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Iaccarino L, Llibre-Guerra JJ, McDade E, Edwards L, Gordon B, Benzinger T, Hassenstab J, Kramer JH, Li Y, Miller BL, Miller Z, Morris JC, Mundada N, Perrin RJ, Rosen HJ, Soleimani-Meigooni D, Strom A, Tsoy E, Wang G, Xiong C, Allegri R, Chrem P, Vazquez S, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Salloway S, Fox NC, Day GS, Gorno-Tempini ML, Boxer AL, La Joie R, Bateman R, Rabinovici GD. Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2024; 6:fcae159. [PMID: 38784820 PMCID: PMC11114609 DOI: 10.1093/braincomms/fcae159] [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: 06/06/2023] [Revised: 03/14/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.
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Affiliation(s)
- Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge J Llibre-Guerra
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason Hassenstab
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - David Soleimani-Meigooni
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Elena Tsoy
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Silvia Vazquez
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Department of Neuroscience, Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, Indiana, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Stephen Salloway
- Memory & Aging Program, Butler Hospital, Brown University in Providence, RI 02906, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
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2
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Yang Z, Kinney JW, Cordes D. Uptake of 18F-AV45 in the Putamen Provides Additional Insights into Alzheimer's Disease beyond the Cortex. Biomolecules 2024; 14:157. [PMID: 38397394 PMCID: PMC10886857 DOI: 10.3390/biom14020157] [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: 11/14/2023] [Revised: 01/09/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024] Open
Abstract
Cortical uptake in brain amyloid positron emission tomography (PET) is increasingly used for the biological diagnosis of Alzheimer's disease (AD); however, the clinical and biological relevance of the striatum beyond the cortex in amyloid PET scans remains unclear. A total of 513 amyloid-positive participants having 18F-AV45 amyloid PET scans available were included in the analysis. The associations between cognitive scores and striatal uptake were analyzed. The participants were categorized into three groups based on the residual from the linear fitting between 18F-AV45 uptake in the putamen and the cortex in the order of HighP > MidP > LowP group. We then examined the differences between these three groups in terms of clinical diagnosis, APOE genotype, CSF phosphorylated tau (ptau) concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate to evaluate the additional insights provided by the putamen beyond the cortex. The 18F-AV45 uptake in the putamen was more strongly associated with ADAS-cog13 and MoCA scores (p < 0.001) compared to the uptake in the caudate nucleus. Despite comparable cortical uptakes, the HighP group had a two-fold higher risk of being ε4-homozygous or diagnosed with AD dementia compared to the LowP group. These three groups had significantly different CSF ptau concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate. These findings suggest that the assessment of 18F-AV45 uptake in the putamen is of unique value for evaluating disease severity and predicting disease progression.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA;
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA;
| | - Jefferson W. Kinney
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, 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 89154, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA;
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA;
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
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3
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Llibre-Guerra JJ, Iaccarino L, Coble D, Edwards L, Li Y, McDade E, Strom A, Gordon B, Mundada N, Schindler SE, Tsoy E, Ma Y, Lu R, Fagan AM, Benzinger TLS, Soleimani-Meigooni D, Aschenbrenner AJ, Miller Z, Wang G, Kramer JH, Hassenstab J, Rosen HJ, Morris JC, Miller BL, Xiong C, Perrin RJ, Allegri R, Chrem P, Surace E, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Fox NC, Day G, Gorno-Tempini ML, Boxer AL, La Joie R, Rabinovici GD, Bateman R. Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2023; 5:fcad280. [PMID: 37942088 PMCID: PMC10629466 DOI: 10.1093/braincomms/fcad280] [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: 06/01/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.
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Affiliation(s)
| | - Leonardo Iaccarino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dean Coble
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Amelia Strom
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Elena Tsoy
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yinjiao Ma
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Tammie L S Benzinger
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - David Soleimani-Meigooni
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Zachary Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Howard J Rosen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Bruce L Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
- Department of Pathology and Immunology, Washington University in St Louis, St. Louis, MO 63108, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Ezequiel Surace
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D Rabinovici
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
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Aloisio S, Satolli S, Bellini G, Lopriore P. Parkinsonism in complex neurogenetic disorders: lessons from hereditary dementias, adult-onset ataxias and spastic paraplegias. Neurol Sci 2023; 44:3379-3388. [PMID: 37648940 PMCID: PMC10495519 DOI: 10.1007/s10072-023-07044-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
Parkinsonism is a syndrome characterized by bradykinesia in combination with either rest tremor, rigidity, or both. These features are the cardinal manifestations of Parkinson's disease, the most common cause of parkinsonism, and atypical parkinsonian disorders. However, parkinsonism can be a manifestation of complex neurological and neurodegenerative genetically determined disorders, which have a vast and heterogeneous motor and non-motor phenotypic features. Hereditary dementias, adult-onset ataxias and spastic paraplegias represent only few of this vast group of neurogenetic diseases. This review will provide an overview of parkinsonism's clinical features within adult-onset neurogenetic diseases which a neurologist could face with. Understanding parkinsonism and its characteristics in the context of the aforementioned neurological conditions may provide insights into pathophysiological mechanisms and have important clinical implications, including diagnostic and therapeutic aspects.
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Affiliation(s)
- Simone Aloisio
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sara Satolli
- Molecular Medicine for Neurodegenerative and Neuromuscular Diseases Unit, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Gabriele Bellini
- Department of Clinical and Experimental Medicine, Neurological Institute, University of Pisa, Pisa, Italy
| | - Piervito Lopriore
- Department of Clinical and Experimental Medicine, Neurological Institute, University of Pisa, Pisa, Italy.
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Wang Z, Pang J, Zhou R, Qi J, Shi X, Han B, Man X, Wang Q, Sun J. Differences in resting-state brain networks and gray matter between APOE ε2 and APOE ε4 carriers in non-dementia elderly. Front Psychiatry 2023; 14:1197987. [PMID: 37636817 PMCID: PMC10449453 DOI: 10.3389/fpsyt.2023.1197987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Background Apolipoprotein E (APOE) ε2 and APOE ε4 are the most distinct alleles among the three APOE alleles, both structurally and functionally. However, differences in cognition, brain function, and brain structure between the two alleles have not been comprehensively reported in the literature, especially in non-demented elderly individuals. Methods A neuropsychological test battery was used to evaluate the differences in cognitive performance in five cognitive domains. Independent component analysis (ICA) and voxel-based morphometry (VBM) were used separately to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data and the structure MRI data between the two groups. Finally, correlations between differential brain regions and neuropsychological tests were calculated. Results APOE ε2 carriers had better cognitive performance in general cognitive, memory, attention, and executive function than APOE ε4 carriers (all p < 0.05). In ICA analyses of rs-fMRI data, the difference in the resting-state functional connectivity (rsFC) between two groups is shown in 7 brain networks. In addition, VBM analyses of the T1-weighted image revealed that APOE ε2 carriers had a larger thalamus and right postcentral gyrus volume and a smaller bilateral putamen volume than APOE ε4 carriers. Finally, differences in brain function and structure may be might be the reason that APOE ε2 carriers are better than APOE ε4 carriers in cognitive performance. Conclusion These findings suggest that there are significant differences in brain function and structure between APOE ε2 carriers and APOE ε4 carriers, and these significant differences are closely related to their cognitive performance.
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Affiliation(s)
- Zhiyuan Wang
- Institute of Integrative Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruizhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianjiao Qi
- Department of Emergency Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xianglong Shi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bin Han
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xu Man
- Institute of Integrative Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingqing Wang
- Department of Emergency Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jinping Sun
- Department of Emergency Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
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6
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Chen CD, McCullough A, Gordon B, Joseph-Mathurin N, Flores S, McKay NS, Hobbs DA, Hornbeck R, Fagan AM, Cruchaga C, Goate AM, Perrin RJ, Wang G, Li Y, Shi X, Xiong C, Pontecorvo MJ, Klein G, Su Y, Klunk WE, Jack C, Koeppe R, Snider BJ, Berman SB, Roberson ED, Brosch J, Surti G, Jiménez-Velázquez IZ, Galasko D, Honig LS, Brooks WS, Clarnette R, Wallon D, Dubois B, Pariente J, Pasquier F, Sanchez-Valle R, Shcherbinin S, Higgins I, Tunali I, Masters CL, van Dyck CH, Masellis M, Hsiung R, Gauthier S, Salloway S, Clifford DB, Mills S, Supnet-Bell C, McDade E, Bateman RJ, Benzinger TLS. Longitudinal head-to-head comparison of 11C-PiB and 18F-florbetapir PET in a Phase 2/3 clinical trial of anti-amyloid-β monoclonal antibodies in dominantly inherited Alzheimer's disease. Eur J Nucl Med Mol Imaging 2023; 50:2669-2682. [PMID: 37017737 PMCID: PMC10330155 DOI: 10.1007/s00259-023-06209-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/18/2023] [Indexed: 04/06/2023]
Abstract
PURPOSE Pittsburgh Compound-B (11C-PiB) and 18F-florbetapir are amyloid-β (Aβ) positron emission tomography (PET) radiotracers that have been used as endpoints in Alzheimer's disease (AD) clinical trials to evaluate the efficacy of anti-Aβ monoclonal antibodies. However, comparing drug effects between and within trials may become complicated if different Aβ radiotracers were used. To study the consequences of using different Aβ radiotracers to measure Aβ clearance, we performed a head-to-head comparison of 11C-PiB and 18F-florbetapir in a Phase 2/3 clinical trial of anti-Aβ monoclonal antibodies. METHODS Sixty-six mutation-positive participants enrolled in the gantenerumab and placebo arms of the first Dominantly Inherited Alzheimer Network Trials Unit clinical trial (DIAN-TU-001) underwent both 11C-PiB and 18F-florbetapir PET imaging at baseline and during at least one follow-up visit. For each PET scan, regional standardized uptake value ratios (SUVRs), regional Centiloids, a global cortical SUVR, and a global cortical Centiloid value were calculated. Longitudinal changes in SUVRs and Centiloids were estimated using linear mixed models. Differences in longitudinal change between PET radiotracers and between drug arms were estimated using paired and Welch two sample t-tests, respectively. Simulated clinical trials were conducted to evaluate the consequences of some research sites using 11C-PiB while other sites use 18F-florbetapir for Aβ PET imaging. RESULTS In the placebo arm, the absolute rate of longitudinal change measured by global cortical 11C-PiB SUVRs did not differ from that of global cortical 18F-florbetapir SUVRs. In the gantenerumab arm, global cortical 11C-PiB SUVRs decreased more rapidly than global cortical 18F-florbetapir SUVRs. Drug effects were statistically significant across both Aβ radiotracers. In contrast, the rates of longitudinal change measured in global cortical Centiloids did not differ between Aβ radiotracers in either the placebo or gantenerumab arms, and drug effects remained statistically significant. Regional analyses largely recapitulated these global cortical analyses. Across simulated clinical trials, type I error was higher in trials where both Aβ radiotracers were used versus trials where only one Aβ radiotracer was used. Power was lower in trials where 18F-florbetapir was primarily used versus trials where 11C-PiB was primarily used. CONCLUSION Gantenerumab treatment induces longitudinal changes in Aβ PET, and the absolute rates of these longitudinal changes differ significantly between Aβ radiotracers. These differences were not seen in the placebo arm, suggesting that Aβ-clearing treatments may pose unique challenges when attempting to compare longitudinal results across different Aβ radiotracers. Our results suggest converting Aβ PET SUVR measurements to Centiloids (both globally and regionally) can harmonize these differences without losing sensitivity to drug effects. Nonetheless, until consensus is achieved on how to harmonize drug effects across radiotracers, and since using multiple radiotracers in the same trial may increase type I error, multisite studies should consider potential variability due to different radiotracers when interpreting Aβ PET biomarker data and, if feasible, use a single radiotracer for the best results. TRIAL REGISTRATION ClinicalTrials.gov NCT01760005. Registered 31 December 2012. Retrospectively registered.
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Affiliation(s)
- Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Washington University School of Medicine, 660 South Euclid, Campus Box 8225, St. Louis, MO, 63110, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nicole S McKay
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Diana A Hobbs
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison M Goate
- Department of Genetics and Genomic Sciences, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xinyu Shi
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael J Pontecorvo
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
- Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Yi Su
- Banner Alzheimer's Institute, Banner Health, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - B Joy Snider
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Erik D Roberson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jared Brosch
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ghulam Surti
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | | | - Douglas Galasko
- Department of Neurology, University of California San Diego, San Diego, CA, USA
| | | | - William S Brooks
- Prince of Wales Medical Research Institute, University of New South Wales, Sydney, NSW, Australia
| | - Roger Clarnette
- Department of Internal Medicine, University of Western Australia, Crawley, WA, Australia
| | - David Wallon
- Department of Neurology and CNR-MAJ, Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, F-76000, Rouen, France
| | - Bruno Dubois
- Sorbonne Université, AP-HP, GRC No. 21, APM, Hôpital de La Pitié-Salpêtrière, Paris, France
- Institut du Cerveau Et de La Moelle Épinière, INSERM U1127, CNRS UMR 7225, Paris, France
- Institut de La Mémoire Et de La Maladie d'Alzheimer, Département de Neurologie, Hôpital de La Pitié-Salpêtrière, Paris, France
| | - Jérémie Pariente
- Department of Neurology, Hôpital Pierre-Paul Riquet, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
- Toulouse NeuroImaging Centre, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Florence Pasquier
- Univ. Lille, INSERM, CHU Lille, 59000, Lille, France
- CNR-MAJ, Labex DISTALZ, LiCEND, 59000, Lille, France
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Fundació Clínic Per a La Recerca Biomèdica, University of Barcelona, Barcelona, Spain
| | | | | | - Ilke Tunali
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | | | | | - Robin Hsiung
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Serge Gauthier
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Steve Salloway
- Alpert Medical School of Brown University, Providence, RI, USA
| | - David B Clifford
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Susan Mills
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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7
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Tada T, Hara K, Fujita N, Ito Y, Yamaguchi H, Ohdake R, Kawabata K, Ogura A, Kato T, Yokoi T, Masuda M, Abe S, Miyao S, Naganawa S, Katsuno M, Watanabe H, Sobue G, Kato K. Comparative examination of the pons and corpus callosum as reference regions for quantitative evaluation in positron emission tomography imaging for Alzheimer's disease using 11C-Pittsburgh Compound-B. Ann Nucl Med 2023:10.1007/s12149-023-01843-y. [PMID: 37160863 DOI: 10.1007/s12149-023-01843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/24/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Standardised uptake value ratio (SUVR) is usually obtained by dividing the SUV of the region of interest (ROI) by that of the cerebellar cortex. Cerebellar cortex is not a valid reference in cases where amyloid β deposition or lesions are present. Only few studies have evaluated the use of other regions as references. We compared the validity of the pons and corpus callosum as reference regions for the quantitative evaluation of brain positron emission tomography (PET) using 11C-PiB compared to the cerebellar cortex. METHODS We retrospectively evaluated data from 86 subjects with or without Alzheimer's disease (AD). All subjects underwent magnetic resonance imaging, PET imaging, and cognitive function testing. For the quantitative analysis, three-dimensional ROIs were automatically placed, and SUV and SUVR were obtained. We compared these values between AD and healthy control (HC) groups. RESULTS SUVR data obtained using the pons and corpus callosum as reference regions strongly correlated with that using the cerebellar cortex. The sensitivity and specificity were high when either the pons or corpus callosum was used as the reference region. However, the SUV values of the corpus callosum were different between AD and HC (p < 0.01). CONCLUSIONS Our data suggest that the pons and corpus callosum might be valid reference regions.
