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Llamas Rodríguez J, van der Kouwe AJW, Oltmer J, Rosenblum E, Mercaldo N, Fischl B, Marshall M, Frosch MP, Augustinack JC. Entorhinal vessel density correlates with phosphorylated tau and TDP-43 pathology. Alzheimers Dement 2024. [PMID: 38877668 DOI: 10.1002/alz.13896] [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: 02/28/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION The entorhinal cortex (EC) and perirhinal cortex (PC) are vulnerable to Alzheimer's disease. A triggering factor may be the interaction of vascular dysfunction and tau pathology. METHODS We imaged post mortem human tissue at 100 μm3 with 7 T magnetic resonance imaging and manually labeled individual blood vessels (mean = 270 slices/case). Vessel density was quantified and compared per EC subfield, between EC and PC, and in relation to tau and TAR DNA-binding protein 43 (TDP-43) semiquantitative scores. RESULTS PC was more vascularized than EC and vessel densities were higher in posterior EC subfields. Tau and TDP-43 strongly correlated with vasculature density and subregions with severe tau at the preclinical stage had significantly greater vessel density than those with low tau burden. DISCUSSION These data impact cerebrovascular maps, quantification of subfield vasculature, and correlation of vasculature and pathology at early stages. The ordered association of vessel density, and tau or TDP-43 pathology, may be exploited in a predictive context. HIGHLIGHTS Vessel density correlates with phosphorylated tau (p-tau) burden in entorhinal and perirhinal cortices. Perirhinal area 35 and posterior entorhinal cortex showed greatest p-tau burden but also the highest vessel density in the preclinical phase of Alzheimer's disease. We combined an ex vivo magnetic resonance imaging model and histopathology to demonstrate the 3D reconstruction of intracortical vessels and its spatial relationship to the pathology.
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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
| | - André J W van der Kouwe
- 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
- Department of Digital Health & Innovation, Vivantes Netzwerk für Gesundheit GmbH, Berlin, Germany
| | - Emma Rosenblum
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Nathaniel Mercaldo
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- MGH Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Michael Marshall
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory for 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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Nair G, Sun R, Merkle H, Xu Q, Hoskin K, Bree K, Dodd S, Koretsky AP. A method to image brain tissue frozen at autopsy. Neuroimage 2024; 296:120680. [PMID: 38857819 DOI: 10.1016/j.neuroimage.2024.120680] [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: 02/29/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 06/12/2024] Open
Abstract
Magnetic Resonance Imaging (MRI) can provide the location and signal characteristics of pathological regions within a postmortem tissue block, thereby improving the efficiency of histopathological studies. However, such postmortem-MRI guided histopathological studies have so far only been performed on fixed samples as imaging tissue frozen at the time of extraction, while preserving its integrity, is significantly more challenging. Here we describe the development of cold-postmortem-MRI, which can preserve tissue integrity and help target techniques such as transcriptomics. As a first step, RNA integrity number (RIN) was used to determine the rate of tissue biomolecular degradation in mouse brains placed at various temperatures between -20 °C and +20 °C for up to 24 h. Then, human tissue frozen at the time of autopsy was immersed in 2-methylbutane, sealed in a bio-safe tissue chamber, and cooled in the MRI using a recirculating chiller to determine MRI signal characteristics. The optimal imaging temperature, which did not show significant RIN deterioration for over 12 h, at the same time giving robust MRI signal and contrast between brain tissue types was deemed to be -7 °C. Finally, MRI was performed on human tissue blocks at this optimal imaging temperatures using a magnetization-prepared rapid gradient echo (MPRAGE, isotropic resolution between 0.3-0.4 mm) revealing good gray-white matter contrast and revealing subpial, subcortical, and deep white matter lesions. RINs measured before and after imaging revealed no significant changes (n = 3, p = 0.18, paired t-test). In addition to improving efficiency of downstream processes, imaging tissue at sub-zero temperatures may also improve our understanding of compartment specificity of MRI signal.
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Affiliation(s)
- Govind Nair
- Quantitative MRI Core, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Dr, Bethesda, MD 20893, USA.
| | - Roy Sun
- Quantitative MRI Core, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Dr, Bethesda, MD 20893, USA
| | - Hellmut Merkle
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA
| | - Qing Xu
- Human Brain Collection Core, National Institute of Mental Health, National Institutes of Health, Bethesda, USA
| | - Kyra Hoskin
- Quantitative MRI Core, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Dr, Bethesda, MD 20893, USA
| | - Kendyl Bree
- Quantitative MRI Core, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Dr, Bethesda, MD 20893, USA
| | - Stephen Dodd
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA
| | - Alan P Koretsky
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA
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Hickling AL, Clark IA, Wu YI, Maguire EA. Automated protocols for delineating human hippocampal subfields from 3 Tesla and 7 Tesla magnetic resonance imaging data. Hippocampus 2024; 34:302-308. [PMID: 38593279 DOI: 10.1002/hipo.23606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions.
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Affiliation(s)
- Alice L Hickling
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian A Clark
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Yan I Wu
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
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4
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts CJ, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, La Joie R, Wang L, Bedard ML, Buendia E, Chung E, Denning A, Del Mar Arroyo-Jiménez M, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Del Pilar Marcos-Rabal M, Iñiguez de Onzoño Martin MM, Lopez MM, de la Rosa Prieto C, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. Hippocampus 2024; 34:241-260. [PMID: 38415962 PMCID: PMC11039382 DOI: 10.1002/hipo.23602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/25/2024] [Accepted: 02/04/2024] [Indexed: 02/29/2024]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 μm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
- Department of Psychology, Wayne State University, Detroit, Michigan, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
| | - Lei Wang
- The Ohio State University, Columbus, Ohio, USA
| | - Madigan L Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Esther Buendia
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Eunice Chung
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Amanda Denning
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - David J Irwin
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Monica Munoz Lopez
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Theresa Schuck
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Alicia Vela
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
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Almudhry M, Wagner MW, Longoni G, Yea C, Vidarsson L, Ertl-Wagner B, Yeh EA. Brain Volumes in Opsoclonus-Myoclonus Ataxia Syndrome: A Longitudinal Study. J Child Neurol 2024; 39:129-134. [PMID: 38544431 PMCID: PMC11102640 DOI: 10.1177/08830738241240181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/27/2024] [Accepted: 03/03/2024] [Indexed: 05/18/2024]
Abstract
INTRODUCTION Little is known about the longitudinal trajectory of brain growth in children with opsoclonus-myoclonus ataxia syndrome. We performed a longitudinal evaluation of brain volumes in pediatric opsoclonus-myoclonus ataxia syndrome patients compared with age- and sex-matched healthy children. PATIENTS AND METHODS This longitudinal case-control study included brain magnetic resonance imaging (MRI) scans from consecutive pediatric opsoclonus-myoclonus ataxia syndrome patients (2009-2020) and age- and sex-matched healthy control children. FreeSurfer analysis provided automatic volumetry of the brain. Paired t tests were performed on the curvature of growth trajectories, with Bonferroni correction. RESULTS A total of 14 opsoclonus-myoclonus ataxia syndrome patients (12 female) and 474 healthy control children (406 female) were included. Curvature of the growth trajectories of the cerebral white and gray matter, cerebellar white and gray matter, and brainstem differed significantly between opsoclonus-myoclonus ataxia syndrome patients and healthy control children (cerebral white matter, P = .01; cerebral gray matter, P = .01; cerebellar white matter, P < .001; cerebellar gray matter, P = .049; brainstem, P < .01). DISCUSSION/CONCLUSION We found abnormal brain maturation in the supratentorial brain, brainstem, and cerebellum in children with opsoclonus-myoclonus ataxia syndrome.
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Affiliation(s)
- Montaha Almudhry
- Program in Neurosciences and Mental Health, SickKids Research Institute, The Hospital for Sick Children, Canada
| | - Matthias W. Wagner
- Division of Neuroradiology, Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany
| | - Giulia Longoni
- Program in Neurosciences and Mental Health, SickKids Research Institute, The Hospital for Sick Children, Canada
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Carmen Yea
- Program in Neurosciences and Mental Health, SickKids Research Institute, The Hospital for Sick Children, Canada
| | - Logi Vidarsson
- Division of Neuroradiology, Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Birgit Ertl-Wagner
- Program in Neurosciences and Mental Health, SickKids Research Institute, The Hospital for Sick Children, Canada
- Division of Neuroradiology, Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - E. Ann Yeh
- Program in Neurosciences and Mental Health, SickKids Research Institute, The Hospital for Sick Children, Canada
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
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6
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Nair G, Sun R, Merkle H, Hoskin K, Bree K, Dodd S, Koretsky A. Postmortem MRI of Tissue Frozen at Autopsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576456. [PMID: 38313300 PMCID: PMC10836069 DOI: 10.1101/2024.01.20.576456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Introduction Postmortem MRI provides insight into location of pathology within tissue blocks, enabling efficient targeting of histopathological studies. While postmortem imaging of fixed tissue is gaining popularity, imaging tissue frozen at the time of extraction is significantly more challenging. Methods Tissue integrity was examined using RNA integrity number (RIN), in mouse brains placed between -20 °C and 20 °C for up to 24 hours, to determine the highest temperature that could potentially be used for imaging without tissue degeneration. Human tissue frozen at the time of autopsy was sealed in a tissue chamber filled with 2-methylbutane to prevent contamination of the MRI components. The tissue was cooled to a range of temperatures in a 9.4T MRI using a recirculating aqueous ethylene glycol solution. MRI was performed using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with inversion time of 1400 ms to null the signal from 2-methylbutane bath, isotropic resolution between 0.3-0.4 mm, and scan time of about 4 hours was used to study the anatomical details of the tissue block. Results and Discussion A temperature of -7 °C was chosen for imaging as it was below the highest temperature that did not show significant RIN deterioration for over 12 hours, at the same time gave robust imaging signal and contrast between brain tissue types. Imaging performed on various human tissue blocks revealed good gray-white matter contrast and revealing subpial, subcortical, and deep white matter lesions typical of multiple sclerosis enabling further spatially targeted studies. Conclusion Here, we describe a new method to image cold tissue, while maintaining tissue integrity and biosafety during scanning. In addition to improving efficiency of downstream processes, imaging tissue at sub-zero temperatures may also improve our understanding of compartment specificity of MRI signal.
