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Title: | Can cognitive training reignite compensatory mechanisms in advanced multiple sclerosis patients? An explorative morphological network approach | Authors: | FRIESKE, Joana Pareto, Deborah García-Vidal, Aran CUYPERS, Koen MEESEN, Raf Alonso, Juli Arévalo, Maria Jesus Galán, Ingrid Renom, Marta Vidal-Jordana, Ángela Auger, Cristina Montalban, Xavier Rovira, Àlex Sastre-Garriga, Jaume |
Issue Date: | 2022 | Publisher: | PERGAMON-ELSEVIER SCIENCE LTD | Source: | NEUROSCIENCE, | Status: | Early view | Abstract: | Multiple Sclerosis (MS) has been shown to significantly impair brain connectivity, as alterations in functional and structural networks have been identified and associated with clinical status, particularly cognitive deficits. We aimed to identify structural connectivity changes in grey matter networks following cognitive rehabilitation (CR) in persons with MS (PwMS). Fifteen long-standing PwMS underwent a 5-week CR-program and 5 healthy controls (HC) were also investigated. T1-weighted MRI scans and neuropsychological tests were obtained before and after CR. T1-weighted scans were used to examine grey matter networks with graph analytic parameters [betweenness centrality (BC), degree (D), clustering (Cl), path length (PL) and small world properties: connectedness, gamma and lambda values]. Results were analysed at the whole brain level and for each brain lobe. Before CR, PwMS displayed lower values for D in the left parietal lobe (p=0.009) compared to HC. After CR, significant increases in Cl located in frontal (p=0.024) and temporal (p=0.026) regions in PwMS were accompanied by significant decreases in PL located in the right parietal lobe (p=0.025) and BC globally (p=0.010). Overall, CR may prevent a network worsening in long-standing PwMS by increasing local efficiency of the brain and therefore facilitating compensation mechanisms. | Keywords: | Cognitive rehabilitation;Connectivity;Grey matter networks;MRI;MS | Document URI: | http://hdl.handle.net/1942/37262 | ISSN: | 0306-4522 | e-ISSN: | 1873-7544 | DOI: | 10.1016/j.neuroscience.2022.03.031 | ISI #: | 000830324900002 | Category: | A2 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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