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Okuyama C, Higashi T, Ishizu K, Oishi N, Kusano K, Ito M, Kagawa S, Okina T, Suzuki N, Hasegawa H, Nagahama Y, Watanabe H, Ono M, Yamauchi H. New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map. Ann Nucl Med 2024; 38:763-773. [PMID: 38907835 PMCID: PMC11339116 DOI: 10.1007/s12149-024-01956-y] [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: 04/29/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
OBJECTIVE This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses. METHODS Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects. RESULTS In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern. CONCLUSIONS Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment.
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Affiliation(s)
- Chio Okuyama
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan.
| | - Tatsuya Higashi
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan.
- Department of Molecular Imaging and Theranostics, National Institute of Quantum Science and Technology, Chiba, Japan.
| | - Koichi Ishizu
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kuninori Kusano
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Radiology, Shiga General Hospital, Moriyama, Japan
| | - Miki Ito
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Radiology, Shiga General Hospital, Moriyama, Japan
| | - Shinya Kagawa
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
| | - Tomoko Okina
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Norio Suzuki
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Hiroshi Hasegawa
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
| | - Yasuhiro Nagahama
- Department of Neurology, Shiga General Hospital, Moriyama, Japan
- Department of Psychiatry and Neurology, Kawasaki Memorial Hospital, Kawasaki, Japan
| | - Hiroyuki Watanabe
- Department of Patho-Fundamental Bioanalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Masahiro Ono
- Department of Patho-Fundamental Bioanalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Hiroshi Yamauchi
- Clinical Research Center, Shiga General Hospital, Moriyama, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Rodriguez-Vieitez E, Kumar A, Malarte ML, Ioannou K, Rocha FM, Chiotis K. Imaging Neuroinflammation: Quantification of Astrocytosis in a Multitracer PET Approach. Methods Mol Biol 2024; 2785:195-218. [PMID: 38427196 DOI: 10.1007/978-1-0716-3774-6_13] [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: 03/02/2024]
Abstract
The recent progress in the development of in vivo biomarkers is rapidly changing how neurodegenerative diseases are conceptualized and diagnosed and how clinical trials are designed today. Alzheimer's disease (AD) - the most common neurodegenerative disorder - is characterized by a complex neuropathology involving the deposition of extracellular amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFTs) of hyperphosphorylated tau proteins, accompanied by the activation of glial cells, i.e., astrocytes and microglia, and neuroinflammatory response, leading to neurodegeneration and cognitive dysfunction. An increasing diversity of positron emission tomography (PET) imaging radiotracers is available to selectively target the different pathophysiological processes of AD. Along with the success of Aβ PET and the more recent tau PET imaging, there is a great interest to develop PET tracers to image glial reactivity and neuroinflammation. While most research to date has focused on imaging microgliosis, there is an upsurge of interest in imaging reactive astrocytes in the AD continuum. There is increasing evidence that reactive astrocytes are morphologically and functionally heterogeneous, with different subtypes that express different markers and display various homeostatic or detrimental roles across disease stages. Therefore, multiple biomarkers are desirable to unravel the complex phenomenon of reactive astrocytosis. In the field of in vivo PET imaging in AD, the research concerning reactive astrocytes has predominantly focused on targeting monoamine oxidase B (MAO-B), most often using either 11C-deuterium-L-deprenyl (11C-DED) or 18F-SMBT-1 PET tracers. Additionally, imidazoline2 binding (I2BS) sites have been imaged using 11C-BU99008 PET. Recent studies in our group using 11C-DED PET imaging suggest that astrocytosis may be present from the early stages of disease development in AD. This chapter provides a detailed description of the practical approach used for the analysis of 11C-DED PET imaging data in a multitracer PET paradigm including 11C-Pittsburgh compound B (11C-PiB) and 18F-fluorodeoxyglucose (18F-FDG). The multitracer PET approach allows investigating the comparative regional and temporal patterns of in vivo brain astrocytosis, fibrillar Aβ deposition, glucose metabolism, and brain structural changes. It may also contribute to understanding the potential role of novel plasma biomarkers of reactive astrocytes, in particular the glial fibrillary acidic protein (GFAP), at different stages of disease progression. This chapter attempts to stimulate further research in the field, including the development of novel PET tracers that may allow visualizing different aspects of the complex astrocytic and microglial response in neurodegenerative diseases. Progress in the field will contribute to the incorporation of PET imaging of glial reactivity and neuroinflammation as biomarkers with clinical application and motivate further investigation on glial cells as therapeutic targets in AD and other neurodegenerative diseases.
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Affiliation(s)
- Elena Rodriguez-Vieitez
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
- Division of Neurogeriatrics, 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
| | - Mona-Lisa Malarte
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Konstantinos Ioannou
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Filipa M Rocha
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Konstantinos Chiotis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
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3
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Tada T, Hara K, Fujita N, Ito Y, Yamaguchi H, Ohdake R, Kawabata K, Ogura A, Kato T, Yokoi T, Masuda M, Abe S, Miyao S, Naganawa S, Katsuno M, Watanabe H, Sobue G, Kato K. Comparative examination of the pons and corpus callosum as reference regions for quantitative evaluation in positron emission tomography imaging for Alzheimer's disease using 11C-Pittsburgh Compound-B. Ann Nucl Med 2023:10.1007/s12149-023-01843-y. [PMID: 37160863 DOI: 10.1007/s12149-023-01843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/24/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Standardised uptake value ratio (SUVR) is usually obtained by dividing the SUV of the region of interest (ROI) by that of the cerebellar cortex. Cerebellar cortex is not a valid reference in cases where amyloid β deposition or lesions are present. Only few studies have evaluated the use of other regions as references. We compared the validity of the pons and corpus callosum as reference regions for the quantitative evaluation of brain positron emission tomography (PET) using 11C-PiB compared to the cerebellar cortex. METHODS We retrospectively evaluated data from 86 subjects with or without Alzheimer's disease (AD). All subjects underwent magnetic resonance imaging, PET imaging, and cognitive function testing. For the quantitative analysis, three-dimensional ROIs were automatically placed, and SUV and SUVR were obtained. We compared these values between AD and healthy control (HC) groups. RESULTS SUVR data obtained using the pons and corpus callosum as reference regions strongly correlated with that using the cerebellar cortex. The sensitivity and specificity were high when either the pons or corpus callosum was used as the reference region. However, the SUV values of the corpus callosum were different between AD and HC (p < 0.01). CONCLUSIONS Our data suggest that the pons and corpus callosum might be valid reference regions.
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Affiliation(s)
- Tomohiro Tada
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Naotoshi Fujita
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Yoshinori Ito
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan
| | - Hiroshi Yamaguchi
- Nagoya University Radioisotope Research Center Medical Branch, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Reiko Ohdake
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Medical University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Anjo Kosei Hospital, 28 Higashihirokute Anjo-Cho, Anjo, 446-8602, Japan
| | - Takamasa Yokoi
- Department of Neurology, Toyohashi Municipal Hospital, 50 Hachikennishi, Aotake-Cho, Toyohashi, 441-8570, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Okazaki City Hospital, 1-3 Gosyoai, Kouryuji-Cho, Okazaki, 444-8553, Japan
| | - Shinji Abe
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Shinichi Miyao
- Department of Neurology, Meitetsu Hospital, 2-26-11 Sakou, Nishiku, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Clinical Research Education, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Hirohisa Watanabe
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Gen Sobue
- Aichi Medical University, 1-1 Yazakokarimata, Nagakute, Japan
| | - Katsuhiko Kato
- Functional Medical Imaging, Biomedical Imaging Sciences, Division of Advanced Information Health Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan.
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Smith AM, Obuchowski NA, Foster NL, Klein G, Mozley PD, Lammertsma AA, Wahl RL, Sunderland JJ, Vanderheyden JL, Benzinger TLS, Kinahan PE, Wong DF, Perlman ES, Minoshima S, Matthews D. The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification. J Nucl Med 2023; 64:294-303. [PMID: 36137760 PMCID: PMC9902844 DOI: 10.2967/jnumed.122.264031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 02/04/2023] Open
Abstract
A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.
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Affiliation(s)
- Anne M Smith
- Siemens Medical Solutions USA, Inc., Knoxville, Tennessee;
| | | | - Norman L Foster
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - P David Mozley
- Weill Medical College of Cornell University, New York, New York
| | - Adriaan A Lammertsma
- Amsterdam Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - John J Sunderland
- Division of Nuclear Medicine, Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - Paul E Kinahan
- Department of Radiology, School of Medicine, University of Washington, Seattle, Washington
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; and
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Ghisays V, Lopera F, Goradia DD, Protas HD, Malek-Ahmadi MH, Chen Y, Devadas V, Luo J, Lee W, Baena A, Bocanegra Y, Guzmán-Vélez E, Pardilla-Delgado E, Vila-Castelar C, Fox-Fuller JT, Hu N, Clayton D, Thomas RG, Alvarez S, Espinosa A, Acosta-Baena N, Giraldo MM, Rios-Romenets S, Langbaum JB, Chen K, Su Y, Tariot PN, Quiroz YT, Reiman EM. PET evidence of preclinical cerebellar amyloid plaque deposition in autosomal dominant Alzheimer's disease-causing Presenilin-1 E280A mutation carriers. NEUROIMAGE-CLINICAL 2021; 31:102749. [PMID: 34252876 PMCID: PMC8278433 DOI: 10.1016/j.nicl.2021.102749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/21/2021] [Accepted: 06/26/2021] [Indexed: 11/16/2022]
Abstract
PET evidence of cerebellar Aβ deposition in unimpaired (CU) PSEN1 E280A kindred. Cerebellar Aβ PET SUVR began to distinguish CU carriers from non-carriers at age 34. Cortical and cerebellar Aβ PET SUVR are positively associated in CU carriers. Cerebellar florbetapir SUVR correlated with lower composite score in CU carriers.