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Affiliation(s)
- Govind Nair
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Roy Sun
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Hellmut Merkle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Kyra Hoskin
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Kendyl Bree
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Stephen Dodd
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
| | - Alan Koretsky
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda
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7
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts C, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, Joie RL, Wang L, Bedard M, Buendia E, Chung E, Denning A, Arroyo-Jiménez MDM, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Marcos-Rabal MDP, Martin MMIDO, Lopez MM, Prieto CDLR, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.24.542054. [PMID: 37292729 PMCID: PMC10245880 DOI: 10.1101/2023.05.24.542054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the cortices that make up the parahippocampal gyrus (entorhinal and parahippocampal cortices) and the adjacent Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 µm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized (20X resolution) slices with 5 mm spacing. Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed more gradually. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed human neuroimaging research on the MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain, Caen-Normandie University, Caen-Normandie, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Arnold Bakker
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco USA
| | - Lei Wang
- The Ohio State University, Columbus, OH, USA
| | - Madigan Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Eunice Chung
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | - Alicia Vela
- University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | | | | | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
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8
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Costantini I, Morgan L, Yang J, Balbastre Y, Varadarajan D, Pesce L, Scardigli M, Mazzamuto G, Gavryusev V, Castelli FM, Roffilli M, Silvestri L, Laffey J, Raia S, Varghese M, Wicinski B, Chang S, Chen IA, Wang H, Cordero D, Vera M, Nolan J, Nestor K, Mora J, Iglesias JE, Garcia Pallares E, Evancic K, Augustinack JC, Fogarty M, Dalca AV, Frosch MP, Magnain C, Frost R, van der Kouwe A, Chen SC, Boas DA, Pavone FS, Fischl B, Hof PR. A cellular resolution atlas of Broca's area. SCIENCE ADVANCES 2023; 9:eadg3844. [PMID: 37824623 PMCID: PMC10569704 DOI: 10.1126/sciadv.adg3844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/03/2023] [Indexed: 10/14/2023]
Abstract
Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.
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Affiliation(s)
- Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Biology, University of Florence, Florence, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Leah Morgan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Division of Physiology, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Vladislav Gavryusev
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Bioretics srl, Cesena, Italy
| | | | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Jessie Laffey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sophia Raia
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bridget Wicinski
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shuaibin Chang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | | | - Hui Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Devani Cordero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Matthew Vera
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jackson Nolan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kimberly Nestor
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Erendira Garcia Pallares
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian V. Dalca
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew P. Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Caroline Magnain
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Andre van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Shih-Chi Chen
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - David A. Boas
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- HST, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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9
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Quek YE, Bourgeat P, Fung YL, Vogrin SJ, Collins SJ, Bowden SC. Validating ASHS-T1 automated entorhinal and transentorhinal cortical segmentation in Alzheimer's disease. Psychiatry Res Neuroimaging 2023; 335:111707. [PMID: 37639979 DOI: 10.1016/j.pscychresns.2023.111707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/25/2023] [Accepted: 08/09/2023] [Indexed: 08/31/2023]
Abstract
The current study aimed to validate entorhinal and transentorhinal cortical volumes measured by the automated segmentation tool Automatic Segmentation of Hippocampal Subfields (ASHS-T1). The study sample comprised 34 healthy controls (HCs), 37 individuals with amnestic mild cognitive impairment (aMCI), and 29 individuals with Alzheimer's disease (AD) dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Entorhinal and transentorhinal cortical volumes were assessed using ASHS-T1, manual segmentation, as well as a widely used automated segmentation tool, FreeSurfer v6.0.1. Mean differences, intraclass correlation coefficients, and Bland-Altman plots were computed. ASHS-T1 tended to underestimate entorhinal and transentorhinal cortical volumes relative to manual segmentation and FreeSurfer. There was variable consistency and low agreement between ASHS-T1 and manual segmentation volumes. There was low-to-moderate consistency and low agreement between ASHS-T1 and FreeSurfer volumes. There was a trend toward higher consistency and agreement for the entorhinal cortex in the aMCI and AD groups compared to the HC group. Despite the differences in volume measurements, ASHS-T1 was sensitive to entorhinal and transentorhinal cortical atrophy in both early and late disease stages. Based on the current study, ASHS-T1 appears to be a promising tool for automated entorhinal and transentorhinal cortical volume measurement in individuals with likely underlying AD.
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Affiliation(s)
- Yi-En Quek
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Yi Leng Fung
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Simon J Vogrin
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Steven J Collins
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia; Department of Medicine (RMH), The University of Melbourne, Parkville, Victoria, Australia
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
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10
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Bosulu J, Allaire MA, Tremblay-Grénier L, Luo Y, Eickhoff S, Hétu S. "Wanting" versus "needing" related value: An fMRI meta-analysis. Brain Behav 2022; 12:e32713. [PMID: 36000558 PMCID: PMC9480935 DOI: 10.1002/brb3.2713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/04/2022] [Indexed: 11/30/2022] Open
Abstract
Consumption and its excesses are sometimes explained by imbalance of need or lack of control over "wanting." "Wanting" assigns value to cues that predict rewards, whereas "needing" assigns value to biologically significant stimuli that one is deprived of. Here we aimed at studying how the brain activation patterns related to value of "wanted" stimuli differs from that of "needed" stimuli using activation likelihood estimation neuroimaging meta-analysis approaches. We used the perception of a cue predicting a reward for "wanting" related value and the perception of food stimuli in a hungry state as a model for "needing" related value. We carried out separate, contrasts, and conjunction meta-analyses to identify differences and similarities between "wanting" and "needing" values. Our overall results for "wanting" related value show consistent activation of the ventral tegmental area, striatum, and pallidum, regions that both activate behavior and direct choice, while for "needing" related value, we found an overall consistent activation of the middle insula and to some extent the caudal-ventral putamen, regions that only direct choice. Our study suggests that wanting has more control on consumption and behavioral activation.
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Affiliation(s)
- Juvenal Bosulu
- Faculté Des Arts et des Sciences, Université de Montréal, Montréal, Canada
| | | | | | - Yi Luo
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Sébastien Hétu
- Faculté Des Arts et des Sciences, Université de Montréal, Montréal, Canada
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11
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Oltmer J, Slepneva N, Llamas Rodriguez J, Greve DN, Williams EM, Wang R, Champion SN, Lang-Orsini M, Nestor K, Fernandez-Ros N, Fischl B, Frosch MP, Magnain C, van der Kouwe AJW, Augustinack JC. Quantitative and histologically validated measures of the entorhinal subfields in ex vivo MRI. Brain Commun 2022; 4:fcac074. [PMID: 35620167 PMCID: PMC9128374 DOI: 10.1093/braincomms/fcac074] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/04/2022] [Accepted: 03/17/2022] [Indexed: 12/15/2022] Open
Abstract
Neuroimaging studies have routinely used hippocampal volume as a measure of Alzheimer's disease severity, but hippocampal changes occur too late in the disease process for potential therapies to be effective. The entorhinal cortex is one of the first cortical areas affected by Alzheimer's disease; its neurons are especially vulnerable to neurofibrillary tangles. Entorhinal atrophy also relates to the conversion from non-clinical to clinical Alzheimer's disease. In neuroimaging, the human entorhinal cortex has so far mostly been considered in its entirety or divided into a medial and a lateral region. Cytoarchitectonic differences provide the opportunity for subfield parcellation. We investigated the entorhinal cortex on a subfield-specific level-at a critical time point of Alzheimer's disease progression. While MRI allows multidimensional quantitative measurements, only histology provides enough accuracy to determine subfield boundaries-the pre-requisite for quantitative measurements within the entorhinal cortex. This study used histological data to validate ultra-high-resolution 7 Tesla ex vivo MRI and create entorhinal subfield parcellations in a total of 10 pre-clinical Alzheimer's disease and normal control cases. Using ex vivo MRI, eight entorhinal subfields (olfactory, rostral, medial intermediate, intermediate, lateral rostral, lateral caudal, caudal, and caudal limiting) were characterized for cortical thickness, volume, and pial surface area. Our data indicated no influence of sex, or Braak and Braak staging on volume, cortical thickness, or pial surface area. The volume and pial surface area for mean whole entorhinal cortex were 1131 ± 55.72 mm3 and 429 ± 22.6 mm2 (mean ± SEM), respectively. The subfield volume percentages relative to the entire entorhinal cortex were olfactory: 18.73 ± 1.82%, rostral: 14.06 ± 0.63%, lateral rostral: 14.81 ± 1.22%, medial intermediate: 6.72 ± 0.72%, intermediate: 23.36 ± 1.85%, lateral caudal: 5.42 ± 0.33%, caudal: 10.99 ± 1.02%, and caudal limiting: 5.91 ± 0.40% (all mean ± SEM). Olfactory and intermediate subfield revealed the most extensive intra-individual variability (cross-subject variance) in volume and pial surface area. This study provides validated measures. It maps individuality and demonstrates human variability in the entorhinal cortex, providing a baseline for approaches in individualized medicine. Taken together, this study serves as a ground-truth validation study for future in vivo comparisons and treatments.
