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Groot C, Smith R, Collij LE, Mastenbroek SE, Stomrud E, Binette AP, Leuzy A, Palmqvist S, Mattsson-Carlgren N, Strandberg O, Cho H, Lyoo CH, Frisoni GB, Peretti DE, Garibotto V, La Joie R, Soleimani-Meigooni DN, Rabinovici G, Ossenkoppele R, Hansson O. Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment. JAMA Neurol 2024; 81:845-856. [PMID: 38857029 PMCID: PMC11165418 DOI: 10.1001/jamaneurol.2024.1612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Importance An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. Objective To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. Design, Setting, and Participants This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures Tau PET, Aβ PET, and MRI. Main Outcomes and Measures Positive results on tau PET (temporal meta-region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. Results In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. Conclusions and Relevance In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.
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Affiliation(s)
- Colin Groot
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Lyduine E. Collij
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Sophie E. Mastenbroek
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Giovanni B. Frisoni
- Memory Clinic, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - Debora E. Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging, Geneva, Switzerland
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
| | - David N. Soleimani-Meigooni
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Gil Rabinovici
- Department of Neurology, Memory and Aging Center, University of California, San Francisco
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
- Associate Editor, JAMA Neurology
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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Xie L, Zhao J, Li Y, Bai J. PET brain imaging in neurological disorders. Phys Life Rev 2024; 49:100-111. [PMID: 38574584 DOI: 10.1016/j.plrev.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/06/2024]
Abstract
Brain disorders are a series of conditions with damage or loss of neurons, such as Parkinson's disease (PD), Alzheimer's disease (AD), or drug dependence. These individuals have gradual deterioration of cognitive, motor, and other central nervous system functions affected. This degenerative trajectory is intricately associated with dysregulations in neurotransmitter systems. Positron Emission Tomography (PET) imaging, employing radiopharmaceuticals and molecular imaging techniques, emerges as a crucial tool for detecting brain biomarkers. It offers invaluable insights for early diagnosis and distinguishing brain disorders. This article comprehensively reviews the application and progress of conventional and novel PET imaging agents in diagnosing brain disorders. Furthermore, it conducts a thorough analysis on merits and limitations. The article also provides a forward-looking perspective in the future development directions of PET imaging agents for diagnosing brain disorders and proposes potential innovative strategies. It aims to furnish clinicians and researchers with an all-encompassing overview of the latest advancements and forthcoming trends in the utilization of PET imaging for diagnosing brain disorders.
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Affiliation(s)
- Lijun Xie
- Faculty of Life science and Technology, Kunming University of Science and Technology, Kunming 650500, PR China; Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China; Department of Nuclear Medicine, First Affiliated Hospital of Kunming Medical University, Kunming 650032, PR China
| | - Jihua Zhao
- Department of Nuclear Medicine, First Affiliated Hospital of Kunming Medical University, Kunming 650032, PR China
| | - Ye Li
- Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China.
| | - Jie Bai
- Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China.
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3
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Yang C, Lu K, Li J, Wu H, Chen W. Rapid Construction of 18F-Triazolyl-tetrazines through the Click Reaction. J Org Chem 2024. [PMID: 38875503 DOI: 10.1021/acs.joc.4c00574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
Due to the fast reaction rate, 18F-labeled tetrazines have been widely applied in positron emission tomography (PET) imaging in cancer research and drug discovery. In this work, several functional 18F-triazolyl-tetrazines were rapidly obtained through an optimized copper-catalyzed alkyene-azide cycloaddition reaction system in >99% radiochemical conversions. Notably, the commonly used 18F-labeled azides were isolated through cartridges and directly used for cycloadditions, which greatly simplified the labeling procedure. The assembled triazolyl-tetrazines demonstrated high in vitro stability and reaction kinetics, exhibiting considerable potential for the development of PET agents.
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Affiliation(s)
- Cheng Yang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug, and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, and Frontiers Science Center for Disease Related Molecular Network West China Hospital, Sichuan University, Huaxi Research Building, 001 4th Keyuan Road, Chengdu 610041, China
| | - Kai Lu
- Department of Nuclear Medicine and Clinical Nuclear Medicine Research Lab, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jie Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug, and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, and Frontiers Science Center for Disease Related Molecular Network West China Hospital, Sichuan University, Huaxi Research Building, 001 4th Keyuan Road, Chengdu 610041, China
| | - Haoxing Wu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug, and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, and Frontiers Science Center for Disease Related Molecular Network West China Hospital, Sichuan University, Huaxi Research Building, 001 4th Keyuan Road, Chengdu 610041, China
| | - Wei Chen
- Department of Nuclear Medicine and Clinical Nuclear Medicine Research Lab, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Jiang J, Shi R, Lu J, Wang M, Zhang Q, Zhang S, Wang L, Alberts I, Rominger A, Zuo C, Shi K. Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network. Neuroimage 2024; 291:120593. [PMID: 38554780 DOI: 10.1016/j.neuroimage.2024.120593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/17/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVE The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Rong Shi
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Qi Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Shuoyan Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Luyao Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, 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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Wang YT, Therriault J, Servaes S, Tissot C, Rahmouni N, Macedo AC, Fernandez-Arias J, Mathotaarachchi SS, Benedet AL, Stevenson J, Ashton NJ, Lussier FZ, Pascoal TA, Zetterberg H, Rajah MN, Blennow K, Gauthier S, Rosa-Neto P. Sex-specific modulation of amyloid-β on tau phosphorylation underlies faster tangle accumulation in females. Brain 2024; 147:1497-1510. [PMID: 37988283 PMCID: PMC10994548 DOI: 10.1093/brain/awad397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/26/2023] [Accepted: 10/28/2023] [Indexed: 11/23/2023] Open
Abstract
Females are disproportionately affected by dementia due to Alzheimer's disease. Despite a similar amyloid-β (Aβ) load, a higher load of neurofibrillary tangles (NFTs) is seen in females than males. Previous literature has proposed that Aβ and phosphorylated-tau (p-tau) synergism accelerates tau tangle formation, yet the effect of biological sex in this process has been overlooked. In this observational study, we examined longitudinal neuroimaging data from the TRIAD and ADNI cohorts from Canada and USA, respectively. We assessed 457 participants across the clinical spectrum of Alzheimer's disease. All participants underwent baseline multimodal imaging assessment, including MRI and PET, with radioligands targeting Aβ plaques and tau tangles, respectively. CSF data were also collected. Follow-up imaging assessments were conducted at 1- and 2-year intervals for the TRIAD cohort and 1-, 2- and 4-year intervals for the ADNI cohort. The upstream pathological events contributing to faster tau progression in females were investigated-specifically, whether the contribution of Aβ and p-tau synergism to accelerated tau tangle formation is modulated by biological sex. We hypothesized that cortical Aβ predisposes tau phosphorylation and tangle accumulation in a sex-specific manner. Findings revealed that Aβ-positive females presented higher CSF p-tau181 concentrations compared with Aβ-positive males in both the TRIAD (P = 0.04, Cohen's d = 0.51) and ADNI (P = 0.027, Cohen's d = 0.41) cohorts. In addition, Aβ-positive females presented faster NFT accumulation compared with their male counterparts (TRIAD: P = 0.026, Cohen's d = 0.52; ADNI: P = 0.049, Cohen's d = 1.14). Finally, the triple interaction between female sex, Aβ and CSF p-tau181 was revealed as a significant predictor of accelerated tau accumulation at the 2-year follow-up visit (Braak I: P = 0.0067, t = 2.81; Braak III: P = 0.017, t = 2.45; Braak IV: P = 0.002, t = 3.17; Braak V: P = 0.006, t = 2.88; Braak VI: P = 0.0049, t = 2.93). Overall, we report sex-specific modulation of cortical Aβ in tau phosphorylation, consequently facilitating faster NFT progression in female individuals over time. This presents important clinical implications and suggests that early intervention that targets Aβ plaques and tau phosphorylation may be a promising therapeutic strategy in females to prevent the further accumulation and spread of tau aggregates.
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Affiliation(s)
- Yi-Ting Wang
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Arthur Cassa Macedo
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Jaime Fernandez-Arias
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Sulantha S Mathotaarachchi
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Andréa L Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Mölndal, Sweden
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Mölndal, Sweden
- Centre for Age-Related Medicine, Stavanger University Hospital, 4011 Stavanger, Norway
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, London SE5 9RX, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London SE5 8AF, UK
| | - Firoza Z Lussier
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Tharick A Pascoal
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 1PJ, UK
- UK Dementia Research Institute at UCL, London WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | | | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 41 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
- Montreal Neurological Institute, Montreal, QC H3A 2B4, Canada
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Ziontz J, Harrison TM, Chen X, Giorgio J, Adams JN, Wang Z, Jagust W. Behaviorally meaningful functional networks mediate the effect of Alzheimer's pathology on cognition. Cereb Cortex 2024; 34:bhae134. [PMID: 38602736 PMCID: PMC11008686 DOI: 10.1093/cercor/bhae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/25/2024] [Accepted: 03/12/2024] [Indexed: 04/12/2024] Open
Abstract
Tau pathology is associated with cognitive impairment in both aging and Alzheimer's disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.
