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Cagol A, Tsagkas C, Granziera C. Advanced Brain Imaging in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:335-357. [PMID: 38942520 DOI: 10.1016/j.nic.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.
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Affiliation(s)
- Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland; Department of Health Sciences, University of Genova, Via A. Pastore, 1 16132 Genova, Italy. https://twitter.com/CagolAlessandr0
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), 10 Center Drive, Bethesda, MD 20892, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland.
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2
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Bongratz F, Rickmann AM, Wachinger C. Neural deformation fields for template-based reconstruction of cortical surfaces from MRI. Med Image Anal 2024; 93:103093. [PMID: 38281362 DOI: 10.1016/j.media.2024.103093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 12/19/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods separate the surface registration from the surface extraction, which is computationally inefficient and prone to distortions. We introduce Vox2Cortex-Flow (V2C-Flow), a deep mesh-deformation technique that learns a deformation field from a brain template to the cortical surfaces of an MRI scan. To this end, we present a geometric neural network that models the deformation-describing ordinary differential equation in a continuous manner. The network architecture comprises convolutional and graph-convolutional layers, which allows it to work with images and meshes at the same time. V2C-Flow is not only very fast, requiring less than two seconds to infer all four cortical surfaces, but also establishes vertex-wise correspondences to the template during reconstruction. In addition, V2C-Flow is the first approach for cortex reconstruction that models white matter and pial surfaces jointly, therefore avoiding intersections between them. Our comprehensive experiments on internal and external test data demonstrate that V2C-Flow results in cortical surfaces that are state-of-the-art in terms of accuracy. Moreover, we show that the established correspondences are more consistent than in FreeSurfer and that they can directly be utilized for cortex parcellation and group analyses of cortical thickness.
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Affiliation(s)
- Fabian Bongratz
- Laboratory for Artificial Intelligence in Medical Imaging, Department of Radiology, Technical University of Munich, Munich 81675, Germany; Munich Center for Machine Learning, Munich, Germany.
| | - Anne-Marie Rickmann
- Laboratory for Artificial Intelligence in Medical Imaging, Department of Radiology, Technical University of Munich, Munich 81675, Germany; Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-University, Munich 80336, Germany
| | - Christian Wachinger
- Laboratory for Artificial Intelligence in Medical Imaging, Department of Radiology, Technical University of Munich, Munich 81675, Germany; Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-University, Munich 80336, Germany; Munich Center for Machine Learning, Munich, Germany
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3
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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Hammond BP, Panda SP, Kaushik DK, Plemel JR. Microglia and Multiple Sclerosis. ADVANCES IN NEUROBIOLOGY 2024; 37:445-456. [PMID: 39207707 DOI: 10.1007/978-3-031-55529-9_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Multiple sclerosis (MS) is a devastating autoimmune disease that leads to profound disability. This disability arises from the stochastic, regional loss of myelin-the insulating sheath surrounding neurons-in the central nervous system (CNS). The demyelinated regions are dominated by the brain's resident macrophages: microglia. Microglia perform a variety of functions in MS and are thought to initiate and perpetuate demyelination through their interactions with peripheral immune cells that traffic into the brain. However, microglia are also likely essential for recruiting and promoting the differentiation of cells that can restore lost myelin in a process known as remyelination. Given these seemingly opposing functions, an overarching beneficial or detrimental role is yet to be ascribed to these immune cells. In this chapter, we will discuss microglia dynamics throughout the MS disease course and probe the apparent dichotomy of microglia as the drivers of both demyelination and remyelination.
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Affiliation(s)
- Brady P Hammond
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Sharmistha P Panda
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Deepak K Kaushik
- Division of Biomedical Sciences, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Jason R Plemel
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada.
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada.
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5
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Zeng X, Puonti O, Sayeed A, Herisse R, Mora J, Evancic K, Varadarajan D, Balbastre Y, Costantini I, Scardigli M, Ramazzotti J, DiMeo D, Mazzamuto G, Pesce L, Brady N, Cheli F, Pavone FS, Hof PR, Frost R, Augustinack J, van der Kouwe A, Iglesias JE, Fischl B. Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570416. [PMID: 38106176 PMCID: PMC10723438 DOI: 10.1101/2023.12.06.570416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 μm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
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Affiliation(s)
- Xiangrui Zeng
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Oula Puonti
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Areej Sayeed
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Rogeny Herisse
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Irene Costantini
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Biology, University of Florence, Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | | | - Danila DiMeo
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Italy
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Niamh Brady
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Franco Cheli
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Italy
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Jean Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - André van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
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6
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Woodfield J, Braun KPJ, van Schooneveld MMJ, Bastin ME, Chin RFM. Efficient organisation of the contralateral hemisphere connectome is associated with improvement in intelligence quotient after paediatric epilepsy surgery. Epilepsy Behav 2023; 149:109521. [PMID: 37944287 DOI: 10.1016/j.yebeh.2023.109521] [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: 09/02/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Aims of epilepsy surgery in childhood include optimising seizure control and facilitating cognitive development. Predicting which children will improve cognitively is challenging. We investigated the association of the pre-operative structural connectome of the contralateral non-operated hemisphere with improvement in intelligence quotient (IQ) post-operatively. METHODS Consecutive children who had undergone unilateral resective procedures for epilepsy at a single centre were retrospectively identified. We included those with pre-operative volume T1-weighted non-contrast brain magnetic resonance imaging (MRI), no visible contralateral MRI abnormalities, and both pre-operative and two years post-operative IQ assessment. The MRI of the hemisphere contralateral to the side of resection was anatomically parcellated into 34 cortical regions and the covariance of cortical thickness between regions was used to create binary and weighted group connectomes. RESULTS Eleven patients with a post-operative IQ increase of at least 10 points at two years were compared with twenty-four patients with no change in IQ score. Children who gained at least 10 IQ points post-operatively had a more efficiently structured contralateral hemisphere connectome with higher global efficiency (0.74) compared to those whose IQ did not change at two years (0.58, p = 0.014). This was consistent across thresholds and both binary and weighted networks. There were no statistically significant group differences in age, sex, age at onset of epilepsy, pre-operative IQ, mean cortical thickness, side or site of procedure, two year post-operative Engel scores or use of anti-seizure medications between the two groups. CONCLUSIONS Surgical procedures to reduce or stop seizures may allow children with an efficiently structured contralateral hemisphere to achieve their cognitive potential.
