Multiple Sclerosis Resource Centre

Welcome to the Multiple Sclerosis Resource Centre. This website is intended for international healthcare professionals with an interest in Multiple Sclerosis. By clicking the link below you are declaring and confirming that you are a healthcare professional

You are here

Association of cognitive impairment and lesion volumes in multiple sclerosis – A MRI study

Clinical Neurology and Neurosurgery, Volume 127, December 2014, Pages 54-58

Abstract

Cognitive impairment (CI) can be demonstrated in 40–65% of multiple sclerosis (MS) patients, sometimes starting from the early stages of the disease. The objective of this study was a community-based investigation of FLAIR-hyperintense lesion volumes (LV) and their association with CI in patients with relapsing remitting (RR) MS. The neurocognitive assessment was conducted with the brief cognitive screening instrument, MUSIC. Magnetic resonance imaging (MRI) scans were obtained with a 1.5 Tesla Sigma Magnetom MRI scanner. We conducted a stepwise multiple regression analysis to assess the relative contribution of the main clinical, demographic and MRI-variables in predicting cognitive impairment. We recruited 78 patients with RRMS. The mean MUSIC score was 20.6 ± 5.4. Forty five percent of patients (n = 35, mean score 15.1 ± 3.3) had CI and 55% (n = 43, mean score 24.4 ± 2.5) had no sign of CI. In the correlation analysis of the MUSIC subtests only the interference test correlated negatively with the LV (r = −0.23). Multivariate linear regression analysis using MUSIC as the dependent continuous variable revealed LV and disability severity as independent factors associated with MUSIC (p value of the regression model < 0.001; adjustedR-square = 0.11). The results of the present study suggest an association between white matter damage and CI in MS. We could demonstrate an association between attention difficulties and the LV in MS patients.

Trial Registration: ClinicalTrials.gov Identifier: NCT01250665 and NCT01250678 .

Keywords: Multiple sclerosis, Cognitive impairment, Lesion volume, MRI.

1. Introduction

Neurocognitive impairment (CI) has been identified in 40–65% of multiple sclerosis (MS) patients [1] , and are already evident at the early stages of the disease [2] . Although in the past the relevance of CI was underestimated by clinicians, it is now accepted as a critical aspect of disease progression in MS [3] . Cognitive impairment is a leading cause of disability in MS and it can have profound socio-economic consequences for patients and their families[4], [5], and [6]. The most common CIs found in MS patients are difficulties in information retention and processing speed, verbal abilities, sustained attention and executive functions [7] .

Structural MRI studies have established WM lesions in multiple sclerosis (MS) and investigated their association with gray matter (GM) atrophy and clinical scores[8] and [9]. Recently, a number of magnetic resonance imaging (MRI) studies investigated neurobiological factors associated with CI in MS[10], [11], [12], [13], [14], [15], and [16]. Previously, it was generally assumed that the association of CI and white matter (WM) lesions is rather modest in MS[16] and [17]. This may be explained in part due to the heterogeneity of the WM lesions with varying degrees of edema, inflammation, demyelination, remyelination, gliosis, and axonal loss [18] . Recent findings suggest a prominent role of WM lesions in the development of CI, especially the contribution of information-processing speed is higher than formerly assumed[19] and [12]. Cognitive impairment in MS due to WM damage may be an outcome of the interruption of crucial tracts between functional units which can be accompanied by insufficient compensatory reorganization of functional neuronal networks subsequently leading to CI [20] . The objective of this study was to investigate FLAIR-hyperintense lesion volumes (LV) in relation to CI in patients with relapsing remitting (RR) MS. By using an effectively applicable score for standard clinical practice (Multiple Sclerosis Inventory Cognition, MUSIC) we specifically aimed at translational application of the findings, stressing the significance of short assessment scales for their clinical usefulness in assessing cognitive impairment and relating it to MRI findings. In comparison to previous studies our cohort reflects the recent improvements in diagnosis and treatment of MS with shorter disease duration and milder disability. We hypothesized that the higher the LV the more severe the CI in RRMS.