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Affiliation(s)
- Jacob Ziontz
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Xi Chen
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Joseph Giorgio
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, University Dr, Callaghan, Newcastle, NSW 2305, Australia
| | - Jenna N Adams
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, 1400 Biological Sciences III, University of California, Irvine, Irvine, CA 92697, United States
| | - Zehao Wang
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - William Jagust
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, United States
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7
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Su Y, Protas H, Luo J, Chen K, Alosco ML, Adler CH, Balcer LJ, Bernick C, Au R, Banks SJ, Barr WB, Coleman MJ, Dodick DW, Katz DI, Marek KL, McClean MD, McKee AC, Mez J, Daneshvar DH, Palmisano JN, Peskind ER, Turner RW, Wethe JV, Rabinovici G, Johnson K, Tripodis Y, Cummings JL, Shenton ME, Stern RA, Reiman EM. Flortaucipir tau PET findings from former professional and college American football players in the DIAGNOSE CTE research project. Alzheimers Dement 2024; 20:1827-1838. [PMID: 38134231 PMCID: PMC10984430 DOI: 10.1002/alz.13602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/27/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION Tau is a key pathology in chronic traumatic encephalopathy (CTE). Here, we report our findings in tau positron emission tomography (PET) measurements from the DIAGNOSE CTE Research Project. METHOD We compare flortaucipir PET measures from 104 former professional players (PRO), 58 former college football players (COL), and 56 same-age men without exposure to repetitive head impacts (RHI) or traumatic brain injury (unexposed [UE]); characterize their associations with RHI exposure; and compare players who did or did not meet diagnostic criteria for traumatic encephalopathy syndrome (TES). RESULTS Significantly elevated flortaucipir uptake was observed in former football players (PRO+COL) in prespecified regions (p < 0.05). Association between regional flortaucipir uptake and estimated cumulative head impact exposure was only observed in the superior frontal region in former players over 60 years old. Flortaucipir PET was not able to differentiate TES groups. DISCUSSION Additional studies are needed to further understand tau pathology in CTE and other individuals with a history of RHI.
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Affiliation(s)
- Yi Su
- Banner Alzheimer's Institute and Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Hillary Protas
- Banner Alzheimer's Institute and Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Ji Luo
- Banner Alzheimer's Institute and Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Kewei Chen
- Banner Alzheimer's Institute and Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Michael L. Alosco
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Charles H. Adler
- Department of NeurologyMayo Clinic College of Medicine, Mayo Clinic ArizonaScottsdaleArizonaUSA
| | - Laura J. Balcer
- Departments of NeurologyNYU Grossman School of MedicineNew YorkNew YorkUSA
- Department of Population Health and OphthalmologyNYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Charles Bernick
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
- Department of NeurologyUniversity of WashingtonSeattleWashingtonUSA
| | - Rhoda Au
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyFraminghamMassachusettsUSA
- Slone Epidemiology Center; Departments of Anatomy & Neurobiology, Neurology, and MedicineDepartment of EpidemiologyBoston University Chobanian & Avedisian School of Medicine; Boston University School of Public HealthBostonMassachusettsUSA
| | - Sarah J. Banks
- Departments of Neuroscience and PsychiatryUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - William B. Barr
- Departments of NeurologyNYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Michael J. Coleman
- Departments of Psychiatry and RadiologyPsychiatry Neuroimaging LaboratoryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - David W. Dodick
- Department of NeurologyMayo Clinic College of Medicine, Mayo Clinic ArizonaScottsdaleArizonaUSA
| | - Douglas I. Katz
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Encompass Health Braintree Rehabilitation HospitalBraintreeMassachusettsUSA
| | - Kenneth L. Marek
- Institute for Neurodegenerative Disorders, Invicro, LLCNew HavenConnecticutUSA
| | - Michael D. McClean
- Department of Environmental HealthBoston University School of Public HealthBostonMassachusettsUSA
| | - Ann C. McKee
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- VA Boston Healthcare SystemBostonMassachusettsUSA
| | - Jesse Mez
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyFraminghamMassachusettsUSA
| | - Daniel H. Daneshvar
- Department of Physical Medicine & RehabilitationMassachusetts General Hospital, Spaulding Rehabilitation Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Joseph N. Palmisano
- Boston University Alzheimer's Disease Research Center, Boston University CTE Center, Biostatistics and Epidemiology Data Analytics Center (BEDAC), Boston University School of Public HealthBostonMassachusettsUSA
| | - Elaine R. Peskind
- Department of Psychiatry and Behavioral SciencesVA Northwest Mental Illness Research, Education, and Clinical Center, VA Puget Sound Health Care System; University of Washington School of MedicineSeattleWashingtonUSA
| | - Robert W. Turner
- Department of Clinical Research & LeadershipThe George Washington University School of Medicine & Health SciencesWashingtonDistrict of ColumbiaUSA
| | - Jennifer V. Wethe
- Department of Psychiatry and PsychologyMayo Clinic School of Medicine, Mayo Clinic ArizonaScottsdaleArizonaUSA
| | - Gil Rabinovici
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Keith Johnson
- Gordon Center for Medical Imaging, Mass General Research Institute, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yorghos Tripodis
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Jeffrey L. Cummings
- Department of Brain HealthChambers‐Grundy Center for Transformative NeuroscienceSchool of Integrated Health Sciences, University of Nevada Las VegasLas VegasNevadaUSA
| | - Martha E. Shenton
- Departments of Psychiatry and RadiologyPsychiatry Neuroimaging LaboratoryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Robert A. Stern
- Department of NeurologyBoston University Alzheimer's Disease Research CenterBoston University CTE CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Eric M. Reiman
- Banner Alzheimer's Institute and Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
- University of Arizona, Arizona State University, Translational Genomics Research InstitutePhoenixArizonaUSA
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8
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Aguero C, Dhaynaut M, Amaral AC, Moon SH, Neelamegam R, Scapellato M, Carazo-Casas C, Kumar S, El Fakhri G, Johnson K, Frosch MP, Normandin MD, Gómez-Isla T. Head-to-head comparison of [ 18F]-Flortaucipir, [ 18F]-MK-6240 and [ 18F]-PI-2620 postmortem binding across the spectrum of neurodegenerative diseases. Acta Neuropathol 2024; 147:25. [PMID: 38280071 PMCID: PMC10822013 DOI: 10.1007/s00401-023-02672-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/19/2023] [Accepted: 12/19/2023] [Indexed: 01/29/2024]
Abstract
We and others have shown that [18F]-Flortaucipir, the most validated tau PET tracer thus far, binds with strong affinity to tau aggregates in Alzheimer's (AD) but has relatively low affinity for tau aggregates in non-AD tauopathies and exhibits off-target binding to neuromelanin- and melanin-containing cells, and to hemorrhages. Several second-generation tau tracers have been subsequently developed. [18F]-MK-6240 and [18F]-PI-2620 are the two that have garnered most attention. Our recent data indicated that the binding pattern of [18F]-MK-6240 closely parallels that of [18F]-Flortaucipir. The present study aimed at the direct comparison of the autoradiographic binding properties and off-target profile of [18F]-Flortaucipir, [18F]-MK-6240 and [18F]-PI-2620 in human tissue specimens, and their potential binding to monoamine oxidases (MAO). Phosphor-screen and high resolution autoradiographic patterns of the three tracers were studied in the same postmortem tissue material from AD and non-AD tauopathies, cerebral amyloid angiopathy, synucleopathies, transactive response DNA-binding protein 43 (TDP-43)-frontotemporal lobe degeneration and controls. Our results show that the three tracers show nearly identical autoradiographic binding profiles. They all strongly bind to neurofibrillary tangles in AD but do not seem to bind to a significant extent to tau aggregates in non-AD tauopathies pointing to their limited utility for the in vivo detection of non-AD tau lesions. None of them binds to lesions containing β-amyloid, α-synuclein or TDP-43 but they all show strong off-target binding to neuromelanin and melanin-containing cells, as well as weaker binding to areas of hemorrhage. The autoradiographic binding signals of the three tracers are only weakly displaced by competing concentrations of selective MAO-B inhibitor deprenyl but not by MAO-A inhibitor clorgyline suggesting that MAO enzymes do not appear to be a significant binding target of any of them. These findings provide relevant insights for the correct interpretation of the in vivo behavior of these three tau PET tracers.
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Affiliation(s)
- Cinthya Aguero
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
| | - Maeva Dhaynaut
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ana C Amaral
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
| | - S-H Moon
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ramesh Neelamegam
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Margaret Scapellato
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
| | - Carlos Carazo-Casas
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
| | - Sunny Kumar
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Keith Johnson
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew P Frosch
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Marc D Normandin
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Teresa Gómez-Isla
- MassGeneral Institute for NeuroDegenerative Disease, Charlestown, MA, USA.
- Department of Neurology, Massachusetts General Hospital, WACC Suite 715, 15th Parkman St., Boston, MA, 02114, USA.
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9
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Gogola A, Cohen AD, Snitz B, Minhas D, Tudorascu D, Ikonomovic MD, Shaaban CE, Doré V, Matan C, Bourgeat P, Mason NS, Leuzy A, Aizenstein H, Mathis CA, Lopez OL, Lopresti BJ, Villemagne VL. Implementation and Assessment of Tau Thresholds in Non-Demented Individuals as Predictors of Cognitive Decline in Tau Imaging Studies. J Alzheimers Dis 2024; 100:S75-S92. [PMID: 39121123 DOI: 10.3233/jad-240543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Background Tau accumulation in Alzheimer's disease is associated with short term clinical progression and faster rates of cognitive decline in individuals with high amyloid-β deposition. Defining an optimal threshold of tau accumulation predictive of cognitive decline remains a challenge. Objective We tested the ability of regional tau PET sensitivity and specificity thresholds to predict longitudinal cognitive decline. We also tested the predictive performance of thresholds in the proposed new NIA-AA biological staging for Alzheimer's disease where multiple levels of tau positivity are used to stage participants. Methods 18F-flortaucipir scans from 301 non-demented participants were processed and sampled. Four cognitive measures were assessed longitudinally. Regional standardized uptake value ratios were split into infra- and suprathreshold groups at baseline using previously derived thresholds. Survival analysis, log rank testing, and Generalized Estimation Equations assessed the relationship between the application of regional sensitivity/specificity thresholds and change in cognitive measures as well as tau threshold performance in predicting cognitive decline within the new NIA-AA biological staging. Results The meta temporal region was best for predicting risk of short-term cognitive decline in suprathreshold, as compared to infrathreshold participants. When applying multiple levels of tau positivity, each subsequent level of tau identified cognitive decline at earlier timepoints. Conclusions When using 18F-flortaucipir, meta temporal suprathreshold classification was associated with increased risk of cognitive decline, suggesting that abnormal tau deposition in the cortex predicts decline. Likewise, the application of multiple levels of tau clearly predicts the distinctive cognitive trajectories in the new NIA-AA biological staging framework.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beth Snitz
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Milos D Ikonomovic
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - C Elizabeth Shaaban
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pierrick Bourgeat
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Antoine Leuzy
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, AZ, USA
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Oscar L Lopez
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
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10
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Tavares-Júnior JWL, Ciurleo GCV, Feitosa EDAAF, Oriá RB, Braga-Neto P. The Clinical Aspects of COVID and Alzheimer's Disease: A Round-Up of Where Things Stand and Are Headed. J Alzheimers Dis 2024; 99:1159-1171. [PMID: 38848177 DOI: 10.3233/jad-231368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
The link between long COVID-19 and brain/cognitive impairments is concerning and may foster a worrisome worldwide emergence of novel cases of neurodegenerative diseases with aging. This review aims to update the knowledge, crosstalk, and possible intersections between the Post-COVID Syndrome (PCS) and Alzheimer's disease (AD). References included in this review were obtained from PubMed searches conducted between October 2023 and November 2023. PCS is a very heterogenous and poorly understood disease with recent evidence of a possible association with chronic diseases such as AD. However, more scientific data is required to establish the link between PCS and AD.