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Affiliation(s)
- Julie Woodfield
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Department of Clinical Neurosciences, NHS Lothian, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom.
| | - Kees P J Braun
- Department of Paediatric Neurology, University Medical Center Utrecht, Utrecht, the Netherlands; Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Monique M J van Schooneveld
- Department of Paediatric Psychology, Sector of Neuropsychology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Richard F M Chin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom; Royal Hospital for Children and Young People, NHS Lothian, Edinburgh, United Kingdom
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7
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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8
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Okawa R, Yasui G, Mihara B, Hayashi N. Optimization of the fluid-attenuated inversion recovery (FLAIR) imaging for use in autopsy imaging of the brain region using synthetic MRI. Technol Health Care 2023; 31:661-674. [PMID: 36093648 DOI: 10.3233/thc-220230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The failure of cerebrospinal fluid (CSF) signal suppression in postmortem fluid-attenuated inversion recovery (FLAIR) of the brain is a problem. OBJECTIVE The present study was to clarify the relationship between the temperature of deceased persons and CSF T1, and to optimize the postmortem brain FLAIR imaging method using synthetic MRI. METHODS Forehead temperature was measured in 15 deceased persons. Next, synthetic MRI of the brain was performed, the CSF T1 was measured, and the optimal TI was calculated. Two types of FLAIR images were obtained with the clinical and optimal TI. The relationship between forehead temperature and the CSF T1 and optimal TI was evaluated. The optimized FLAIR images were physically and visually evaluated. RESULTS The CSF T1 and optimal TI were strongly correlated with forehead temperature. Comparing the average SNR and CNR ratios and visual evaluation scores of the two FLAIR images, those captured with the optimal TI showed statistically lower SNR, higher CNR, and higher visual evaluation scores (p< 0.01). CONCLUSIONS Synthetic MRI enables the quantification of the CSF T1 resulting from postmortem temperature decreases and calculation of the optimal TI, which could aid in improving the failure of CSF signal suppression and in optimizing postmortem brain FLAIR imaging.
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Affiliation(s)
- Ryuya Okawa
- Department of Diagnostic Imaging, Institute of Brain and Blood Vessels, Mihara Memorial Hospital, Isesaki, Japan
- Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan
| | - Go Yasui
- Department of Diagnostic Imaging, Institute of Brain and Blood Vessels, Mihara Memorial Hospital, Isesaki, Japan
| | - Ban Mihara
- Department of Neurology, Institute of Brain and Blood Vessels, Mihara Memorial Hospital, Isesaki, Japan
| | - Norio Hayashi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan
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9
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Comparison of FreeSurfer and CAT12 Software in Parcel-Based Cortical Thickness Calculations. Brain Topogr 2022; 35:572-582. [PMID: 36208399 DOI: 10.1007/s10548-022-00919-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/27/2022] [Indexed: 11/02/2022]
Abstract
Several approaches have emerged to measure the cortical thickness (CT), which can be broadly divided into surface-based and voxel-based algorithms. We aimed to compare parcel-based CT estimation of the widely used FreeSurfer (FS) software and CAT12 software, which is a widely used voxel-based approach, and evaluate the test-retest (TRT) reliability of both methods. MRI images of 417 healthy individuals were analyzed. TRT reliability was performed on 60 participants. The mean CT of the parcels of the Desikan-Killiany atlas were calculated both in FS and CAT12. Linear mixed model was performed to compare the two methods and the TRT reliability, and paired-sample t-test for post-hoc analyses. Linear regression analyses were utilized to examine the regressions between the two methods and between different sessions with each method. CT values calculated using the two methods were significantly correlated (R2adj = 0.67). The significant interaction effect between the method and the parcels were due to larger CT values of FS in 32 of 68 parcels, whereas CT values of CAT12 were higher in 31 of 68 parcels. The TRT reliabilities of both approaches were excellent (FS R2adj = 0.95, CAT12 R2adj = 0.93). We conclude that both techniques can provide equally valid results for groups comparisons or follow-up studies as long as they are not mixed with each other.
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10
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Stellmann JP, Wanke N, Maarouf A, Gellißen S, Heesen C, Audoin B, Gold SM, Zaaraoui W, Poettgen J. Cognitive performance shows domain specific associations with regional cortical thickness in multiple sclerosis. NEUROIMAGE-CLINICAL 2021; 30:102606. [PMID: 33744503 PMCID: PMC7985400 DOI: 10.1016/j.nicl.2021.102606] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/15/2021] [Accepted: 02/15/2021] [Indexed: 11/25/2022]
Abstract
Cognitive impairment correlates with loss of cortical thickness in MS. Cognitive tests show distinctive regional associations with cortical thickness. Some regions, such as the left insula, correlate with multiple tests. Associations patterns seem reproducible in patients with very mild impairment. Better localization of cognitive functions may improve future MRI studies.
Multiple Sclerosis (MS) patients often suffer from significant cognitive impairment. Earlier research has shown relationships between regional cortical atrophy and cognitive deterioration. However, due to a large number of neuropsychological assessments and a heterogenous pattern of cognitive deficits in MS patients, reported associations patterns are also heterogenous. Using an extensive neuropsychological battery of 23 different tasks, we explored domain (attention/information processing, memory, spatial processing, executive functioning) and task-specific associations with regional cortical thickness in a representative sample of MS patients (N = 97). Cortical regions associated with multiple cognitive tasks in the left hemisphere were predominantly located in the inferior insula (attention p < 0.001, memory p = 0.047, spatial processing p = 0.004, executive functioning p = 0.037), the gyrus frontalis superior (attention p = 0.015, memory p = 0.037, spatial processing p = 0.033, executive functioning p = 0.017) and temporal medial (attention p < 0.001, memory two clusters p = 0.016 and p < 0.001, executive functioning p = 0.016). In the right hemisphere, we detected the strongest association in the sulcus interparietalis with five cluster (attention SDMT p = 0.003 and TAP_DA p < 0.001; memory Rey recall p = 0.013 and VLMT verbal learning p = 0.016; spatial processing Rey copy p < 0.001). We replicated parts of our results in an independent sample of 30 mildly disabled MS patients. Moreover, comparisons to 29 healthy controls showed that the regional associations seemed to represent rather pathophysiological dependency than a physiological one. We believe that our results may prove useful in diagnosis and rehabilitation of cognitive impairments and may serve as guidance in future magnetic resonance imaging (MRI) studies.
