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Spruyt L, Mlinarič T, Dusart N, Reinartz M, Meade G, Van Hulle MM, Van Laere K, Dupont P, Vandenberghe R. EEG-based graph network analysis in relation to regional tau in asymptomatic Alzheimer's disease. Brain Commun 2025; 7:fcaf138. [PMID: 40255689 PMCID: PMC12008720 DOI: 10.1093/braincomms/fcaf138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2024] [Revised: 03/07/2025] [Accepted: 04/13/2025] [Indexed: 04/22/2025] Open
Abstract
Tau aggregation in early affected regions in the asymptomatic stage of Alzheimer's disease marks a transitional phase between stable asymptomatic amyloid positivity and the clinically manifest stage. How this early region tau aggregation covertly affects brain function during this asymptomatic stage remains unclear. In this study, 83 participants underwent a 128 electrodes resting-state EEG, a dynamic 100 min tau PET scan (18F-MK6240), an amyloid PET scan, a structural T1 MRI scan and neuropsychological assessment. Tau PET data quality control led to a final sample of 66 subjects. Based on the clinical and cognitive status, amyloid and tau PET biomarkers, the group was composed of 37 cognitively unimpaired amyloid negative subjects, 14 cognitively unimpaired amyloid positive subjects and 15 patients with prodromal Alzheimer's disease. We calculated the average undirected weighted Phase Lag Index in the alpha frequency band with eyes closed and used this as weights for the graph and analysed the global clustering coefficient and characteristic path length in sensor space. As a primary objective, we assessed how these global graph measures correlated with tau PET values, in an a priori defined early metaVOI, comprised of the entorhinal and perirhinal cortex, hippocampus, parahippocampus and fusiform cortex. As secondary analyses, we investigated which specific brain regions were mainly implicated, what the contribution was of amyloid, the effect of electrode density and the relation to cognitive performance. In the overall group and within the cognitively unimpaired amyloid positive subgroup, tau aggregation was associated with a decrease in global clustering coefficient and an increase in characteristic path length. These changes reflect the initial disintegration of the small-world brain network during the transitional phase, even before clinical symptoms are apparent. The correlations are most prominent in the perirhinal cortex, indicating that global deterioration of the network is already present early in the Alzheimer's disease pathology. We obtained similar results with only taking 64 electrodes into account. To conclude, we found that in the asymptomatic stage of Alzheimer's disease, tau PET load in medial temporal cortex is associated with global electrophysiological measures of network disintegration. The study demonstrates the potential value of high-density EEG in the era of biologically defined Alzheimer's disease for characterizing brain function in the asymptomatic stage.
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Affiliation(s)
- Laure Spruyt
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
| | - Tjaša Mlinarič
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, Leuven Brain institute, KU Leuven, Leuven 3000, Belgium
| | - Nathalie Dusart
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
| | - Gabriela Meade
- Division of Speech Pathology, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Marc M Van Hulle
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, Leuven Brain institute, KU Leuven, Leuven 3000, Belgium
| | - Koen Van Laere
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven 3000, Belgium
- Division of Nuclear Medicine, UZ Leuven, Leuven 3000, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Brain Institute (LBI), KU Leuven, Leuven 3000, Belgium
- Department of Neurology, UZ Leuven, Leuven 3000, Belgium
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2
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De Meyer S, Schaeverbeke JM, Luckett ES, Reinartz M, Blujdea ER, Cleynen I, Dupont P, Van Laere K, Vanbrabant J, Stoops E, Vanmechelen E, di Molfetta G, Zetterberg H, Ashton NJ, Teunissen CE, Poesen K, Vandenberghe R. Plasma pTau181 and pTau217 predict asymptomatic amyloid accumulation equally well as amyloid PET. Brain Commun 2024; 6:fcae162. [PMID: 39051027 PMCID: PMC11267224 DOI: 10.1093/braincomms/fcae162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/25/2024] [Accepted: 05/22/2024] [Indexed: 07/27/2024] Open