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Affiliation(s)
- Jan Oltmer
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA
| | - Natalya Slepneva
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Josue Llamas Rodriguez
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Douglas N. Greve
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA
| | - Emily M. Williams
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Ruopeng Wang
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Melanie Lang-Orsini
- Department of Neuropathology, Massachusetts General
Hospital, Boston, MA, USA
| | - Kimberly Nestor
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Nídia Fernandez-Ros
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA,CSAIL, Cambridge, MA, USA
| | - Matthew P. Frosch
- Department of Neuropathology, Massachusetts General
Hospital, Boston, MA, USA
| | - Caroline Magnain
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA
| | - Andre J. W. van der Kouwe
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos
Center, Massachusetts General Hospital, Charlestown, MA, USA,Harvard Medical School, Boston, MA,
USA,Correspondence to: Jean C. Augustinack Department of
Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts
General Hospital Building 149, 13th St Room 2301 Charlestown, MA 02129, USA
E-mail:
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12
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Bouchard AE, Dickler M, Renauld E, Lenglos C, Ferland F, Rouillard C, Leblond J, Fecteau S. Brain morphometry in adults with gambling disorder. J Psychiatr Res 2021; 141:66-73. [PMID: 34175744 DOI: 10.1016/j.jpsychires.2021.06.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 05/20/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
Little is known regarding the brain substrates of Gambling Disorder, including surface brain morphometry, and whether these are linked to the clinical profile. A better understanding of the brain substrates will likely help determine targets to treat patients. Hence, the aim of this study was two-fold, that is to examine surface-based morphometry in 17 patients with gambling disorder as compared to norms of healthy individuals (2713 and 2790 subjects for cortical and subcortical anatomical scans, respectively) and to assess the clinical relevance of morphometry in patients with Gambling Disorder. This study measured brain volume, surface and thickness in Gambling Disorder. We compared these measures to those of a normative database that controlled for factors such as age and sex. We also tested for correlations with gambling-related behaviors, such as gambling severity and duration, impulsivity, and depressive symptoms (assessed using the South Oaks Gambling Screen, years of gambling, Barratt Impulsiveness Scale, and Beck Depression Inventory, respectively). Patients displayed thinner prefrontal and parietal cortices, greater volume and thickness of the occipital and the entorhinal cortices, and greater volume of subcortical regions as compared to the norms of healthy individuals. There were positive correlations between surface area of occipital regions and depressive symptoms. This work contributes to better characterize the brain substrates of Gambling Disorder, which appear to resemble those of substance use disorders and Internet Gaming Disorder.
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Affiliation(s)
- Amy E Bouchard
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Maya Dickler
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Emmanuelle Renauld
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Christophe Lenglos
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Francine Ferland
- Centre de réadaptation en dépendance du CIUSSS de la Capitale-Nationale, 2525 chemin de la Canardière, Quebec City, Quebec, G1J 2G3, Canada.
| | - Claude Rouillard
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; Axe Neurosciences, Centre de recherche du CHU de Québec, 2705 boul. Laurier, Quebec City, Quebec, G1V 4G2, Canada.
| | - Jean Leblond
- Centre interdisciplinaire de recherche en réadaptation et intégration sociale, 525 boul. Wilfrid-Hamel, Quebec City, Quebec, G1M 2S8, Canada.
| | - Shirley Fecteau
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
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13
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Baram AB, Muller TH, Nili H, Garvert MM, Behrens TEJ. Entorhinal and ventromedial prefrontal cortices abstract and generalize the structure of reinforcement learning problems. Neuron 2021; 109:713-723.e7. [PMID: 33357385 PMCID: PMC7889496 DOI: 10.1016/j.neuron.2020.11.024] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/09/2020] [Accepted: 11/19/2020] [Indexed: 11/25/2022]
Abstract
Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalize it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new environments. Spatial structural transfer is mediated by cells in entorhinal and (in humans) medial prefrontal cortices, which maintain their co-activation structure across different environments and behavioral states. Here, using fMRI, we show that entorhinal and ventromedial prefrontal cortex (vmPFC) representations perform a much broader role in generalizing the structure of problems. We introduce a task-remapping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensory properties. We show that, as with space, entorhinal representations are preserved across different RL problems only if task structure is preserved. In vmPFC and ventral striatum, representations of prediction error also depend on task structure.
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Affiliation(s)
- Alon Boaz Baram
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
| | - Timothy Howard Muller
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Mona Maria Garvert
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK; Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany
| | - Timothy Edward John Behrens
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3AR, UK
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14
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Omidvarnia A, Zalesky A, Mansour L S, Van De Ville D, Jackson GD, Pedersen M. Temporal complexity of fMRI is reproducible and correlates with higher order cognition. Neuroimage 2021; 230:117760. [PMID: 33486124 DOI: 10.1016/j.neuroimage.2021.117760] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/18/2020] [Accepted: 01/05/2021] [Indexed: 02/08/2023] Open
Abstract
It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.
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Affiliation(s)
- Amir Omidvarnia
- Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Graeme D Jackson
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Neurology, Austin Health, Melbourne, Australia.
| | - Mangor Pedersen
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
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15
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Hallab A, Lange C, Apostolova I, Özden C, Gonzalez-Escamilla G, Klutmann S, Brenner W, Grothe MJ, Buchert R. Impairment of Everyday Spatial Navigation Abilities in Mild Cognitive Impairment Is Weakly Associated with Reduced Grey Matter Volume in the Medial Part of the Entorhinal Cortex. J Alzheimers Dis 2020; 78:1149-1159. [PMID: 33104026 DOI: 10.3233/jad-200520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Research in rodents identified specific neuron populations encoding information for spatial navigation with particularly high density in the medial part of the entorhinal cortex (ERC), which may be homologous with Brodmann area 34 (BA34) in the human brain. OBJECTIVE The aim of this study was to test whether impaired spatial navigation frequently occurring in mild cognitive impairment (MCI) is specifically associated with neurodegeneration in BA34. METHODS The study included baseline data of MCI patients enrolled in the Alzheimer's Disease Neuroimaging Initiative with high-resolution structural MRI, brain FDG PET, and complete visuospatial ability scores of the Everyday Cognition test (VS-ECog) within 30 days of PET. A standard mask of BA34 predefined in MNI space was mapped to individual native space to determine grey matter volume and metabolic activity in BA34 on MRI and on (partial volume corrected) FDG PET, respectively. The association of the VS-ECog sum score with grey matter volume and metabolic activity in BA34, APOE4 carrier status, age, education, and global cognition (ADAS-cog-13 score) was tested by linear regression. BA28, which constitutes the lateral part of the ERC, was used as control region. RESULTS The eligibility criteria led to inclusion of 379 MCI subjects. The VS-ECog sum score was negatively correlated with grey matter volume in BA34 (β= -0.229, p = 0.022) and age (β= -0.124, p = 0.036), and was positively correlated with ADAS-cog-13 (β= 0.175, p = 0.003). None of the other predictor variables contributed significantly. CONCLUSION Impairment of spatial navigation in MCI is weakly associated with BA34 atrophy.
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Affiliation(s)
- Asma Hallab
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cansu Özden
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gabriel Gonzalez-Escamilla
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.,Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.,Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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16
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Tustison NJ, Holbrook AJ, Avants BB, Roberts JM, Cook PA, Reagh ZM, Duda JT, Stone JR, Gillen DL, Yassa MA. Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study. J Alzheimers Dis 2020; 71:165-183. [PMID: 31356207 DOI: 10.3233/jad-190283] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | | | - Brian B Avants
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Jared M Roberts
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachariah M Reagh
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jeffrey T Duda
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James R Stone
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Daniel L Gillen
- Department of Statistics, University of California, Irvine, CA, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
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17
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Fung YL, Ng KET, Vogrin SJ, Meade C, Ngo M, Collins SJ, Bowden SC. Comparative Utility of Manual versus Automated Segmentation of Hippocampus and Entorhinal Cortex Volumes in a Memory Clinic Sample. J Alzheimers Dis 2020; 68:159-171. [PMID: 30814357 DOI: 10.3233/jad-181172] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Structural neuroimaging is a useful non-invasive biomarker commonly employed to evaluate the integrity of mesial temporal lobe structures that are typically compromised in Alzheimer's disease. Advances in quantitative neuroimaging have permitted the development of automated segmentation protocols (e.g., FreeSurfer) with significantly increased efficiency compared to earlier manual techniques. While these protocols have been found to be suitable for large-scale, multi-site research studies, we were interested in assessing the practical utility and reliability of automated FreeSurfer protocols compared to manual volumetry on routinely acquired clinical scans. Independent validation studies with newer automated segmentation protocols are scarce. Two FreeSurfer protocols for each of two regions of interest-the hippocampus and entorhinal cortex-were compared against manual volumetry. High reliability and agreement was found between FreeSurfer and manual hippocampal protocols, however, there was lower reliability and agreement between FreeSurfer and manual entorhinal protocols. Although based on a the relatively small sample of subjects drawn from a memory clinic (n = 27), our study findings suggest further refinements to improve measurement error and most accurately depict true regional brain volumes using automated segmentation protocols are required, especially for non-hippocampal mesial temporal structures, to achieve maximal utility for routine clinical evaluations.
