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Chadwick W, Maudsley S, Hull W, Havolli E, Boshoff E, Hill MDW, Goetghebeur PJD, Harrison DC, Nizami S, Bedford DC, Coope G, Real K, Thiemermann C, Maycox P, Carlton M, Cole SL. The oDGal Mouse: A Novel, Physiologically Relevant Rodent Model of Sporadic Alzheimer's Disease. Int J Mol Sci 2023; 24:ijms24086953. [PMID: 37108119 PMCID: PMC10138655 DOI: 10.3390/ijms24086953] [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: 03/03/2023] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 04/29/2023] Open
Abstract
Sporadic Alzheimer's disease (sAD) represents a serious and growing worldwide economic and healthcare burden. Almost 95% of current AD patients are associated with sAD as opposed to patients presenting with well-characterized genetic mutations that lead to AD predisposition, i.e., familial AD (fAD). Presently, the use of transgenic (Tg) animals overexpressing human versions of these causative fAD genes represents the dominant research model for AD therapeutic development. As significant differences in etiology exist between sAD and fAD, it is perhaps more appropriate to develop novel, more sAD-reminiscent experimental models that would expedite the discovery of effective therapies for the majority of AD patients. Here we present the oDGal mouse model, a novel model of sAD that displays a range of AD-like pathologies as well as multiple cognitive deficits reminiscent of AD symptomology. Hippocampal cognitive impairment and pathology were delayed with N-acetyl-cysteine (NaC) treatment, which strongly suggests that reactive oxygen species (ROS) are the drivers of downstream pathologies such as elevated amyloid beta and hyperphosphorylated tau. These features demonstrate a desired pathophenotype that distinguishes our model from current transgenic rodent AD models. A preclinical model that presents a phenotype of non-genetic AD-like pathologies and cognitive deficits would benefit the sAD field, particularly when translating therapeutics from the preclinical to the clinical phase.
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Affiliation(s)
- Wayne Chadwick
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2000 Antwerp, Belgium
| | - William Hull
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Enes Havolli
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Eugene Boshoff
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark D W Hill
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | | | - David C Harrison
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sohaib Nizami
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - David C Bedford
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Gareth Coope
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Katia Real
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Christoph Thiemermann
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Peter Maycox
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark Carlton
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sarah L Cole
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
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2
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N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI- 1H-MRS Pilot Study. Diagnostics (Basel) 2023; 13:diagnostics13040654. [PMID: 36832141 PMCID: PMC9955118 DOI: 10.3390/diagnostics13040654] [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: 01/10/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (1H-MRS). The regions of interest (ROIs) for 1H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.
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3
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Cobigo Y, Goh MS, Wolf A, Staffaroni AM, Kornak J, Miller BL, Rabinovici GD, Seeley WW, Spina S, Boxer AL, Boeve BF, Wang L, Allegri R, Farlow M, Mori H, Perrin RJ, Kramer J, Rosen HJ. Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling. Neuroimage Clin 2022; 36:103144. [PMID: 36030718 PMCID: PMC9428846 DOI: 10.1016/j.nicl.2022.103144] [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: 10/04/2021] [Revised: 07/20/2022] [Accepted: 08/02/2022] [Indexed: 01/18/2023]
Abstract
Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.
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Affiliation(s)
- Yann Cobigo
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States.