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Affiliation(s)
- Tomohiro Tada
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Naotoshi Fujita
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Yoshinori Ito
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan
| | - Hiroshi Yamaguchi
- Nagoya University Radioisotope Research Center Medical Branch, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Reiko Ohdake
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Medical University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Anjo Kosei Hospital, 28 Higashihirokute Anjo-Cho, Anjo, 446-8602, Japan
| | - Takamasa Yokoi
- Department of Neurology, Toyohashi Municipal Hospital, 50 Hachikennishi, Aotake-Cho, Toyohashi, 441-8570, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Okazaki City Hospital, 1-3 Gosyoai, Kouryuji-Cho, Okazaki, 444-8553, Japan
| | - Shinji Abe
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Shinichi Miyao
- Department of Neurology, Meitetsu Hospital, 2-26-11 Sakou, Nishiku, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Clinical Research Education, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Hirohisa Watanabe
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Gen Sobue
- Aichi Medical University, 1-1 Yazakokarimata, Nagakute, Japan
| | - Katsuhiko Kato
- Functional Medical Imaging, Biomedical Imaging Sciences, Division of Advanced Information Health Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan.
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Rafii MS, Aisen PS. Detection and treatment of Alzheimer's disease in its preclinical stage. NATURE AGING 2023; 3:520-531. [PMID: 37202518 PMCID: PMC11110912 DOI: 10.1038/s43587-023-00410-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/29/2023] [Indexed: 05/20/2023]
Abstract
Longitudinal multimodal biomarker studies reveal that the continuum of Alzheimer's disease (AD) includes a long latent phase, referred to as preclinical AD, which precedes the onset of symptoms by decades. Treatment during the preclinical AD phase offers an optimal opportunity for slowing the progression of disease. However, trial design in this population is complex. In this Review, we discuss the recent advances in accurate plasma measurements, new recruitment approaches, sensitive cognitive instruments and self-reported outcomes that have facilitated the successful launch of multiple phase 3 trials for preclinical AD. The recent success of anti-amyloid immunotherapy trials in symptomatic AD has increased the enthusiasm for testing this strategy at the earliest feasible stage. We provide an outlook for standard screening of amyloid accumulation at the preclinical stage in clinically normal individuals, during which effective therapy to delay or prevent cognitive decline can be initiated.
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Affiliation(s)
- Michael S Rafii
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, Los Angeles, CA, USA.
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, Los Angeles, CA, USA
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9
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Chen P, Zhao K, Zhang H, Wei Y, Wang P, Wang D, Song C, Yang H, Zhang Z, Yao H, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhou B, Jiang T, Zhou Y, Lu J, Han Y, Zhang X, Liu B, Liu Y. Altered global signal topography in Alzheimer's disease. EBioMedicine 2023; 89:104455. [PMID: 36758481 PMCID: PMC9941064 DOI: 10.1016/j.ebiom.2023.104455] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/31/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI). METHODS fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics. FINDINGS Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (Padj < 0.05). Notably, topographical GS changes in these regions correlated with cognitive ability (P < 0.05). The changes in GS topography also correlated with the changes in functional network segregation (ρ = 0.5). Moreover, the genes identified based on GS topographical changes were enriched in pathways associated with AD and neurodegenerative diseases. INTERPRETATION Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD. FUNDING Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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10
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Ringman JM, Dorrani N, Fernández SG, Signer R, Martinez-Agosto J, Lee H, Douine ED, Qiao Y, Shi Y, D’Orazio L, Pawar S, Robbie L, Kashani AH, Singer M, Byers JT, Magaki S, Guzman S, Sagare A, Zlokovic B, Cederbaum S, Nelson S, Sheikh-Bahaei N, Chui HC, Chávez-Gutiérrez L, Vinters HV. Characterization of spastic paraplegia in a family with a novel PSEN1 mutation. Brain Commun 2023; 5:fcad030. [PMID: 36895955 PMCID: PMC9991506 DOI: 10.1093/braincomms/fcad030] [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: 11/29/2021] [Revised: 09/09/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Spastic paraparesis has been described to occur in 13.7% of PSEN1 mutations and can be the presenting feature in 7.5%. In this paper, we describe a family with a particularly young onset of spastic paraparesis due to a novel mutation in PSEN1 (F388S). Three affected brothers underwent comprehensive imaging protocols, two underwent ophthalmological evaluations and one underwent neuropathological examination after his death at age 29. Age of onset was consistently at age 23 with spastic paraparesis, dysarthria and bradyphrenia. Pseudobulbar affect followed with progressive gait problems leading to loss of ambulation in the late 20s. Cerebrospinal fluid levels of amyloid-β, tau and phosphorylated tau and florbetaben PET were consistent with Alzheimer's disease. Flortaucipir PET showed an uptake pattern atypical for Alzheimer's disease, with disproportionate signal in posterior brain areas. Diffusion tensor imaging showed decreased mean diffusivity in widespread areas of white matter but particularly in areas underlying the peri-Rolandic cortex and in the corticospinal tracts. These changes were more severe than those found in carriers of another PSEN1 mutation, which can cause spastic paraparesis at a later age (A431E), which were in turn more severe than among persons carrying autosomal dominant Alzheimer's disease mutations not causing spastic paraparesis. Neuropathological examination confirmed the presence of cotton wool plaques previously described in association with spastic parapresis and pallor and microgliosis in the corticospinal tract with severe amyloid-β pathology in motor cortex but without unequivocal disproportionate neuronal loss or tau pathology. In vitro modelling of the effects of the mutation demonstrated increased production of longer length amyloid-β peptides relative to shorter that predicted the young age of onset. In this paper, we provide imaging and neuropathological characterization of an extreme form of spastic paraparesis occurring in association with autosomal dominant Alzheimer's disease, demonstrating robust diffusion and pathological abnormalities in white matter. That the amyloid-β profiles produced predicted the young age of onset suggests an amyloid-driven aetiology though the link between this and the white matter pathology remains undefined.
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Affiliation(s)
- John M Ringman
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | | | - Sara Gutiérrez Fernández
- Department of Neurosciences, VIB-KU Leuven Center for Brain & Disease Research, Leuven 3000, Belgium
- Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Rebecca Signer
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | | | - Hane Lee
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Emilie D Douine
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Yuchuan Qiao
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
| | - Yonggang Shi
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
| | - Lina D’Orazio
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Sanjay Pawar
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Leah Robbie
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Amir H Kashani
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Maxwell Singer
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Joshua T Byers
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Shino Magaki
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Sam Guzman
- Department of Pathology, Keck School of Medicine at USC, Los Angeles, CA 90033, USA
| | - Abhay Sagare
- Zilkha Neurogenetics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Berislav Zlokovic
- Zilkha Neurogenetics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Stephen Cederbaum
- Department of Pediatrics, UCLA, Los Angeles, CA 90095, USA
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Stanley Nelson
- Department of Pediatrics, UCLA, Los Angeles, CA 90095, USA
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Nasim Sheikh-Bahaei
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Lucía Chávez-Gutiérrez
- Department of Neurosciences, VIB-KU Leuven Center for Brain & Disease Research, Leuven 3000, Belgium
- Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Harry V Vinters
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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Kim JE, Lee DK, Hwang JH, Kim CM, Kim Y, Lee JH, Lee JM, Roh JH. Regional Comparison of Imaging Biomarkers in the Striatum between Early- and Late-onset Alzheimer's Disease. Exp Neurobiol 2022; 31:401-408. [PMID: 36631848 PMCID: PMC9841745 DOI: 10.5607/en22022] [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: 06/09/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 01/13/2023] Open
Abstract
Striatal changes in the pathogenesis of Alzheimer's disease (AD) is not fully understood yet. We compared structural and functional image differences in the striatum between patients with early onset AD (EOAD) and late onset AD (LOAD) to investigate whether EOAD harbors autosomal dominant AD like imaging findings. The clinical, neuropsychological and neuroimaging biomarkers of 77 probable AD patients and 107 elderly subjects with normal cognition (NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 dataset were analyzed. Enrolled each subject completed a 3-Tesla MRI, baseline 18F-FDG-PET, and baseline 18F-AV-45 (Florbetapir) amyloid PET studies. AD patients were divided into two groups based on the onset age of clinical symptoms (EOAD <65 yrs; LOAD ≥65 yrs). A standardized uptake value ratio of the striatum and subcortical structures was obtained from both amyloid and FDG-PET scans. Structural MR imaging analysis was conducted using a parametric boundary description protocol, SPHARM-PDM. Of the 77 AD patients, 18 were EOAD and 59 were LOAD. Except for age of symptom onset, there were no statistically significant differences between the groups in demographics and detailed neuropsychological test results. 18F-AV-45 amyloid PET showed marked β-amyloid accumulation in the bilateral caudate nucleus and left pallidum in the EOAD group. Intriguingly, the caudate nucleus and putamen showed maintained glucose metabolism in the EOAD group compared to the LOAD group. Our image findings in the striatum of EOAD patients suggest that sporadic EOAD may share some pathophysiological changes noted in autosomal dominant AD.
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Affiliation(s)
- Ji Eun Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea,Department of Neurology, Inje University Ilsan Paik Hospital, Goyang 10380, Korea
| | - Dong-Kyun Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Ji Hye Hwang
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea,Department of Neurology, Keimyung University Daegu Dongsan Hospital, Daegu 42601, Korea
| | - Chan-Mi Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Yeji Kim
- Department of Artificial Intelligence, Hanyang University, Seoul 04763, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea,
Jong-Min Lee, TEL: 82-2-2220-0697, FAX: 82-2-2296-5943, e-mail:
| | - Jee Hoon Roh
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea,Department of Biomedical Sciences and Department of Physiology, Korea University College of Medicine, Seoul 02841, Korea,Department of Neurology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea,To whom correspondence should be addressed. Jee Hoon Roh, TEL: 82-2-2286-1275, FAX: 82-2-474-4691, e-mail:
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Xia M, Gao C, Wang H, Shang J, Liu R, You Y, Zang W, Zhang J. Novel PSEN1 (P284S) Mutation Causes Alzheimer's Disease with Cerebellar Amyloid β-Protein Deposition. Curr Alzheimer Res 2022; 19:523-529. [PMID: 35850649 PMCID: PMC9933047 DOI: 10.2174/1567205019666220718151357] [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: 01/22/2022] [Revised: 05/14/2022] [Accepted: 05/17/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND/OBJECTIVE AD-associated PSEN1 mutations exhibit high clinical heterogeneity. The discovery of these mutations and the analysis of their associations with cases such as EOAD should be critical to understanding AD's pathogenesis. METHODS We performed clinical analysis, neuroimaging, target region capture and high-throughput sequencing, and Sanger sequencing in a family of 3 generations. The underlying Alzheimer's pathology was evaluated using biomarker evidence obtained from cerebrospinal fluid (CSF) amyloid testing and 18F-florbetapir (AV-45) PET imaging. RESULTS Target region capture sequencing revealed a novel heterozygous C to T missense point mutation at the base position 284 (c.850 C>T) located in exon 8 of the PSEN1 gene, resulting in a Prolineto- Serine substitution (P284S) at codon position 850. The mutation was also identified by Sanger sequencing in 2 family members, including proband and her daughter and was absent in the other 4 unaffected family members and 50 control subjects. Cerebrospinal fluid (CSF) amyloid test exhibited biomarker evidence of underlying Alzheimer's pathology. 18F-florbetapir (AV-45) PET imaging indicated extensive cerebral cortex and cerebellar Aβ deposition. CONCLUSIONS We discovered a novel PSEN1 pathogenic mutation, P284S, observed for the first time in a Chinese family with early-onset AD.
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Affiliation(s)
- Mingrong Xia
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Department of Neurology, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,These authors contributed equally to this manuscript.
| | - Chenhao Gao
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450003, Henan, China; ,These authors contributed equally to this manuscript.
| | - Huayuan Wang
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Department of Neurology, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China;
| | - Junkui Shang
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450003, Henan, China;
| | - Ruijie Liu
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China;
| | - Yang You
- Department of Radiology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China
| | - Weizhou Zang
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Department of Neurology, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Address correspondence to these authors at the Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; E-mails: (J.Z.) and (W.Z.)
| | - Jiewen Zhang
- Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Department of Neurology, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; ,Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450003, Henan, China; ,Address correspondence to these authors at the Department of Neurology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou 450003, Henan, China; E-mails: (J.Z.) and (W.Z.)