2. Materials and methods

2.1. Subjects

Patients were eligible with an age of 18–60 years and diagnosis of definite relapsing remitting multiple sclerosis (RRMS) according to the 2010 revisions to the McDonald criteria [21] . The study was approved by the local Ethics committee and written informed consent was attained from all participating patients. Disease duration was defined as time since first manifestation of MS. Neurological disability was assessed by Kurtzke expanded disability status scale (EDSS) [22] . Depressive symptoms were measured by the Beck depression inventory (BDI) [23] . Patients were tested for cognitive dysfunction and depressive symptoms in one visit between March and April 2011. We recruited 78 patients: 78 with diagnosis of RRMS at our outpatient clinic between 21 and 60 years of age at presentation. Forty-two patients were treated with natalizumab (ntz),n = 3 intramuscular IFN-β1a 30 μg once weekly, n = 20 s.c. IFN-β1b 250 μg every other day,n = 2 s.c. IFN-β1a 22 μg orn = 3 IFN-β1a 44 μg three times weekly and no treatmentn = 8.

2.2. Neurocognitive assessment

The neurocognitive evaluation was conducted with the brief cognitive screening instrument, MUSIC. It consists of 5 subtests to assess the cognitive core deficits in MS: memory and attention are mainly tested in “word list A” and “word list B” (also for assessing set-shifting capacity) for immediate recall and “word list A delayed” for delayed recall; the “verbal fluency” subtest is predominately measuring mental flexibility; information processing speed and inhibitory control are basically captured with a Stroop test called “interference test without interference” and “interference test with interference condition”, respectively. MUSIC also includes 3 items to examine fatigue [22] . The scale is ranging from 0 to 30 (higher scores indicating less cognitive impairment): 20–30, 16–19, 11–15 and ≤10 points indicate no cognitive dysfunction, mild cognitive dysfunction, moderate cognitive dysfunction and severe cognitive dysfunction, respectively.

2.3. Image acquisition

MRI scans were obtained in the radiological institute of the Cantonal Hospital St. Gall (1.5 Tesla Sigma Magnetom Scanner, Siemens AG, Germany). They include axial pre- and post-gadolinium T1 weighted (T1w), sagittal T1w, sagittal and axial fluid attenuated inversion recovery (FLAIR) sequences and axial T2 weighted spin-echo sequences.

2.4. Image analysis

Marking and measurement of focal WM lesions was performed at the Medical Image Analysis Center, University Hospital in Basel, using commercial semi-automatic software (AMIRA 3.1.1; Mercury Computer Systems Inc.). Hyperintense lesions were identified on the T2 and FLAIR images and manually outlined on the FLAIR images. Subsequently, volumes were calculated.

2.5. Statistical calculations

Patient characteristics were summarized using mean ± standard deviation (or in the case of nonparametric data, median and range) or count and percentage, as appropriate. Categorical data were compared with the Chi-square test or Fisher's exact test. By means of the Mann–WhitneyUtest, we compared the nonparametric data. The correlation between MUSIC and LV was estimated using the Pearson correlation coefficient, for which a 95% confidence interval was constructed using the FisherZtransformation. We conducted a stepwise multiple regression analysis to assess the relative contribution of the main clinical, demographic and MRI-variables in predicting cognitive impairment. All tests have been performed two-sided. A statistical level ofp < 0.05 was considered significant. Analyses were performed with Statistica Version 10, Tulsa, Oklahoma, USA.

3. Results

The mean MUSIC score was 20.6 ± 5.4. The 5 subtests’ weighted scores of MUSIC were as follows: mean score 3.0 ± 1.1 (range 0–4; 4 maximum possible score) word list A; 3.8 ± 0.4 (2–4; 4) word list B; 1.8 ± 0.5 (1–3; 4) mental flexibility tested by verbal fluency; interference (Stroop) test: 3.4 ± 2.2 (0–6; 6) and 3.5 ± 2.0 (2–4; 5), picture naming without interference and with interference, respectively; 4.9 ± 2.2 (0–7; 7) word list A delayed for delayed recall. According to MUSIC 20 patients were categorized with mild cognitive dysfunction, 11 with moderate cognitive dysfunction and 4 with severe cognitive dysfunction, respectively. As the number of patients for each cognitive impairment subcategory was small we decided to form one group with cognitive impairments using the predefined <20 cut-off score. Forty five per cent of patients (n = 35, mean score 15.1 ± 3.3) had cognitive dysfunction (CI+: MUSIC score < 20) and 55% (n = 43, mean score 24.4 ± 2.5) had no sign of cognitive impairment (CI−: score ≥20). Patients’ demographic and clinical characteristics are given in Table 1 .