Abstract
The dynamic phase of preclinical Alzheimer's disease, as characterized by accumulating cortical amyloid-β, is a window of opportunity for amyloid-β-lowering therapies to have greater efficacy. Biomarkers that accurately predict amyloid-β accumulation may be of critical importance for participant inclusion in secondary prevention trials and thus enhance development of early Alzheimer's disease therapies. We compared the abilities of baseline plasma pTau181, pTau217 and amyloid-β PET load to predict future amyloid-β accumulation in asymptomatic elderly. In this longitudinal cohort study, baseline plasma pTau181 and pTau217 were quantified using single molecule array assays in cognitively unimpaired elderly selected from the community-recruited F-PACK cohort based on the availability of baseline plasma samples and longitudinal amyloid-β PET data (median time interval = 5 years, range 2-10 years). The predictive abilities of pTau181, pTau217 and PET-based amyloid-β measures for PET-based amyloid-β accumulation were investigated using receiver operating characteristic analyses, correlations and stepwise regression analyses. We included 75 F-PACK subjects (mean age = 70 years, 48% female), of which 16 were classified as amyloid-β accumulators [median (interquartile range) Centiloid rate of change = 3.42 (1.60) Centiloids/year). Plasma pTau181 [area under the curve (95% confidence interval) = 0.72 (0.59-0.86)] distinguished amyloid-β accumulators from non-accumulators with similar accuracy as pTau217 [area under the curve (95% confidence interval) = 0.75 (0.62-0.88) and amyloid-β PET [area under the curve (95% confidence interval) = 0.72 (0.56-0.87)]. Plasma pTau181 and pTau217 strongly correlated with each other (r = 0.93, Pfalse discovery rate < 0.001) and, together with amyloid-β PET, similarly correlated with amyloid-β rate of change (r pTau181 = 0.33, r pTau217 = 0.36, r amyloid-β PET = 0.35, all Pfalse discovery rate ≤ 0.01). Addition of plasma pTau181, plasma pTau217 or amyloid-β PET to a linear demographic model including age, sex and APOE-ε4 carriership similarly improved the prediction of amyloid-β accumulation (ΔAkaike information criterion ≤ 4.1). In a multimodal biomarker model including all three biomarkers, each biomarker lost their individual predictive ability. These findings indicate that plasma pTau181, plasma pTau217 and amyloid-β PET convey overlapping information and therefore predict the dynamic phase of asymptomatic amyloid-β accumulation with comparable performances. In clinical trial recruitment, confirmatory PET scans following blood-based prescreening might thus not provide additional value for detecting participants in these early disease stages who are destined to accumulate cortical amyloid-β. Given the moderate performances, future studies should investigate whether integrating plasma pTau species with other factors can improve performance and thus enhance clinical and research utility.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Jolien M Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Laboratory for Complex Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Elena R Blujdea
- Neurochemistry Laboratory, Department of Clinical Chemistry, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Isabelle Cleynen
- Laboratory for Complex Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
- Division of Nuclear Medicine, UZ Leuven, 3000 Leuven, Belgium
| | | | | | | | - Guglielmo di Molfetta
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, S-431 80 Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- UK Dementia Research Institute at UCL, WC1N 3BG London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, S-431 80 Mölndal, Sweden
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Laboratory Medicine Department, UZ Leuven, 3000 Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Neurology Department, UZ Leuven, 3000 Leuven, Belgium
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3
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Cui L, Zhang Z, Guo Y, Li Y, Xie F, Guo Q. Category Switching Test: A Brief Amyloid-β-Sensitive Assessment Tool for Mild Cognitive Impairment. Assessment 2024; 31:543-556. [PMID: 37081801 DOI: 10.1177/10731911231167537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
