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  A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis

Jandric, D., Parker, G. J., Haroon, H., Tomassini, V., Muhlert, N., & Lipp, I. (2022). A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis. NeuroImage: Clinical, 34: 102995. doi:10.1016/j.nicl.2022.102995.

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 Creators:
Jandric, Danka1, Author
Parker, Geoff J.M.2, 3, Author
Haroon, Hamied1, Author
Tomassini, Valentina4, 5, 6, Author
Muhlert, Nils1, Author
Lipp, Ilona4, 7, Author              
Affiliations:
1Division of Neuroscience & Experimental Psychology, School of Psychological Sciences, University of Manchester, United Kingdom, ou_persistent22              
2Centre for Medical Image Computing, University College London, United Kingdom, ou_persistent22              
3Bioxydyn Limited, Manchester, United Kingdom, ou_persistent22              
4Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom, ou_persistent22              
5Institute of Advanced Biomedical Technologies (ITAB), Gabriele D'Annunzio University, Chieti-Pescara, Italy, ou_persistent22              
6Multiple Sclerosis Centre, Gabriele D'Annunzio University, Chieti-Pescara, Italy, ou_persistent22              
7Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Free keywords: Multiple sclerosis; Cognitive impairment; MRI; Tractometry; Brain connectivity
 Abstract: Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.

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Language(s): eng - English
 Dates: 2022-03-042021-12-212022-03-232022-03-24
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.nicl.2022.102995
Other: online ahead of print
PMID: 35349892
PMC: PMC8958271
 Degree: -

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Project name : -
Grant ID : MR/N013751/11
Funding program : Medical Research Council Doctoral Training Partnership
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Title: NeuroImage: Clinical
Source Genre: Journal
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Publ. Info: Elsevier
Pages: - Volume / Issue: 34 Sequence Number: 102995 Start / End Page: - Identifier: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582