Background In contrast to sporadic Alzheimer’s disease, autosomal dominant Alzheimer’s disease (ADAD) is associated with greater neuropathological evidence of cerebellar amyloid plaque (Aβ) deposition. In this study, we used positron emission tomography (PET) measurements of fibrillar Aβ burden to characterize the presence and age at onset of cerebellar Aβ deposition in cognitively unimpaired (CU) Presenilin-1 (PSEN1) E280A mutation carriers from the world’s largest extended family with ADAD. Methods 18F florbetapir and 11C Pittsburgh compound B (PiB) PET data from two independent studies – API ADAD Colombia Trial (NCT01998841) and Colombia-Boston (COLBOS) longitudinal biomarker study were included. The tracers were selected independently by the respective sponsors prior to the start of each study and used exclusively throughout. Template-based cerebellar Aβ-SUVR (standard-uptake value ratios) using a known-to-be-spared pons reference region (cerebellar SUVR_pons), to a) compare 28–56-year-old CU carriers and non-carriers; b) estimate the age at which cerebellar SUVR_pons began to differ significantly in carrier and non-carrier groups; and c) characterize in carriers associations with age, cortical SUVR_pons, delayed recall memory, and API ADAD composite score. Results Florbetapir and PiB cerebellar SUVR_pons were significantly higher in carriers than non-carriers (p < 0.0001). Cerebellar SUVR_pons began to distinguish carriers from non-carriers at age 34, 10 years before the carriers’ estimated age at mild cognitive impairment onset. Florbetapir and PiB cerebellar SUVR_pons in carriers were positively correlated with age (r = 0.44 & 0.69, p < 0.001), cortical SUVR_pons (r = 0.55 & 0.69, p < 0.001), and negatively correlated with delayed recall memory (r = −0.21 & −0.50, p < 0.05, unadjusted for cortical SUVR_pons) and API ADAD composite (r = −0.25, p < 0.01, unadjusted for cortical SUVR_pons in florbetapir API ADAD cohort). Conclusion This PET study provides evidence of cerebellar Aβ plaque deposition in CU carriers starting about a decade before the clinical onset of ADAD. Additional studies are needed to clarify the impact of using a cerebellar versus pons reference region on the power to detect and track ADAD changes, even in preclinical stages of this disorder.
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Affiliation(s)
- Valentina Ghisays
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Francisco Lopera
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Dhruman D Goradia
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Hillary D Protas
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Michael H Malek-Ahmadi
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yinghua Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Vivek Devadas
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Ji Luo
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Wendy Lee
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Ana Baena
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Yamile Bocanegra
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | | | | | - Joshua T Fox-Fuller
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Boston University, Boston, MA, USA
| | - Nan Hu
- Genentech Inc., South San Francisco, CA, USA
| | | | - Ronald G Thomas
- University of California San Diego School of Medicine, La Jolla, CA, USA
| | | | - Alejandro Espinosa
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | - Margarita M Giraldo
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | - Jessica B Langbaum
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Arizona State University, Tempe, AZ, USA; University of Arizona, Tucson, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Pierre N Tariot
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yakeel T Quiroz
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia; Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Arizona State University, Tempe, AZ, USA; University of Arizona, Tucson, AZ, USA; Translational Genomics Research Institute, Phoenix, AZ, USA.
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Heeman F, Hendriks J, Lopes Alves I, Ossenkoppele R, Tolboom N, van Berckel BNM, Lammertsma AA, Yaqub M. [ 11C]PIB amyloid quantification: effect of reference region selection. EJNMMI Res 2020; 10:123. [PMID: 33074395 PMCID: PMC7572969 DOI: 10.1186/s13550-020-00714-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 09/24/2020] [Indexed: 11/24/2022] Open
Abstract
Background The standard reference region (RR) for amyloid-beta (Aβ) PET studies is the cerebellar grey matter (GMCB), while alternative RRs have mostly been utilized without prior validation against the gold standard. This study compared five commonly used RRs to gold standard plasma input-based quantification using the GMCB. Methods Thirteen subjects from a test–retest (TRT) study and 30 from a longitudinal study were retrospectively included (total: 17 Alzheimer’s disease, 13 mild cognitive impairment, 13 controls). Dynamic [11C]PiB PET (90 min) and T1-weighted MR scans were co-registered and time–activity curves were extracted for cortical target regions and the following RRs: GMCB, whole cerebellum (WCB), white matter brainstem/pons (WMBS), whole brainstem (WBS) and eroded subcortical white matter (WMES). A two-tissue reversible plasma input model (2T4k_Vb) with GMCB as RR, reference Logan and the simplified reference tissue model were used to derive distribution volume ratios (DVRs), and standardized uptake value (SUV) ratios were calculated for 40–60 min and 60–90 min intervals. Parameter variability was evaluated using TRT scans, and correlations and agreements with the gold standard (DVR from 2T4k_Vb with GMCB RR) were also assessed. Next, longitudinal changes in SUVs (both intervals) were assessed for each RR. Finally, the ability to discriminate between visually Aβ positive and Aβ negative scans was assessed. Results All RRs yielded stable TRT performance (max 5.1% variability), with WCB consistently showing lower variability. All approaches were able to discriminate between Aβ positive and Aβ negative scans, with highest effect sizes obtained for GMCB (range − 0.9 to − 0.7), followed by WCB (range − 0.8 to − 0.6). Furthermore, all approaches provided good correlations with the gold standard (r ≥ 0.78), while the highest bias (as assessed by the regression slope) was observed using WMES (range slope 0.52–0.67), followed by WBS (range slope 0.58–0.92) and WMBS (range slope 0.62–0.91). Finally, RR SUVs were stable across a period of 2.6 years for all except WBS and WMBS RRs (60–90 min interval). Conclusions GMCB and WCB are considered the best RRs for quantifying amyloid burden using [11C]PiB PET.
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Affiliation(s)
- Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - Janine Hendriks
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Neurology and Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.,Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Nelleke Tolboom
- Imaging Division, Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
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Cho SH, Choe YS, Park S, Kim YJ, Kim HJ, Jang H, Kim SJ, Kim JP, Jung YH, Kim BC, Na DL, Moon SH, Seo SW. Appropriate reference region selection of 18F-florbetaben and 18F-flutemetamol beta-amyloid PET expressed in Centiloid. Sci Rep 2020; 10:14950. [PMID: 32917930 PMCID: PMC7486392 DOI: 10.1038/s41598-020-70978-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/27/2020] [Indexed: 12/01/2022] Open
Abstract
The Centiloid (CL) is a method for standardizing amyloid beta (Aβ) quantification through different ligands and methods. To find the most appropriate reference region to reduce the variance in the Aβ CL unit between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM), we conducted head-to-head comparisons from 56 participants using the direct comparison of FBB-FMM CL (dcCL) method with four reference regions: cerebellar gray (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons. The FBB and FMM dcCL units were highly correlated in four reference regions: WC (R2 = 0.97), WC + B (R2 = 0.98), CG (R2 = 0.92), and pons (R2 = 0.98). WC showed the largest effect size in both FBB and FMM. Comparison of the variance of the dcCL values within the young control group showed that with FBB, WC + B had the smallest variance and with FMM, the WC had the smallest variance. Additionally, WC + B showed the smallest absolute difference between FBB and FMM, followed by the WC, pons, and CG. We found that it would be reasonable to use the WC or WC + B as the reference region when converting FBB and FMM SUVRs into dcCL, which can increase the accuracy of standardizing FBB and FMM PET results.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. .,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea. .,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea.
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8
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Tsai HH, Pasi M, Tsai LK, Chen YF, Chen YW, Tang SC, Gurol ME, Yen RF, Jeng JS. Superficial Cerebellar Microbleeds and Cerebral Amyloid Angiopathy. Stroke 2020; 51:202-208. [PMID: 31726962 DOI: 10.1161/strokeaha.119.026235] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background and Purpose—
The differentiation between cerebral amyloid angiopathy (CAA) and hypertensive small vessel disease in primary intracerebral hemorrhage is mainly based on hemorrhagic neuroimaging markers in the supratentorial regions, and the cause for cerebellar microbleeds remains unknown. Our aim was to investigate whether superficial cerebellar microbleeds are more likely to be related to CAA rather than hypertensive small vessel disease.