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Affiliation(s)
| | - Gabriella Cunha Vieira Ciurleo
- Department of Clinical Medicine, Neurology Section, Faculty of Medicine, Federal University of Ceará (UFC), Fortaleza, CE, Brazil
- Department of Morphology and Institute of Biomedicine, Laboratory of the Biology of Tissue Healing, Ontogeny and Nutrition, School of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil
| | | | - Reinaldo B Oriá
- Department of Clinical Medicine, Neurology Section, Faculty of Medicine, Federal University of Ceará (UFC), Fortaleza, CE, Brazil
- Department of Morphology and Institute of Biomedicine, Laboratory of the Biology of Tissue Healing, Ontogeny and Nutrition, School of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Pedro Braga-Neto
- Department of Clinical Medicine, Neurology Section, Faculty of Medicine, Federal University of Ceará (UFC), Fortaleza, CE, Brazil
- Center of Health Sciences, State University of Ceará, Fortaleza, CE, Brazil
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11
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Zhuang X, Cordes D, Bender AR, Nandy R, Oh EC, Kinney J, Caldwell JZ, Cummings J, Miller J. Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements. J Alzheimers Dis 2024; 100:843-862. [PMID: 38943387 PMCID: PMC11307063 DOI: 10.3233/jad-231321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/01/2024]
Abstract
Background Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA
- Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, NV, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- University of Colorado Boulder, Boulder, CO, USA
| | - Andrew R. Bender
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Rajesh Nandy
- Department of Biostatistics and Epidemiology, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Edwin C. Oh
- Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA
- Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, NV, USA
- Department of Internal Medicine, School of Medicine, University of Nevada, Las Vegas, NV, USA
| | - Jefferson Kinney
- Interdisciplinary Neuroscience PhD Program, University of Nevada, Las Vegas, NV, USA
- Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, NV, USA
| | | | - Jeffrey Cummings
- Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, NV, USA
| | - Justin Miller
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
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12
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Strobel J, Müller HP, Ludolph AC, Beer AJ, Sollmann N, Kassubek J. New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review. Cells 2023; 12:2776. [PMID: 38132096 PMCID: PMC10742083 DOI: 10.3390/cells12242776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.
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Affiliation(s)
- Joachim Strobel
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Hans-Peter Müller
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
| | - Albert C. Ludolph
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
| | - Ambros J. Beer
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany;
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan Kassubek
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
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13
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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14
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Wang YTT, Rosa-Neto P, Gauthier S. Advanced brain imaging for the diagnosis of Alzheimer disease. Curr Opin Neurol 2023; 36:481-490. [PMID: 37639461 DOI: 10.1097/wco.0000000000001198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
PURPOSE OF REVIEW The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). RECENT FINDINGS Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. SUMMARY Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.
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15
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Lepinay E, Cicchetti F. Tau: a biomarker of Huntington's disease. Mol Psychiatry 2023; 28:4070-4083. [PMID: 37749233 DOI: 10.1038/s41380-023-02230-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 07/31/2023] [Accepted: 08/11/2023] [Indexed: 09/27/2023]
Abstract
Developing effective treatments for patients with Huntington's disease (HD)-a neurodegenerative disorder characterized by severe cognitive, motor and psychiatric impairments-is proving extremely challenging. While the monogenic nature of this condition enables to identify individuals at risk, robust biomarkers would still be extremely valuable to help diagnose disease onset and progression, and especially to confirm treatment efficacy. If measurements of cerebrospinal fluid neurofilament levels, for example, have demonstrated use in recent clinical trials, other proteins may prove equal, if not greater, relevance as biomarkers. In fact, proteins such as tau could specifically be used to detect/predict cognitive affectations. We have herein reviewed the literature pertaining to the association between tau levels and cognitive states, zooming in on Alzheimer's disease, Parkinson's disease and traumatic brain injury in which imaging, cerebrospinal fluid, and blood samples have been interrogated or used to unveil a strong association between tau and cognition. Collectively, these areas of research have accrued compelling evidence to suggest tau-related measurements as both diagnostic and prognostic tools for clinical practice. The abundance of information retrieved in this niche of study has laid the groundwork for further understanding whether tau-related biomarkers may be applied to HD and guide future investigations to better understand and treat this disease.
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Affiliation(s)
- Eva Lepinay
- Centre de Recherche du CHU de Québec, Axe Neurosciences, Québec, QC, Canada
- Département de Psychiatrie & Neurosciences, Université Laval, Québec, QC, Canada
| | - Francesca Cicchetti
- Centre de Recherche du CHU de Québec, Axe Neurosciences, Québec, QC, Canada.
- Département de Psychiatrie & Neurosciences, Université Laval, Québec, QC, Canada.
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16
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Xie F, Ni R. Collection on molecular imaging in neurodegeneration. Eur J Nucl Med Mol Imaging 2023; 50:3166-3167. [PMID: 37480370 DOI: 10.1007/s00259-023-06347-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Affiliation(s)
- Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China.
| | - Ruiqing Ni
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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Quattrini G, Ferrari C, Pievani M, Geviti A, Ribaldi F, Scheffler M, Frisoni GB, Garibotto V, Marizzoni M. Unsupervised [ 18F]Flortaucipir cutoffs for tau positivity and staging in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2023; 50:3265-3275. [PMID: 37272955 PMCID: PMC10542510 DOI: 10.1007/s00259-023-06280-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
PURPOSE Several [18F]Flortaucipir cutoffs have been proposed for tau PET positivity (T+) in Alzheimer's disease (AD), but none were data-driven. The aim of this study was to establish and validate unsupervised T+ cutoffs by applying Gaussian mixture models (GMM). METHODS Amyloid negative (A-) cognitively normal (CN) and amyloid positive (A+) AD-related dementia (ADRD) subjects from ADNI (n=269) were included. ADNI (n=475) and Geneva Memory Clinic (GMC) cohorts (n=98) were used for validation. GMM-based cutoffs were extracted for the temporal meta-ROI, and validated against previously published cutoffs and visual rating. RESULTS GMM-based cutoffs classified less subjects as T+, mainly in the A- CN (<3.4% vs >28.5%) and A+ CN (<14.5% vs >42.9%) groups and showed higher agreement with visual rating (ICC=0.91 vs ICC<0.62) than published cutoffs. CONCLUSION We provided reliable data-driven [18F]Flortaucipir cutoffs for in vivo T+ detection in AD. These cutoffs might be useful to select participants in clinical and research studies.
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Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | - Clarissa Ferrari
- FONDAZIONE POLIAMBULANZA ISTITUTO OSPEDALIERO via Bissolati, 57, 25124, Brescia, Italy
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Andrea Geviti
- Unit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy
| | - Federica Ribaldi
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1205, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre, Faculty of Medicine, University of Geneva, 1205, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, 1205, Geneva, Switzerland
- Centre for Biomedical Imaging (CIBM), 1205, Geneva, Switzerland
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
- Biological Psychiatric Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.
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18
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Wagatsuma K, Miwa K, Akamatsu G, Yamao T, Kamitaka Y, Sakurai M, Fujita N, Hanaoka K, Matsuda H, Ishii K. Toward standardization of tau PET imaging corresponding to various tau PET tracers: a multicenter phantom study. Ann Nucl Med 2023; 37:494-503. [PMID: 37243882 DOI: 10.1007/s12149-023-01847-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Tau positron emission tomography (PET) imaging is a recently developed non-invasive tool that can detect the density and extension of tau neurofibrillary tangles. Tau PET tracers have been validated to harmonize and accelerate their development and implementation in clinical practice. Whereas standard protocols including injected dose, uptake time, and duration have been determined for tau PET tracers, reconstruction parameters have not been standardized. The present study conducted phantom experiments based on tau pathology to standardize quantitative tau PET imaging parameters and optimize reconstruction conditions of PET scanners at four Japanese sites according to the results of phantom experiments. METHODS The activity of 4.0 and 2.0 kBq/mL for Hoffman 3D brain and cylindrical phantoms, respectively, was estimated from published studies of brain activity using [18F]flortaucipir, [18F]THK5351, and [18F]MK6240. We developed an original tau-specific volume of interest template for the brain based on pathophysiological tau distribution in the brain defined as Braak stages. We acquired brain and cylindrical phantom images using four PET scanners. Iteration numbers were determined as contrast and recover coefficients (RCs) in gray (GM) and white (WM) matter, and the magnitude of the Gaussian filter was determined from image noise. RESULTS Contrast and RC converged at ≥ 4 iterations, the error rates of RC for GM and WM were < 15% and 1%, respectively, and noise was < 10% in Gaussian filters of 2-4 mm in images acquired using the four scanners. Optimizing the reconstruction conditions for phantom tau PET images acquired by each scanner improved contrast and image noise. CONCLUSIONS The phantom activity was comprehensive for first- and second-generation tau PET tracers. The mid-range activity that we determined could be applied to later tau PET tracers. We propose an analytical tau-specific VOI template based on tau pathophysiological changes in patients with AD to standardize tau PET imaging. Phantom images reconstructed under the optimized conditions for tau PET imaging achieved excellent image quality and quantitative accuracy.
