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Kim SJ, Jang H, Yoo H, Na DL, Ham H, Kim HJ, Kim JP, Farrar G, Moon SH, Seo SW. Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET. Clin Nucl Med 2024; 49:1-8. [PMID: 38048354 DOI: 10.1097/rlu.0000000000004937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
PURPOSE The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional β-amyloid (Aβ) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aβ pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. PATIENTS AND METHODS We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. RESULTS The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aβ involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aβ deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. CONCLUSIONS The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.
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Affiliation(s)
| | | | - Heejin Yoo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center
| | | | | | | | | | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, United Kingdom
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Sun Y, Qiao Y, Guo J, Hou W, Chen Y, Peng D. The preservation of right cingulum fibers in subjective cognitive decline of preclinical phase of Alzheimer's disease. Front Aging Neurosci 2023; 15:1223697. [PMID: 37965494 PMCID: PMC10642356 DOI: 10.3389/fnagi.2023.1223697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction Subjective cognitive decline (SCD) with a positive amyloid burden has been recognized as the earliest clinical symptom of the preclinical phase of Alzheimers disease (AD), providing invaluable opportunities to improve our understanding of the natural history of AD and determine strategies for early therapeutic interventions. Methods The microstructure of white matter in patients showing SCD in the preclinical phase of AD (SCD of pre-AD) was evaluated using diffusion images, and voxel-wise fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivities were assessed and compared among participant groups. Significant clusters in the tracts were extracted to determine their associations with alterations in the cognitive domains. Results We found that individuals with SCD of pre-AD may have subclinical episodic memory impairment associated with the global amyloid burden. Meanwhile, we found significantly reduced FA and λ1 in the right cingulum (cingulate and hippocampus) in AD dementia, while significantly increased FA and decreased MD as well as λ23 in the SCD of pre-AD group in comparison with the HC group. Discussion In conclusion, increased white matter microstructural integrity in the right cingulum (cingulate and hippocampus) may indicate compensation for short-term episodic memory in individuals with SCD of pre-AD in comparison with individuals with AD and healthy elderly individuals.
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Affiliation(s)
- Yu Sun
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Yanan Qiao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Jing Guo
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Wenjie Hou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
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Cui L, Zhang Z, Huang YL, Xie F, Guan YH, Lo CYZ, Guo YH, Jiang JH, Guo QH. Brain amyloid-β deposition associated functional connectivity changes of ultra-large structural scale in mild cognitive impairment. Brain Imaging Behav 2023; 17:494-506. [PMID: 37188840 DOI: 10.1007/s11682-023-00780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In preclinical Alzheimer's disease, neuro-functional changes due to amyloid-β (Aβ) deposition are not synchronized in different brain lobes and subcortical nuclei. This study aimed to explore the correlation between brain Aβ burden, connectivity changes in an ultra-large structural scale, and cognitive function in mild cognitive impairment. Participants with mild cognitive impairment were recruited and underwent florbetapir (F18-AV45) PET, resting-state functional MRI, and multidomain neuropsychological tests. AV-45 standardized uptake value ratio (SUVR) and functional connectivity of all participants were calculated. Of the total 144 participants, 72 were put in the low Aβ burden group and 72 in the high Aβ burden group. In the low Aβ burden group, all connectivities between lobes and nuclei had no correlation with SUVR. In the high Aβ burden group, SUVR showed negative correlations with the Subcortical-Occipital connectivity (r=-0.36, P = 0.02) and Subcortical-Parietal connectivity (r=-0.26, P = 0.026). Meanwhile, in the high Aβ burden group, SUVR showed positive correlations with the Temporal-Prefrontal connectivity (r = 0.27, P = 0.023), Temporal-Occipital connectivity (r = 0.24, P = 0.038), and Temporal-Parietal connectivity (r = 0.32, P = 0.006). Subcortical to Occipital and Parietal connectivities had positive correlations with general cognition, language, memory, and executive function. Temporal to Prefrontal, Occipital, and Parietal connectivities had negative correlations with memory function, executive function, and visuospatial function, and a positive correlation with language function. In conclusion, Individuals with mild cognitive impairment with high Aβ burden have Aβ-related bidirectional functional connectivity changes between lobes and subcortical nuclei that are associated with cognitive decline in multiple domains. These connectivity changes reflect neurological impairment and failed compensation.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Yan-Lu Huang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Yi-Hui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yi-Han Guo
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jie-Hui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China.
| | - Qi-Hao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
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Wang ZB, Tan L, Gao PY, Ma YH, Fu Y, Sun Y, Yu JT. Associations of the A/T/N profiles in PET, CSF, and plasma biomarkers with Alzheimer's disease neuropathology at autopsy. Alzheimers Dement 2023; 19:4421-4435. [PMID: 37506291 DOI: 10.1002/alz.13413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
INTRODUCTION To examine the extent to which positron emission tomography (PET)-, cerebrospinal fluid (CSF)-, and plasma-related amyloid-β/tau/neurodegeneration (A/T/N) biomarkers are associated with Alzheimer's disease (AD) neuropathology at autopsy. METHODS A total of 100 participants who respectively underwent antemortem biomarker measurements and postmortem neuropathology were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined the associations of PET-, CSF-, and plasma-related A/T/N biomarkers in combinations or alone with AD neuropathological changes (ADNC). RESULTS PET- and CSF-related A/T/N biomarkers in combination showed high concordance with the ADNC stage and alone showed high accuracy in discriminating autopsy-confirmed AD. However, the plasma-related A/T/N biomarkers alone showed better discriminative performance only when combined with apolipoprotein E (APO)E ε4 genotype. DISCUSSION This study supports that PET- and CSF-related A/T/N profiles can be used to predict accurately the stages of AD neuropathology. For diagnostic settings, PET-, CSF-, and plasma-related A/T/N biomarkers are all useful diagnostic tools to detect the presence of AD neuropathology. HIGHLIGHTS PET- and CSF-related A/T/N biomarkers in combination can accurately predict the specific stages of AD neuropathology. PET- and CSF-related A/T/N biomarkers alone may serve as a precise diagnostic tool for detecting AD neuropathology at autopsy. Plasma-related A/T/N biomarkers may need combined risk factors when used as a diagnostic tool. Aβ PET and CSF p-tau181/Aβ42 were most consistent with Aβ pathology, while tau PET and CSF p-tau181/Aβ42 were most consistent with tau pathology.
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Affiliation(s)
- Zhi-Bo Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Sun
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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Le PG, Le HTN, Kim HE, Cho S. SAM-Support-Based Electrochemical Sensor for Aβ Biomarker Detection of Alzheimer's Disease. BIOSENSORS 2023; 13:809. [PMID: 37622895 PMCID: PMC10452698 DOI: 10.3390/bios13080809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
Alzheimer's disease has taken the spotlight as a neurodegenerative disease which has caused crucial issues to both society and the economy. Specifically, aging populations in developed countries face an increasingly serious problem due to the increasing budget for patient care and an inadequate labor force, and therefore a solution is urgently needed. Recently, diverse techniques for the detection of Alzheimer's biomarkers have been researched and developed to support early diagnosis and treatment. Among them, electrochemical biosensors and electrode modification proved their effectiveness in the detection of the Aβ biomarker at appropriately low concentrations for practice and point-of-care application. This review discusses the production and detection ability of amyloid beta, an Alzheimer's biomarker, by electrochemical biosensors with SAM support for antibody conjugation. In addition, future perspectives on SAM for the improvement of electrochemical biosensors are also proposed and discussed.
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Affiliation(s)
- Phan Gia Le
- Department of Electronic Engineering, Gachon University, Seongnam-si 13120, Republic of Korea; (P.G.L.); (H.T.N.L.)
| | - Hien T. Ngoc Le
- Department of Electronic Engineering, Gachon University, Seongnam-si 13120, Republic of Korea; (P.G.L.); (H.T.N.L.)
| | - Hee-Eun Kim
- Department of Dental Hygiene, Gachon University, Incheon 21936, Republic of Korea;
| | - Sungbo Cho
- Department of Electronic Engineering, Gachon University, Seongnam-si 13120, Republic of Korea; (P.G.L.); (H.T.N.L.)
- Department of Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, Republic of Korea
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Park CJ, Kim SY, Kim JH, Son NH, Park JY, Jeong YH, Kim HJ, Park J, Kim WJ. Evaluation of glymphatic system activity using diffusion tensor image analysis along the perivascular space and amyloid PET in older adults with objectively normal cognition: a preliminary study. Front Aging Neurosci 2023; 15:1221667. [PMID: 37577357 PMCID: PMC10413261 DOI: 10.3389/fnagi.2023.1221667] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023] Open
Abstract
Objectives Diffusion tensor image analysis along the perivascular space (DTI-ALPS) is a recently introduced method for the assessment of the glymphatic system without the need for contrast injection. The purpose of our study was to assess the glymphatic system in cognitively normal older adults with or without subjective cognitive decline (SCD) using DTI-ALPS, and correlating with amyloid PET. Design and participants To evaluate the glymphatic system in cognitively normal older adults using DTI-ALPS, we built a prospective cohort including a total of 123 objectively cognitively normal older adults with or without SCD. The ALPS index was calculated from DTI MRI and was assessed by correlating it with standardized uptake value ratios (SUVRs) from amyloid PET and clinically relevant variables. The study subjects were also divided into amyloid "positive" and "negative" groups based on the result of amyloid PET, and the ALPS indices between those two groups were compared. Results The ALPS index was not significantly different between the normal and SCD groups (P = 0.897). The mean ALPS index from the amyloid positive and amyloid negative group was 1.31 and 1.35, respectively, which showed no significant difference (P = 0.308). Among the SUVRs from variable cortices, that of the paracentral cortex was negatively correlated with the ALPS index (r = -0.218, P = 0.016). Multivariate linear regression revealed that older age (coefficient, -0.007) and higher SUVR from the paracentral cortex (coefficient, -0.101) were two independent variables with a significant association with a lower ALPS index (P = 0.015 and 0.045, respectively). Conclusion DTI-ALPS may not be useful for evaluation of the glymphatic system in subjects with SCD. Older age was significantly associated with lower ALPS index. Greater amyloid deposition in the paracentral cortex was significantly associated with lower glymphatic activity in cognitively normal older adults. These results should be validated in future studies on the relationships between ALPS index and other fundamental compartments in glymphatic system, such as perivenous space and the meningeal lymphatic vessels.
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Affiliation(s)
- Chae Jung Park
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Sang-Young Kim
- MR Clinical Science, Philips Healthcare Korea, Seoul, Republic of Korea
| | - Jun Hwee Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu, Republic of Korea
| | - Jin Young Park
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Yong Hyu Jeong
- Department of Nuclear Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Hyun Jeong Kim
- Department of Nuclear Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
| | - Jaesub Park
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Woo Jung Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea
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Wang SM, Kang DW, Um YH, Kim S, Kim REY, Kim D, Lee CU, Lim HK. Cognitive Normal Older Adults with APOE-2 Allele Show a Distinctive Functional Connectivity Pattern in Response to Cerebral Aβ Deposition. Int J Mol Sci 2023; 24:11250. [PMID: 37511008 PMCID: PMC10380008 DOI: 10.3390/ijms241411250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/29/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The ε2 allele of apolipoprotein E (ε2) has neuroprotective effects against beta-amyloid (Aβ) pathology in Alzheimer's disease (AD). However, its impact on the functional connectivity and hub efficiency in cognitively normal older adults (CN) with ε2 is unclear. We investigated the functional connectivity differences in the default mode network (DMN), salience network, and central executive network (CEN) between A-PET-negative (N = 29) and A-PET-positive (N = 15) CNs with ε2/ε2 or ε2/ε3 genotypes. The A-PET-positive CNs exhibited a lower anterior DMN functional connectivity, higher posterior DMN functional connectivity, and increased CEN functional connectivity compared to the A-PET-negative CNs. Cerebral Aβ retention was negatively correlated with anterior DMN functional connectivity and positively correlated with posterior DMN and anterior CEN functional connectivity. A graph theory analysis showed that the A-PET-positive CNs displayed a higher betweenness centrality in the middle frontal gyrus (left) and medial fronto-parietal regions (left). The betweenness centrality in the middle frontal gyrus (left) was positively correlated with Aβ retention. Our findings reveal a reversed anterior-posterior dissociation in the DMN functional connectivity and heightened CEN functional connectivity in A-PET-positive CNs with ε2. Hub efficiencies, measured by betweenness centrality, were increased in the DMN and CEN of the A-PET-positive CNs with ε2. These results suggest unique functional connectivity responses to Aβ pathology in CN individuals with ε2.