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Affiliation(s)
- Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
| | - Nadine Wanke
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Department of Cognitive Psychology, Institute of Psychology, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany
| | - Adil Maarouf
- APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Susanne Gellißen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Martinistr. 52, 20246 Hamburg, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
| | - Bertrand Audoin
- APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Charité Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany; Charité Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Wafaa Zaaraoui
- APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Jana Poettgen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
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11
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Hidalgo de la Cruz M, Valsasina P, Gobbi C, Gallo A, Zecca C, Bisecco A, Tedeschi G, Filippi M, Rocca MA. Longitudinal cortical thinning progression differs across multiple sclerosis phenotypes and is clinically relevant: A multicentre study. Mult Scler 2020; 27:827-840. [DOI: 10.1177/1352458520940548] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Longitudinal evolution of cortical thickness (CTh) in different MS phenotypes has been rarely studied. Aim: To investigate the regional pattern and 1-year progression of cortical thinning in relapsing-remitting (RR) and progressive (P) MS. Methods: 3T high-resolution T1-weighted magnetic resonance imaging (MRI) was obtained from 86 patients (75 RRMS, 11 PMS) and 34 healthy controls (HC) at three European sites at baseline and 1-year follow-up. Using FreeSurfer, baseline CTh between-group differences, longitudinal CTh changes and their correlations with clinical and MRI variables were assessed. Results: Baseline frontal, parietal and sensorimotor atrophy was found in MS versus HC. Such pattern was driven by RRMS, while PMS showed additional parietal, insular and sensorimotor cortical atrophy versus RRMS. At 1-year versus baseline, additional frontal and temporal cortical thinning was detected in RRMS patients, while a widespread CTh reduction was found in PMS patients (significant at time-by-group interaction vs RRMS). In MS, baseline fronto-parietal atrophy correlated with more severe disability and higher lesion volume. Baseline inferior parietal CTh decrease and 1-year temporal cortical thinning correlated with more severe disability. Conclusion: Parieto-temporal baseline CTh abnormalities and thinning pattern over time characterized the main MS clinical phenotypes and were associated with 1-year disability worsening.
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Affiliation(s)
- Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Claudio Gobbi
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, Civic Hospital, Lugano, Switzerland/Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, and 3T MRI Center, University of Campania ‘Luigi Vanvitelli’, Naples, Italy
| | - Chiara Zecca
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, Civic Hospital, Lugano, Switzerland/Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, and 3T MRI Center, University of Campania ‘Luigi Vanvitelli’, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, and 3T MRI Center, University of Campania ‘Luigi Vanvitelli’, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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12
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Preziosa P, Kiljan S, Steenwijk MD, Meani A, van de Berg WDJ, Schenk GJ, Rocca MA, Filippi M, Geurts JJG, Jonkman LE. Axonal degeneration as substrate of fractional anisotropy abnormalities in multiple sclerosis cortex. Brain 2020; 142:1921-1937. [PMID: 31168614 DOI: 10.1093/brain/awz143] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/14/2019] [Accepted: 04/09/2019] [Indexed: 12/11/2022] Open
Abstract
Cortical microstructural abnormalities are associated with clinical and cognitive deterioration in multiple sclerosis. Using diffusion tensor MRI, a higher fractional anisotropy has been found in cortical lesions versus normal-appearing cortex in multiple sclerosis. The pathological substrates of this finding have yet to be definitively elucidated. By performing a combined post-mortem diffusion tensor MRI and histopathology study, we aimed to define the histopathological substrates of diffusivity abnormalities in multiple sclerosis cortex. Sixteen subjects with multiple sclerosis and 10 age- and sex-matched non-neurological control donors underwent post-mortem in situ at 3 T MRI, followed by brain dissection. One hundred and ten paraffin-embedded tissue blocks (54 from multiple sclerosis patients, 56 from non-neurological controls) were matched to the diffusion tensor sequence to obtain regional diffusivity measures. Using immunohistochemistry and silver staining, cortical density of myelin, microglia, astrocytes and axons, and density and volume of neurons and glial cells were evaluated. Correlates of diffusivity abnormalities with histological markers were assessed through linear mixed-effects models. Cortical lesions (77% subpial) were found in 27/54 (50%) multiple sclerosis cortical regions. Multiple sclerosis normal-appearing cortex had a significantly lower fractional anisotropy compared to cortex from non-neurological controls (P = 0.047), whereas fractional anisotropy in demyelinated cortex was significantly higher than in multiple sclerosis normal-appearing cortex (P = 0.012) but not different from non-neurological control cortex (P = 0.420). Compared to non-neurological control cortex, both multiple sclerosis normal-appearing and demyelinated cortices showed a lower density of axons perpendicular to the cortical surface (P = 0.012 for both) and of total axons (parallel and perpendicular to cortical surface) (P = 0.028 and 0.012). In multiple sclerosis, demyelinated cortex had a lower density of myelin (P = 0.004), parallel (P = 0.018) and total axons (P = 0.029) versus normal-appearing cortex. Regarding the pathological substrate, in non-neurological controls, cortical fractional anisotropy was positively associated with density of perpendicular, parallel, and total axons (P = 0.031 for all). In multiple sclerosis, normal-appearing cortex fractional anisotropy was positively associated with perpendicular and total axon density (P = 0.031 for both), while associations with myelin, glial and total cells and parallel axons did not survive multiple comparison correction. Demyelinated cortex fractional anisotropy was positively associated with density of neurons, and total cells and negatively with microglia density, without surviving multiple comparison correction. Our results suggest that a reduction of perpendicular axons in normal-appearing cortex and of both perpendicular and parallel axons in demyelinated cortex may underlie the substrate influencing cortical microstructural coherence and being responsible for the different patterns of fractional anisotropy changes occurring in multiple sclerosis cortex.
