4.3 Article

The link between resting-state functional connectivity and cognition in MS patients

Journal

MULTIPLE SCLEROSIS JOURNAL
Volume 20, Issue 3, Pages 338-348

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/1352458513495584

Keywords

default network; salience network; right frontoparietal network; cognitive impairment; left frontoparietal network; Resting state functional connectivity

Funding

  1. Brainglot project of the CONSOLIDER-INGENIO Programme [CSD2007-00012]
  2. MINECO [PSI2010-20168]
  3. Universitat Jaume I [P1.1B2011-09]
  4. Biogen Idec

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Objective: The objective of this paper is to explore differences in resting-state functional connectivity between cognitively impaired and preserved multiple sclerosis (MS) patients. Methods: Sixty MS patients and 18 controls were assessed with the Brief Repeatable Battery of Neuropsychological Tests (BRB-N). A global Z score of the BRB-N was obtained and allowed us to classify MS patients as cognitively impaired and cognitively preserved (n = 30 per group). Functional connectivity was assessed by independent component analysis of resting-state networks (RSNs) related to cognition: the default mode network, left and right frontoparietal and salience network. Between-group differences were evaluated and a regression analysis was performed to describe relationships among cognitive status, functional connectivity and radiological variables. Results: Compared to cognitively preserved patients and healthy controls, cognitively impaired patients showed a lesser degree of functional connectivity in all RSNs explored. Cognitively preserved patients presented less connectivity than the control group in the left frontoparietal network. Global Z scores were positively and negatively correlated with brain parenchymal fraction and lesion volume, respectively. Conclusion: Decreased cognitive performance is accompanied by reduced resting state functional connectivity and directly related to brain damage. These results support the use of connectivity as a powerful tool to monitor and predict cognitive impairment in MS patients.

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