Methods—
Two hundred seventy-five consecutive patients with intracerebral hemorrhage were retrospectively reviewed from a prospectively maintained hospital-based stroke registry. Eighty-five (33.1%) patients had cerebellar microbleeds and were categorized into superficial (gray matter, vermis), deep (white matter, deep nucleus, cerebellar peduncle), or mixed type based on the location of cerebellar hemorrhagic lesions. Amyloid imaging was obtained using 11C-Pittsburgh Compound B–positron emission tomography in a subgroup of patients. The associations between cerebellar microbleed locations and the type of small vessel disease (CAA versus hypertensive small vessel disease) based on distribution of supratentorial hemorrhagic lesions as well as other magnetic resonance imaging and positron emission tomography markers were analyzed.
Results—
The presence of cerebellar microbleed was independently associated with supratentorial microbleed and lacunar infarcts (both
P
<0.01). Strictly superficial cerebellar microbleeds were significantly related to CAA–intracerebral hemorrhage, cortical superficial siderosis and high-grade enlarged perivascular space in centrum semiovale (all
P
<0.05); deep or mixed cerebellar microbleeds were related to hypertension and deep microbleed (all
P
<0.05). In multivariable models, superficial cerebellar microbleeds were independently associated with CAA–intracerebral hemorrhage (
P
=0.03). Of 33 patients assessed by amyloid positron emission tomography, cerebral and cerebellar amyloid load (standardized uptake value ratio) was higher in patients with superficial cerebellar microbleeds compared with deep/mixed cerebellar microbleeds (cerebrum standardized uptake value ratio [reference: cerebellum] 1.33±0.24 versus 1.05±0.09,
P
<0.001; cerebellum standardized uptake value ratio [reference: pons] 0.58±0.08 versus 0.51±0.09,
P
=0.03).
Conclusions—
Patients with strictly superficial cerebellar microbleeds are associated with a clinicoradiological diagnosis of CAA as well as increased cerebral and cerebellar amyloid deposition on Pittsburgh Compound B–positron emission tomography, suggesting underlying CAA pathology.
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Affiliation(s)
- Hsin-Hsi Tsai
- From the Departments of Neurology, National Taiwan University Hospital Bei-Hu Branch, Taipei (H.-H.T.)
- Departments of Neurology (H.-H.T., L.-K.T., Y.-W.C., S.-C.T., J.-S.J.), National Taiwan University Hospital, Taipei
| | - Marco Pasi
- Univ-Lille, Inserm U1171, CHU Lille (Department of Neurology, Stroke Unit), France (M.P.)
| | - Li-Kai Tsai
- Departments of Neurology (H.-H.T., L.-K.T., Y.-W.C., S.-C.T., J.-S.J.), National Taiwan University Hospital, Taipei
| | - Ya-Fang Chen
- Department of Medical Imaging (Y.-F.C.), National Taiwan University Hospital, Taipei
| | - Yu-Wei Chen
- Departments of Neurology (H.-H.T., L.-K.T., Y.-W.C., S.-C.T., J.-S.J.), National Taiwan University Hospital, Taipei
- Department of Neurology, Landseed International Hospital, Taoyuan (Y.-W.C.)
| | - Sung-Chun Tang
- Departments of Neurology (H.-H.T., L.-K.T., Y.-W.C., S.-C.T., J.-S.J.), National Taiwan University Hospital, Taipei
| | - M. Edip Gurol
- Graduate institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei (H.-H.T.)
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston (M.E.G.)
| | - Ruoh-Fang Yen
- Department of Nuclear Medicine (R.-F.Y.), National Taiwan University Hospital, Taipei
| | - Jiann-Shing Jeng
- Departments of Neurology (H.-H.T., L.-K.T., Y.-W.C., S.-C.T., J.-S.J.), National Taiwan University Hospital, Taipei
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9
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La Joie R, Ayakta N, Seeley WW, Borys E, Boxer AL, DeCarli C, Doré V, Grinberg LT, Huang E, Hwang JH, Ikonomovic MD, Jack C, Jagust WJ, Jin LW, Klunk WE, Kofler J, Lesman-Segev OH, Lockhart SN, Lowe VJ, Masters CL, Mathis CA, McLean CL, Miller BL, Mungas D, O'Neil JP, Olichney JM, Parisi JE, Petersen RC, Rosen HJ, Rowe CC, Spina S, Vemuri P, Villemagne VL, Murray ME, Rabinovici GD. Multisite study of the relationships between antemortem [ 11C]PIB-PET Centiloid values and postmortem measures of Alzheimer's disease neuropathology. Alzheimers Dement 2019; 15:205-216. [PMID: 30347188 PMCID: PMC6368897 DOI: 10.1016/j.jalz.2018.09.001] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/08/2018] [Accepted: 09/03/2018] [Indexed: 10/28/2022]
Abstract
INTRODUCTION We sought to establish the relationships between standard postmortem measures of AD neuropathology and antemortem [11C]PIB-positron emission tomography ([11C]PIB-PET) analyzed with the Centiloid (CL) method, a standardized scale for Aβ-PET quantification. METHODS Four centers contributed 179 participants encompassing a broad range of clinical diagnoses, PET data, and autopsy findings. RESULTS CL values increased with each CERAD neuritic plaque score increment (median -3 CL for no plaques and 92 CL for frequent plaques) and nonlinearly with Thal Aβ phases (increases were detected starting at phase 2) with overlap between scores/phases. PET-pathology associations were comparable across sites and unchanged when restricting the analyses to the 56 patients who died within 2 years of PET. A threshold of 12.2 CL detected CERAD moderate-to-frequent neuritic plaques (area under the curve = 0.910, sensitivity = 89.2%, specificity = 86.4%), whereas 24.4 CL identified intermediate-to-high AD neuropathological changes (area under the curve = 0.894, sensitivity = 84.1%, specificity = 87.9%). DISCUSSION Our study demonstrated the robustness of a multisite Centiloid [11C]PIB-PET study and established a range of pathology-based CL thresholds.
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Affiliation(s)
- Renaud La Joie
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
| | - Nagehan Ayakta
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA
| | - William W Seeley
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ewa Borys
- Department of Pathology, Stritch School of Medicine, Loyola University, Maywood, IL, USA
| | - Adam L Boxer
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, CA, USA
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, Heidelberg, Victoria, Australia
| | - Lea T Grinberg
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Eric Huang
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ji-Hye Hwang
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA
| | - Lee-Way Jin
- Alzheimer's Disease Center, Department of Pathology, University of California Davis, CA, USA
| | - William E Klunk
- Department of Neurology, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA; Alzheimer's Disease Research Center, University of Pittsburgh, PA, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pennsylvania, USA
| | - Orit H Lesman-Segev
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Samuel N Lockhart
- Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA; Department of Internal Medicine, Division of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN, USA
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Catriona L McLean
- Department of Anatomical Pathology, Alfred Hospital, Melbourne, Australia
| | - Bruce L Miller
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Daniel Mungas
- Department of Neurology, University of California, Davis, CA, USA
| | - James P O'Neil
- Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA; Biomedical Isotope Facility, MBIB Division, Lawrence Berkeley National Laboratory, CA, USA
| | - John M Olichney
- Department of Neurology, University of California, Davis, CA, USA
| | - Joseph E Parisi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Howard J Rosen
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, Heidelberg, Victoria, Australia
| | - Salvatore Spina
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, Heidelberg, Victoria, Australia; The Florey Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Gil D Rabinovici
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Helen Wills Neuroscience Institute, University of California Berkeley, CA, USA
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10
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Abstract
PURPOSE Longitudinal studies into the variability of F-Flutemetamol uptake are lacking. METHODS/PATIENTS Therefore, the current study examined change in F-Flutemetamol uptake in 19 nondemented older adults (65 to 82 y old) who were either cognitively intact or had Mild Cognitive Impairment (MCI) who were scanned twice across 3.6 years. RESULTS Baseline and follow-up composite SUVRs were significantly correlated (0.96, P<0.001). Significant increases in the composite SUVR from baseline to follow-up were observed (P=0.002). For the total sample, the average difference over this time period when using the composite SUVR was 6.8%. Similar results were seen in subsets of the total sample (MCI vs. cognitively intact, amyloid positive vs. negative). Finally, a Reliable Change Index that exceeded ±0.046 SUVR units would indicate a significant change of F-Flutemetamol. CONCLUSIONS The current results extend the limited literature on longitudinal variability of F-Flutemetamol uptake across 3.6 years, which should give clinicians and researchers more confidence in the stability of this amyloid imaging agent in longer therapeutic and prevention trials in cognitive decline in MCI and Alzheimer disease.
