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Whitfield JF, Rennie K, Chakravarthy B. Alzheimer's Disease and Its Possible Evolutionary Origin: Hypothesis. Cells 2023; 12:1618. [PMID: 37371088 DOI: 10.3390/cells12121618] [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: 04/01/2023] [Revised: 05/29/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The enormous, 2-3-million-year evolutionary expansion of hominin neocortices to the current enormity enabled humans to take over the planet. However, there appears to have been a glitch, and it occurred without a compensatory expansion of the entorhinal cortical (EC) gateway to the hippocampal memory-encoding system needed to manage the processing of the increasing volume of neocortical data converging on it. The resulting age-dependent connectopathic glitch was unnoticed by the early short-lived populations. It has now surfaced as Alzheimer's disease (AD) in today's long-lived populations. With advancing age, processing of the converging neocortical data by the neurons of the relatively small lateral entorhinal cortex (LEC) inflicts persistent strain and high energy costs on these cells. This may result in their hyper-release of harmless Aβ1-42 monomers into the interstitial fluid, where they seed the formation of toxic amyloid-β oligomers (AβOs) that initiate AD. At the core of connectopathic AD are the postsynaptic cellular prion protein (PrPC). Electrostatic binding of the negatively charged AβOs to the positively charged N-terminus of PrPC induces hyperphosphorylation of tau that destroys synapses. The spread of these accumulating AβOs from ground zero is supported by Aβ's own production mediated by target cells' Ca2+-sensing receptors (CaSRs). These data suggest that an early administration of a strongly positively charged, AβOs-interacting peptide or protein, plus an inhibitor of CaSR, might be an effective AD-arresting therapeutic combination.
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Affiliation(s)
- James F Whitfield
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
| | - Kerry Rennie
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
| | - Balu Chakravarthy
- Human Health Therapeutics, National Research Council, Ottawa, ON K1A 0R6, Canada
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2
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Llamas-Rodríguez J, Oltmer J, Marshall M, Champion S, Frosch MP, Augustinack JC. TDP-43 and tau concurrence in the entorhinal subfields in primary age-related tauopathy and preclinical Alzheimer's disease. Brain Pathol 2023:e13159. [PMID: 37037195 DOI: 10.1111/bpa.13159] [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: 02/16/2023] [Accepted: 03/24/2023] [Indexed: 04/12/2023] Open
Abstract
Phosphorylated tau (p-tau) pathology correlates strongly with cognitive decline and is a pathological hallmark of Alzheimer's Disease (AD). In recent years, phosphorylated transactive response DNA-binding protein (pTDP-43) has emerged as a common comorbidity, found in up to 70% of all AD cases (Josephs et al., Acta Neuropathol, 131(4), 571-585; Josephs, Whitwell, et al., Acta Neuropathol, 127(6), 811-824). Current staging schemes for pTDP-43 in AD and primary age-related tauopathy (PART) track its progression throughout the brain, but the distribution of pTDP-43 within the entorhinal cortex (EC) at the earliest stages has not been studied. Moreover, the exact nature of p-tau and pTDP-43 co-localization is debated. We investigated the selective vulnerability of the entorhinal subfields to phosphorylated pTDP-43 pathology in preclinical AD and PART postmortem tissue. Within the EC, posterior-lateral subfields showed the highest semi-quantitative pTDP-43 density scores, while the anterior-medial subfields had the lowest. On the rostrocaudal axis, pTDP-43 scores were higher posteriorly than anteriorly (p < 0.010), peaking at the posterior-most level (p < 0.050). Further, we showed the relationship between pTDP-43 and p-tau in these regions at pathology-positive but clinically silent stages. P-tau and pTDP-43 presented a similar pattern of affected subregions (p < 0.0001) but differed in density magnitude (p < 0.0001). P-tau burden was consistently higher than pTDP-43 at every anterior-posterior level and in most EC subfields. These findings highlight pTDP-43 burden heterogeneity within the EC and the posterior-lateral subfields as the most vulnerable regions within stage II of the current pTDP-43 staging schemes for AD and PART. The EC is a point of convergence for p-tau and pTDP-43 and identifying its most vulnerable neuronal populations will prove key for early diagnosis and disease intervention.
