmetric tau distribution is associated with an early age at onset and poor prognosis in Alzheimer's disease. Neuroimage Clin 2023; 38:103416. [PMID: 37137254 PMCID: PMC10176076 DOI: 10.1016/j.nicl.2023.103416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/13/2023] [Accepted: 04/22/2023] [Indexed: 05/05/2023]
Abstract
PURPOSE Left-right asymmetry, an important feature of brain development, has been implicated in neurodegenerative diseases, although it's less discussed in typical Alzheimer's disease (AD). We sought to investigate whether asymmetric tau deposition plays a potential role in AD heterogeneity. METHODS Two independent cohorts consisting of patients with mild cognitive impairment due to AD and AD dementia with tau PET imaging were enrolled [the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with 18F-Flortaucipir, the Shanghai Memory Study (SMS) cohort with 18F-Florzolotau]. Based on the absolute global tau interhemispheric differences, each cohort was divided into two groups (asymmetric versus symmetric tau distribution). The two groups were cross-sectionally compared in terms of demographic, cognitive characteristics, and pathological burden. The cognitive decline trajectories were analyzed longitudinally. RESULTS Fourteen (23.3%) and 42 (48.3%) patients in the ADNI and SMS cohorts showed an asymmetric tau distribution, respectively. An asymmetric tau distribution was associated with an earlier age at disease onset (proportion of early-onset AD: ADNI/SMS/combined cohorts, p = 0.093/0.026/0.001) and more severe pathological burden (i.e., global tau burden: ADNI/SMS cohorts, p < 0.001/= 0.007). And patients with an asymmetric tau distribution were characterized by a steeper cognitive decline longitudinally (i.e., the annual decline of Mini-Mental Status Examination score: ADNI/SMS/combined cohorts, p = 0.053 / 0.035 / < 0.001). CONCLUSIONS Asymmetry in tau deposition, which may be associated with an earlier age at onset, more severe pathological burden, and a steeper cognitive decline, is potentially an important characteristic of AD heterogeneity.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Zhengwei Zhang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jimin Hong
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Clement
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | | | - Saineng Ding
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; Department of Informatics, Technische Universität München, Munich, Germany
| | - Zhenxu Xiao
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland; Department of Informatics, Technische Universität München, Munich, Germany
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Qianhua Zhao
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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28
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Scotton WJ, Shand C, Todd E, Bocchetta M, Cash DM, VandeVrede L, Heuer H, Young AL, Oxtoby N, Alexander DC, Rowe JB, Morris HR, Boxer AL, Rohrer JD, Wijeratne PA. Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning. Brain Commun 2023; 5:fcad048. [PMID: 36938523 PMCID: PMC10016410 DOI: 10.1093/braincomms/fcad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/22/2022] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy-Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy-Richardson, 52 with a progressive supranuclear palsy-cortical variant (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech/language, or progressive supranuclear palsy-corticobasal), and 17 with a progressive supranuclear palsy-subcortical variant (progressive supranuclear palsy-parkinsonism or progressive supranuclear palsy-progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a 'subcortical' subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a 'cortical' subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy-subcortical cases and 81% of progressive supranuclear palsy-Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy-cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the 'subcortical' subtype was associated with worse clinical severity scores compared to the 'cortical subtype' (progressive supranuclear palsy rating scale and Unified Parkinson's Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.