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Affiliation(s)
- Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-Ku, Sagamihara, Kanagawa, 252-0373, Japan.
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan.
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima City, Fukushima, 960-1295, Japan
| | - Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-Ku, Chiba, 263-8555, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima City, Fukushima, 960-1295, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan
| | - Minoru Sakurai
- Clinical Imaging Center for Healthcare, Nippon Medical School, 1-12-15, Sendagi, Bunkyo-Ku, Tokyo, 113-0022, Japan
| | - Naotoshi Fujita
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikarigaoka, Fukushima City, Fukushima, 960-1295, Japan
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-115, Yatsuyamada, Koriyama, 963-8052, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-Cho, Itabashi-Ku, Tokyo, 173-0015, Japan
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19
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Matsuda H, Yamao T. Tau positron emission tomography in patients with cognitive impairment and suspected Alzheimer's disease. Fukushima J Med Sci 2023; 69:85-93. [PMID: 37302841 PMCID: PMC10480511 DOI: 10.5387/fms.2023-08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Alzheimer's disease (AD) is diagnosed by the presence of both amyloid β and tau proteins. Recent advances in molecular PET imaging have made it possible to assess the accumulation of these proteins in the living brain. PET ligands have been developed that bind to 3R/4R tau in AD, but not to 3R tau or 4R tau alone. Of the first-generation PET ligands, 18F-flortaucipir has recently been approved by the Food and Drug Administration. Several second-generation PET probes with less off-target binding have been developed and are being applied clinically. Visual interpretation of tau PET should be based on neuropathological neurofibrillary tangle staging instead of a simple positive or negative classification. Four visual read classifications have been proposed: "no uptake," "medial temporal lobe (MTL) only," "MTL AND," and "outside MTL." As an adjunct to visual interpretation, quantitative analysis has been proposed using MRI-based native space FreeSurfer parcellations. The standardized uptake value ratio of the target area is measured using the cerebellar gray matter as a reference region. In the near future, the Centiloid scale of tau PET is expected to be used as a harmonized value for standardizing each analytical method or PET ligand used, similar to amyloid PET.
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Affiliation(s)
- Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University
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20
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Horie K, Salvadó G, Barthélemy NR, Janelidze S, Li Y, He Y, Saef B, Chen CD, Jiang H, Strandberg O, Pichet Binette A, Palmqvist S, Sato C, Sachdev P, Koyama A, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Mattsson-Carlgren N, Stomrud E, Ossenkoppele R, Schindler SE, Hansson O, Bateman RJ. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer's disease. Nat Med 2023; 29:1954-1963. [PMID: 37443334 PMCID: PMC10427417 DOI: 10.1038/s41591-023-02443-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Aggregated insoluble tau is one of two defining features of Alzheimer's disease. Because clinical symptoms are strongly correlated with tau aggregates, drug development and clinical diagnosis need cost-effective and accessible specific fluid biomarkers of tau aggregates; however, recent studies suggest that the fluid biomarkers currently available cannot specifically track tau aggregates. We show that the microtubule-binding region (MTBR) of tau containing the residue 243 (MTBR-tau243) is a new cerebrospinal fluid (CSF) biomarker specific for insoluble tau aggregates and compared it to multiple other phosphorylated tau measures (p-tau181, p-tau205, p-tau217 and p-tau231) in two independent cohorts (BioFINDER-2, n = 448; and Knight Alzheimer Disease Research Center, n = 219). MTBR-tau243 was most strongly associated with tau-positron emission tomography (PET) and cognition, whereas showing the lowest association with amyloid-PET. In combination with p-tau205, MTBR-tau243 explained most of the total variance in tau-PET burden (0.58 ≤ R2 ≤ 0.75) and the performance in predicting cognitive measures (0.34 ≤ R2 ≤ 0.48) approached that of tau-PET (0.44 ≤ R2 ≤ 0.52). MTBR-tau243 levels longitudinally increased with insoluble tau aggregates, unlike CSF p-tau species. CSF MTBR-tau243 is a specific biomarker of tau aggregate pathology, which may be utilized in interventional trials and in the diagnosis of patients. Based on these findings, we propose to revise the A/T/(N) criteria to include MTBR-tau243 as representing insoluble tau aggregates ('T').
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Grants
- P30 AG066444 NIA NIH HHS
- R01 AG070941 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P01 AG026276 NIA NIH HHS
- P30 NS048056 NINDS NIH HHS
- S10 OD025214 NIH HHS
- The Tracy Family SILQ Center established by the Tracy Family, Richard Frimel and Gary Werths, GHR Foundation, David Payne, and the Willman Family brought together by The Foundation for Barnes-Jewish Hospital.
- Eisai industry grant
- The European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie action grant agreement No 101061836, from Greta och Johan Kocks research grants and, travel grants from the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- The Swedish Research Council (2016-00906), the Knut and Alice Wallenberg foundation (2017-0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Cure Alzheimer’s fund, the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020-0314) and the Swedish federal government under the ALF agreement (2018-Projekt0279)
- The Knight ADRC developmental project
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Affiliation(s)
- Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai Inc., Nutley, NJ, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Nicolas R Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hong Jiang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Chihiro Sato
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| | - Randall J Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.
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21
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Villemagne VL, Leuzy A, Bohorquez SS, Bullich S, Shimada H, Rowe CC, Bourgeat P, Lopresti B, Huang K, Krishnadas N, Fripp J, Takado Y, Gogola A, Minhas D, Weimer R, Higuchi M, Stephens A, Hansson O, Doré V. CenTauR: Toward a universal scale and masks for standardizing tau imaging studies. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12454. [PMID: 37424964 PMCID: PMC10326476 DOI: 10.1002/dad2.12454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION Recently, an increasing number of tau tracers have become available. There is a need to standardize quantitative tau measures across tracers, supporting a universal scale. We developed several cortical tau masks and applied them to generate a tau imaging universal scale. METHOD One thousand forty-five participants underwent tau scans with either 18F-flortaucipir, 18F-MK6240, 18F-PI2620, 18F-PM-PBB3, 18F-GTP1, or 18F-RO948. The universal mask was generated from cognitively unimpaired amyloid beta (Aβ)- subjects and Alzheimer's disease (AD) patients with Aβ+. Four additional regional cortical masks were defined within the constraints of the universal mask. A universal scale, the CenTauRz, was constructed. RESULTS None of the regions known to display off-target signal were included in the masks. The CenTauRz allows robust discrimination between low and high levels of tau deposits. DISCUSSION We constructed several tau-specific cortical masks for the AD continuum and a universal standard scale designed to capture the location and degree of abnormality that can be applied across tracers and across centers. The masks are freely available at https://www.gaain.org/centaur-project.
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Affiliation(s)
- Victor L. Villemagne
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
| | - Antoine Leuzy
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | | | | | - Hitoshi Shimada
- Department of Functional Brain ImagingNational Institutes for Quantum and Radiological Science and TechnologyChibaJapan
- Brain Research InstituteNiigata UniversityNiigataJapan
| | - Christopher C. Rowe
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- Florey Department of Neurosciences & Mental HealthThe University of MelbourneMelbourneParkvilleAustralia
- The Australian Dementia Network (ADNeT)MelbourneVictoriaAustralia
| | - Pierrick Bourgeat
- Health and Biosecurity FlagshipThe Australian eHealth Research CentreCSIROBrisbaneQueenslandAustralia
| | - Brian Lopresti
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Kun Huang
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
| | - Natasha Krishnadas
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- Florey Institute of Neurosciences & Mental HealthParkvilleVictoriaAustralia
| | - Jurgen Fripp
- Health and Biosecurity FlagshipThe Australian eHealth Research CentreCSIROBrisbaneQueenslandAustralia
| | - Yuhei Takado
- Department of Functional Brain ImagingNational Institutes for Quantum and Radiological Science and TechnologyChibaJapan
| | - Alexandra Gogola
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Davneet Minhas
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Makoto Higuchi
- Department of Functional Brain ImagingNational Institutes for Quantum and Radiological Science and TechnologyChibaJapan
| | | | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalMalmöSweden
| | - Vincent Doré
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- Health and Biosecurity FlagshipThe Australian eHealth Research CentreCSIROHeidelbergVictoriaAustralia
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22
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Park SW, Yeo NY, Kim Y, Byeon G, Jang JW. Deep learning application for the classification of Alzheimer's disease using 18F-flortaucipir (AV-1451) tau positron emission tomography. Sci Rep 2023; 13:8096. [PMID: 37208383 PMCID: PMC10198973 DOI: 10.1038/s41598-023-35389-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/17/2023] [Indexed: 05/21/2023] Open
Abstract
The positron emission tomography (PET) with 18F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of 18F-flortaucipir-PET images and multimodal data integration in the differentiation of CU from MCI or AD through DL. We used cross-sectional data (18F-flortaucipir-PET images, demographic and neuropsychological score) from the ADNI. All data for subjects (138 CU, 75 MCI, 63 AD) were acquired at baseline. The 2D convolutional neural network (CNN)-long short-term memory (LSTM) and 3D CNN were conducted. Multimodal learning was conducted by adding the clinical data with imaging data. Transfer learning was performed for classification between CU and MCI. The AUC for AD classification from CU was 0.964 and 0.947 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.947, and 0.976 in multimodal learning. The AUC for MCI classification from CU had 0.840 and 0.923 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.845, and 0.850 in multimodal learning. The 18F-flortaucipir PET is effective for the classification of AD stage. Furthermore, the effect of combination images with clinical data increased the performance of AD classification.