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Affiliation(s)
- Josué Llamas-Rodríguez
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Jan Oltmer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Michael Marshall
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Samantha Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew P Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean C Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
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3
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Zachlod D, Kedo O, Amunts K. Anatomy of the temporal lobe: From macro to micro. HANDBOOK OF CLINICAL NEUROLOGY 2022; 187:17-51. [PMID: 35964970 DOI: 10.1016/b978-0-12-823493-8.00009-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The temporal cortex encompasses a large number of different areas ranging from the six-layered isocortex to the allocortex. The areas support auditory, visual, and language processing, as well as emotions and memory. The primary auditory cortex is found at the Heschl gyri, which develop early in ontogeny with the Sylvian fissure, a deep and characteristic fissure that separates the temporal lobe from the parietal and frontal lobes. Gyri and sulci as well as brain areas vary between brains and between hemispheres, partly linked to the functional organization of language and lateralization. Interindividual variability in anatomy makes a direct comparison between different brains in structure-functional analysis often challenging, but can be addressed by applying cytoarchitectonic probability maps of the Julich-Brain atlas. We review the macroanatomy of the temporal lobe, its variability and asymmetry at the macro- and the microlevel, discuss the relationship to brain areas and their microstructure, and emphasize the advantage of a multimodal approach to address temporal lobe organization. We review recent data on combined cytoarchitectonic and molecular architectonic studies of temporal areas, and provide links to their function.
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Affiliation(s)
- Daniel Zachlod
- Institute of Neuroscience and Medicine, INM-1, Research Centre Juelich, Juelich, Germany
| | - Olga Kedo
- Institute of Neuroscience and Medicine, INM-1, Research Centre Juelich, Juelich, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, INM-1, Research Centre Juelich, Juelich, Germany; C&O Vogt Institute for Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
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4
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Sanchez JS, Becker JA, Jacobs HIL, Hanseeuw BJ, Jiang S, Schultz AP, Properzi MJ, Katz SR, Beiser A, Satizabal CL, O'Donnell A, DeCarli C, Killiany R, El Fakhri G, Normandin MD, Gómez-Isla T, Quiroz YT, Rentz DM, Sperling RA, Seshadri S, Augustinack J, Price JC, Johnson KA. The cortical origin and initial spread of medial temporal tauopathy in Alzheimer's disease assessed with positron emission tomography. Sci Transl Med 2021; 13:eabc0655. [PMID: 33472953 PMCID: PMC7978042 DOI: 10.1126/scitranslmed.abc0655] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022]
Abstract
Advances in molecular positron emission tomography (PET) have enabled anatomic tracking of brain pathology in longitudinal studies of normal aging and dementia, including assessment of the central model of Alzheimer's disease (AD) pathogenesis, according to which TAU pathology begins focally but expands catastrophically under the influence of amyloid-β (Aβ) pathology to mediate neurodegeneration and cognitive decline. Initial TAU deposition occurs many years before Aβ in a specific area of the medial temporal lobe. Building on recent work that enabled focus of molecular PET measurements on specific TAU-vulnerable convolutional temporal lobe anatomy, we applied an automated anatomic sampling method to quantify TAU PET signal in 443 adult participants from several observational studies of aging and AD, spanning a wide range of ages, Aβ burdens, and degrees of clinical impairment. We detected initial cortical emergence of tauopathy near the rhinal sulcus in clinically normal people and, in a subset with longitudinal 2-year follow-up data (n = 104), tracked Aβ-associated spread of TAU from this site first to nearby neocortex of the temporal lobe and then to extratemporal regions. Greater rate of TAU spread was associated with baseline measures of both global Aβ burden and medial temporal lobe TAU. These findings are consistent with clinicopathological correlation studies of Alzheimer's tauopathy and enable precise tracking of AD-related TAU progression for natural history studies and prevention therapeutic trials.
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Affiliation(s)
- Justin S Sanchez
- Massachusetts General Hospital, Boston, MA 02114, USA.
