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

Event related potential and response time give evidence for a physiological reserve in cognitive functioning in relapsing–remitting multiple sclerosis

Journal of the Neurological Sciences, Available online 16 June 2015


Cognitive dysfunction is common in multiple sclerosis (MS). Different factors may moderate the degree of cognitive deficit. The aim of the present study was to distinguish different mechanisms for cognitive reserve in relapsing–remitting MS (RRMS). The effects of clinical variables (physical disability, depression), premorbid intelligence (years of education, vocabulary knowledge), visual event-related potential measures (P300) and response time (RT) were studied in RRMS patients (n = 71) and healthy subjects (n = 89). Patients with high P300 amplitude and short RT had better cognitive performance. This effect was significantly weaker in controls. High P300 and short RT may be physiological markers of a cognitive reserve in RRMS. In contrast, the association between cognitive scores and premorbid intelligence was similar in patients and in control subjects. The effects of physiological reserve and clinical variables were studied in a hierarchical linear regression model of cognitive performance in RRMS. P300 amplitude and RT explained a considerable amount of variance in global cognitive performance (34%, p < 0.001). The effects of P300 and RT were not moderated by premorbid intelligence. Physical disability and depression added significantly to explained variance, and the final model accounted for 44% (p < 0.001) of the variation. We conclude that physiological reserve is the strongest moderator of cognitive impairment in RRMS.



  • A physiological reserve operates to attenuate cognitive impairment in RRMS.
  • High P300 amplitude and short RT indicate a large physiological reserve.
  • High premorbid intelligence is not protective against cognitive impairment.
  • Physiological reserve, disability and depression predict 44% of the impairment.

Keywords: Multiple sclerosis, Cognitive impairment, Cognitive reserve, P300, Response time.

1. Introduction

Cognitive impairment is common in multiple sclerosis (MS) and not restricted to advanced stages or progressive subtypes of the disease [1] . The prevalence has been estimated to be 22–40% in relapsing–remitting MS (RRMS) [1], [2], and [3]. It correlates with physical disability [2], [3], and [4] and depression [3], [5], [6], and [7], but not with self-reported fatigue when controlling for concomitant depression [3], [8], and [9]. Furthermore, cognitive impairment correlates with brain magnetic resonance imaging (MRI), especially measures of brain atrophy, but the explained variance remains moderate [10] . Thus the correlation between disease burden and cognitive status is often modest. This is not unique for MS and it has foremost been described in Alzheimer's disease (AD) [11] . Higher levels of premorbid verbal intelligence and educational attainment are associated with a slower deterioration in AD [11] . This has been attributed to a larger cognitive reserve in subjects with higher premorbid intelligence, attenuating the effects of the disease process on cognitive functioning. Recent cross-sectional studies in populations of mixed sub-groups of MS have reported a moderating effect of premorbid intelligence on the cognitive dysfunction related to MRI indices of MS pathology [12], [13], and [14].

Formal education and vocabulary knowledge are commonly used as surrogate markers for premorbid intelligence in studies of cognitive reserve [15] and [16]. However, when using education as a marker for premorbid intelligence it is important to recognize the pervasive effect of education on neuropsychological test performance seen both in healthy individuals and patients [17] . To support the reserve hypothesis the correlation between education (or vocabulary knowledge) and cognitive performance needs to be significantly stronger among patients than in healthy controls [18], [19], and [20]. The cognitive reserve model as proposed by Stern accounts for the physiological variability in synaptic organization or relative utilization of different brain regions. We have previously studied event-related potentials (ERP) and response time (RT) in patients with RRMS [21] . Parietal and central P300 amplitude and RT were normal in RRMS, but patients with low P300 amplitude and long RT had a deficit in cognitive performance. The association between cognitive performance and these physiological parameters was significantly stronger in the patient population than in the healthy controls. This association indicated that high P300 amplitude and fast RT may be protective against cognitive impairment in the RRMS subgroup.

The objectives of the present study were to distinguish different mechanisms for cognitive reserve in RRMS:

  • Is cognitive impairment in RRMS influenced by premorbid intelligence?
  • How much of the variance in cognitive function is explained by clinical and physiological predictors of cognitive impairment?
  • Are the associations of P300 amplitude and RT with cognitive performance moderated by premorbid intelligence?

