Patients with multiple sclerosis (MS) demonstrate slower and more variable performance on attention and information processing speed tasks. Greater variability in cognitive task performance has been shown to be an important predictor of neurologic status and provides a unique measure of cognitive performance in MS patients.
Objectives:
This study investigated alterations in resting-state functional connectivity associated with within-person performance variability in MS patients.
Methods:
Relapsing–remitting MS patients and matched healthy controls completed structural MRI and resting-state fMRI (rsfMRI) scans, as well as tests of information processing speed. Performance variability was calculated from reaction time tests of processing speed. rsfMRI connectivity was investigated within regions associated with the default mode network (DMN). Relations between performance variability and functional connectivity in the DMN within MS patients were evaluated.
Results:
MS patients demonstrated greater reaction time performance variability compared to healthy controls (p<0.05). For MS patients, more stable performance on a complex processing speed task was associated with greater resting-state connectivity between the ventral medial prefrontal cortex and the frontal pole.
Conclusions:
Among MS patients, greater functional connectivity between medial prefrontal and frontal pole regions appears to facilitate performance stability on complex speed-dependent information processing tasks.
ChiaravallotiNDDeLucaJ. Cognitive impairment in multiple sclerosis. Lancet Neurol2008; 7: 1139–1151.
2.
BodlingAMDenneyDRLynchSG. Individual variability in speed of information processing: An index of cognitive impairment in multiple sclerosis. Neuropsychology2012; 26: 357–367.
3.
BruceJMBruceASArnettPA. Response variability is associated with self-reported cognitive fatigue in multiple sclerosis. Neuropsychology2010; 24: 77–83.
4.
WojtowiczMBerriganLIFiskJD. Intra-individual variability as a measure of information processing difficulties in multiple sclerosis. Int J M S Care2012; 14: 77–83.
5.
WojtowiczMOmisadeAFiskJD. Indices of cognitive dysfunction in relapsing-remitting multiple sclerosis: Intra-individual variability, processing speed, and attention network efficiency. J Int Neuropsychol Soc2013; 19: 551–558.
6.
BenedictRHBZivadinovR. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol2011; 7: 332–342.
7.
StaffenWMairAZaunerH. Cognitive function and fMRI in patients with multiple sclerosis: Evidence for compensatory cortical activation during an attention task. Brain2002; 125: 1275–1282.
8.
FoxMDRaichleME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci2007; 8: 700–711.
9.
BucknerRLAndrews-HannaJRSchacterDL. The brain’s default network: Anatomy, function, and relevance to disease. Ann N Y Acad Sci2008; 1124: 1–38.
10.
RoccaMAValsasinaPAbsintaM. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology2010; 74: 1252–1259.
11.
FaivreARicoAZaaraouiW. Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler2012; 18: 1251–1258.
12.
BonavitaSGalloASaccoR. Distributed changes in connectivity in multiple sclerosis. Multiple sclerosis journal2011; 17: 411–422.
13.
SumowskiJFWylieGRLeavittVM. Default network activity is a sensitive and specific biomarker of memory in multiple sclerosis. Mult Scler2013; 19: 199–208.
14.
FranciottiRFalascaNWBonanniL. Default network is not hypoactive in dementia with fluctuating cognition: An Alzheimer disease/dementia with Lewy bodies comparison. Neurobiol Aging2013; 34: 1148–1158.
15.
RomboutsSABarkhofFGoekoopR. Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: An fMRI study. Hum Brain Mapp2005; 26: 231–239.
16.
GradyCLProtznerABKovacevicN. A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cereb Cortex2010; 20: 1432–1447.
McdonaldWICompstonAEdanG. Recommended diagnostic criteria for multiple sclerosis : Guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Ann Neurol2001; XX: 121–127.
19.
FiskJDobleS. Construction and validation of a fatigue impact scale for daily administration (D-FIS). Qual Life Res2002; 11: 263–272.
20.
TombaughTNReesLM. Computerized Test of Information Processing. Toronto: Multi-Health System Inc, 2008.
JenkinsonMSmithS. A global optimisation method for robust affine registration of brain images. Med Image Anal2001; 5: 143–156.
23.
SchmidtPGaserCArsicM. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012; 59: 3774–3783.
24.
PhillipsMDGrossmanRIMikiY. Comparison of T2 lesion volume and magnetization transfer ratio histogram analysis and of atrophy and measures of lesion burden in patients with multiple sclerosis. Am J Neuroradiol1998; 19: 1055–1060.
25.
ToroRFoxPTPausT. Functional coactivation map of the human brain. Cereb Cortex2008; 18: 2553–2559.
26.
UddinLQKellyAMBiswalBB. Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Hum Brain Mapp2009; 30: 625–637.
27.
GreiciusMDKrasnowBReissAL. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA2003; 100: 253–258.
28.
RoosendaalSDSchoonheimMMHulstHE. Resting state networks change in clinically isolated syndrome. Brain2010; 133: 1612–1621.
29.
DamoiseauxJSGreiciusMD. Greater than the sum of its parts: A review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct2009; 213: 525–533.
30.
GilbertSJSpenglerSSimonsJS. Differential functions of lateral and medial rostral prefrontal cortex (area 10) revealed by brain-behavior associations. Cereb Cortex2006; 16: 1783–1789.
31.
GilbertSJSpenglerSSimonsJS. Functional specialization within rostral prefrontal cortex (area 10): A meta-analysis. J Cogn Neurosci2006; 18: 932–948.
32.
StussDT. Functions of the frontal lobes: Relation to executive functions. J Int Neuropsychol Soc2011; 17: 759–765.
33.
MaineroCCaramiaFPozzilliC. fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. Neuroimage2004; 21: 858–867.
34.
AudoinBIbarrolaDRanjevaJ-P. Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS. Hum Brain Mapp2003; 20: 51–58.
35.
PennerIRauschM. Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. J Neurol2003; 250: 461–472.
36.
Van DijkKRHeddenTVenkataramanAEvansKCLazarSWBucknerRL. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of neurophysiology2010; 103(1): 297–321.
37.
BennettCMWolfordGLMillerMB. The principled control of false positives in neuroimaging. Social cognitive and affective neuroscience2009; 4(4): 417–22.