Table 1 Demographic, clinical, and magnetic resonance imaging characteristics of cognitively impaired and cognitively unimpaired patients with relapsing–remitting multiple sclerosis.

Characteristic Total (n = 78) Cognitively impaired (n = 35) Cognitively preserved (n = 43) P value U statistic

z
Age, mean (range), median y 38.7 ± 10.1

(60–21)

Median 37
40.4

(60–22)

Median 41
37.4

(58–21)

Median 35
0.18 609.0

1.351
Female, n (%) 41 (53) 21 (60) 20 (47) 0.45  
Disease duration mean (range), y 6.7 ± 4.9

(21.0–0.4)
7.7 ± 5.7

(21.0–0.4)
5.9 ± 4.0

(17.0–0.8)
0.241 626.5

1.173
ARR, mean (range) 0.87 ± 0.75

(4–0.1)
0.91 ± 0.77

(4–0.1)
0.85 ± 0.74

(1.60–0.18)
0.50 660.5

0.674
EDSS score, mean (range) 2.3 ± 1.1

(5.5–0)
2.6 ± 1.0

(5–1)
2.2 ± 1.2

(5.5–0)
0.048 * 548.5

1.976
BDI score, mean (range) 8.8 ± 7.1

(30–0)
9.0 ± 6.4

(23–0)
8.6 ± 7.6

(30–0)
0.54 584.0

0.61
MUSIC score, mean (range) 20.6 ± 5.4

(28–6)
15.1 ± 3.3

(19–6)
24.4 ± 2.5

(28–20)
<0.001 ** 0

7.519
T2FlVol, mean (range) 6535 ± 8017

(35010–66)
8212 ± 9022

(28314–294)
5342 ± 7084

(35010–66)
0.07 532.0

1.181

* Significant p < 0.05.

** Highly significant p < 0.001.

Abbreviations:n, number; ARR, annualized relapse rate since diagnosis of MS; EDSS, Expanded Disability Status Scale; FSS, Fatigue Severity Scale; T1w/T2w MRI, T1/T2 weighted magnetic resonance images; FSS, Fatigue Severity Scale; BDI, Beck's Depression Inventory.

Comparing the clinical outcomes between CI+ and CI− subjects, we found that besides the MUSIC, the burden of neurological disability as measured by the EDSS was significantly higher in the CI+ group as compared to the CI− group. There was a trend for higher LV in the cognitively impaired group (see Table 1 ). In the correlation analysis of the MUSIC subtests only the interference test significantly correlated with the LV (r = −0.23, <0.05; see Table 2 ). Multivariate linear regression analysis using MUSIC as the dependent continuous variable revealed LV and EDSS as independent factors associated with MUSIC (p value of the regression model < 0.001; adjustedR-square = 0.11; Table 3 ). Age, annualized relapse rate (ARR) since diagnosis of MS, medication, BDI did not have in this model an influence on the prediction of CI.

Table 2 Multiple Sclerosis Inventory Cognition (MUSIC) and subtests Pearson correlation with T2 lesion volume.

MUSIC Subtest Pearson correlation
Word list A 0.18
Word list B −0.11
Mental flexibility 0.06
Interference test −0.23 *
Word list delayed −0.07
Music −0.23 *

* Significant correlation with p-value < 0.05.

Table 3 Linear regression analysis using Multiple Sclerosis Inventory Cognition (MUSIC) as the dependent variable.

  Regression coefficient β SE of regression coefficient β Standardized coefficient β SE of standardized coefficient β T p Partial correlation
Constant 26.36 2.38     11.092 <0.001*  
EDSS −1.497 0.569 −0.358 0.121 −2.623 0.011* −0.306
T2 Volume −0.0002 0.00008 −0.265 0.123 −2.173 0.033* −0.257
Disease duration 0.198 0.146 0.173 0.127 1.363 0.177 0.164
Gender −1.373 1.320 −0.118 0.113 −1.040 0.302 −0.126

Abbreviation: SE, standard error.