2. Methods

2.1. Patients

Clinical data, cognitive test scores, P300 amplitude and RT data in RRMS-patients (n = 72) and healthy control subjects (n = 89), were obtained from previously published data sets [3] and [21]. All patients were diagnosed with RRMS and they were recruited at the Department of Neurology at the Karolinska University Hospital in Stockholm/Solna. Healthy control individuals were randomly selected with the aid of the Swedish population registry. The protocol was approved by the regional ethics committee (Regionala etikprövningsnämnden i Stockholm), and the study was conducted in accordance with Good Clinical Practice guidelines and the principles of the Declaration of Helsinki. Patient and control groups were similar for sex, age and education ( Table 1 ). Premorbid intelligence was estimated by education (number of years in school and higher education) and vocabulary knowledge (performance in Vocabulary, SRB:1) [22] and [23].

Table 1 Demographic and clinical data of study population. EDSS, Expanded Disability Status Scale; MSSS, Multiple Sclerosis Severity Scores; BDI, Beck Depression Inventory; FSS, Fatigue Severity Scale; n.s., non-significant.

  Patients (n = 72) Control subjects (n = 89) p value
Female sex (%) 71.0 57.0 n.s.
Left handedness (%) 12.5 9.0 n.s.
  Mean ± SD (range) Mean ± SD (range)  
Age (years) 37.9 ± 10.0 (22–61) 38.2 ± 11.5 (21–60) n.s.
Education (years) 13.8 ± 2.8 (8–21) 14.1 ± 2.5 (9–21) n.s.
Disease duration (years) 9.3 ± 6.5 (0.5–31)
EDSS (scale 0–10) 2.7 ± 1.5 (0–7.5)
MSSS (scale 0.01–9.99) 4.1 ± 2.2 (0.5–9.1)
BDI (scale 0–63) 8.8 ± 7.3 (0–44) 4.0 ± 4.2 (0–21) < 0.0001
FSS (scale 1–7) 3.9 ± 1.8 (1–7) 2.6 ± 1.0 (1–5.7) < 0.0001

2.2. Neuropsychological tests and clinical instruments

The neuropsychological tests and the cognitive domains are listed in Table 2 .

Table 2 Test grouping in cognitive domains and scores in the patient population. n.s. = non-significant.

Cognitive domain Cognitive test z-Score
Benton Visual Retention Test a Memory, visual − 0.12 (n.s.)
Vocabulary Test b Verbal ability − 0.29 (p = 0.03)
Controlled Oral Word Association Test c
Digit Span Test, forward d Attention − 0.88 (p < 0.0001)
Digit Span Test, backwards d
Digit Span Test, total d
Trail Making Test, conditions 1, 2, 3 and 5 c
Color–Word Interference Test, conditions 1 and 2 c
Controlled Oral Word Association Test c Executive functions − 0.92 (p < 0.0001)
Color–Word Interference Test, conditions 1–4 c
Trail Making Test, conditions 1–5 c
Digit Span Test, backwards d
Benton Visual Retention Test a Visual perception/organization − 0.49 (p = 0.002)
Block Design Test d
Digit Symbol Coding Test d
Symbol Search Test d
Digit Symbol Coding Test d Processing speed − 0.64 (p < 0.0001)
Symbol Search Test d
Controlled Oral Word Association Test c
All tests Global score − 0.71 (p < 0.0001)

a BVRT-5, Form C, Administration A [45] .

b SRB:1 [22] and [23].

c Delis–Kaplan Executive Function System (D-KEFS) [46] .

d Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) [47] .

Physical disability was assessed by Kurtzke Expanded Disability Status Scale (EDSS) [24] , and the Multiple Sclerosis Severity Score (MSSS) [25] was used to assess disease severity. Symptoms of depression were assessed by the Beck Depression Inventory (BDI) [26] and the scale was separated into its non-somatic (BDI-NS) and somatic (BDI-S) components [3] and [27]. Fatigue was scored with the Fatigue Severity Scale (FSS) [28] . Seven patients and 7 control subjects had visual acuity less than 1.0, but none of these had poor vision that could interfere with the testing procedure  [21] .