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Affiliation(s)
- Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Regina E Y Kim
- Research Institute, Neurophet Inc., Seoul 08380, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul 08380, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Jack CR, Wiste HJ, Algeciras-Schimnich A, Figdore DJ, Schwarz CG, Lowe VJ, Ramanan VK, Vemuri P, Mielke MM, Knopman DS, Graff-Radford J, Boeve BF, Kantarci K, Cogswell PM, Senjem ML, Gunter JL, Therneau TM, Petersen RC. Predicting amyloid PET and tau PET stages with plasma biomarkers. Brain 2023; 146:2029-2044. [PMID: 36789483 PMCID: PMC10151195 DOI: 10.1093/brain/awad042] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/20/2022] [Accepted: 01/21/2023] [Indexed: 02/16/2023] Open
Abstract
Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Dan J Figdore
- Department of Laboratory Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
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Chen L, Lyu Y, Zhang X, Zheng L, Li Q, Ding D, Chen F, Liu Y, Li W, Zhang Y, Huang Q, Wang Z, Xie T, Zhang Q, Sima Y, Li K, Xu S, Ren T, Xiong M, Wu Y, Song J, Yuan L, Yang H, Zhang XB, Tan W. Molecular imaging: design mechanism and bioapplications. Sci China Chem 2023. [DOI: 10.1007/s11426-022-1461-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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10
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Hao SW, Li TR, Han C, Han Y, Cai YN. Associations Between Levels of Peripheral NCAPH2 Promoter Methylation and Different Stages of Alzheimer's Disease: A Cross-Sectional Study. J Alzheimers Dis 2023; 92:899-909. [PMID: 36806511 DOI: 10.3233/jad-221211] [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: 02/19/2023]
Abstract
BACKGROUND Several studies have examined NCAPH2 methylation in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), but little is known of NCAPH2 methylation in subjective cognitive decline (SCD). OBJECTIVE To examine whether methylation of peripheral NCAPH2 are differentially changed at various phases of AD, and whether it could serve as a diagnostic biomarker for SCD. METHODS A total of 40 AD patients, 52 aMCI patients, 148 SCD patients, and 193 cognitively normal controls (NCs) were recruited in the current case-control study. Besides, 54 cognitively normal individuals have received amyloid positron emission tomography (amyloid PET) scans. Using bisulfite pyrosequencing method, we measured blood DNA methylation in the NCAPH2 gene promoter. RESULTS The main outcomes were: 1) For SCD, there was no significant difference between SCD and NC regarding NCAPH2 methylation; 2) For aMCI, NCAPH2 methylation at CpG2 were significantly lower in aMCI compared with NC and SCD in the entire population and male subgroup; 3) For AD, NCAPH2 methylation at CpG1 were significantly lower in AD compared with NC among females; 4) A relationship with apolipoprotein E (APOE) ɛ4 status was shown. Receiver operating characteristic (ROC) analysis by combining NCAPH2 methylation, age, education, and APOEɛ4 status could distinguish between patients with aMCI (area under the curve (AUC): 0.742) and AD (AUC: 0.873) from NCs. CONCLUSION NCAPH2 methylation levels were altered at the aMCI and AD stage and may be convenient and cost-effective biomarkers of AD and aMCI.
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Affiliation(s)
- Shu-Wen Hao
- Department of Neurobiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China.,Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, China
| | - Tao-Ran Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Chao Han
- National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan-Ning Cai
- Department of Neurobiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Biobank, Xuanwu Hospital, Capital Medical University, Beijing, China
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Hoenig MC, Drzezga A. Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective. J Neurochem 2023; 164:325-345. [PMID: 35226362 DOI: 10.1111/jnc.15598] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 11/28/2022]
Abstract
With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.
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Affiliation(s)
- Merle C Hoenig
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Alexander Drzezga
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany
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12
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Yang Z, Banks SJ, Ritter AR, Cummings JL, Sreenivasan K, Kinney JW, Caldwell JK, Wong CG, Miller JB, Cordes D. Microglial Imaging in Alzheimer's Disease and Its Relationship to Brain Amyloid: A Human 18F-GE180 PET Study. J Alzheimers Dis 2023; 96:1505-1514. [PMID: 37980664 PMCID: PMC10894577 DOI: 10.3233/jad-230631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/27/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Emerging evidence suggests a potential causal role of neuroinflammation in Alzheimer's disease (AD). Using positron emission tomography (PET) to image overexpressed 18 kDA translocator protein (TSPO) by activated microglia has gained increasing interest. The uptake of 18F-GE180 TSPO PET was observed to co-localize with inflammatory markers and have a two-stage association with amyloid PET in mice. Very few studies evaluated the diagnostic power of 18F-GE180 PET in AD population and its interpretation in human remains controversial about whether it is a marker of microglial activation or merely reflects disrupted blood-brain barrier integrity in humans. OBJECTIVE The goal of this study was to study human GE180 from the perspective of the previous animal observations. METHODS With data from twenty-four participants having 18F-GE180 and 18F-AV45 PET scans, we evaluated the group differences of 18F-GE180 uptake between participants with and without cognitive impairment. An association analysis of 18F-GE180 and 18F-AV45 was then conducted to test if the relationship in humans is consistent with the two-stage association in AD mouse model. RESULTS Elevated 18F-GE180 was observed in participants with cognitive impairment compared to those with normal cognition. No regions showed reduced 18F-GE180 uptake. Consistent with mouse model, a two-stage association between 18F-GE180 and 18F-AV45 was observed. CONCLUSIONS 18F-GE180 PET imaging showed promising utility in detecting pathological alterations in a symptomatic AD population. Consistent two-stage association between 18F-GE180 and amyloid PET in human and mouse suggested that 18F-GE180 uptake in human might be considerably influenced by microglial activation.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | | | - Aaron R. Ritter
- Hoag’s Pickup Family Neurosciences Institute, Newport Beach, CA, USA
| | - Jeffrey L. Cummings
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Karthik Sreenivasan
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Jefferson W. Kinney
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | | | - Christina G. Wong
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Justin B. Miller
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA
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13
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Kim J, Choe YS, Park Y, Kim Y, Kim JP, Jang H, Kim HJ, Na DL, Cho SJ, Moon SH, Seo SW. Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels. Front Aging Neurosci 2023; 15:1124445. [PMID: 36936497 PMCID: PMC10017468 DOI: 10.3389/fnagi.2023.1124445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Background Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.
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Affiliation(s)
- Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yuhyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Samsung Medical Center, Stem Cell and Regenerative Medicine Institute, Seoul, Republic of Korea
| | - Soo-Jin Cho
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- *Correspondence: Seung Hwan Moon,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Sang Won Seo,
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14
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Collij LE, Salvadó G, de Wilde A, Altomare D, Shekari M, Gispert JD, Bullich S, Stephens A, Barkhof F, Scheltens P, Bouwman F, van der Flier WM. Quantification of [
18
F]florbetaben amyloid‐PET imaging in a mixed memory clinic population: The ABIDE project. Alzheimers Dement 2022. [DOI: 10.1002/alz.12886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Lyduine E. Collij
- Department of Radiology and Nuclear Medicine Amsterdam University Medical Center Amsterdam Neuroscience Amsterdam The Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- Clinical Memory Research Unit Department of Clinical Sciences Lund University Malmö Sweden
| | - Arno de Wilde
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE) University of Geneva Geneva Switzerland
- Memory Center Geneva University Hospitals Geneva Switzerland
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain
- Pompeu Fabra University Barcelona Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain
- IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales y Nanomedicina (CIBER‐BBN) Madrid Spain
| | | | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine Amsterdam University Medical Center Amsterdam Neuroscience Amsterdam The Netherlands
- Centre for Medical Image Computing and Queen Square Institute of Neurology UCL London UK
| | - Philip Scheltens
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Femke Bouwman
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
| | - Wiesje M. van der Flier
- Department of Neurology Alzheimer Center Amsterdam Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
- Department of Epidemiology & Data Science Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands
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15
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Kim KY, Park J, Jeong YH, Kim HJ, Lee E, Park JY, Kim E, Kim WJ. Plasma amyloid-beta oligomer is related to subjective cognitive decline and brain amyloid status. Alzheimers Res Ther 2022; 14:162. [PMID: 36324157 PMCID: PMC9632136 DOI: 10.1186/s13195-022-01104-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
Background Subjective cognitive decline (SCD) is a target for Alzheimer’s disease prediction. Plasma amyloid-beta oligomer (AβO), the pathogenic form of Aβ in blood, has recently been proposed as a novel blood-based biomarker of AD prediction by representing brain Aβ deposition. The relationship between plasma AβO, brain Aβ deposition, and SCD in individuals with normal objective cognition has not been investigated. Methods In this cross-sectional study, we analyzed 126 participants with normal objective cognition. More SCD symptoms were expressed as higher scores of the Subjective Cognitive Decline Questionnaire (SCDQ) and Memory Age-associated Complaint Questionnaire (MACQ). The plasma AβO level of each participant was measured twice for validation and expressed as a concentration (ng/mL) and a ratio relative to the mean value of two internal standards. Brain Aβ deposition was assessed by [18F] flutemetamol positron emission tomography (PET) and expressed as standard uptake value ratio (SUVR). Associations of SCDQ and MACQ with plasma AβO levels or SUVR were analyzed in multiple linear regression models. The association between plasma AβO level and flutemetamol PET positivity was assessed in logistic regression and receiver operative characteristic analyses. Results Overall, participants were 73.3 years old with female predominance (69.0%). After adjustment for confounders, high SCDQ and MACQ scores were associated with the high plasma AβO levels as both concentrations and ratios (ratios: standardized coefficient = 0.246 and p = 0.023 for SCDQ, standardized coefficient = 0.209 and p = 0.029 for MACQ; concentrations: standardized coefficient = 0.257 and p = 0.015 for SCDQ, standardized coefficient = 0.217 and p = 0.021 for MACQ). In contrast, SCDQ and MACQ were not significantly associated with SUVRs (p = 0.134 for SCDQ, p = 0.079 for MACQ). High plasma AβO levels were associated with flutemetamol PET (+) with an area under the curve of 0.694 (ratio) or 0.662 (concentration). Combined with APOE e4, plasma AβO presented area under the curves of 0.789 (ratio) and 0.783 (concentration). Conclusions Our findings indicate that the high plasma AβO level could serve as a potential surrogate biomarker of severe SCD and the presence of brain Aβ deposition in individuals with normal objective cognition.