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Affiliation(s)
- Paolo Preziosa
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Svenja Kiljan
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alessandro Meani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Wilma D J van de Berg
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Geert J Schenk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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13
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Sastre-Garriga J, Pareto D, Battaglini M, Rocca MA, Ciccarelli O, Enzinger C, Wuerfel J, Sormani MP, Barkhof F, Yousry TA, De Stefano N, Tintoré M, Filippi M, Gasperini C, Kappos L, Río J, Frederiksen J, Palace J, Vrenken H, Montalban X, Rovira À. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat Rev Neurol 2020; 16:171-182. [PMID: 32094485 PMCID: PMC7054210 DOI: 10.1038/s41582-020-0314-x] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2020] [Indexed: 11/08/2022]
Abstract
Early evaluation of treatment response and prediction of disease evolution are key issues in the management of people with multiple sclerosis (MS). In the past 20 years, MRI has become the most useful paraclinical tool in both situations and is used clinically to assess the inflammatory component of the disease, particularly the presence and evolution of focal lesions - the pathological hallmark of MS. However, diffuse neurodegenerative processes that are at least partly independent of inflammatory mechanisms can develop early in people with MS and are closely related to disability. The effects of these neurodegenerative processes at a macroscopic level can be quantified by estimation of brain and spinal cord atrophy with MRI. MRI measurements of atrophy in MS have also been proposed as a complementary approach to lesion assessment to facilitate the prediction of clinical outcomes and to assess treatment responses. In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group critically review the application of brain and spinal cord atrophy in clinical practice in the management of MS, considering the role of atrophy measures in prognosis and treatment monitoring and the barriers to clinical use of these measures. On the basis of this review, the group makes consensus statements and recommendations for future research.
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Affiliation(s)
- Jaume Sastre-Garriga
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - Deborah Pareto
- Section of Neuroradiology and Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Olga Ciccarelli
- NMR Research Unit, University College London Queen Square Institute of Neurology, London, UK
- National Institute for Health Research Biomedical Research Centre, University College London Hospitals, London, UK
| | - Christian Enzinger
- Department of Neurology and Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Jens Wuerfel
- Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Maria P Sormani
- Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS, Ospedale Policlinico San Martino, Genoa, Italy
| | - Frederik Barkhof
- National Institute for Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tarek A Yousry
- NMR Research Unit, University College London Queen Square Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, University College London Hospitals National Hospital for Neurology and Neurosurgery, University College London Institute of Neurology, London, UK
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Mar Tintoré
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Gasperini
- Multiple Sclerosis Center, Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital, University of Basel, Basel, Switzerland
| | - Jordi Río
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jette Frederiksen
- Department of Neurology, Rigshospitalet-Glostrup and University of Copenhagen, Glostrup, Denmark
| | - Jackie Palace
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
- Division of Neurology, St Michael's Hospital, University of Toronto, Toronto, Canada
| | - Àlex Rovira
- Section of Neuroradiology and Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
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14
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Suh JS, Minuzzi L, Raamana PR, Davis A, Hall GB, Harris J, Hassel S, Zamyadi M, Arnott SR, Alders GL, Sassi RB, Milev R, Lam RW, MacQueen GM, Strother SC, Kennedy SH, Frey BN. An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report. NEUROIMAGE-CLINICAL 2020; 25:102178. [PMID: 32036277 PMCID: PMC7011077 DOI: 10.1016/j.nicl.2020.102178] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/25/2019] [Accepted: 01/10/2020] [Indexed: 11/28/2022]
Abstract
Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10-20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2-10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=-29,9,54.5,-7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.
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Affiliation(s)
- Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Luciano Minuzzi
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Pradeep Reddy Raamana
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Andrew Davis
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Jacqueline Harris
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Roberto B Sassi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University and Providence Care Hospital, Kingston, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Canadian Biomarker Integration Network for Depression, St. Michael's Hospital, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Benicio N Frey
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
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15
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Silva BA, Ferrari CC. Cortical and meningeal pathology in progressive multiple sclerosis: a new therapeutic target? Rev Neurosci 2019; 30:221-232. [PMID: 30048237 DOI: 10.1515/revneuro-2018-0017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 05/04/2018] [Indexed: 12/31/2022]
Abstract
Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease that involves an intricate interaction between the central nervous system and the immune system. Nevertheless, its etiology is still unknown. MS exhibits different clinical courses: recurrent episodes with remission periods ('relapsing-remitting') that can evolve to a 'secondary progressive' form or persistent progression from the onset of the disease ('primary progressive'). The discovery of an effective treatment and cure has been hampered due to the pathological and clinical heterogeneity of the disease. Historically, MS has been considered as a disease exclusively of white matter. However, patients with progressive forms of MS present with cortical lesions associated with meningeal inflammation along with physical and cognitive disabilities. The pathogenesis of the cortical lesions has not yet been fully described. Animal models that represent both the cortical and meningeal pathologies will be critical in addressing MS pathogenesis as well as the design of specific treatments. In this review, we will address the state-of-the-art diagnostic and therapeutic alternatives and the development of strategies to discover new therapeutic approaches, especially for the progressive forms.