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11
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12
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Thordardottir S, Rodriguez-Vieitez E, Almkvist O, Ferreira D, Saint-Aubert L, Kinhult-Ståhlbom A, Thonberg H, Schöll M, Westman E, Wall A, Eriksdotter M, Zetterberg H, Blennow K, Nordberg A, Graff C. Reduced penetrance of the PSEN1 H163Y autosomal dominant Alzheimer mutation: a 22-year follow-up study. ALZHEIMERS RESEARCH & THERAPY 2018; 10:45. [PMID: 29747683 PMCID: PMC5944151 DOI: 10.1186/s13195-018-0374-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 04/17/2018] [Indexed: 12/14/2022]
Abstract
Background The range of onset ages within some PSEN1 families is wide, and a few cases of reduced penetrance of PSEN1 mutations have been reported. However, published data on reduced penetrance have been limited to clinical histories, often collected retrospectively and lacking biomarker information. We present a case of reduced penetrance of the PSEN1 H163Y mutation in a carrier prospectively followed for 22 years. Methods Two brothers (A and B), both carriers of the H163Y mutation, were followed between 1995 and 2017. They underwent repeated clinical evaluations, neuropsychological assessments, and cerebrospinal fluid analyses, as well as brain imaging examinations with structural magnetic resonance, [18F]fluorodeoxyglucose positron emission tomography, and [11C]Pittsburgh compound B positron emission tomography. Results Brother A was followed between 44 and 64 years of age. Cognitive symptoms due to Alzheimer’s disease set in at the age of 54. Gradual worsening of symptoms resulted in admittance to a nursing home owing to dependence on others for all activities of daily living. He showed a curvilinear decline in cognitive function on neuropsychological tests, and changes on magnetic resonance imaging, positron emission tomography, and biomarkers in the cerebrospinal fluid supported a clinical diagnosis of Alzheimer’s disease. Brother A died at the age of 64 and fulfilled the criteria for definitive Alzheimer’s disease according to neuropathological examination results. Brother B was followed between the ages of 43 and 65 and showed no cognitive deterioration on repeated neuropsychological test occasions. In addition, no biomarker evidence of Alzheimer’s disease pathology was detected, either on imaging examinations or in cerebrospinal fluid. Conclusions The average (SD) age of symptom onset for PSEN1 H163Y is 51 ± 7 years according to previous studies. However, we present a case of a biomarker-verified reduction in penetrance in a mutation carrier who was still symptom-free at the age of 65. This suggests that other genetic, epigenetic, and/or environmental factors modify the onset age. Electronic supplementary material The online version of this article (10.1186/s13195-018-0374-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Steinunn Thordardottir
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, 141 57, Huddinge, Sweden.,Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden
| | - Elena Rodriguez-Vieitez
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, 141 57, Huddinge, Sweden
| | - Ove Almkvist
- Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden.,Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, 141 57, Huddinge, Sweden.,Department of Psychology, Stockholm University, 106 91, Stockholm, Sweden
| | - Daniel Ferreira
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, 141 57, Huddinge, Sweden
| | - Laure Saint-Aubert
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, 141 57, Huddinge, Sweden.,Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | - Anne Kinhult-Ståhlbom
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, 141 57, Huddinge, Sweden.,Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden
| | - Håkan Thonberg
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, 141 57, Huddinge, Sweden.,Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, 413 45, Gothenburg, Sweden.,Clinical Memory Research Unit, Lund University, 212 24, Malmö, Sweden
| | - Eric Westman
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, 141 57, Huddinge, Sweden
| | - Anders Wall
- Uppsala University, Department of Surgical Sciences, Section of Nuclear Medicine & PET, 751 85, Uppsala, Sweden
| | - Maria Eriksdotter
- Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden.,Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, 141 57, Huddinge, Sweden
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 431 80, Mölndal, Sweden.,UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at UCL, London, WC1N 3BG, UK.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Mölndal, Sweden
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 431 80, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Mölndal, Sweden
| | - Agneta Nordberg
- Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden.,Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, 141 57, Huddinge, Sweden
| | - Caroline Graff
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Neurogeriatrics, 141 57, Huddinge, Sweden. .,Theme Aging, Karolinska University Hospital Huddinge, 141 86, Stockholm, Sweden.
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13
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Su Y, Flores S, Hornbeck RC, Speidel B, Vlassenko AG, Gordon BA, Koeppe RA, Klunk WE, Xiong C, Morris JC, Benzinger TLS. Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies. NEUROIMAGE-CLINICAL 2018; 19:406-416. [PMID: 30035025 PMCID: PMC6051499 DOI: 10.1016/j.nicl.2018.04.022] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 04/17/2018] [Accepted: 04/22/2018] [Indexed: 01/18/2023]
Abstract
Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p < 0.05) differences in the variability and group mean values. Because of these differences, even after expression in CL, the 95% specificity amyloid positivity thresholds derived from different analytic approaches varied from 5.7 CL to 11.9 CL, and the reliable worsening threshold varied from −2.0 CL to 11.0 CL. Although this difference is in part due to the dependency of the threshold determination methodology on the statistical characteristics of the measurements. When amyloid measurements obtained from different centers are combined for analysis, one should not expect Centiloid conversion to eliminate all the differences in amyloid burden measurements due to variabilities in underlying acquisition protocols and analysis techniques. The Centiloid approach brings amyloid burden measurements into a common scale. The Centiloid value inherits the characteristics of the underlying method. The Centiloid value derived from different analysis techniques remains different. The amyloid positivity thresholds in Centiloid are sensitive to implementation.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA.
| | - Shaney Flores
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Russ C Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Benjamin Speidel
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Andrei G Vlassenko
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Robert A Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - William E Klunk
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
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14
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Shokouhi S, Campbell D, Brill AB, Gwirtsman HE. Longitudinal Positron Emission Tomography in Preventive Alzheimer's Disease Drug Trials, Critical Barriers from Imaging Science Perspective. Brain Pathol 2018; 26:664-71. [PMID: 27327527 PMCID: PMC5958602 DOI: 10.1111/bpa.12399] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 06/16/2016] [Indexed: 12/30/2022] Open
Abstract
Recent Alzheimer's trials have recruited cognitively normal people at risk for Alzheimer's dementia. Due to the lack of clinical symptoms in normal population, conventional clinical outcome measures are not suitable for these early trials. While several groups are developing new composite cognitive tests that could serve as potential outcome measures by detecting subtle cognitive changes in normal people, there is a need for longitudinal brain imaging techniques that can correlate with temporal changes in these new tests and provide additional objective measures of neuropathological changes in brain. Positron emission tomography (PET) is a nuclear medicine imaging procedure based on the measurement of annihilation photons after positron emission from radiolabeled molecules that allow tracking of biological processes in body, including the brain. PET is a well-established in vivo imaging modality in Alzheimer's disease diagnosis and research due to its capability of detecting abnormalities in three major hallmarks of this disease. These include (1) amyloid beta plaques; (2) neurofibrillary tau tangles and (3) decrease in neuronal activity due to loss of nerve cell connection and death. While semiquantitative PET imaging techniques are commonly used to set discrete cut-points to stratify abnormal levels of amyloid accumulation and neurodegeneration, they are suboptimal for detecting subtle longitudinal changes. In this study, we have identified and discussed four critical barriers in conventional longitudinal PET imaging that may be particularly relevant for early Alzheimer's disease studies. These include within and across subject heterogeneity of AD-affected brain regions, PET intensity normalization, neuronal compensations in early disease stages and cerebrovascular amyloid deposition.
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Affiliation(s)
- Sepideh Shokouhi
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
| | - Desmond Campbell
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
| | - Aaron B Brill
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center
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15
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Renard D, Collombier L, Demattei C, Wacongne A, Charif M, Ayrignac X, Azakri S, Gaillard N, Boudousq V, Lehmann S, Menjot de Champfleur N, Thouvenot E. Cerebrospinal Fluid, MRI, and Florbetaben-PET in Cerebral Amyloid Angiopathy-Related Inflammation. J Alzheimers Dis 2018; 61:1107-1117. [DOI: 10.3233/jad-170843] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Dimitri Renard
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
| | | | | | - Anne Wacongne
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
| | - Mahmoud Charif
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | - Xavier Ayrignac
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | - Souhayla Azakri
- Department of Neurology, CHU Montpellier, Hôpital Gui de Chauliac, France
| | | | - Vincent Boudousq
- Department of Nuclear Medicine, CHU Nîmes, Hôpital Caremeau, France
| | - Sylvain Lehmann
- Laboratoire de Biochimie-Protéomique Clinique – IRMB – CRB – Inserm U11183, CHU Montpellier, Hôpital St-Eloi – Université Montpellier, France
| | | | - Eric Thouvenot
- Department of Neurology, CHU Nîmes, Hôpital Caremeau, France
- Institut de Génomique Fonctionnelle, UMR5203, Université Montpellier, Montpellier, France
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16
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Rodriguez-Vieitez E, Nordberg A. Imaging Neuroinflammation: Quantification of Astrocytosis in a Multitracer PET Approach. Methods Mol Biol 2018; 1750:231-251. [PMID: 29512077 DOI: 10.1007/978-1-4939-7704-8_16] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The recent progress in the development of in vivo biomarkers is rapidly changing how neurodegenerative diseases are conceptualized and diagnosed, and how clinical trials are designed today. Alzheimer's disease (AD)-the most common neurodegenerative disorder-is characterized by a complex neuropathology involving the deposition of extracellular amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFT) of hyperphosphorylated tau proteins, accompanied by the activation of glial cells-astrocytes and microglia-and neuroinflammatory responses, leading to neurodegeneration and cognitive dysfunction. An increasing diversity of positron emission tomography (PET) imaging radiotracers are available to selectively target the different pathophysiological processes of AD. Along with the success of Aβ PET and the more recent tau PET imaging, there is also a great interest to develop PET tracers to image glial activation and neuroinflammation. While most research to date has focused on imaging microgliosis, recent studies using 11C-deuterium-L-deprenyl (11C-DED) PET imaging suggest that astrocytosis may be present from very early stages of disease development in AD. This chapter provides a detailed description of the practical approach used for the analysis of 11C-DED PET imaging data in a multitracer PET paradigm including 11C-Pittsburgh compound B (11C-PiB) and 18F-fluorodeoxyglucose (18F-FDG). The multitracer PET approach allows investigating the comparative regional and temporal patterns of in vivo brain astrocytosis, fibrillar Aβ deposition, and glucose metabolism in patients at different stages of disease progression. This chapter attempts to stimulate further research in the field, including the development of novel PET tracers that may allow visualizing different aspects of the complex astrocytic and microglial responses in neurodegenerative diseases. Progress in the field will contribute to the incorporation of PET imaging of glial activation and neuroinflammation as biomarkers with clinical application, and motivate further investigation on glial cells as therapeutic targets in AD and other neurodegenerative diseases.