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Affiliation(s)
- Keun You Kim
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.412479.dDepartment of Psychiatry, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jaesub Park
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.416665.60000 0004 0647 2391Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | - Yong Hyu Jeong
- grid.15444.300000 0004 0470 5454Department of Nuclear Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Hyun Jeong Kim
- grid.15444.300000 0004 0470 5454Department of Nuclear Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Eun Lee
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.15444.300000 0004 0470 5454Department of Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Young Park
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.15444.300000 0004 0470 5454Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Gyeonggi Yongin, Republic of Korea
| | - Eosu Kim
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.15444.300000 0004 0470 5454Department of Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Woo Jung Kim
- grid.15444.300000 0004 0470 5454Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea ,grid.15444.300000 0004 0470 5454Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Gyeonggi Yongin, Republic of Korea
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16
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Morrison MS, Aparicio HJ, Blennow K, Zetterberg H, Ashton NJ, Karikari TK, Tripodis Y, Martin B, Palmisano JN, Sugarman MA, Frank B, Steinberg EG, Turk KW, Budson AE, Au R, Goldstein LE, Jun GR, Kowall NW, Killiany R, Qiu WQ, Stern RA, Mez J, McKee AC, Stein TD, Alosco ML. Ante-mortem plasma phosphorylated tau (181) predicts Alzheimer's disease neuropathology and regional tau at autopsy. Brain 2022; 145:3546-3557. [PMID: 35554506 PMCID: PMC10233293 DOI: 10.1093/brain/awac175] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/07/2022] [Accepted: 05/02/2022] [Indexed: 11/14/2022] Open
Abstract
Blood-based biomarkers such as tau phosphorylated at threonine 181 (phosphorylated-tau181) represent an accessible, cost-effective and scalable approach for the in vivo detection of Alzheimer's disease pathophysiology. Plasma-pathological correlation studies are needed to validate plasma phosphorylated-tau181 as an accurate and reliable biomarker of Alzheimer's disease neuropathological changes. This plasma-to-autopsy correlation study included participants from the Boston University Alzheimer's Disease Research Center who had a plasma sample analysed for phosphorylated-tau181 between 2008 and 2018 and donated their brain for neuropathological examination. Plasma phosphorelated-tau181 was measured with single molecule array technology. Of 103 participants, 62 (60.2%) had autopsy-confirmed Alzheimer's disease. Average time between blood draw and death was 5.6 years (standard deviation = 3.1 years). Multivariable analyses showed higher plasma phosphorylated-tau181 concentrations were associated with increased odds for having autopsy-confirmed Alzheimer's disease [AUC = 0.82, OR = 1.07, 95% CI = 1.03-1.11, P < 0.01; phosphorylated-tau standardized (z-transformed): OR = 2.98, 95% CI = 1.50-5.93, P < 0.01]. Higher plasma phosphorylated-tau181 levels were associated with increased odds for having a higher Braak stage (OR = 1.06, 95% CI = 1.02-1.09, P < 0.01) and more severe phosphorylated-tau across six cortical and subcortical brain regions (ORs = 1.03-1.06, P < 0.05). The association between plasma phosphorylated-tau181 and Alzheimer's disease was strongest in those who were demented at time of blood draw (OR = 1.25, 95%CI = 1.02-1.53), but an effect existed among the non-demented (OR = 1.05, 95% CI = 1.01-1.10). There was higher discrimination accuracy for Alzheimer's disease when blood draw occurred in years closer to death; however, higher plasma phosphorylated-tau181 levels were associated with Alzheimer's disease even when blood draw occurred >5 years from death. Ante-mortem plasma phosphorylated-tau181 concentrations were associated with Alzheimer's disease neuropathology and accurately differentiated brain donors with and without autopsy-confirmed Alzheimer's disease. These findings support plasma phosphorylated-tau181 as a scalable biomarker for the detection of Alzheimer's disease.
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Affiliation(s)
- Madeline S Morrison
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Hugo J Aparicio
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London WC1N 3BG, UK
| | - Nicholas J Ashton
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden
| | - Thomas K Karikari
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden
| | - Yorghos Tripodis
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Brett Martin
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA 02118, USA
| | - Joseph N Palmisano
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA 02118, USA
| | - Michael A Sugarman
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Brandon Frank
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Eric G Steinberg
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Katherine W Turk
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- VA Boston Healthcare System, U.S. Department of Veteran Affairs, Jamaica Plain, MA 02130, USA
| | - Andrew E Budson
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- VA Boston Healthcare System, U.S. Department of Veteran Affairs, Jamaica Plain, MA 02130, USA
| | - Rhoda Au
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Lee E Goldstein
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Biomedical Engineering, Boston University College of Engineering, Boston, MA 02215, USA
- Department of Electrical and Computer Engineering, Boston University College of Engineering, Boston, MA 02215, USA
| | - Gyungah R Jun
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Neil W Kowall
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- VA Boston Healthcare System, U.S. Department of Veteran Affairs, Jamaica Plain, MA 02130, USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ronald Killiany
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
- Center for Biomedical Imaging, Boston University School of Medicine, Boston, MA 02118, USA
| | - Wei Qiao Qiu
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA 02118, USA
| | - Robert A Stern
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurosurgery, Boston University School of Medicine, Boston, MA 02118, USA
| | - Jesse Mez
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ann C McKee
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- VA Boston Healthcare System, U.S. Department of Veteran Affairs, Jamaica Plain, MA 02130, USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- VA Bedford Healthcare System, U.S. Department of Veteran Affairs, Bedford, MA 01730, USA
| | - Thor D Stein
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- VA Boston Healthcare System, U.S. Department of Veteran Affairs, Jamaica Plain, MA 02130, USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- VA Bedford Healthcare System, U.S. Department of Veteran Affairs, Bedford, MA 01730, USA
| | - Michael L Alosco
- Boston University Alzheimer’s Disease Research Center and CTE Center, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
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17
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Reinartz M, Luckett ES, Schaeverbeke J, De Meyer S, Adamczuk K, Thal DR, Van Laere K, Dupont P, Vandenberghe R. Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth. Eur J Nucl Med Mol Imaging 2022; 49:3772-3786. [PMID: 35522322 PMCID: PMC9399207 DOI: 10.1007/s00259-022-05808-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/19/2022] [Indexed: 11/29/2022]
Abstract
Purpose End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05808-7.
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Affiliation(s)
- Mariska Reinartz
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Emma Susanne Luckett
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | | | - Dietmar Rudolf Thal
- Department of Pathology, UZ Leuven, Leuven, Belgium.,Laboratory of Neuropathology, KU Leuven, Leuven, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium.,Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium. .,Neurology Department, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
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18
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Duan P, Chen KJ, Wijegunawardena G, Dregni AJ, Wang HK, Wu H, Hong M. Binding Sites of a Positron Emission Tomography Imaging Agent in Alzheimer's β-Amyloid Fibrils Studied Using 19F Solid-State NMR. J Am Chem Soc 2022; 144:1416-1430. [PMID: 35015530 PMCID: PMC8855532 DOI: 10.1021/jacs.1c12056] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Amyloid imaging by positron emission tomography (PET) is an important method for diagnosing neurodegenerative disorders such as Alzheimer's disease. Many 11C- and 18F-labeled PET tracers show varying binding capacities, specificities, and affinities for their target proteins. The structural basis of these variations is poorly understood. Here we employ 19F and 13C solid-state NMR to investigate the binding sites of a PET ligand, flutemetamol, to the 40-residue Alzheimer's β-amyloid peptide (Aβ40). Analytical high-performance liquid chromatography and 19F NMR spectra show that flutemetamol binds the current Aβ40 fibril polymorph with a stoichiometry of one ligand per four to five peptides. Half of the ligands are tightly bound while the other half are loosely bound. 13C and 15N chemical shifts indicate that this Aβ40 polymorph has an immobilized N-terminus, a non-β-sheet His14, and a non-β-sheet C-terminus. We measured the proximity of the ligand fluorine to peptide residues using 19F-13C and 19F-1H rotational-echo double-resonance (REDOR) experiments. The spectra show that three segments in the peptide, 12VHH14, 18VFF20, and 39VV40, lie the closest to the ligand. REDOR-constrained docking simulations indicate that these three segments form multiple binding sites, and the ligand orientations and positions at these sites are similar across different Aβ polymorphs. Comparison of the flutemetamol-interacting residues in Aβ40 with the small-molecule binding sites in other amyloid proteins suggest that conjugated aromatic compounds preferentially bind β-sheet surface grooves lined by aromatic, polar, and charged residues. These motifs may explain the specificity of different PET tracers to different amyloid proteins.
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Affiliation(s)
- Pu Duan
- Department of Chemistry, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139, United States
| | - Kelly J. Chen
- Department of Chemistry, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139, United States
| | - Gayani Wijegunawardena
- Department of Chemistry and Biochemistry, Wichita State University, 1845 Fairmount St, Wichita, KS 67260, United States
| | - Aurelio J. Dregni
- Department of Chemistry, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139, United States
| | - Harrison K. Wang
- Department of Chemistry, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139, United States
| | - Haifan Wu
- Department of Chemistry and Biochemistry, Wichita State University, 1845 Fairmount St, Wichita, KS 67260, United States
| | - Mei Hong
- Department of Chemistry, Massachusetts Institute of Technology, 170 Albany Street, Cambridge, MA 02139, United States
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19
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Ni R, Nitsch RM. Recent Developments in Positron Emission Tomography Tracers for Proteinopathies Imaging in Dementia. Front Aging Neurosci 2022; 13:751897. [PMID: 35046791 PMCID: PMC8761855 DOI: 10.3389/fnagi.2021.751897] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
An early detection and intervention for dementia represent tremendous unmet clinical needs and priorities in society. A shared feature of neurodegenerative diseases causing dementia is the abnormal accumulation and spreading of pathological protein aggregates, which affect the selective vulnerable circuit in a disease-specific pattern. The advancement in positron emission tomography (PET) biomarkers has accelerated the understanding of the disease mechanism and development of therapeutics for Alzheimer's disease and Parkinson's disease. The clinical utility of amyloid-β PET and the clinical validity of tau PET as diagnostic biomarker for Alzheimer's disease continuum have been demonstrated. The inclusion of biomarkers in the diagnostic criteria has introduced a paradigm shift that facilitated the early and differential disease diagnosis and impacted on the clinical management. Application of disease-modifying therapy likely requires screening of patients with molecular evidence of pathological accumulation and monitoring of treatment effect assisted with biomarkers. There is currently still a gap in specific 4-repeat tau imaging probes for 4-repeat tauopathies and α-synuclein imaging probes for Parkinson's disease and dementia with Lewy body. In this review, we focused on recent development in molecular imaging biomarkers for assisting the early diagnosis of proteinopathies (i.e., amyloid-β, tau, and α-synuclein) in dementia and discussed future perspectives.
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Affiliation(s)
- Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Roger M. Nitsch
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
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20
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Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases-A Resting State fMRI Study. Life (Basel) 2021; 11:life11101108. [PMID: 34685479 PMCID: PMC8538280 DOI: 10.3390/life11101108] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/10/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022] Open
Abstract
Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer’s disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer’s related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK-MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC.