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Affiliation(s)
- Yi Leng Fung
- School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Kelly E T Ng
- School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Simon J Vogrin
- Centre for Clinical Neuroscience and Neurological Research, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Catherine Meade
- Centre for Clinical Neuroscience and Neurological Research, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Michael Ngo
- Centre for Clinical Neuroscience and Neurological Research, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Steven J Collins
- Centre for Clinical Neuroscience and Neurological Research, St Vincent's Hospital, Fitzroy, Victoria, Australia.,Department of Medicine (RMH), The University of Melbourne, Parkville, Victoria, Australia
| | - Stephen C Bowden
- School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Centre for Clinical Neuroscience and Neurological Research, St Vincent's Hospital, Fitzroy, Victoria, Australia
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18
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Dallaire-Théroux C, Beheshti I, Potvin O, Dieumegarde L, Saikali S, Duchesne S. Braak neurofibrillary tangle staging prediction from in vivo MRI metrics. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:599-609. [PMID: 31517022 PMCID: PMC6731211 DOI: 10.1016/j.dadm.2019.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Alzheimer's disease diagnosis requires postmortem visualization of amyloid and tau deposits. As brain atrophy can provide assessment of consequent neurodegeneration, our objective was to predict postmortem neurofibrillary tangles (NFT) from in vivo MRI measurements. METHODS All participants with neuroimaging and neuropathological data from the Alzheimer's Disease Neuroimaging Initiative, the National Alzheimer's Coordinating Center and the Rush Memory and Aging Project were selected (n = 186). Two hundred and thirty two variables were extracted from last MRI before death using FreeSurfer. Nonparametric correlation analysis and multivariable support vector machine classification were performed to provide a predictive model of Braak NFT staging. RESULTS We demonstrated that 59 of our MRI variables, mostly temporal lobe structures, were significantly associated with Braak NFT stages (P < .005). We obtained a 62.4% correct classification rate for discrimination between transentorhinal, limbic, and isocortical groups. DISCUSSION Structural neuroimaging may therefore be considered as a potential biomarker for early detection of Alzheimer's disease-associated neurofibrillary degeneration.
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Affiliation(s)
- Caroline Dallaire-Théroux
- CERVO Brain Research Center, Quebec City, Quebec, Canada
- Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada
| | - Iman Beheshti
- CERVO Brain Research Center, Quebec City, Quebec, Canada
| | - Olivier Potvin
- CERVO Brain Research Center, Quebec City, Quebec, Canada
| | | | - Stephan Saikali
- Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada
- Department of pathology, Centre Hospitalier Universitaire de Quebec, Quebec City, Quebec, Canada
| | - Simon Duchesne
- CERVO Brain Research Center, Quebec City, Quebec, Canada
- Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada
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19
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Vallejo Ramirez PP, Zammit J, Vanderpoorten O, Riche F, Blé FX, Zhou XH, Spiridon B, Valentine C, Spasov SE, Oluwasanya PW, Goodfellow G, Fantham MJ, Siddiqui O, Alimagham F, Robbins M, Stretton A, Simatos D, Hadeler O, Rees EJ, Ströhl F, Laine RF, Kaminski CF. OptiJ: Open-source optical projection tomography of large organ samples. Sci Rep 2019; 9:15693. [PMID: 31666606 PMCID: PMC6821862 DOI: 10.1038/s41598-019-52065-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/09/2019] [Indexed: 12/20/2022] Open
Abstract
The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples.
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Affiliation(s)
- Pedro P Vallejo Ramirez
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Joseph Zammit
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Oliver Vanderpoorten
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Fergus Riche
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Francois-Xavier Blé
- Clinical Discovery Unit, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Xiao-Hong Zhou
- Bioscience, Respiratory, Inflammation and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Bogdan Spiridon
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | | | - Simeon E Spasov
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | | | - Gemma Goodfellow
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Marcus J Fantham
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Omid Siddiqui
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Farah Alimagham
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Miranda Robbins
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Andrew Stretton
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Dimitrios Simatos
- Sensor CDT 2015-2016 student cohort, University of Cambridge, Cambridge, UK
| | - Oliver Hadeler
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Eric J Rees
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Florian Ströhl
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Romain F Laine
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Medical Research Council Laboratory for Molecular Cell Biology (LMCB), University College London, Gower Street, London, WC1E 6BT, UK
| | - Clemens F Kaminski
- Laser Analytics Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
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20
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Wang Y, Hao L, Zhang Y, Zuo C, Wang D. Entorhinal cortex volume, thickness, surface area and curvature trajectories over the adult lifespan. Psychiatry Res Neuroimaging 2019; 292:47-53. [PMID: 31521943 DOI: 10.1016/j.pscychresns.2019.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 08/30/2019] [Accepted: 09/05/2019] [Indexed: 01/23/2023]
Abstract
The entorhinal cortex (ERC) acts as a connection between the hippocampus and temporal cortex and plays a key role in memory retrieval and navigation. The morphology of this brain region changes with age. However, there are few quantitative magnetic resonance imaging studies of ERC morphology across the healthy adult lifespan. In this study, we quantified ERC volume, thickness, surface area, and curvature in a large number of subjects spanning seven decades of life. Using structural MRI data from 563 healthy subjects ranging from 19 to 86 years of age, we explored the adult lifespan trajectory of ERC volume, thickness, surface and curvature. ERC volume, thickness, and surface area initially increased with age, reaching a peak at about 32 years, 40 years, and 50 years of age, respectively, after which they decreased with age. ERC volume and surface area were hemispherically leftward asymmetric, whereas ERC thickness was hemispherically rightward asymmetric, with no gender differences. The direction of asymmetry differed across the measures. This informs previous inconsistencies in reports of ERC asymmetry. ERC aging began in mid-adulthood. At this stage of life, it may be important to adopt some strategies to reduce the effects of aging on cognition.
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Affiliation(s)
- Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lei Hao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yuning Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Institute of Psychiatry, Psychology, & Neurosciences, King's College London, London, UK
| | - Chenyi Zuo
- College of Educational Science, Anhui Normal University, Wuhu, China.
| | - Daoyang Wang
- College of Educational Science, Anhui Normal University, Wuhu, China.
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21
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Xie L, Wisse LEM, Pluta J, de Flores R, Piskin V, Manjón JV, Wang H, Das SR, Ding S, Wolk DA, Yushkevich PA. Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. Hum Brain Mapp 2019; 40:3431-3451. [PMID: 31034738 PMCID: PMC6697377 DOI: 10.1002/hbm.24607] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 04/15/2019] [Indexed: 12/14/2022] Open
Abstract
Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Laura E. M. Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - John Pluta
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Robin de Flores
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Virgine Piskin
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Jose V. Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universidad Politécnica de ValenciaValenciaSpain
| | | | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Song‐Lin Ding
- Allen Institute for Brain ScienceSeattleWashington
- Institute of Neuroscience, School of Basic Medical SciencesGuangzhou Medical UniversityGuangzhouPeople's Republic of China
| | - David A. Wolk
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania
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22
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Dalton MA, McCormick C, De Luca F, Clark IA, Maguire EA. Functional connectivity along the anterior-posterior axis of hippocampal subfields in the ageing human brain. Hippocampus 2019; 29:1049-1062. [PMID: 31058404 PMCID: PMC6849752 DOI: 10.1002/hipo.23097] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 04/02/2019] [Accepted: 04/22/2019] [Indexed: 12/19/2022]
Abstract
While age‐related volumetric changes in human hippocampal subfields have been reported, little is known about patterns of subfield functional connectivity (FC) in the context of healthy ageing. Here we investigated age‐related changes in patterns of FC down the anterior–posterior axis of each subfield. Using high resolution structural MRI we delineated the dentate gyrus (DG), CA fields (including separating DG from CA3), the subiculum, pre/parasubiculum, and the uncus in healthy young and older adults. We then used high resolution resting state functional MRI to measure FC in each group and to directly compare them. We first examined the FC of each subfield in its entirety, in terms of FC with other subfields and with neighboring cortical regions, namely, entorhinal, perirhinal, posterior parahippocampal, and retrosplenial cortices. Next, we analyzed subfield to subfield FC within different portions along the hippocampal anterior–posterior axis, and FC of each subfield portion with the neighboring cortical regions of interest. In general, the FC of the older adults was similar to that observed in the younger adults. We found that, as in the young group, the older group displayed intrinsic FC between the subfields that aligned with the tri‐synaptic circuit but also extended beyond it, and that FC between the subfields and neighboring cortical areas differed markedly along the anterior–posterior axis of each subfield. We observed only one significant difference between the young and older groups. Compared to the young group, the older participants had significantly reduced FC between the anterior CA1‐subiculum transition region and the transentorhinal cortex, two brain regions known to be disproportionately affected during the early stages of age‐related tau accumulation. Overall, these results contribute to ongoing efforts to characterize human hippocampal subfield connectivity, with implications for understanding hippocampal function and its modulation in the ageing brain.
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Affiliation(s)
- Marshall A Dalton
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Cornelia McCormick
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Flavia De Luca
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian A Clark
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
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23
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Dalton MA, McCormick C, Maguire EA. Differences in functional connectivity along the anterior-posterior axis of human hippocampal subfields. Neuroimage 2019; 192:38-51. [PMID: 30840906 PMCID: PMC6503073 DOI: 10.1016/j.neuroimage.2019.02.066] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 02/21/2019] [Accepted: 02/27/2019] [Indexed: 12/24/2022] Open
Abstract
There is a paucity of information about how human hippocampal subfields are functionally connected to each other and to neighbouring extra-hippocampal cortices. In particular, little is known about whether patterns of functional connectivity (FC) differ down the anterior-posterior axis of each subfield. Here, using high resolution structural MRI we delineated the hippocampal subfields in healthy young adults. This included the CA fields, separating DG/CA4 from CA3, separating the pre/parasubiculum from the subiculum, and also segmenting the uncus. We then used high resolution resting state functional MRI to interrogate FC. We first analysed the FC of each hippocampal subfield in its entirety, in terms of FC with other subfields and with the neighbouring regions, namely entorhinal, perirhinal, posterior parahippocampal and retrosplenial cortices. Next, we analysed FC for different portions of each hippocampal subfield along its anterior-posterior axis, in terms of FC between different parts of a subfield, FC with other subfield portions, and FC of each subfield portion with the neighbouring cortical regions of interest. We found that intrinsic functional connectivity between the subfields aligned generally with the tri-synaptic circuit but also extended beyond it. Our findings also revealed that patterns of functional connectivity between the subfields and neighbouring cortical areas differed markedly along the anterior-posterior axis of each hippocampal subfield. Overall, these results contribute to ongoing efforts to characterise human hippocampal subfield connectivity, with implications for understanding hippocampal function. High resolution resting state functional MRI scans were collected. We investigated functional connectivity (FC) of human hippocampal subfields. We specifically examined FC along the anterior-posterior axis of subfields. FC between subfields extended beyond the canonical tri-synaptic circuit. Different portions of subfields showed different patterns of FC with neocortex.
