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Resting-state functional MRI shows altered default-mode network functional connectivity in Duchenne muscular dystrophy patients

Lookup NU author(s): Dr Nathalie Doorenweerd, Dr Chiara Marini Bettolo, Dr Kieren Hollingsworth, Professor Volker Straub

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Abstract

© 2021, The Author(s).Duchenne muscular dystrophy (DMD) is an X-linked recessive neuromuscular disorder caused by absence of dystrophin protein. Dystrophin is expressed in muscle, but also in the brain. Difficulties with attention/inhibition, working memory and information processing are well described in DMD patients but their origin is poorly understood. The default mode network (DMN) is one of the networks involved in these processes. Therefore we aimed to assess DMN connectivity in DMD patients compared to matched controls, to better understand the cognitive profile in DMD. T1-weighted and resting state functional MRI scans were acquired from 33 DMD and 24 male age-matched controls at two clinical sites. Scans were analysed using FMRIB Software Library (FSL). Differences in the DMN were assessed using FSL RANDOMISE, with age as covariate and threshold-free cluster enhancement including multiple comparison correction. Post-hoc analyses were performed on the visual network, executive control network and fronto-parietal network with the same methods. In DMD patients, the level of connectivity was higher in areas within the control DMN (hyperconnectivity) and significant connectivity was found in areas outside the control DMN. No hypoconnectivity was found and no differences in the visual network, executive control network and fronto-parietal network. We showed differences both within and in areas outside the DMN in DMD. The specificity of our findings to the DMN can help provide a better understanding of the attention/inhibition, working memory and information processing difficulties in DMD.


Publication metadata

Author(s): Doorenweerd N, de Rover M, Marini-Bettolo C, Hollingsworth KG, Niks EH, Hendriksen JGM, Kan HE, Straub V

Publication type: Article

Publication status: Published

Journal: Brain Imaging and Behavior

Year: 2021

Pages: epub ahead of print

Online publication date: 03/01/2021

Acceptance date: 24/11/2020

ISSN (print): 1931-7557

ISSN (electronic): 1931-7565

Publisher: Springer

URL: https://doi.org/10.1007/s11682-020-00422-3

DOI: 10.1007/s11682-020-00422-3


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