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21
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Cho SH, Choe YS, Kim YJ, Kim HJ, Jang H, Kim Y, Kim SE, Kim SJ, Kim JP, Jung YH, Kim BC, Lockhart SN, Farrar G, Na DL, Moon SH, Seo SW. Head-to-Head Comparison of 18F-Florbetaben and 18F-Flutemetamol in the Cortical and Striatal Regions. J Alzheimers Dis 2021; 76:281-290. [PMID: 32474468 DOI: 10.3233/jad-200079] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) amyloid PET have been developed and approved for clinical use. It is important to understand the distinct features of these ligands to compare and correctly interpret the results of different amyloid PET studies. OBJECTIVE We performed a head-to-head comparison of FBB and FMM to compare with regard to imaging characteristics, including dynamic range of retention, and differences in quantitative measurements between the two ligands in cortical, striatal, and white matter (WM) regions. METHODS Paired FBB and FMM PET images were acquired in 107 participants. Correlations of FBB and FMM amyloid deposition in the cortex, striatum, and WM were investigated and compared in different reference regions (cerebellar gray matter (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons). RESULTS The cortical SUVR (R2 = 0.97) and striatal SUVR (R2 = 0.95) demonstrated an excellent linear correlation between FBB and FMM using a WC as reference region. There was no difference in the cortical SUVR ratio between the two ligands (p = 0.90), but the striatal SUVR ratio was higher in FMM than in FBB (p < 0.001). Also, the effect size of differences in striatal SUVR seemed to be higher with FMM (2.61) than with FBB (2.34). These trends were similarly observed according to four different reference regions (CG, WC, WC + B, and pons). CONCLUSION Our findings suggest that FMM might be better than FBB to detect amyloid burden in the striatum, although both ligands are comparable for imaging AD pathology in vivo.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Si Eun Kim
- Departments of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Samuel N Lockhart
- Internal Medicine - Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea.,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
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22
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Wang YJ, Hu H, Yang YX, Zuo CT, Tan L, Yu JT. Regional Amyloid Accumulation and White Matter Integrity in Cognitively Normal Individuals. J Alzheimers Dis 2021; 74:1261-1270. [PMID: 32176644 DOI: 10.3233/jad-191350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent studies have shown that amyloid-β (Aβ) burden influenced white matter (WM) integrity before the onset of dementia. OBJECTIVE To assess whether the effects of Aβ burden on WM integrity in cognitively normal (CN) individuals were regionally specific. METHODS Our cohort consisted of 71 CNs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database who underwent both AV45 amyloid-PET and diffusion tensor imaging. Standardized uptake value ratio (SUVR) was computed across four bilateral regions of interest (ROIs) corresponding to four stages of in vivo amyloid staging model (Amyloid stages I-IV). Linear regression models were conducted in entire CN group and between APOEɛ4 carriers and non-carriers. RESULTS Our results indicated that higher global Aβ-SUVR was associated with higher mean diffusivity (MD) in the entire CN group (p = 0.023), and with both higher MD (p = 0.015) and lower fractional anisotropy (FA) (p = 0.026) in APOEɛ4 carriers. Subregion analysis showed that higher Amyloid stage I-II Aβ-SUVRs were associated with higher MD (Stage-1: p = 0.030; Stage-2: p = 0.016) in the entire CN group, and with both higher MD (Stage-1: p = 0.004; Stage-2: p = 0.010) and lower FA (Stage-1: p = 0.022; Stage-2: p = 0.014) in APOEɛ4 carriers. No associations were found in APOEɛ4 non-carriers and in Amyloid stage III-IV ROIs. CONCLUSIONS Our results indicated that the effects of Aβ burden on WM integrity in CNs might be regionally specific, particularly in Amyloid stage I-II ROIs, and modulated by APOEɛ4 status.
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Affiliation(s)
- Ya-Juan Wang
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Yu-Xiang Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China.,Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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23
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Bao W, Xie F, Zuo C, Guan Y, Huang YH. PET Neuroimaging of Alzheimer's Disease: Radiotracers and Their Utility in Clinical Research. Front Aging Neurosci 2021; 13:624330. [PMID: 34025386 PMCID: PMC8134674 DOI: 10.3389/fnagi.2021.624330] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 02/23/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's Disease (AD), the leading cause of senile dementia, is a progressive neurodegenerative disorder affecting millions of people worldwide and exerting tremendous socioeconomic burden on all societies. Although definitive diagnosis of AD is often made in the presence of clinical manifestations in late stages, it is now universally believed that AD is a continuum of disease commencing from the preclinical stage with typical neuropathological alterations appearing decades prior to its first symptom, to the prodromal stage with slight symptoms of amnesia (amnestic mild cognitive impairment, aMCI), and then to the terminal stage with extensive loss of basic cognitive functions, i.e., AD-dementia. Positron emission tomography (PET) radiotracers have been developed in a search to meet the increasing clinical need of early detection and treatment monitoring for AD, with reference to the pathophysiological targets in Alzheimer's brain. These include the pathological aggregations of misfolded proteins such as β-amyloid (Aβ) plagues and neurofibrillary tangles (NFTs), impaired neurotransmitter system, neuroinflammation, as well as deficient synaptic vesicles and glucose utilization. In this article we survey the various PET radiotracers available for AD imaging and discuss their clinical applications especially in terms of early detection and cognitive relevance.
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Affiliation(s)
- Weiqi Bao
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Yiyun Henry Huang
- Department of Radiology and Biomedical Imaging, PET Center, Yale University School of Medicine, New Haven, CT, United States
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24
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Ni R, Röjdner J, Voytenko L, Dyrks T, Thiele A, Marutle A, Nordberg A. In vitro Characterization of the Regional Binding Distribution of Amyloid PET Tracer Florbetaben and the Glia Tracers Deprenyl and PK11195 in Autopsy Alzheimer's Brain Tissue. J Alzheimers Dis 2021; 80:1723-1737. [PMID: 33749648 PMCID: PMC8150513 DOI: 10.3233/jad-201344] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Emerging evidence indicates a central role of gliosis in Alzheimer's disease (AD) pathophysiology. However, the regional distribution and interaction of astrogliosis and microgliosis in association with amyloid-β (Aβ) still remain uncertain. OBJECTIVE Here we studied the pathological profiles in autopsy AD brain by using specific imaging tracers. METHODS Autopsy brain tissues of AD (n = 15, age 70.4±8.5 years) and control cases (n = 12, age 76.6±10.9) were examined with homogenate binding assays, autoradiography for Aβ plaques (3H-florbetaben/3H-PIB), astrogliosis (3H-L-deprenyl), and microgliosis (3H-PK11195/3H-FEMPA), as well as immunoassays. RESULTS In vitro saturation analysis revealed high-affinity binding sites of 3H-florbetaben, 3H-L-deprenyl, and 3H-PK11195/3H-FEMPA in the frontal cortex of AD cases. In vitro3H-florbetaben binding increased across cortical and subcortical regions of AD compared to control with the highest binding in the frontal and parietal cortices. The in vitro3H-L-deprenyl binding showed highest binding in the hippocampus (dentate gyrus) followed by cortical and subcortical regions of AD while the GFAP expression was upregulated only in the hippocampus compared to control. The in vitro3H-PK11195 binding was solely increased in the parietal cortex and the hippocampus of AD compared to control. The 3H-florbetaben binding positively correlated with the 3H-L-deprenyl binding in the hippocampus and parietal cortex of AD and controls. Similarly, a positive correlation was observed between 3H-florbetaben binding and GFAP expression in hippocampus of AD and control. CONCLUSION The use of multi-imaging tracers revealed different regional pattern of changes in autopsy AD brain with respect to amyloid plaque pathology versus astrogliosis and microgliosis.
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Affiliation(s)
- Ruiqing Ni
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Jennie Röjdner
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Larysa Voytenko
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Amelia Marutle
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, The Aging Brain Unit, Karolinska University Hospital, Stockholm, Sweden
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25
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Lopes Alves I, Heeman F, Collij LE, Salvadó G, Tolboom N, Vilor-Tejedor N, Markiewicz P, Yaqub M, Cash D, Mormino EC, Insel PS, Boellaard R, van Berckel BNM, Lammertsma AA, Barkhof F, Gispert JD. Strategies to reduce sample sizes in Alzheimer's disease primary and secondary prevention trials using longitudinal amyloid PET imaging. Alzheimers Res Ther 2021; 13:82. [PMID: 33875021 PMCID: PMC8056524 DOI: 10.1186/s13195-021-00819-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/26/2021] [Indexed: 01/07/2023]
Abstract
BACKGROUND Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials. METHODS Baseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only. RESULTS Although highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR). CONCLUSION Sample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.
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Affiliation(s)
- Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Nelleke Tolboom
- Imaging Division, Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Natàlia Vilor-Tejedor
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Pawel Markiewicz
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - David Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Philip S Insel
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain.
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Schwarz AJ. The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders. Neurotherapeutics 2021; 18:686-708. [PMID: 33846962 PMCID: PMC8423963 DOI: 10.1007/s13311-021-01027-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
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Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
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Collij LE, Ingala S, Top H, Wottschel V, Stickney KE, Tomassen J, Konijnenberg E, ten Kate M, Sudre C, Lopes Alves I, Yaqub MM, Wink AM, Van ‘t Ent D, Scheltens P, van Berckel BN, Visser PJ, Barkhof F, Braber AD. White matter microstructure disruption in early stage amyloid pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12124. [PMID: 33816751 PMCID: PMC8015832 DOI: 10.1002/dad2.12124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. METHODS We included 179 individuals from the European Medical Information Framework for AD (EMIF-AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract-level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. RESULTS Regression analyses showed a non-linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. DISCUSSION Early amyloid deposition is associated with changes in WM microstructure. The non-linear relationship might reflect multiple stages of axonal damage.
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Affiliation(s)
- Lyduine E. Collij
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Silvia Ingala
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Herwin Top
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Viktor Wottschel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Jori Tomassen
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Mara ten Kate
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Carole Sudre
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Isadora Lopes Alves
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Maqsood M. Yaqub
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Alle Meije Wink
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Dennis Van ‘t Ent
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
| | - Philip Scheltens
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Bart N.M. van Berckel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Pieter Jelle Visser
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience (MHeNS), Alzheimer Centrum LimburgMaastricht UniversityMaastrichtThe Netherlands
- Department of NeurobiologyCare Sciences Division of NeurogeriatricsKarolinska InstitutetStockholmSweden
| | - Frederik Barkhof
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Anouk Den Braber
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
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28
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Collij LE, Salvadó G, Shekari M, Lopes Alves I, Reimand J, Wink AM, Zwan M, Niñerola-Baizán A, Perissinotti A, Scheltens P, Ikonomovic MD, Smith APL, Farrar G, Molinuevo JL, Barkhof F, Buckley CJ, van Berckel BNM, Gispert JD. Visual assessment of [ 18F]flutemetamol PET images can detect early amyloid pathology and grade its extent. Eur J Nucl Med Mol Imaging 2021; 48:2169-2182. [PMID: 33615397 PMCID: PMC8175297 DOI: 10.1007/s00259-020-05174-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/20/2020] [Indexed: 11/08/2022]
Abstract
Purpose To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR. Methods [18F]Flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0–5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden’s index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [18F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density. Results VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERADSOT-based classification (i.e., any region mCERADSOT > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density. Conclusion VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05174-2.
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Affiliation(s)
- Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Juhan Reimand
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia.,Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Marissa Zwan
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Andrés Perissinotti
- Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Philip Scheltens
- Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
| | | | | | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Universitat Pompeu Fabra, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands.,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
| | | | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands. .,Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1117, 1108 HV, Amsterdam, The Netherlands.
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. .,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. .,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. .,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. .,Alzheimer Prevention Program, BarcelonaBeta Brain Research Center (BBRC), C/ Wellington, 30, 08005, Barcelona, Spain.
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Lee S, Cho EJ, Kwak HB. Personalized Healthcare for Dementia. Healthcare (Basel) 2021; 9:healthcare9020128. [PMID: 33525656 PMCID: PMC7910906 DOI: 10.3390/healthcare9020128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 01/07/2023] Open
Abstract
Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.
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Affiliation(s)
- Seunghyeon Lee
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
- Department of Chemical Engineering, Inha University, Incheon 22212, Korea
| | - Eun-Jeong Cho
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
| | - Hyo-Bum Kwak
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
- Correspondence: ; Tel.: +82-32-860-8183
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30
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Bao YW, Chau ACM, Chiu PKC, Shea YF, Kwan JSK, Chan FHW, Mak HKF. Heterogeneity of Amyloid Binding in Cognitively Impaired Patients Consecutively Recruited from a Memory Clinic: Evaluating the Utility of Quantitative 18F-Flutemetamol PET-CT in Discrimination of Mild Cognitive Impairment from Alzheimer's Disease and Other Dementias. J Alzheimers Dis 2021; 79:819-832. [PMID: 33361593 PMCID: PMC7902948 DOI: 10.3233/jad-200890] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker. OBJECTIVE 18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer's disease (AD), and 2) MCI from other non-AD dementias (OD). METHODS 109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare). RESULTS The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively. CONCLUSION 18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.