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - J Alex Becker
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Heidi I L Jacobs
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, 6211 LK, Netherlands
| | - Bernard J Hanseeuw
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- Université Catholique de Louvain, Brussels B-1348, Belgium
| | - Shu Jiang
- Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Aaron P Schultz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael J Properzi
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Samantha R Katz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Alexa Beiser
- Boston University School of Medicine, Boston, MA 02118, USA
- Boston University School of Public Health, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
| | - Claudia L Satizabal
- Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA
| | - Adrienne O'Donnell
- Boston University School of Public Health, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
| | | | - Ron Killiany
- Boston University School of Medicine, Boston, MA 02118, USA
- Boston University School of Public Health, Boston, MA 02118, USA
| | - Georges El Fakhri
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Marc D Normandin
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Teresa Gómez-Isla
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Yakeel T Quiroz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Grupo de Neurociencias, Universidad de Antioquia, Antioquia 050010, Colombia
| | - Dorene M Rentz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Reisa A Sperling
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sudha Seshadri
- Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA
| | - Jean Augustinack
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Julie C Price
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Boston, MA 02114, USA.
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
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5
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Niemantsverdriet E, Ribbens A, Bastin C, Benoit F, Bergmans B, Bier JC, Bladt R, Claes L, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER). J Alzheimers Dis 2019; 63:1509-1522. [PMID: 29782314 PMCID: PMC6004934 DOI: 10.3233/jad-171140] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD). Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available. Results: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.
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Affiliation(s)
- Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | | | - Roxanne Bladt
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | | | - Peter Paul De Deyn
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, UZ Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Namur, Université catholique de Louvain, Yvoir, Belgium.,Université catholique de Louvain, Institute of Neuroscience (IoNS), Louvain-la-Neuve (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, KU Leuven, Leuven, Belgium.,Geriatric Medicine and Memory Clinic, University Hospital Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
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6
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Ding SL, Royall JJ, Sunkin SM, Ng L, Facer BAC, Lesnar P, Guillozet-Bongaarts A, McMurray B, Szafer A, Dolbeare TA, Stevens A, Tirrell L, Benner T, Caldejon S, Dalley RA, Dee N, Lau C, Nyhus J, Reding M, Riley ZL, Sandman D, Shen E, van der Kouwe A, Varjabedian A, Wright M, Zöllei L, Dang C, Knowles JA, Koch C, Phillips JW, Sestan N, Wohnoutka P, Zielke HR, Hohmann JG, Jones AR, Bernard A, Hawrylycz MJ, Hof PR, Fischl B, Lein ES. Comprehensive cellular-resolution atlas of the adult human brain. J Comp Neurol 2017; 524:3127-481. [PMID: 27418273 PMCID: PMC5054943 DOI: 10.1002/cne.24080] [Citation(s) in RCA: 209] [Impact Index Per Article: 29.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 07/11/2016] [Accepted: 07/13/2016] [Indexed: 12/12/2022]
Abstract
Detailed anatomical understanding of the human brain is essential for unraveling its functional architecture, yet current reference atlases have major limitations such as lack of whole‐brain coverage, relatively low image resolution, and sparse structural annotation. We present the first digital human brain atlas to incorporate neuroimaging, high‐resolution histology, and chemoarchitecture across a complete adult female brain, consisting of magnetic resonance imaging (MRI), diffusion‐weighted imaging (DWI), and 1,356 large‐format cellular resolution (1 µm/pixel) Nissl and immunohistochemistry anatomical plates. The atlas is comprehensively annotated for 862 structures, including 117 white matter tracts and several novel cyto‐ and chemoarchitecturally defined structures, and these annotations were transferred onto the matching MRI dataset. Neocortical delineations were done for sulci, gyri, and modified Brodmann areas to link macroscopic anatomical and microscopic cytoarchitectural parcellations. Correlated neuroimaging and histological structural delineation allowed fine feature identification in MRI data and subsequent structural identification in MRI data from other brains. This interactive online digital atlas is integrated with existing Allen Institute for Brain Science gene expression atlases and is publicly accessible as a resource for the neuroscience community. J. Comp. Neurol. 524:3127–3481, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, 98109.
| | - Joshua J Royall
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | | | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | | | - Bergen McMurray
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Tim A Dolbeare
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Allison Stevens
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Lee Tirrell
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Thomas Benner
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | | | - Rachel A Dalley
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Christopher Lau
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Melissa Reding
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Zackery L Riley
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - David Sandman
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Elaine Shen
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Ani Varjabedian
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Michelle Wright
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Lilla Zöllei
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Chinh Dang
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - James A Knowles
- Zilkha Neurogenetic Institute, and Department of Psychiatry, University of Southern California, Los Angeles, California, 90033
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - John W Phillips
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Nenad Sestan
- Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, Connecticut, 06510
| | - Paul Wohnoutka
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - H Ronald Zielke
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, Maryland, 21201
| | - John G Hohmann
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Allan R Jones
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, Washington, 98109
| | | | - Patrick R Hof
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 11029
| | - Bruce Fischl
- Department of Radiology, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, 02129
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, Washington, 98109.