| | - Matthew S. Goh
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Amy Wolf
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam M. Staffaroni
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - John Kornak
- University of California, San Francisco, Department of Epidemiology and Biostatistics, United States
| | - Bruce L. Miller
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Gil D. Rabinovici
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - William W. Seeley
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Salvatore Spina
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam L. Boxer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | | | - Lei Wang
- Northwestern University Feinberg School of Medicine, Department of Psychiatry and Behavioral Sciences and Department Radiology, United States
| | - Ricardo Allegri
- FLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia), Argentina
| | | | - Hiroshi Mori
- Osaka City University Medical School, Department of Neurosciences, Japan
| | | | - Joel Kramer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Howard J. Rosen
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
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4
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Franzmeier N, Ren J, Damm A, Monté-Rubio G, Boada M, Ruiz A, Ramirez A, Jessen F, Düzel E, Rodríguez Gómez O, Benzinger T, Goate A, Karch CM, Fagan AM, McDade E, Buerger K, Levin J, Duering M, Dichgans M, Suárez-Calvet M, Haass C, Gordon BA, Lim YY, Masters CL, Janowitz D, Catak C, Wolfsgruber S, Wagner M, Milz E, Moreno-Grau S, Teipel S, Grothe MJ, Kilimann I, Rossor M, Fox N, Laske C, Chhatwal J, Falkai P, Perneczky R, Lee JH, Spottke A, Boecker H, Brosseron F, Fliessbach K, Heneka MT, Nestor P, Peters O, Fuentes M, Menne F, Priller J, Spruth EJ, Franke C, Schneider A, Westerteicher C, Speck O, Wiltfang J, Bartels C, Araque Caballero MÁ, Metzger C, Bittner D, Salloway S, Danek A, Hassenstab J, Yakushev I, Schofield PR, Morris JC, Bateman RJ, Ewers M. The BDNF Val66Met SNP modulates the association between beta-amyloid and hippocampal disconnection in Alzheimer's disease. Mol Psychiatry 2021; 26:614-628. [PMID: 30899092 PMCID: PMC6754794 DOI: 10.1038/s41380-019-0404-6] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/19/2019] [Accepted: 02/14/2019] [Indexed: 01/29/2023]
Abstract
In Alzheimer's disease (AD), a single-nucleotide polymorphism in the gene encoding brain-derived neurotrophic factor (BDNFVal66Met) is associated with worse impact of primary AD pathology (beta-amyloid, Aβ) on neurodegeneration and cognitive decline, rendering BDNFVal66Met an important modulating factor of cognitive impairment in AD. However, the effect of BDNFVal66Met on functional networks that may underlie cognitive impairment in AD is poorly understood. Using a cross-validation approach, we first explored in subjects with autosomal dominant AD (ADAD) from the Dominantly Inherited Alzheimer Network (DIAN) the effect of BDNFVal66Met on resting-state fMRI assessed functional networks. In seed-based connectivity analysis of six major large-scale networks, we found a stronger decrease of hippocampus (seed) to medial-frontal connectivity in the BDNFVal66Met carriers compared to BDNFVal homozogytes. BDNFVal66Met was not associated with connectivity in any other networks. Next, we tested whether the finding of more pronounced decrease in hippocampal-medial-frontal connectivity in BDNFVal66Met could be also found in elderly subjects with sporadically occurring Aβ, including a group with subjective cognitive decline (N = 149, FACEHBI study) and a group ranging from preclinical to AD dementia (N = 114, DELCODE study). In both of these independently recruited groups, BDNFVal66Met was associated with a stronger effect of more abnormal Aβ-levels (assessed by biofluid-assay or amyloid-PET) on hippocampal-medial-frontal connectivity decreases, controlled for hippocampus volume and other confounds. Lower hippocampal-medial-frontal connectivity was associated with lower global cognitive performance in the DIAN and DELCODE studies. Together these results suggest that BDNFVal66Met is selectively associated with a higher vulnerability of hippocampus-frontal connectivity to primary AD pathology, resulting in greater AD-related cognitive impairment.
