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Jagust WJ, Mattay VS, Krainak DM, Wang SJ, Weidner LD, Hofling AA, Koo H, Hsieh P, Kuo PH, Farrar G, Marzella L. Quantitative Brain Amyloid PET. J Nucl Med 2024; 65:670-678. [PMID: 38514082 PMCID: PMC11064834 DOI: 10.2967/jnumed.123.265766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
Since the development of amyloid tracers for PET imaging, there has been interest in quantifying amyloid burden in the brains of patients with Alzheimer disease. Quantitative amyloid PET imaging is poised to become a valuable approach in disease staging, theranostics, monitoring, and as an outcome measure for interventional studies. Yet, there are significant challenges and hurdles to overcome before it can be implemented into widespread clinical practice. On November 17, 2022, the U.S. Food and Drug Administration, Society of Nuclear Medicine and Molecular Imaging, and Medical Imaging and Technology Alliance cosponsored a public workshop comprising experts from academia, industry, and government agencies to discuss the role of quantitative brain amyloid PET imaging in staging, prognosis, and longitudinal assessment of Alzheimer disease. The workshop discussed a range of topics, including available radiopharmaceuticals for amyloid imaging; the methodology, metrics, and analytic validity of quantitative amyloid PET imaging; its use in disease staging, prognosis, and monitoring of progression; and challenges facing the field. This report provides a high-level summary of the presentations and the discussion.
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Affiliation(s)
| | - Venkata S Mattay
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland;
| | - Daniel M Krainak
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Sue-Jane Wang
- Division of Biometrics I, Office of Biostatistics, Office of Translational Sciences, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Lora D Weidner
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - A Alex Hofling
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Hayoung Koo
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | | | | | | | - Libero Marzella
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
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Kim CM, Diez I, Bueichekú E, Ahn S, Montal V, Sepulcre J. Spatiotemporal Correlation between Amyloid and Tau Accumulations Underlies Cognitive Changes in Aging. J Neurosci 2024; 44:e0488232023. [PMID: 38123362 PMCID: PMC10869152 DOI: 10.1523/jneurosci.0488-23.2023] [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: 03/17/2023] [Revised: 10/03/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023] Open
Abstract
It is poorly known how Aβ and tau accumulations associate at the spatiotemporal level in the in vivo human brain to impact cognitive changes in older adults prior to AD symptoms onset. In this study, we used a graph theory-based spatiotemporal analysis to characterize the cortical patterns of Aβ and tau deposits and their relationship with cognitive changes in the Harvard Aging Brain Study (HABS) cohort. We found that the temporal accumulations of interlinked Aβ and tau pathology display distinctive spatiotemporal correlations associated with early cognitive decline. Notably, we observed that baseline Aβ deposits-Thal amyloid phase Ⅱ-related to future increase of tau deposits, Braak stages Ⅰ-Ⅳ, both displaying linkage to the decline in multi-domain cognitive scores. We also found unimodal tau-to-tau and cognitive impairment associations in broad areas of Braak stages Ⅰ-Ⅳ. The unimodal Aβ-to-Aβ progressions were not associated with cognitive changes. Our results revealed a multifaceted correlation of the spatiotemporal Aβ and tau associations with cognitive decline over time, in which tau-to-tau and tau-Aβ interactions, and not Aβ independently, might be critical contributors to clinical trajectories toward AD in older adults.
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Affiliation(s)
- Chan-Mi Kim
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Ibai Diez
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown 02129, Massachusetts
| | - Elisenda Bueichekú
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Sung Ahn
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
| | - Victor Montal
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona, Barcelona 08041, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid 28029, Spain
| | - Jorge Sepulcre
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston 02114, Massachusetts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown 02129, Massachusetts
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Honhar P, Matuskey D, Carson RE, Hillmer AT. Improving SUVR quantification by correcting for radiotracer clearance in tissue. J Cereb Blood Flow Metab 2024; 44:296-309. [PMID: 37589538 PMCID: PMC10993874 DOI: 10.1177/0271678x231196804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 08/18/2023]
Abstract
Standardized Uptake Value Ratio (SUVR) is a widely reported semi-quantitative positron emission tomography (PET) outcome measure, partly because of its ease of measurement from short scan durations. However, in brain, SUVR is often a biased estimator of the gold-standard distribution volume ratio (DVR) due to non-equilibrium conditions, i.e., clearance of the radiotracer in relevant tissues. Factors that affect radiotracer metabolism and clearance such as medication or subject groups could lead to artificial differences in SUVR. This work developed a correction that reduces the bias in SUVR (estimated from a short 15-30 min PET imaging session) by accounting for the effects of tracer clearance observed during the late SUVR time window. The proposed correction takes the form of a one-step non-linear algebraic transform of SUVR that is a function of radiotracer dependent parameters such as clearance rates from the reference and target tissues, and population averaged reference region clearance rate (k 2 , ref ). An important observation was the need for accurate estimation of radiotracer clearance rate in target tissue, which was addressed with a regression based model. Simulations and human data from two different radiotracers (healthy controls for [11C]LSN3172176, healthy controls and Parkinson's disease subjects for [18F]FE-PE2I) were used to validate the correction and evaluate its benefits and limitations. SUVR correction in human data significantly reduced mean SUVR bias across brain regions and subjects (from ∼25% for SUVR to <10% for corrected SUVR). This correction also significantly reduced the variability of this bias across brain regions for both tracers (approximately 50% for [11C]LSN3172176, 20% for [18F]FE-PE2I). Future work should investigate the benefits of using corrected SUVR in other populations and with different tracers.
