Literature DB >> 26637488

Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant.

Martijn D Steenwijk1, Jeroen J G Geurts2, Marita Daams3, Betty M Tijms4, Alle Meije Wink5, Lisanne J Balk4, Prejaas K Tewarie4, Bernard M J Uitdehaag4, Frederik Barkhof5, Hugo Vrenken6, Petra J W Pouwels7.   

Abstract

Grey matter atrophy is common in multiple sclerosis. However, in contrast with other neurodegenerative diseases, it is unclear whether grey matter atrophy in multiple sclerosis is a diffuse 'global' process or develops, instead, according to distinct anatomical patterns. Using source-based morphometry we searched for anatomical patterns of co-varying cortical thickness and assessed their relationships with white matter pathology, physical disability and cognitive functioning. Magnetic resonance imaging was performed at 3 T in 208 patients with long-standing multiple sclerosis (141 females; age = 53.7 ± 9.6 years; disease duration = 20.2 ± 7.1 years) and 60 age- and sex-matched healthy controls. Spatial independent component analysis was performed on cortical thickness maps derived from 3D T1-weighted images across all subjects to identify co-varying patterns. The loadings, which reflect the presence of each cortical thickness pattern in a subject, were compared between patients with multiple sclerosis and healthy controls with generalized linear models. Stepwise linear regression analyses were used to assess whether white matter pathology was associated with these loadings and to identify the cortical thickness patterns that predict measures of physical and cognitive dysfunction. Ten cortical thickness patterns were identified, of which six had significantly lower loadings in patients with multiple sclerosis than in controls: the largest loading differences corresponded to the pattern predominantly involving the bilateral temporal pole and entorhinal cortex, and the pattern involving the bilateral posterior cingulate cortex. In patients with multiple sclerosis, overall white matter lesion load was negatively associated with the loadings of these two patterns. The final model for physical dysfunction as measured with Expanded Disability Status Scale score (adjusted R(2) = 0.297; P < 0.001) included the predictors age, overall white matter lesion load, the loadings of two cortical thickness patterns (bilateral sensorimotor cortex and bilateral insula), and global cortical thickness. The final model predicting average cognition (adjusted R(2) = 0.469; P < 0.001) consisted of age, the loadings of two cortical thickness patterns (bilateral posterior cingulate cortex and bilateral temporal pole), overall white matter lesion load and normal-appearing white matter integrity. Although white matter pathology measures were part of the final clinical regression models, they explained limited incremental variance (to a maximum of 4%). Several cortical atrophy patterns relevant for multiple sclerosis were found. This suggests that cortical atrophy in multiple sclerosis occurs largely in a non-random manner and develops (at least partly) according to distinct anatomical patterns. In addition, these cortical atrophy patterns showed stronger associations with clinical (especially cognitive) dysfunction than global cortical atrophy.
© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  MRI; grey matter; multiple sclerosis; neurodegeneration; white matter

Mesh:

Year:  2015        PMID: 26637488     DOI: 10.1093/brain/awv337

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  76 in total

1.  Localised grey matter atrophy in multiple sclerosis is network-based: a coordinate-based meta-analysis.

Authors:  F L Chiang; Q Wang; F F Yu; R S Romero; S Y Huang; P M Fox; B Tantiwongkosi; P T Fox
Journal:  Clin Radiol       Date:  2019-08-14       Impact factor: 2.350

2.  Single-subject independent component analysis-based intensity normalization in non-quantitative multi-modal structural MRI.

Authors:  Sebastian Papazoglou; Jens Würfel; Friedemann Paul; Alexander U Brandt; Michael Scheel
Journal:  Hum Brain Mapp       Date:  2017-04-22       Impact factor: 5.038

3.  Global and regional annual brain volume loss rates in physiological aging.

Authors:  Sven Schippling; Ann-Christin Ostwaldt; Per Suppa; Lothar Spies; Praveena Manogaran; Carola Gocke; Hans-Jürgen Huppertz; Roland Opfer
Journal:  J Neurol       Date:  2017-01-04       Impact factor: 4.849

4.  Single scan quantitative gradient recalled echo MRI for evaluation of tissue damage in lesions and normal appearing gray and white matter in multiple sclerosis.

Authors:  Biao Xiang; Jie Wen; Anne H Cross; Dmitriy A Yablonskiy
Journal:  J Magn Reson Imaging       Date:  2018-08-29       Impact factor: 4.813

5.  Postmortem validation of MRI cortical volume measurements in MS.

Authors:  Veronica Popescu; Roel Klaver; Adriaan Versteeg; Pieter Voorn; Jos W R Twisk; Frederik Barkhof; Jeroen J G Geurts; Hugo Vrenken
Journal:  Hum Brain Mapp       Date:  2016-03-04       Impact factor: 5.038

6.  Word-finding difficulty is a prevalent disease-related deficit in early multiple sclerosis.

Authors:  Rachel Brandstadter; Michelle Fabian; Victoria M Leavitt; Stephen Krieger; Anusha Yeshokumar; Ilana Katz Sand; Sylvia Klineova; Claire S Riley; Christina Lewis; Gabrielle Pelle; Fred D Lublin; Aaron E Miller; James F Sumowski
Journal:  Mult Scler       Date:  2019-11-19       Impact factor: 6.312

7.  Mindboggling morphometry of human brains.

Authors:  Arno Klein; Satrajit S Ghosh; Forrest S Bao; Joachim Giard; Yrjö Häme; Eliezer Stavsky; Noah Lee; Brian Rossa; Martin Reuter; Elias Chaibub Neto; Anisha Keshavan
Journal:  PLoS Comput Biol       Date:  2017-02-23       Impact factor: 4.475

Review 8.  Causes, effects and connectivity changes in MS-related cognitive decline.

Authors:  Carolina de Medeiros Rimkus; Martijn D Steenwijk; Frederik Barkhof
Journal:  Dement Neuropsychol       Date:  2016 Jan-Mar

9.  In vivo detection of connectivity between cortical and white matter lesions in early MS.

Authors:  Jan-Mendelt Tillema; Stephen D Weigand; Jay Mandrekar; Yunhong Shu; Claudia F Lucchinetti; Istvan Pirko; John D Port
Journal:  Mult Scler       Date:  2016-10-03       Impact factor: 6.312

10.  Long-standing multiple sclerosis neurodegeneration: volumetric magnetic resonance imaging comparison to Parkinson's disease, mild cognitive impairment, Alzheimer's disease, and elderly healthy controls.

Authors:  Dejan Jakimovski; Niels Bergsland; Michael G Dwyer; Jesper Hagemeier; Deepa P Ramasamy; Kinga Szigeti; Thomas Guttuso; David Lichter; David Hojnacki; Bianca Weinstock-Guttman; Ralph H B Benedict; Robert Zivadinov
Journal:  Neurobiol Aging       Date:  2020-02-08       Impact factor: 4.673

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