1
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Parent O, Bussy A, Devenyi GA, Dai A, Costantino M, Tullo S, Salaciak A, Bedford S, Farzin S, Béland ML, Valiquette V, Villeneuve S, Poirier J, Tardif CL, Dadar M, Chakravarty MM. Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging. Brain Commun 2023; 5:fcad279. [PMID: 37953840 PMCID: PMC10636521 DOI: 10.1093/braincomms/fcad279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/05/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.
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Affiliation(s)
- Olivier Parent
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Aurélie Bussy
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Gabriel Allan Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Dai
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Salaciak
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Saashi Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sarah Farzin
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Marie-Lise Béland
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Vanessa Valiquette
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sylvia Villeneuve
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Judes Poirier
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Molecular Neurobiology Unit, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Medicine, McGill University, Montreal, Quebec H4A 3J1, Canada
| | - Christine Lucas Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Mahsa Dadar
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
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2
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Tullo S, Miranda AS, Del Cid-Pellitero E, Lim MP, Gallino D, Attaran A, Patel R, Novikov V, Park M, Beraldo FH, Luo W, Shlaifer I, Durcan TM, Bussey TJ, Saksida LM, Fon EA, Prado VF, Prado MAM, Chakravarty MM. Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms. J Neurochem 2023. [PMID: 37804203 DOI: 10.1111/jnc.15967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/31/2023] [Accepted: 09/11/2023] [Indexed: 10/09/2023]
Abstract
Significant evidence suggests that misfolded alpha-synuclein (aSyn), a major component of Lewy bodies, propagates in a prion-like manner contributing to disease progression in Parkinson's disease (PD) and other synucleinopathies. In fact, timed inoculation of M83 hemizygous mice with recombinant human aSyn preformed fibrils (PFF) has shown symptomatic deficits after substantial spreading of pathogenic alpha-synuclein, as detected by markers for the phosphorylation of S129 of aSyn. However, whether accumulated toxicity impact human-relevant cognitive and structural neuroanatomical measures is not fully understood. Here we performed a single unilateral striatal PFF injection in M83 hemizygous mice, and using two assays with translational potential, ex vivo magnetic resonance imaging (MRI) and touchscreen testing, we examined the combined neuroanatomical and behavioral impact of aSyn propagation. In PFF-injected mice, we observed widespread atrophy in bilateral regions that project to or receive input from the injection site using MRI. We also identified early deficits in reversal learning prior to the emergence of motor symptoms. Our findings highlight a network of regions with related cellular correlates of pathology that follow the progression of aSyn spreading, and that affect brain areas relevant for reversal learning. Our experiments suggest that M83 hemizygous mice injected with human PFF provides a model to understand how misfolded aSyn affects human-relevant pre-clinical measures and suggest that these pre-clinical biomarkers could be used to detect early toxicity of aSyn and provide better translational measures between mice and human disease.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Aline S Miranda
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Departamento de Morfologia, Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Esther Del Cid-Pellitero
- McGill Parkinson Program, Neurodegenerative Diseases Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Mei Peng Lim
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Daniel Gallino
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Anoosha Attaran
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Raihaan Patel
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
- Department of Biological & Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Vladislav Novikov
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Megan Park
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Flavio H Beraldo
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Wen Luo
- Early Drug Discovery Unit, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Irina Shlaifer
- Early Drug Discovery Unit, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Thomas M Durcan
- Early Drug Discovery Unit, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Timothy J Bussey
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Lisa M Saksida
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Edward A Fon
- McGill Parkinson Program, Neurodegenerative Diseases Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Vania F Prado
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Anatomy & Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Marco A M Prado
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
- Department of Anatomy & Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