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Affiliation(s)
- Nicolai Franzmeier
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Jinyi Ren
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Alexander Damm
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Gemma Monté-Rubio
- grid.477255.60000 0004 1765 5601Fundació ACE, Alzheimer Treatment and Research Center, Barcelona, Spain
| | - Mercè Boada
- grid.477255.60000 0004 1765 5601Fundació ACE, Alzheimer Treatment and Research Center, Barcelona, Spain ,grid.451322.30000 0004 1770 9462CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Madrid, Spain
| | - Agustín Ruiz
- grid.477255.60000 0004 1765 5601Fundació ACE, Alzheimer Treatment and Research Center, Barcelona, Spain ,grid.451322.30000 0004 1770 9462CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Madrid, Spain
| | - Alfredo Ramirez
- grid.6190.e0000 0000 8580 3777Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany ,grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Frank Jessen
- grid.6190.e0000 0000 8580 3777Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Emrah Düzel
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Octavio Rodríguez Gómez
- grid.477255.60000 0004 1765 5601Fundació ACE, Alzheimer Treatment and Research Center, Barcelona, Spain ,grid.451322.30000 0004 1770 9462CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Madrid, Spain
| | - Tammie Benzinger
- grid.4367.60000 0001 2355 7002Department of Radiology, Washington University in St Louis, St Louis, MO USA ,grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA
| | - Alison Goate
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Ronald M. Loeb Center for Alzheimer’s Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Celeste M. Karch
- grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Anne M. Fagan
- grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Neurology, Washington University in St. Louis, St. Louis, MO USA
| | - Eric McDade
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University in St. Louis, St. Louis, MO USA
| | - Katharina Buerger
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marco Duering
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Martin Dichgans
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Marc Suárez-Calvet
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.430077.7Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Catalonia Spain ,grid.5252.00000 0004 1936 973XFaculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Christian Haass
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XFaculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Brian A. Gordon
- grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Psychological and Brain Sciences, Washington University, St. Louis, MO USA
| | - Yen Ying Lim
- grid.1008.90000 0001 2179 088XThe Florey Institute, The University of Melbourne, Parkville, VIC Australia
| | - Colin L. Masters
- grid.1008.90000 0001 2179 088XThe Florey Institute, The University of Melbourne, Parkville, VIC Australia
| | - Daniel Janowitz
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Cihan Catak
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Steffen Wolfsgruber
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael Wagner
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Esther Milz
- grid.6190.e0000 0000 8580 3777Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Sonia Moreno-Grau
- grid.477255.60000 0004 1765 5601Fundació ACE, Alzheimer Treatment and Research Center, Barcelona, Spain ,grid.451322.30000 0004 1770 9462CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Madrid, Spain
| | - Stefan Teipel
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany ,grid.413108.f0000 0000 9737 0454Department of Psychosomatic Medicine, University Hospital Rostock, Rostock, Germany
| | - Michel J Grothe
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Ingo Kilimann
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Martin Rossor
- grid.83440.3b0000000121901201Dementia Research Centre, University College London, Queen Square, London, UK
| | - Nick Fox
- grid.83440.3b0000000121901201Dementia Research Centre, University College London, Queen Square, London, UK
| | - Christoph Laske
- grid.428620.aHertie Institute for Clinical Brain Research, Tübingen, Germany ,grid.424247.30000 0004 0438 0426Germany and German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Jasmeer Chhatwal
- grid.38142.3c000000041936754XMassachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA USA
| | - Peter Falkai
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Robert Perneczky
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany ,grid.7445.20000 0001 2113 8111Neuroepidemiology and Ageing Research Unit, School of Public Health, The Imperial College of Science, Technology and Medicine, London, UK
| | - Jae-Hong Lee
- grid.413967.e0000 0001 0842 2126Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Annika Spottke
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ,grid.10388.320000 0001 2240 3300Department of Neurology, University of Bonn, Bonn, Germany
| | - Henning Boecker
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ,grid.10388.320000 0001 2240 3300Department of Radiology, University of Bonn, Bonn, Germany
| | - Frederic Brosseron
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Fliessbach
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael T. Heneka
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Peter Nestor
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ,grid.1003.20000 0000 9320 7537Queensland Brain Institute, University of Queensland, Brisbane, QLD Australia
| | - Oliver Peters
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Manuel Fuentes
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Felix Menne
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Josef Priller
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Neuropsychiatry, Charité, Berlin, Germany
| | - Eike J. Spruth
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Neuropsychiatry, Charité, Berlin, Germany
| | - Christiana Franke
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ,grid.6363.00000 0001 2218 4662Department of Neuropsychiatry, Charité, Berlin, Germany
| | - Anja Schneider
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Christine Westerteicher
- grid.10388.320000 0001 2240 3300Department of Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Oliver Speck
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265Leibniz Institute for Neurobiology, Magdeburg, Germany ,grid.452320.20000 0004 0404 7236Center for Behavioral Brain Sciences, Magdeburg, Germany ,grid.5807.a0000 0001 1018 4307Department for Biomedical Magnetic Resonance, Institute for Physics, Otto-von-Guericke University, Magdeburg, Germany
| | - Jens Wiltfang
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany ,grid.7450.60000 0001 2364 4210Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany ,grid.7311.40000000123236065iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Claudia Bartels
- grid.7450.60000 0001 2364 4210Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | - Miguel Ángel Araque Caballero
- grid.5252.00000 0004 1936 973XInstitute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Coraline Metzger
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Daniel Bittner
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Stephen Salloway
- grid.40263.330000 0004 1936 9094Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI USA
| | - Adrian Danek
- grid.5252.00000 0004 1936 973XDepartment of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jason Hassenstab
- grid.4367.60000 0001 2355 7002Department of Neurology, Washington University in St. Louis, St. Louis, MO USA
| | - Igor Yakushev
- grid.6936.a0000000123222966Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - Peter R. Schofield
- grid.250407.40000 0000 8900 8842Neuroscience Research Australia, Barker Street Randwick, Sydney, NSW 2031 Australia ,grid.1005.40000 0004 4902 0432School of Medical Sciences, University of New South Wales, Sydney, NSW 2052 Australia
| | - John C. Morris
- grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Neurology, Washington University in St. Louis, St. Louis, MO USA
| | - Randall J. Bateman
- grid.4367.60000 0001 2355 7002Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Neurology, Washington University in St. Louis, St. Louis, MO USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.