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Affiliation(s)
- Berenice Anabel Silva
- Institute of Basic Science and Experimental Medicine (ICBME), University Institute, Italian Hospital, Potosi 4240 (C1199ABB), CABA, Buenos Aires, Argentina.,Leloir Institute Foundation, Institute for Biochemical Investigations of Buenos Aires, (IIBBA, CONICET), Patricias Argentinas 435 (C1405BWE), Buenos Aires, Argentina, e-mail:
| | - Carina Cintia Ferrari
- Institute of Basic Science and Experimental Medicine (ICBME), University Institute, Italian Hospital, Potosi 4240 (C1199ABB), CABA, Buenos Aires, Argentina.,Leloir Institute Foundation, Institute for Biochemical Investigations of Buenos Aires, (IIBBA, CONICET), Patricias Argentinas 435 (C1405BWE), Buenos Aires, Argentina
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16
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Wong APY, French L, Leonard G, Perron M, Pike GB, Richer L, Veillette S, Pausova Z, Paus T. Inter-Regional Variations in Gene Expression and Age-Related Cortical Thinning in the Adolescent Brain. Cereb Cortex 2019; 28:1272-1281. [PMID: 28334178 DOI: 10.1093/cercor/bhx040] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Indexed: 12/16/2022] Open
Abstract
Age-related decreases in cortical thickness observed during adolescence may be related to fluctuations in sex and stress hormones. We examine this possibility by relating inter-regional variations in age-related cortical thinning (data from the Saguenay Youth Study) to inter-regional variations in expression levels of relevant genes (data from the Allen Human Brain Atlas); we focus on genes coding for glucocorticoid receptor (NR3C1), androgen receptor (AR), progesterone receptor (PGR), and estrogen receptors (ESR1 and ESR2). Across 34 cortical regions (Desikan-Killiany parcellation), age-related cortical thinning varied as a function of mRNA expression levels of NR3C1 in males (R2 = 0.46) and females (R2 = 0.30) and AR in males only (R2 = 0.25). Cortical thinning did not vary as a function of expression levels of PGR, ESR1, or ESR2 in either sex; this might be due to the observed low consistency of expression profiles of these 3 genes across donors. Inter-regional levels of the NR3C1 and AR expression interacted with each other vis-à-vis cortical thinning: age-related cortical thinning varied as a function of NR3C1 mRNA expression in brain regions with low (males: R2 = 0.64; females: R2 = 0.58) but not high (males: R2 = 0.0045; females: R2 = 0.15) levels of AR mRNA expression. These results suggest that glucocorticoid and androgen receptors contribute to cortical maturation during adolescence.
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Affiliation(s)
- Angelita Pui-Yee Wong
- Department of Psychology, University of Toronto, Toronto M5S 3G3, Canada.,Rotman Research Institute, Baycrest, Toronto M6A 2E1, Canada
| | - Leon French
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto M5T 1R8, Canada
| | - Gabriel Leonard
- Montreal Neurological Institute, McGill University, Montréal H3A 2B4, Canada
| | - Michel Perron
- ECOBES, Cégep de Jonquière, Jonquière G7X 7W2, Canada.,University of Quebec in Chicoutimi, Chicoutimi G7H 2B1, Canada
| | - G Bruce Pike
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 4N1, Canada
| | - Louis Richer
- University of Quebec in Chicoutimi, Chicoutimi G7H 2B1, Canada
| | - Suzanne Veillette
- ECOBES, Cégep de Jonquière, Jonquière G7X 7W2, Canada.,University of Quebec in Chicoutimi, Chicoutimi G7H 2B1, Canada
| | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto M5G 1X8, Canada
| | - Tomáš Paus
- Department of Psychology, University of Toronto, Toronto M5S 3G3, Canada.,Rotman Research Institute, Baycrest, Toronto M6A 2E1, Canada.,Department of Psychiatry, University of Toronto, Toronto M5T 1R8, Canada.,Child Mind Institute, New York, NY 10022, USA
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17
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Kojima M, Yassin W, Owada K, Aoki Y, Kuwabara H, Natsubori T, Iwashiro N, Gonoi W, Takao H, Kasai K, Abe O, Kano Y, Yamasue H. Neuroanatomical Correlates of Advanced Paternal and Maternal Age at Birth in Autism Spectrum Disorder. Cereb Cortex 2019; 29:2524-2532. [PMID: 29800092 DOI: 10.1093/cercor/bhy122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Indexed: 12/22/2022] Open
Abstract
Although advanced paternal and maternal age at birth (PA/MA) increases the risk of autism spectrum disorder (ASD), the underlying neurobiological mechanisms are not fully understood. To explore the neuroanatomical correlates of advanced PA/MA, the current study conducted brain morphometric analyses in 39 high-functioning adult males with ASD and 39 age-, intellectual level-, and parental socioeconomic background-matched, typically developed (TD) males. Whole-brain analysis revealed that the regional gray matter volume (GMV) in bilateral posterior cingulate cortex (PCC) and precuneus (PCU) were significantly smaller in the individuals with ASD than in TD subjects (false discovery rate-corrected P = 0.014). Additional analyses of the constituents of GMV reduction in these brain regions revealed that the cortical thickness of the right ventral PCC was significantly thinner (P = 0.014) and the surface area of bilateral PCU was significantly smaller (left: P = 0.001; right: P = 0.049) in the adults with ASD, compared with TD subjects. Although the analyses were exploratory, the thinner cortical thickness of right ventral PCC was significantly correlated with older PA in the ASD individuals (P = 0.028). The current findings shed new light on the neurobiological mechanisms underlying the link between advanced PA and ASD.
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Affiliation(s)
- Masaki Kojima
- Department of Child Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Walid Yassin
- Department of Child Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Keiho Owada
- Department of Child Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Yuta Aoki
- Department of Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, The University of Hamamatsu School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka, Japan
| | - Tatsunobu Natsubori
- Department of Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Norichika Iwashiro
- Department of Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Wataru Gonoi
- Department of Radiology, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Hidemasa Takao
- Department of Radiology, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Yukiko Kano
- Department of Child Neuropsychiatry, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, The University of Hamamatsu School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka, Japan
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18
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Høgestøl EA, Kaufmann T, Nygaard GO, Beyer MK, Sowa P, Nordvik JE, Kolskår K, Richard G, Andreassen OA, Harbo HF, Westlye LT. Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis. Front Neurol 2019; 10:450. [PMID: 31114541 PMCID: PMC6503038 DOI: 10.3389/fneur.2019.00450] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/12/2019] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course and severity. Seventy-six MS patients [71% females, mean age 34.8 years (range 21-49) at inclusion] were examined with brain MRI at three time points with a mean total follow up period of 4.4 years (±0.4 years). We used additional cross-sectional MRI data from 235 HC for case-control comparison. We applied a machine learning model trained on an independent set of 3,208 HC to estimate individual brain age and to calculate the difference between estimated and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in individuals with MS. MS patients showed significantly higher BAG (4.4 ± 6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 × 10-6). Longitudinal estimates of BAG in MS patients showed high reliability and suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (SE = 0.15) years compared to chronological aging (p = 0.008). Multiple regression analyses revealed higher rate of brain aging in patients with more brain atrophy (Cohen's D = 0.86, p = 4.3 × 10-15) and increased white matter lesion load (WMLL) (Cohen's D = 0.55, p = 0.015). On average, patients with MS had significantly higher BAG compared to HC. Progressive brain aging in patients with MS was related to brain atrophy and increased WMLL. No significant clinical associations were found in our sample, future studies are warranted on this matter. Brain age estimation is a promising method for evaluation of subtle brain changes in MS, which is important for predicting clinical outcome and guide choice of intervention.