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Affiliation(s)
- Marshall A Dalton
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Cornelia McCormick
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK.
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24
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Bouyeure A, Germanaud D, Bekha D, Delattre V, Lefèvre J, Pinabiaux C, Mangin JF, Rivière D, Fischer C, Chiron C, Hertz-Pannier L, Noulhiane M. Three-Dimensional Probabilistic Maps of Mesial Temporal Lobe Structures in Children and Adolescents' Brains. Front Neuroanat 2018; 12:98. [PMID: 30498435 PMCID: PMC6249374 DOI: 10.3389/fnana.2018.00098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/29/2018] [Indexed: 11/13/2022] Open
Abstract
The hippocampus and the adjacent perirhinal, entorhinal, temporopolar, and parahippocampal cortices are interconnected in a hierarchical MTL system crucial for memory processes. A probabilistic description of the anatomical location and spatial variability of MTL cortices in the child and adolescent brain would help to assess structure-function relationships. The rhinal sulcus (RS) and the collateral sulcus (CS) that border MTL cortices and influence their morphology have never been described in these populations. In this study, we identified the aforementioned structures on magnetic resonance images of 38 healthy subjects aged 7-17 years old. Relative to sulcal morphometry in the MTL, we showed RS-CS conformation is an additional factor of variability in the MTL that is not explained by other variables such as age, sex and brain volume; with an innovative method using permutation testing of the extrema of structures of interest, we showed that RS-SC conformation was not associated with differences of location of MTL sulci. Relative to probabilistic maps, we offered for the first time a systematic mapping of MTL structures in children and adolescent, mapping all the structures of the MTL system while taking sulcal morphology into account. Our results, with the probabilistic maps described here being freely available for download, will help to understand the anatomy of this region and help functional and clinical studies to accurately test structure-function hypotheses in the MTL during development. Free access to MTL pediatric atlas: http://neurovault.org/collections/2381/.
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Affiliation(s)
- Antoine Bouyeure
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - David Germanaud
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
- Université Paris Diderot, Sorbonne Paris Cité, AP-HP, Hôpital Robert-Debré, DHU Protect, Service de Neurologie Pédiatrique et des Maladies Métaboliques, Paris, France
| | - Dhaif Bekha
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Victor Delattre
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Julien Lefèvre
- CNRS, ENSAM, LSIS UMR 7296, Aix Marseille University, Toulon University, Toulon, France
| | - Charlotte Pinabiaux
- Université Paris Ouest Nanterre La Défense, Laboratoire CHArt (EA 4004), Nanterre, France
| | | | - Denis Rivière
- CEA, University Paris Saclay, NeuroSpin, UNATI, Gif-sur-Yvette, France
| | - Clara Fischer
- CEA, University Paris Saclay, NeuroSpin, UNATI, Gif-sur-Yvette, France
| | - Catherine Chiron
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Lucie Hertz-Pannier
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Marion Noulhiane
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
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Ali S, Wörz S, Amunts K, Eils R, Axer M, Rohr K. Rigid and non-rigid registration of polarized light imaging data for 3D reconstruction of the temporal lobe of the human brain at micrometer resolution. Neuroimage 2018; 181:235-251. [PMID: 30018015 DOI: 10.1016/j.neuroimage.2018.06.084] [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: 04/06/2018] [Revised: 06/22/2018] [Accepted: 06/30/2018] [Indexed: 10/28/2022] Open
Abstract
To understand the spatial organization as well as long- and short-range connections of the human brain at microscopic resolution, 3D reconstruction of histological sections is important. We approach this challenge by reconstructing series of unstained histological sections of multi-scale (1.3μm and 64μm) and multi-modal 3D polarized light imaging (3D-PLI) data. Since spatial coherence is lost during the sectioning procedure, image registration is the major step in 3D reconstruction. We propose a non-rigid registration method which comprises of a novel multi-modal similarity metric and an improved regularization scheme to cope with deformations inevitably introduced during the sectioning procedure, as well as a rigid registration approach using a robust similarity metric for improved initial alignment. We also introduce a multi-scale feature-based localization and registration approach for mapping of 1.3μm sections to 64μm sections and a scale-adaptive method that can handle challenging sections with large semi-global deformations due to tissue splits. We have applied our registration method to 126 consecutive sections of the temporal lobe of the human brain with 64μm and 1.3μm resolution. Each step of the registration method was quantitatively evaluated using 10 different sections and manually determined ground truth, and a quantitative comparison with previous methods was performed. Visual assessment of the reconstructed volumes and comparison with reference volumes confirmed the high quality of the registration result.
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Affiliation(s)
- Sharib Ali
- Dept. of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, BIOQUANT, IPMB, University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany.
| | - Stefan Wörz
- Dept. of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, BIOQUANT, IPMB, University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine 1, Research Centre Jülich, Germany; Cécile and Oskar Vogt Institute of Brain Research, Heinrich Heine University Düsseldorf, University Hospital Düsseldorf, Germany
| | - Roland Eils
- Dept. of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, BIOQUANT, IPMB, University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany
| | - Markus Axer
- Institute of Neuroscience and Medicine 1, Research Centre Jülich, Germany
| | - Karl Rohr
- Dept. of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, BIOQUANT, IPMB, University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany
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26
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Berron D, Neumann K, Maass A, Schütze H, Fliessbach K, Kiven V, Jessen F, Sauvage M, Kumaran D, Düzel E. Age-related functional changes in domain-specific medial temporal lobe pathways. Neurobiol Aging 2018; 65:86-97. [DOI: 10.1016/j.neurobiolaging.2017.12.030] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 12/19/2017] [Accepted: 12/19/2017] [Indexed: 11/25/2022]
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Fischl B, Sereno MI. Microstructural parcellation of the human brain. Neuroimage 2018; 182:219-231. [PMID: 29496612 DOI: 10.1016/j.neuroimage.2018.01.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 12/27/2022] Open
Abstract
The human cerebral cortex is composed of a mosaic of areas thought to subserve different functions. The parcellation of the cortex into areas has a long history and has been carried out using different combinations of structural, connectional, receptotopic, and functional properties. Here we give a brief overview of the history of cortical parcellation, and explore different microstructural properties and analysis techniques that can be used to define the borders between different regions. We show that accounting for the 3D geometry of the highly folded human cortex is especially critical for accurate parcellation. We close with some thoughts on future directions and best practices for combining modalities.
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Affiliation(s)
- Bruce Fischl
- Department of Radiology, Harvard Medical School, United States; Athinoula A. Martinos Center for Biomedical Imaging Mass, General Hospital, United States; Division of Health Sciences and Technology and Engineering and Computer Science MIT, Cambridge, MA, United States.
| | - Martin I Sereno
- Department of Psychology, SDSU Imaging Center, San Diego State University, San Diego, CA 92182, United States.
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Yousaf T, Dervenoulas G, Politis M. Advances in MRI Methodology. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:31-76. [DOI: 10.1016/bs.irn.2018.08.008] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Girard H, Potvin O, Nugent S, Dallaire-Théroux C, Cunnane S, Duchesne S. Faster progression from MCI to probable AD for carriers of a single-nucleotide polymorphism associated with type 2 diabetes. Neurobiol Aging 2017; 64:157.e11-157.e17. [PMID: 29338921 DOI: 10.1016/j.neurobiolaging.2017.11.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 11/27/2022]
Abstract
Sporadic Alzheimer's disease (AD), as opposed to its autosomal dominant form, is likely caused by a complex interaction of genetic, environmental, and health lifestyle factors. Twin studies indicate that sporadic AD heritability could be between 58% and 79%, around half of which is explained by the ε4 allele of the apolipoprotein E (APOE4). We hypothesized that genes associated with known risk factors for AD, namely hypertension, hypercholesterolemia, obesity, diabetes, and cardiovascular disease, would contribute significantly to the remaining heritability. We analyzed 22 AD-associated single-nucleotide polymorphisms (SNPs), associated with these risk factors, that were included in the sequencing data of the Alzheimer's Disease Neuroimaging Initiative 1 data set, which included 355 participants with mild cognitive impairment (MCI). We built survival models with the selected SNPs to predict progression of MCI to probable AD over the 10-year follow-up of the study. The rs391300 SNP, located on the serine racemase (SRR) gene and linked to increased susceptibility to type 2 diabetes, was associated with progression from MCI to probable AD.
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Affiliation(s)
- Hugo Girard
- Centre de recherche CERVO de l'Institut universitaire en santé mentale de Québec, Québec, Canada; Département de radiologie, Faculté de médecine, Université Laval, Québec, Canada
| | - Olivier Potvin
- Centre de recherche CERVO de l'Institut universitaire en santé mentale de Québec, Québec, Canada
| | - Scott Nugent
- Centre de recherche CERVO de l'Institut universitaire en santé mentale de Québec, Québec, Canada
| | - Caroline Dallaire-Théroux
- Centre de recherche CERVO de l'Institut universitaire en santé mentale de Québec, Québec, Canada; Faculté de médecine, Université Laval, Québec, Canada
| | - Stephen Cunnane
- Département de médecine, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Canada
| | - Simon Duchesne
- Centre de recherche CERVO de l'Institut universitaire en santé mentale de Québec, Québec, Canada; Département de radiologie, Faculté de médecine, Université Laval, Québec, Canada.