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Affiliation(s)
- Praveen Honhar
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - David Matuskey
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Ansel T Hillmer
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Visser M, O'Brien JT, Mak E. In vivo imaging of synaptic density in neurodegenerative disorders with positron emission tomography: A systematic review. Ageing Res Rev 2024; 94:102197. [PMID: 38266660 DOI: 10.1016/j.arr.2024.102197] [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: 11/17/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2 A (SV2A) enables quantification of synaptic density in the living human brain. Assessing the regional distribution and severity of synaptic density loss will contribute to our understanding of the pathological processes that precede atrophy in neurodegeneration. In this systematic review, we provide a discussion of in vivo SV2A PET imaging research for quantitative assessment of synaptic density in various dementia conditions: amnestic Mild Cognitive Impairment and Alzheimer's disease, Frontotemporal dementia, Progressive supranuclear palsy and Corticobasal degeneration, Parkinson's disease and Dementia with Lewy bodies, Huntington's disease, and Spinocerebellar Ataxia. We discuss the main findings concerning group differences and clinical-cognitive correlations, and explore relations between SV2A PET and other markers of pathology. Additionally, we touch upon synaptic density in healthy ageing and outcomes of radiotracer validation studies. Studies were identified on PubMed and Embase between 2018 and 2023; last searched on the 3rd of July 2023. A total of 36 studies were included, comprising 5 on normal ageing, 21 clinical studies, and 10 validation studies. Extracted study characteristics were participant details, methodological aspects, and critical findings. In summary, the small but growing literature on in vivo SV2A PET has revealed different spatial patterns of synaptic density loss among various neurodegenerative disorders that correlate with cognitive functioning, supporting the potential role of SV2A PET imaging for differential diagnosis. SV2A PET imaging shows tremendous capability to provide novel insights into the aetiology of neurodegenerative disorders and great promise as a biomarker for synaptic density reduction. Novel directions for future synaptic density research are proposed, including (a) longitudinal imaging in larger patient cohorts of preclinical dementias, (b) multi-modal mapping of synaptic density loss onto other pathological processes, and (c) monitoring therapeutic responses and assessing drug efficacy in clinical trials.
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Affiliation(s)
- Malouke Visser
- Department of Psychiatry, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, United Kingdom; Neuropsychology and Rehabilitation Psychology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - John T O'Brien
- Department of Psychiatry, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, United Kingdom.
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Kanel P, Carli G, Vangel R, Roytman S, Bohnen NI. Challenges and innovations in brain PET analysis of neurodegenerative disorders: a mini-review on partial volume effects, small brain region studies, and reference region selection. Front Neurosci 2023; 17:1293847. [PMID: 38099203 PMCID: PMC10720329 DOI: 10.3389/fnins.2023.1293847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/13/2023] [Indexed: 12/17/2023] Open
Abstract
Positron Emission Tomography (PET) brain imaging is increasingly utilized in clinical and research settings due to its unique ability to study biological processes and subtle changes in living subjects. However, PET imaging is not without its limitations. Currently, bias introduced by partial volume effect (PVE) and poor signal-to-noise ratios of some radiotracers can hamper accurate quantification. Technological advancements like ultra-high-resolution scanners and improvements in radiochemistry are on the horizon to address these challenges. This will enable the study of smaller brain regions and may require more sophisticated methods (e.g., data-driven approaches like unsupervised clustering) for reference region selection and to improve quantification accuracy. This review delves into some of these critical aspects of PET molecular imaging and offers suggested strategies for improvement. This will be illustrated by showing examples for dopaminergic and cholinergic nerve terminal ligands.