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Affiliation(s)
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gro O. Nygaard
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Mona K. Beyer
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Knut Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hanne F. Harbo
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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19
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Cerebral cortical thickness after treatment with desvenlafaxine succinate in major depressive disorder. Neuroreport 2019; 30:378-382. [DOI: 10.1097/wnr.0000000000001211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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Canna A, Russo AG, Ponticorvo S, Manara R, Pepino A, Sansone M, Di Salle F, Esposito F. Automated search of control points in surface-based morphometry. Neuroimage 2018; 176:56-70. [DOI: 10.1016/j.neuroimage.2018.04.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/25/2018] [Accepted: 04/15/2018] [Indexed: 12/13/2022] Open
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21
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Cerqueira JJ, Compston DAS, Geraldes R, Rosa MM, Schmierer K, Thompson A, Tinelli M, Palace J. Time matters in multiple sclerosis: can early treatment and long-term follow-up ensure everyone benefits from the latest advances in multiple sclerosis? J Neurol Neurosurg Psychiatry 2018; 89:844-850. [PMID: 29618493 PMCID: PMC6204938 DOI: 10.1136/jnnp-2017-317509] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 02/02/2018] [Accepted: 02/22/2018] [Indexed: 12/26/2022]
Affiliation(s)
- João J Cerqueira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | | | - Ruth Geraldes
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mario M Rosa
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Klaus Schmierer
- Blizard Institute, Barts and The London School of Medicine and Dentistry, London, UK
| | - Alan Thompson
- Faculty of Brain Sciences, University College London, London, UK
| | - Michela Tinelli
- LSE Enterprise, London School of Economics, London, UK
- Personal Social Services research Unit (PSSRU), London School of Economics and Political Science, London, UK
| | - Jacqueline Palace
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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22
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Hemond CC, Chu R, Tummala S, Tauhid S, Healy BC, Bakshi R. Whole-brain atrophy assessed by proportional- versus registration-based pipelines from 3T MRI in multiple sclerosis. Brain Behav 2018; 8:e01068. [PMID: 30019857 PMCID: PMC6085901 DOI: 10.1002/brb3.1068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 06/11/2018] [Accepted: 06/20/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND AND PURPOSE Whole-brain atrophy is a standard outcome measure in multiple sclerosis (MS) clinical trials as assessed by various software tools. The effect of processing method on the validity of such data obtained from high-resolution 3T MRI is not known. We compared two commonly used methods of quantifying whole-brain atrophy. METHODS Three-dimensional T1-weighted and FLAIR images were obtained at 3T in MS (n = 61) and normal control (NC, n = 30) groups. Whole-brain atrophy was assessed by two automated pipelines: (a) SPM8 to derive brain parenchymal fraction (BPF, proportional-based method); (b) SIENAX to derive normalized brain parenchymal volume (BPV, registration method). We assessed agreement between BPF and BPV, as well their relationship to Expanded Disability Status Scale (EDSS) score, timed 25-foot walk (T25FW), cognition, and cerebral T2 (FLAIR) lesion volume (T2LV). RESULTS Brain parenchymal fraction and BPV showed only partial agreement (r = 0.73) in the MS group, and r = 0.28 in NC. Both methods showed atrophy in MS versus NC (BPF p < 0.01, BPV p < 0.05). Within MS group comparisons, BPF (p < 0.05) but not BPV (p > 0.05) correlated with EDSS score. BPV (p = 0.03) but not BPF (p = 0.08) correlated with T25FW. Both metrics correlated with T2LV (p < 0.05) and cognitive subscales. BPF (p < 0.05) but not BPV (p > 0.05) showed lower brain volume in cognitively impaired (n = 23) versus cognitively preserved (n = 38) patients. However, direct comparisons of BPF and BPV sensitivities to atrophy and clinical correlations were not statistically significant. CONCLUSION Whole-brain atrophy metrics may not be interchangeable between proportional- and registration-based automated pipelines from 3T MRI in patients with MS.
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Affiliation(s)
- Christopher C Hemond
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
| | - Renxin Chu
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
| | - Subhash Tummala
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
| | - Shahamat Tauhid
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
| | - Brian C Healy
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
| | - Rohit Bakshi
- Laboratory for Neuroimaging Research, Department of Neurology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts.,Laboratory for Neuroimaging Research, Department of Radiology, Brigham & Women's Hospital, Partners MS Center, Ann Romney Center for Neurologic Diseases, Harvard Medical School, Boston, Massachusetts
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23
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Keser Z, Hasan KM, Mwangi B, Younes K, Khayat-Khoei M, Kamali A, Lincoln JA, Nelson FM. Quantitative Limbic System Mapping of Main Cognitive Domains in Multiple Sclerosis. Front Neurol 2018; 9:132. [PMID: 29593633 PMCID: PMC5857595 DOI: 10.3389/fneur.2018.00132] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 02/22/2018] [Indexed: 12/03/2022] Open
Abstract
Background and objective Cognitive impairment (CI) is common in multiple sclerosis (MS), but underlying mechanisms and their imaging correlates are not completely understood. The gray and white matter structures of the limbic system (LS) play crucial roles in different aspects of cognition. To investigate their role in MS related CI, and since a detailed evaluations are lacking in the literature, we used a comprehensive neuroimaging approach to evaluate CI’s correlations with the main components of the LS. Methods Ten non-cognitively impaired MS patients and 30 MS patients with diagnosed CI, who underwent a comprehensive neuropsychological evaluation were included in the analysis. Microstructural integrity, volumetry of main limbic gray and white matter structures and cortical thickness were assessed for associations with CI. Results Fornix and cingulum/cingulate cortices were found to be the strongest correlates of CI in MS. As expected, LS’ gray and white matter structures were involved in various cognitive functions. Uncinate fasciculi showed significant correlation with verbal and visuospatial learning and memory, phonemic and semantic fluency; hippocampi with visuospatial skills, phonemic and semantic fluency, executive functions, and processing speed; thalami with verbal learning, visuospatial skills, semantic fluency; and amygdala with verbal recognition discrimination. Conclusion This comprehensive neuroimaging approach elucidated the role of the main limbic structures in cognitive functions associated with MS-related CI.