The Category Switching Test (CaST) is a verbal fluency test with active semantic category switching. This study aimed to explore the association between CaST performance and brain amyloid-β (Aβ) burden in patients with mild cognitive impairment (MCI) and the neurofunctional mechanisms. A total of 112 participants with MCI underwent Florbetapir positron emission tomography, resting-state functional magnetic resonance imaging, and a neuropsychological test battery. The high Aβ burden group had worse CaST performance than the low-burden group. CaST score and left middle temporal gyrus fractional amplitude of low-frequency fluctuations (fALFF) related inversely to the global Florbetapir standardized uptake value rate. Functional connectivity between the left middle temporal gyrus and frontal lobe decreased widely and correlated with CaST score in the high Aβ burden group. Thus, CaST score and left middle temporal gyrus fALFF were valuable in discriminating high Aβ burden. CaST might be useful in screening for MCI with high Aβ burden.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihan Guo
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Yuehua Li
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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4
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De Meyer S, Blujdea ER, Schaeverbeke J, Reinartz M, Luckett ES, Adamczuk K, Van Laere K, Dupont P, Teunissen CE, Vandenberghe R, Poesen K. Longitudinal associations of serum biomarkers with early cognitive, amyloid and grey matter changes. Brain 2024; 147:936-948. [PMID: 37787146 DOI: 10.1093/brain/awad330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 10/04/2023] Open
Abstract
Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-β (Aβ) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [n = 185, mean age (range) = 69 (53-84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aβ1-42/Aβ1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aβ-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aβ-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3-11.0) years]. Matching plasma measurements were available for Aβ1-42/Aβ1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (βGFAP×Time = -0.021, PFDR = 0.007 and βNfL×Time = -0.031, PFDR = 0.002) and language (βGFAP×Time = -0.021, PFDR = 0.002 and βNfL×Time = -0.018, PFDR = 0.03) domains. Low serum Aβ1-42/Aβ1-40 equally but independently predicted memory decline (βAβ1-42/Aβ1-40×Time = -0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aβ1-42/Aβ1-40 predicted Aβ accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aβ1-42/Aβ1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aβ1-42/Aβ1-40 decreased only in Aβ-PET-negative elderly. NfL increases associated with declining memory (βNfLchange×Time = -0.030, PFDR = 0.006) and language (βNfLchange×Time = -0.021, PFDR = 0.02) function and serum Aβ1-42/Aβ1-40 decreases associated with declining language function (βAβ1-42/Aβ1-40×Time = -0.020, PFDR = 0.04). GFAP increases associated with Aβ accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aβ accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aβ1-42/Aβ1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aβ-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aβ-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aβ1-42/Aβ1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Elena R Blujdea
- Neurochemistry Laboratory, Amsterdam UMC, 1081 HZ Amsterdam, The Netherlands
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
| | - Koen Van Laere
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
- Division of Nuclear Medicine, UZ Leuven, 3000 Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | | | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Neurology, UZ Leuven, 3000 Leuven, Belgium
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Laboratory Medicine, UZ Leuven, 3000 Leuven, Belgium
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Zhang X, Zeng Q, Wang Y, Jin Y, Qiu T, Li K, Luo X, Wang S, Xu X, Liu X, Zhao S, Li Z, Hong L, Li J, Zhong S, Zhang T, Huang P, Zhang B, Zhang M, Chen Y. Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline. Brain Commun 2024; 6:fcae033. [PMID: 38425749 PMCID: PMC10903975 DOI: 10.1093/braincomms/fcae033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/10/2024] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer's disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer's disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer's disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer's disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.