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Sundermann EE, Maki PM, Reddy S, Bondi MW, Biegon A. Women's higher brain metabolic rate compensates for early Alzheimer's pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12121. [PMID: 33251322 PMCID: PMC7678742 DOI: 10.1002/dad2.12121] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 11/16/2022]
Abstract
INTRODUCTION The female advantage in brain metabolic function may confer cognitive resilience against Alzheimer's disease (AD). METHODS A total of 1259 participants (44% women; 52% mild cognitive impairment; 18% AD) aged 55 to 90 from the Alzheimer's Disease Neuroimaging Initiative (ANDI) completed tests of global cognition, verbal memory, and executive function, and neuroimaging assessments of regional glucose metabolism, hippocampal volume (HV), and amyloid beta (Aβ). We examined sex differences in brain metabolism and cognition by AD biomarker quartiles (Aβ, HV). We then examined if metabolism mediates sex differences in cognition. RESULTS Metabolism was higher in women versus men when pathology was mild-to-moderate (quartiles 2 to 3). Women outperformed men on all cognitive outcomes at ≥1 biomarker quartile, reflecting minimal-to-moderate pathology; however, these differences were eliminated/attenuated after adjusting for metabolism. The female advantage in verbal memory was also observed at minimal pathology quartiles but was unchanged after metabolism adjustment. DISCUSSION Women's greater brain metabolism may confer cognitive resilience against early AD.
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Affiliation(s)
- Erin E. Sundermann
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Pauline M. Maki
- Departments of PsychiatryPsychology and Obstetrics & GynecologyUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - Sarah Reddy
- Departments of Radiology and NeurologyState University of New York at Stony BrookNew YorkUSA
| | - Mark W. Bondi
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Veterans Affairs San Diego Healthcare SystemLa JollaCaliforniaUSA
| | - Anat Biegon
- Departments of Radiology and NeurologyState University of New York at Stony BrookNew YorkUSA
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Avants BB, Hutchison RM, Mikulskis A, Salinas-Valenzuela C, Hargreaves R, Beaver J, Chiao P. Amyloid beta-positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity. Neurobiol Aging 2018; 74:191-201. [PMID: 30471630 DOI: 10.1016/j.neurobiolaging.2018.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 09/06/2018] [Accepted: 10/02/2018] [Indexed: 12/20/2022]
Abstract
Amyloid beta (Aβ) deposition and cognitive decline are key features of Alzheimer's disease. The relationship between Aβ status and changes in neuronal function over time, however, remains unclear. We evaluated the effect of baseline Aβ status on reference region spontaneous brain activity (SBA-rr) using resting-state functional magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in patients with mild cognitive impairment. Patients (N = 62, [43 Aβ-positive]) from the Alzheimer's Disease Neuroimaging Initiative were divided into Aβ-positive and Aβ-negative groups via prespecified cerebrospinal fluid Aβ42 or 18F-florbetapir positron emission tomography standardized uptake value ratio cutoffs measured at baseline. We analyzed interaction of biomarker-confirmed Aβ status with SBA-rr change over a 2-year period using mixed-effects modeling. SBA-rr differences between Aβ-positive and Aβ-negative subjects increased significantly over time within subsystems of the default and visual networks. Changes exhibit an interaction with memory performance over time but were independent of glucose metabolism. Results reinforce the value of resting-state functional magnetic resonance imaging in evaluating Alzheimer''s disease progression and suggest spontaneous neuronal activity changes are concomitant with cognitive decline.