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13
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Morris JC, Weiner M, Xiong C, Beckett L, Coble D, Saito N, Aisen PS, Allegri R, Benzinger TLS, Berman SB, Cairns NJ, Carrillo MC, Chui HC, Chhatwal JP, Cruchaga C, Fagan AM, Farlow M, Fox NC, Ghetti B, Goate AM, Gordon BA, Graff-Radford N, Day GS, Hassenstab J, Ikeuchi T, Jack CR, Jagust WJ, Jucker M, Levin J, Massoumzadeh P, Masters CL, Martins R, McDade E, Mori H, Noble JM, Petersen RC, Ringman JM, Salloway S, Saykin AJ, Schofield PR, Shaw LM, Toga AW, Trojanowski JQ, Vöglein J, Weninger S, Bateman RJ, Buckles VD. Autosomal dominant and sporadic late onset Alzheimer's disease share a common in vivo pathophysiology. Brain 2022; 145:3594-3607. [PMID: 35580594 PMCID: PMC9989348 DOI: 10.1093/brain/awac181] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/12/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.
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Affiliation(s)
- John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael Weiner
- Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Laurel Beckett
- Department of Public Health Sciences, School of Medicine, University of California; Davis, Davis, CA, USA
| | - Dean Coble
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Naomi Saito
- Department of Public Health Sciences, School of Medicine, University of California; Davis, Davis, CA, USA
| | - Paul S Aisen
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Neuropsychology and Neuropsychiatry, Institute for Neurological Research (FLENI), Buenos Aires, Argentina
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nigel J Cairns
- College of Medicine and Health and the Living Systems Institute, University of Exeter, Exeter, UK
| | | | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Martin Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease and UK Dementia Research Institute, UCL Institute of Neurology, London, UK
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer’s Disease, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | | | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Mathias Jucker
- Cell Biology of Neurological Diseases Group, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Johannes Levin
- DZNE Munich, Munich Cluster of Systems Neurology (SyNergy) and Ludwig-Maximilians-Universität, Munich, Germany
| | - Parinaz Massoumzadeh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Colin L Masters
- Florey Institute, University of Melbourne, Melbourne, Australia
| | - Ralph Martins
- Sir James McCusker Alzheimer’s Disease Research Unit, Edith Cowan University, Nedlands, Australia
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hiroshi Mori
- Department of Neuroscience, Osaka City University Medical School, Osaka City, Japan
| | - James M Noble
- Department of Neurology, Taub Institute for Research on Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | | | - John M Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephen Salloway
- Department of Neurology, Butler Hospital and Alpert Medical School of Brown University, Providence, RI, 02906, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Peter R Schofield
- Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE) and Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | | | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Virginia D Buckles
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
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14
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Cistaro A, Quartuccio N, Cassalia L, Vai D, Guerra UP, Atzori C, Rainero I, Imperiale D. Brain 18 F-Florbetapir PET/CT Findings in an Early-onset Alzheimer Disease Patient Carrying Presenilin-1 G378E Mutation. Alzheimer Dis Assoc Disord 2022; 36:347-349. [PMID: 34132671 DOI: 10.1097/wad.0000000000000461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/26/2021] [Indexed: 02/07/2023]
Abstract
Positron emission tomography (PET) with 18 F-Fluorodeoxyglucose ( 18 F-FDG) plays an outstanding role in the diagnostic work-up of dementia. Amyloid PET imaging is a complementary imaging technique for the early detection of Alzheimer disease (AD). β-amyloid precursor protein ( APP ), Presenilin-1 ( PSEN1 ) and Presenilin-2 ( PSEN2 ) are the 3 main causative genes responsible for autosomal dominant early-onset Alzheimer disease (EOAD). This is the first report of 18 F-Florbetapir amyloid imaging findings in a 35-year-old male patient with EOAD carrying the G378E mutation in PSEN1 gene. Brain computed tomography (CT) and magnetic resonance imaging scans showed remarkable cerebral atrophy with dilatation of the cerebrospinal fluid spaces; furthermore, a 18 F-Florbetapir PET/CT scan demonstrated also widespread remarkable accumulation of the amyloid tracer in the cerebral cortex, with reduction of the normal contrast between white and gray matter and flattening of the external cortical margins. Furthermore, PET/CT showed intense 18 F-florbetapir uptake in the striatum and in the thalamus bilaterally. Our case supports the usefulness of amyloid PET imaging in the diagnostic work-up of EOAD.
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Affiliation(s)
- Angelina Cistaro
- Nuclear Medicine Department, Ospedali Galliera, Genoa
- AIMN Neuroimaging Study Group, Milan
| | - Natale Quartuccio
- AIMN Neuroimaging Study Group, Milan
- Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, Palermo
| | - Laura Cassalia
- Department of Radiology, Institute of Radiology, "Magna Grecia" University, Catanzaro
| | - Daniela Vai
- Neurology Unit and Human TSE Regional Center, "Amedeo di Savoia" & "Maria Vittoria" Hospital, Turin
| | | | - Cristiana Atzori
- Neurology Unit and Human TSE Regional Center, "Amedeo di Savoia" & "Maria Vittoria" Hospital, Turin
| | - Innocenzo Rainero
- Neurology I, Department of Neuroscience "Rita Levi Montalcini," University of Torino, Torino, Italy
| | - Daniele Imperiale
- Neurology Unit and Human TSE Regional Center, "Amedeo di Savoia" & "Maria Vittoria" Hospital, Turin
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15
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Miao D, Zhou X, Wu X, Chen C, Tian L. Hippocampal morphological atrophy and distinct patterns of structural covariance network in Alzheimer's disease and mild cognitive impairment. Front Psychol 2022; 13:980954. [PMID: 36160522 PMCID: PMC9505506 DOI: 10.3389/fpsyg.2022.980954] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
Elucidating distinct morphological atrophy patterns of Alzheimer's disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD.
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Affiliation(s)
- Dawei Miao
- School of Automation, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaoguang Zhou
- School of Automation, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaoyuan Wu
- School of Economics and Management, Minjiang University, Fuzhou, China
| | - Chengdong Chen
- School of Economics and Management, Minjiang University, Fuzhou, China
| | - Le Tian
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
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16
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Mao C, Hou B, Li J, Chu S, Huang X, Wang J, Dong L, Liu C, Feng F, Peng B, Gao J. Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer's Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort. Curr Alzheimer Res 2022; 19:618-627. [PMID: 36065913 DOI: 10.2174/1567205019666220905145756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/25/2022] [Accepted: 06/28/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker. OBJECTIVE We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients. METHODS One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform. RESULTS The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed. CONCLUSION Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jie Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing 100730, China
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17
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Abrahamson EE, Kofler JK, Becker CR, Price JC, Newell KL, Ghetti B, Murrell JR, McLean CA, Lopez OL, Mathis CA, Klunk WE, Villemagne VL, Ikonomovic MD. 11C-PiB PET can underestimate brain amyloid-β burden when cotton wool plaques are numerous. Brain 2022; 145:2161-2176. [PMID: 34918018 PMCID: PMC9630719 DOI: 10.1093/brain/awab434] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/02/2021] [Accepted: 10/20/2021] [Indexed: 09/01/2023] Open
Abstract
Individuals with familial Alzheimer's disease due to PSEN1 mutations develop high cortical fibrillar amyloid-β load but often have lower cortical 11C-Pittsburgh compound B (PiB) retention than Individuals with sporadic Alzheimer's disease. We hypothesized this is influenced by limited interactions of Pittsburgh compound B with cotton wool plaques, an amyloid-β plaque type common in familial Alzheimer's disease but rare in sporadic Alzheimer's disease. Histological sections of frontal and temporal cortex, caudate nucleus and cerebellum were obtained from 14 cases with sporadic Alzheimer's disease, 12 cases with familial Alzheimer's disease due to PSEN1 mutations, two relatives of a PSEN1 mutation carrier but without genotype information and three non-Alzheimer's disease cases. Sections were processed immunohistochemically using amyloid-β-targeting antibodies and the fluorescent amyloid stains cyano-PiB and X-34. Plaque load was quantified by percentage area analysis. Frozen homogenates from the same brain regions from five sporadic Alzheimer's disease and three familial Alzheimer's disease cases were analysed for 3H-PiB in vitro binding and concentrations of amyloid-β1-40 and amyloid-β1-42. Nine sporadic Alzheimer's disease, three familial Alzheimer's disease and three non-Alzheimer's disease participants had 11C-PiB PET with standardized uptake value ratios calculated using the cerebellum as the reference region. Cotton wool plaques were present in the neocortex of all familial Alzheimer's disease cases and one sporadic Alzheimer's disease case, in the caudate nucleus from four familial Alzheimer's disease cases, but not in the cerebellum. Cotton wool plaques immunolabelled robustly with 4G8 and amyloid-β42 antibodies but weakly with amyloid-β40 and amyloid-βN3pE antibodies and had only background cyano-PiB fluorescence despite labelling with X-34. Relative to amyloid-β plaque load, cyano-Pittsburgh compound B plaque load was similar in sporadic Alzheimer's disease while in familial Alzheimer's disease it was lower in the neocortex and the caudate nucleus. In both regions, insoluble amyloid-β1-42 and amyloid-β1-40 concentrations were similar in familial Alzheimer's disease and sporadic Alzheimer's disease groups, while 3H-PiB binding was lower in the familial Alzheimer's disease than the sporadic Alzheimer's disease group. Higher amyloid-β1-42 concentration associated with higher 3H-PiB binding in sporadic Alzheimer's disease but not familial Alzheimer's disease. 11C-PiB retention correlated with region-matched post-mortem amyloid-β plaque load; however, familial Alzheimer's disease cases with abundant cotton wool plaques had lower 11C-PiB retention than sporadic Alzheimer's disease cases with similar amyloid-β plaque loads. PiB has limited ability to detect amyloid-β aggregates in cotton wool plaques and may underestimate total amyloid-β plaque burden in brain regions with abundant cotton wool plaques.
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Affiliation(s)
- Eric E Abrahamson
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Pittsburgh VA Healthcare System, Pittsburgh, PA, USA
| | - Julia K Kofler
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carl R Becker
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Julie C Price
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Massachusetts General Hospital, A. A. Martinos Center for Biomedical Imaging, Cambridge, MA, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, IN, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, IN, USA
| | - Jill R Murrell
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Catriona A McLean
- Victorian Brain Bank, The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh School of Medicine. Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Pittsburgh VA Healthcare System, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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18
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Queder N, Phelan MJ, Taylor L, Tustison N, Doran E, Hom C, Nguyen D, Lai F, Pulsifer M, Price J, Kreisl WC, Rosas HD, Krinsky‐McHale S, Brickman AM, Yassa MA, Schupf N, Silverman W, Lott IT, Head E, Mapstone M, Keator DB, Ances BM, Andrews HF, Bell K, Birn RM, Brickman AM, Bulova P, Cheema A, Chen K, Christian BT, Clare I, Clark L, Cohen AD, Constantino JN, Doran EW, Fagan A, Feingold E, Foroud TM, Handen BL, Hartley SL, Head E, Henson R, Hom C, Honig L, Ikonomovic MD, Johnson SC, Jordan C, Kamboh MI, Keator D, Klunk WE, Kofler JK, Kreisl WC, Krinsky‐McHale SJ, Lai F, Lao P, Laymon C, Lee JH, Lott IT, Lupson V, Mapstone M, Mathis CA, Minhas DS, Nadkarni N, O'Bryant S, Pang D, Petersen M, Price JC, Pulsifer M, Reiman E, Rizvi B, Rosas HD, Schupf N, Silverman WP, Tudorascu DL, Tumuluru R, Tycko B, Varadarajan B, White DA, Yassa MA, Zaman S, Zhang F. Joint-label fusion brain atlases for dementia research in Down syndrome. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12324. [PMID: 35634535 PMCID: PMC9131930 DOI: 10.1002/dad2.12324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/28/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023]
Abstract
Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community. Highlight Down syndrome (DS) joint-label-fusion atlases provide accurate positron emission tomography (PET) amyloid measurements.A disorder-specific DS atlas is better than a neurotypical atlas for PET quantification.It is not necessary to use a disease-state-specific atlas for quantification in aged DS.Dorsal striatum results vary, possibly due to this region and dementia progression.