- Department of Biological & Biomedical Engineering, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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3
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Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T, Lai MC, Lombardo MV, Di Martino A, Devenyi GA, Chakravarty MM, Alexander-Bloch AF, Seidlitz J, Baron-Cohen S, Bethlehem RA. The impact of quality control on cortical morphometry comparisons in autism. Imaging Neurosci (Camb) 2023; 1:1-21. [PMID: 38495338 PMCID: PMC10938341 DOI: 10.1162/imag_a_00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/11/2023] [Accepted: 09/13/2023] [Indexed: 03/19/2024]
Abstract
Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Alfredo Ortiz-Rosa
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
| | - Jenna M. Schabdach
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Tom Piercy
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | | | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - M. Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, McGill University, Montreal, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Aaron F. Alexander-Bloch
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough, United Kingdom
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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4
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MacDonald DN, Bedford SA, Olafson E, Park MTM, Devenyi GA, Tullo S, Patel R, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Smith E, Shinohara RT, Spencer MD, Suckling J, Taylor MJ, Thurm A, Lai MC, Chakravarty MM. Characterizing Subcortical Structural Heterogeneity in Autism. bioRxiv 2023:2023.08.28.554882. [PMID: 37693556 PMCID: PMC10491091 DOI: 10.1101/2023.08.28.554882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Autism presents with significant phenotypic and neuroanatomical heterogeneity, and neuroimaging studies of the thalamus, globus pallidus and striatum in autism have produced inconsistent and contradictory results. These structures are critical mediators of functions known to be atypical in autism, including sensory gating and motor function. We examined both volumetric and fine-grained localized shape differences in autism using a large (n=3145, 1045-1318 after strict quality control), cross-sectional dataset of T1-weighted structural MRI scans from 32 sites, including both males and females (assigned-at-birth). We investigated three potentially important sources of neuroanatomical heterogeneity: sex, age, and intelligence quotient (IQ), using a meta-analytic technique after strict quality control to minimize non-biological sources of variation. We observed no volumetric differences in the thalamus, globus pallidus, or striatum in autism. Rather, we identified a variety of localized shape differences in all three structures. Including age, but not sex or IQ, in the statistical model improved the fit for both the pallidum and striatum, but not for the thalamus. Age-centered shape analysis indicated a variety of age-dependent regional differences. Overall, our findings help confirm that the neurodevelopment of the striatum, globus pallidus and thalamus are atypical in autism, in a subtle location-dependent manner that is not reflected in overall structure volumes, and that is highly non-uniform across the lifespan.
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Affiliation(s)
- David N. MacDonald
- Integrated Program in Neuroscience, McGill University
- Cerebral Imaging Centre, Douglas Mental Health University Institute
| | - Saashi A. Bedford
- Integrated Program in Neuroscience, McGill University
- Cerebral Imaging Centre, Douglas Mental Health University Institute
- Autism Research Centre, Department of Psychiatry, University of Cambridge
| | - Emily Olafson
- Cerebral Imaging Centre, Douglas Mental Health University Institute
- Department of Neuroscience, Weill Cornell Graduate School of Medical Sciences
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute
- Department of Psychiatry, McGill University
| | - Stephanie Tullo
- Integrated Program in Neuroscience, McGill University
- Cerebral Imaging Centre, Douglas Mental Health University Institute
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute
- Department of Biological and Biomedical Engineering, McGill University
| | | | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge
| | | | - Lindsay R. Chura
- Autism Research Centre, Department of Psychiatry, University of Cambridge
| | - Michael C. Craig
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London
- National Autism Unit, Bethlem Royal Hospital, London, UK
| | - Christine Ecker
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, GoetheUniversity
| | - Dorothea L. Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich,Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Rosemary J. Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge
| | - Rhoshel Lenroot
- Dept.of Psychiatry and Behavioral Sciences, University of New Mexico
| | - Jason P. Lerch
- Program in Neurosciences and Mental Health, The Hospital for Sick Children
- Department of Medical Biophysics, University of Toronto
- Wellcome Centre for Integrative Neuroimaging, University of Oxford
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia
| | | | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of MentalHealth
| | - Amber N. V. Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
| | - Elizabeth Smith
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Michael D. Spencer