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Affiliation(s)
- Prabesh Kanel
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson’s Disease Research, University of Michigan, Ann Arbor, MI, United States
- Parkinson’s Foundation Research Center of Excellence, University of Michigan, Ann Arbor, MI, United States
| | - Giulia Carli
- Morris K. Udall Center of Excellence for Parkinson’s Disease Research, University of Michigan, Ann Arbor, MI, United States
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Robert Vangel
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Stiven Roytman
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Nicolaas I. Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Morris K. Udall Center of Excellence for Parkinson’s Disease Research, University of Michigan, Ann Arbor, MI, United States
- Parkinson’s Foundation Research Center of Excellence, University of Michigan, Ann Arbor, MI, United States
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
- Neurology Service and GRECC, Veterans Administration Ann Arbor Healthcare System, Ann Arbor, MI, United States
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Bartos LM, Kirchleitner SV, Blobner J, Wind K, Kunze LH, Holzgreve A, Gold L, Zatcepin A, Kolabas ZI, Ulukaya S, Weidner L, Quach S, Messerer D, Bartenstein P, Tonn JC, Riemenschneider MJ, Ziegler S, von Baumgarten L, Albert NL, Brendel M. 18 kDa translocator protein positron emission tomography facilitates early and robust tumor detection in the immunocompetent SB28 glioblastoma mouse model. Front Med (Lausanne) 2022; 9:992993. [PMID: 36325388 PMCID: PMC9621314 DOI: 10.3389/fmed.2022.992993] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/02/2022] [Indexed: 10/29/2023] Open
Abstract
INTRODUCTION The 18 kDa translocator protein (TSPO) receives growing interest as a biomarker in glioblastoma. Mouse models can serve as an important tool for the investigation of biomarkers in glioblastoma, but several glioblastoma models indicated only low TSPO-PET signals in contrast to high TSPO-PET signals of human glioblastoma. Thus, we aimed to investigate TSPO-PET imaging in the syngeneic immunocompetent SB28 mouse model, which is thought to closely represent the tumor microenvironment (TME) of human glioblastoma. METHODS Dynamic TSPO-PET/CT imaging was performed for 60 min after injection of 13.6 ± 4.2 MBq [18F]GE-180. Contrast enhanced CT (ceCT) was acquired prior to PET and served for assessment of tumor volumes and attenuation correction. SB28 and sham mice were imaged at an early (week-1; n = 6 SB28, n = 6 sham) and a late time-point (week-3; n = 8 SB28, n = 9 sham) after inoculation. Standard of truth ex vivo tumor volumes were obtained for SB28 mice at the late time-point. Tracer kinetics were analyzed for the lesion site and the carotid arteries to establish an image derived input function (IDIF). TSPO-PET and ceCT lesion volumes were compared with ex vivo volumes by calculation of root-mean-square-errors (RMSE). Volumes of distribution (VTmax/mean) in the lesion were calculated using carotid IDIF and standardized uptake values (SUVmax/mean) were obtained for a 40-60 min time frame. RESULTS Higher uptake rate constants (K1) were observed for week-1 SB28 tumor lesions when compared to week-3 SB28 tumor lesions. Highest agreement between TSPO-PET lesion volumes and ex vivo tumor volumes was achieved with a 50% maximum threshold (RMSE-VT: 39.7%; RMSE-SUV: 34.4%), similar to the agreement of ceCT tumor volumes (RMSE: 30.1%). Lesions of SB28 mice had higher PET signal when compared to sham mice at week-1 (VTmax 6.6 ± 2.9 vs. 3.9 ± 0.8, p = 0.035; SUVmax 2.3 ± 0.5 vs. 1.2 ± 0.1, p < 0.001) and PET signals remained at a similar level at week-3 (VTmax 5.0 ± 1.6 vs. 2.7 ± 0.8, p = 0.029; SUVmax 1.9 ± 0.5 vs. 1.2 ± 0.2, p = 0.0012). VTmax correlated with SUVmax (R 2 = 0.532, p < 0.001). CONCLUSION TSPO-PET imaging of immunocompetent SB28 mice facilitates early detection of tumor signals over sham lesions. SB28 tumors mirror high TSPO-PET signals of human glioblastoma and could serve as a valuable translational model to study TSPO as an imaging biomarker.
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Affiliation(s)
- Laura M. Bartos
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | | | - Jens Blobner
- Department of Neurosurgery, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Karin Wind
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lea H. Kunze
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lukas Gold
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Artem Zatcepin
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Zeynep Ilgin Kolabas
- Helmholtz Center, Institute for Tissue Engineering and Regenerative Medicine (iTERM), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital of Munich, Ludwig- Maximilians University Munich, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Munich, Germany
| | - Selin Ulukaya
- Helmholtz Center, Institute for Tissue Engineering and Regenerative Medicine (iTERM), Munich, Germany
- Faculty of Biology, Master of Science Program in Molecular and Cellular Biology, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Lorraine Weidner
- Department of Neuropathology, Regensburg University Hospital, Regensburg, Germany
| | - Stefanie Quach
- Department of Neurosurgery, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Denise Messerer
- Department of Cardiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- SyNergy, University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Joerg C. Tonn
- Department of Neurosurgery, University Hospital of Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Louisa von Baumgarten
- Department of Neurosurgery, University Hospital of Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nathalie L. Albert
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- SyNergy, University of Munich, Munich, Germany
- DZNE – German Center for Neurodegenerative Diseases, Munich, Germany
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