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Affiliation(s)
- Brian B Avants
- Biogen employee while completing work, 225 Binney Street, Cambridge, Massachusetts, 02142, USA.
| | | | - Alvydas Mikulskis
- Biogen employee while completing work, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
| | | | | | - John Beaver
- Biogen, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
| | - Ping Chiao
- Biogen, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
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Possible Clues for Brain Energy Translation via Endolysosomal Trafficking of APP-CTFs in Alzheimer's Disease. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2018; 2018:2764831. [PMID: 30420907 PMCID: PMC6215552 DOI: 10.1155/2018/2764831] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 07/14/2018] [Accepted: 08/19/2018] [Indexed: 02/07/2023]
Abstract
Vascular dysfunctions, hypometabolism, and insulin resistance are high and early risk factors for Alzheimer's disease (AD), a leading neurological disease associated with memory decline and cognitive dysfunctions. Early defects in glucose transporters and glycolysis occur during the course of AD progression. Hypometabolism begins well before the onset of early AD symptoms; this timing implicates the vulnerability of hypometabolic brain regions to beta-secretase 1 (BACE-1) upregulation, oxidative stress, inflammation, synaptic failure, and cell death. Despite the fact that ketone bodies, astrocyte-neuron lactate shuttle, pentose phosphate pathway (PPP), and glycogenolysis compensate to provide energy to the starving AD brain, a considerable energy crisis still persists and increases during disease progression. Studies that track brain energy metabolism in humans, animal models of AD, and in vitro studies reveal striking upregulation of beta-amyloid precursor protein (β-APP) and carboxy-terminal fragments (CTFs). Currently, the precise role of CTFs is unclear, but evidence supports increased endosomal-lysosomal trafficking of β-APP and CTFs through autophagy through a vague mechanism. While intracellular accumulation of Aβ is attributed as both the cause and consequence of a defective endolysosomal-autophagic system, much remains to be explored about the other β-APP cleavage products. Many recent works report altered amino acid catabolism and expression of several urea cycle enzymes in AD brains, but the precise cause for this dysregulation is not fully explained. In this paper, we try to connect the role of CTFs in the energy translation process in AD brain based on recent findings.
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Dyrba M, Grothe MJ, Mohammadi A, Binder H, Kirste T, Teipel SJ. Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging. J Alzheimers Dis 2018; 65:731-746. [DOI: 10.3233/jad-161197] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
| | - Abdolreza Mohammadi
- Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
| | - Harald Binder
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Thomas Kirste
- Mobile Multimedia Information Systems Group (MMIS), University of Rostock, Rostock, Germany
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany
- Clinic for Psychosomatic and Psychotherapeutic Medicine, University Medical Center Rostock, Rostock, Germany
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Happ C, Greven S, Schmid VJ. The impact of model assumptions in scalar-on-image regression. Stat Med 2018; 37:4298-4317. [PMID: 30132932 DOI: 10.1002/sim.7915] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 06/20/2018] [Accepted: 06/27/2018] [Indexed: 11/11/2022]
Abstract
Complex statistical models such as scalar-on-image regression often require strong assumptions to overcome the issue of nonidentifiability. While in theory, it is well understood that model assumptions can strongly influence the results, this seems to be underappreciated, or played down, in practice. This article gives a systematic overview of the main approaches for scalar-on-image regression with a special focus on their assumptions. We categorize the assumptions and develop measures to quantify the degree to which they are met. The impact of model assumptions and the practical usage of the proposed measures are illustrated in a simulation study and in an application to neuroimaging data. The results show that different assumptions indeed lead to quite different estimates with similar predictive ability, raising the question of their interpretability. We give recommendations for making modeling and interpretation decisions in practice based on the new measures and simulations using hypothetic coefficient images and the observed data.