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Affiliation(s)
- Zafer Keser
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Khader M Hasan
- Department of Diagnostic and Interventional Radiology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Benson Mwangi
- Department of Psychiatry, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Kyan Younes
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Mahsa Khayat-Khoei
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Arash Kamali
- Department of Diagnostic and Interventional Radiology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - John A Lincoln
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Flavia M Nelson
- Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
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24
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Bauer CM, Cabral HJ, Killiany RJ. Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer's Disease and Prediction of Cognitive Decline. Diagnostics (Basel) 2018; 8:diagnostics8010014. [PMID: 29415470 PMCID: PMC5871997 DOI: 10.3390/diagnostics8010014] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/08/2018] [Accepted: 01/31/2018] [Indexed: 11/29/2022] Open
Abstract
Alzheimer’s Disease (AD) and mild cognitive impairment (MCI) are associated with widespread changes in brain structure and function, as indicated by magnetic resonance imaging (MRI) morphometry and 18-fluorodeoxyglucose position emission tomography (FDG PET) metabolism. Nevertheless, the ability to differentiate between AD, MCI and normal aging groups can be difficult. Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET metabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer’s Disease Neuroimaging Initiative. The secondary goal was to determine the neuroimaging variables from MRI, FDG PET and CSF biomarkers that can predict future cognitive decline within each group. To achieve these aims, a series of multivariate stepwise logistic and linear regression models were generated. Combining all neuroimaging modalities and cognitive test scores significantly improved the index of discrimination, especially at the earliest stages of the disease, whereas MRI gray matter morphometry variables best predicted future cognitive decline compared to other neuroimaging variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores and CSF biomarkers may provide significantly better discrimination than any modality alone.
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Affiliation(s)
- Corinna M Bauer
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA.
| | - Howard J Cabral
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
| | - Ronald J Killiany
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA.
- Department of Anatomy and Neurobiology, Center for Biomedical Imaging, Boston University School of Medicine, Boston, MA 02118, USA.
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25
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Abstract
Understanding the clinico-radiological paradox is important in the search for more sensitive and specific surrogates of relapses and disability progression (such that they can be used to inform treatment choices in individual people with multiple sclerosis) and to gain a better understanding of the pathophysiological basis of disability in multiple sclerosis (to identify and assess key therapeutic targets). In this brief review, we will consider themes and issues underlying the clinico-radiological paradox and recent advances in its resolution.
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Affiliation(s)
- Declan Chard
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH), Biomedical Research Centre, London, UK.,NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
| | - S Anand Trip
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH), Biomedical Research Centre, London, UK.,NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
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26
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Righart R, Biberacher V, Jonkman LE, Klaver R, Schmidt P, Buck D, Berthele A, Kirschke JS, Zimmer C, Hemmer B, Geurts JJG, Mühlau M. Cortical pathology in multiple sclerosis detected by the T1/T2-weighted ratio from routine magnetic resonance imaging. Ann Neurol 2017; 82:519-529. [PMID: 28833433 PMCID: PMC5698772 DOI: 10.1002/ana.25020] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 06/20/2017] [Accepted: 06/22/2017] [Indexed: 11/08/2022]
Abstract
OBJECTIVE In multiple sclerosis, neuropathological studies have shown widespread changes in the cerebral cortex. In vivo imaging is critical, because the histopathological substrate of most measurements is unknown. METHODS Using a novel magnetic resonance imaging analysis technique, based on the ratio of T1- and T2-weighted signal intensities, we studied the cerebral cortex of a large cohort of patients in early stages of multiple sclerosis. A total of 168 patients with clinically isolated syndrome or relapsing-remitting multiple sclerosis (Expanded Disability Status Scale: median = 1, range = 0-3.5) and 80 age- and sex-matched healthy controls were investigated. We also searched for the histopathological substrate of the T1/T2-weighted ratio by combining postmortem imaging and histopathology in 9 multiple sclerosis brain donors. RESULTS Patients showed lower T1/T2-weighted ratio values in parietal and occipital areas. The 4 most significant clusters appeared in the medial occipital and posterior cingulate cortex (each left and right). The decrease of the T1/T2-weighted ratio in the posterior cingulate was related to performance in attention. Analysis of the T1/T2-weighted ratio values of postmortem imaging yielded a strong correlation with dendrite density but none of the other parameters including myelin. INTERPRETATION The T1/T2-weighted ratio decreases in early stages of multiple sclerosis in a widespread manner, with a preponderance of posterior areas and with a contribution to attentional performance; it seems to reflect dendrite pathology. As the method is broadly available and applicable to available clinical scans, we believe that it is a promising candidate for studying and monitoring cortical pathology or therapeutic effects in multiple sclerosis. Ann Neurol 2017;82:519-529.