The neurophysiological data included in the present study were P300 amplitude over the parietal and central regions, and RT in response to a visual choice reaction. These were the variables with the strongest correlation to cognitive performance in patients and controls [21] .

2.3. Statistics

Cognitive scores, P300 data and RT were adjusted for the effects of age and sex as identified in linear regression analysis of data from the healthy control subjects. Subsequently, the cognitive scores were adjusted for the effects of education [3] , with some exceptions as indicated in the Results . All data were expressed as z-scores, where z = (measured value − controls' mean value) / controls' S.D. Correlation analyses were performed with parametric and/or non-parametric methods as indicated. Bonferroni corrections were performed for multiple independent comparisons. Calculations were performed with Matlab R2013b with Statistics Toolbox (MathWorks Inc.) and IBM SPSS Statistics version 20.0.

3. Results

3.1. Physical disability, depression, fatigue and cognitive function in RRMS patients

Patients were on average mildly disabled with a mean EDSS of 2.7. They had significantly more symptoms of depression and fatigue compared to controls (both p < 0.0001). Patients also had deficits in cognitive function relative to controls (global score − 0.71, p < 0.0001). Executive functions, attention and processing speed were the most affected cognitive domains ( Table 2 ). One patient was an outlier with a global score of − 8.1 S.D. and was excluded from the regression analyses.

3.2. Effect of premorbid intelligence on cognitive function

The cognitive test scores (adjusted for age and sex) were plotted across years of education ( Fig. 1 ). The global score had a positive correlation with education in patients (r = 0.102, p = 0.007) as well as in control subjects (r = 0.085, p = 0.001). When the test scores obtained in the patients had been adjusted for the effect of education as measured in the controls, there was no residual effect of education, neither on the global score (r = 0.02, p = 0.60) nor on any of the six domain scores (not illustrated). Vocabulary knowledge is relatively resistant to brain damage [17] . In agreement with this, vocabulary knowledge was not significantly different in patients (z = − 0.20) and it did not correlate with EDSS or disease duration. Fig. 2 illustrates the association between cognitive test scores (adjusted for age and sex) and vocabulary. The global score had a similar association with vocabulary in patients (r = 0.29, p = 0.004) and control subjects (r = 0.23, p = 0.0003). When the test scores of the patients were adjusted for the effects of vocabulary as measured in the healthy control subjects there was no residual effect of vocabulary on the domain scores or on the global score (not illustrated, r = 0.13, p = 0.19).


Fig. 1 Correlation between global cognitive score and education in patients and control subjects. All data z-scored. Lines fitted to data with robust regression. Vocabulary excluded from the global mean of all test scores.


Fig. 2 Correlation between global cognitive score and vocabulary knowledge in patients and control subjects. All data z-scored. Lines fitted to data with robust regression. Vocabulary excluded from the global mean of all test scores.

3.3. Effect of P300 and RT on cognitive function

Cognitive performance in patients had a positive correlation with parietal and central P300 amplitude and a negative correlation with visual and auditory RT. The correlations were strongest for visual stimuli in the patients and were absent or weaker in controls [21] . In the parietal region, visual P300 amplitude correlated with the global score in patients (r = 0.44, p < 0.0001) but not in control subjects (r = 0.12, n.s.). In the central region the correlation between visual P300 amplitude and global cognitive performance was significant in both patients (r = 0.23, p = 0.003) and controls (r = 0.14, p = 0.04). Visual RT was more strongly correlated with global score in patients (r = − 0.53, p < 0.0001), than in controls (r = − 0.21, p = 0.0005).

3.4. Correlation with disease burden

Parametric and non-parametric correlation analyses were performed between visual P300 amplitude and EDSS, MSSS and disease duration, respectively (not illustrated). The same correlations were repeated with visual RT. None of the correlations were significant, indicating that P300 amplitude and RT were not associated with disease burden in the present sample of RRMS patients. Neither were P300 amplitude and RT significantly correlated with BDI-NS which was the strongest clinical predictor for cognitive impairment in the patients [3] (not illustrated).