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Affiliation(s)
- Sang Won Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea
| | - Na Young Yeo
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gihwan Byeon
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, Republic of Korea.
- Department of Medical Informatics, Kangwon National University, Chuncheon, Republic of Korea.
- Department of Big Data Medical Convergence, Kangwon National University, Chuncheon, Republic of Korea.
- School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.
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23
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Miyamoto M, Okuyama C, Kagawa S, Kusano K, Takahashi M, Takahata K, Jang MK, Yamauchi H. Radiation dosimetry and pharmacokinetics of the tau PET tracer florzolotau (18F) in healthy Japanese subjects. Ann Nucl Med 2023; 37:300-309. [PMID: 36890399 PMCID: PMC10129982 DOI: 10.1007/s12149-023-01828-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/14/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVE Abnormal aggregation of tau in the brain is a major contributing factor in various neurodegenerative diseases. Florzolotau (18F) (florzolotau, APN-1607, PM-PBB3) has been shown to be a probe for tau fibrils in an animal model and patients with Alzheimer's disease and those with non-Alzheimer's disease tauopathies. The objective of this study is to evaluate the safety, pharmacokinetics, and radiation dose following a single intravenous administration of florzolotau in healthy Japanese subjects. METHODS Three healthy male Japanese subjects aged between 20 and 64 were enrolled in this study. Subjects were determined to be eligible based on the screening assessments at the study site. Subjects received a single intravenous dose of 195.0 ± 0.5 MBq of florzolotau and underwent the whole-body PET scan 10 times in total to calculate absorbed doses to major organs/tissues and effective dose. Radioactivities in whole blood and urine were also measured for pharmacokinetic evaluation. Absorbed doses to major organs/tissues and effective dose were estimated using the medical internal radiation dose (MIRD) method. Vital signs, electrocardiography (ECG), and blood tests were done for safety evaluation. RESULTS The intravenous injection of florzolotau was well tolerated. There were no adverse events or clinically detectable pharmacologic effects related to the tracer in any subjects. No significant changes in vital signs and ECG were observed. The highest mean initial uptake at 15 min after injection was in the liver (29.0 ± 4.0%ID), intestine (4.69 ± 1.65%ID), and brain (2.13 ± 0.18%ID). The highest absorbed dose was 508 μGy/MBq of the gallbladder wall, followed by the liver of 79.4 μGy/MBq, the pancreas of 42.5 μGy/MBq, and the upper large intestine of 34.2 μGy/MBq. The effective dose was calculated as 19.7 μSv/MBq according to the tissue weighting factor reported by ICRP-103. CONCLUSION Florzolotau intravenous injection was well tolerated in healthy male Japanese subjects. The effective dose was determined as 3.61 mSv when 185 MBq florzolotau was given.
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Affiliation(s)
| | - Chio Okuyama
- Shiga Medical Center Research Institute, Moriyama, Japan
| | - Shinya Kagawa
- Shiga Medical Center Research Institute, Moriyama, Japan
| | | | | | - Keisuke Takahata
- Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, Chiba, Japan
| | | | - Hiroshi Yamauchi
- Shiga Medical Center Research Institute, Moriyama, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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24
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Park SW, Yeo NY, Lee J, Lee SH, Byun J, Park DY, Yum S, Kim JK, Byeon G, Kim Y, Jang JW. Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography. Biomed Eng Online 2023; 22:40. [PMID: 37120537 PMCID: PMC10149022 DOI: 10.1186/s12938-023-01107-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/25/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. RESULTS Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. CONCLUSIONS The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.
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Affiliation(s)
- Sang Won Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Informatics, Kangwon National University, Chuncheon, Korea
- School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Na Young Yeo
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Jinsu Lee
- Department of Data Science Research Center, Seoul National University Hospital, Seoul, Korea
| | - Suk-Hee Lee
- Department of Statistics, Kangwon National University, Chuncheon, Korea
| | - Junghyun Byun
- Department of Healthcare, Radiation Health Institute, Hydro & Nuclear Co., Ltd., Seongnam, Korea
| | - Dong Young Park
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Sujin Yum
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Jung-Kyeom Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
| | - Gihwan Byeon
- School of Medicine, Kangwon National University, Chuncheon, Korea
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea
- School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon, Gangwon, 24289, Republic of Korea.
- Department of Medical Informatics, Kangwon National University, Chuncheon, Korea.
- School of Medicine, Kangwon National University, Chuncheon, Korea.
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea.
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25
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Barthélemy NR, Saef B, Li Y, Gordon BA, He Y, Horie K, Stomrud E, Salvadó G, Janelidze S, Sato C, Ovod V, Henson RL, Fagan AM, Benzinger TLS, Xiong C, Morris JC, Hansson O, Bateman RJ, Schindler SE. CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer's disease. NATURE AGING 2023; 3:391-401. [PMID: 37117788 PMCID: PMC10154225 DOI: 10.1038/s43587-023-00380-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/03/2023] [Indexed: 04/30/2023]
Abstract
Cerebrospinal fluid (CSF) amyloid-β peptide (Aβ)42/Aβ40 and the concentration of tau phosphorylated at site 181 (p-tau181) are well-established biomarkers of Alzheimer's disease (AD). The present study used mass spectrometry to measure concentrations of nine phosphorylated and five nonphosphorylated tau species and phosphorylation occupancies (percentage phosphorylated/nonphosphorylated) at ten sites. In the present study we show that, in 750 individuals with a median age of 71.2 years, CSF pT217/T217 predicted the presence of brain amyloid by positron emission tomography (PET) slightly better than Aβ42/Aβ40 (P = 0.02). Furthermore, for individuals with positive brain amyloid by PET (n = 263), CSF pT217/T217 was more strongly correlated with the amount of amyloid (Spearman's ρ = 0.69) than Aβ42/Aβ40 (ρ = -0.42, P < 0.0001). In two independent cohorts of participants with symptoms of AD dementia (n = 55 and n = 90), CSF pT217/T217 and pT205/T205 were better correlated with tau PET measures than CSF p-tau181 concentration. These findings suggest that CSF pT217/T217 and pT205/T205 represent improved CSF biomarkers of amyloid and tau pathology in AD.
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Affiliation(s)
- Nicolas R Barthélemy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA.
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Kanta Horie
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Chihiro Sato
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
| | - Rachel L Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center for Neurodegenerative Biology, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
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26
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Fu JF, Lois C, Sanchez J, Becker JA, Rubinstein ZB, Thibault E, Salvatore AN, Sari H, Farrell ME, Guehl NJ, Normandin MD, Fakhri GE, Johnson KA, Price JC. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [ 18F]MK-6240 PET uptake. J Cereb Blood Flow Metab 2023; 43:581-594. [PMID: 36420769 PMCID: PMC10063833 DOI: 10.1177/0271678x221142139] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/17/2022] [Accepted: 11/06/2022] [Indexed: 11/25/2022]
Abstract
[18F]MK-6240 meningeal/extracerebral off-target binding may impact tau quantification. We examined the kinetics and longitudinal changes of extracerebral and reference regions. [18F]MK-6240 PET was performed in 24 cognitively-normal and eight cognitively-impaired subjects, with arterial samples in 13 subjects. Follow-up scans at 6.1 ± 0.5 (n = 25) and 13.3 ± 0.9 (n = 16) months were acquired. Extracerebral and reference region (cerebellar gray matter (CerGM)-based, cerebral white matter (WM), pons) uptake were evaluated using standardized uptake values (SUV90-110), spectral analysis, and distribution volume. Longitudinal changes in SUV90-110 were examined. The impact of reference region on target region outcomes, partial volume correction (PVC) and regional erosion were evaluated. Eroded WM and pons showed lower variability, lower extracerebral contamination, and lower longitudinal changes than CerGM-based regions. CerGM-based regions resulted larger cross-sectional effect sizes for group differentiation. Extracerebral signal was high in 50% of subjects and exhibited irreversible kinetics and nonsignificant longitudinal changes over one-year but was highly variable at subject-level. PVC resulted in higher variability in reference region uptake and longitudinal changes. Our results suggest that eroded CerGM may be preferred for cross-sectional, whilst eroded WM or pons may be preferred for longitudinal [18F]MK-6240 studies. For CerGM, erosion was necessary (preferred over PVC) to address the heterogenous nature of extracerebral signal.