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Affiliation(s)
- Yi-Wen Bao
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Anson C M Chau
- Department of Medical Imaging, The University of Hong Kong (Shenzhen) Teaching Hospital , The University of Hong Kong, Hong Kong SAR, China
| | - Patrick Ka-Chun Chiu
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Yat Fung Shea
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Joseph S K Kwan
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Felix Hon Wai Chan
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
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Abstract
Amyloid-β (Aβ) PET imaging has now been available for over 15 years. The ability to detect Aβ in vivo has greatly improved the clinical and research landscape of Alzheimer's disease (AD) and other neurodegenerative conditions. Aβ imaging provides very reliable, accurate, and reproducible measurements of regional and global Aβ burden in the brain. It has proved invaluable in anti-Aβ therapy trials, and is now recognized as a powerful diagnostic tool. The appropriate use of Aβ PET, when combined with comprehensive clinical evaluation by a dementia-trained specialist, can improve the accuracy of a clinical diagnosis of AD and substantially alter management. It can assist in differentiating AD from other neurodegenerative conditions, often by its ability to rule out the presence of Aβ. When combined with tau imaging, further increase in specificity for the diagnosis of AD can be achieved. The integration of Aβ PET, in conjunction with biomarkers of tau, neurodegeneration and neuroinflammation, into large, longitudinal, observational cohort studies continues to increase our understanding of the development of AD. Its incorporation into clinical trials has been pivotal in defining the most effective anti-Aβ biological therapies and optimal dosing so that effective disease modifying therapy now appears imminent. Aβ deposition is a gradual and protracted process, permitting a wide treatment window for anti-Aβ therapies and Aβ PET has made trials in this preclinical AD period feasible. Continuing improvement in Aβ tracer target to background ratio is allowing trials in earlier AD that tailor drug dosage to Aβ level. The quest to standardize quantification and define universally applicable thresholds for all Aβ tracers has produced the Centiloid method. Centiloid values that correlate well with neuropathologic findings and prognosis have been identified. Rapid cloud-based automated individual scan analysis is now possible and does not require MRI. Challenges remain, particularly around cross camera standardized uptake value ratio variation that need to be addressed. This review will compare available Aβ radiotracers, discuss approaches to quantification, as well as the clinical and research applications of Aβ PET.
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Affiliation(s)
- Natasha Krishnadas
- Florey Department of Neurosciences and Mental Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia; Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia; Health and Biosecurity Flagship, The Australian eHealth Research Centre, CSIRO, Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia; The Australian Dementia Network (ADNeT), Melbourne, Australia; The University of Melbourne, Victoria, Australia.
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Teipel SJ, Temp AGM, Levin F, Dyrba M, Grothe MJ. Association of PET-based stages of amyloid deposition with neuropathological markers of Aβ pathology. Ann Clin Transl Neurol 2021; 8:29-42. [PMID: 33137247 PMCID: PMC7818279 DOI: 10.1002/acn3.51238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/22/2020] [Accepted: 10/02/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To determine if PET-based stages of regional amyloid deposition are associated with neuropathological phases of Aβ pathology. METHODS We applied data-driven regional frequency-based and a-priori striatum-based PET staging approaches to ante-mortem 18F-Florbetapir-PET scans of 30 cases from the Alzheimer's Disease Neuroimaging Initiative autopsy cohort, and used Bayesian regression analysis to study the associations of these in vivo amyloid stages with neuropathological Thal phases of regional Aβ plaque distribution and with semi-quantitative ratings of neocortical and striatal plaque densities. RESULTS Bayesian regression revealed extreme evidence for an association of both PET-based staging approaches with Thal phases, and these associations were about 44 times more likely for frequency-based stages and 89 times more likely for striatum-based stages than for global cortical 18F-Florbetapir-PET signal. Early (i.e., neocortical-only) PET-based amyloid stages also predicted the absence of striatal/diencephalic cored plaques. Receiver operating characteristics curves revealed highly accurate discrimination between low/high Thal phases and the presence/absence of regional plaques. The median areas under the curve were 0.99 for frequency-based staging (95% credibility interval 0.97-1.00), 0.93 for striatum-based staging (0.83-1.00), and 0.87 for global 18F-Florbetapir-PET signal (0.72-0.98). INTERPRETATION Our data indicate that both regional frequency- and striatum-based amyloid-PET staging approaches were superior to standard global amyloid-PET signal for differentiating between low and high degrees of regional amyloid pathology spread. Despite this, we found no evidence for the ability of either staging scheme to differentiate between low and moderate degrees of amyloid pathology which may be particularly relevant for early, preclinical stages of Alzheimer's disease.
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Affiliation(s)
- Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Department of Psychosomatic MedicineUniversity Medicine RostockRostockGermany
| | - Anna G. M. Temp
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Fedor Levin
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
- Servicio de Neurología y Neurofisiología ClínicaUnidad de Trastornos del MovimientoInstituto de Biomedicina de SevillaHospital Universitario Virgen del Rocío/CSICUniversidad de SevillaSevilleSpain
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Leuzy A, Lilja J, Buckley CJ, Ossenkoppele R, Palmqvist S, Battle M, Farrar G, Thal DR, Janelidze S, Stomrud E, Strandberg O, Smith R, Hansson O. Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load. Neurology 2020; 95:e2834-e2844. [PMID: 33077542 PMCID: PMC7734735 DOI: 10.1212/wnl.0000000000011031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 08/03/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate a novel β-amyloid (Aβ)-PET-based quantitative measure (Aβ accumulation index [Aβ index]), including the assessment of its ability to discriminate between participants based on Aβ status using visual read, CSF Aβ42/Aβ40, and post-mortem neuritic plaque burden as standards of truth. METHODS One thousand one hundred twenty-one participants (with and without cognitive impairment) were scanned with Aβ-PET: Swedish BioFINDER, n = 392, [18F]flutemetamol; Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 692, [18F]florbetapir; and a phase 3 end-of-life study, n = 100, [18F]flutemetamol. The relationships between Aβ index and standardized uptake values ratios (SUVR) from Aβ-PET were assessed. The diagnostic performances of Aβ index and SUVR were compared with visual reads, CSF Aβ42/Aβ40, and Aβ histopathology used as reference standards. RESULTS Strong associations were observed between Aβ index and SUVR (R 2: BioFINDER 0.951, ADNI 0.943, end-of-life, 0.916). Both measures performed equally well in differentiating Aβ-positive from Aβ-negative participants, with areas under the curve (AUCs) of 0.979 to 0.991 to detect abnormal visual reads, AUCs of 0.961 to 0.966 to detect abnormal CSF Aβ42/Aβ40, and AUCs of 0.820 to 0.823 to detect abnormal Aβ histopathology. Both measures also showed a similar distribution across postmortem-based Aβ phases (based on anti-Aβ 4G8 antibodies). Compared to models using visual read alone, the addition of the Aβ index resulted in a significant increase in AUC and a decrease in Akaike information criterion to detect abnormal Aβ histopathology. CONCLUSION The proposed Aβ index showed a tight association to SUVR and carries an advantage over the latter in that it does not require the definition of regions of interest or the use of MRI. Aβ index may thus prove simpler to implement in clinical settings and may also facilitate the comparison of findings using different Aβ-PET tracers. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that the Aβ accumulation index accurately differentiates Aβ-positive from Aβ-negative participants compared to Aβ-PET visual reads, CSF Aβ42/Aβ40, and Aβ histopathology.
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Affiliation(s)
- Antoine Leuzy
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium.
| | - Johan Lilja
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Christopher J Buckley
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Rik Ossenkoppele
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Sebastian Palmqvist
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Mark Battle
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Gill Farrar
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Dietmar R Thal
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Shorena Janelidze
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Erik Stomrud
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Olof Strandberg
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Ruben Smith
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
| | - Oskar Hansson
- From the Clinical Memory Research Unit (A.L., J.L., R.O., S.P., S.J., E.S., O.S., R.S., O.H.), Department of Clinical Sciences, Lund University, Malmö; Department of Surgical Sciences, Nuclear Medicine and PET (J.L.), Uppsala University; Hermes Medical Solutions (J.L.), Stockholm, Sweden; GE Healthcare Life Sciences (C.J.B., M.B., G.F.), Amersham, UK; VU University Medical Center (R.O.), Neuroscience Campus Amsterdam, the Netherlands; Department of Neurology (S.P., R.S.) and Memory Clinic (E.S., O.H.), Skåne University Hospital, Lund, Sweden; Department of Imaging and Pathology (D.R.T.), Laboratory of Neuropathology, and Leuven Brain Institute (D.R.T.), Campus Gasthuisberg; and Department of Pathology (D.R.T.), UZ-Leuven, Belgium
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Dani M, Wood M, Mizoguchi R, Fan Z, Edginton T, Hinz R, Win Z, Brooks DJ, Edison P. Tau Aggregation Correlates with Amyloid Deposition in Both Mild Cognitive Impairment and Alzheimer's Disease Subjects. J Alzheimers Dis 2020; 70:455-465. [PMID: 31256120 DOI: 10.3233/jad-181168] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Amyloid plaque and tau-containing neurofibrillary tangles are important features of Alzheimer's disease (AD). However, the relationship between these processes is still debated. OBJECTIVE We aimed to investigate local and distant relationships between tau and amyloid deposition in the cortex in mild cognitive impairment (MCI) and AD using PET imaging. METHODS Seventy-nine subjects (51 controls, 13 amyloid-positive MCI subjects, and 15 amyloid positive AD subjects) underwent MRI and 18F-flutemetamol PET. All MCI/AD subjects and 8 healthy controls as well as 33 healthy control subjects from the ADNI dataset also had 18F-AV1451 PET. Regional and distant correlations were examined after sampling target-to-cerebellar ratio images. Biological parametric mapping was used to evaluate voxel level correlations locally. RESULTS We found multiple clusters of voxels with highly significant positive correlations throughout the association cortex in both MCI and AD subjects. CONCLUSION The multiple clusters of positive correlations indicate that tau and amyloid may interact locally and be involved in disease progression. Our findings suggest that targeting both pathologies may be required.