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Affiliation(s)
- Xinyi Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yanbo Wang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yu Jin
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, Linyi People’s Hospital, 276003, Linyi, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Zheyu Li
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Luwei Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Tianyi Zhang
- Department of Neurology, The First Affiliated Hospital of Zhejiang University School of Medicine, 310003, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, China
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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7
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Xiang C, Ai W, Zhang Y, Alzheimer's Disease Neuroimaging Initiative. Language dysfunction correlates with cognitive impairments in older adults without dementia mediated by amyloid pathology. Front Neurol 2023; 14:1051382. [PMID: 37265466 PMCID: PMC10230042 DOI: 10.3389/fneur.2023.1051382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Background Previous studies have explored the application of non-invasive biomarkers of language dysfunction for the early detection of Alzheimer's disease (AD). However, language dysfunction over time may be quite heterogeneous within different diagnostic groups. Method Patient demographics and clinical data were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for the participants without dementia who had measures of cerebrospinal fluid (CSF) biomarkers and language dysfunction. We analyzed the effect of longitudinal neuropathological and clinical correlates in the pathological process of semantic fluency and confrontation naming. The mediation effects of AD biomarkers were also explored by the mediation analysis. Result There were 272 subjects without dementia included in this analysis. Higher rates of decline in semantic fluency and confrontation naming were associated with a higher risk of progression to MCI or AD, and a greater decline in cognitive abilities. Moreover, the rate of change in semantic fluency was significantly associated with Aβ deposition, while confrontation naming was significantly associated with both amyloidosis and tau burden. Mediation analyses revealed that both confrontation naming and semantic fluency were partially mediated by the Aβ aggregation. Conclusion In conclusion, the changes in language dysfunction may partly stem from the Aβ deposition, while confrontation naming can also partly originate from the increase in tau burden. Therefore, this study sheds light on how language dysfunction is partly constitutive of mild cognitive impairment and dementia and therefore is an important clinical predictor.
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Affiliation(s)
- Chunchen Xiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weiping Ai
- Department of Neurology, Zhangjiakou First Hospital, Zhangjiakou, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
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8
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Dysfunktionen der Sprache
sind mit Amyloid-Last
assoziiert. FORTSCHRITTE DER NEUROLOGIE · PSYCHIATRIE 2022. [DOI: 10.1055/a-1831-9884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Störungen der Sprache können bei Menschen mit Alzheimer-Demenz
(AD) schon in frühen Stadien beobachtet werden. Möglicherweise
bestehen auf neurophysiologischer Ebene schon lange vor Diagnosestellung
Defizite, die aber durch Adaptationsprozesse im Gehirn ausgeglichen werden
können. Dafür sprechen die Untersuchungsergebnisse von Mariska
Reinertz vom Labor für kognitive Neurologie der Universität
Leuven (Belgien) und Kollegen.
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Shah D, Gsell W, Wahis J, Luckett ES, Jamoulle T, Vermaercke B, Preman P, Moechars D, Hendrickx V, Jaspers T, Craessaerts K, Horré K, Wolfs L, Fiers M, Holt M, Thal DR, Callaerts-Vegh Z, D'Hooge R, Vandenberghe R, Himmelreich U, Bonin V, De Strooper B. Astrocyte calcium dysfunction causes early network hyperactivity in Alzheimer's disease. Cell Rep 2022; 40:111280. [PMID: 36001964 PMCID: PMC9433881 DOI: 10.1016/j.celrep.2022.111280] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/30/2022] [Accepted: 08/05/2022] [Indexed: 12/15/2022] Open
Abstract
Dysfunctions of network activity and functional connectivity (FC) represent early events in Alzheimer’s disease (AD), but the underlying mechanisms remain unclear. Astrocytes regulate local neuronal activity in the healthy brain, but their involvement in early network hyperactivity in AD is unknown. We show increased FC in the human cingulate cortex several years before amyloid deposition. We find the same early cingulate FC disruption and neuronal hyperactivity in AppNL-F mice. Crucially, these network disruptions are accompanied by decreased astrocyte calcium signaling. Recovery of astrocytic calcium activity normalizes neuronal hyperactivity and FC, as well as seizure susceptibility and day/night behavioral disruptions. In conclusion, we show that astrocytes mediate initial features of AD and drive clinically relevant phenotypes. The cingulate cortex of humans and mice shows early functional deficits in AD Astrocyte calcium signaling is decreased before the presence of amyloid plaques Recovery of astrocyte calcium signals mitigates neuronal hyperactivity Recovery of astrocytes normalizes cingulate connectivity and behavior disruptions
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Affiliation(s)
- Disha Shah
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium.