- Autism Research Centre, Department of Psychiatry, University of Cambridge
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge
| | - Margot J. Taylor
- Program in Neurosciences and Mental Health, The Hospital for Sick Children
- Diagnostic Imaging, The Hospital for Sick Children
| | - Audrey Thurm
- Section on Behavioral Pediatrics, National Institute of Mental Health
| | | | - Meng-Chuan Lai
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto
- Autism Research Centre, Department of Psychiatry, University of Cambridge
- Program in Neurosciences and Mental Health, The Hospital for Sick Children
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine
| | - M. Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University
- Cerebral Imaging Centre, Douglas Mental Health University Institute
- Department of Psychiatry, McGill University
- Department of Biological and Biomedical Engineering, McGill University
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5
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Parent O, Olafson E, Bussy A, Tullo S, Blostein N, Dai A, Salaciak A, Bedford SA, Farzin S, Béland ML, Valiquette V, Tardif CL, Devenyi GA, Chakravarty MM. High spatial overlap but diverging age-related trajectories of cortical magnetic resonance imaging markers aiming to represent intracortical myelin and microstructure. Hum Brain Mapp 2023; 44:3023-3044. [PMID: 36896711 PMCID: PMC10171508 DOI: 10.1002/hbm.26259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/15/2022] [Accepted: 02/18/2023] [Indexed: 03/11/2023] Open
Abstract
Statistical effects of cortical metrics derived from standard T1- and T2-weighted magnetic resonance imaging (MRI) images, such as gray-white matter contrast (GWC), boundary sharpness coefficient (BSC), T1-weighted/T2-weighted ratio (T1w/T2w), and cortical thickness (CT), are often interpreted as representing or being influenced by intracortical myelin content with little empirical evidence to justify these interpretations. We first examined spatial correspondence with more biologically specific microstructural measures, and second compared between-marker age-related trends with the underlying hypothesis that different measures primarily driven by similar changes in myelo- and microstructural underpinnings should be highly related. Cortical MRI markers were derived from MRI images of 127 healthy subjects, aged 18-81, using cortical surfaces that were generated with the CIVET 2.1.0 pipeline. Their gross spatial distributions were compared with gene expression-derived cell-type densities, histology-derived cytoarchitecture, and quantitative R1 maps acquired on a subset of participants. We then compared between-marker age-related trends in their shape, direction, and spatial distribution of the linear age effect. The gross anatomical distributions of cortical MRI markers were, in general, more related to myelin and glial cells than neuronal indicators. Comparing MRI markers, our results revealed generally high overlap in spatial distribution (i.e., group means), but mostly divergent age trajectories in the shape, direction, and spatial distribution of the linear age effect. We conclude that the microstructural properties at the source of spatial distributions of MRI cortical markers can be different from microstructural changes that affect these markers in aging.
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Affiliation(s)
- Olivier Parent
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, New York City, New York, USA
| | - Aurélie Bussy
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Nadia Blostein
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Electrical Engineering Department, Polytechnique Montreal, Montreal, Quebec, Canada
| | - Alyssa Dai
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Saashi A Bedford
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sarah Farzin
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Marie-Lise Béland
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Vanessa Valiquette
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Christine L Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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6
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Pascoal TA, Chamoun M, Lax E, Wey HY, Shin M, Ng KP, Kang MS, Mathotaarachchi S, Benedet AL, Therriault J, Lussier FZ, Schroeder FA, DuBois JM, Hightower BG, Gilbert TM, Zürcher NR, Wang C, Hopewell R, Chakravarty M, Savard M, Thomas E, Mohaddes S, Farzin S, Salaciak A, Tullo S, Cuello AC, Soucy JP, Massarweh G, Hwang H, Kobayashi E, Hyman BT, Dickerson BC, Guiot MC, Szyf M, Gauthier S, Hooker JM, Rosa-Neto P. [ 11C]Martinostat PET analysis reveals reduced HDAC I availability in Alzheimer's disease. Nat Commun 2022; 13:4171. [PMID: 35853847 PMCID: PMC9296476 DOI: 10.1038/s41467-022-30653-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 05/04/2022] [Indexed: 11/26/2022] Open
Abstract