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Affiliation(s)
- Melanie Dani
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK
| | - Melanie Wood
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK
| | - Ruth Mizoguchi
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK
| | - Zhen Fan
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK
| | - Trudi Edginton
- Department of Psychology, City University of London, London, UK
| | - Rainer Hinz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Zarni Win
- Imperial College Healthcare NHS Trust, Charing Cross Hospital, London, UK
| | - David James Brooks
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK.,Department of Nuclear Medicine, Aarhus University, Aarhus, Denmark.,Institute of Neuroscience, University of Newcastle upon Tyne, Newcastle University Campus for Ageing and Vitality, Newcastle, UK
| | - Paul Edison
- Neurology Imaging Unit, Department of Medicine, Imperial College London, London, UK
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Koychev I, Hofer M, Friedman N. Correlation of Alzheimer Disease Neuropathologic Staging with Amyloid and Tau Scintigraphic Imaging Biomarkers. J Nucl Med 2020; 61:1413-1418. [PMID: 32764121 DOI: 10.2967/jnumed.119.230458] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/30/2020] [Indexed: 11/16/2022] Open
Abstract
PET neuroimaging of amyloid-β (Aβ) provides an in vivo biomarker for pathologic changes associated with Alzheimer disease (AD). Aβ-targeted agents have been approved by the Food and Drug Administration, with additional agents, most notably targeting tau, currently under clinical investigation and one approved in May 2020. These agents, along with nonscintigraphic biomarkers from blood and cerebrospinal fluid, have provided an opportunity to investigate the pathogenesis, prodromal changes, and time course of the disease in living individuals. The current understanding is that the neuropathologic changes of the AD continuum begin up to 25 y before the onset of clinical symptomatology. The opportunities afforded by in vivo biomarkers of AD, whether by serum, cerebrospinal fluid examination or PET, have transformed the design of AD therapeutic trials by shifting focus to the preclinical stages of disease. Future disease-modifying therapies, should they be forthcoming, will rely heavily on the use of approved biomarkers or biomarkers currently under investigation to confirm the presence of target pathology. Understanding the progressive neuropathologic changes that occur in AD-and how scintigraphic findings relate to these changes-will help the interpreting physician to fully appreciate the implications of the scintigraphic findings and provide a basis to interpret the examinations. The recently adopted National Institute on Aging-Alzheimer Association guidelines define postmortem AD neuropathologic changes as a composite score based on 3 elements. These elements are the extent of involvement (spread) by cerebral Aβ based on the progression model defined by the Thal Aβ phases, the extent of involvement (spread) by neurofibrillary tangles (composed of hyperphosphorylated tau proteins) based on the progression model defined by Braak, and the Consortium to Establish a Registry for Alzheimer's Disease score, which describes the density of neuritic plaques based on certain key locations in the neocortex. This paper will review the 3 elements that define the National Institute on Aging-Alzheimer's Association scoring system and discusses current evidence on how these elements relate to findings based on Aβ and tau PET scintigraphy.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Monika Hofer
- Department of Neuropathology, Oxford University Hospitals, Oxford, United Kingdom; and
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Guo T, Shaw LM, Trojanowski JQ, Jagust WJ, Landau SM. Association of CSF Aβ, amyloid PET, and cognition in cognitively unimpaired elderly adults. Neurology 2020; 95:e2075-e2085. [PMID: 32759202 DOI: 10.1212/wnl.0000000000010596] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 04/28/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE To compare CSF β-amyloid (Aβ) and florbetapir PET measurements in cognitively unimpaired (CU) elderly adults in order to detect the earliest abnormalities and compare their predictive effect for cognitive decline. METHODS A total of 259 CU individuals were categorized as abnormal (+) or normal (-) on CSF Aβ1-42/Aβ1-40 analyzed with mass spectrometry and Aβ PET measured with 18F-florbetapir. Simultaneous longitudinal measurements of CSF and PET were compared for 39 individuals who were unambiguously Aβ-negative at baseline (CSF-/PET-). We also examined the relationship between baseline CSF/PET group membership and longitudinal changes in CSF Aβ, Aβ PET, and cognition. RESULTS The proportions of individuals in each discordant group were similar (8.1% CSF+/PET- and 7.7% CSF-/PET+). Among baseline Aβ-negative (CSF-/PET-) individuals with longitudinal CSF and PET measurements, a larger proportion subsequently worsened on CSF Aβ (odds ratio 4 [95% confidence interval (CI) 1.1, 22.1], p = 0.035) than Aβ PET over 3.5 ± 1.0 years. Compared to CSF-/PET- individuals, CSF+/PET- individuals had faster (estimate 0.009 [95% CI 0.005, 0.013], p < 0.001) rates of Aβ PET accumulation over 4.4 ± 1.7 years, while CSF-/PET+ individuals had faster (estimate -0.492 [95% CI -0.861, -0.123], p = 0.01) rates of cognitive decline over 4.5 ± 1.9 years. CONCLUSIONS The proportions of discordant PET and CSF Aβ-positive individuals were similar cross-sectionally. However, unambiguously Aβ-negative (CSF-/PET-) individuals are more likely to show subsequent worsening on CSF than PET, supporting the idea that CSF detects the earliest Aβ changes. In discordant cases, only PET abnormality predicted cognitive decline, suggesting that abnormal Aβ PET changes are a later phenomenon in cognitively normal individuals.
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Affiliation(s)
- Tengfei Guo
- From the Helen Wills Neuroscience Institute (T.G., W.J.J., S.M.L.), University of California; Molecular Biophysics and Integrated Bioimaging (T.G., W.J.J., S.M.L.), Lawrence Berkeley National Laboratory, Berkeley, CA; and Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.
| | - Leslie M Shaw
- From the Helen Wills Neuroscience Institute (T.G., W.J.J., S.M.L.), University of California; Molecular Biophysics and Integrated Bioimaging (T.G., W.J.J., S.M.L.), Lawrence Berkeley National Laboratory, Berkeley, CA; and Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - John Q Trojanowski
- From the Helen Wills Neuroscience Institute (T.G., W.J.J., S.M.L.), University of California; Molecular Biophysics and Integrated Bioimaging (T.G., W.J.J., S.M.L.), Lawrence Berkeley National Laboratory, Berkeley, CA; and Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - William J Jagust
- From the Helen Wills Neuroscience Institute (T.G., W.J.J., S.M.L.), University of California; Molecular Biophysics and Integrated Bioimaging (T.G., W.J.J., S.M.L.), Lawrence Berkeley National Laboratory, Berkeley, CA; and Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Susan M Landau
- From the Helen Wills Neuroscience Institute (T.G., W.J.J., S.M.L.), University of California; Molecular Biophysics and Integrated Bioimaging (T.G., W.J.J., S.M.L.), Lawrence Berkeley National Laboratory, Berkeley, CA; and Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Collij LE, Heeman F, Salvadó G, Ingala S, Altomare D, de Wilde A, Konijnenberg E, van Buchem M, Yaqub M, Markiewicz P, Golla SSV, Wottschel V, Wink AM, Visser PJ, Teunissen CE, Lammertsma AA, Scheltens P, van der Flier WM, Boellaard R, van Berckel BNM, Molinuevo JL, Gispert JD, Schmidt ME, Barkhof F, Lopes Alves I. Multitracer model for staging cortical amyloid deposition using PET imaging. Neurology 2020; 95:e1538-e1553. [PMID: 32675080 PMCID: PMC7713745 DOI: 10.1212/wnl.0000000000010256] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 03/20/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]stage1 2.00, HRstage2 3.53, HRstage3 4.55, HRstage4 9.91, p < 0.001; OASIS: n = 469, HRstage4 4.80, p < 0.001). CONCLUSION The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.
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Affiliation(s)
- Lyduine E Collij
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Fiona Heeman
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Gemma Salvadó
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Silvia Ingala
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Daniele Altomare
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Arno de Wilde
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Elles Konijnenberg
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Marieke van Buchem
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Maqsood Yaqub
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pawel Markiewicz
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Sandeep S V Golla
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Viktor Wottschel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Alle Meije Wink
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Pieter Jelle Visser
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Charlotte E Teunissen
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Adriaan A Lammertsma
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Philip Scheltens
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Wiesje M van der Flier
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Ronald Boellaard
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Bart N M van Berckel
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - José Luis Molinuevo
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Juan Domingo Gispert
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Mark E Schmidt
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Frederik Barkhof
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium
| | - Isadora Lopes Alves
- From Department of Radiology and Nuclear Medicine (L.E.C., F.H., S.I., M.Y., S.S.V.G., V.W., A.M.W., A.A.L., R.B., B.N.M.v.B., F.B., I.L.A.), Neurochemistry Laboratory (C.E.T.), Alzheimer Center (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), and Department of Neurology (D.A., A.d.W., E.K., M.v.B., P.J.V., P.S., W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation (G.S., J.L.M., J.D.G.), Barcelona; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (J.D.G.); Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (J.L.M.), Madrid; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.L.M., J.D.G.), Barcelona; Universitat Pompeu Fabra (J.L.M., J.D.G.), Barcelona, Spain; Laboratory of Neuroimaging of Aging (D.A.), University of Geneva; Memory Clinic (D.A.), University Hospital of Geneva, Switzerland; Centre for Medical Image Computing (P.M., F.B.), Medical Physics and Biomedical Engineering, University College London, London, UK; and Janssen Pharmaceutica NV (M.E.S.), Beerse, Belgium.
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Jonkman LE, Steenwijk MD, Boesen N, Rozemuller AJM, Barkhof F, Geurts JJG, Douw L, van de Berg WDJ. Relationship between β-amyloid and structural network topology in decedents without dementia. Neurology 2020; 95:e532-e544. [PMID: 32661099 PMCID: PMC7455348 DOI: 10.1212/wnl.0000000000009910] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 01/14/2020] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia. METHODS Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures. RESULTS Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10-3, p = 0.014), degree (β = -0.47, p = 0.034), and clustering (β = -0.55 × 10-2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10-3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, p < 0.001) and local efficiency (β = 1.01 × 10-2, p = 0.014) and precuneus for clustering (β = -0.91 × 10-2, p = 0.017). There was no relationship between p-tau and network topology. CONCLUSION This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.
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Affiliation(s)
- Laura E Jonkman
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK.
| | - Martijn D Steenwijk
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Nicky Boesen
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Annemieke J M Rozemuller
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Frederik Barkhof
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Jeroen J G Geurts
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Linda Douw
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Wilma D J van de Berg
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
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Turner RS, Stubbs T, Davies DA, Albensi BC. Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias. Front Neurol 2020; 11:496. [PMID: 32582013 PMCID: PMC7290039 DOI: 10.3389/fneur.2020.00496] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/06/2020] [Indexed: 12/21/2022] Open
Abstract
Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.
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Affiliation(s)
- R Scott Turner
- Department of Neurology, Georgetown University, Washington, DC, United States
| | - Terry Stubbs
- ActivMed, Practices & Research, Methuen, MA, United States
| | - Don A Davies
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Ismail R, Parbo P, Madsen LS, Hansen AK, Hansen KV, Schaldemose JL, Kjeldsen PL, Stokholm MG, Gottrup H, Eskildsen SF, Brooks DJ. The relationships between neuroinflammation, beta-amyloid and tau deposition in Alzheimer's disease: a longitudinal PET study. J Neuroinflammation 2020; 17:151. [PMID: 32375809 PMCID: PMC7203856 DOI: 10.1186/s12974-020-01820-6] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 04/17/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The aim of this longitudinal study was to assess with positron emission tomography (PET) the relationship between levels of inflammation and the loads of aggregated β-amyloid and tau at baseline and again after 2 years in prodromal Alzheimer's disease. METHODS Forty-three subjects with mild cognitive impairment (MCI) had serial 11C-PK11195 PET over 2 years to measure inflammation changes, and 11C-PiB PET to determine β-amyloid fibril load; 22 also had serial 18F-Flortaucipir PET to determine tau tangle load. Cortical surface statistical mapping was used to localise areas showing significant changes in tracer binding over time and to interrogate correlations between tracer binding of the tracers at baseline and after 2 years. RESULTS Those MCI subjects with high 11C-PiB uptake at baseline (classified as prodromal Alzheimer's disease) had raised inflammation levels which significantly declined across cortical regions over 2 years although their β-amyloid levels continued to rise. Those MCI cases who had low/normal 11C-PiB uptake at baseline but their levels then rose over 2 years were classified as prodromal AD with low Thal phase 1-2 amyloid deposition at baseline. They showed levels of cortical inflammation which correlated with their rising β-amyloid load. Those MCI cases with baseline low 11C-PiB uptake that remained stable were classified as non-AD, and they showed no correlated inflammation levels. Finally, MCI cases which showed both high 11C-PiB and 18F-Flortaucipir uptake at baseline (MCI due to AD) showed a further rise in their tau tangle load over 2 years with a correlated rise in levels of inflammation. CONCLUSIONS Our baseline and 2-year imaging findings are compatible with a biphasic trajectory of inflammation in Alzheimer's disease: MCI cases with low baseline but subsequently rising β-amyloid load show correlated levels of microglial activation which then later decline when the β-amyloid load approaches AD levels. Later, as tau tangles form in β-amyloid positive MCI cases with prodromal AD, the rising tau load is associated with higher levels of inflammation.