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Affiliation(s)
- Nazek Queder
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA,Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Michael J. Phelan
- Institute for Memory Impairments and Neurological DisordersUniversity of California IrvineIrvineCaliforniaUSA
| | - Lisa Taylor
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Nicholas Tustison
- Department of RadiologyUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Eric Doran
- Department of PediatricsUniversity of CaliforniaIrvine Medical CenterOrangeCaliforniaUSA
| | - Christy Hom
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Dana Nguyen
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Florence Lai
- Massachusetts General HospitalHarvard UniversityBostonMassachusettsUSA
| | - Margaret Pulsifer
- Massachusetts General HospitalHarvard UniversityBostonMassachusettsUSA
| | - Julie Price
- Massachusetts General HospitalHarvard UniversityBostonMassachusettsUSA
| | | | - Herminia D. Rosas
- Massachusetts General HospitalHarvard UniversityBostonMassachusettsUSA
| | - Sharon Krinsky‐McHale
- New York State Institute for Basic Research in Developmental DisabilitiesNew YorkNew YorkUSA
| | - Adam M. Brickman
- Department of NeurologyColumbia UniversityNew YorkNew YorkUSA,Taub Institute for Research on Alzheimer's Disease and the Aging BrainDepartment of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Michael A. Yassa
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA,Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA,Department of NeurologyUniversity of California IrvineIrvineCaliforniaUSA
| | - Nicole Schupf
- Department of NeurologyColumbia UniversityNew YorkNew YorkUSA,Taub Institute for Research on Alzheimer's Disease and the Aging BrainDepartment of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Wayne Silverman
- Department of PediatricsUniversity of CaliforniaIrvine Medical CenterOrangeCaliforniaUSA
| | - Ira T. Lott
- Department of PediatricsUniversity of CaliforniaIrvine Medical CenterOrangeCaliforniaUSA
| | - Elizabeth Head
- Department of Pathology & Laboratory MedicineUniversity of California IrvineIrvineCaliforniaUSA
| | - Mark Mapstone
- Department of NeurologyUniversity of California IrvineIrvineCaliforniaUSA
| | - David B. Keator
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
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19
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Beach TG. A History of Senile Plaques: From Alzheimer to Amyloid Imaging. J Neuropathol Exp Neurol 2022; 81:387-413. [PMID: 35595841 DOI: 10.1093/jnen/nlac030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Senile plaques have been studied in postmortem brains for more than 120 years and the resultant knowledge has not only helped us understand the etiology and pathogenesis of Alzheimer disease (AD), but has also pointed to possible modes of prevention and treatment. Within the last 15 years, it has become possible to image plaques in living subjects. This is arguably the single greatest advance in AD research since the identification of the Aβ peptide as the major plaque constituent. The limitations and potentialities of amyloid imaging are still not completely clear but are perhaps best glimpsed through the perspective gained from the accumulated postmortem histological studies. The basic morphological classification of plaques into neuritic, cored and diffuse has been supplemented by sophisticated immunohistochemical and biochemical analyses and increasingly detailed mapping of plaque brain distribution. Changes in plaque classification and staging have in turn contributed to changes in the definition and diagnostic criteria for AD. All of this information continues to be tested by clinicopathological correlations and it is through the insights thereby gained that we will best be able to employ the powerful tool of amyloid imaging.
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Affiliation(s)
- Thomas G Beach
- From the Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, USA
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20
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García Vicente A, Tello Galán M, Pena Pardo F, Amo-Salas M, Mondejar Marín B, Navarro Muñoz S, Rueda Medina I, Poblete García V, Marsal Alonso C, Soriano Castrejón Á. Aumento de la confianza en la interpretación del PET con 18F-Florbetaben: “machine learning” basado en la aproximación cuantitativa. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2021.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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21
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Levitis E, Vogel JW, Funck T, Hachinski V, Gauthier S, Vöglein J, Levin J, Gordon BA, Benzinger T, Iturria-Medina Y, Evans AC. Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease. Brain Commun 2022; 4:fcac085. [PMID: 35602652 PMCID: PMC9116976 DOI: 10.1093/braincomms/fcac085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/05/2021] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Amyloid-beta deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, the latter of which is caused by mutations in genes involved in amyloid-beta processing. Despite amyloid-beta deposition being a centrepiece to both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, some differences between these Alzheimer's disease subtypes have been observed with respect to the spatial pattern of amyloid-beta. Previous work has shown that the spatial pattern of amyloid-beta in individuals spanning the sporadic Alzheimer's disease spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of amyloid-beta across neuronal connections and is constrained by individual rates of amyloid-beta production and clearance. However, it has not been investigated whether amyloid-beta deposition in the rarer autosomal-dominant Alzheimer's disease can be modelled in the same way, and if so, how congruent the spreading patterns of amyloid-beta across sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of amyloid-beta across three different large-scale imaging datasets (2 sporadic Alzheimer's disease, 1 autosomal-dominant Alzheimer's disease). We applied the epidemic spreading model separately to the Alzheimer's Disease Neuroimaging initiative (n = 737), the Open Access Series of Imaging Studies (n = 510) and the Dominantly Inherited Alzheimer's Network (n = 249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately and further identified the most likely subject-specific epicentre of amyloid-beta spread. Using epicentres defined in previous work in sporadic Alzheimer's disease, the epidemic spreading model provided moderate prediction of the regional pattern of amyloid-beta deposition across all three datasets. We further find that, whilst the most likely epicentre for most amyloid-beta-positive subjects overlaps with the default mode network, 13% of autosomal-dominant Alzheimer's disease individuals were best characterized by a striatal origin of amyloid-beta spread. These subjects were also distinguished by being younger than autosomal-dominant Alzheimer's disease subjects with a default mode network amyloid-beta origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most autosomal-dominant Alzheimer's disease patients express amyloid-beta spreading patterns similar to those of sporadic Alzheimer's disease, but that there may be a subset of autosomal-dominant Alzheimer's disease patients with a separate, striatal phenotype.
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Affiliation(s)
- Elizabeth Levitis
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence to: Elizabeth Levitis Magnuson Clinical Center Room 4N244, MSC 1367 Bethesda, MD 20814, USA E-mail:
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Thomas Funck
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Serge Gauthier
- McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases, Munich, Germany,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Yasser Iturria-Medina
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence may also be addressed to: Alan C. Evans Montreal Neurological Institute Montreal, H3A 2B4, Quebec Canada E-mail:
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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22
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Kobayashi R, Kawakatsu S, Hayashi H, Morioka D, Hara N, Ikeuchi T, Otani K. Focal striatal amyloid deposition in Alzheimer's disease caused by APP p.V717I mutation: Longitudinal positron emission tomography study. Geriatr Gerontol Int 2022; 22:360-362. [PMID: 35199912 DOI: 10.1111/ggi.14361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/02/2022] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Ryota Kobayashi
- Department of Psychiatry, Yamagata University School of Medicine, Yamagata, Japan
| | - Shinobu Kawakatsu
- Department of Neuropsychiatry, Aizu Medical Center, Fukushima Medical University, Aizuwakamatsu, Japan
| | - Hiroshi Hayashi
- Department of Occupational Therapy, Fukushima Medical University School of Health Sciences, Fukushima, Japan
| | - Daichi Morioka
- Department of Psychiatry, Yamagata University School of Medicine, Yamagata, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Koichi Otani
- Department of Psychiatry, Yamagata University School of Medicine, Yamagata, Japan
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23
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Vogel JW, Tosun D. Multiple Cortical to Striatal Accumulation Trajectories of β-Amyloid: Do All Roads Lead to Rome? Neurology 2022; 98:695-696. [PMID: 35338076 DOI: 10.1212/wnl.0000000000200191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.,Veterans Affairs San Francisco, CA, USA
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24
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Chhatwal JP, Schultz SA, McDade E, Schultz AP, Liu L, Hanseeuw BJ, Joseph-Mathurin N, Feldman R, Fitzpatrick CD, Sparks KP, Levin J, Berman SB, Renton AE, Esposito BT, Fernandez MV, Sung YJ, Lee JH, Klunk WE, Hofmann A, Noble JM, Graff-Radford N, Mori H, Salloway SM, Masters CL, Martins R, Karch CM, Xiong C, Cruchaga C, Perrin RJ, Gordon BA, Benzinger TLS, Fox NC, Schofield PR, Fagan AM, Goate AM, Morris JC, Bateman RJ, Johnson KA, Sperling RA. Variant-dependent heterogeneity in amyloid β burden in autosomal dominant Alzheimer's disease: cross-sectional and longitudinal analyses of an observational study. Lancet Neurol 2022; 21:140-152. [PMID: 35065037 PMCID: PMC8956209 DOI: 10.1016/s1474-4422(21)00375-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 10/13/2021] [Accepted: 10/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Insights gained from studying individuals with autosomal dominant Alzheimer's disease have broadly influenced mechanistic hypotheses, biomarker development, and clinical trials in both sporadic and dominantly inherited Alzheimer's disease. Although pathogenic variants causing autosomal dominant Alzheimer's disease are highly penetrant, there is substantial heterogeneity in levels of amyloid β (Aβ) between individuals. We aimed to examine whether this heterogeneity is related to disease progression and to investigate the association with mutation location within PSEN1, PSEN2, or APP. METHODS We did cross-sectional and longitudinal analyses of data from the Dominantly Inherited Alzheimer's Network (DIAN) observational study, which enrols individuals from families affected by autosomal dominant Alzheimer's disease. 340 participants in the DIAN study who were aged 18 years or older, had a history of autosomal dominant Alzheimer's disease in their family, and who were enrolled between September, 2008, and June, 2019, were included in our analysis. 206 participants were carriers of pathogenic mutations in PSEN1, PSEN2, or APP, and 134 were non-carriers. 62 unique pathogenic variants were identified in the cohort and were grouped in two ways. First, we sorted variants in PSEN1, PSEN2, or APP by the affected protein domain. Second, we divided PSEN1 variants according to position before or after codon 200. We examined variant-dependent variability in Aβ biomarkers, specifically Pittsburgh-Compound-B PET (PiB-PET) signal, levels of CSF Aβ1-42 (Aβ42), and levels of Aβ1-40 (Aβ40). FINDINGS Cortical and striatal PiB-PET signal showed striking variant-dependent variability using both grouping approaches (p<0·0001), despite similar progression on the clinical dementia rating (p>0·7), and CSF Aβ42 levels (codon-based grouping: p=0·49; domain-based grouping: p=0·095). Longitudinal PiB-PET signal also varied across codon-based groups, mirroring cross-sectional analyses. INTERPRETATION Autosomal dominant Alzheimer's disease pathogenic variants showed highly differential temporal and regional patterns of PiB-PET signal, despite similar functional progression. These findings suggest that although increased PiB-PET signal is generally seen in autosomal dominant Alzheimer's disease, higher levels of PiB-PET signal at an individual level might not reflect more severe or more advanced disease. Our results have high relevance for ongoing clinical trials in autosomal dominant Alzheimer's disease, including those using Aβ PET as a surrogate marker of disease progression. Additionally, and pertinent to both sporadic and autosomal dominant Alzheimer's disease, our results suggest that CSF and PET measures of Aβ levels are not interchangeable and might reflect different Aβ-driven pathobiological processes. FUNDING National Institute on Aging, Doris Duke Charitable Foundation, German Center for Neurodegenerative Diseases, Japanese Agency for Medical Research and Development.
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Affiliation(s)
- Jasmeer P Chhatwal
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA.
| | - Stephanie A Schultz
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Aaron P Schultz
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA
| | - Lei Liu
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA
| | - Bernard J Hanseeuw
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Université Catholique de Louvain, Brussels, Belgium
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Rebecca Feldman
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Colleen D Fitzpatrick
- Massachusetts General Hospital, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA
| | | | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians Universität München, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases, Munich, Germany
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alan E Renton
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bianca T Esposito
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Yun Ju Sung
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Jae Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - William E Klunk
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Anna Hofmann
- German Center for Neurodegenerative Disease, Tübingen, Germany
| | - James M Noble
- Columbia University Irving Medical Center, Department of Neurology, New York, NY, USA
| | | | - Hiroshi Mori
- Osaka City University, Sumiyoshi Ward, Osaka, Japan
| | - Steven M Salloway
- Butler Hospital, Memory and Aging Program, Brown University Alpert Medical School, Providence, RI, USA
| | - Colin L Masters
- The University of Melbourne, Melbourne, VIC, Australia; Florey Institute, Melbourne, VIC, Australia
| | - Ralph Martins
- Department of Biomedical Sciences, Macquarie University, Sydney, NSW, Australia
| | - Celeste M Karch
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Richard J Perrin
- Department of Pathology, Washington University in St Louis, St Louis, MO, USA
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Nick C Fox
- UCL Queen Square Institute of Neurology, Dementia Research Centre, London, UK
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Keith A Johnson
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA
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25
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Eger SJ, Le Guen Y, Khan RR, Hall JN, Kennedy G, Zaharchuk G, Couthouis J, Brooks WS, Velakoulis D, Napolioni V, Belloy ME, Dalgard CL, Mormino EC, Gitler AD, Greicius MD. Confirming Pathogenicity of the F386L PSEN1 Variant in a South Asian Family With Early-Onset Alzheimer Disease. Neurol Genet 2021; 8:e647. [PMID: 34901437 PMCID: PMC8655848 DOI: 10.1212/nxg.0000000000000647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/20/2021] [Indexed: 11/15/2022]
Abstract
Objectives The F386L PSEN1 variant has been reported in 1 Japanese family with limited clinical information. We aimed to prove that F386L is pathogenic by demonstrating that it segregates with early-onset Alzheimer disease (AD). Methods Eight individuals in a South Asian family provided DNA for genetic testing and underwent a neurologic examination. Results The female proband was diagnosed with AD at age 45 years and died at age 49 years. She had a CSF biomarker profile consistent with AD, and her florbetaben PET scan was amyloid positive with high uptake in the striatum. Her MRI showed no prominent white matter disease. Her affected relatives had an age at onset range of 38–57 years and had imaging and biomarker profiles similar to hers. Discussion The results presented here, in conjunction with the prior report, confirm the pathogenicity of F386L. Furthermore, our study highlights the importance of studying families from underrepresented populations to identify or confirm the pathogenicity of rare variants that may be specific to certain genetic ancestries.
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Affiliation(s)
- Sarah J Eger
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Yann Le Guen
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Raiyan R Khan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Jacob N Hall
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Gabriel Kennedy
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Greg Zaharchuk
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Julien Couthouis
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - William S Brooks
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Dennis Velakoulis
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Valerio Napolioni
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Michaël E Belloy
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Clifton L Dalgard
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Aaron D Gitler
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, (S.J.E., Y.L.G., G.K., M.E.B., E.C.M., M.D.G.); Department of Computer Science, Columbia University, New York, NY (R.R.K.) the Neurology Center of Southern California, Temecula, CA (J.N.H.); Department of Radiology, Stanford University School of Medicine, Stanford, CA (G.Z.) Department of Genetics, Stanford University School of Medicine, Stanford, CA (J.C., A.D.G.); Neuroscience Research Australia, Randwick NSW 2031, Australia (W.S.B); the University of New South Wales, Sydney NSW 2052, Australia (W.S.B.); Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville VIC 3050, Australia (D.V.); School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy (V.N); Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.); the American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD (C.L.D.)