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Affiliation(s)
- Ruthger Righart
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Viola Biberacher
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, the Netherlands
| | - Roel Klaver
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, the Netherlands
| | - Paul Schmidt
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,Department of Statistics, Ludwig Maximilian University of Munich, Munich, Germany
| | - Dorothea Buck
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, the Netherlands
| | - Mark Mühlau
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
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27
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Ramus F, Altarelli I, Jednoróg K, Zhao J, Scotto di Covella L. Neuroanatomy of developmental dyslexia: Pitfalls and promise. Neurosci Biobehav Rev 2017; 84:434-452. [PMID: 28797557 DOI: 10.1016/j.neubiorev.2017.08.001] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 07/28/2017] [Accepted: 08/02/2017] [Indexed: 01/18/2023]
Abstract
Investigations into the neuroanatomical bases of developmental dyslexia have now spanned more than 40 years, starting with the post-mortem examination of a few individual brains in the 60s and 70s, and exploding in the 90s with the widespread use of MRI. The time is now ripe to reappraise the considerable amount of data gathered with MRI using different types of sequences (T1, diffusion, spectroscopy) and analysed using different methods (manual, voxel-based or surface-based morphometry, fractional anisotropy and tractography, multivariate analyses…). While selective reviews of mostly small-scale studies seem to provide a coherent view of the brain disruptions that are typical of dyslexia, involving left perisylvian and occipito-temporal regions, we argue that this view may be deceptive and that meta-analyses and large-scale studies rather highlight many inconsistencies and limitations. We discuss problems inherent to small sample size as well as methodological difficulties that still undermine the discovery of reliable neuroanatomical bases of dyslexia, and we outline some recommendations to further improve this research area.
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Affiliation(s)
- Franck Ramus
- Laboratoire de sciences cognitives et psycholinguistique (CNRS, ENS, EHESS, PSL Research University), Ecole Normale Supérieure, 29 rue d'Ulm, 75005 Paris, France.
| | - Irene Altarelli
- Brain and Learning Lab, Campus Biotech, University of Geneva, 9 Chemin des Mines, 1205 Geneva, Switzerland
| | - Katarzyna Jednoróg
- Laboratory of Psychophysiology, Department of Neurophysiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur St, 02-093 Warsaw, Poland
| | - Jingjing Zhao
- School of Psychology, Shaanxi Normal University and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi'an, Shaanxi, 710062, China
| | - Lou Scotto di Covella
- Laboratoire de sciences cognitives et psycholinguistique (CNRS, ENS, EHESS, PSL Research University), Ecole Normale Supérieure, 29 rue d'Ulm, 75005 Paris, France
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28
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Fragoso YD, Willie PR, Goncalves MVM, Brooks JBB. Critical analysis on the present methods for brain volume measurements in multiple sclerosis. ARQUIVOS DE NEURO-PSIQUIATRIA 2017; 75:464-469. [PMID: 28746434 DOI: 10.1590/0004-282x20170072] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 03/30/2017] [Indexed: 11/22/2022]
Abstract
Objective The treatment of multiple sclerosis (MS) has quickly evolved from a time when controlling clinical relapses would suffice, to the present day, when complete disease control is expected. Measurement of brain volume is still at an early stage to be indicative of therapeutic decisions in MS. Methods This paper provides a critical review of potential biases and artifacts in brain measurement in the follow-up of patients with MS. Results Clinical conditions (such as hydration or ovulation), time of the day, type of magnetic resonance machine (manufacturer and potency), brain volume artifacts and different platforms for volumetric assessment of the brain can induce variations that exceed the acceptable physiological rate of annual loss of brain volume. Conclusion Although potentially extremely valuable, brain volume measurement still has to be regarded with caution in MS.
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Affiliation(s)
- Yara Dadalti Fragoso
- Universidade Metropolitana de Santos, Centro de Referência de Esclerose Múltipla, Departamento de Neurologia, Santos SP, Brasil
| | - Paulo Roberto Willie
- Universidade da Região de Joinville, Departamento de Neuroradiologia, Joinville SC, Brasil
| | | | - Joseph Bruno Bidin Brooks
- Universidade Metropolitana de Santos, Centro de Referência de Esclerose Múltipla, Departamento de Neurologia, Santos SP, Brasil
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29
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Volume versus surface-based cortical thickness measurements: A comparative study with healthy controls and multiple sclerosis patients. PLoS One 2017; 12:e0179590. [PMID: 28683072 PMCID: PMC5500013 DOI: 10.1371/journal.pone.0179590] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 06/01/2017] [Indexed: 12/21/2022] Open
Abstract
The cerebral cortex is a highly folded outer layer of grey matter tissue that plays a key role in cognitive functions. In part, alterations of the cortex during development and disease can be captured by measuring the cortical thickness across the whole brain. Available software tools differ with regard to labor intensity and computational demands. In this study, we compared the computational anatomy toolbox (CAT), a recently proposed volume-based tool, with the well-established surface-based tool FreeSurfer. We observed that overall thickness measures were highly inter-correlated, although thickness estimates were systematically lower in CAT than in FreeSurfer. Comparison of multiple sclerosis (MS) patients with age-matched healthy control subjects showed highly comparable clusters of MS-related thinning for both methods. Likewise, both methods yielded comparable clusters of age-related cortical thinning, although correlations between age and average cortical thickness were stronger for FreeSurfer. Our data suggest that, for the analysis of cortical thickness, the volume-based CAT tool can be regarded a considerable alternative to the well-established surface-based FreeSurfer tool.
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30
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Rocca MA, Battaglini M, Benedict RHB, De Stefano N, Geurts JJG, Henry RG, Horsfield MA, Jenkinson M, Pagani E, Filippi M. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology 2016; 88:403-413. [PMID: 27986875 DOI: 10.1212/wnl.0000000000003542] [Citation(s) in RCA: 162] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/18/2016] [Indexed: 01/06/2023] Open
Abstract
Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been applied in research or clinical trial settings. Despite this, the measurement of atrophy in routine clinical practice remains an unmet need. After a short summary of the pathologic substrates of brain atrophy in MS, this review attempts to guide the clinician towards a better understanding of the methods currently used for quantifying brain atrophy in this condition. Important physiologic factors that affect brain volume measures are also considered. Finally, the most recent research on brain atrophy in MS is summarized, including whole brain and various compartments thereof (i.e., white matter, gray matter, selected CNS structures). Current methods provide sufficient precision for cohort studies, but are not adequate for confidently assessing changes in individual patients over the scale of months or a few years.
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Affiliation(s)
- Maria A Rocca
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Marco Battaglini
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ralph H B Benedict
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Nicola De Stefano
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jeroen J G Geurts
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Roland G Henry
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark A Horsfield
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark Jenkinson
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
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