Alzheimer’s disease (AD) is characterized by the brain accumulation of amyloid-β and tau proteins. A growing body of literature suggests that epigenetic dysregulations play a role in the interplay of hallmark proteinopathies with neurodegeneration and cognitive impairment. Here, we aim to characterize an epigenetic dysregulation associated with the brain deposition of amyloid-β and tau proteins. Using positron emission tomography (PET) tracers selective for amyloid-β, tau, and class I histone deacetylase (HDAC I isoforms 1–3), we find that HDAC I levels are reduced in patients with AD. HDAC I PET reduction is associated with elevated amyloid-β PET and tau PET concentrations. Notably, HDAC I reduction mediates the deleterious effects of amyloid-β and tau on brain atrophy and cognitive impairment. HDAC I PET reduction is associated with 2-year longitudinal neurodegeneration and cognitive decline. We also find HDAC I reduction in the postmortem brain tissue of patients with AD and in a transgenic rat model expressing human amyloid-β plus tau pathology in the same brain regions identified in vivo using PET. These observations highlight HDAC I reduction as an element associated with AD pathophysiology. The link between amyloid and tau proteins with Alzheimer’s disease progression remains unclear. Here, the authors propose HDACs I downregulation as an element linking the deleterious effects of brain proteinopathies with disease progression.
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Affiliation(s)
- Tharick A Pascoal
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Departments of Psychiatry and Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Departments of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Elad Lax
- Department of Molecular Biology, Ariel University, Ariel, Israel.,Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Hsiao-Ying Wey
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Kok Pin Ng
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Frederick A Schroeder
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jonathan M DuBois
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Baileigh G Hightower
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Tonya M Gilbert
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Nicole R Zürcher
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Changning Wang
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Robert Hopewell
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mallar Chakravarty
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Emilie Thomas
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Sara Mohaddes
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Sarah Farzin
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Alyssa Salaciak
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Stephanie Tullo
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - A Claudio Cuello
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bradford C Dickerson
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.,Department of Psychology, McGill University, Montreal, QC, Canada
| | | | - Moshe Szyf
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jacob M Hooker
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada. .,Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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7
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Germann J, Gouveia FV, Brentani H, Bedford SA, Tullo S, Chakravarty MM, Devenyi GA. Involvement of the habenula in the pathophysiology of autism spectrum disorder. Sci Rep 2021; 11:21168. [PMID: 34707133 PMCID: PMC8551275 DOI: 10.1038/s41598-021-00603-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 10/13/2021] [Indexed: 11/09/2022] Open
Abstract
The habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6-30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.
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Affiliation(s)
- Jürgen Germann
- grid.231844.80000 0004 0474 0428University Health Network, 399 Bathurst Street, Toronto, ON Canada ,grid.14709.3b0000 0004 1936 8649Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC Canada
| | - Flavia Venetucci Gouveia
- grid.42327.300000 0004 0473 9646Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON Canada
| | - Helena Brentani
- grid.11899.380000 0004 1937 0722Department of Psychiatry, University of Sao Paulo, Medical School, São Paulo, São Paulo Brazil ,grid.500696.cNational Institute of Developmental Psychiatry for Children and Adolescents, CNPq, São Paulo, São Paulo Brazil
| | - Saashi A. Bedford
- grid.14709.3b0000 0004 1936 8649Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC Canada ,grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Stephanie Tullo
- grid.14709.3b0000 0004 1936 8649Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC Canada ,grid.14709.3b0000 0004 1936 8649Integrated Program in Neuroscience, McGill University, Montreal, QC Canada
| | - M. Mallar Chakravarty
- grid.14709.3b0000 0004 1936 8649Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC Canada ,grid.14709.3b0000 0004 1936 8649Department of Biomedical Engineering, McGill University, Montreal, QC Canada ,grid.14709.3b0000 0004 1936 8649Department of Psychiatry, McGill University, Montreal, QC Canada
| | - Gabriel A. Devenyi
- grid.14709.3b0000 0004 1936 8649Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC Canada ,grid.14709.3b0000 0004 1936 8649Department of Psychiatry, McGill University, Montreal, QC Canada
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8
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Olafson E, Bedford SA, Devenyi GA, Patel R, Tullo S, Park MTM, Parent O, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Spencer MD, Suckling J, Taylor MJ, Lai MC, Chakravarty MM. Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder. Cereb Cortex 2021; 31:3338-3352. [PMID: 33693614 PMCID: PMC8196259 DOI: 10.1093/cercor/bhab015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/06/2020] [Accepted: 01/15/2021] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.