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26
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Lloret A, Esteve D, Lloret MA, Cervera-Ferri A, Lopez B, Nepomuceno M, Monllor P. When Does Alzheimer's Disease Really Start? The Role of Biomarkers. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2021; 19:355-364. [PMID: 34690605 DOI: 10.1176/appi.focus.19305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
(Appeared originally in Int J Mol Sci 2019, 20 5536).
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Affiliation(s)
- Ana Lloret
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Daniel Esteve
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Maria-Angeles Lloret
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Ana Cervera-Ferri
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Begoña Lopez
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Mariana Nepomuceno
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
| | - Paloma Monllor
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Avda. Blasco Ibanez, 17, 46010 Valencia, Spain; Department of Clinic Neurophysiology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain; Department of Human Anatomy and Embriology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain; Department of Neurology. University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain
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27
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Levin F, Jelistratova I, Betthauser TJ, Okonkwo O, Johnson SC, Teipel SJ, Grothe MJ. In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer's disease. Alzheimers Res Ther 2021; 13:178. [PMID: 34674764 PMCID: PMC8532333 DOI: 10.1186/s13195-021-00918-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/11/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND We investigated regional amyloid staging characteristics in 11C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer's Prevention. METHODS We analyzed partial volume effect-corrected 11C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.9-76.8 years). Regional amyloid positivity was established using region-specific thresholds. We used four stages from the frequency-based staging of amyloid positivity to characterize individual amyloid deposition. Longitudinal PET data was used to assess the temporal progression of stages and to evaluate the emergence of regional amyloid positivity in participants who were amyloid-negative at baseline. We also assessed the effect of amyloid stage on longitudinal cognitive trajectories. RESULTS The staging model suggested progressive accumulation of amyloid from associative to primary neocortex and gradually involving subcortical regions. Longitudinal PET measurements supported the cross-sectionally estimated amyloid progression. In mixed-effects longitudinal analysis of cognitive follow-up data obtained over an average period of 6.5 years following the baseline PET measurement, amyloid stage II showed a faster decline in executive function, and advanced amyloid stages (III and IV) showed a faster decline across multiple cognitive domains compared to stage 0. CONCLUSIONS Overall, the 11C-PiB-PET-based staging model was generally consistent with previously derived models from 18F-labeled amyloid PET scans and a longitudinal course of amyloid accumulation. Differences in longitudinal cognitive decline support the potential clinical utility of in vivo amyloid staging for risk stratification of the preclinical phase of AD even in middle-aged to older individuals at risk for AD.
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Affiliation(s)
- Fedor Levin
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Irina Jelistratova
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Tobey J Betthauser
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Ozioma Okonkwo
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, s/n, 41013, Seville, Spain.
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28
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Forno G, Lladó A, Hornberger M. Going round in circles-The Papez circuit in Alzheimer's disease. Eur J Neurosci 2021; 54:7668-7687. [PMID: 34656073 DOI: 10.1111/ejn.15494] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/01/2021] [Accepted: 10/12/2021] [Indexed: 11/29/2022]
Abstract
The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.
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Affiliation(s)
- Gonzalo Forno
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,School of Psychology, Universidad de los Andes, Santiago, Chile.,Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, ICBM, Neurosciences Department, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
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29
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Qin Q, Fu L, Wang R, Lyu J, Ma H, Zhan M, Zhou A, Wang F, Zuo X, Wei C. Prominent Striatum Amyloid Retention in Early-Onset Familial Alzheimer's Disease With PSEN1 Mutations: A Pilot PET/MR Study. Front Aging Neurosci 2021; 13:732159. [PMID: 34603009 PMCID: PMC8480470 DOI: 10.3389/fnagi.2021.732159] [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: 06/28/2021] [Accepted: 08/13/2021] [Indexed: 11/17/2022] Open
Abstract
Background: With the advancements of amyloid imaging in recent years, this new imaging diagnostic method has aroused great interest from researchers. Till now, little is known regarding amyloid deposition specialty in patients with early-onset familial Alzheimer's disease (EOFAD), and even less is known about its role in cognitive impairments. Objectives: Our study aimed to evaluate the amyloid deposition in five patients with EOFAD, 15 patients with late-onset sporadic AD, and 12 healthy subjects utilizing 11C-labeled Pittsburgh compound-B (11C-PiB) amyloid PET imaging. Moreover, we figured out the correlation between striatal and cortical standardized uptake value ratios (SUVRs). We also investigated the correlation between 11C-PiB retention and cognitive presentation. Results: All patients with EOFAD showed high amyloid deposition in the striatum, a pattern that is not usually seen in patients with late-onset sporadic AD. The SUVR in the striatum, especially in the amygdala, showed significant correlations with cortex SUVR in EOFAD. However, neither striatal nor cortical 11C-PiB retention was related to cognitive decline. Conclusions: The amyloid distribution in patients with EOFAD differs from late-onset sporadic AD, with higher amyloid deposits in the striatum. Our study also demonstrated positive correlations in 11C-PiB retention between the striatum and other cortical areas. We revealed that the distribution of amyloid in the brain is not random but diffuses following the functional and anatomical connections. However, the degree and pattern of amyloid deposition were not correlated with cognitive deficits.
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Affiliation(s)
- Qi Qin
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Liping Fu
- Department of Nuclear Medicine, China-Japan Friendship Hospital, Beijing, China.,Department of Nuclear Medicine, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ruimin Wang
- Department of Nuclear Medicine, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jihui Lyu
- Center for Cognitive Disorders, Beijing Geriatric Hospital, Beijing, China
| | - Huixuan Ma
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Minmin Zhan
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Aihong Zhou
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Fen Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Xiumei Zuo
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Cuibai Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
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Chen CD, Joseph-Mathurin N, Sinha N, Zhou A, Li Y, Friedrichsen K, McCullough A, Franklin EE, Hornbeck R, Gordon B, Sharma V, Cruchaga C, Goate A, Karch C, McDade E, Xiong C, Bateman RJ, Ghetti B, Ringman JM, Chhatwal J, Masters CL, McLean C, Lashley T, Su Y, Koeppe R, Jack C, Klunk WE, Morris JC, Perrin RJ, Cairns NJ, Benzinger TLS. Comparing amyloid-β plaque burden with antemortem PiB PET in autosomal dominant and late-onset Alzheimer disease. Acta Neuropathol 2021; 142:689-706. [PMID: 34319442 PMCID: PMC8815340 DOI: 10.1007/s00401-021-02342-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 12/31/2022]
Abstract
Pittsburgh compound B (PiB) radiotracer for positron emission tomography (PET) imaging can bind to different types of amyloid-β plaques and blood vessels (cerebral amyloid angiopathy). However, the relative contributions of different plaque subtypes (diffuse versus cored/compact) to in vivo PiB PET signal on a region-by-region basis are incompletely understood. Of particular interest is whether the same staging schemes for summarizing amyloid-β burden are appropriate for both late-onset and autosomal dominant forms of Alzheimer disease (LOAD and ADAD). Here, we compared antemortem PiB PET with follow-up postmortem estimation of amyloid-β burden using stereologic methods to estimate the relative area fraction of diffuse and cored/compact amyloid-β plaques across 16 brain regions in 15 individuals with ADAD and 14 individuals with LOAD. In ADAD, we found that PiB PET correlated with diffuse plaques in the frontal, parietal, temporal, and striatal regions commonly used to summarize amyloid-β burden in PiB PET, and correlated with both diffuse and cored/compact plaques in the occipital lobe and parahippocampal gyrus. In LOAD, we found that PiB PET correlated with both diffuse and cored/compact plaques in the anterior cingulate, frontal lobe (middle frontal gyrus), and parietal lobe, and showed additional correlations with diffuse plaque in the amygdala and occipital lobe, and with cored/compact plaque in the temporal lobe. Thus, commonly used PiB PET summary regions predominantly reflect diffuse plaque burden in ADAD and a mixture of diffuse and cored/compact plaque burden in LOAD. In direct comparisons of ADAD and LOAD, postmortem stereology identified much greater mean amyloid-β plaque burdens in ADAD versus LOAD across almost all brain regions studied. However, standard PiB PET did not recapitulate these stereologic findings, likely due to non-trivial amyloid-β plaque burdens in ADAD within the cerebellum and brainstem-commonly used reference regions in PiB PET. Our findings suggest that PiB PET summary regions correlate with amyloid-β plaque burden in both ADAD and LOAD; however, they might not be reliable in direct comparisons of regional amyloid-β plaque burden between the two forms of AD.
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Affiliation(s)
- Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Namita Sinha
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology, University of Manitoba, Shared Health, Winnipeg, MB, Canada
| | - Aihong Zhou
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Karl Friedrichsen
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Erin E Franklin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Vijay Sharma
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John M Ringman
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jasmeer Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Catriona McLean
- Department of Anatomic Pathology, Alfred Hospital, Melbourne, VIC, Australia
| | - Tammaryn Lashley
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, University College London, London, UK
| | - Yi Su
- Banner Alzheimer's Institute, Banner Health, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Banner Health, Phoenix, AZ, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nigel J Cairns
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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Montoliu-Gaya L, Strydom A, Blennow K, Zetterberg H, Ashton NJ. Blood Biomarkers for Alzheimer's Disease in Down Syndrome. J Clin Med 2021; 10:3639. [PMID: 34441934 PMCID: PMC8397053 DOI: 10.3390/jcm10163639] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 12/15/2022] Open
Abstract
Epidemiological evidence suggests that by the age of 40 years, all individuals with Down syndrome (DS) have Alzheimer's disease (AD) neuropathology. Clinical diagnosis of dementia by cognitive assessment is complex in these patients due to the pre-existing and varying intellectual disability, which may mask subtle declines in cognitive functioning. Cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers, although accurate, are expensive, invasive, and particularly challenging in such a vulnerable population. The advances in ultra-sensitive detection methods have highlighted blood biomarkers as a valuable and realistic tool for AD diagnosis. Studies with DS patients have proven the potential blood-based biomarkers for sporadic AD (amyloid-β, tau, phosphorylated tau, and neurofilament light chain) to be useful in this population. In addition, biomarkers related to other pathologies that could aggravate dementia progression-such as inflammatory dysregulation, energetic imbalance, or oxidative stress-have been explored. This review serves to provide a brief overview of the main findings from the limited neuroimaging and CSF studies, outline the current state of blood biomarkers to diagnose AD in patients with DS, discuss possible past limitations of the research, and suggest considerations for developing and validating blood-based biomarkers in the future.
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Affiliation(s)
- Laia Montoliu-Gaya
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, 431 41 Mölndal, Sweden; (K.B.); (H.Z.); (N.J.A.)
| | - Andre Strydom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK;
- South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
- London Down Syndrome Consortium (LonDowns), London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, 431 41 Mölndal, Sweden; (K.B.); (H.Z.); (N.J.A.)
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, 431 41 Mölndal, Sweden; (K.B.); (H.Z.); (N.J.A.)
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
- UK Dementia Research Institute, University College London, London WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Nicholas James Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, 431 41 Mölndal, Sweden; (K.B.); (H.Z.); (N.J.A.)
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Old Age Psychiatry, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RT, UK
- NIHR Biomedical Research Centre for Mental Health, Biomedical Research Unit for Dementia at South London, Maudsley NHS Foundation, London SE5 8AF, UK
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Kim HR, Jang JH, Ham H, Choo SH, Park J, Kang SH, Hwangbo S, Jang H, Na DL, Seo SW, Baek JH, Kim HJ. A Case of Early-Onset Alzheimer's Disease Mimicking Schizophrenia in a Patient with Presenilin 1 Mutation (S170P). J Alzheimers Dis 2021; 83:1025-1031. [PMID: 34366354 DOI: 10.3233/jad-210650] [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
Atypical psychological symptoms frequently occur in early-onset Alzheimer's disease (EOAD), which makes it difficult to differentiate it from other psychiatric disorders. We report the case of a 28-year-old woman with EOAD, carrying a presenilin-1 mutation (S170P), who was initially misdiagnosed with schizophrenia because of prominent psychiatric symptoms in the first 1-2 years of the disease. Amyloid-β positron emission tomography (PET) showed remarkably high tracer uptake in the striatum and thalamus. Tau PET showed widespread cortical uptake and relatively low uptake in the subcortical and medial temporal regions. Our case advocates for considering EOAD diagnosis for young patients with psychiatric and atypical cognitive symptoms.
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Affiliation(s)
- Hang-Rai Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea.,Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Ja Hyun Jang
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Honggi Ham
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Seung Ho Choo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeongho Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Song Hwangbo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence 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.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Ji Hyun Baek
- Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
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Martinez JL, Zammit MD, West NR, Christian BT, Bhattacharyya A. Basal Forebrain Cholinergic Neurons: Linking Down Syndrome and Alzheimer's Disease. Front Aging Neurosci 2021; 13:703876. [PMID: 34322015 PMCID: PMC8311593 DOI: 10.3389/fnagi.2021.703876] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/17/2021] [Indexed: 12/31/2022] Open
Abstract
Down syndrome (DS, trisomy 21) is characterized by intellectual impairment at birth and Alzheimer’s disease (AD) pathology in middle age. As individuals with DS age, their cognitive functions decline as they develop AD pathology. The susceptibility to degeneration of a subset of neurons, known as basal forebrain cholinergic neurons (BFCNs), in DS and AD is a critical link between cognitive impairment and neurodegeneration in both disorders. BFCNs are the primary source of cholinergic innervation to the cerebral cortex and hippocampus, as well as the amygdala. They play a critical role in the processing of information related to cognitive function and are directly engaged in regulating circuits of attention and memory throughout the lifespan. Given the importance of BFCNs in attention and memory, it is not surprising that these neurons contribute to dysfunctional neuronal circuitry in DS and are vulnerable in adults with DS and AD, where their degeneration leads to memory loss and disturbance in language. BFCNs are thus a relevant cell target for therapeutics for both DS and AD but, despite some success, efforts in this area have waned. There are gaps in our knowledge of BFCN vulnerability that preclude our ability to effectively design interventions. Here, we review the role of BFCN function and degeneration in AD and DS and identify under-studied aspects of BFCN biology. The current gaps in BFCN relevant imaging studies, therapeutics, and human models limit our insight into the mechanistic vulnerability of BFCNs in individuals with DS and AD.