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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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Eliassen CF, Reinvang I, Selnes P, Grambaite R, Fladby T, Hessen E. Biomarkers in subtypes of mild cognitive impairment and subjective cognitive decline. Brain Behav 2017; 7:e00776. [PMID: 28948074 PMCID: PMC5607543 DOI: 10.1002/brb3.776] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Revised: 06/07/2017] [Accepted: 06/12/2017] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Preclinical Alzheimers disease (AD) patients may or may not show cognitive impairment on testing. AD biomarkers are central to the identification of those at low, intermediate, or high risk of later dementia due to AD. We investigated biomarker distribution in those identified as subjective cognitive decline (SCD), amnestic (aMCI), and nonamnestic (naMCI) mild cognitive impairment (MCI) subtypes. In addition, the clinical groups were compared with controls on downstream neuroimaging markers. MATERIALS AND METHODS Cerebrospinal fluid (CSF) amyloid-β42 (A β42) and total tau (t-tau), phosphorylated tau (p-tau), fluorodeoxyglucose (FDG), positron-emission tomography (PET), and MRI neuroimaging measures were collected from 116 memory clinic patients. They were characterized as SCD, aMCI, and naMCI according to comprehensive neuropsychological criteria. ANOVAs were used to assess differences when biomarkers were treated as continuous variables and chi square analyses were used to assess group differences in distribution of biomarkers. RESULTS We did not find any between group differences in Aβ42, nor in p-tau, but we observed elevated t-tau in aMCI and SCD relative to the naMCI group. Significantly lower cortical glucose metabolism (as measured by FDG PET) was found in aMCI relative to SCD and controls, and there was a trend for lower metabolism in naMCI. Significant thinner entorhinal cortex (ERC) was found in aMCI and SCD. As expected biomarkers were significantly more frequently pathological in aMCI than in naMCI and SCD, whereas the naMCI and SCD groups displayed similar pathological biomarker burden. CONCLUSIONS aMCI cases show the most pathologic biomarker burden. Interestingly naMCI and SCD subjects show similar levels of pathological biomarkers albeit the former displayed neuropsychological deficits. That the latter group may represent a risk group is supported by our observation of both elevated CSF tau and thinner ERC relative to controls.
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Affiliation(s)
- Carl F Eliassen
- Department of Psychology University of Oslo Oslo Norway.,Department of Neurology Akershus University Hospital Lørenskog Norway
| | - Ivar Reinvang
- Department of Psychology University of Oslo Oslo Norway
| | - Per Selnes
- Department of Neurology Akershus University Hospital Lørenskog Norway
| | - Ramune Grambaite
- Department of Neurology Akershus University Hospital Lørenskog Norway
| | - Tormod Fladby
- Department of Neurology Akershus University Hospital Lørenskog Norway.,Institute of Clinical Medicine Campus Ahus University of Oslo Oslo Norway
| | - Erik Hessen
- Department of Psychology University of Oslo Oslo Norway.,Department of Neurology Akershus University Hospital Lørenskog Norway
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Strong Evidence for Pattern Separation in Human Dentate Gyrus. J Neurosci 2017; 36:7569-79. [PMID: 27445136 DOI: 10.1523/jneurosci.0518-16.2016] [Citation(s) in RCA: 152] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Accepted: 05/26/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED The hippocampus is proposed to be critical in distinguishing between similar experiences by performing pattern separation computations that create orthogonalized representations for related episodes. Previous neuroimaging studies have provided indirect evidence that the dentate gyrus (DG) and CA3 hippocampal subregions support pattern separation by inferring the nature of underlying representations from the observation of novelty signals. Here, we use ultra-high-resolution fMRI at 7 T and multivariate pattern analysis to provide compelling evidence that the DG subregion specifically sustains representations of similar scenes that are less overlapping than in other hippocampal (e.g., CA3) and medial temporal lobe regions (e.g., entorhinal cortex). Further, we provide evidence that novelty signals within the DG are stimulus specific rather than generic in nature. Our study, in providing a mechanistic link between novelty signals and the underlying representations, constitutes the first demonstration that the human DG performs pattern separation. SIGNIFICANCE STATEMENT A fundamental property of an episodic memory system is the ability to minimize interference between similar episodes. The dentate gyrus (DG) subregion of the hippocampus is widely viewed to realize this function through a computation referred to as pattern separation, which creates distinct nonoverlapping neural codes for individual events. Here, we leveraged 7 T fMRI to test the hypothesis that this region supports pattern separation. Our results demonstrate that the DG supports representations of similar scenes that are less overlapping than those in neighboring subregions. The current study therefore is the first to offer compelling evidence that the human DG supports pattern separation by obtaining critical empirical data at the representational level: the level where this computation is defined.
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Development of a histologically validated segmentation protocol for the hippocampal body. Neuroimage 2017; 157:219-232. [PMID: 28587896 DOI: 10.1016/j.neuroimage.2017.06.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 05/30/2017] [Accepted: 06/02/2017] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Recent findings have demonstrated that hippocampal subfields can be selectively affected in different disease states, which has led to efforts to segment the human hippocampus with in vivo magnetic resonance imaging (MRI). However, no studies have examined the histological accuracy of subfield segmentation protocols. The presence of MRI-visible anatomical landmarks with known correspondence to histology represents a fundamental prerequisite for in vivo hippocampal subfield segmentation. In the present study, we aimed to: 1) develop a novel method for hippocampal body segmentation, based on two MRI-visible anatomical landmarks (stratum lacunosum moleculare [SLM] & dentate gyrus [DG]), and assess its accuracy in comparison to the gold standard direct histological measurements; 2) quantify the accuracy of two published segmentation strategies in comparison to the histological gold standard; and 3) apply the novel method to ex vivo MRI and correlate the results with histology. METHODS Ultra-high resolution ex vivo MRI was performed on six whole cadaveric hippocampal specimens, which were then divided into 22 blocks and histologically processed. The hippocampal bodies were segmented into subfields based on histological criteria and subfield boundaries and areas were directly measured. A novel method was developed using mean percentage of the total SLM distance to define subfield boundaries. Boundary distances and subfield areas on histology were then determined using the novel method and compared to the gold standard histological measurements. The novel method was then used to determine ex vivo MRI measures of subfield boundaries and areas, which were compared to histological measurements. RESULTS For direct histological measurements, the mean percentages of total SLM distance were: Subiculum/CA1 = 9.7%, CA1/CA2 = 78.4%, CA2/CA3 = 97.5%. When applied to histology, the novel method provided accurate measures for CA1/CA2 (ICC = 0.93) and CA2/CA3 (ICC = 0.97) boundaries, but not for the Subiculum/CA1 (ICC = -0.04) boundary. Accuracy was poorer using previous techniques for CA1/CA2 (maximum ICC = 0.85) and CA2/CA3 (maximum ICC = 0.88), with the previously reported techniques also performing poorly in defining the Subiculum/CA1 boundary (maximum ICC = 0.00). Ex vivo MRI measurements using the novel method were linearly related to direct measurements of SLM length (r2 = 0.58), CA1/CA2 boundary (r2 = 0.39) and CA2/CA3 boundary (r2 = 0.47), but not for Subiculum/CA1 boundary (r2 = 0.01). Subfield areas measured with the novel method on histology and ex vivo MRI were linearly related to gold standard histological measures for CA1, CA2, and CA3/CA4/DG. CONCLUSIONS In this initial proof of concept study, we used ex vivo MRI and histology of cadaveric hippocampi to develop a novel segmentation protocol for the hippocampal body. The novel method utilized two anatomical landmarks, SLM & DG, and provided accurate measurements of CA1, CA2, and CA3/CA4/DG subfields in comparison to the gold standard histological measurements. The relationships demonstrated between histology and ex vivo MRI supports the potential feasibility of applying this method to in vivo MRI studies.
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Aganj I, Iglesias JE, Reuter M, Sabuncu MR, Fischl B. Mid-space-independent deformable image registration. Neuroimage 2017; 152:158-170. [PMID: 28242316 PMCID: PMC5432428 DOI: 10.1016/j.neuroimage.2017.02.055] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 02/20/2017] [Indexed: 11/20/2022] Open
Abstract
Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.
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Affiliation(s)
- Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Suite 2301, Charlestown, MA 02129, USA.
| | - Juan Eugenio Iglesias
- Translational Imaging Group, University College London, Malet Place Engineering Building, London WC1E 6BT, UK.
| | - Martin Reuter
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Suite 2301, Charlestown, MA 02129, USA; Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139, USA; German Center for Neurodegenerative Diseases (DZNE), Siegmund-Freud-Straße 27, 53127 Bonn, Germany.
| | - Mert Rory Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Suite 2301, Charlestown, MA 02129, USA; Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139, USA; School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, 300 Rhodes Hall, Ithaca, NY 14853, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Suite 2301, Charlestown, MA 02129, USA; Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139, USA; Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave., Room E25-519, Cambridge, MA 02139, USA.
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Potvin O, Dieumegarde L, Duchesne S. Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols. Neuroimage 2017; 156:43-64. [PMID: 28479474 DOI: 10.1016/j.neuroimage.2017.04.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 02/28/2017] [Accepted: 04/14/2017] [Indexed: 10/19/2022] Open
Abstract
We recently built normative data for FreeSurfer morphometric estimates of cortical regions using its default atlas parcellation (Desikan-Killiany or DK) according to individual and scanner characteristics. We aimed to produced similar normative values for Desikan-Killianny-Tourville (DKT) and ex vivo-based labeling protocols, as well as examine the differences between these three atlases. Surfaces, thicknesses, and volumes of cortical regions were produced using cross-sectional magnetic resonance scans from the same 2713 healthy individuals aged 18-94 years as used in the reported DK norms. Models predicting regional cortical estimates of each hemisphere were produced using age, sex, estimated intracranial volume (eTIV), scanner manufacturer and magnetic field strength (MFS) as predictors. The DKT and DK models generally included the same predictors and produced similar R2. Comparison between DK, DKT, ex vivo atlases normative cortical measures showed that the three protocols generally produced similar normative values.