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Affiliation(s)
- Elena Rodriguez-Vieitez
- Division of Translational Alzheimer Neurobiology, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Agneta Nordberg
- Division of Translational Alzheimer Neurobiology, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
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17
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Yun HJ, Moon SH, Kim HJ, Lockhart SN, Choe YS, Lee KH, Na DL, Lee JM, Seo SW. Centiloid method evaluation for amyloid PET of subcortical vascular dementia. Sci Rep 2017; 7:16322. [PMID: 29176753 PMCID: PMC5701176 DOI: 10.1038/s41598-017-16236-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 10/27/2017] [Indexed: 11/08/2022] Open
Abstract
Reference region selection is important for proper amyloid PET analysis, especially in subcortical vascular dementia (SVaD) patients. We investigated reference region differences between SVaD and Alzheimer's disease (AD) using Centiloid scores. In 57 [C-11] Pittsburgh compound B (PiB) positive (+) AD and 23 PiB (+) SVaD patients, we assessed standardized PiB uptake and Centiloid scores in disease-specific cortical regions, with several reference regions: cerebellar gray (CG), whole cerebellum (WC), WC with brainstem (WC + B), pons, and white matter (WM). We calculated disease group differences from young controls (YC) and YC variance according to reference region. SVaD patients showed large effect sizes (Cohen's d > 0.8) using all reference regions. WM and pons showed larger YC variances than other regions. Findings were similar for AD patients. CG, WC, and WC + B, but not WM or pons, are reliable reference regions for amyloid imaging analysis in SVaD.
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Affiliation(s)
- Hyuk Jin Yun
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763, Korea
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, 02115, MA, USA
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea
| | - Samuel N Lockhart
- Helen Wills Neuroscience Institute, University of California, Berkeley, 94720, CA, USA
- Department of Internal Medicine, Division of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Yearn Seong Choe
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Kyung Han Lee
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea
- Department of Health Sciences and Technology, Sungkyunkwan University, Seoul, 06351, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763, Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea.
- Department of Health Sciences and Technology, Sungkyunkwan University, Seoul, 06351, Korea.
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
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18
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Bhattacharyya S, Egerton A, Kim E, Rosso L, Riano Barros D, Hammers A, Brammer M, Turkheimer FE, Howes OD, McGuire P. Acute induction of anxiety in humans by delta-9-tetrahydrocannabinol related to amygdalar cannabinoid-1 (CB1) receptors. Sci Rep 2017; 7:15025. [PMID: 29101333 PMCID: PMC5670208 DOI: 10.1038/s41598-017-14203-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 10/04/2017] [Indexed: 12/19/2022] Open
Abstract
Use of Cannabis, the most widely used illicit drug worldwide, is associated with acute anxiety, and anxiety disorders following regular use. The precise neural and receptor basis of these effects have not been tested in man. Employing a combination of functional MRI (fMRI) and positron emission tomography (PET), we investigated whether the effects of delta-9-tetrahydrocannabinol (delta-9-THC), the main psychoactive ingredient of cannabis, on anxiety and on amygdala response while processing fearful stimuli were related to local availability of its main central molecular target, cannabinoid-1 (CB1) receptors in man. Fourteen healthy males were studied with fMRI twice, one month apart, following an oral dose of either delta-9-THC (10 mg) or placebo, while they performed a fear-processing task. Baseline availability of the CB1 receptor was studied using PET with [11C]MePPEP, a CB1 inverse agonist radioligand. Relative to the placebo condition, delta-9-THC induced anxiety and modulated right amygdala activation while processing fear. Both these effects were positively correlated with CB1 receptor availability in the right amygdala. These results suggest that the acute effects of cannabis on anxiety in males are mediated by the modulation of amygdalar function by delta-9-THC and the extent of these effects are related to local availability of CB1 receptors.
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Affiliation(s)
- Sagnik Bhattacharyya
- Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK.
| | - Alice Egerton
- Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK
| | - Euitae Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Lula Rosso
- Medical Research Council Clinical Sciences Centre, Hammersmith Hospital, London, UK
| | | | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, 4th floor Lambeth Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Michael Brammer
- Department of Neuroimaging, Centre for Neuroimaging Sciences, PO Box 089, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Centre for Neuroimaging Sciences, PO Box 089, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK
| | - Oliver D Howes
- Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK
- Medical Research Council Clinical Sciences Centre, Hammersmith Hospital, London, UK
| | - Philip McGuire
- Department of Psychosis Studies, King's College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, UK
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19
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Abstract
A compelling need in the field of neurodegenerative diseases is the development and validation of biomarkers for early identification and differential diagnosis. The availability of positron emission tomography (PET) neuroimaging tools for the assessment of molecular biology and neuropathology has opened new venues in the diagnostic design and the conduction of new clinical trials. PET techniques, allowing the in vivo assessment of brain function and pathology changes, are increasingly showing great potential in supporting clinical diagnosis also in the early and even preclinical phases of dementia. This review will summarize the most recent evidence on fluorine-18 fluorodeoxyglucose-, amyloid -, tau -, and neuroinflammation - PET tools, highlighting strengths and limitations and possible new perspectives in research and clinical applications. Appropriate use of PET tools is crucial for a prompt diagnosis and target evaluation of new developed drugs aimed at slowing or preventing dementia.
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Affiliation(s)
- Leonardo Iaccarino
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Arianna Sala
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Paola Caminiti
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy
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20
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Akamatsu G, Ohnishi A, Aita K, Ikari Y, Yamamoto Y, Senda M. Technical Considerations on Scanning and Image Analysis for Amyloid PET in Dementia. Nihon Hoshasen Gijutsu Gakkai Zasshi 2017; 73:298-308. [PMID: 28428473 DOI: 10.6009/jjrt.2017_jsrt_73.4.298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Brain imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), can provide essential and objective information for the early and differential diagnosis of dementia. Amyloid PET is especially useful to evaluate the amyloid-β pathological process as a biomarker of Alzheimer's disease. This article reviews critical points about technical considerations on the scanning and image analysis methods for amyloid PET. Each amyloid PET agent has its own proper administration instructions and recommended uptake time, scan duration, and the method of image display and interpretation. In addition, we have introduced general scanning information, including subject positioning, reconstruction parameters, and quantitative and statistical image analysis. We believe that this article could make amyloid PET a more reliable tool in clinical study and practice.