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Affiliation(s)
- Jose L Martinez
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, United States.,Waisman Center, University of Wisconsin, Madison, WI, United States
| | - Matthew D Zammit
- Waisman Center, University of Wisconsin, Madison, WI, United States.,Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
| | - Nicole R West
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, United States.,Waisman Center, University of Wisconsin, Madison, WI, United States
| | - Bradley T Christian
- Waisman Center, University of Wisconsin, Madison, WI, United States.,Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States.,Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
| | - Anita Bhattacharyya
- Waisman Center, University of Wisconsin, Madison, WI, United States.,Department of Cellular and Regenerative Biology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
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Ghisays V, Lopera F, Goradia DD, Protas HD, Malek-Ahmadi MH, Chen Y, Devadas V, Luo J, Lee W, Baena A, Bocanegra Y, Guzmán-Vélez E, Pardilla-Delgado E, Vila-Castelar C, Fox-Fuller JT, Hu N, Clayton D, Thomas RG, Alvarez S, Espinosa A, Acosta-Baena N, Giraldo MM, Rios-Romenets S, Langbaum JB, Chen K, Su Y, Tariot PN, Quiroz YT, Reiman EM. PET evidence of preclinical cerebellar amyloid plaque deposition in autosomal dominant Alzheimer's disease-causing Presenilin-1 E280A mutation carriers. NEUROIMAGE-CLINICAL 2021; 31:102749. [PMID: 34252876 PMCID: PMC8278433 DOI: 10.1016/j.nicl.2021.102749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/21/2021] [Accepted: 06/26/2021] [Indexed: 11/16/2022]
Abstract
PET evidence of cerebellar Aβ deposition in unimpaired (CU) PSEN1 E280A kindred. Cerebellar Aβ PET SUVR began to distinguish CU carriers from non-carriers at age 34. Cortical and cerebellar Aβ PET SUVR are positively associated in CU carriers. Cerebellar florbetapir SUVR correlated with lower composite score in CU carriers.
Background In contrast to sporadic Alzheimer’s disease, autosomal dominant Alzheimer’s disease (ADAD) is associated with greater neuropathological evidence of cerebellar amyloid plaque (Aβ) deposition. In this study, we used positron emission tomography (PET) measurements of fibrillar Aβ burden to characterize the presence and age at onset of cerebellar Aβ deposition in cognitively unimpaired (CU) Presenilin-1 (PSEN1) E280A mutation carriers from the world’s largest extended family with ADAD. Methods 18F florbetapir and 11C Pittsburgh compound B (PiB) PET data from two independent studies – API ADAD Colombia Trial (NCT01998841) and Colombia-Boston (COLBOS) longitudinal biomarker study were included. The tracers were selected independently by the respective sponsors prior to the start of each study and used exclusively throughout. Template-based cerebellar Aβ-SUVR (standard-uptake value ratios) using a known-to-be-spared pons reference region (cerebellar SUVR_pons), to a) compare 28–56-year-old CU carriers and non-carriers; b) estimate the age at which cerebellar SUVR_pons began to differ significantly in carrier and non-carrier groups; and c) characterize in carriers associations with age, cortical SUVR_pons, delayed recall memory, and API ADAD composite score. Results Florbetapir and PiB cerebellar SUVR_pons were significantly higher in carriers than non-carriers (p < 0.0001). Cerebellar SUVR_pons began to distinguish carriers from non-carriers at age 34, 10 years before the carriers’ estimated age at mild cognitive impairment onset. Florbetapir and PiB cerebellar SUVR_pons in carriers were positively correlated with age (r = 0.44 & 0.69, p < 0.001), cortical SUVR_pons (r = 0.55 & 0.69, p < 0.001), and negatively correlated with delayed recall memory (r = −0.21 & −0.50, p < 0.05, unadjusted for cortical SUVR_pons) and API ADAD composite (r = −0.25, p < 0.01, unadjusted for cortical SUVR_pons in florbetapir API ADAD cohort). Conclusion This PET study provides evidence of cerebellar Aβ plaque deposition in CU carriers starting about a decade before the clinical onset of ADAD. Additional studies are needed to clarify the impact of using a cerebellar versus pons reference region on the power to detect and track ADAD changes, even in preclinical stages of this disorder.
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Affiliation(s)
- Valentina Ghisays
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Francisco Lopera
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Dhruman D Goradia
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Hillary D Protas
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Michael H Malek-Ahmadi
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yinghua Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Vivek Devadas
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Ji Luo
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Wendy Lee
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Ana Baena
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Yamile Bocanegra
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | | | | | - Joshua T Fox-Fuller
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Boston University, Boston, MA, USA
| | - Nan Hu
- Genentech Inc., South San Francisco, CA, USA
| | | | - Ronald G Thomas
- University of California San Diego School of Medicine, La Jolla, CA, USA
| | | | - Alejandro Espinosa
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | - Margarita M Giraldo
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | - Jessica B Langbaum
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Arizona State University, Tempe, AZ, USA; University of Arizona, Tucson, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Pierre N Tariot
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yakeel T Quiroz
- Neurosciences Group of Antioquia, Universidad de Antioquia, Medellín, Colombia; Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Arizona State University, Tempe, AZ, USA; University of Arizona, Tucson, AZ, USA; Translational Genomics Research Institute, Phoenix, AZ, USA.
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Zammit MD, Tudorascu DL, Laymon CM, Hartley SL, Ellison PA, Zaman SH, Ances BM, Johnson SC, Stone CK, Sabbagh MN, Mathis CA, Klunk WE, Cohen AD, Handen BL, Christian BT. Neurofibrillary tau depositions emerge with subthreshold cerebral beta-amyloidosis in down syndrome. NEUROIMAGE-CLINICAL 2021; 31:102740. [PMID: 34182407 PMCID: PMC8252122 DOI: 10.1016/j.nicl.2021.102740] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/20/2021] [Accepted: 06/21/2021] [Indexed: 01/04/2023]
Abstract
Neurofibrillary tau deposition in Down syndrome follows the Braak staging pathology. Neurofibrillary tau emerges in individuals with very low amyloid burden. There is a short latency between the onset of amyloid and tau in Down syndrome. Elevated tau was observed in Braak stages I-II with very low amyloid burden, and in stages III-VI with greater amyloid burden.
Introduction Adults with Down syndrome are genetically predisposed to develop Alzheimer’s disease and accumulate beta-amyloid plaques (Aβ) early in life. While Aβ has been heavily studied in Down syndrome, its relationship with neurofibrillary tau is less understood. The aim of this study was to evaluate neurofibrillary tau deposition in individuals with Down syndrome with varying levels of Aβ burden. Methods A total of 161 adults with Down syndrome (mean age = 39.2 (8.50) years) and 40 healthy, non-Down syndrome sibling controls (43.2 (12.6) years) underwent T1w-MRI, [C-11]PiB and [F-18]AV-1451 PET scans. PET images were converted to units of standardized uptake value ratios (SUVrs). Aβ burden was calculated using the amyloid load metric (AβL); a measure of global Aβ burden that improves quantification from SUVrs by suppressing the nonspecific binding signal component and computing the specific Aβ signal from all Aβ-carrying voxels from the image. Regional tau was assessed using control-standardized AV-1451 SUVr. Control-standardized SUVrs were compared across Down syndrome groups of Aβ-negative (A-) (AβL < 13.3), subthreshold A+ (13.3 ≤ AβL < 20) and conventionally A+ (AβL ≥ 20) individuals. The subthreshold A + group was identified as having significantly higher Aβ burden compared to the A- group, but not high enough to satisfy a conventional A + classification. Results A large-sized association that survived adjustment for chronological age, mental age (assessed using the Peabody Picture Vocabulary Test), and imaging site was observed between AβL and AV-1451 within each Braak region (p < .05). The A + group showed significantly higher AV-1451 retention across all Braak regions compared to the A- and subthreshold A + groups (p < .05). The subthreshold A + group showed significantly higher AV-1451 retention in Braak regions I-III compared to an age-matched sample from the A- group (p < .05). Discussion These results show that even the earliest detectable Aβ accumulation in Down syndrome is accompanied by elevated tau in the early Braak stage regions. This early detection of tau can help characterize the tau accumulation phase during preclinical Alzheimer’s disease progression in Down syndrome and suggests that there may be a relatively narrow window after Aβ accumulation begins to prevent the downstream cascade of events that leads to Alzheimer’s disease.
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Affiliation(s)
- Matthew D Zammit
- University of Wisconsin-Madison Waisman Center, Madison, WI, USA; University of Wisconsin-Madison Department of Medical Physics, Madison, WI, USA.
| | - Dana L Tudorascu
- University of Pittsburgh Department of Psychiatry, Pittsburgh, PA, USA
| | - Charles M Laymon
- University of Pittsburgh Department of Radiology, Pittsburgh, PA, USA; University of Pittsburgh Department of Bioengineering, Pittsburgh, PA, USA
| | - Sigan L Hartley
- University of Wisconsin-Madison Waisman Center, Madison, WI, USA
| | - Paul A Ellison
- University of Wisconsin-Madison Department of Medical Physics, Madison, WI, USA
| | - Shahid H Zaman
- Cambridge Intellectual Disability Research Group, University of Cambridge, Cambridge, UK
| | - Beau M Ances
- Washington University in St. Louis Department of Neurology, St. Louis, MO, USA
| | - Sterling C Johnson
- University of Wisconsin-Madison Alzheimer's Disease Research Center, Madison, WI, USA
| | - Charles K Stone
- University of Wisconsin-Madison Department of Medicine, Madison, WI, USA
| | | | - Chester A Mathis
- University of Pittsburgh Department of Psychiatry, Pittsburgh, PA, USA
| | - William E Klunk
- University of Pittsburgh Department of Psychiatry, Pittsburgh, PA, USA
| | - Ann D Cohen
- University of Pittsburgh Department of Psychiatry, Pittsburgh, PA, USA
| | - Benjamin L Handen
- University of Pittsburgh Department of Psychiatry, Pittsburgh, PA, USA
| | - Bradley T Christian
- University of Wisconsin-Madison Waisman Center, Madison, WI, USA; University of Wisconsin-Madison Department of Medical Physics, Madison, WI, USA
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Almkvist O, Brüggen K, Nordberg A. Subcortical and Cortical Regions of Amyloid-β Pathology Measured by 11C-PiB PET Are Differentially Associated with Cognitive Functions and Stages of Disease in Memory Clinic Patients. J Alzheimers Dis 2021; 81:1613-1624. [PMID: 33967046 DOI: 10.3233/jad-201612] [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: 01/13/2023]
Abstract
BACKGROUND The effect of regional brain amyloid-β (Aβ) pathology on specific cognitive functions is incompletely known. OBJECTIVE The relationship between Aβ and cognitive functions was investigated in this cross-sectional multicenter study of memory clinic patients. METHODS The participants were patients diagnosed with Alzheimer's disease (AD, n = 83), mild cognitive impairment (MCI, n = 60), and healthy controls (HC, n = 32), who had been scanned by 11C-PiB PET in 13 brain regions of both hemispheres and who had been assessed by cognitive tests covering seven domains. RESULTS Hierarchic multiple regression analyses were performed on each cognitive test as dependent variable, controlling for demographic characteristics and APOE status (block 1) and PiB measures in 13 brain regions (block 2) as independent variables. The model was highly significant for each cognitive test and most strongly for tests of episodic memory (learning and retention) versus PiB in putamen, visuospatially demanding tests (processing and retention) versus the occipital lobe, semantic fluency versus the parietal lobe, attention versus posterior gyrus cinguli, and executive function versus nucleus accumbens. In addition, education had a positively and APOE status a negatively significant effect on cognitive tests. CONCLUSION Five subcortical and cortical regions with Aβ pathology are differentially associated with cognitive functions and stages of disease in memory clinic patients.