| | - Willy Gsell
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Jérôme Wahis
- Laboratory of Glia Biology, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Tarik Jamoulle
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Ben Vermaercke
- Neuro-electronics Research Flanders, 3000 Leuven, Belgium
| | - Pranav Preman
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Daan Moechars
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Véronique Hendrickx
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Tom Jaspers
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Katleen Craessaerts
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Katrien Horré
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Leen Wolfs
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Mark Fiers
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Matthew Holt
- Laboratory of Glia Biology, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium
| | - Dietmar Rudolf Thal
- Laboratory for Neuropathology, Department of Imaging and Pathology, LBI, KU Leuven, 3000 Leuven, Belgium
| | | | - Rudi D'Hooge
- Laboratory of Biological Psychology, KU-Leuven, 3000 Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
| | - Vincent Bonin
- Neuro-electronics Research Flanders, 3000 Leuven, Belgium
| | - Bart De Strooper
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain and Disease Research, KU Leuven, 3000 Leuven, Belgium; UK Dementia Research Institute at University College London, WC1E 6BT London, UK.
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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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De Meyer S, Vanbrabant J, Schaeverbeke JM, Reinartz M, Luckett ES, Dupont P, Van Laere K, Stoops E, Vanmechelen E, Poesen K, Vandenberghe R. Phospho-specific plasma p-tau181 assay detects clinical as well as asymptomatic Alzheimer's disease. Ann Clin Transl Neurol 2022; 9:734-746. [PMID: 35502634 PMCID: PMC9082389 DOI: 10.1002/acn3.51553] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Plasma phosphorylated-tau-181 (p-tau181) reliably detects clinical Alzheimer's disease (AD) as well as asymptomatic amyloid-β (Aβ) pathology, but is consistently quantified with assays using antibody AT270, which cross-reacts with p-tau175. This study investigates two novel phospho-specific assays for plasma p-tau181 and p-tau231 in clinical and asymptomatic AD. METHODS Plasma p-tau species were quantified with Simoa in 44 AD patients, 40 spouse controls and an independent cohort of 151 cognitively unimpaired (CU) elderly who underwent Aβ-PET. Simoa plasma Aβ42 measurements were available in a CU subset (N = 69). Receiver operating characteristics and Aβ-PET associations were used to evaluate biomarker validity. RESULTS The novel plasma p-tau181 and p-tau231 assays did not show cross-reactivity. Plasma p-tau181 accurately detected clinical AD (area under the curve (AUC) = 0.98, 95% CI 0.95-1.00) as well as asymptomatic Aβ pathology (AUC = 0.84, 95% CI 0.76-0.92), while plasma p-tau231 did not (AUC = 0.74, 95% CI 0.63-0.85 and 0.61, 95% CI 0.52-0.71, respectively). Plasma p-tau181, but not p-tau231, detected asymptomatic Aβ pathology more accurately than age, sex and APOE combined (AUC = 0.64). In asymptomatic elderly, correlations between plasma p-tau181 and Aβ pathology were observed throughout the cerebral cortex (ρ = 0.40, p < 0.0001), with focal associations within AD-vulnerable regions, particularly the precuneus. The plasma Aβ42/p-tau181 ratio did not reflect asymptomatic Aβ pathology better than p-tau181 alone. INTERPRETATION The novel plasma p-tau181 assay is an accurate tool to detect clinical as well as asymptomatic AD and provides a phospho-specific alternative to currently employed immunoassays.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | | | - Jolien M. Schaeverbeke
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Emma S. Luckett
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Koen Van Laere
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and PathologyKU LeuvenLeuvenBelgium
- Division of Nuclear MedicineUZ LeuvenLeuvenBelgium
| | | | | | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Neurology DepartmentUZ LeuvenLeuvenBelgium
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