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Affiliation(s)
- Jessie Fanglu Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Cristina Lois
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Justin Sanchez
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - J Alex Becker
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Zoe B Rubinstein
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Emma Thibault
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew N Salvatore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hasan Sari
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | | | - Nicolas J Guehl
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Marc D Normandin
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Georges El Fakhri
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Keith A Johnson
- Harvard Medical School, Department of Radiology, Boston, MA, USA
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Julie C Price
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
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27
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Malarte ML, Gillberg PG, Kumar A, Bogdanovic N, Lemoine L, Nordberg A. Discriminative binding of tau PET tracers PI2620, MK6240 and RO948 in Alzheimer's disease, corticobasal degeneration and progressive supranuclear palsy brains. Mol Psychiatry 2023; 28:1272-1283. [PMID: 36447011 PMCID: PMC10005967 DOI: 10.1038/s41380-022-01875-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022]
Abstract
Recent mechanistic and structural studies have challenged the classical tauopathy classification approach and revealed the complexity and heterogeneity of tau pathology in Alzheimer's disease (AD) and primary tauopathies such as corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP), progressing beyond distinct tau isoforms. In this multi-tau tracer study, we focused on the new second-generation tau PET tracers PI2620, MK6240 and RO948 to investigate this tau complexity in AD, CBD, and PSP brains using post-mortem radioligand binding studies and autoradiography of large and small frozen brain sections. Saturation binding studies indicated multiple binding sites for 3H-PI2620 in AD, CBD and PSP brains with different binding affinities (Kd ranging from 0.2 to 0.7 nM) and binding site densities (following the order: BmaxAD > BmaxCBD > BmaxPSP). Competitive binding studies complemented these findings, demonstrating the presence of two binding sites [super-high affinity (SHA): IC50(1) = 8.1 pM; and high affinity (HA): IC50(2) = 4.9 nM] in AD brains. Regional binding distribution studies showed that 3H-PI2620 could discriminate between AD (n = 6) and control cases (n = 9), especially in frontal cortex and temporal cortex tissue (p < 0.001) as well as in the hippocampal region (p = 0.02). 3H-PI2620, 3H-MK6240 and 3H-RO948 displayed similar binding behaviour in AD brains (in both homogenate competitive studies and one large frozen hemispherical brain section autoradiography studies) in terms of binding affinities, number of sites and regional patterns. Our small section autoradiography studies in the frontal cortex of CBD (n = 3) and PSP brains (n = 2) showed high specificity for 3H-PI2620 but not for 3H-MK6240 or 3H-RO948. Our findings clearly demonstrate different binding properties among the second-generation tau PET tracers, which may assist in further understanding of tau heterogeneity in AD versus non-AD tauopathies and suggests potential for development of pure selective 4R tau PET tracers.
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Grants
- Stiftelsen för Strategisk Forskning (Swedish Foundation for Strategic Research)
- Stiftelsen Olle Engkvist Byggmästare
- Svenska Forskningsrådet Formas (Swedish Research Council Formas)
- Stockholms Läns Landsting (Stockholm County Council)
- Hjärnfonden (Swedish Brain Foundation)
- Stockholm County Council -Karolinska Institute regional agreement on medical training and clinical research (ALF grant),the Swedish Alzheimer Foundation, the Foundation for Old Servants, Gun and Bertil Stohne’s Foundation, the KI Foundation for Geriatric Diseases, the Swedish Dementia Foundation, the Center for Innovative Medicine (CIMED) Region Stockholm, the Michael J Fox Foundation (MJFF-019728), the Alzheimer Association USA (AARF -21-848395), and the Recherche sur Alzheimer Foundation (Paris, France).
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Affiliation(s)
- Mona-Lisa Malarte
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Per-Göran Gillberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Amit Kumar
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Nenad Bogdanovic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Laëtitia Lemoine
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden.
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28
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Heeman F, Visser D, Yaqub M, Verfaillie S, Timmers T, Pijnenburg YA, van der Flier WM, van Berckel BN, Boellaard R, Lammertsma AA, Golla SS. Precision estimates of relative and absolute cerebral blood flow in Alzheimer's disease and cognitively normal individuals. J Cereb Blood Flow Metab 2023; 43:369-378. [PMID: 36271598 PMCID: PMC9941867 DOI: 10.1177/0271678x221135270] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Alzheimer's disease is characterized by regional reductions in cerebral blood flow (CBF). Although the gold standard for measuring CBF is [15O]H2O PET, proxies of relative CBF, derived from the early distribution phase of amyloid and tau tracers, have gained attention. The present study assessed precision of [15O]H2O derived relative and absolute CBF, and compared precision of these measures with that of (relative) CBF proxies. Dynamic [15O]H2O, [18F]florbetapir and [18F]flortaucipir PET test-retest (TrT) datasets with eleven, nine and fourteen subjects, respectively, were included. Analyses were performed using an arterial input model and/or a simplified reference tissue model, depending on the data available. Relative CBF values (i.e. K1/K1' and/or R1) were obtained using cerebellar cortex as reference tissue and TrT repeatability (i.e. precision) was calculated and compared between tracers, parameters and clinical groups. Relative CBF had significantly better TrT repeatability than absolute CBF derived from [15O]H2O (r = -0.53), while best TrT repeatability was observed for [18F]florbetapir and [18F]flortaucipir R1 (r = -0.23, r = -0.33). Furthermore, only R1 showed, better TrT repeatability for cognitively normal individuals. High precision of CBF proxies could be due to a compensatory effect of the extraction fraction, although changes in extraction fraction could also bias these proxies, but not the gold standard.
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Affiliation(s)
- Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Denise Visser
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sander Verfaillie
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Medical Psychology, Amsterdam Public Health, Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, Netherlands
| | - Tessa Timmers
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Yolande Al Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart Nm van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sandeep Sv Golla
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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29
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Mohammadi Z, Alizadeh H, Marton J, Cumming P. The Sensitivity of Tau Tracers for the Discrimination of Alzheimer's Disease Patients and Healthy Controls by PET. Biomolecules 2023; 13:290. [PMID: 36830659 PMCID: PMC9953528 DOI: 10.3390/biom13020290] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/12/2023] [Accepted: 01/25/2023] [Indexed: 02/09/2023] Open
Abstract
Hyperphosphorylated tau aggregates, also known as neurofibrillary tangles, are a hallmark neuropathological feature of Alzheimer's disease (AD). Molecular imaging of tau by positron emission tomography (PET) began with the development of [18F]FDDNP, an amyloid β tracer with off-target binding to tau, which obtained regional specificity through the differing distributions of amyloid β and tau in AD brains. A concerted search for more selective and affine tau PET tracers yielded compounds belonging to at least eight structural categories; 18F-flortaucipir, known variously as [18F]-T807, AV-1451, and Tauvid®, emerged as the first tau tracer approved by the American Food and Drug Administration. The various tau tracers differ concerning their selectivity over amyloid β, off-target binding at sites such as monoamine oxidase and neuromelanin, and degree of uptake in white matter. While there have been many reviews of molecular imaging of tau in AD and other conditions, there has been no systematic comparison of the fitness of the various tracers for discriminating between AD patient and healthy control (HC) groups. In this narrative review, we endeavored to compare the binding properties of the various tau tracers in vitro and the effect size (Cohen's d) for the contrast by PET between AD patients and age-matched HC groups. The available tracers all gave good discrimination, with Cohen's d generally in the range of two-three in culprit brain regions. Overall, Cohen's d was higher for AD patient groups with more severe illness. Second-generation tracers, while superior concerning off-target binding, do not have conspicuously higher sensitivity for the discrimination of AD and HC groups. We suppose that available pharmacophores may have converged on a maximal affinity for tau fibrils, which may limit the specific signal imparted in PET studies.
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Affiliation(s)
- Zohreh Mohammadi
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5166/15731, Iran
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5166/15731, Iran
| | - Hadi Alizadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5166/15731, Iran
| | - János Marton
- ABX Advanced Biochemical Compounds Biomedizinische Forschungsreagenzien GmbH, Heinrich-Glaeser-Straße 10-14, D-01454 Radeberg, Germany
| | - Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Freiburgstraße 18, CH-3010 Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, QLD 4059, Australia
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30
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Oliveira Hauer K, Pawlik D, Leuzy A, Janelidze S, Hall S, Hansson O, Smith R. Performance of [ 18F]RO948 PET, MRI and CSF neurofilament light in the differential diagnosis of progressive supranuclear palsy. Parkinsonism Relat Disord 2023; 106:105226. [PMID: 36442367 DOI: 10.1016/j.parkreldis.2022.11.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 11/19/2022]
Abstract
INTRODUCTION The diagnosis of progressive supranuclear palsy (PSP) is often challenging since PSP may clinically resemble other neurodegenerative disorders. Recently, the tau PET tracer [18F]RO948, a potential new biomarker for PSP, was developed. The aim of this study was to determine the ability of three different biomarkers, including [18F]RO948 PET, to distinguish PSP patients from healthy controls and from patients with α-synucleinopathies. METHODS Patients with PSP (n = 23), α-synucleinopathies (n = 47) and healthy controls (n = 61) were included from the BioFINDER-2 study. [18F]RO948 standardized uptake value ratios (SUVR), magnetic resonance imaging midbrain/pons ratio, and cerebrospinal fluid neurofilament light (NfL) levels were compared between diagnostic groups individually and in combination. RESULTS [18F]RO948 PET SUVR in the globus pallidus, NfL, and midbrain/pons area ratios were all able to differentiate PSP patients from controls and from patients with α-synucleinopathies ([18F]RO948 [mean ± SD]: controls 1.24 ± 0.22; PSP 1.47 ± 0.4; PD 1.18 ± 0.2; DLB 1.25 ± 0.24, p < 0.05), (NfL pg/mL [mean ± SD]: controls 1055 ± 569; PSP 2197 ± 1010; PD 1038 ± 416; DLB 1548 ± 687, p < 0.001) and (midbrain/pons ratio [mean ± SD]: controls 0.46 ± 0.07; PSP 0.34 ± 0.09; PD 0.43 ± 0.06; DLB 0.40 ± 0.07, p < 0.01). Receiver operating characteristic (ROC) analyses indicated that combining the three biomarkers resulted in the highest area under the ROC values (0.94 [0.88-1.00]) for separating controls from PSP and (0.92 [0.85-0.99]) for separating PSP from α-synucleinopathies. CONCLUSIONS All studied biomarkers could individually separate PSP from controls and α-synucleinopathies patients at a group level. The optimal prediction models included NfL and midbrain/pons ratio for separating controls from PSP and all three biomarkers for separating PSP from α-synucleinopathies.
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Affiliation(s)
- Kevin Oliveira Hauer
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Daria Pawlik
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden; Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Antoine Leuzy
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Shorena Janelidze
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Sara Hall
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Ruben Smith
- Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden; Department of Neurology, Skåne University Hospital, Lund, Sweden.