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Affiliation(s)
- Rola Ismail
- Department of Clinical Medicine, PET-Centre, Aarhus University, Aarhus, Denmark.
| | - Peter Parbo
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, DK-8200, Aarhus N, Denmark
| | | | - Allan K Hansen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, DK-8200, Aarhus N, Denmark
| | - Kim V Hansen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, DK-8200, Aarhus N, Denmark
| | - Jeppe L Schaldemose
- Department of Clinical Medicine, PET-Centre, Aarhus University, Aarhus, Denmark
| | - Pernille L Kjeldsen
- Department of Clinical Medicine, PET-Centre, Aarhus University, Aarhus, Denmark
| | - Morten G Stokholm
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, DK-8200, Aarhus N, Denmark
| | - Hanne Gottrup
- Dept. of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Simon F Eskildsen
- Centre of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
| | - David J Brooks
- Department of Clinical Medicine, PET-Centre, Aarhus University, Aarhus, Denmark
- Institute of Neuroscience, University of Newcastle upon Tyne, Tyne, UK
- Department of Medicine, Imperial College London, London, UK
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Lopes Alves I, Collij LE, Altomare D, Frisoni GB, Saint‐Aubert L, Payoux P, Kivipelto M, Jessen F, Drzezga A, Leeuwis A, Wink AM, Visser PJ, van Berckel BN, Scheltens P, Gray KR, Wolz R, Stephens A, Gismondi R, Buckely C, Gispert JD, Schmidt M, Ford L, Ritchie C, Farrar G, Barkhof F, Molinuevo JL. Quantitative amyloid PET in Alzheimer's disease: the AMYPAD prognostic and natural history study. Alzheimers Dement 2020; 16:750-758. [PMID: 32281303 PMCID: PMC7984341 DOI: 10.1002/alz.12069] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/12/2019] [Accepted: 12/23/2019] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) Prognostic and Natural History Study (PNHS) aims at understanding the role of amyloid imaging in the earliest stages of Alzheimer's disease (AD). AMYPAD PNHS adds (semi-)quantitative amyloid PET imaging to several European parent cohorts (PCs) to predict AD-related progression as well as address methodological challenges in amyloid PET. METHODS AMYPAD PNHS is an open-label, prospective, multi-center, cohort study recruiting from multiple PCs. Around 2000 participants will undergo baseline amyloid positron emission tomography (PET), half of whom will be invited for a follow-up PET 12 at least 12 months later. RESULTS Primary include several amyloid PET measurements (Centiloid, SUVr, BPND , R1 ), and secondary are their changes from baseline, relationship to other amyloid markers (cerebrospinal fluid and visual assessment), and predictive value of AD-related decline. EXPECTED IMPACT Determining the role of amyloid PET for the understanding of this complex disease and potentially improving secondary prevention trials.
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Affiliation(s)
- Isadora Lopes Alves
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
- Memory ClinicUniversity Hospital of GenevaGenevaSwitzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
- Memory ClinicUniversity Hospital of GenevaGenevaSwitzerland
| | - Laure Saint‐Aubert
- Department of Nuclear MedicineImaging PoleToulouse, University HospitalToulouseFrance
- Toulouse NeuroImaging CenterUniversité de Toulouse, Inserm, UPSToulouseFrance
| | - Pierre Payoux
- Department of Nuclear MedicineImaging PoleToulouse, University HospitalToulouseFrance
- Toulouse NeuroImaging CenterUniversité de Toulouse, Inserm, UPSToulouseFrance
| | - Miia Kivipelto
- Department of Geriatric MedicineKarolinska University Hospital HuddingeStockholmSweden
| | - Frank Jessen
- Department of Nuclear MedicineUniversity of CologneCologneGermany
| | | | - Annebet Leeuwis
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | - Bart N.M. van Berckel
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Philip Scheltens
- Department of Neurology, Amsterdam UMCVrije Universiteit AmsterdamAlzheimercenterAmsterdamthe Netherlands
| | | | | | | | | | | | - Juan Domingo Gispert
- Barcelona β Brain Research CenterBarcelonaSpain
- Centro de Investigación Biomédica en Red de BioingenieríaBiomateriales y Nanomedicina (CIBER‐BBN)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | | | - Lisa Ford
- Janssen Pharmaceutica RNDTitusvilleNew JerseyUSA
| | - Craig Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Gill Farrar
- GE HealthcareLife SciencesAmershamUnited Kingdom
| | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
- Centre for Medical Image ComputingMedical Physics and Biomedical Engineering, UCLLondonUnited Kingdom
| | - José Luis Molinuevo
- Barcelona β Brain Research CenterBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
| | - the AMYPAD Consortium
- Department of Radiology and Nuclear MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamthe Netherlands
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Trends in the public health significance, definitions of disease, and implications for prevention of Alzheimer’s disease. CURR EPIDEMIOL REP 2020; 7:68-76. [DOI: 10.1007/s40471-020-00231-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Weng CC, Hsiao IT, Yang QF, Yao CH, Tai CY, Wu MF, Yen TC, Jang MK, Lin KJ. Characterization of 18F-PM-PBB3 ( 18F-APN-1607) Uptake in the rTg4510 Mouse Model of Tauopathy. Molecules 2020; 25:molecules25071750. [PMID: 32290239 PMCID: PMC7181044 DOI: 10.3390/molecules25071750] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/05/2020] [Accepted: 04/07/2020] [Indexed: 12/03/2022] Open
Abstract
Misfolding, aggregation, and cerebral accumulation of tau deposits are hallmark features of Alzheimer’s disease. Positron emission tomography study of tau can facilitate the development of anti-tau treatment. Here, we investigated a novel tau tracer 18F-PM-PBB3 (18F-APN-1607) in a mouse model of tauopathy. Dynamic PET scans were collected in groups of rTg4510 transgenic mice at 2–11 months of age. Associations between distribution volume ratios (DVR) and standardized uptake value ratios (SUVR) with cerebellum reference were used to determine the optimal scanning time and uptake pattern for each age. Immunohistochemistry staining of neurofibrillary tangles and autoradiography study was performed for ex vivo validation. An SUVR 40–70 min was most consistently correlated with DVR and was used in further analyses. Significant increased 18F-PM-PBB3 uptake in the brain cortex was found in six-month-old mice (+28.9%, p < 0.05), and increased further in the nine-month-old group (+38.8%, p < 0.01). The trend of increased SUVR value remained evident in the hippocampus and striatum regions except for cortex where uptake becomes slightly reduced in 11-month-old animals (+37.3%, p < 0.05). Radioactivity distributions from autoradiography correlate well to the presence of human tau (HT7 antibody) and hyperphosphorylated tau (antibody AT8) from the immunohistochemistry study of the adjacent brain sections. These findings supported that the 40–70 min 18F-PM-PBB3 PET scan with SUVR measurement can detect significantly increased tau deposits in a living rTg4510 transgenic mouse models as early as six-months-old. The result exhibited promising dynamic imaging capability of this novel tau tracer, and the above image characteristics should be considered in the design of longitudinal preclinical tau image studies.
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Affiliation(s)
- Chi-Chang Weng
- HARC and Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan; (C.C.-W.); (I.-T.H.); (Q.-F.Y.)
- Department of Nuclear Medicine and Center for Advanced Molecular Imaging and Translation, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Ing-Tsung Hsiao
- HARC and Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan; (C.C.-W.); (I.-T.H.); (Q.-F.Y.)
- Department of Nuclear Medicine and Center for Advanced Molecular Imaging and Translation, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Qing-Fang Yang
- HARC and Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan; (C.C.-W.); (I.-T.H.); (Q.-F.Y.)
| | - Cheng-Hsiang Yao
- Department of Nuclear Medicine and Center for Advanced Molecular Imaging and Translation, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Chin-Yin Tai
- APRINOIA Therapeutics Inc., Taipei 11503, Taiwan; (C.-Y.T.); (M.-F.W.); (T.-C.Y.); (M.-K.J.)
| | - Meng-Fang Wu
- APRINOIA Therapeutics Inc., Taipei 11503, Taiwan; (C.-Y.T.); (M.-F.W.); (T.-C.Y.); (M.-K.J.)
| | - Tzu-Chen Yen
- APRINOIA Therapeutics Inc., Taipei 11503, Taiwan; (C.-Y.T.); (M.-F.W.); (T.-C.Y.); (M.-K.J.)
| | - Ming-Kuei Jang
- APRINOIA Therapeutics Inc., Taipei 11503, Taiwan; (C.-Y.T.); (M.-F.W.); (T.-C.Y.); (M.-K.J.)
| | - Kun-Ju Lin
- Department of Nuclear Medicine and Center for Advanced Molecular Imaging and Translation, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Correspondence:
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A kinetics-based approach to amyloid PET semi-quantification. Eur J Nucl Med Mol Imaging 2020; 47:2175-2185. [PMID: 31982991 DOI: 10.1007/s00259-020-04689-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 01/07/2020] [Indexed: 10/25/2022]
Abstract
PURPOSE To develop and validate a semi-quantification method (time-delayed ratio, TDr) applied to amyloid PET scans, based on tracer kinetics information. METHODS The TDr method requires two static scans per subject: one early (~ 0-10 min after the injection) and one late (typically 50-70 min or 90-100 min after the injection, depending on the tracer). High perfusion regions are delineated on the early scan and applied onto the late scan. A SUVr-like ratio is calculated between the average intensities in the high perfusion regions and the late scan hotspot. TDr was applied to a naturalistic multicenter dataset of 143 subjects acquired with [18F]florbetapir. TDr values are compared to visual evaluation, cortical-cerebellar SUVr, and to the geometrical semi-quantification method ELBA. All three methods are gauged versus the heterogeneity of the dataset. RESULTS TDr shows excellent agreement with respect to the binary visual assessment (AUC = 0.99) and significantly correlates with both validated semi-quantification methods, reaching a Pearson correlation coefficient of 0.86 with respect to ELBA. CONCLUSIONS TDr is an alternative approach to previously validated ones (SUVr and ELBA). It requires minimal image processing; it is independent on predefined regions of interest and does not require MR registration. Besides, it takes advantage on the availability of early scans which are becoming common practice while imposing a negligible added patient discomfort.
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Hanseeuw BJ, Jonas V, Jackson J, Betensky RA, Rentz DM, Johnson KA, Sperling RA, Donovan NJ. Association of anxiety with subcortical amyloidosis in cognitively normal older adults. Mol Psychiatry 2020; 25:2599-2607. [PMID: 30116029 PMCID: PMC6377864 DOI: 10.1038/s41380-018-0214-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 05/30/2018] [Accepted: 06/20/2018] [Indexed: 01/05/2023]
Abstract
Late-life anxiety has been associated with increased progression from normal cognition to amnestic MCI, suggesting that anxiety may be a neuropsychiatric symptom of Alzheimer's disease (AD) pathological changes and a possible marker of anatomical progression in preclinical AD. This study examined whether cortical or subcortical amyloidosis, indicating earlier or later stages of preclinical AD, was associated with greater self-reported anxiety among 118 cognitively normal volunteers, aged 65-90 years, and whether this association was stronger in APOEε4 carriers. Participants underwent Pittsburgh Compound B Positron Emission Tomography (PiB-PET) to assess fibrillar amyloid-β burden in cortical and subcortical regions, and measurement of anxiety using the Hospital Anxiety and Depression Scale-anxiety subscale. Higher PiB-PET measures in the subcortex (striatum, amygdala, and thalamus), but not in the cortex, were associated with greater anxiety, adjusting for demographics, cognition, and depression. Findings were similar using a cortico-striatal staging system and continuous PET measurements. Anxiety was highest in APOEε4 carriers with subcortical amyloidosis. This work supports in vivo staging of amyloid-β deposition in both cortical and subcortical regions as a promising approach to the study of neuropsychiatric symptoms such as anxiety in cognitively normal older individuals. Elevated anxiety symptoms in combination with high-risk biological factors such as APOEε4 and subcortical amyloid-β may identify participants closest to MCI for secondary prevention trials.
