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

Abstract

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.

Keywords

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

Get full access to this article

View all access options for this article.

References

Hawellek

Hipp

Lewis

. Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci U S A 2011; 108: 19066–19071.

Audoin

Ibarrola

Ranjeva

. Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS. Hum Brain Mapp 2003; 20: 51–58.

Au Duong

Boulanouar

Audoin

. Modulation of effective connectivity inside the working memory network in patients at the earliest stage of multiple sclerosis. Neuroimage 2005; 24: 533–538.

Au Duong

Audoin

Boulanouar

. Altered functional connectivity related to white matter changes inside the working memory network at the very early stage of MS. J Cereb Blood Flow Metab 2005; 25: 1245–1253.

Cader

Cifelli

Abu-Omar

. Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain 2006; 129: 527–537.

Forn

Barros-Loscertales

Escudero

. Cortical reorganization during PASAT task in MS patients with preserved working memory functions. Neuroimage 2006; 31: 686–691.

Forn

Barros-Loscertales

Escudero

. Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory N-back task. Hum Brain Mapp 2007; 28: 424–430.

Forn

Rocca

Valsasina

. Functional magnetic resonance imaging correlates of cognitive performance in patients with a clinically isolated syndrome suggestive of multiple sclerosis at presentation: An activation and connectivity study. Mult Scler 2012; 18: 153–163.

Bonnet

Allard

Dilharreguy

. Cognitive compensation failure in multiple sclerosis. Neurology 2010; 75: 1241–1248.

10.

Cifelli

Matthews

PM.

Cerebral plasticity in multiple sclerosis: Insights from fMRI. Mult Scler 2002; 8: 193–199.

11.

Fox

Greicius

. Clinical applications of resting state functional connectivity. Front Syst Neurosci 2010; 4: 19.

12.

Rocca

Valsasina

Absinta

. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010; 74: 1252–1259.

13.

Bonavita

Gallo

Sacco

. Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Mult Scler 2011; 17: 411–422.

14.

Roosendaal

Schoonheim

Hulst

. Resting state networks change in clinically isolated syndrome. Brain 2010; 133: 1612–1621.

15.

Hulst

Geurts

. Gray matter imaging in multiple sclerosis: What have we learned? BMC Neurol 2011; 11: 153.

16.

Sumowski

Wylie

Leavitt

. Default network activity is a sensitive and specific biomarker of memory in multiple sclerosis. Mult Scler 2013; 19: 199–208.

17.

Faivre

Rico

Zaaraoui

. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler 2012; 18: 1251–1258.

18.

Polman

Reingold

Banwell

. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69: 292–302.

19.

Kurtzke

. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983; 33: 1444–1452.

20.

Sepulcre

Vanotti

Hernández

. Cognitive impairment in patients with multiple sclerosis using the Brief Repeatable Battery-Neuropsychology test. Mult Scler 2006; 12: 187–195.

21.

Calabrese

Agosta

Rinaldi

. Cortical lesions and atrophy associated with cognitive impairment in relapsing–remitting multiple sclerosis. Arch Neurol 2009; 66: 1144–1150.

22.

Sanfilipo

Benedict

RHB

Zivadinov

. Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: The proportion vs. residual method. Neuroimage 2004; 22: 1732–1743.

23.

Ceccarelli

Bakshi

Neema

. MRI in multiple sclerosis: A review of the current literature. Curr Opin Neurol 2012; 25: 402–409.

24.

Calhoun

Adali

Pearlson

. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001; 14: 140–151.

25.

Bell

Sejnowski

. An information-maximization approach to blind separation and blind deconvolution. Neural Comput 1995; 7: 1129–1159.

26.

Himberg

Hyvärinen

Esposito

. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 2004; 22: 1214–1222.

27.

Erhardt

Rachakonda

Bedrick

. Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 2011; 32: 2075–2095.

28.

Raichle

MacLeod

Snyder

. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682.

29.

Damoiseaux

Rombouts

Barkhof

. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 2006; 103: 13848–13853.

30.

Menon

Uddin

. Saliency, switching, attention and control: A network model of insula function. Brain Struct Funct 2010; 214: 655–667.

31.

Loitfelder

Filippi

Rocca

. Abnormalities of resting state functional connectivity are related to sustained attention deficits in MS. PloS One 2012; 7: e42862.

32.

Rocca

Valsasina

Martinelli

. Large-scale neuronal network dysfunction in relapsing–remitting multiple sclerosis. Neurology 2012; 79: 1449–1457.

33.

Schoonheim

Geurts

Barkhof

. The limits of functional reorganization in multiple sclerosis. Neurology 2010; 74: 1246–1247.

34.

Rocca

Gatti

Agosta

. Influence of task complexity during coordinated hand and foot movements in MS patients with and without fatigue. A kinematic and functional MRI study. J Neurol 2009; 256: 470–482.

35.

Filippi

Rocca

. MRI and cognition in multiple sclerosis. Neurol Sci 2010; 3 (Suppl 2): S231–S234.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

8.88 MB