R = 0.40494275;R2 = 0.16397863; correctedR2 = 0.11406690;F = 3.285;p = 0.016; SD of the estimation: 5.1613; constant: 26.361; standard deviation: 2.377;t(67) = 11.092;p < 0.001.

4. Discussion

The results of the present study suggest that WM lesions are significantly associated with MS-related CI. Our observations confirm previous results from imaging studies[16], [17], [25], and [26]by demonstrating an interrelationship between WM lesions and CI in MS patients. It extends their findings by showing an association between attention difficulties (captured mainly by the interference test) and the LV in MS patients. The WM LV significantly predicted Symbol Digit Modalities Test (SDMT) performance in 91 patients with MS and clinically isolated syndrome [19] . Low SDMT scores correlated with higher lesion frequency in WM regions in a group of 142 consecutive patients with RRMS [27] . The SDMT is a widely used measure of attention, visual scanning and tracking, and psychomotor speed [28] . In general, impairment in sustained attention is one of the domain-specific deficits frequently encountered in MS-associated CI [16] . As stated earlier, this may partly be related to the heterogeneity of the WM lesions in terms of their individual histopathological composition and subsequent deterioration of the related WM tracts and gray matter structures. Moreover, with the progression of the disease, WM abnormalities change from predominantly focal to more diffuse lesions and an increase of demyelination in the GM occurs [29] . It would be particularly interesting to investigate in future studies the association of cognitive impairment with the spatiotemporal distribution of white matter lesions. As it was demonstrated during disease progression that MS patients with WM lesions in the callosal body, corticospinal tract and periventricular tracts show particular vulnerability to neurodegeneration [30] . Additionally, WM lesion localizations could be associated with specific cognitive impairments. For example, frontal and parietal lesions have been correlated with poor performance on tests of complex attention and verbal working memory [31] . Attention is a multifaceted cognitive function and encompasses different characteristics like alertness, vigilance, selective, focused and divided attention [32] . Attention deficits are already present early in MS and even in patients with clinically isolated syndrome before the onset of definitive MS[17] and [33]. Attention deficit is clinically highly relevant as only attention and information processing speed correlated with overall health related quality of life [33] . Patients with attention deficits may profit from behavioral treatment [34] .

Previous study results assessing the correlation of T2 hyperintense WM LV in cognitively impaired patients versus cognitively unimpaired patients with MS were contradictory as a significant correlation was found by some studies but not all[16] and [17]. The different outcomes of the studies may be due to methodological disparities. Our patient cohort had milder disability (EDSS 2.3 ± 1.1) as in a recently published study which could demonstrate an association between SDMT and T2 WM LV, i.e. with EDSS 1.8 ± 1.1 [27] . Additionally, our study participants had a considerable shorter mean disease duration (6.7 ± 4.9 years). The milder mean disability of our patient cohort and that of recent clinical studies reflect the present patient population which have changed due to increased diagnostic sensitivity in the last decade. The McDonald Criteria and the widespread availability of MRI have resulted in earlier diagnosis of MS with a high degree of specificity and sensitivity [35] , allowing for better participation of patients in treatment decisions and earlier treatment initiation [21] . Thus the results of our study are highly relevant for general neurology practice populations.

The EDSS score, indicating disability severity, was significantly higher in cognitively impaired than in cognitively preserved patients, as seen in other studies [25] . For example in the study by Patti et al. most patients had mild disability, as in our study population [25] . Intriguingly, EDSS was an independent predictor of cognitive performance in our linear regression model. A recent study found out that early cognitive impairment in MS predicts disability outcome, measured by EDSS, seven years later [34] . Furthermore, there is an association between disability progression and regional GM atrophy in RRMS patients [37] . We did not also capture in our study spinal cord abnormalities. These results underline the importance of neurocognitive evaluation in combination with MRI measurements and clinical parameters, like relapse rates and disability severity, to obtain a more comprehensive assessment of disease activity in MS patients.