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Affiliation(s)
- Go Akamatsu
- Division of Molecular Imaging, Institute of Biomedical Research and Innovation.,Division of Radiological Technology, Institute of Biomedical Research and Innovation
| | - Akihito Ohnishi
- Division of Molecular Imaging, Institute of Biomedical Research and Innovation.,Department of Radiology, Kobe University Hospital
| | - Kazuki Aita
- Division of Molecular Imaging, Institute of Biomedical Research and Innovation
| | - Yasuhiko Ikari
- Division of Molecular Imaging, Institute of Biomedical Research and Innovation
| | - Yasuji Yamamoto
- Department of Biosignal Pathophysiology, Graduate School of Medicine, Kobe University.,Medical Center for Student Health, Kobe University
| | - Michio Senda
- Division of Molecular Imaging, Institute of Biomedical Research and Innovation
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21
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Su Y, Blazey TM, Owen CJ, Christensen JJ, Friedrichsen K, Joseph-Mathurin N, Wang Q, Hornbeck RC, Ances BM, Snyder AZ, Cash LA, Koeppe RA, Klunk WE, Galasko D, Brickman AM, McDade E, Ringman JM, Thompson PM, Saykin AJ, Ghetti B, Sperling RA, Johnson KA, Salloway SP, Schofield PR, Masters CL, Villemagne VL, Fox NC, Förster S, Chen K, Reiman EM, Xiong C, Marcus DS, Weiner MW, Morris JC, Bateman RJ, Benzinger TLS. Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group. PLoS One 2016; 11:e0152082. [PMID: 27010959 PMCID: PMC4807073 DOI: 10.1371/journal.pone.0152082] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 03/08/2016] [Indexed: 02/04/2023] Open
Abstract
Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer's Network (DIAN), an autosomal dominant Alzheimer's disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer's disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
| | - Tyler M. Blazey
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Christopher J. Owen
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Jon J. Christensen
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Karl Friedrichsen
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Nelly Joseph-Mathurin
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Qing Wang
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Russ C. Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Abraham Z. Snyder
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Lisa A. Cash
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Robert A. Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - William E. Klunk
- University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Douglas Galasko
- University of California San Diego, La Jolla, California, United States of America
| | - Adam M. Brickman
- Columbia University, New York, New York, United States of America
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - John M. Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Paul M. Thompson
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Andrew J. Saykin
- Department of Radiology, Indiana University, Indianapolis, Indiana, United States of America
| | - Bernardino Ghetti
- Department of Radiology, Indiana University, Indianapolis, Indiana, United States of America
| | - Reisa A. Sperling
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Keith A. Johnson
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephen P. Salloway
- Butler Hospital and Brown University, Providence, Rhode Island, United States of America
| | - Peter R. Schofield
- Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Colin L. Masters
- The Florey Institute and the University of Melbourne, Parkville, Victoria, Australia
| | - Victor L. Villemagne
- The Florey Institute and the University of Melbourne, Parkville, Victoria, Australia
| | - Nick C. Fox
- Dememtia Research Centre, Institute of Neurology, London, Great Britain
| | - Stefan Förster
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) München/Tübingen and Dept. of Nuclear Medicine, Technische Universität München, München, Germany
| | - Kewei Chen
- Banner Alzheimer’s Institute, Banner Health, 901 E. Willetta Street, Phoenix, Arizona, United States of America
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Banner Health, 901 E. Willetta Street, Phoenix, Arizona, United States of America
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Michael W. Weiner
- Department of Radiology, University of California San Francisco, San Francisco, California, United States of America
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
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22
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Carbonell F, Zijdenbos AP, Charil A, Grand’Maison M, Bedell BJ. Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images. J Nucl Med 2015; 56:1351-8. [DOI: 10.2967/jnumed.115.158774] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 06/16/2015] [Indexed: 01/30/2023] Open
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23
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Su Y, Blazey TM, Snyder AZ, Raichle ME, Hornbeck RC, Aldea P, Morris JC, Benzinger TLS. Quantitative amyloid imaging using image-derived arterial input function. PLoS One 2015; 10:e0122920. [PMID: 25849581 PMCID: PMC4388540 DOI: 10.1371/journal.pone.0122920] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 02/24/2015] [Indexed: 11/19/2022] Open
Abstract
Amyloid PET imaging is an indispensable tool widely used in the investigation, diagnosis and monitoring of Alzheimer’s disease (AD). Currently, a reference region based approach is used as the mainstream quantification technique for amyloid imaging. This approach assumes the reference region is amyloid free and has the same tracer influx and washout kinetics as the regions of interest. However, this assumption may not always be valid. The goal of this work is to evaluate an amyloid imaging quantification technique that uses arterial region of interest as the reference to avoid potential bias caused by specific binding in the reference region. 21 participants, age 58 and up, underwent Pittsburgh compound B (PiB) PET imaging and MR imaging including a time-of-flight (TOF) MR angiography (MRA) scan and a structural scan. FreeSurfer based regional analysis was performed to quantify PiB PET data. Arterial input function was estimated based on coregistered TOF MRA using a modeling based technique. Regional distribution volume (VT) was calculated using Logan graphical analysis with estimated arterial input function. Kinetic modeling was also performed using the estimated arterial input function as a way to evaluate PiB binding (DVRkinetic) without a reference region. As a comparison, Logan graphical analysis was also performed with cerebellar cortex as reference to obtain DVRREF. Excellent agreement was observed between the two distribution volume ratio measurements (r>0.89, ICC>0.80). The estimated cerebellum VT was in line with literature reported values and the variability of cerebellum VT in the control group was comparable to reported variability using arterial sampling data. This study suggests that image-based arterial input function is a viable approach to quantify amyloid imaging data, without the need of arterial sampling or a reference region. This technique can be a valuable tool for amyloid imaging, particularly in population where reference normalization may not be accurate.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
| | - Tyler M. Blazey
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Abraham Z. Snyder
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Marcus E. Raichle
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Russ C. Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Patricia Aldea
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Department of Neurosurgery, Washington University School of Medicine, Saint Louis, Missouri, United States of America
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24
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Blennow K, Mattsson N, Schöll M, Hansson O, Zetterberg H. Amyloid biomarkers in Alzheimer's disease. Trends Pharmacol Sci 2015; 36:297-309. [PMID: 25840462 DOI: 10.1016/j.tips.2015.03.002] [Citation(s) in RCA: 349] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Revised: 03/02/2015] [Accepted: 03/05/2015] [Indexed: 02/06/2023]
Abstract
Aggregation of amyloid-β (Aβ) into oligomers, fibrils, and plaques is central in the molecular pathogenesis of Alzheimer's disease (AD), and is the main focus of AD drug development. Biomarkers to monitor Aβ metabolism and aggregation directly in patients are important for further detailed study of the involvement of Aβ in disease pathogenesis and to monitor the biochemical effect of drugs targeting Aβ in clinical trials. Furthermore, if anti-Aβ disease-modifying drugs prove to be effective clinically, amyloid biomarkers will be of special value in the clinic to identify patients with brain amyloid deposition at risk for progression to AD dementia, to enable initiation of treatment before neurodegeneration is too severe, and to monitor drug effects on Aβ metabolism or pathology to guide dosage. Two types of amyloid biomarker have been developed: Aβ-binding ligands for use in positron emission tomography (PET) and assays to measure Aβ42 in cerebrospinal fluid (CSF). In this review, we present the rationales behind these biomarkers and compare their ability to measure Aβ plaque load in the brain. We also review possible shortcomings and the need of standardization of both biomarkers, as well as their implementation in the clinic.
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Affiliation(s)
- Kaj Blennow
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden; The Torsten Söderberg Professorship at the Royal Swedish Academy of Sciences.
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden; Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Michael Schöll
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Department of Clinical Neuroscience and Rehabilitation, University of Gothenburg, Gothenburg, Sweden
| | - Oskar Hansson
- Department of Clinical Sciences, Lund University, Lund, Sweden; Clinical Memory Research unit, Clinical Sciences, Lund University, Lund, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden; UCL Institute of Neurology, Queen Square, London, UK
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Brendel M, Högenauer M, Delker A, Sauerbeck J, Bartenstein P, Seibyl J, Rominger A. Improved longitudinal [(18)F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction. Neuroimage 2014; 108:450-9. [PMID: 25482269 DOI: 10.1016/j.neuroimage.2014.11.055] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 11/20/2014] [Accepted: 11/26/2014] [Indexed: 10/24/2022] Open
Abstract
Amyloid positron-emission-tomography (PET) offers an important research and diagnostic tool for investigating Alzheimer's disease (AD). The majority of amyloid PET studies have used the cerebellum as a reference region, and clinical studies have not accounted for atrophy-based partial volume effects (PVE). Longitudinal studies using cerebellum as reference tissue have revealed only small mean increases and high inter-subject variability in amyloid binding. We aimed to test the effects of different reference regions and PVE-correction (PVEC) on the discriminatory power and longitudinal performance of amyloid PET. We analyzed [(18)F]-AV45 PET and T1-weighted MRI data of 962 subjects at baseline and two-year follow-up data of 258 subjects. Cortical composite volume-of-interest (VOI) values (COMP) for tracer uptake were generated using either full brain atlas VOIs, gray matter segmented VOIs or gray matter segmented VOIs after VOI-based PVEC. Standard-uptake-value ratios (SUVR) were calculated by scaling the COMP values to uptake in cerebellum (SUVRCBL), brainstem (SUVRBST) or white matter (SUVRWM). Mean SUV, SUVR, and changes after PVEC were compared at baseline between diagnostic groups of healthy controls (HC; N=316), mild cognitive impairment (MCI; N=483) and AD (N=163). Receiver operating characteristics (ROC) were calculated for the discriminations between HC, MCI and AD, and expressed as area under the curve (AUC). Finally, the longitudinal [(18)F]-AV45-PET data were used to analyze the impact of quantitation procedures on apparent changes in amyloid load over time. Reference region SUV was most constant between diagnosis groups for the white matter. PVEC led to decreases of COMP-SUV in HC (-18%) and MCI (-10%), but increases in AD (+7%). Highest AUCs were found when using PVEC with white matter scaling for the contrast between HC/AD (0.907) or with brainstem scaling for the contrast between HC/MCI (0.658). Longitudinal increases were greatest in all diagnosis groups with application of PVEC, and inter-subject variability was lowest for the white matter reference. Thus, discriminatory power of [(18)F]-AV45-PET was improved by use of a VOI-based PVEC and white matter or brainstem rather than cerebellum reference region. Detection of longitudinal amyloid increases was optimized with PVEC and white matter reference tissue.