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Affiliation(s)
- Emily Olafson
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Neuroscience, Weill Cornell Graduate School of Medical Sciences, New York City, NY 10021, USA
| | - Saashi A Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal H3A 2B4, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal H3A 2B4, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
| | - Min Tae M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London N6A 3K7, ON, Canada
| | - Olivier Parent
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Departement de Psychologie, Universite de Montreal, Montreal, QC, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, Toronto M4G 1R8, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Simon Baron-Cohen
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Lindsay R Chura
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Michael C Craig
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- National Autism Unit, Bethlem Royal Hospital, London BR3 3BX, UK
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of the Goethe University, Frankfurt am Main 60528, Germany
| | - Dorothea L Floris
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen 6525 HR, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen 02.275, The Netherlands
| | - Rosemary J Holt
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Rhoshel Lenroot
- Department of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jason P Lerch
- Department of Medical Biophysics, The University of Toronto, Toronto, ON M5G 1L7, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Michael V Lombardo
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Amber N V Ruigrok
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Michael D Spencer
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - John Suckling
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Margot J Taylor
- Diagnostic Imaging, The Hospital for Sick Children, Toronto M5G 1X8, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto M5G 1X8, Canada
- Department of Medical Imaging, University of Toronto, Toronto M5G 1X8, Canada
| | | | - Meng-Chuan Lai
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto M6J 1H4, Canada
- Department of Psychiatry, University of Toronto, Toronto M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
- Department of Psychiatry, The Hospital for Sick Children, Toronto M5G 1X8, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
- Department of Psychiatry, McGill University, Montreal H3A 2B4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal H3A 2B4, Canada
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9
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Plitman E, Bussy A, Valiquette V, Salaciak A, Patel R, Cupo L, Béland ML, Tullo S, Tardif CL, Rajah MN, Near J, Devenyi GA, Chakravarty MM. The impact of the Siemens Tim Trio to Prisma upgrade and the addition of volumetric navigators on cortical thickness, structure volume, and 1H-MRS indices: An MRI reliability study with implications for longitudinal study designs. Neuroimage 2021; 238:118172. [PMID: 34082116 DOI: 10.1016/j.neuroimage.2021.118172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 11/30/2022] Open
Abstract
Many magnetic resonance imaging (MRI) measures are being studied longitudinally to explore topics such as biomarker detection and clinical staging. A pertinent concern to longitudinal work is MRI scanner upgrades. When upgrades occur during the course of a longitudinal MRI neuroimaging investigation, there may be an impact on the compatibility of pre- and post-upgrade measures. Similarly, subject motion is another issue that may be detrimental to MRI work and embedding volumetric navigators (vNavs) within acquisition sequences has emerged as a technique that allows for prospective motion correction. Our research group recently underwent an upgrade from a Siemens MAGNETOM 3T Tim Trio system to a Siemens MAGNETOM 3T Prisma Fit system. The goals of the current work were to: 1) investigate the impact of this upgrade on commonly used structural imaging measures and proton magnetic resonance spectroscopy indices ("Prisma Upgrade protocol") and 2) examine structural imaging measures in a sequence with vNavs alongside a standard acquisition sequence ("vNav protocol"). While high reliability was observed for most of the investigated MRI outputs, suboptimal reliability was observed for certain indices. Across the scanner upgrade, increases in frontal, temporal, and cingulate cortical thickness (CT) and thalamus volume, along with decreases in parietal CT and amygdala, globus pallidus, hippocampus, and striatum volumes, were observed. No significant impact of the upgrade was found in 1H-MRS analyses. Further, CT estimates were found to be larger in MPRAGE acquisitions compared to vNav-MPRAGE acquisitions mainly within temporal areas, while the opposite was found mostly in parietal brain regions. The results from this work should be considered in longitudinal study designs and comparable prospective motion correction investigations are warranted in cases of marked head movement.