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7
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Human anterolateral entorhinal cortex volumes are associated with cognitive decline in aging prior to clinical diagnosis. Neurobiol Aging 2017; 57:195-205. [DOI: 10.1016/j.neurobiolaging.2017.04.025] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 04/26/2017] [Accepted: 04/28/2017] [Indexed: 11/20/2022]
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8
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Lindemer ER, Greve DN, Fischl BR, Augustinack JC, Salat DH. Regional staging of white matter signal abnormalities in aging and Alzheimer's disease. Neuroimage Clin 2017; 14:156-165. [PMID: 28180074 PMCID: PMC5279704 DOI: 10.1016/j.nicl.2017.01.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/02/2017] [Accepted: 01/20/2017] [Indexed: 11/07/2022]
Abstract
White matter lesions, quantified as 'white matter signal abnormalities' (WMSA) on neuroimaging, are common incidental findings on brain images of older adults. This tissue damage is linked to cerebrovascular dysfunction and is associated with cognitive decline. The regional distribution of WMSA throughout the cerebral white matter has been described at a gross scale; however, to date no prior study has described regional patterns relative to cortical gyral landmarks which may be important for understanding functional impact. Additionally, no prior study has described how regional WMSA volume scales with total global WMSA. Such information could be used in the creation of a pathologic 'staging' of WMSA through a detailed regional characterization at the individual level. Magnetic resonance imaging data from 97 cognitively-healthy older individuals (OC) aged 52-90 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were processed using a novel WMSA labeling procedure described in our prior work. WMSA were quantified regionally using a procedure that segments the cerebral white matter into 35 bilateral units based on proximity to landmarks in the cerebral cortex. An initial staging was performed by quantifying the regional WMSA volume in four groups based on quartiles of total WMSA volume (quartiles I-IV). A consistent spatial pattern of WMSA accumulation was observed with increasing quartile. A clustering procedure was then used to distinguish regions based on patterns of scaling of regional WMSA to global WMSA. Three patterns were extracted that showed high, medium, and non-scaling with global WMSA. Regions in the high-scaling cluster included periventricular, caudal and rostral middle frontal, inferior and superior parietal, supramarginal, and precuneus white matter. A data-driven staging procedure was then created based on patterns of WMSA scaling and specific regional cut-off values from the quartile analyses. Individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were then additionally staged, and significant differences in the percent of each diagnostic group in Stages I and IV were observed, with more AD individuals residing in Stage IV and more OC and MCI individuals residing in Stage I. These data demonstrate a consistent regional scaling relationship between global and regional WMSA that can be used to classify individuals into one of four stages of white matter disease. White matter staging could play an important role in a better understanding and the treatment of cerebrovascular contributions to brain aging and dementia.
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Affiliation(s)
- Emily R. Lindemer
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R. Fischl
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Jean C. Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA, USA
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9
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Augustinack JC, van der Kouwe AJW. Postmortem imaging and neuropathologic correlations. HANDBOOK OF CLINICAL NEUROLOGY 2016; 136:1321-39. [PMID: 27430472 DOI: 10.1016/b978-0-444-53486-6.00069-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Postmortem imaging refers to scanning autopsy specimens using magnetic resonance imaging (MRI) or optical imaging. This chapter summarizes postmortem imaging and its usefulness in brain mapping. Standard in vivo MRI has limited resolution due to time constraints and does not deliver cortical boundaries (e.g., Brodmann areas). Postmortem imaging offers a means to obtain ultra-high-resolution images with appropriate contrast for delineating cortical regions. Postmortem imaging provides the ability to validate MRI properties against histologic stained sections. This approach has enabled probabilistic mapping that is based on ex vivo MRI contrast, validated to histology, and subsequently mapped on to an in vivo model. This chapter emphasizes structural imaging, which can be validated with histologic assessment. Postmortem imaging has been applied to neuropathologic studies as well. This chapter includes many ex vivo studies, but focuses on studies of the medial temporal lobe, often involved in neurologic disease. New research using optical imaging is also highlighted.