The linear regression model, however, could only explain 11% of the variance in CI. The relatively low prediction power could be due to methodological limitations of our study, as conventional MRI is not able to capture abnormalities in the normally appearing WM (NAWM) and damage of the GM such as atrophy or GM lesions. Damages in the NAWM have a profound influence on cognitive functions (i.e. attention, information processing speed and executive functions) which are prominently affected in MS [18] . Interestingly, multivariate pattern classification of gray matter pathology is able to reliably differentiate MS patients with a benign clinical course from patients with more disease activity [38] . Recently, the impact of cortical lesions and atrophy on cognition has been shown using double inversion recovery (DIR) sequences and 3 T scanners, respectively[11], [13], [39], and [40].

A possible limitation of this study is the use of FLAIR sequences. In comparison to T2 weighted images they have more CSF flow-related artifacts and insufficient contrast in the posterior fossa [41] . In particular the sensitivity of the FLAIR technique for detecting small lesion load changes is low [42] . Contrary, the FLAIR sequences also have the obvious advantage of detecting juxtacortical lesions very reliably which are associated with processing speed and verbal memory measures in MS patients [43] . Additionally, a major limitation of our study in regard to other longitudinal studies is that we used a cross-sectional design. As we have conducted our study in one outpatient clinic the sample size is relatively small and poses an obvious limitation. We observed that 45–65% of the study subjects had signs of CI which is in the range of previous studies [44] . Depressive symptoms did not correlate with the LV and were not explanatory for CI in our dataset as demonstrated in previous studies [31] .

5. Conclusion

Despite the high prevalence of CI in MS, and its relation to MRI measurements, cognitive function is not assessed routinely in clinical practice. The perception that cognitive assessments are time-consuming and difficult to interpret has contributed to the failure to incorporate cognitive testing into standard clinical evaluation of patients with MS. However, our findings suggest that WM lesions are associated with CI and in particular with attention deficits. The strength of this study is its naturalistic design, thus the findings can probably be generalized to standard care in MS outpatient clinics.

Disclosure

This work was supported by grants from Bayer AG Switzerland and Biogen Idec Switzerland AG. MY and BT have received travel grants from Merck Serono, Biogen Idec Switzerland AG and Bayer AG Switzerland.