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Affiliation(s)
- William J Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Emily Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Hilary Heuer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Neil Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - James B Rowe
- Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, Medical Research Council Cognition and Brain Sciences Unit, Cambridge CB2 0QQ, UK
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK
- Movement Disorders Centre, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
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29
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Schork NJ, Elman JA. Pathway-specific polygenic risk scores correlate with clinical status and Alzheimer's-related biomarkers. Res Sq 2023:rs.3.rs-2583037. [PMID: 36909609 PMCID: PMC10002839 DOI: 10.21203/rs.3.rs-2583037/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Background: APOE is the largest genetic risk factor for sporadic Alzheimer's disease (AD), but there is a substantial polygenic component as well. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk associated with different molecular processes and pathways. Variability at the genetic level may contribute to the extensive phenotypic heterogeneity of Alzheimer's disease (AD). Here, we examine polygenic risk impacting specific pathways associated with AD and examined its relationship with clinical status and AD biomarkers of amyloid, tau, and neurodegeneration (A/T/N). Methods: A total of 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with genotyping data were included. Sets of variants identified from a pathway analysis of AD GWAS summary statistics were combined into clusters based on their assigned pathway. We constructed pathway-specific PRSs for each participant and tested their associations with diagnostic status (AD vs cognitively normal), abnormal levels of amyloid and ptau (positive vs negative), and hippocampal volume. The APOE region was excluded from all PRSs, and analyses controlled for APOE -ε4 carrier status. Results: Thirteen pathway clusters were identified relating to categories such as immune response, amyloid precursor processing, protein localization, lipid transport and binding, tyrosine kinase, and endocytosis. Eight pathway-specific PRSs were significantly associated with AD dementia diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau positivity was additionally associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs, suggesting a strong synergistic effect of all loci contributing to the global AD PRS. Conclusions: Pathway PRS may contribute to understanding separable disease processes, but do not appear to add significant power for predictive purposes. These findings demonstrate that, although genetic risk for AD is widely distributed, AD-phenotypes may be preferentially associated with risk in specific pathways. Defining genetic risk along multiple dimensions at the individual level may help clarify the etiological heterogeneity in AD.
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30
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Shand C, Markiewicz PJ, Cash DM, Alexander DC, Donohue MC, Barkhof F, Oxtoby NP. Heterogeneity in Preclinical Alzheimer's Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling. medRxiv 2023:2023.02.07.23285572. [PMID: 36798314 PMCID: PMC9934776 DOI: 10.1101/2023.02.07.23285572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Importance Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline - and perhaps response to treatment - differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials. Objective Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value. Design Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used. Setting The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America. Participants Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI). Main Outcomes and Measures Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET. Results We included 1,240 Aβ+ participants (and 407 Aβ- controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes - Typical, Cortical, Subcortical - comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (Typical: +0.09/yr, [0.06,0.12], P<.0001; and Cortical: +0.07/yr, [0.04,0.10], P<.0001). Conclusions and Relevance In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.
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Affiliation(s)
- Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Michael C Donohue
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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31
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Sun Y, Zhao Y, Hu K, Wang M, Liu Y, Liu B. Distinct spatiotemporal subtypes of amyloid deposition are associated with diverging disease profiles in cognitively normal and mild cognitive impairment individuals. Transl Psychiatry 2023; 13:35. [PMID: 36732496 PMCID: PMC9895066 DOI: 10.1038/s41398-023-02328-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/04/2023] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
We aimed to investigate the relationship between spatiotemporal changes of amyloid deposition and Alzheimer's disease (AD) profiles in cognitively normal (CN) and those with mild cognitive impairment (MCI). Using a data-driven method and amyloid-PET data, we identified and validated two subtypes in two independent datasets (discovery dataset: N = 548, age = 72.4 ± 6.78, 49% female; validation dataset: N = 348, age = 74.9 ± 8.16, 47% female) from the Alzheimer's Disease Neuroimaging Initiative across a range of individuals who were CN or had MCI. The two subtypes showed distinct regional progression patterns and presented distinct genetic, clinical and biomarker characteristics. The cortex-priority subtype was more likely to show typical clinical syndromes of symptomatic AD and vice versa. Furthermore, the regional progression patterns were associated with clinical and biomarker profiles. In sum, our findings suggest that the spatiotemporal variants of amyloid depositions are in close association with disease trajectories; these findings may provide insight into the disease monitoring and enrollment of therapeutic trials in AD.