3.5. Regression analysis of cognitive impairment

Both physiological and clinical factors influenced global cognitive function in patients. The strength of these predictors was studied in a hierarchical multiple linear regression model. Visual RT and P300 amplitude (parietal) were entered in Block 1 because they were the strongest predictors for cognitive performance and they were not dependent on the clinical predictors as demonstrated above. EDSS and BDI-NS were entered in Blocks 2 and 3, respectively, because they were previously identified as the strongest clinical predictors for global cognitive impairment in patients [3] . Surrogate markers of premorbid intelligence were not included in the model because they were not predictors of cognitive impairment in the patients and they did not moderate the effects of P300 or RT on cognitive performance. The results of the analysis are shown in Table 3 . P300 amplitude and RT explained most of the variance (adjusted r2 = 0.335). The clinical predictors (Blocks 2 and 3) added significant variance, and the final model had an adjusted r2 of 0.444 (p < 0.001). The regression analysis was repeated with the different cognitive domains as the dependent variable, respectively. P300 amplitude and RT (Block 1) explained most of the variance for executive functions (29%), attention (23%), processing speed (23%), visual perception/organization (19%) and verbal ability (16%). Memory was not predicted by RT and P300 or any of the clinical predictors. EDSS (Block 2) and BDI-NS (Block 3) increased explained variance for executive functions (40%) and processing speed (37%) in the final model. EDSS, but not BDI-NS, increased the explained variance for attention (final model 33%). On the other hand, BDI-NS and not EDSS, increased the variance for visual perception and organization (final model 30%). The regression model for verbal ability was not improved by any of the clinical predictors.

Table 3 Hierarchical regression model for cognitive impairment in RRMS. Dependent variables (adjusted for age, sex and education) as indicated. Independent variables were visual P300 amplitude (P300) and response time to visual stimuli (RT) in Block 1. Expanded Disability Status Scale (EDSS) was added in Block 2, and the non-somatic part of Beck Depression Inventory (BDI-NS) was added in Block 3. n.s. = non-significant.

Model r2 Adj. r2 Std. error r2 change Sign. change Sign. model
Global score
RT + P300 0.354 0.335 0.658 0.354 0.000 0.000
RT + P300 + EDSS 0.422 0.397 0.627 0.068 0.006 0.000
RT + P300 + EDSS + BDI-NS 0.476 0.444 0.602 0.054 0.011 0.000
Executive functions
RT + P300 0.309 0.289 0.789 0.309 0.000 0.000
RT + P300 + EDSS 0.390 0.363 0.747 0.081 0.004 0.000
RT + P300 + EDSS + BDI-NS 0.431 0.396 0.727 0.041 0.033 0.000
Processing speed
RT + P300 0.249 0.226 0.774 0.249 0.000 0.000
RT + P300 + EDSS 0.329 0.299 0.737 0.080 0.006 0.000
RT + P300 + EDSS + BDI-NS 0.409 0.373 0.697 0.080 0.004 0.000
RT + P300 0.254 0.232 0.845 0.254 0.000 0.000
RT + P300 + EDSS 0.339 0.310 0.801 0.085 0.004 0.000
RT + P300 + EDSS + BDI-NS 0.367 0.328 0.790 0.027 n.s. 0.000
Visual perception and organization
RT + P300 0.215 0.192 0.800 0.215 0.000 0.000
RT + P300 + EDSS 0.248 0.214 0.789 0.032 n.s. 0.000
RT + P300 + EDSS + BDI-NS 0.342 0.302 0.744 0.095 0.003 0.000
Verbal ability
RT + P300 0.182 0.158 0.745 0.182 0.001 0.001
RT + P300 + EDSS 0.202 0.166 0.741 0.020 n.s. 0.002
RT + P300 + EDSS + BDI-NS 0.213 0.165 0.742 0.011 n.s. 0.003
RT + P300 0.038 0.010 1.299 0.038 n.s. n.s.
RT + P300 + EDSS 0.043 0.000 1.306 0.004 n.s. n.s.
RT + P300 + EDSS + BDI-NS 0.118 0.065 1.263 0.076 0.020 n.s.