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Affiliation(s)
- Clara Happ
- Department of Statistics, LMU Munich, Munich, Germany
| | - Sonja Greven
- Department of Statistics, LMU Munich, Munich, Germany
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10
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Montal V, Vilaplana E, Alcolea D, Pegueroles J, Pasternak O, González-Ortiz S, Clarimón J, Carmona-Iragui M, Illán-Gala I, Morenas-Rodríguez E, Ribosa-Nogué R, Sala I, Sánchez-Saudinós MB, García-Sebastian M, Villanúa J, Izagirre A, Estanga A, Ecay-Torres M, Iriondo A, Clerigue M, Tainta M, Pozueta A, González A, Martínez-Heras E, Llufriu S, Blesa R, Sanchez-Juan P, Martínez-Lage P, Lleó A, Fortea J. Cortical microstructural changes along the Alzheimer's disease continuum. Alzheimers Dement 2017; 14:340-351. [PMID: 29080407 DOI: 10.1016/j.jalz.2017.09.013] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 09/14/2017] [Accepted: 09/20/2017] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Cortical mean diffusivity (MD) and free water fraction (FW) changes are proposed biomarkers for Alzheimer's disease (AD). METHODS We included healthy control subjects (N = 254), mild cognitive impairment (N = 41), and AD dementia (N = 31) patients. Participants underwent a lumbar puncture and a 3 T magnetic resonance imaging. Healthy control subjects were classified following National Institute on Aging-Alzheimer's Association stages (stage 0, N = 220; stage 1, N = 25; and stage 2/3, N = 9). We assessed the cortical MD, cortical FW, and cortical thickness (CTh) changes along the AD continuum. RESULTS Microstructural and macrostructural changes show a biphasic trajectory. Stage 1 subjects showed increased CTh and decreased MD and FW with respect the stage 0 subjects. Stage 2/3 subjects showed decreased CTh and increased cortical MD and FW, changes that were more widespread in symptomatic stages. DISCUSSION These results support a biphasic model of changes in AD, which could affect the selection of patients for clinical trials and the use of magnetic resonance imaging as a surrogate marker of disease modification.
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Affiliation(s)
- Victor Montal
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Eduard Vilaplana
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Daniel Alcolea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Jordi Pegueroles
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jordi Clarimón
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - María Carmona-Iragui
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Ignacio Illán-Gala
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Estrella Morenas-Rodríguez
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Roser Ribosa-Nogué
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Isabel Sala
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - María-Belén Sánchez-Saudinós
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Maite García-Sebastian
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Jorge Villanúa
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain; Donostia Unit, Osatek SA, Donostia University Hospital, San Sebastian, Spain
| | - Andrea Izagirre
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Ainara Estanga
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Mirian Ecay-Torres
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Ane Iriondo
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Montserrat Clerigue
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Ana Pozueta
- Servicio de Neurología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Andrea González
- Servicio de Neurología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Eloy Martínez-Heras
- Center for Neuroimmunology, Hospital Clinic Barcelona, IDIBAPS, Barcelona, Spain
| | - Sara Llufriu
- Center for Neuroimmunology, Hospital Clinic Barcelona, IDIBAPS, Barcelona, Spain
| | - Rafael Blesa
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Pascual Sanchez-Juan
- Servicio de Neurología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Pablo Martínez-Lage
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain; Center for Research and Advanced Therapies and Memory Clinic, Fundacion CITA-Alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Alberto Lleó
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain
| | - Juan Fortea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain.