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Affiliation(s)
- Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuxin Zhao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ke Hu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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32
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Collij LE, Farrar G, Valléz García D, Bader I, Shekari M, Lorenzini L, Pemberton H, Altomare D, Pla S, Loor M, Markiewicz P, Yaqub M, Buckley C, Frisoni GB, Nordberg A, Payoux P, Stephens A, Gismondi R, Visser PJ, Ford L, Schmidt M, Birck C, Georges J, Mett A, Walker Z, Boada M, Drzezga A, Vandenberghe R, Hanseeuw B, Jessen F, Schöll M, Ritchie C, Lopes Alves I, Gispert JD, Barkhof F. The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact. Front Neurol 2023; 13:1063598. [PMID: 36761917 PMCID: PMC9907029 DOI: 10.3389/fneur.2022.1063598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/08/2022] [Indexed: 01/22/2023] Open
Abstract
Background Amyloid-β (Aβ) accumulation is considered the earliest pathological change in Alzheimer's disease (AD). The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) consortium is a collaborative European framework across European Federation of Pharmaceutical Industries Associations (EFPIA), academic, and 'Small and Medium-sized enterprises' (SME) partners aiming to provide evidence on the clinical utility and cost-effectiveness of Positron Emission Tomography (PET) imaging in diagnostic work-up of AD and to support clinical trial design by developing optimal quantitative methodology in an early AD population. The AMYPAD studies In the Diagnostic and Patient Management Study (DPMS), 844 participants from eight centres across three clinical subgroups (245 subjective cognitive decline, 342 mild cognitive impairment, and 258 dementia) were included. The Prognostic and Natural History Study (PNHS) recruited pre-dementia subjects across 11 European parent cohorts (PCs). Approximately 1600 unique subjects with historical and prospective data were collected within this study. PET acquisition with [18F]flutemetamol or [18F]florbetaben radiotracers was performed and quantified using the Centiloid (CL) method. Results AMYPAD has significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Main contributions so far include the validation of the dual-time window acquisition protocol to derive the fully quantitative non-displaceable binding potential (BP ND ), assess the value of this metric in the context of clinical trials, improve PET-sensitivity to emerging Aβ burden and utilize its available regional information, establish the quantitative accuracy of the Centiloid method across tracers and support implementation of quantitative amyloid-PET measures in the clinical routine. Future steps The AMYPAD consortium has succeeded in recruiting and following a large number of prospective subjects and setting up a collaborative framework to integrate data across European PCs. Efforts are currently ongoing in collaboration with ARIDHIA and ADDI to harmonize, integrate, and curate all available clinical data from the PNHS PCs, which will become openly accessible to the wider scientific community.
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Affiliation(s)
- Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,*Correspondence: Lyduine E. Collij ✉
| | | | - David Valléz García
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Ilona Bader
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | | | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Hugh Pemberton
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Sandra Pla
- Synapse Research Management Partners, Barcelona, Spain
| | - Mery Loor
- Synapse Research Management Partners, Barcelona, Spain
| | - Pawel Markiewicz
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | | | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Pierre Payoux
- Department of Nuclear Medicine, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Andrew Stephens
- Life Molecular Imaging GmbH, Berlin, Baden-Württemberg, Germany
| | | | - Pieter Jelle Visser
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Lisa Ford
- Janssen Pharmaceutica NV, Beerse, Belgium
| | | | | | | | - Anja Mett
- GE Healthcare, Amersham, United Kingdom
| | - Zuzana Walker
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Mercé Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Alexander Drzezga
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Rik Vandenberghe
- Faculty of Medicine, University Hospitals Leuven, Leuven, Brussels, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Brussels, Belgium
| | - Frank Jessen
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | | | - Juan Domingo Gispert
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
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33
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Li J, Mountz EJ, Mizuno A, Shah AM, Weinstein A, Cohen AD, Klunk WE, Snitz BE, Aizenstein HJ, Karim HT. Functional Asymmetry During Working Memory and Its Association with Markers of Alzheimer's Disease in Cognitively Normal Older Adults. J Alzheimers Dis 2023; 95:1077-1089. [PMID: 37638440 DOI: 10.3233/jad-230379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
BACKGROUND Amyloid-β (Aβ) deposits asymmetrically early in Alzheimer's disease (AD). This process is variable and has been associated with asymmetric hypometabolism. OBJECTIVE We investigated whether neural asymmetry during working memory and executive function processing was associated with AD genetic risk and markers of AD as well as other brain neuropathology biomarkers, cognitive function, and cognitive reserve in cognitively normal older adults. METHODS We analyzed data from 77 cognitively healthy, older adults who completed functional magnetic resonance imaging, positron emission tomography, and cognitive testing. We identified regions of significant activation and asymmetry during the Digital Symbol Substitution Task (DSST). We examined associations between regions with significant hemispheric asymmetry (directional and absolute) and global cerebral Aβ, cerebral glucose metabolism, white matter hyperintensities, APOE ɛ4 allele status, DSST reaction time, age, sex, education, and cognitive function. RESULTS Asymmetry was not associated with several factors including cognitive function, Aβ, and white matter hyperintensities. The presence of at least one ɛ4 APOE allele in participants was associated with less asymmetric activation in the angular gyrus (right dominant activation). Greater education was associated with less asymmetric activation in mediodorsal thalamus (left dominant activation). CONCLUSIONS Genetic risk of AD was associated with lower asymmetry in angular gyrus activation, while greater education was associated with lower asymmetry in mediodorsal thalamus activation. Changes in asymmetry may reflect components of compensation or cognitive reserve. Asymmetric neural recruitment during working memory may be related to maintenance of cognitive function in cognitively normal older adults.