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Affiliation(s)
- Ove Almkvist
- Division of Clinical Geriatrics, Department of Neurobiology Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden.,Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Katharina Brüggen
- Division of Clinical Geriatrics, Department of Neurobiology Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Department of Neurobiology Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden
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Luckett PH, McCullough A, Gordon BA, Strain J, Flores S, Dincer A, McCarthy J, Kuffner T, Stern A, Meeker KL, Berman SB, Chhatwal JP, Cruchaga C, Fagan AM, Farlow MR, Fox NC, Jucker M, Levin J, Masters CL, Mori H, Noble JM, Salloway S, Schofield PR, Brickman AM, Brooks WS, Cash DM, Fulham MJ, Ghetti B, Jack CR, Vöglein J, Klunk W, Koeppe R, Oh H, Su Y, Weiner M, Wang Q, Swisher L, Marcus D, Koudelis D, Joseph-Mathurin N, Cash L, Hornbeck R, Xiong C, Perrin RJ, Karch CM, Hassenstab J, McDade E, Morris JC, Benzinger TLS, Bateman RJ, Ances BM. Modeling autosomal dominant Alzheimer's disease with machine learning. Alzheimers Dement 2021; 17:1005-1016. [PMID: 33480178 PMCID: PMC8195816 DOI: 10.1002/alz.12259] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
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Affiliation(s)
| | | | - Brian A Gordon
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jeremy Strain
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shaney Flores
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aylin Dincer
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - John McCarthy
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Todd Kuffner
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ari Stern
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Karin L Meeker
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Jasmeer P Chhatwal
- Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Carlos Cruchaga
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Anne M Fagan
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Mathias Jucker
- German Center for Neurodegenerative Disease, Tübingen, Germany
| | - Johannes Levin
- Ludwig Maximilian University of Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, Australia
| | - Hiroshi Mori
- Osaka City University, Sumiyoshi Ward, Osaka, Japan
| | - James M Noble
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, G.H. Sergievsky Center and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Peter R Schofield
- Neuroscience Research Australia, Randwick, NSW, Australia
- University of New South Wales, Sydney, NSW, Australia
| | | | - William S Brooks
- Neuroscience Research Australia, Randwick, NSW, Australia
- University of New South Wales, Sydney, NSW, Australia
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Michael J Fulham
- Department of Molecular Imaging, Royal Prince Alfred Hospital, Missenden Road, Camperdown, NSW, Australia
- University of Sydney, Sydney, NSW, Australia
| | | | | | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases, Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - William Klunk
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Hwamee Oh
- Brown University, Providence, Rhode Island, USA
| | - Yi Su
- Banner Alzheimer Institute, Phoenix, Arizona, USA
| | | | - Qing Wang
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Laura Swisher
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Dan Marcus
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | | | - Lisa Cash
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Russ Hornbeck
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chengjie Xiong
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Celeste M Karch
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Eric McDade
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - John C Morris
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | | | - Beau M Ances
- Washington University in St. Louis, St. Louis, Missouri, USA
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Bucci M, Savitcheva I, Farrar G, Salvadó G, Collij L, Doré V, Gispert JD, Gunn R, Hanseeuw B, Hansson O, Shekari M, Lhommel R, Molinuevo JL, Rowe C, Sur C, Whittington A, Buckley C, Nordberg A. A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [ 18F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 2021; 48:2183-2199. [PMID: 33844055 PMCID: PMC8175298 DOI: 10.1007/s00259-021-05311-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 03/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. METHODS A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD). RESULTS Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. CONCLUSIONS Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.
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Affiliation(s)
- Marco Bucci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Amersham, UK
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Vincent Doré
- Austin Health, University of Melbourne, Melbourne, Australia.,Health and Biosecurity, CSIRO, Parkville, Australia
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), 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 Bioingenieriá, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Roger Gunn
- Invicro, London, UK.,Division of Brain Sciences, Department of Medicine, Imperial College, London, UK
| | - Bernard Hanseeuw
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmo, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Renaud Lhommel
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), 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 de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Christopher Rowe
- Austin Health, University of Melbourne, Melbourne, Australia.,Department of Medicine, The University of Melbourne, Melbourne, Australia
| | | | | | | | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. .,Department of Aging, Karolinska University Hospital, Stockholm, Sweden.
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García Vicente AM, Tello Galán MJ, Pena Pardo FJ, Amo-Salas M, Mondejar Marín B, Navarro Muñoz S, Rueda Medina I, Poblete García VM, Marsal Alonso C, Soriano Castrejón Á. Increasing the confidence of 18F-Florbetaben PET interpretations: Machine learning quantitative approximation. Rev Esp Med Nucl Imagen Mol 2021; 41:153-163. [DOI: 10.1016/j.remnie.2021.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 01/27/2021] [Indexed: 11/28/2022]
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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Lanzillotta C, Di Domenico F. Stress Responses in Down Syndrome Neurodegeneration: State of the Art and Therapeutic Molecules. Biomolecules 2021; 11:biom11020266. [PMID: 33670211 PMCID: PMC7916967 DOI: 10.3390/biom11020266] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 12/11/2022] Open
Abstract
Down syndrome (DS) is the most common genomic disorder characterized by the increased incidence of developing early Alzheimer’s disease (AD). In DS, the triplication of genes on chromosome 21 is intimately associated with the increase of AD pathological hallmarks and with the development of brain redox imbalance and aberrant proteostasis. Increasing evidence has recently shown that oxidative stress (OS), associated with mitochondrial dysfunction and with the failure of antioxidant responses (e.g., SOD1 and Nrf2), is an early signature of DS, promoting protein oxidation and the formation of toxic protein aggregates. In turn, systems involved in the surveillance of protein synthesis/folding/degradation mechanisms, such as the integrated stress response (ISR), the unfolded stress response (UPR), and autophagy, are impaired in DS, thus exacerbating brain damage. A number of pre-clinical and clinical studies have been applied to the context of DS with the aim of rescuing redox balance and proteostasis by boosting the antioxidant response and/or inducing the mechanisms of protein re-folding and clearance, and at final of reducing cognitive decline. So far, such therapeutic approaches demonstrated their efficacy in reverting several aspects of DS phenotype in murine models, however, additional studies aimed to translate these approaches in clinical practice are still needed.
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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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Sanchez JS, Hanseeuw BJ, Lopera F, Sperling RA, Baena A, Bocanegra Y, Aguillon D, Guzmán-Vélez E, Pardilla-Delgado E, Ramirez-Gomez L, Vila-Castelar C, Martinez JE, Fox-Fuller JT, Ramos C, Ochoa-Escudero M, Alvarez S, Jacobs HIL, Schultz AP, Gatchel JR, Becker JA, Katz SR, Mayblyum DV, Price JC, Reiman EM, Johnson KA, Quiroz YT. Longitudinal amyloid and tau accumulation in autosomal dominant Alzheimer's disease: findings from the Colombia-Boston (COLBOS) biomarker study. Alzheimers Res Ther 2021; 13:27. [PMID: 33451357 PMCID: PMC7811244 DOI: 10.1186/s13195-020-00765-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/26/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Neuroimaging studies of autosomal dominant Alzheimer's disease (ADAD) enable characterization of the trajectories of cerebral amyloid-β (Aβ) and tau accumulation in the decades prior to clinical symptom onset. Longitudinal rates of regional tau accumulation measured with positron emission tomography (PET) and their relationship with other biomarker and cognitive changes remain to be fully characterized in ADAD. METHODS Fourteen ADAD mutation carriers (Presenilin-1 E280A) and 15 age-matched non-carriers from the Colombian kindred underwent 2-3 sessions of Aβ (11C-Pittsburgh compound B) and tau (18F-flortaucipir) PET, structural magnetic resonance imaging, and neuropsychological evaluation over a 2-4-year follow-up period. Annualized rates of change for imaging and cognitive variables were compared between carriers and non-carriers, and relationships among baseline measurements and rates of change were assessed within carriers. RESULTS Longitudinal measurements were consistent with a sequence of ADAD-related changes beginning with Aβ accumulation (16 years prior to expected symptom onset, EYO), followed by entorhinal cortex (EC) tau (9 EYO), neocortical tau (6 EYO), hippocampal atrophy (6 EYO), and cognitive decline (4 EYO). Rates of tau accumulation among carriers were most rapid in parietal neocortex (~ 9%/year). EC tau PET signal at baseline was a significant predictor of subsequent neocortical tau accumulation and cognitive decline within carriers. CONCLUSIONS Our results are consistent with the sequence of biological changes in ADAD implied by cross-sectional studies and highlight the importance of EC tau as an early biomarker and a potential link between Aβ burden and neocortical tau accumulation in ADAD.
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Affiliation(s)
- Justin S Sanchez
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - Bernard J Hanseeuw
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | - Reisa A Sperling
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hoospital, Harvard Medical School, Boston, MA, USA
| | - Ana Baena
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | - Yamile Bocanegra
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | - David Aguillon
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | | | | | | | | | - Jairo E Martinez
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - Joshua T Fox-Fuller
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
- Boston University, Boston, MA, USA
| | - Claudia Ramos
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia
| | | | | | - Heidi I L Jacobs
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Aaron P Schultz
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - Jennifer R Gatchel
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - J Alex Becker
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | - Samantha R Katz
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | | | - Julie C Price
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
| | | | - Keith A Johnson
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hoospital, Harvard Medical School, Boston, MA, USA
| | - Yakeel T Quiroz
- Massachusetts General Hoospital, Harvard Medical School, Boston, MA, USA.
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellin, Colombia.
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Zammit MD, Laymon CM, Tudorascu DL, Hartley SL, Piro‐Gambetti B, Johnson SC, Stone CK, Mathis CA, Zaman SH, Klunk WE, Handen BL, Cohen AD, Christian BT. Patterns of glucose hypometabolism in Down syndrome resemble sporadic Alzheimer's disease except for the putamen. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 12:e12138. [PMID: 33490360 PMCID: PMC7804861 DOI: 10.1002/dad2.12138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/09/2020] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Adults with Down syndrome (DS) are predisposed to Alzheimer's disease (AD) and the relationship between cognition and glucose metabolism in this population has yet to be evaluated. METHODS Adults with DS (N = 90; mean age [standard deviation] = 38.0 [8.30] years) underwent [C-11]Pittsburgh compound B (PiB) and [F-18]fluorodeoxyglucose (FDG) positron emission tomography scans. Associations among amyloid beta (Aβ), FDG, and measures of cognition were explored. Interregional FDG metabolic connectivity was assessed to compare cognitively stable DS and mild cognitive impairment/AD (MCI-DS/AD). RESULTS Negative associations between Aβ and FDG were evident in regions affected in sporadic AD. A positive association was observed in the putamen, which is the brain region showing the earliest increases in Aβ deposition. Both Aβ and FDG were associated with measures of cognition, and metabolic connectivity distinguished cases of MCI-DS/AD from cognitively stable DS. DISCUSSION Associations among Aβ, FDG, and cognition reveal that neurodegeneration in DS resembles sporadic AD with the exception of the putamen, highlighting the usefulness of FDG in monitoring neurodegeneration in DS.
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Affiliation(s)
| | - Charles M. Laymon
- Department of Radiology, University of PittsburghPittsburghPennsylvaniaUSA
- Department of Bioengineering, University of PittsburghPittsburghPennsylvaniaUSA
| | - Dana L. Tudorascu
- Department of Psychiatry, University of PittsburghPittsburghPennsylvaniaUSA
| | - Sigan L. Hartley
- University of Wisconsin‐Madison Waisman CenterMadisonWisconsinUSA
| | | | - Sterling C. Johnson
- University of Wisconsin‐Madison Alzheimer's Disease Research CenterMadisonWisconsinUSA
| | - Charles K. Stone
- Department of Medicine, University of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Chester A. Mathis
- Department of Radiology, University of PittsburghPittsburghPennsylvaniaUSA
| | - Shahid H. Zaman
- University of Cambridge Intellectual Disability Research GroupCambridgeUK
| | - William E. Klunk
- Department of Psychiatry, University of PittsburghPittsburghPennsylvaniaUSA
| | - Benjamin L. Handen
- Department of Psychiatry, University of PittsburghPittsburghPennsylvaniaUSA
| | - Ann D. Cohen
- Department of Psychiatry, University of PittsburghPittsburghPennsylvaniaUSA
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Brown SSG, Mak E, Zaman S. Multi-Modal Imaging in Down's Syndrome: Maximizing Utility Through Innovative Neuroimaging Approaches. Front Neurol 2021; 11:629463. [PMID: 33488507 PMCID: PMC7817620 DOI: 10.3389/fneur.2020.629463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 12/08/2020] [Indexed: 11/13/2022] Open
Abstract
In recent decades, the field of neuroimaging has experienced a surge of popularity and innovation which has led to significant advancements in the understanding of neurological disease, if not immediate clinical translation. In the case of Down's syndrome, a complex interplay of neurodevelopmental and neurodegenerative processes occur as a result of the trisomy of chromosome 21. The substantial potential impact of improved clinical intervention and the limited research under-taken to date make it a prime candidate for longitudinal neuroimaging-based study. However, as with a multitude of other multifaceted brain-based disorders, singular utilization of lone modality imaging has limited interpretability and applicability. Indeed, a present challenge facing the neuroimaging community as a whole is the methodological integration of multi-modal imaging to enhance clinical understanding. This review therefore aims to assess the current literature in Down's syndrome utilizing a multi-modal approach with regards to improvement upon consideration of a single modality. Additionally, we discuss potential avenues of future research that may effectively combine structural, functional and molecular-based imaging techniques for the significant benefit of the understanding of Down's syndrome pathology.
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Affiliation(s)
- Stephanie S. G. Brown
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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Zammit MD, Tudorascu DL, Laymon CM, Hartley SL, Zaman SH, Ances BM, Johnson SC, Stone CK, Mathis CA, Klunk WE, Cohen AD, Handen BL, Christian BT. PET measurement of longitudinal amyloid load identifies the earliest stages of amyloid-beta accumulation during Alzheimer's disease progression in Down syndrome. Neuroimage 2021; 228:117728. [PMID: 33421595 PMCID: PMC7953340 DOI: 10.1016/j.neuroimage.2021.117728] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/14/2020] [Accepted: 12/27/2020] [Indexed: 12/17/2022] Open
Abstract
Introduction: Adults with Down syndrome (DS) are predisposed to Alzheimer’s disease (AD) and reveal early amyloid beta (Aβ) pathology in the brain. Positron emission tomography (PET) provides an in vivo measure of Aβ throughout the AD continuum. Due to the high prevalence of AD in DS, there is need for longitudinal imaging studies of Aβ to better characterize the natural history of Aβ accumulation, which will aid in the staging of this population for clinical trials aimed at AD treatment and prevention. Methods: Adults with DS (N = 79; Mean age (SD) = 42.7 (7.28) years) underwent longitudinal [C-11]Pittsburgh compound B (PiB) PET. Global Aβ burden was quantified using the amyloid load metric (AβL). Modeled PiB images were generated from the longitudinal AβL data to visualize which regions are most susceptible to Aβ accumulation in DS. AβL change was evaluated across Aβ(−), Aβ-converter, and Aβ(+) groups to assess longitudinal Aβ trajectories during different stages of AD-pathology progression. AβL change values were used to identify Aβ-accumulators within the Aβ(−) group prior to reaching the Aβ(+) threshold (previously reported as 20 AβL) which would have resulted in an Aβ-converter classification. With knowledge of trajectories of Aβ(−) accumulators, a new cutoff of Aβ(+) was derived to better identify subthreshold Aβ accumulation in DS. Estimated sample sizes necessary to detect a 25% reduction in annual Aβ change with 80% power (alpha 0.01) were determined for different groups of Aβ-status. Results: Modeled PiB images revealed the striatum, parietal cortex and precuneus as the regions with earliest detected Aβ accumulation in DS. The Aβ(−) group had a mean AβL change of 0.38 (0.58) AβL/year, while the Aβ-converter and Aβ(+) groups had change of 2.26 (0.66) and 3.16 (1.34) AβL/year, respectively. Within the Aβ(−) group, Aβ-accumulators showed no significant difference in AβL change values when compared to Aβ-converter and Aβ(+) groups. An Aβ(+) cutoff for subthreshold Aβ accumulation was derived as 13.3 AβL. The estimated sample size necessary to detect a 25% reduction in Aβ was 79 for Aβ(−) accumulators and 59 for the Aβ-converter/Aβ(+) group in DS. Conclusion: Longitudinal AβL changes were capable of distinguishing Aβ accumulators from non-accumulators in DS. Longitudinal imaging allowed for identification of subthreshold Aβ accumulation in DS during the earliest stages of AD-pathology progression. Detection of active Aβ deposition evidenced by subthreshold accumulation with longitudinal imaging can identify DS individuals at risk for AD development at an earlier stage.