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Preziosa P, Pagani E, Mesaros S, Riccitelli GC, Dackovic J, Drulovic J, Filippi M, Rocca MA. Progression of regional atrophy in the left hemisphere contributes to clinical and cognitive deterioration in multiple sclerosis: A 5-year study. Hum Brain Mapp 2017; 38:5648-5665. [PMID: 28792103 DOI: 10.1002/hbm.23755] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/22/2017] [Accepted: 07/25/2017] [Indexed: 01/18/2023] Open
Abstract
In this longitudinal study, we investigated the regional patterns of focal lesions accumulation, and gray (GM) and white matter (WM) atrophy progression over a five-year follow-up (FU) in multiple sclerosis (MS) patients and their association with clinical and cognitive deterioration. Neurological, neuropsychological and brain MRI (dual-echo and 3D T1-weighted sequences) assessments were prospectively performed at baseline (T0) and after a median FU of 4.9 years from 66 MS patients (including relapse-onset and primary progressive MS) and 16 matched controls. Lesion probability maps were obtained. Longitudinal changes of GM and WM volumes and their association with clinical and cognitive deterioration were assessed using tensor-based morphometry and SPM12. At FU, 36/66 (54.5%) MS patients showed a significant disability worsening, 14/66 (21.2%) evolved to a worse clinical phenotype, and 18/63 (28.6%) developed cognitive deterioration. At T0, compared to controls, MS patients showed a widespread pattern of GM atrophy, involving cortex, deep GM and cerebellum, and atrophy of the majority of WM tracts, which further progressed at FU (P < 0.001, uncorrected). Compared to stable patients, those with clinical and cognitive worsening showed a left-lateralized pattern of GM and WM atrophy, involving deep GM, fronto-temporo-parieto-occipital regions, cerebellum, and several WM tracts (P < 0.001, uncorrected).GM and WM atrophy of relevant brain regions occur in MS after 5 years. A different vulnerability of the two brain hemispheres to irreversible structural damage may be among the factors contributing to clinical and cognitive worsening in these patients. Hum Brain Mapp 38:5648-5665, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Sarlota Mesaros
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Gianna C Riccitelli
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jelena Dackovic
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jelena Drulovic
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:9060124. [PMID: 29065663 PMCID: PMC5499252 DOI: 10.1155/2017/9060124] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 03/22/2017] [Accepted: 04/30/2017] [Indexed: 01/27/2023]
Abstract
Background. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because information about the condition's severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI) classification. However, distinguishing between Alzheimer's brain data and healthy brain data in older adults (age > 60) is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT) for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA). The reduced feature vector is sent to feed-forward neural network (FNN) to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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14
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Zhang Y, Wang S, Phillips P, Yang J, Yuan TF. Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease. J Alzheimers Dis 2016; 50:1163-79. [PMID: 26836190 DOI: 10.3233/jad-150988] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. OBJECTIVE Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. METHODS We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. RESULTS The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. CONCLUSIONS We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.
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Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, Guilin, Guangxi, China
| | - Shuihua Wang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3d Printing Equipment And Manufacturing, Nanjing, Jiangsu, China
| | - Ti-Fei Yuan
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
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15
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Araque Caballero MÁ, Klöppel S, Dichgans M, Ewers M. Spatial Patterns of Longitudinal Gray Matter Change as Predictors of Concurrent Cognitive Decline in Amyloid Positive Healthy Subjects. J Alzheimers Dis 2016; 55:343-358. [DOI: 10.3233/jad-160327] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Miguel Ángel Araque Caballero
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Stefan Klöppel
- Freiburg Brain Imaging, Departments of Neurology and Psychiatry, University Medical Center Freiburg, Freiburg, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
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16
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Knopman DS, Jack CR, Lundt ES, Weigand SD, Vemuri P, Lowe VJ, Kantarci K, Gunter JL, Senjem ML, Mielke MM, Machulda MM, Roberts RO, Boeve BF, Jones DT, Petersen RC. Evolution of neurodegeneration-imaging biomarkers from clinically normal to dementia in the Alzheimer disease spectrum. Neurobiol Aging 2016; 46:32-42. [PMID: 27460147 PMCID: PMC5018437 DOI: 10.1016/j.neurobiolaging.2016.06.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Revised: 05/20/2016] [Accepted: 06/08/2016] [Indexed: 12/11/2022]
Abstract
The availability of antemortem biomarkers for Alzheimer's disease (AD) enables monitoring the evolution of neurodegenerative processes in real time. Pittsburgh compound B (PIB) positron emission tomography (PET) was used to select participants in the Mayo Clinic Study of Aging and the Mayo Alzheimer's Disease Research Center with elevated β-amyloid, designated as "A+," and hippocampal volume and (18)fluorodeoxyglucose (FDG) positron emission tomography were used to characterize participants as having evidence of neurodegeneration ("N+") at the baseline evaluation. There were 145 clinically normal (CN) A+ individuals, 62 persons with mild cognitive impairment (MCI) who were A+ and 20 with A+ AD dementia. Over a period of 1-6 years, MCI A+N+ individuals showed declines in medial temporal, lateral temporal, lateral parietal, and to a lesser extent, medial parietal regions for both FDG standardized uptake value ratio and gray matter volume that exceeded declines seen in the CN A+N+ group. The AD dementia group showed declines in the same regions on FDG standardized uptake value ratio and gray matter volume with rates that exceeded that in MCI A+N+. Expansion of regional involvement and faster rate of neurodegeneration characterizes progression in the AD pathway.