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Affiliation(s)
- Jinghang Li
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth J Mountz
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Akiko Mizuno
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashti M Shah
- Physician Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Andrea Weinstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beth E Snitz
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Howard J Aizenstein
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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34
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Schork NJ, Elman JA. Pathway-Specific Polygenic Risk Scores Correlate with Clinical Status and Alzheimer's Disease-Related Biomarkers. J Alzheimers Dis 2023; 95:915-929. [PMID: 37661888 PMCID: PMC10697039 DOI: 10.3233/jad-230548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
BACKGROUND APOE is the largest genetic risk factor for Alzheimer's disease (AD), but there is a substantial polygenic component. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk across molecular processes and pathways that contribute to heterogeneity of disease presentation. OBJECTIVE We examined polygenic risk impacting specific AD-associated pathways and its relationship with clinical status and biomarkers of amyloid, tau, and neurodegeneration (A/T/N). METHODS We analyzed data from 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied pathway analysis and clustering to identify AD-associated "pathway clusters" and construct pathway-specific PRSs (excluding the APOE region). We tested associations with diagnostic status, abnormal levels of amyloid and ptau, and hippocampal volume. RESULTS Thirteen pathway clusters were identified, and eight pathway-specific PRSs were significantly associated with AD diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau-positivity was also associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs. CONCLUSIONS Pathway PRS may contribute to understanding separable disease processes, but do not add significant power for predictive purposes. These findings demonstrate that AD-phenotypes may be preferentially associated with risk in specific pathways, and defining genetic risk along multiple dimensions may clarify etiological heterogeneity in AD. This approach to delineate pathway-specific PRS can be used to study other complex diseases.
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Affiliation(s)
- Nicholas J. Schork
- The Translational Genomics Research Institute, Quantitative Medicine and Systems Biology, Phoenix, AZ, USA
- Department of Psychiatry University of California, San Diego, La Jolla, CA, USA
| | - Jeremy A. Elman
- Department of Psychiatry University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
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35
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Chen P, Yao H, Tijms BM, Wang P, Wang D, Song C, Yang H, Zhang Z, Zhao K, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhang X, Jiang T, Zhou Y, Lu J, Han Y, Liu B, Zhou B, Liu Y. Four Distinct Subtypes of Alzheimer's Disease Based on Resting-State Connectivity Biomarkers. Biol Psychiatry 2022; 93:759-769. [PMID: 36137824 DOI: 10.1016/j.biopsych.2022.06.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/19/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. METHODS We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. RESULTS Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. CONCLUSIONS These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC - Location VUmc, The Netherlands
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Kun Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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36
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Vogel JW, Tosun D. Multiple Cortical to Striatal Accumulation Trajectories of β-Amyloid: Do All Roads Lead to Rome? Neurology 2022; 98:695-696. [PMID: 35338076 DOI: 10.1212/wnl.0000000000200191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.,Veterans Affairs San Francisco, CA, USA
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