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Affiliation(s)
- Bernard J. Hanseeuw
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,grid.32224.350000 0004 0386 9924Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,grid.7942.80000 0001 2294 713XDepartment of Neurology, Cliniques Universitaires Saint-Luc, Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Victoria Jonas
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA
| | - Jonathan Jackson
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA
| | - Rebecca A. Betensky
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA
| | - Dorene M. Rentz
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Keith A. Johnson
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,grid.32224.350000 0004 0386 9924Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Reisa A. Sperling
- grid.32224.350000 0004 0386 9924Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA ,Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Nancy J. Donovan
- Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA ,grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 USA
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Kim JP, Kim J, Kim Y, Moon SH, Park YH, Yoo S, Jang H, Kim HJ, Na DL, Seo SW, Seong JK. Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes. Eur J Nucl Med Mol Imaging 2019; 47:1971-1983. [PMID: 31884562 PMCID: PMC7299909 DOI: 10.1007/s00259-019-04663-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/16/2019] [Indexed: 01/18/2023]
Abstract
Purpose We developed a machine learning–based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes. Methods A total of 337 18F-florbetaben PET scans obtained at Samsung Medical Center were assessed. We defined a feature vector representing the change in PET tracer uptake from grey to white matter. Using support vector machine (SVM) regression and SVM classification, we quantified the cortical uptake as predicted regional cortical tracer uptake (pRCTU) and categorised the scans as positive and negative. Positive scans were further classified into two stages according to the striatal uptake. We compared outcome parameters among stages and further assessed the association between the pRCTU and outcome variables. Finally, we performed path analysis to determine mediation effects between PET variables. Results The classification accuracy was 97.3% for cortical amyloid positivity and 91.1% for striatal positivity. The left frontal and precuneus/posterior cingulate regions, as well as the anterior portion of the striatum, were important in determination of stages. The clinical scores and magnetic resonance imaging parameters showed negative associations with PET stage. However, except for the hippocampal volume, most outcomes were associated with the stage through the complete mediation effect of pRCTU. Conclusion Using a machine learning algorithm, we achieved high accuracy for in vivo amyloid PET staging. The in vivo amyloid stage was associated with cognitive function and cerebral atrophy mostly through the mediation effect of cortical amyloid. Electronic supplementary material The online version of this article (10.1007/s00259-019-04663-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Jeonghun Kim
- Department of Bio-convergence Engineering, Korea University, Seoul, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, South Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea
| | - Sole Yoo
- Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Seoul, South Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, South Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea. .,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea.
| | - Joon-Kyung Seong
- Department of Bio-convergence Engineering, Korea University, Seoul, South Korea. .,School of Biomedical Engineering, Korea University, Seoul, South Korea. .,Department of Artificial Intelligence, Korea University, Seoul, South Korea.
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Chandra A, Valkimadi PE, Pagano G, Cousins O, Dervenoulas G, Politis M. Applications of amyloid, tau, and neuroinflammation PET imaging to Alzheimer's disease and mild cognitive impairment. Hum Brain Mapp 2019; 40:5424-5442. [PMID: 31520513 PMCID: PMC6864887 DOI: 10.1002/hbm.24782] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 07/29/2019] [Accepted: 08/18/2019] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is a devastating and progressive neurodegenerative disease for which there is no cure. Mild cognitive impairment (MCI) is considered a prodromal stage of the disease. Molecular imaging with positron emission tomography (PET) allows for the in vivo visualisation and tracking of pathophysiological changes in AD and MCI. PET is a very promising methodology for differential diagnosis and novel targets of PET imaging might also serve as biomarkers for disease-modifying therapeutic interventions. This review provides an overview of the current status and applications of in vivo molecular imaging of AD pathology, specifically amyloid, tau, and microglial activation. PET imaging studies were included and evaluated as potential biomarkers and for monitoring disease progression. Although the majority of radiotracers showed the ability to discriminate AD and MCI patients from healthy controls, they had various limitations that prevent the recommendation of a single technique or tracer as an optimal biomarker. Newer research examining amyloid, tau, and microglial PET imaging in combination suggest an alternative approach in studying the disease process.
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Affiliation(s)
- Avinash Chandra
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Polytimi-Eleni Valkimadi
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Gennaro Pagano
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Oliver Cousins
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - George Dervenoulas
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Marios Politis
- Neurodegeneration Imaging Group (NIG), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
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Fantoni E, Collij L, Lopes Alves I, Buckley C, Farrar G. The Spatial-Temporal Ordering of Amyloid Pathology and Opportunities for PET Imaging. J Nucl Med 2019; 61:166-171. [PMID: 31836683 DOI: 10.2967/jnumed.119.235879] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 12/03/2019] [Indexed: 12/24/2022] Open
Abstract
Although clinical routine focuses on dichotomous and visual interpretation of amyloid PET, regional image assessment in research settings may yield additional opportunities. Understanding the regional-temporal evolution of amyloid pathology may enable earlier identification of subjects in the Alzheimer Disease pathologic continuum, as well as a finer-grained assessment of pathology beyond traditional dichotomous measures. This review summarizes current research in the detection of regional amyloid deposition patterns and its potential for staging amyloid pathology. Pathology studies, cross-sectional and longitudinal PET-only studies, and comparative PET and autopsy studies are included. Despite certain differences, cortical deposition generally precedes striatal pathology, and in PET-only studies, medial cortical regions are seen to accumulate amyloid earlier than lateral regions. Based on regional amyloid PET, multiple studies have developed and implemented models for staging amyloid pathology that could improve subject selection into secondary prevention trials and visual assessment in clinical routine.
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Affiliation(s)
- Enrico Fantoni
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St. Giles, Buckinghamshire, United Kingdom; and
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Christopher Buckley
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St. Giles, Buckinghamshire, United Kingdom; and
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Thal DR, Ronisz A, Tousseyn T, Rijal Upadhaya A, Balakrishnan K, Vandenberghe R, Vandenbulcke M, von Arnim CAF, Otto M, Beach TG, Lilja J, Heurling K, Chakrabarty A, Ismail A, Buckley C, Smith APL, Kumar S, Farrar G, Walter J. Different aspects of Alzheimer's disease-related amyloid β-peptide pathology and their relationship to amyloid positron emission tomography imaging and dementia. Acta Neuropathol Commun 2019; 7:178. [PMID: 31727169 PMCID: PMC6854805 DOI: 10.1186/s40478-019-0837-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD)-related amyloid β-peptide (Aβ) pathology in the form of amyloid plaques and cerebral amyloid angiopathy (CAA) spreads in its topographical distribution, increases in quantity, and undergoes qualitative changes in its composition of modified Aβ species throughout the pathogenesis of AD. It is not clear which of these aspects of Aβ pathology contribute to AD progression and to what extent amyloid positron emission tomography (PET) reflects each of these aspects. To address these questions three cohorts of human autopsy cases (in total n = 271) were neuropathologically and biochemically examined for the topographical distribution of Aβ pathology (plaques and CAA), its quantity and its composition. These parameters were compared with neurofibrillary tangle (NFT) and neuritic plaque pathology, the degree of dementia and the results from [18F]flutemetamol amyloid PET imaging in cohort 3. All three aspects of Aβ pathology correlated with one another, the estimation of Aβ pathology by [18F]flutemetamol PET, AD-related NFT pathology, neuritic plaques, and with the degree of dementia. These results show that one aspect of Aβ pathology can be used to predict the other two, and correlates well with the development of dementia, advancing NFT and neuritic plaque pathology. Moreover, amyloid PET estimates all three aspects of Aβ pathology in-vivo. Accordingly, amyloid PET-based estimates for staging of amyloid pathology indicate the progression status of amyloid pathology in general and, in doing so, also of AD pathology. Only 7.75% of our cases deviated from this general association.
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50
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de Wilde A, van der Flier WM, Pelkmans W, Bouwman F, Verwer J, Groot C, van Buchem MM, Zwan M, Ossenkoppele R, Yaqub M, Kunneman M, Smets EMA, Barkhof F, Lammertsma AA, Stephens A, van Lier E, Biessels GJ, van Berckel BN, Scheltens P. Association of Amyloid Positron Emission Tomography With Changes in Diagnosis and Patient Treatment in an Unselected Memory Clinic Cohort: The ABIDE Project. JAMA Neurol 2019; 75:1062-1070. [PMID: 29889941 DOI: 10.1001/jamaneurol.2018.1346] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance Previous studies have evaluated the diagnostic effect of amyloid positron emission tomography (PET) in selected research cohorts. However, these research populations do not reflect daily practice, thus hampering clinical implementation of amyloid imaging. Objective To evaluate the association of amyloid PET with changes in diagnosis, diagnostic confidence, treatment, and patients' experiences in an unselected memory clinic cohort. Design, Setting, and Participants Amyloid PET using fluoride-18 florbetaben was offered to 866 patients who visited the tertiary memory clinic at the VU University Medical Center between January 2015 and December 2016 as part of their routine diagnostic dementia workup. Of these patients, 476 (55%) were included, 32 (4%) were excluded, and 358 (41%) did not participate. To enrich this sample, 31 patients with mild cognitive impairment from the University Medical Center Utrecht memory clinic were included. For each patient, neurologists determined a preamyloid and postamyloid PET diagnosis that existed of both a clinical syndrome (dementia, mild cognitive impairment, or subjective cognitive decline) and a suspected etiology (Alzheimer disease [AD] or non-AD), with a confidence level ranging from 0% to 100%. In addition, the neurologist determined patient treatment in terms of ancillary investigations, medication, and care. Each patient received a clinical follow-up 1 year after being scanned. Main Outcomes and Measures Primary outcome measures were post-PET changes in diagnosis, diagnostic confidence, and patient treatment. Results Of the 507 patients (mean [SD] age, 65 (8) years; 201 women [39%]; mean [SD] Mini-Mental State Examination score, 25 [4]), 164 (32%) had AD dementia, 70 (14%) non-AD dementia, 114 (23%) mild cognitive impairment, and 159 (31%) subjective cognitive decline. Amyloid PET results were positive for 242 patients (48%). The suspected etiology changed for 125 patients (25%) after undergoing amyloid PET, more often due to a negative (82 of 265 [31%]) than a positive (43 of 242 [18%]) PET result (P < .01). Post-PET changes in suspected etiology occurred more frequently in patients older (>65 years) than younger (<65 years) than the typical age at onset of 65 years (74 of 257 [29%] vs 51 of 250 [20%]; P < .05). Mean diagnostic confidence (SD) increased from 80 (13) to 89 (13%) (P < .001). In 123 patients (24%), there was a change in patient treatment post-PET, mostly related to additional investigations and therapy. Conclusions and Relevance This prospective diagnostic study provides a bridge between validating amyloid PET in a research setting and implementing this diagnostic tool in daily clinical practice. Both amyloid-positive and amyloid-negative results had substantial associations with changes in diagnosis and treatment, both in patients with and without dementia.
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Affiliation(s)
- Arno de Wilde
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Wiesje Pelkmans
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Jurre Verwer
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Colin Groot
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marieke M van Buchem
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marissa Zwan
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Rik Ossenkoppele
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Marleen Kunneman
- Department of Medical Psychology, Amsterdam Neuroscience, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Ellen M A Smets
- Department of Medical Psychology, Amsterdam Neuroscience, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, England
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | | | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Bart N van Berckel
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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