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Ferreira D, Mohanty R, Murray ME, Nordberg A, Kantarci K, Westman E. The hippocampal sparing subtype of Alzheimer's disease assessed in neuropathology and in vivo tau positron emission tomography: a systematic review. Acta Neuropathol Commun 2022; 10:166. [PMID: 36376963 PMCID: PMC9664780 DOI: 10.1186/s40478-022-01471-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022] Open
Abstract
Neuropathology and neuroimaging studies have identified several subtypes of Alzheimer's disease (AD): hippocampal sparing AD, typical AD, and limbic predominant AD. An unresolved question is whether hippocampal sparing AD cases can present with neurofibrillary tangles (NFT) in association cortices while completely sparing the hippocampus. To address that question, we conducted a systematic review and performed original analyses on tau positron emission tomography (PET) data. We searched EMBASE, PubMed, and Web of Science databases until October 2022. We also implemented several methods for AD subtyping on tau PET to identify hippocampal sparing AD cases. Our findings show that seven out of the eight reviewed neuropathologic studies included cases at Braak stages IV or higher and therefore, could not identify hippocampal sparing cases with NFT completely sparing the hippocampus. In contrast, tau PET did identify AD participants with tracer retention in the association cortex while completely sparing the hippocampus. We conclude that tau PET can identify hippocampal sparing AD cases with NFT completely sparing the hippocampus. Based on the accumulating data, we suggest two possible pathways of tau spread: (1) a canonical pathway with early involvement of transentorhinal cortex and subsequent involvement of limbic regions and association cortices, and (2) a less common pathway that affects association cortices with limbic involvement observed at end stages of the disease or not at all.
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Affiliation(s)
- Daniel Ferreira
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden. .,Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden
| | | | - Agneta Nordberg
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Huddinge, Sweden
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Eric Westman
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden. .,Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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32
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Wang R, Gao H, Xie H, Jia Z, Chen Q. Molecular imaging biomarkers in familial frontotemporal lobar degeneration: Progress and prospects. Front Neurol 2022; 13:933217. [PMID: 36051222 PMCID: PMC9424494 DOI: 10.3389/fneur.2022.933217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/25/2022] [Indexed: 12/01/2022] Open
Abstract
Familial frontotemporal lobar degeneration (FTLD) is a pathologically heterogeneous group of neurodegenerative diseases with diverse genotypes and clinical phenotypes. Three major mutations were reported in patients with familial FTLD, namely, progranulin (GRN), microtubule-associated protein tau (MAPT), and the chromosome 9 open reading frame 72 (C9orf72) repeat expansion, which could cause neurodegenerative pathological changes years before symptom onset. Noninvasive quantitative molecular imaging with PET or single-photon emission CT (SPECT) allows for selective visualization of the molecular targets in vivo to investigate brain metabolism, perfusion, neuroinflammation, and pathophysiological changes. There was increasing evidence that several molecular imaging biomarkers tend to serve as biomarkers to reveal the early brain abnormalities in familial FTLD. Tau-PET with 18F-flortaucipir and 11C-PBB3 demonstrated the elevated tau position in patients with FTLD and also showed the ability to differentiate patterns among the different subtypes of the mutations in familial FTLD. Furthermore, dopamine transporter imaging with the 11C-DOPA and 11C-CFT in PET and the 123I-FP-CIT in SPECT revealed the loss of dopaminergic neurons in the asymptomatic and symptomatic patients of familial FTLD. In addition, PET imaging with the 11C-MP4A has demonstrated reduced acetylcholinesterase (AChE) activity in patients with FTLD, while PET with the 11C-DAA1106 and 11C-PK11195 revealed an increased level of microglial activation associated with neuroinflammation even before the onset of symptoms in familial FTLD. 18F-fluorodeoxyglucose (FDG)-PET indicated hypometabolism in FTLD with different mutations preceded the atrophy on MRI. Identifying molecular imaging biomarkers for familial FTLD is important for the in-vivo assessment of underlying pathophysiological changes with disease progression and future disease-modifying therapy. We review the recent progress of molecular imaging in familial FTLD with focused on the possible implication of these techniques and their prospects in specific mutation types.
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Affiliation(s)
- Ruihan Wang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Hui Gao
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Hongsheng Xie
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Qin Chen
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Qin Chen
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33
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Groot C, Villeneuve S, Smith R, Hansson O, Ossenkoppele R. Tau PET Imaging in Neurodegenerative Disorders. J Nucl Med 2022; 63:20S-26S. [PMID: 35649647 DOI: 10.2967/jnumed.121.263196] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/09/2022] [Indexed: 11/16/2022] Open
Abstract
The advent of PET ligands that bind tau pathology has enabled the quantification and visualization of tau pathology in aging and in Alzheimer disease (AD). There is strong evidence from neuropathologic studies that the most widely used tau PET tracers (i.e., 18F-flortaucipir, 18F-MK6240, 18F-RO948, and 18F-PI2620) bind tau aggregates formed in AD in the more advanced (i.e., ≥IV) Braak stages. However, tracer binding in most non-AD tauopathies is weaker and overlaps to a large extent with known off-target binding regions, limiting the quantification and visualization of non-AD tau pathology in vivo. Off-target binding is generally present in the substantia nigra, basal ganglia, pituitary, choroid plexus, longitudinal sinuses, meninges, or skull in a tracer-specific manner. Most cross-sectional studies use the inferior aspect of the cerebellar gray matter as a reference region, whereas for longitudinal analyses, an eroded white matter reference region is sometimes selected. No consensus has yet been reached on whether to use partial-volume correction of tau PET data. Although an increased neocortical tau PET signal is rare in cognitively unimpaired individuals, even in amyloid-β-positive cases, such a signal holds important prognostic information because preliminary data suggest that an elevated tau PET signal predicts cognitive decline over time. Also, in symptomatic stages of AD (i.e., mild cognitive impairment or AD dementia), tau PET shows great potential as a prognostic marker because an elevated baseline tau PET retention forecasts future cognitive decline and brain atrophy. For differential diagnostic use, the primary utility of tau PET is to differentiate AD dementia from other neurodegenerative diseases, as is in line with the conditions for the approval of 18F-flortaucipir by the U.S. Food and Drug Administration for clinical use. The differential diagnostic performance drops substantially at the mild-cognitive-impairment stage of AD, and there is no sufficient evidence for detection of sporadic non-AD primary tauopathies at the individual level for any of the currently available tau PET tracers. In conclusion, while the field is currently addressing outstanding methodologic issues, tau PET is gradually moving toward clinical application as a diagnostic and possibly prognostic marker in dementia expert centers and as a tool for selecting participants, assessing target engagement, and monitoring treatment effects in clinical trials.
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Affiliation(s)
- Colin Groot
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Location VUMC, Amsterdam, The Netherlands
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.,Douglas Mental Health University Institute, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada; and
| | - Ruben Smith
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden; .,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Location VUMC, Amsterdam, The Netherlands
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Klyucherev TO, Olszewski P, Shalimova AA, Chubarev VN, Tarasov VV, Attwood MM, Syvänen S, Schiöth HB. Advances in the development of new biomarkers for Alzheimer's disease. Transl Neurodegener 2022; 11:25. [PMID: 35449079 PMCID: PMC9027827 DOI: 10.1186/s40035-022-00296-z] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 03/28/2022] [Indexed: 12/16/2022] Open
Abstract
Alzheimer's disease (AD) is a complex, heterogeneous, progressive disease and is the most common type of neurodegenerative dementia. The prevalence of AD is expected to increase as the population ages, placing an additional burden on national healthcare systems. There is a large need for new diagnostic tests that can detect AD at an early stage with high specificity at relatively low cost. The development of modern analytical diagnostic tools has made it possible to determine several biomarkers of AD with high specificity, including pathogenic proteins, markers of synaptic dysfunction, and markers of inflammation in the blood. There is a considerable potential in using microRNA (miRNA) as markers of AD, and diagnostic studies based on miRNA panels suggest that AD could potentially be determined with high accuracy for individual patients. Studies of the retina with improved methods of visualization of the fundus are also showing promising results for the potential diagnosis of the disease. This review focuses on the recent developments of blood, plasma, and ocular biomarkers for the diagnosis of AD.
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Affiliation(s)
- Timofey O Klyucherev
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, Sweden.,Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Pawel Olszewski
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, Sweden
| | - Alena A Shalimova
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, Sweden.,Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vladimir N Chubarev
- Institute of Translational Medicine and Biotechnology, I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vadim V Tarasov
- Department of Pharmacology, Institute of Pharmacy, I. M. Sechenov First Moscow State Medical University, Moscow, Russia.,Institute of Translational Medicine and Biotechnology, I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Misty M Attwood
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, Sweden
| | - Stina Syvänen
- Department of Public Health and Caring Sciences, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, Uppsala, Sweden.
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35
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Zhang Y, Wu KM, Yang L, Dong Q, Yu JT. Tauopathies: new perspectives and challenges. Mol Neurodegener 2022; 17:28. [PMID: 35392986 PMCID: PMC8991707 DOI: 10.1186/s13024-022-00533-z] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tauopathies are a class of neurodegenerative disorders characterized by neuronal and/or glial tau-positive inclusions. MAIN BODY Clinically, tauopathies can present with a range of phenotypes that include cognitive/behavioral-disorders, movement disorders, language disorders and non-specific amnestic symptoms in advanced age. Pathologically, tauopathies can be classified based on the predominant tau isoforms that are present in the inclusion bodies (i.e., 3R, 4R or equal 3R:4R ratio). Imaging, cerebrospinal fluid (CSF) and blood-based tau biomarkers have the potential to be used as a routine diagnostic strategy and in the evaluation of patients with tauopathies. As tauopathies are strongly linked neuropathologically and genetically to tau protein abnormalities, there is a growing interest in pursuing of tau-directed therapeutics for the disorders. Here we synthesize emerging lessons on tauopathies from clinical, pathological, genetic, and experimental studies toward a unified concept of these disorders that may accelerate the therapeutics. CONCLUSIONS Since tauopathies are still untreatable diseases, efforts have been made to depict clinical and pathological characteristics, identify biomarkers, elucidate underlying pathogenesis to achieve early diagnosis and develop disease-modifying therapies.