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Affiliation(s)
| | | | - Andreas Delker
- Dept. of Nuclear Medicine, University of Munich, Germany
| | | | | | | | - Axel Rominger
- Dept. of Nuclear Medicine, University of Munich, Germany.
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Daniela P, Orazio S, Alessandro P, Mariano NF, Leonardo I, Pasquale Anthony DR, Giovanni F, Carlo C. A survey of FDG- and amyloid-PET imaging in dementia and GRADE analysis. BIOMED RESEARCH INTERNATIONAL 2014; 2014:785039. [PMID: 24772437 PMCID: PMC3977528 DOI: 10.1155/2014/785039] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 01/29/2014] [Indexed: 12/25/2022]
Abstract
PET based tools can improve the early diagnosis of Alzheimer's disease (AD) and differential diagnosis of dementia. The importance of identifying individuals at risk of developing dementia among people with subjective cognitive complaints or mild cognitive impairment has clinical, social, and therapeutic implications. Within the two major classes of AD biomarkers currently identified, that is, markers of pathology and neurodegeneration, amyloid- and FDG-PET imaging represent decisive tools for their measurement. As a consequence, the PET tools have been recognized to be of crucial value in the recent guidelines for the early diagnosis of AD and other dementia conditions. The references based recommendations, however, include large PET imaging literature based on visual methods that greatly reduces sensitivity and specificity and lacks a clear cut-off between normal and pathological findings. PET imaging can be assessed using parametric or voxel-wise analyses by comparing the subject's scan with a normative data set, significantly increasing the diagnostic accuracy. This paper is a survey of the relevant literature on FDG and amyloid-PET imaging aimed at providing the value of quantification for the early and differential diagnosis of AD. This allowed a meta-analysis and GRADE analysis revealing high values for PET imaging that might be useful in considering recommendations.
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Affiliation(s)
- Perani Daniela
- Nuclear Medicine Department, Vita-Salute San Raffaele University, San Raffaele Hospital and Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | - Schillaci Orazio
- Nuclear Medicine Department, University of Rome “Tor Vergata” and IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Padovani Alessandro
- Department of Medical and Experimental Sciences, Unit of Neurology, Brescia University, 25123 Brescia, Italy
| | - Nobili Flavio Mariano
- Department of Neuroscience Ophthalmology and Genetics, University of Genoa, 16132 Genoa, Italy
| | - Iaccarino Leonardo
- Nuclear Medicine Department, Vita-Salute San Raffaele University, San Raffaele Hospital and Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy
| | | | - Frisoni Giovanni
- IRCCS Centro San Giovanni di Dio Fatebenefratelli, and Memory Clinic and LANVIE, Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 1225 Geneva, Switzerland
| | - Caltagirone Carlo
- University of Rome Tor Vergata and IRCSS S. Lucia, 00142 Rome, Italy
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PET Neuroimaging: The White Elephant Packs His Trunk? Neuroimage 2014; 84:1094-100. [DOI: 10.1016/j.neuroimage.2013.08.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Revised: 08/07/2013] [Accepted: 08/11/2013] [Indexed: 01/30/2023] Open
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Fast R, Rodell A, Gjedde A, Mouridsen K, Alstrup AK, Bjarkam CR, West MJ, Berendt M, Møller A. PiB Fails to Map Amyloid Deposits in Cerebral Cortex of Aged Dogs with Canine Cognitive Dysfunction. Front Aging Neurosci 2013; 5:99. [PMID: 24416017 PMCID: PMC3874561 DOI: 10.3389/fnagi.2013.00099] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Accepted: 12/10/2013] [Indexed: 11/25/2022] Open
Abstract
Dogs with Canine Cognitive Dysfunction (CCD) accumulate amyloid beta (Aβ) in the brain. As the cognitive decline and neuropathology of these old dogs share features with Alzheimer’s disease (AD), the relation between Aβ and cognitive decline in animal models of cognitive decline is of interest to the understanding of AD. However, the sensitivity of the biomarker Pittsburgh Compound B (PiB) to the presence of Aβ in humans and in other mammalian species is in doubt. To test the sensitivity and assess the distribution of Aβ in dog brain, we mapped the brains of dogs with signs of CCD (n = 16) and a control group (n = 4) of healthy dogs with radioactively labeled PiB ([11C]PiB). Structural magnetic resonance imaging brain scans were obtained from each dog. Tracer washout analysis yielded parametric maps of PiB retention in brain. In the CCD group, dogs had significant retention of [11C]PiB in the cerebellum, compared to the cerebral cortex. Retention in the cerebellum is at variance with evidence from brains of humans with AD. To confirm the lack of sensitivity, we stained two dog brains with the immunohistochemical marker 6E10, which is sensitive to the presence of both Aβ and Aβ precursor protein (AβPP). The 6E10 stain revealed intracellular material positive for Aβ or AβPP, or both, in Purkinje cells. The brains of the two groups of dogs did not have significantly different patterns of [11C]PiB binding, suggesting that the material detected with 6E10 is AβPP rather than Aβ. As the comparison with the histological images revealed no correlation between the [11C]PiB and Aβ and AβPP deposits in post-mortem brain, the marked intracellular staining implies intracellular involvement of amyloid processing in the dog brain. We conclude that PET maps of [11C]PiB retention in brain of dogs with CCD fundamentally differ from the images obtained in most humans with AD.
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Affiliation(s)
- Rikke Fast
- Department of Clinical Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Frederiksberg , Denmark
| | - Anders Rodell
- Centre of Functionally Integrative Neuroscience (CFIN), Aarhus University , Aarhus , Denmark ; Department of Nuclear Medicine and PET Center, Aarhus University Hospital , Aarhus , Denmark
| | - Albert Gjedde
- Centre of Functionally Integrative Neuroscience (CFIN), Aarhus University , Aarhus , Denmark ; Department of Nuclear Medicine and PET Center, Aarhus University Hospital , Aarhus , Denmark ; Department of Neuroscience and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen , Denmark
| | - Kim Mouridsen
- Centre of Functionally Integrative Neuroscience (CFIN), Aarhus University , Aarhus , Denmark
| | - Aage K Alstrup
- Department of Nuclear Medicine and PET Center, Aarhus University Hospital , Aarhus , Denmark
| | - Carsten R Bjarkam
- Department of Biomedicine, Faculty of Health, University of Aarhus , Aarhus , Denmark ; Department of Neurosurgery, Aarhus University Hospital , Aarhus , Denmark
| | - Mark J West
- Department of Biomedicine, Faculty of Health, University of Aarhus , Aarhus , Denmark
| | - Mette Berendt
- Department of Clinical Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen , Frederiksberg , Denmark
| | - Arne Møller
- Centre of Functionally Integrative Neuroscience (CFIN), Aarhus University , Aarhus , Denmark ; Department of Nuclear Medicine and PET Center, Aarhus University Hospital , Aarhus , Denmark
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Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease. Proc Natl Acad Sci U S A 2013; 110:E4502-9. [PMID: 24194552 DOI: 10.1073/pnas.1317918110] [Citation(s) in RCA: 280] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Major imaging biomarkers of Alzheimer's disease include amyloid deposition [imaged with [(11)C]Pittsburgh compound B (PiB) PET], altered glucose metabolism (imaged with [(18)F]fluro-deoxyglucose PET), and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, whole-brain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.
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Ikoma Y, Edison P, Ramlackhansingh A, Brooks DJ, Turkheimer FE. Reference region automatic extraction in dynamic [(11)C]PIB. J Cereb Blood Flow Metab 2013; 33:1725-31. [PMID: 23921900 PMCID: PMC3824180 DOI: 10.1038/jcbfm.2013.133] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Revised: 06/16/2013] [Accepted: 07/10/2013] [Indexed: 11/09/2022]
Abstract
The positron emission tomography (PET) radiotracer [(11)C]Pittsburgh Compound B (PIB) is a marker of amyloid plaque deposition in brain, and binding potential is usually quantified using the cerebellum as a reference where the specific binding is negligible. The use of the cerebellum as a reference, however, has been questioned by the reported cerebellar [(11)C]PIB retention in familial Alzheimer's disease (AD) subjects. In this work, we developed a supervised clustering procedure for the automatic extraction of a reference region in [(11)C]PIB studies. Supervised clustering models each gray matter voxel as the linear combination of three predefined kinetic classes, normal and lesion gray matter, and blood pool, and extract reference voxels in which the contribution of the normal gray matter class is high. In the validation with idiopathic AD subjects, supervised clustering extracted reference voxels mostly in the cerebellum that indicated little specific [(11)C]PIB binding, and total distribution volumes of the extracted region were lower than those of the cerebellum. Next, the methodology was applied to the familial AD cohort where the cerebellar amyloid load had been demonstrated previously, resulting in higher binding potential compared with that obtained with the cerebellar reference. The supervised clustering method is a useful tool for the accurate quantification of [(11)C]PIB studies.