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Affiliation(s)
- Olivier Potvin
- Centre de recherche CERVO Research Centre, 2601, de la Canardière, Québec, Canada G1J 2G3
| | - Louis Dieumegarde
- Centre de recherche CERVO Research Centre, 2601, de la Canardière, Québec, Canada G1J 2G3
| | - Simon Duchesne
- Centre de recherche CERVO Research Centre, 2601, de la Canardière, Québec, Canada G1J 2G3; Département de radiologie, Faculté de médecine, Université Laval, 1050, avenue de la Médecine, Québec, Canada G1V 0A6.
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Almdahl IS, Lauridsen C, Selnes P, Kalheim LF, Coello C, Gajdzik B, Møller I, Wettergreen M, Grambaite R, Bjørnerud A, Bråthen G, Sando SB, White LR, Fladby T. Cerebrospinal Fluid Levels of Amyloid Beta 1-43 Mirror 1-42 in Relation to Imaging Biomarkers of Alzheimer's Disease. Front Aging Neurosci 2017; 9:9. [PMID: 28223932 PMCID: PMC5293760 DOI: 10.3389/fnagi.2017.00009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 01/12/2017] [Indexed: 11/24/2022] Open
Abstract
Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed. Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent 18F-Flutemetamol PET. Seventy-eight subjects were assessed with 18F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging. Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with 18F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing. Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.
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Affiliation(s)
- Ina S Almdahl
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Camilla Lauridsen
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology Trondheim, Norway
| | - Per Selnes
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Lisa F Kalheim
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Christopher Coello
- Preclinical PET/CT, Institute of Basic Medical Sciences, University of Oslo Oslo, Norway
| | | | - Ina Møller
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim Trondheim, Norway
| | - Marianne Wettergreen
- Department of Neurology, Akershus University HospitalLørenskog, Norway; Department of Clinical Molecular Biology (EpiGen), Institute of Clinical Medicine, University of Oslo - Akershus University HospitalLørenskog, Norway
| | - Ramune Grambaite
- Department of Neurology, Akershus University Hospital Lørenskog, Norway
| | - Atle Bjørnerud
- The Intervention Centre, Oslo University Hospital Oslo, Norway
| | - Geir Bråthen
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Sigrid B Sando
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Linda R White
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Tormod Fladby
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
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Wisse LE, Daugherty AM, Olsen RK, Berron D, Carr VA, Stark CE, Amaral RS, Amunts K, Augustinack JC, Bender AR, Bernstein JD, Boccardi M, Bocchetta M, Burggren A, Chakravarty MM, Chupin M, Ekstrom A, de Flores R, Insausti R, Kanel P, Kedo O, Kennedy KM, Kerchner GA, LaRocque KF, Liu X, Maass A, Malykhin N, Mueller SG, Ofen N, Palombo DJ, Parekh MB, Pluta JB, Pruessner JC, Raz N, Rodrigue KM, Schoemaker D, Shafer AT, Steve TA, Suthana N, Wang L, Winterburn JL, Yassa MA, Yushkevich PA, la Joie R. A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus 2017; 27:3-11. [PMID: 27862600 PMCID: PMC5167633 DOI: 10.1002/hipo.22671] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 10/06/2016] [Accepted: 10/17/2016] [Indexed: 01/08/2023]
Abstract
The advent of high-resolution magnetic resonance imaging (MRI) has enabled in vivo research in a variety of populations and diseases on the structure and function of hippocampal subfields and subdivisions of the parahippocampal gyrus. Because of the many extant and highly discrepant segmentation protocols, comparing results across studies is difficult. To overcome this barrier, the Hippocampal Subfields Group was formed as an international collaboration with the aim of developing a harmonized protocol for manual segmentation of hippocampal and parahippocampal subregions on high-resolution MRI. In this commentary we discuss the goals for this protocol and the associated key challenges involved in its development. These include differences among existing anatomical reference materials, striking the right balance between reliability of measurements and anatomical validity, and the development of a versatile protocol that can be adopted for the study of populations varying in age and health. The commentary outlines these key challenges, as well as the proposed solution of each, with concrete examples from our working plan. Finally, with two examples, we illustrate how the harmonized protocol, once completed, is expected to impact the field by producing measurements that are quantitatively comparable across labs and by facilitating the synthesis of findings across different studies. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Laura E.M. Wisse
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ana M. Daugherty
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Champaign, USA
| | - Rosanna K. Olsen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - David Berron
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany
| | - Valerie A. Carr
- Department of Psychology, Stanford University, Palo Alto, USA
- Department of Psychology, San Jose State University, San Jose, USA
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
| | - Robert S.C. Amaral
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jean C. Augustinack
- AA Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, USA
| | - Andrew R. Bender
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Jeffrey D. Bernstein
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | - Marina Boccardi
- LANVIE Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Alison Burggren
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Marie Chupin
- INSERM, CNRS, UMR-S975, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
| | - Arne Ekstrom
- Center for Neuroscience, University of California Davis, Davis, USA
- Department of Psychology, University of California Davis, Davis, USA
| | - Robin de Flores
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory and C.R.I.B., School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Prabesh Kanel
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Olga Kedo
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
| | - Kristen M. Kennedy
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Geoffrey A. Kerchner
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | | | - Xiuwen Liu
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Anne Maass
- School of Public Health and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, USA
| | - Nicolai Malykhin
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
- The Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Susanne G. Mueller
- Department of Radiology, University of California, San Francisco, USA
- Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, USA
| | - Noa Ofen
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | | | - Mansi B. Parekh
- Department of Radiology, Stanford University, Palo Alto, USA
| | - John B. Pluta
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jens C. Pruessner
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Naftali Raz
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Karen M. Rodrigue
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Dorothee Schoemaker
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Andrea T. Shafer
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Trevor A. Steve
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, USA
| | - Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Julie L. Winterburn
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
- Department of Neurology, University of California Irvine, Irvine, USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Renaud la Joie
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
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Dickerson BC, Brickhouse M, McGinnis S, Wolk DA. Alzheimer's disease: The influence of age on clinical heterogeneity through the human brain connectome. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2016; 6:122-135. [PMID: 28239637 PMCID: PMC5318292 DOI: 10.1016/j.dadm.2016.12.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION One major factor that influences the heterogeneity of Alzheimer's disease (AD) is age: younger AD patients more frequently exhibit atypical forms of AD. We propose that this age-related heterogeneity can be understood better by considering age-related differences in atrophy in the context of large-scale brain networks subserving cognitive functions that contribute to memory. METHODS We examined data from 75 patients with mild AD dementia from Alzheimer's Disease Neuroimaging Initiative. These individuals were chosen because they have cerebrospinal fluid amyloid and p-tau levels in the range suggesting the presence of AD neuropathology, and because they were either younger than age 65 years early-onset AD (EOAD) or age 80 years or older late-onset AD (LOAD). RESULTS In the EOAD group, the most prominent atrophy was present in the posterior cingulate cortex, whereas in the LOAD group, atrophy was most prominent in the medial temporal lobe. Structural covariance analysis showed that the magnitude of atrophy in these epicenters is strongly correlated with a distributed atrophy pattern similar to distinct intrinsic connectivity networks in the healthy brain. An examination of memory performance in EOAD dementia versus LOAD dementia demonstrated relatively more prominent impairment in encoding in the EOAD group than in the LOAD group, with similar performance in memory storage in LOAD and EOAD but greater impairment in semantic memory in LOAD than in EOAD. DISCUSSION The observations provide novel insights about age as a major factor contributing to the heterogeneity in the topography of AD-related cortical atrophy.
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Affiliation(s)
- Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Michael Brickhouse
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Scott McGinnis
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Zhou M, Zhang F, Zhao L, Qian J, Dong C. Entorhinal cortex: a good biomarker of mild cognitive impairment and mild Alzheimer's disease. Rev Neurosci 2016; 27:185-95. [PMID: 26444348 DOI: 10.1515/revneuro-2015-0019] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 08/01/2015] [Indexed: 11/15/2022]
Abstract
Entorhinal cortex (EC), thought to be the location of the earliest lesions in Alzheimer's disease (AD), has been widely studied in recent years. With the irreversible pathological changes of AD, there is an urgent need to find biomarkers that can be used to predict the presence of the disease before it is clinically expressed. The aim of this review is to summarize and analyze recent findings that are relevant to the important role of EC in the diagnosis of mild cognitive impairment (MCI) and mild AD and to describe a range of neuroimaging techniques used to define the EC boundary. A comprehensive literature search for articles published up to May 2015 was performed. Our research highlights the finding that atrophy in EC reflects the early pathological changes of AD and can be a strong predictor of prodromal AD. The early changes in EC are a good imaging biomarker that can be used to discriminate individuals with MCI from normal control subjects. A larger degree of atrophy in EC predicts increased disease severity, and the right EC in patients with mild AD exhibited greater changes than the left side. In addition, the EC seems to have an obvious advantage over the hippocampus as a biomarker when predicting future conversion to AD in individuals with MCI, and it may be of help in following the course of disease progression. In this review, we also summarize the main differences observed between the hippocampus and the EC when differentiating diseases. These findings will hopefully provide an opportunity for the effective prevention and early treatment of AD.
