title: Patterns of white matter damage are non-random and associated with cognitive function in secondary progressive multiple sclerosis.
abstract: In multiple sclerosis (MS), white matter damage is thought to contribute to cognitive dysfunction, which is especially prominent in secondary progressive MS (SPMS). While studies in healthy subjects have revealed patterns of correlated fractional anisotropy (FA) across white matter tracts, little is known about the underlying patterns of white matter damage in MS. In the present study, we aimed to map the SPMS-related covariance patterns of microstructural white matter changes, and investigated whether or not these patterns were associated with cognitive dysfunction. Diffusion MRI was acquired from 30 SPMS patients and 32 healthy controls (HC). A tensor model was fitted and FA maps were processed using tract-based spatial statistics (TBSS) in order to obtain a skeletonised map for each subject. The skeletonised FA maps of patients only were decomposed into 18 spatially independent components (ICs) using independent component analysis. Comprehensive cognitive assessment was conducted to evaluate five cognitive domains. Correlations between cognitive performance and (1) severity of FA abnormalities of the extracted ICs (i.e. z-scores relative to FA values of HC) and (2) IC load (i.e. FA covariance of a particular IC) were examined. SPMS patients showed lower FA values of all examined patterns of correlated FA (i.e. spatially independent components) than HC (p < 0.01). Tracts visually assigned to the supratentorial commissural class were most severely damaged (z = - 3.54; p < 0.001). Reduced FA was significantly correlated with reduced IC load (i.e. FA covariance) (r = 0.441; p < 0.05). Lower mean FA and component load of the supratentorial projection tracts and limbic association tracts classes were associated with worse cognitive function, including executive function, working memory and verbal memory. Despite the presence of white matter damage, it was possible to reveal patterns of FA covariance across SPMS patients. This could indicate that white matter tracts belonging to the same cluster, and thus with similar characteristics, tend to follow similar trends during neurodegeneration. Furthermore, these underlying FA patterns might help to explain cognitive dysfunction in SPMS.
authors.affiliation: Department of Anatomy and Neurosciences, VU Medical Centre, Amsterdam, The Netherlands; NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom.
authors.forename: K A
authors.initials: KA
authors.lastname: Meijer
authors.affiliation: Clinical Imaging Centre, Brighton and Sussex Medical School, Brighton, United Kingdom; Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Cercignani
authors.affiliation: School of Psychological Sciences, University of Manchester, Manchester, United Kingdom.
authors.forename: N
authors.initials: N
authors.lastname: Muhlert
authors.affiliation: NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom; School of Psychology and Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.
authors.forename: V
authors.initials: V
authors.lastname: Sethi
authors.affiliation: NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom; NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom.
authors.forename: D
authors.initials: D
authors.lastname: Chard
authors.affiliation: Department of Anatomy and Neurosciences, VU Medical Centre, Amsterdam, The Netherlands.
authors.forename: J J G
authors.initials: JJ
authors.lastname: Geurts
authors.affiliation: NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom; NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom.
authors.forename: O
authors.initials: O
authors.lastname: Ciccarelli
mesh_terms: D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D016880:Anisotropy; D001931:Brain Mapping; D003072:Cognition Disorders / Q000000981:diagnostic imaging / Q000209:etiology*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D056784:Leukoencephalopathies / Q000000981:diagnostic imaging / Q000209:etiology*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000150:complications* / Q000000981:diagnostic imaging; D009483:Neuropsychological Tests; D010808:Physical Examination; D012720:Severity of Illness Index
keywords: Cognition; Diffusion tensor imaging; Independent component analysis; MRI; Secondary progressive multiple sclerosis; Tract-based spatial statistics