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Affiliation(s)
- Yoko Ikoma
- Biophysics Group, Molecular Imaging Center, National Institute of Radiological Sciences, Chiba, Japan
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Chételat G, La Joie R, Villain N, Perrotin A, de La Sayette V, Eustache F, Vandenberghe R. Amyloid imaging in cognitively normal individuals, at-risk populations and preclinical Alzheimer's disease. Neuroimage Clin 2013; 2:356-65. [PMID: 24179789 PMCID: PMC3777672 DOI: 10.1016/j.nicl.2013.02.006] [Citation(s) in RCA: 260] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 02/10/2013] [Accepted: 02/23/2013] [Indexed: 01/18/2023]
Abstract
Recent developments of PET amyloid ligands have made it possible to visualize the presence of Aβ deposition in the brain of living participants and to assess the consequences especially in individuals with no objective sign of cognitive deficits. The present review will focus on amyloid imaging in cognitively normal elderly, asymptomatic at-risk populations, and individuals with subjective cognitive decline. It will cover the prevalence of amyloid-positive cases amongst cognitively normal elderly, the influence of risk factors for AD, the relationships to cognition, atrophy and prognosis, longitudinal amyloid imaging and ethical aspects related to amyloid imaging in cognitively normal individuals. Almost ten years of research have led to a few consensual and relatively consistent findings: some cognitively normal elderly have Aβ deposition in their brain, the prevalence of amyloid-positive cases increases in at-risk populations, the prognosis for these individuals is worse than for those with no Aβ deposition, and significant increase in Aβ deposition over time is detectable in cognitively normal elderly. More inconsistent findings are still under debate; these include the relationship between Aβ deposition and cognition and brain volume, the sequence and cause-to-effect relations between the different AD biomarkers, and the individual outcome associated with an amyloid positive versus negative scan. Preclinical amyloid imaging also raises important ethical issues. While amyloid imaging is definitely useful to understand the role of Aβ in early stages, to define at-risk populations for research or for clinical trial, and to assess the effects of anti-amyloid treatments, we are not ready yet to translate research results into clinical practice and policy. More researches are needed to determine which information to disclose from an individual amyloid imaging scan, the way of disclosing such information and the impact on individuals and on society.
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Affiliation(s)
- Gaël Chételat
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
| | - Renaud La Joie
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
| | - Nicolas Villain
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
| | - Audrey Perrotin
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
| | - Vincent de La Sayette
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
- CHU de Caen, Service de Neurologie, Caen, France
| | - Francis Eustache
- INSERM, U1077 Caen, France
- Université de Caen Basse-Normandie, UMR-S1077, Caen, France
- Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France
- CHU de Caen, U1077 Caen, France
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, University of Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Institute of Neuroscience and Disease, University of Leuven, Belgium
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Edison P, Carter SF, Rinne JO, Gelosa G, Herholz K, Nordberg A, Brooks DJ, Hinz R. Comparison of MRI based and PET template based approaches in the quantitative analysis of amyloid imaging with PIB-PET. Neuroimage 2012; 70:423-33. [PMID: 23261639 DOI: 10.1016/j.neuroimage.2012.12.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2012] [Revised: 11/10/2012] [Accepted: 12/10/2012] [Indexed: 11/15/2022] Open
Abstract
RATIONALE [(11)C]Pittsburgh compound-B (PIB) has been the most widely used positron emission tomography (PET) imaging agent for brain amyloid. Several longitudinal studies evaluating the progression of Alzheimer's disease (AD), and numerous therapeutic intervention studies are underway using [(11)C]PIB PET as an AD biomarker. Quantitative analysis of [(11)C]PIB data requires the definition of regional volumes of interest. This investigation systematically compared two data analysis routes both using a probabilistic brain atlas with 11 bilateral regions. Route 1 used individually segmented structural magnetic resonance images (MRI) for each subject while Route 2 used a standardised [(11)C]PIB PET template. METHODS A total of 54 subjects, 20 with probable Alzheimer's disease (AD), 14 with amnestic Mild Cognitive Impairment (MCI) and 20 age-matched healthy controls, were scanned at two imaging centres either in London (UK) or in Turku (Finland). For all subjects structural volumetric MRI and [(11)C]PIB PET scans were acquired. Target-to-cerebellum ratios 40 min to 60 min post injection were used as outcome measures. Regional read outs for grey matter target regions were generated for both routes. Based on a composite neocortical, frontal, posterior cingulate, combined posterior cingulate and frontal cortical regions, scans were categorised into either 'PIB negative' (PIB-) or 'PIB positive' (PIB+) using previously reported cut-off target-to-cerebellar ratios of 1.41, 1.5 and 1.6, respectively. RESULTS Target-to-cerebellum ratios were greater when defined with a [(11)C]PIB PET template than with individual MRIs for all cortical regions regardless of diagnosis. This difference was highly significant for controls (p<0.001, paired samples t-test), less significant for MCIs and borderline for ADs. Assignment of subjects to raised or normal categories was the same with both routes with a 1.6 cut-off while with lower cut off using frontal cortex, and combined frontal cortex and posterior cingulate demonstrated similar results, while posterior cingulate alone demonstrated significantly higher proportion of controls as amyloid positive by Route 2. CONCLUSIONS Definition of cortical grey matter regions is more accurate when individually segmented MRIs (Route 1) were used rather than a population-based PET template (Route 2). The impact of this difference depends on the grey-to-white matter contrast in the PET images; specifically seen in healthy controls with high white matter and low grey matter uptake. When classifying AD, MCI and control subjects as normal or abnormal using large cortical regions; discordance was found between the MRI and template approach for those few subjects who presented with cortex-to-cerebellum ratios very close to the pre-assigned cut-off. However, posterior cingulate alone demonstrated significant discordance in healthy controls using template based approach. This study, therefore, demonstrates that the use of a [(11)C]PIB PET template (Route 2) is adequate for clinical diagnostic purposes, while MRI based analysis (Route 1) remains more appropriate for clinical research.
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Affiliation(s)
- P Edison
- Division of Neuroscience, Imperial College London, Hammersmith Campus, London, UK.
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Fleisher AS, Chen K, Quiroz YT, Jakimovich LJ, Gomez MG, Langois CM, Langbaum JBS, Ayutyanont N, Roontiva A, Thiyyagura P, Lee W, Mo H, Lopez L, Moreno S, Acosta-Baena N, Giraldo M, Garcia G, Reiman RA, Huentelman MJ, Kosik KS, Tariot PN, Lopera F, Reiman EM. Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer's disease kindred: a cross-sectional study. Lancet Neurol 2012; 11:1057-65. [PMID: 23137949 DOI: 10.1016/s1474-4422(12)70227-2] [Citation(s) in RCA: 177] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND Fibrillar amyloid-β (Aβ) is thought to begin accumulating in the brain many years before the onset of clinical impairment in patients with Alzheimer's disease. By assessing the accumulation of Aβ in people at risk of genetic forms of Alzheimer's disease, we can identify how early preclinical changes start in individuals certain to develop dementia later in life. We sought to characterise the age-related accumulation of Aβ deposition in presenilin 1 (PSEN1) E280A mutation carriers across the spectrum of preclinical disease. METHODS Between Aug 1 and Dec 6, 2011, members of the familial Alzheimer's disease Colombian kindred aged 18-60 years were recruited from the Alzheimer's Prevention Initiative's registry at the University of Antioquia, Medellín, Colombia. Cross-sectional assessment using florbetapir PET was done in symptomatic mutation carriers with mild cognitive impairment or mild dementia, asymptomatic carriers, and asymptomatic non-carriers. These assessments were done at the Banner Alzheimer's Institute in Phoenix, AZ, USA. A cortical grey matter mask consisting of six predefined regions.was used to measure mean cortical florbetapir PET binding. Cortical-to-pontine standard-uptake value ratios were used to characterise the cross-sectional accumulation of fibrillar Aβ deposition in carriers and non-carriers with regression analysis and to estimate the trajectories of fibrillar Aβ deposition. FINDINGS We enrolled a cohort of 11 symptomatic individuals, 19 presymptomatic mutation carriers, and 20 asymptomatic non-carriers, ranging in age from 20 to 56 years. There was greater florbetapir binding in asymptomatic PSEN1 E280A mutation carriers than in age matched non-carriers. Fibrillar Aβ began to accumulate in PSEN 1E280A mutation carriers at a mean age of 28·2 years (95% CI 27·3-33·4), about 16 years and 21 years before the predicted median ages at mild cognitive impairment and dementia onset, respectively. (18)F florbetapir binding rose steeply over the next 9·4 years and plateaued at a mean age of 37·6 years (95% CI 35·3-40·2), about 6 and 11 years before the expected respective median ages at mild cognitive impairment and dementia onset. Prominent florbetapir binding was seen in the anterior and posterior cingulate, precuneus, and parietotemporal and frontal grey matter, as well as in the basal ganglia. Binding in the basal ganglia was not seen earlier or more prominently than in other regions. INTERPRETATION These findings contribute to the understanding of preclinical familial Alzheimer's disease and help set the stage for assessment of amyloid-modifying treatments in the prevention of familial Alzheimer's disease. FUNDING Avid Radiopharmaceuticals, Banner Alzheimer's Foundation, Nomis Foundation, Anonymous Foundation, Forget Me Not Initiative, Colciencias, National Institute on Aging, and the State of Arizona.
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