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Affiliation(s)
- Eric Plitman
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
| | - Aurélie Bussy
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Vanessa Valiquette
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Lani Cupo
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Marie-Lise Béland
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Christine Lucas Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - M Natasha Rajah
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychology, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Jamie Near
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
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10
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Bussy A, Patel R, Plitman E, Tullo S, Salaciak A, Bedford SA, Farzin S, Béland ML, Valiquette V, Kazazian C, Tardif CL, Devenyi GA, Chakravarty MM. Hippocampal shape across the healthy lifespan and its relationship with cognition. Neurobiol Aging 2021; 106:153-168. [PMID: 34280848 DOI: 10.1016/j.neurobiolaging.2021.03.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/02/2021] [Accepted: 03/29/2021] [Indexed: 01/18/2023]
Abstract
The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Aurélie Bussy
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| | - Raihaan Patel
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Saashi A Bedford
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sarah Farzin
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Marie-Lise Béland
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Vanessa Valiquette
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Christina Kazazian
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Christine L Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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11
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Bussy A, Plitman E, Patel R, Salaciak A, Farzin S, Bedford S, Béland M, Tullo S, Devenyi GA, Chakravarty M. Volumetric, shape and microstructural alterations of the hippocampal subfields in healthy aging. Alzheimers Dement 2020. [DOI: 10.1002/alz.039589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Aurelie Bussy
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
| | - Eric Plitman
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
| | - Sarah Farzin
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
| | - Saashi Bedford
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
| | - Marie‐Lise Béland
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
| | - Stephanie Tullo
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
| | - Gabriel A. Devenyi
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- Department of Psychiatry McGill University Montreal QC Canada
| | - Mallar Chakravarty
- Computational Brain Anatomy Laboratory ‐ Cerebral Imaging Centre ‐ Douglas Mental Health University Institute Verdun QC Canada
- McGill University Montreal QC Canada
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12
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Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, Wlodarski N, Tardif CL, Breitner JCS, Chakravarty MM. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp 2019; 40:5269-5288. [PMID: 31452289 PMCID: PMC6864890 DOI: 10.1002/hbm.24771] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/17/2019] [Accepted: 08/05/2019] [Indexed: 01/18/2023] Open
Abstract
While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age‐related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1‐weighted/T2‐weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18–83. In accordance with previous cross‐sectional studies of adults, we observed age‐related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age‐related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U‐shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Saashi A Bedford
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Sarah Farzin
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Nancy Wlodarski
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Christine L Tardif
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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- Centre for the Studies on the Prevention of AD, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - John C S Breitner
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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13
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Tullo S, Devenyi GA, Patel R, Park MTM, Collins DL, Chakravarty MM. Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci Data 2018; 5:180107. [PMID: 29917012 PMCID: PMC6007088 DOI: 10.1038/sdata.2018.107] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/06/2018] [Indexed: 11/09/2022] Open
Abstract
Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Min Tae M Park
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - D Louis Collins
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
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14
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Abstract
Nail brittleness is a common complaint characterized by weak inelastic nails that split, flake and crumble. It may be a consequence of factors that alter nail plate production and/or factors that damage the already keratinised nail plate. It is often idiopathic. It can also be caused by many dermatological and systemic diseases, nutritional deficiencies, drugs and traumas. Environmental and occupational factors that produce progressive dehydration of the nail plate have an important role in nail brittleness. Treatment of brittle nails is often difficult. Preventative measures, together with oral supplementation of vitamins (especially biotin), oligo-elements and amino acids, can be useful in improving nail strength. Cosmetic treatment affords camouflage and a degree of protection.
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Affiliation(s)
- M Iorizzo
- Department of Dermatology, University of Bologna, Bologna, Italy
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