References

  • [1] M. Sailer, B. Fischl, D. Salat, C. Tempelmann, M.A. Schönfeld, E. Busa, et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain. 2004;126:1734-1744
  • [2] M.P. Amato, M.L. Bartolozzi, V. Zipoli, E. Portaccio, M. Mortilla, L. Guidi, et al. Neocortical volume decrease in relapsing–remitting MS patients with mild cognitive impairment. Neurology. 2004;63:89-93
  • [3] S.L. Hauser, J.R. Chan, J.R. Oksenberg. Multiple sclerosis: prospects and promise. Ann Neurol. 2013;74(3):317-327
  • [4] J.D. Fisk, A. Pontefract, P.G. Ritvo, C.J. Archibald, T.J. Murray. The impact of fatigue on patients with multiple sclerosis. Can J Neurol Sci. 1994;21:9-14
  • [5] B.I. Glanz, I.R. Dégano, D.J. Rintell, T. Chitnis, H.L. Weiner, B.C. Healy. Work productivity in relapsing multiple sclerosis: associations with disability, depression, fatigue, anxiety, cognition, and health-related quality of life. Value Health. 2012;15(8):1029-1035
  • [6] A. Ruet, M. Deloire, D. Hamel, J.-C. Ouallet, K. Petry, B. Brochet. Cognitive impairment, health-related quality of life and vocational status at early stages of multiple sclerosis: a 7-year longitudinal study. J Neurol. 2012;01:1-9
  • [7] A. Achiron, M. Polliack, S.M. Rao, Y. Barak, M. Lavie, N. Appelboim, et al. Cognitive patterns and progression in multiple sclerosis: construction and validation of percentile curves. J Neurol Neurosurg Psychiatry. 2005;76:744-749
  • [8] K. Bendfeldt, J.O. Blumhagen, H. Egger, P. Loetscher, N. Denier, P. Kuster, et al. Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing–remitting multiple sclerosis. Hum Brain Mapp. 2010;31(10):1542-1555
  • [9] K. Bendfeldt, P. Kuster, S. Traud, H. Egger, S. Winklhofer, N. Mueller-Lenke, et al. Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis – a longitudinal voxel-based morphometry study. Neuroimage. 2009;45(1):60-67
  • [10] G. Riccitelli, M.A. Rocca, E. Pagani, M.E. Rodegher, P. Rossi, A. Falini, et al. Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp. 2010;32(10):1535-1543
  • [11] F. Nelson, S. Datta, N. Garcia, N.L. Rozario, F. Perez, G. Cutter, et al. Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Mult Scler J. 2011;17(9):1122-1129
  • [12] M. Khalil, C. Enzinger, C. Langkammer, K. Petrovic, M. Loitfelder, M. Tscherner, et al. Cognitive impairment in relation to MRI metrics in patients with clinically isolated syndrome. Mult Scler. 2011;17(2):173-180
  • [13] F. Rinaldi, M. Calabrese, P. Grossi, M. Puthenparampil, P. Perini, P. Gallo. Cortical lesions and cognitive impairment in multiple sclerosis. Neurol Sci. 2010;31(Suppl. 2):S235-S237
  • [14] M. Summers, L. Fisniku, V. Anderson, D. Miller, L. Cipolotti, M. Ron. Cognitive impairment in relapsing–remitting multiple sclerosis can be predicted by imaging performed several years earlier. Mult Scler. 2008;14(2):197-204
  • [15] S.D. Roosendaal, K. Bendfeldt, H. Vrenken, C.H. Polman, S. Borgwardt, E.W. Radue, et al. Grey matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult Scler. 2011;17(9):1098-1106
  • [16] M.P. Amato, V. Zipoli, E. Portaccio. Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci. 2006;245(1/2):41-46
  • [17] M. Rovaris, G. Comi, M. Filippi. MRI markers of destructive pathology in multiple sclerosis-related cognitive dysfunction. J Neurol Sci. 2006;245(1/2):111-116
  • [18] M. Filippi, M.A. Rocca, R.H. Benedict, J. DeLuca, J.J. Geurts, S.A. Rombouts, et al. The contribution of MRI in assessing cognitive impairment in multiple sclerosis. Neurology. 2010;75(23):2121-2128
  • [19] A. Papadopoulou, N. Müller-Lenke, Y. Naegelin, G. Kalt, K. Bendfeldt, P. Kuster, et al. Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Mult Scler. 2013;19(10):1290-1296
  • [20] S.A. Helekar, J.C. Shin, B.J. Mattson, K. Bartley, M. Stosic, T. Saldana-King, et al. Functional brain network changes associated with maintenance of cognitive function in multiple sclerosis. Front Hum Neurosci. 2010;4:219
  • [21] C.H. Polman, S.C. Reingold, B. Banwell, M. Clanet, J.A. Cohen, M. Filippi, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292-302
  • [22] J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology. 1983;33:1444-1452
  • [23] A.T. Beck, G.K. Brown, R.A. Steer. Psychometric characteristics of the Scale for Suicide Ideation with psychiatric outpatients. Behav Res Ther. 1997;35:1039-1046