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Affiliation(s)
- David S Knopman
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA.
| | - Clifford R Jack
- Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Emily S Lundt
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Stephen D Weigand
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Prashanthi Vemuri
- Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Val J Lowe
- Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Kejal Kantarci
- Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Mary M Machulda
- Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Psychiatry, Division of Psychology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Rosebud O Roberts
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA; Mayo Clinic Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN, USA; Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN, USA
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17
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Sarro L, Senjem ML, Lundt ES, Przybelski SA, Lesnick TG, Graff-Radford J, Boeve BF, Lowe VJ, Ferman TJ, Knopman DS, Comi G, Filippi M, Petersen RC, Jack CR, Kantarci K. Amyloid-β deposition and regional grey matter atrophy rates in dementia with Lewy bodies. Brain 2016; 139:2740-2750. [PMID: 27452602 PMCID: PMC5035818 DOI: 10.1093/brain/aww193] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 06/15/2016] [Accepted: 06/20/2016] [Indexed: 12/17/2022] Open
Abstract
Alzheimer's disease pathology frequently coexists with Lewy body disease at autopsy in patients with probable dementia with Lewy bodies. More than half of patients with probable dementia with Lewy bodies have high amyloid-β deposition as measured with 11C-Pittsburgh compound B binding on positron emission tomography. Biomarkers of amyloid-β deposition precede neurodegeneration on magnetic resonance imaging during the progression of Alzheimer's disease, but little is known about how amyloid-β deposition relates to longitudinal progression of atrophy in patients with probable dementia with Lewy bodies. We investigated the associations between baseline 11C-Pittsburgh compound B binding on positron emission tomography and the longitudinal rates of grey matter atrophy in a cohort of clinically diagnosed patients with dementia with Lewy bodies (n = 20), who were consecutively recruited to the Mayo Clinic Alzheimer's Disease Research Centre. All patients underwent 11C-Pittsburgh compound B positron emission tomography and magnetic resonance imaging examinations at baseline. Follow-up magnetic resonance imaging was performed after a mean (standard deviation) interval of 2.5 (1.1) years. Regional grey matter loss was determined on three-dimensional T1-weighted magnetic resonance imaging with the tensor-based morphometry-symmetric normalization technique. Linear regression was performed between baseline 11C-Pittsburgh compound B standard unit value ratio and longitudinal change in regional grey matter volumes from an in-house modified atlas. We identified significant associations between greater baseline 11C-Pittsburgh compound B standard unit value ratio and greater grey matter loss over time in the posterior cingulate gyrus, lateral and medial temporal lobe, and occipital lobe as well as caudate and putamen nuclei, after adjusting for age (P < 0.05). Greater baseline 11C-Pittsburgh compound B standard unit value ratio was also associated with greater ventricular expansion rates (P < 0.01) and greater worsening over time in Clinical Dementia Rating Scale, sum of boxes (P = 0.02). In conclusion, in patients with probable dementia with Lewy bodies, higher amyloid-β deposition at baseline is predictive of faster neurodegeneration in the cortex and also in the striatum. This distribution is suggestive of possible interactions among amyloid-β, tau and α-synuclein aggregates, which needs further investigation. Furthermore, higher amyloid-β deposition at baseline predicts a faster clinical decline over time in patients with probable dementia with Lewy bodies.
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Affiliation(s)
- Lidia Sarro
- 1 Department of Radiology, Mayo Clinic, Rochester, MN, USA 2 Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy 3 Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Matthew L Senjem
- 1 Department of Radiology, Mayo Clinic, Rochester, MN, USA 4 Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Emily S Lundt
- 5 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- 5 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- 5 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | | | - Val J Lowe
- 1 Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Tanis J Ferman
- 7 Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Giancarlo Comi
- 3 Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- 2 Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy 3 Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Ronald C Petersen
- 5 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA 6 Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Kejal Kantarci
- 1 Department of Radiology, Mayo Clinic, Rochester, MN, USA
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18
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Longitudinal brain structural changes in preclinical Alzheimer's disease. Alzheimers Dement 2016; 13:499-509. [DOI: 10.1016/j.jalz.2016.08.010] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 08/22/2016] [Accepted: 08/23/2016] [Indexed: 01/30/2023]
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