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Affiliation(s)
- Matthew D Zammit
- University of Wisconsin-Madison, Waisman Center, 1500 Highland Avenue, Madison, WI 53705, United States.
| | - Dana L Tudorascu
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, United States.
| | - Charles M Laymon
- University of Pittsburgh, Department of Radiology, Pittsburgh, PA, United States; University of Pittsburgh, Department of Bioengineering, Pittsburgh, PA, United States.
| | - Sigan L Hartley
- University of Wisconsin-Madison, Waisman Center, 1500 Highland Avenue, Madison, WI 53705, United States.
| | - Shahid H Zaman
- Cambridge Intellectual Disability Research Group, University of Cambridge, Cambridge, United Kingdom.
| | - Beau M Ances
- Washington University in St. Louis Department of Neurology, St. Louis, MO, United States.
| | - Sterling C Johnson
- University of Wisconsin-Madison, Alzheimer's Disease Research Center, Madison, WI, United States.
| | - Charles K Stone
- University of Wisconsin-Madison, Department of Medicine, Madison, WI, United States.
| | - Chester A Mathis
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, United States.
| | - William E Klunk
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, United States
| | - Ann D Cohen
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, United States.
| | - Benjamin L Handen
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, United States.
| | - Bradley T Christian
- University of Wisconsin-Madison, Waisman Center, 1500 Highland Avenue, Madison, WI 53705, United States.
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Abstract
The presenilin-1 (PSEN1) L226F mutation has been linked to very early onset of prominent behavioral and psychiatric disturbances followed by cognitive decline within a few years. We report a novel case of early-onset Alzheimer disease that was originally diagnosed as psychotic depression in a patient with this gene mutation. We also compare our patient's clinical data to those of other cases of this mutation that have been described in the literature. Because atypical behavioral and psychiatric disturbances in young (<40 years) individuals can herald Alzheimer disease, a tight collaboration between psychiatrists and neurologists is crucial for an early diagnosis.
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Tudorascu DL, Laymon CM, Zammit M, Minhas DS, Anderson SJ, Ellison PA, Zaman S, Ances BM, Sabbagh M, Johnson SC, Mathis CA, Klunk WE, Handen BL, Christian BT, Cohen AD. Relationship of amyloid beta and neurofibrillary tau deposition in Neurodegeneration in Aging Down Syndrome (NiAD) study at baseline. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2020; 6:e12096. [PMID: 33163613 PMCID: PMC7602678 DOI: 10.1002/trc2.12096] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 09/11/2020] [Indexed: 11/07/2022]
Abstract
IMPORTANCE Adults with Down syndrome (DS) are at high-risk of revealing Alzheimer's disease (AD) pathology, in part due to the triplication of chromosome 21 encoding the amyloid precursor protein. Adults with DS are uniformly affected by AD pathology by their 30's and have a 70% to 80% chance of clinical dementia by their 60's. Our previous studies have assessed longitudinal changes in amyloid beta (Aβ) accumulation in DS. OBJECTIVE The goal of the present study was to assess the presence of brain tau using [18F]AV-1451 positron emission tomography (PET) in DS and to assess the relationship of brain tau pathology to Aβ using Pittsburgh Compound B (PiB)-PET. DESIGN Cohort study. SETTING Multi-center study. PARTICIPANTS Participants consisted of a sample of individuals with DS and sibling controls recruited from the community; exclusion criteria included contraindications for magnetic resonance imaging (MRI) and/or a medical or psychiatric condition that impaired cognitive functioning. EXPOSURES PET brain scans to assess Aβ ([11C]PiB) and tau ([18F]AV-1451) burden. MAIN OUTCOMES AND MEASURES Multiple linear regression models (adjusted for chronological age, sex and performance site) were used to examine associations between regional [18F]AV-1451 standard uptake value ratio (SUVR) (based on regions associated with Braak stages 1-6) and global [11C]PiB SUVR (as both a continuous and dichotomous variable). RESULTS A cohort of 156 participants (mean age = 39.05, SD(8.4)) were examined. These results revealed a significant relationship between in vivo Aβ and tau pathology in DS. As a dichotomous variable, [18F]AV-1451 retention was higher in each Braak region in PiB(+) participants. We also found, based on our statistical models, starting with the Braak 3 region of interest (ROI), an acceleration of [18F]AV-1451 SUVR deposition with [11C]PiB SUVR increases.
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Affiliation(s)
- DL Tudorascu
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - CM Laymon
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - M Zammit
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - DS Minhas
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - SJ Anderson
- Department of BiostatisticsUniversity of PittsburghPittsburghUSA
| | - PA Ellison
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - S Zaman
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | - BM Ances
- Department of NeurologyWashington UniversitySt. LouisMissouriUSA
| | - M Sabbagh
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNVUSA
| | - SC Johnson
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - CA Mathis
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - WE Klunk
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - BL Handen
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - BT Christian
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - AD Cohen
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
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Rafii MS, Ances BM, Schupf N, Krinsky‐McHale SJ, Mapstone M, Silverman W, Lott I, Klunk W, Head E, Christian B, Lai F, Rosas HD, Zaman S, Petersen ME, Strydom A, Fortea J, Handen B, O'Bryant S. The AT(N) framework for Alzheimer's disease in adults with Down syndrome. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12062. [PMID: 33134477 PMCID: PMC7588820 DOI: 10.1002/dad2.12062] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/04/2020] [Indexed: 12/15/2022]
Abstract
The National Institute on Aging in conjunction with the Alzheimer's Association (NIA-AA) recently proposed a biological framework for defining the Alzheimer's disease (AD) continuum. This new framework is based upon the key AD biomarkers (amyloid, tau, neurodegeneration, AT[N]) instead of clinical symptoms and represents the latest understanding that the pathological processes underlying AD begin decades before the manifestation of symptoms. By using these same biomarkers, individuals with Down syndrome (DS), who are genetically predisposed to developing AD, can also be placed more precisely along the AD continuum. The A/T(N) framework is therefore thought to provide an objective manner by which to select and enrich samples for clinical trials. This new framework is highly flexible and allows the addition of newly confirmed AD biomarkers into the existing AT(N) groups. As biomarkers for other pathological processes are validated, they can also be added to the AT(N) classification scheme, which will allow for better characterization and staging of AD in DS. These biological classifications can then be merged with clinical staging for an examination of factors that impact the biological and clinical progression of the disease. Here, we leverage previously published guidelines for the AT(N) framework to generate such a plan for AD among adults with DS.
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Affiliation(s)
- Michael S. Rafii
- Alzheimer's Therapeutic Research Institute (ATRI)Keck School of MedicineUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Beau M. Ances
- Center for Advanced Medicine NeuroscienceWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Nicole Schupf
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain/G.H. Sergievsky CenterColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of EpidemiologyMailman School of Public HealthColumbia UniversityNew YorkNew YorkUSA
- Department of NeurologyNeurological Institute of New York, Columbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of PsychiatryColumbia University Medical CenterNew YorkNew YorkUSA
| | - Sharon J. Krinsky‐McHale
- Department of PsychologyNYS Institute for Basic Research in Developmental DisabilitiesStaten IslandNew YorkUSA
| | - Mark Mapstone
- Department of NeurologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Wayne Silverman
- Department of PediatricsSchool of MedicineUniversity of CaliforniaIrvineCaliforniaUSA
| | - Ira Lott
- Department of PediatricsSchool of MedicineUniversity of CaliforniaIrvineCaliforniaUSA
| | - William Klunk
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Elizabeth Head
- Department of PathologyGillespie Neuroscience Research Facility, University of CaliforniaIrvineCaliforniaUSA
| | - Brad Christian
- Department of Medical Physics and PsychiatryUniversity of Wisconsin MadisonMadisonWisconsinUSA
| | - Florence Lai
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolCharlestownMassachusettsUSA
| | - H. Diana Rosas
- Departments of Neurology and RadiologyMassachusetts General HospitalHarvard Medical SchoolCharlestownMassachusettsUSA
| | - Shahid Zaman
- Department of PsychiatrySchool of Clinical MedicineUniversity of CambridgeCambridgeUK
- Cambridgeshire and Peterborough NHS Foundation TrustFulbourn HospitalCambridgeUK
| | - Melissa E. Petersen
- Department of Family Medicine and Institute for Translational ResearchUniversity of North Texas Health Science CenterFort WorthTexasUSA
| | - Andre Strydom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Juan Fortea
- Sant Pau Memory UnitDepartment of NeurologyHospital de la Santa Creu i Sant PauBiomedical Research Institute Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Benjamin Handen
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Sid O'Bryant
- Institute for Translational Research and Department of Pharmacology and NeuroscienceUniversity of North Texas Health Science CenterFort WorthTexasUSA
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Ikonomovic MD, Buckley CJ, Abrahamson EE, Kofler JK, Mathis CA, Klunk WE, Farrar G. Post-mortem analyses of PiB and flutemetamol in diffuse and cored amyloid-β plaques in Alzheimer's disease. Acta Neuropathol 2020; 140:463-476. [PMID: 32772265 PMCID: PMC7498488 DOI: 10.1007/s00401-020-02175-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/27/2020] [Accepted: 06/04/2020] [Indexed: 01/22/2023]
Abstract
Specificity and sensitivity of positron emission tomography (PET) radiopharmaceuticals targeting fibrillar amyloid-β (Aβ) deposits is high for detection of neuritic Aβ plaques, a mature form of Aβ deposits which often have dense Aβ core (i.e., cored plaques). However, imaging-to-autopsy validation studies of amyloid PET radioligands have identified several false positive cases all of which had mainly diffuse Aβ plaques (i.e., plaques without neuritic pathology or dense amyloid core), and high amyloid PET signal was reported in the striatum where diffuse plaques predominate in Alzheimer's disease (AD). Relative contributions of different plaque types to amyloid PET signal is unclear, particularly in neocortical areas where they are intermixed in AD. In vitro binding assay and autoradiography were performed using [3H]flutemetamol and [3H]Pittsburgh Compound-B (PiB) in frozen brain homogenates from 30 autopsy cases including sporadic AD and non-AD controls with a range of brain Aβ burden and plaque density. Fixed tissue sections of frontal cortex and caudate from 10 of the AD cases were processed for microscopy using fluorescent derivatives of flutemetamol (cyano-flutemetamol) and PiB (cyano-PiB) and compared to Aβ immunohistochemistry and pan-amyloid (X-34) histology. Using epifluorescence microscopy, percent area coverage and fluorescence output values of cyano-PiB- and cyano-flutemetamol-labeled plaques in two-dimensional microscopic fields were then calculated and combined to obtain integrated density measurements. Using confocal microscopy, we analysed total fluorescence output of the entire three-dimensional volume of individual cored plaques and diffuse plaques labeled with cyano-flutemetamol or cyano-PiB. [3H]Flutemetamol and [3H]PiB binding values in tissue homogenates correlated strongly and their binding pattern in tissue sections, as seen on autoradiograms, overlapped the pattern of Aβ-immunoreactive plaques on directly adjacent sections. Cyano-flutemetamol and cyano-PiB fluorescence was prominent in cored plaques and less so in diffuse plaques. Across brain regions and cases, percent area coverage of cyano-flutemetamol-labeled plaques correlated strongly with cyano-PiB-labeled and Aβ-immunoreactive plaques. For both ligands, plaque burden, calculated as percent area coverage of all Aβ plaque types, was similar in frontal cortex and caudate regions, while integrated density values were significantly greater in frontal cortex, which contained both cored plaques and diffuse plaques, compared to the caudate, which contained only diffuse plaques. Three-dimensional analysis of individual plaques labeled with either ligand showed that total fluorescence output of a single cored plaque was equivalent to total fluorescence output of approximately three diffuse plaques of similar volume. Our results indicate that [18F]flutemetamol and [11C]PiB PET signal is influenced by both diffuse plaques and cored plaques, and therefore is likely a function of plaque size and density of Aβ fibrils in plaques. Brain areas with large volumes/frequencies of diffuse plaques could yield [18F]flutemetamol and [11C]PiB PET retention levels comparable to brain regions with a lower volume/frequency of cored plaques.
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Affiliation(s)
- Milos D Ikonomovic
- Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- University of Pittsburgh School of Medicine, Thomas Detre Hall of the WPIC, Room 1421, 3811 O'Hara Street, Pittsburgh, 15213-2593, PA, USA.
| | | | - Eric E Abrahamson
- Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Julia K Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - William E Klunk
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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