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Affiliation(s)
- Jean C Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.
| | - André J W van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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10
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Hasan KM, Mwangi B, Cao B, Keser Z, Tustison NJ, Kochunov P, Frye RE, Savatic M, Soares J. Entorhinal Cortex Thickness across the Human Lifespan. J Neuroimaging 2015; 26:278-82. [PMID: 26565394 DOI: 10.1111/jon.12297] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 07/25/2015] [Accepted: 08/14/2015] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND PURPOSE Human entorhinal cortex (ERC) connects the temporal neocortex with hippocampus and is essential for memory retrieval and navigation. Markedly, there have been only few quantitative MRI works on the ERC geometric measurements in pediatric and adult healthy subjects across the lifespan. Here, we sought to fill this gap in knowledge by quantifying the ERC thickness in a very large cohort of subjects spanning 9 decades of life. METHODS Using magnetic resonance imaging data from multiple centers (IXI, MMRR, NKI, OASIS combined with the NIH-Child Dev database and locally recruited healthy subjects), we analyzed the lifespan trajectory of ERC thickness in 1,660 healthy controls ranging from 2 to 94 years of age. RESULTS The ERC thickness increased with age, reached a peak at about 44 years, and then decreased with age. ERC thickness is hemispherically rightward-asymmetric with no gender differences. Mean ERC thickness was found to vary between 2.943 ± .438 mm and 3.525 ± .355 mm across different age populations. Also, more pronounced loss of the ERC thickness in healthy aging men was noticeable. DISCUSSION Our report with high spatial resolution brain MRI data from 1,660 healthy controls provided important clues about ERC thickness across lifespan. We believe that our report will pave the way for the future studies investigating distinct neural pathologies related with cognitive dysfunctions.
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Affiliation(s)
- Khader M Hasan
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center, Houston, TX
| | - Benson Mwangi
- Department of Diagnostic and Psychiatry, University of Texas Health Science Center, Houston, TX
| | - Bo Cao
- Department of Diagnostic and Psychiatry, University of Texas Health Science Center, Houston, TX
| | - Zafer Keser
- Department of Diagnostic and Physical Medicine and Rehabilitation, University of Texas Health Science Center, Houston, TX
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
| | | | - Richard E Frye
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Mirjana Savatic
- Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX
| | - Jair Soares
- Department of Diagnostic and Psychiatry, University of Texas Health Science Center, Houston, TX
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11
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Raslau FD, Augustinack JC, Klein AP, Ulmer JL, Mathews VP, Mark LP. Memory Part 3: The Role of the Fornix and Clinical Cases. AJNR Am J Neuroradiol 2015; 36:1604-8. [PMID: 26045575 DOI: 10.3174/ajnr.a4371] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- F D Raslau
- From the Department of Radiology (F.D.R.), University of Kentucky, Lexington, Kentucky
| | - J C Augustinack
- Department of Radiology (J.C.A.), Harvard Medical School, Boston, Massachusetts Athinoula A. Martinos Center for Biomedical Imaging (J.C.A.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - A P Klein
- Department of Radiology (A.P.K., J.L.U., V.P.M., L.P.M.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - J L Ulmer
- Department of Radiology (A.P.K., J.L.U., V.P.M., L.P.M.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - V P Mathews
- Department of Radiology (A.P.K., J.L.U., V.P.M., L.P.M.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - L P Mark
- Department of Radiology (A.P.K., J.L.U., V.P.M., L.P.M.), Medical College of Wisconsin, Milwaukee, Wisconsin.
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12
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Mah L, Binns MA, Steffens DC. Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer disease. Am J Geriatr Psychiatry 2015; 23:466-76. [PMID: 25500120 PMCID: PMC4390420 DOI: 10.1016/j.jagp.2014.10.005] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Revised: 10/14/2014] [Accepted: 10/21/2014] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To test the hypothesis that anxiety in amnestic mild cognitive impairment (aMCI) increases rates of conversion to Alzheimer disease (AD) and to identify potential neural mechanisms underlying such an association. METHODS Participants (N = 376) with aMCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were studied over a median period of 36 months. A Cox proportional-hazards model was used to assess the association between anxiety severity ratings on the Neuropsychiatric Inventory Questionnaire and AD risk. Other variables were depression, memory loss, and MRI-derived AD-related regions of interest (ROIs), including hippocampal, amygdalar, entorhinal cortical (EC) volumes, and EC thickness, In addition, a linear regression model was used to determine the effect of anxiety in aMCI on rates of atrophy within ROIs. RESULTS Anxiety severity increased rate of aMCI conversion to AD, after controlling for depression and cognitive decline. The association between anxiety and AD remained significant even with inclusion of ROI baseline values or atrophy rates as explanatory variables. Further, anxiety status predicted greater rates of decrease in EC volume. An association between anxiety and EC thickness missed significance. CONCLUSION Anxiety symptoms in aMCI predict conversion to AD, over and beyond the effects of depression, memory loss, or atrophy within AD neuroimaging biomarkers. These findings, together with the greater EC atrophy rate predicted by anxiety, are compatible with the hypothesis that anxiety is not a prodromal noncognitive feature of AD but may accelerate decline toward AD through direct or indirect effects on EC.