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Xie L, Pluta JB, Das SR, Wisse LEM, Wang H, Mancuso L, Kliot D, Avants BB, Ding SL, Manjón JV, Wolk DA, Yushkevich PA. Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability. Neuroimage 2016; 144:183-202. [PMID: 27702610 DOI: 10.1016/j.neuroimage.2016.09.070] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 09/28/2016] [Accepted: 09/30/2016] [Indexed: 01/05/2023] Open
Abstract
RATIONAL The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). RESULTS The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - John B Pluta
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | | | - Lauren Mancuso
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Dasha Kliot
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Brian B Avants
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, USA; School of Basic Sciences, Guangzhou Medical University, Guangzhou, China
| | - José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Schwarz CG, Gunter JL, Wiste HJ, Przybelski SA, Weigand SD, Ward CP, Senjem ML, Vemuri P, Murray ME, Dickson DW, Parisi JE, Kantarci K, Weiner MW, Petersen RC, Jack CR. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. NEUROIMAGE-CLINICAL 2016; 11:802-812. [PMID: 28050342 PMCID: PMC5187496 DOI: 10.1016/j.nicl.2016.05.017] [Citation(s) in RCA: 224] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 04/29/2016] [Accepted: 05/27/2016] [Indexed: 01/07/2023]
Abstract
Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12. Evaluate thickness- and volume-based quantitative measures of AD severity Volume- and thickness-based measures perform similarly for separating by diagnosis. Volume-based measures are correlated with head size; thickness-based mostly aren't. We recommend an AD signature measure formed from cortical thickness measures. We recommend thicknesses using ANTs software with input segmentations from SPM12.
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Affiliation(s)
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Chadwick P Ward
- Department of Radiology, 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
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Melissa E Murray
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Dennis W Dickson
- Department of Neuroscience (Neuropathology), Mayo Clinic and Foundation, Jacksonville, FL, USA
| | - Joseph E Parisi
- Department of Laboratory Medicine, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Veterans Affairs, University of California, San Francisco, CA, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
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Three-dimensional probability maps of the rhinal and the collateral sulci in the human brain. Brain Struct Funct 2016; 221:4235-4255. [DOI: 10.1007/s00429-016-1189-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Accepted: 01/12/2016] [Indexed: 10/21/2022]
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Zeidman P, Maguire EA. Anterior hippocampus: the anatomy of perception, imagination and episodic memory. Nat Rev Neurosci 2016; 17:173-82. [PMID: 26865022 PMCID: PMC5358751 DOI: 10.1038/nrn.2015.24] [Citation(s) in RCA: 316] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The brain creates a model of the world around us. We can use this representation to perceive and comprehend what we see at any given moment, but also to vividly re-experience scenes from our past and imagine future (or even fanciful) scenarios. Recent work has shown that these cognitive functions--perception, imagination and recall of scenes and events--all engage the anterior hippocampus. In this Opinion article, we capitalize on new findings from functional neuroimaging to propose a model that links high-level cognitive functions to specific structures within the anterior hippocampus.
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Affiliation(s)
- Peter Zeidman
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK
| | - Eleanor A. Maguire
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK
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Eliassen CF, Selnes P, Selseth Almdahl I, Reinvang I, Fladby T, Hessen E. Hippocampal Subfield Atrophy in Multi-Domain but Not Amnestic Mild Cognitive Impairment. Dement Geriatr Cogn Disord 2016; 40:44-53. [PMID: 25924735 DOI: 10.1159/000381142] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/17/2015] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS To investigate differences in hippocampal (HP) subfields and the adjoining perirhinal and entorhinal cortices (PRC and ERC) between amnestic mild cognitive impairment (aMCI) and multi-domain amnestic MCI (mdMCI) patients, and controls. METHODS Nineteen patients characterized as aMCI were compared with 24 mdMCI patients and 31 controls by means of an automatic HP segmentation procedure. RESULTS We found significant atrophy of the PRC and ERC in aMCI relative to controls, whereas a more pronounced pattern of atrophy in most subfields, including total HP volume, was found in the mdMCI group. The mdMCI group also had a significant cornu ammonis sector 4 region with dentate gyrus, subiculum and total HP atrophy relative to aMCI. CONCLUSION The aMCI group showed atrophy in the PRC and ERC, whereas significantly more affection of the HP subfields was evident in mdMCI. The mdMCI group may thus represent clinical progression relative to aMCI coupled with HP subfield affection.
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Hippocampal subfield atrophy in relation to cerebrospinal fluid biomarkers and cognition in early Parkinson's disease: a cross-sectional study. NPJ PARKINSONS DISEASE 2016; 2:15030. [PMID: 28725691 PMCID: PMC5516586 DOI: 10.1038/npjparkd.2015.30] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 10/23/2015] [Accepted: 12/10/2015] [Indexed: 11/14/2022]
Abstract
Cognition is often affected early in Parkinson’s disease (PD). Lewy body and amyloid β (Aβ) pathology and cortical atrophy may be involved. The aim of this study was to examine whether medial temporal lobe structural changes may be linked to cerebrospinal fluid (CSF) biomarker levels and cognition in early PD. PD patients had smaller volumes of total hippocampus, presubiculum, subiculum, CA2–3, CA4-DG, and hippocampal tail compared with normal controls (NCs). In the PD group, lower CSF Aβ38 and 42 were significant predictors for thinner perirhinal cortex. Lower Aβ42 and smaller presubiculum and subiculum predicted poorer verbal learning and delayed verbal recall. Smaller total hippocampus, presubiculum and subiculum predicted poorer visuospatial copying. Lower Aβ38 and 40 and thinner perirhinal cortex predicted poorer delayed visual reproduction. In conclusion, smaller volumes of hippocampal subfields and subhippocampal cortex thickness linked to lower CSF Aβ levels may contribute to cognitive impairment in early PD. Thirty-three early PD patients (13 without, 5 with subjective, and 15 with mild cognitive impairment) and NC had 3 T magnetic resonance imaging (MRI) scans. The MRI scans were post processed for volumes of hippocampal subfields and entorhinal and perirhinal cortical thickness. Lumbar puncture for CSF biomarkers Aβ38, 40, 42, total tau, phosphorylated tau (Innogenetics), and total α-synuclein (Meso Scale Diagnostics) were performed. Multiple regression analyses were used for between-group comparisons of the MRI measurements in the NC and PD groups and for assessment of CSF biomarkers and neuropsychological tests in relation to morphometry in the PD group.
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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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Interaction between BDNF rs6265 Met allele and low family cohesion is associated with smaller left hippocampal volume in pediatric bipolar disorder. J Affect Disord 2016; 189:94-7. [PMID: 26432032 PMCID: PMC4733573 DOI: 10.1016/j.jad.2015.09.031] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 09/10/2015] [Accepted: 09/18/2015] [Indexed: 02/07/2023]
Abstract
BACKGROUND Genetic and environmental factors are implicated in the onset and evolution of pediatric bipolar disorder, and may be associated to structural brain abnormalities. The aim of our study was to assess the impact of the interaction between the Brain-Derived Neurotrophic Factor (BDNF) rs6265 polymorphism and family functioning on hippocampal volumes of children and adolescents with bipolar disorder, and typically-developing controls. METHODS We evaluated the family functioning cohesion subscale using the Family Environment Scale-Revised, genotyped the BDNF rs6265 polymorphism, and performed structural brain imaging in 29 children and adolescents with bipolar disorder, and 22 healthy controls. RESULTS We did not find significant differences between patients with BD or controls in left or right hippocampus volume (p=0.44, and p=0.71, respectively). However, we detected a significant interaction between low scores on the cohesion subscale and the presence of the Met allele at BNDF on left hippocampal volume of patients with bipolar disorder (F=3.4, p=0.043). None of the factors independently (BDNF Val66Met, cohesion scores) was significantly associated with hippocampal volume differences. LIMITATIONS small sample size, cross-sectional study. CONCLUSIONS These results may lead to a better understanding of the impact of the interaction between genes and environment factors on brain structures associated to bipolar disorder and its manifestations.
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Architectonic Mapping of the Human Brain beyond Brodmann. Neuron 2015; 88:1086-1107. [DOI: 10.1016/j.neuron.2015.12.001] [Citation(s) in RCA: 266] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 10/13/2015] [Accepted: 11/20/2015] [Indexed: 12/25/2022]
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Hippocampal complex atrophy in poststroke and mild cognitive impairment. J Cereb Blood Flow Metab 2015; 35:1729-37. [PMID: 26036934 PMCID: PMC4635227 DOI: 10.1038/jcbfm.2015.110] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2015] [Revised: 03/28/2015] [Accepted: 05/04/2015] [Indexed: 11/09/2022]
Abstract
To investigate putative interacting or distinct pathways for hippocampal complex substructure (HCS) atrophy and cognitive affection in early-stage Alzheimer's disease (AD) and cerebrovascular disease (CVD), we recruited healthy controls, patients with mild cognitive impairment (MCI) and poststroke patients. HCSs were segmented, and quantitative white-matter hyperintensity (WMH) load and cerebrospinal fluid (CSF) amyloid-β concentrations were determined. The WMH load was higher poststroke. All examined HCSs were smaller in amyloid-positive MCI than in controls, and the subicular regions were smaller poststroke. Memory was reduced in amyloid-positive MCI, and psychomotor speed and executive function were reduced in poststroke and amyloid-positive MCI. Size of several HCS correlated with WMH load poststroke and with CSF amyloid-β concentrations in MCI. In poststroke and amyloid-positive MCI, neuropsychological function correlated with WMH load and hippocampal volume. There are similar patterns of HCS atrophy in CVD and early-stage AD, but different HCS associations with WMH and CSF biomarkers. WMHs add to hippocampal atrophy and the archetypal AD deficit delayed recall. In line with mounting evidence of a mechanistic link between primary AD pathology and CVD, these additive effects suggest interacting pathologic processes.
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