  • [25] F. Patti, M.P. Amato, M. Trojano, S. Bastianello, M.R. Tola, B. Goretti, et al. Cognitive impairment and its relation with disease measures in mildly disabled patients with relapsing–remitting multiple sclerosis: baseline results from the Cognitive Impairment in Multiple Sclerosis (COGIMUS) study. Mult Scler. 2009;15(7):779-788
  • [26] N. Shiee, P.L. Bazin, K.M. Zackowski, S.K. Farrell, D.M. Harrison, S.D. Newsome, et al. Revisiting brain atrophy and its relationship to disability in multiple sclerosis. PLoS ONE. 2012;7(5):e37049
  • [27] F. Rossi, A. Giorgio, M. Battaglini, M.L. Stromillo, E. Portaccio, B. Goretti, et al. Relevance of brain lesion location to cognition in relapsing multiple sclerosis. PLoS ONE. 2012;7(11):e44826
  • [28] S.E. O’Bryant, J.D. Humphreys, L. Bauer, R.J. McCaffrey, R.C. Hilsabeck. The influence of ethnicity on Symbol Digit Modalities Test performance: an analysis of a multi-ethnic college and hepatitis C patient sample. Appl Neuropsychol. 2007;14(3):183-188
  • [29] A. Kutzelnigg, C.F. Lucchinetti, C. Stadelmann, W. Brück, H. Rauschka, M. Bergmann, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005;128:2705-2712
  • [30] L. Filli, L. Hofstetter, P. Kuster, S. Traud, N. Mueller-Lenke, Y. Naegelin, et al. Spatiotemporal distribution of white matter lesions in relapsing–remitting and secondary progressive multiple sclerosis. Mult Scler. 2012;18(11):1577-1584
  • [31] R.A. Sperling, C.R. Guttmann, M.J. Hohol, S.K. Warfield, M. Jakab, M. Parente, et al. Regional magnetic resonance imaging lesion burden and cognitive function in multiple sclerosis: a longitudinal study. Arch Neurol. 2001;58(1):115-121
  • [32] J. Guimarães, M.J. Sá. Cognitive dysfunction in multiple sclerosis. Front Neurol. 2012;3:74
  • [33] M. Deloire, E. Salort, M. Bonnet, Y. Arimone, M. Boudineau, H. Amieva, et al. Cognitive impairment as marker of diffuse brain abnormalities in early relapsing remitting multiple sclerosis. J Neurol Neurosurg Psychiatry. 2005;76:519-526
  • [34] M.P. Amato, D. Langdon, X. Montalban, R.H. Benedict, J. Deluca, L.B. Krupp, et al. Treatment of cognitive impairment in multiple sclerosis: position paper. J Neurol. 2013;260(6):1452-1468
  • [35] M. Tintoré, A. Rovira, J. Rio, C. Nos, E. Grivé, J. Sastra-Garriga, et al. New diagnostic criteria for multiple sclerosis: application in first demyelinating episode. Neurology. 2003;60:27-30
  • [37] L. Hofstetter, Y. Naegelin, L. Filli, P. Kuster, S. Traud, R. Smieskova, et al. Progression in disability and regional grey matter atrophy in relapsing–remitting multiple sclerosis. Mult Scler. 2013; [Epub ahead of print]
  • [38] K. Bendfeldt, S. Klöppel, T.E. Nichols, R. Smieskova, P. Kuster, S. Traud, et al. Multivariate pattern classification of gray matter pathology in multiple sclerosis. Neuroimage. 2012;60(1):400-408
  • [39] M. Calabrese, M. Filippi, P. Gallo. Cortical lesions in multiple sclerosis. Nat Rev Neurol. 2010;6(8):438-444
  • [40] M. Calabrese, F. Agosta, F. Rinaldi, I. Mattisi, P. Grossi, A. Favaretto, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing–remitting multiple sclerosis. Arch Neurol. 2009;66(9):1144-1150
  • [41] T. Okuda, Y. Korogi, Y. Shigematsu, T. Sugahara, T. Hirai, I. Ikushima, et al. Brain lesions: when should fluid-attenuated inversion-recovery sequences be used in MR evaluation?. Radiology. 1999;212(3):793-798
  • [42] M. Rovaris, G. Comi, M.A. Rocca, M. Cercignani, B. Colombo, G. Santuccio, et al. Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. Am J Neuroradiol. 1999;20(5):813-820
  • [43] J.J. Randolph, H.A. Wishart, A.J. Saykin, B.C. McDonald, K.R. Schuschu, J.W. MacDonald, et al. FLAIR lesion volume in multiple sclerosis: relation to processing speed and verbal memory. J Int Neuropsychol Soc. 2005;11(2):205-209
  • [44] S.M. Rao, S.C. Reingold, M.A. Ron, O. Lyon-Caen, G. Comi. Workshop on neurobehavioral disorders in multiple sclerosis: diagnosis, underlying disease, natural history, and therapeutic intervention. Arch Neurol. 1993;50:658-662

Footnotes

a Department of Neurology, Kantonsspital St. Gallen, St. Gallen, Switzerland

b Medical Image Analysis Center, University of Basel, Basel, Switzerland

c Department of Psychiatry, University of Basel, Basel, Switzerland

lowast Corresponding author at: Department of Neurology, Cantonal Hospital of Saint Gallen, Rorschacher Strasse 95, Switzerland. Tel.: +41 71 494 11 11; fax: +41 71 494 2895.