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Affiliation(s)
- Linda Mah
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada; Division of Geriatric Psychiatry, University of Toronto, Toronto, Ontario, Canada.
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - David C Steffens
- Department of Psychiatry, University of Connecticut Health Center, Farmington, CT
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13
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Wisse LE, Biessels GJ, Heringa SM, Kuijf HJ, Koek D(HL, Luijten PR, Geerlings MI. Hippocampal subfield volumes at 7T in early Alzheimer's disease and normal aging. Neurobiol Aging 2014; 35:2039-45. [PMID: 24684788 DOI: 10.1016/j.neurobiolaging.2014.02.021] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Revised: 02/13/2014] [Accepted: 02/26/2014] [Indexed: 10/25/2022]
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14
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Augustinack JC, van der Kouwe AJ, Fischl B. Medial temporal cortices in ex vivo magnetic resonance imaging. J Comp Neurol 2013; 521:4177-88. [PMID: 23881818 PMCID: PMC6014627 DOI: 10.1002/cne.23432] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 06/27/2013] [Accepted: 07/10/2013] [Indexed: 12/24/2022]
Abstract
This review focuses on the ex vivo magnetic resonance imaging (MRI) modeling of medial temporal cortices and associated structures, the entorhinal verrucae and the perforant pathway. Typical in vivo MRI has limited resolution due to constraints on scan times and does not show laminae in the medial temporal lobe. Recent studies using ex vivo MRI have demonstrated lamina in the entorhinal, perirhinal, and hippocampal cortices. These studies have enabled probabilistic brain mapping that is based on the ex vivo MRI contrast, validated to histology, and subsequently mapped onto an in vivo spherically warped surface model. Probabilistic maps are applicable to other in vivo studies.
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Affiliation(s)
- Jean C. Augustinack
- Athinoula A Martinos Center, Department of Radiology, MGH, Charlestown, Massachusetts 02129
| | | | - Bruce Fischl
- Athinoula A Martinos Center, Department of Radiology, MGH, Charlestown, Massachusetts 02129
- MIT Computer Science and AI Lab, Cambridge, Massachusetts 02139
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Abstract
AbstractEntorhinal verrucae are unique, small elevations on the surface of entorhinal cortex, formed due to distinctive clustering of large neurons in entorhinal layer II. In Alzheimer’s disease, the verrucae atrophy as a result of neurofibrillary tangle formation and concomitant neuronal loss. Previously, we found significant decreases in verrucae height, width, surface area, and volume even in the mildest stage of Alzheimer’s disease. In this report, we introduce a new method for analyzing verrucae prominence using measures of their curvature. Smoothed surfaces and curvatures were generated using FreeSurfer (http://surfer.nmr.mgh.harvard.edu) from 100 μm3 ex vivo MRI isosurfaces. We examined the positive and negative components of mean curvature AreaNorm(H+/-) and Gaussian curvature AreaNorm(K +/−) in entorhinal cortex. A significant difference was found between entorhinal (n=10) and non-entorhinal cortices (n=9) for both AreaNorm(H+/-) and AreaNorm(K +/−). We also validated our curvature analysis through a comparison with previously published verrucae measures derived from manual labels of individual verrucae. A significant positive correlation was found between mean verrucae height and AreaNorm(H+/-). Both mean verrucae height and volume were significantly positively correlated with AreaNorm(K +/−). These results demonstrate that K and H are accurate metrics for detecting the presence or absence of entorhinal verrucae. Curvature analysis may be a useful and sensitive technique for detecting local surface changes in entorhinal cortex.
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