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Abstract

Background:

Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients.

Objectives:

The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought.

Methods:

A total of 50 patients (EDSS mean (m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS.

Results:

A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area (p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas (p = 0.005, 0.004, respectively).

Conclusion:

I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings.

Keywords

Cognitive impairment multiple sclerosis working memory BOLD fMRI MACFIMS

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References

Nylander

Hafler

. Multiple sclerosis. J Clin Invest 2012; 122(4): 1180–1188.

Noseworthy

Lucchinetti

Rodriguez

. Multiple sclerosis. N Engl J Med 2000; 343(13): 938–952.

Amato

Ponziani

Pracucci

. Cognitive impairment in early-onset multiple sclerosis. Arch Neurol 1995; 52: 168–172.

Filippi

Rocca

Benedict

RHB

. The contribution of MRI in assessing cognitive impairment in multiple sclerosis. Neurology 2010; 75(23): 2121–2128.

Nelson

Datta

Garcia

. Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Mult Scler 2011; 17(9): 1122–1129.

Mainero

Pantano

Caramia

. Brain reorganization during attention and memory tasks in multiple sclerosis: Insights from functional MRI studies. J Neurol Sci 2006; 245(1–2): 93–98.

Guimarães

Sá

. Cognitive dysfunction in multiple sclerosis. Front Neurol 2012; 3: 1–8.

Rao

Leo

Bernardin

. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology 1991; 41(5): 685–691.

Kutzelnigg

Lucchinetti

Stadelmann

. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 2005; 128(11): 2705–2712.

10.

Loitfelder

Fazekas

Petrovic

. Reorganization in cognitive networks with progression of multiple sclerosis: Insights from fMRI. Neurology 2011; 76(6): 526–533.

11.

Kinsinger

Lattie

Mohr

. Relationship between depression, fatigue, subjective cognitive impairment, and objective neuropsychological functioning in patients with multiple sclerosis. Neuropsychology 2010; 24(5): 573–580.

12.

Rocca

Riccitelli

Rodegher

. Functional MR imaging correlates of neuropsychological impairment in primary-progressive multiple sclerosis. AJNR Am J Neuroradiol 2010; 31(7): 1240–1246.

13.

DeLuca

Johnson

Natelson

. Information processing efficiency in chronic fatigue syndrome and multiple sclerosis. Arch Neurol 1993; 50(3): 301–304.

14.

Langdon

Amato

Boringa

. Recommendations for a brief international cognitive assessment for multiple sclerosis (BICAMS). Mult Scler J 2012; 18(6): 891–898.

15.

Bastianello

Giugni

Amato

. Changes in magnetic resonance imaging disease measures over 3 years in mildly disabled patients with relapsing-remitting multiple sclerosis receiving interferon β-1a in the COGnitive Impairment in MUltiple Sclerosis (COGIMUS) study. BMC Neurol 2011; 11(1): 125.

16.

Benedict

RHB

Munschauer

Linn

. Screening for multiple sclerosis cognitive impairment using a self-administered 15-item questionnaire. Mult Scler 2003; 9(1): 95–101.

17.

Sweet

Rao

Primeau

. Functional magnetic resonance imaging of working memory among multiple sclerosis patients. J Neuroimaging 2004; 14(2): 150–157.

18.

Chiaravalloti

Hillary

Ricker

. Cerebral activation patterns during working memory performance in multiple sclerosis using fMRI. J Elinical Exp Neuropsychol 2005; 27(1): 33–54.

19.

Moeller

Steinberg

Schmitz

. Working memory fMRI activation in cocaine dependent subjects: Association with treatment response. Psychiatry Res 2010; 181(3): 174–182.

20.

Benedict

RHB

Cox

Thompson

. Reliable screening for neuropsychological impairment in multiple sclerosis. Mult Scler 2004; 10(6): 675–678.

21.

Benedict

RHB

Zivadinov

. Reliability and validity of neuropsychological screening and assessment strategies in MS. J Neurol 2007; 254(Suppl. 2): II22–II25.

22.

Krüger

Kastrup

Glover

. Neuroimaging at 1.5 T and 3.0 T: Comparison of oxygenation-sensitive magnetic resonance imaging. Mangetic Reson Med 2001; 45(4): 595–604.

23.

Norris

Zysset

Mildner

. An investigation of the value of spin-echo-based fMRI using a Stroop color-word matching task and EPI at 3 T. Neuroimage 2002; 15(3): 719–726.

24.

Wang

Roc

. Reduced susceptibility effects in perfusion fMRI with single-shot spin-echo EPI acquisitions at 1.5 Tesla. Magn Reson Imaging 2004; 22(1): 1–7.

25.

Ogawa

Lee

Kay

. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 1990; 87(24): 9868–9872.

26.

Sajja

Datta

. Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng 2006; 34(1): 142–151.

27.

Narayana

. Global optimization of mutual information: Application to three-dimensional retrospective registration of magnetic resonance images. Comput Med Imaging Graph 2002; 26(4): 277–292.

28.

Datta

Sajja

. Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis. J Magn Reson Imaging 2007; 25(5): 932–937.

29.

Perona

Malik

. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629–639.

30.

Nyúl

Udupa

Zhang

. New variants of a method of MRI scale standardization. IEEE Trans Pattern Anal Mach Intell 2000; 19(2): 143–150.

31.

Tzourio-Mazoyer

Landeau

Papathanassiou

. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15(1): 273–289.

32.

Donaldson

. Measuring recognition memory. J Exp Psychol Gen 1992; 121: 275–277.

33.

Reitan

Wolfson

. Theoretical, methodological, and validational bases of the Halstead-Reitan neuropsychological test battery. In: Hilsenroth

Segal

Hersen

(eds) Comprehensive handbook of psychological assessment. Hoboken, NJ: John Wiley & Sons, 2004, pp. 105–133.

34.

Desikan

Ségonne

Fischl

. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31(3): 968–980.

35.

Rocca

Valsasina

Hulst

. Functional correlates of cognitive dysfunction in multiple sclerosis: A multicenter fMRI study. Hum Brain Mapp 2014; 35: 5799–5814.

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Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis

Abstract

Background:

Objectives:

Methods:

Results:

Conclusion:

Keywords

Get full access to this article

References

Supplementary Material