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Affiliation(s)
- Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Kai-Min Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, 12th Wulumuqi Zhong Road, Shanghai, 200040 China
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36
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Disclosing tau tangles using PET imaging: a pharmacological review of the radiotracers available in 2021. Acta Neurol Belg 2022; 122:263-272. [PMID: 34713414 DOI: 10.1007/s13760-021-01797-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/06/2021] [Indexed: 11/27/2022]
Abstract
Neurological symptoms depend on the topography of the lesions in the nervous system, hence the importance of brain imaging for neurologists. Neurological treatment, however, depends on the biological nature of the lesions. The development of radiotracers specific for the proteinopathies observed in neurodegenerative disorders is, therefore, crucially important for better understanding the relationships between the pathology and the clinical symptoms, as well as the efficacy of therapeutical interventions. The tau protein is involved in several neurodegenerative disorders, that can be distinguished both biologically and clinically as the type of tau isoforms and filaments observed in brain aggregates, and the brain regions affected differ between tauopathies. Over the past few years, several tracers have been developed for imaging tauopathies with positron emission tomography. The present review aims to compare the binding properties of these tracers, with a specific focus on how these properties might be relevant for neurologists using these biomarkers to characterize the pathology of patients presenting with clinical symptoms suspect of a neurodegenerative disorder.
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37
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Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease. Brain Sci 2022; 12:brainsci12020146. [PMID: 35203910 PMCID: PMC8869882 DOI: 10.3390/brainsci12020146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.
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38
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Ni R, Nitsch RM. Recent Developments in Positron Emission Tomography Tracers for Proteinopathies Imaging in Dementia. Front Aging Neurosci 2022; 13:751897. [PMID: 35046791 PMCID: PMC8761855 DOI: 10.3389/fnagi.2021.751897] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
An early detection and intervention for dementia represent tremendous unmet clinical needs and priorities in society. A shared feature of neurodegenerative diseases causing dementia is the abnormal accumulation and spreading of pathological protein aggregates, which affect the selective vulnerable circuit in a disease-specific pattern. The advancement in positron emission tomography (PET) biomarkers has accelerated the understanding of the disease mechanism and development of therapeutics for Alzheimer's disease and Parkinson's disease. The clinical utility of amyloid-β PET and the clinical validity of tau PET as diagnostic biomarker for Alzheimer's disease continuum have been demonstrated. The inclusion of biomarkers in the diagnostic criteria has introduced a paradigm shift that facilitated the early and differential disease diagnosis and impacted on the clinical management. Application of disease-modifying therapy likely requires screening of patients with molecular evidence of pathological accumulation and monitoring of treatment effect assisted with biomarkers. There is currently still a gap in specific 4-repeat tau imaging probes for 4-repeat tauopathies and α-synuclein imaging probes for Parkinson's disease and dementia with Lewy body. In this review, we focused on recent development in molecular imaging biomarkers for assisting the early diagnosis of proteinopathies (i.e., amyloid-β, tau, and α-synuclein) in dementia and discussed future perspectives.
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Affiliation(s)
- Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Roger M. Nitsch
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
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Kim YK. Recent Updates on PET Imaging in Neurodegenerative Diseases. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:453-472. [PMID: 36238518 PMCID: PMC9514517 DOI: 10.3348/jksr.2022.0052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
양전자방출단층촬영(PET)을 이용한 단백질병리의 생체영상기술은 퇴행성 치매의 질병 기전을 이해하는데 필요한 정보를 제공할 뿐 아니라, 질병의 조기 발견과 치료법 개발에서 중요한 역할을 수행하고 있다. 베타아밀로이드와 타우 PET 영상은 인체 뇌병리에 기반한 알츠하이머병 연속체에 대한 진단 바이오마커로 확립되어 조기진단과 감별진단을 용이하게 하고, 질병 예후를 예측하고 있다. 또한, 치매치료제 개발에서 예후 및 대리 바이오마커로의 역할이 커지고 있다. 이 종설에서는 치매를 유발하는 알츠하이머병 및 기타 퇴행성 뇌질환에서 베타아밀로이드와 타우 단백질의 뇌축적을 영상화하는 PET의 최근 임상적 적용과 최근 동향을 살펴보고, 잠재적 유용성을 소개하고자 한다.
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Affiliation(s)
- Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
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40
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Zou J, Park D, Johnson A, Feng X, Pardo M, France J, Tomljanovic Z, Brickman AM, Devanand DP, Luchsinger JA, Kreisl WC, Provenzano FA. Deep learning improves utility of tau PET in the study of Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12264. [PMID: 35005197 PMCID: PMC8719427 DOI: 10.1002/dad2.12264] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification. METHODS 18F-MK6240 (n = 320) and AV-1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)-based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making. RESULTS Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. DISCUSSION CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.
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Affiliation(s)
- James Zou
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - David Park
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Aubrey Johnson
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Xinyang Feng
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Michelle Pardo
- Department of MedicineColumbia University Medical CenterNew YorkNew YorkUSA
| | - Jeanelle France
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Zeljko Tomljanovic
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Adam M. Brickman
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Devangere P. Devanand
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
- New York State Psychiatric Institute and Department of PsychiatryColumbia University Medical CenterNew YorkNew YorkUSA
| | - José A. Luchsinger
- Department of MedicineColumbia University Medical CenterNew YorkNew YorkUSA
- Department of EpidemiologyColumbia University Medical CenterNew YorkNew YorkUSA
| | - William C. Kreisl
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
| | - Frank A. Provenzano
- The Taub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkNew YorkUSA
- Department of NeurologyCollege of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
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Teng E, Manser PT, Sanabria Bohorquez S, Wildsmith KR, Pickthorn K, Baker SL, Ward M, Kerchner GA, Weimer RM. Baseline [ 18F]GTP1 tau PET imaging is associated with subsequent cognitive decline in Alzheimer's disease. Alzheimers Res Ther 2021; 13:196. [PMID: 34852837 PMCID: PMC8638526 DOI: 10.1186/s13195-021-00937-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022]
Abstract
Background The role and implementation of tau PET imaging for predicting subsequent cognitive decline in Alzheimer’s disease (AD) remains uncertain. This study was designed to evaluate the relationship between baseline [18F]GTP1 tau PET and subsequent longitudinal change across multiple cognitive measures over 18 months. Methods Our analyses incorporated data from 67 participants, including cognitively normal controls (n = 10) and β-amyloid (Aβ)-positive individuals ([18F] florbetapir Aβ PET) with prodromal (n = 26), mild (n = 16), or moderate (n = 15) AD. Baseline measurements included cortical volume (MRI), tau burden ([18F]GTP1 tau PET), and cognitive assessments [Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), 13-item version of the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)]. Cognitive assessments were repeated at 6-month intervals over an 18-month period. Associations between baseline [18F]GTP1 tau PET indices and longitudinal cognitive performance were assessed via univariate (Spearman correlations) and multivariate (linear mixed effects models) approaches. The utility of potential prognostic tau PET cut points was assessed with ROC curves. Results Univariate analyses indicated that greater baseline [18F]GTP1 tau PET signal was associated with faster rates of subsequent decline on the MMSE, CDR, and ADAS-Cog13 across regions of interest (ROIs). In multivariate analyses adjusted for baseline age, cognitive performance, cortical volume, and Aβ PET SUVR, the prognostic performance of [18F]GTP1 SUVR was most robust in the whole cortical gray ROI. When AD participants were dichotomized into low versus high tau subgroups based on baseline [18F]GTP1 PET standardized uptake value ratios (SUVR) in the temporal (cutoff = 1.325) or whole cortical gray (cutoff = 1.245) ROIs, high tau subgroups demonstrated significantly more decline on the MMSE, CDR, and ADAS-Cog13. Conclusions Our results suggest that [18F]GTP1 tau PET represents a prognostic biomarker in AD and are consistent with data from other tau PET tracers. Tau PET imaging may have utility for identifying AD patients at risk for more rapid cognitive decline and for stratification and/or enrichment of participant selection in AD clinical trials. Trial registration ClinicalTrials.gov NCT02640092. Registered on December 28, 2015 Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00937-x.
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Affiliation(s)
- Edmond Teng
- Early Clinical Development, Genentech, Inc., South San Francisco, CA, USA.
| | - Paul T Manser
- Clinical Biostatistics, Genentech, Inc., South San Francisco, CA, USA
| | | | | | - Karen Pickthorn
- Early Clinical Development, Genentech, Inc., South San Francisco, CA, USA
| | - Suzanne L Baker
- Clinical Imaging Group, Genentech, Inc., South San Francisco, CA, USA.,Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Michael Ward
- Early Clinical Development, Genentech, Inc., South San Francisco, CA, USA.,Current Address: Alector, Inc., South San Francisco, CA, USA
| | - Geoffrey A Kerchner
- Early Clinical Development, Genentech, Inc., South San Francisco, CA, USA.,Current Address: F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Robby M Weimer
- Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA
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Dartora CM, Borelli WV, Koole M, Marques da Silva AM. Cognitive Decline Assessment: A Review From Medical Imaging Perspective. Front Aging Neurosci 2021; 13:704661. [PMID: 34489675 PMCID: PMC8416532 DOI: 10.3389/fnagi.2021.704661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.
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Affiliation(s)
- Caroline Machado Dartora
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Wyllians Vendramini Borelli
- Neurology Department, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Brain Institute of Rio Grande do Sul, BraIns, Porto Alegre, Brazil
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Ana Maria Marques da Silva
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.,Brain Institute of Rio Grande do Sul, BraIns, Porto Alegre, Brazil.,Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
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