A possible moderating effect of premorbid intelligence (education and vocabulary) on visual P300 amplitude and RT was tested in a hierarchical regression model (not illustrated). The global cognitive score (vocabulary score excluded) adjusted for age and sex was the dependent variable. Education (years) and vocabulary knowledge, respectively, were tested in Block 1 and parietal P300 amplitude and RT, respectively, were tested in Block 2. The interactions education*P300, education*RT, vocabulary*P300, and vocabulary*RT, respectively, were studied in Block 3. None of the interactions were significant. We repeated the testing with the different cognitive domains (executive functions, attention, processing speed, memory, verbal ability and visual perception/organization) as the dependent variable, respectively. Of these, two models showed interactions (p < 0.05) but after correction for multiple comparisons (24 models) these results were not significant.

4. Discussion

The main finding of the present study was that almost half of the variance in cognitive functioning in RRMS is explained by the variance in P300 amplitude and RT together with the level of physical disability and depression. Educational attainment and vocabulary knowledge (premorbid intelligence) influenced cognitive function in both patients and controls, and there was no difference in the strength of correlation. The effects of P300 amplitude and RT on cognition were not moderated by premorbid intelligence.

The present study did not give evidence that higher premorbid intelligence provides a cognitive reserve in RRMS. Patients had the same degree of cognitive impairment, approximately − 0.7 S.D. in global score, independent of years of education or level of vocabulary knowledge. This is in variance with the findings by Sumowski and collaborators describing that a better vocabulary knowledge and educational attainment can prevent or attenuate impairment in cognitive efficiency and memory in MS patients [19] and [20]. Our results are in agreement with two recent studies showing no attenuating effect of years of education [29] and [30]. Note that in these studies, occupational attainment [30] and premorbid verbal IQ [29] did have a beneficial effect on MS-related cognitive impairment, and the present study did not include such proxies. Educational attainment, vocabulary knowledge and various measures of IQ are commonly used as surrogate markers for premorbid intelligence. Such indirect measures of premorbid intelligence introduce an uncertainty which has been pointed out as a major challenge in cognitive reserve research [15] and [31]. Another factor that may explain the different outcome is the degree of cognitive impairment. The present study comprised only RRMS patients and the average cognitive impairment was mild. High premorbid intelligence may have a larger protective effect in MS patients suffering from more severe cognitive impairment [20] . It is therefore possible that certain commonly used proxies of cognitive reserve, such as educational attainment and vocabulary knowledge, will be equally associated with cognitive performance in patients and healthy individuals if disease burden has not yet exceeded some critical level. However, because cognitive reserve in MS is a novel area of research, the importance of replication has been stressed [32] .

P300 amplitude and RT were the strongest predictors of impaired function globally and in five of six included cognitive domains in RRMS. The association between P300, RT and cognitive function was described in more detail in a previous study [21] . We found that cognitive performance is more closely related with amplitude than latency of P300, and that this relation is stronger for visual than auditory stimulation. P300 is related to the cognitive process of stimulus evaluation when there is a decision involved, such as discriminating between two images  [33] . The topographic distribution of the P300 wave is centroparietal with a maximum in the midline [34] and it has mostly been studied in research on dementia. Previous ERP-research in MS patients has focused on P300 latency [35] and [36]. Our findings agree with previous studies showing an association between cognitive function in MS patients and P300 amplitude [37] and RT [38], [39], and [40].

A ‘physiological reserve’ hypothesis can be formulated in the same way as the cognitive reserve hypothesis. Accordingly, patients should rely more on this physiological reserve for their cognitive performance than healthy individuals do. Our results suggest that P300 and RT, at choice reaction tasks, reflect such a physiological reserve in the present RRMS sample. It can be viewed as an important cognition-related neural buffer system helping patients compensate for the negative effects on cognition posed by MS. Individuals with a small physiological reserve as manifested by low P300 amplitude and slow response speed are thus at risk of impaired cognitive function, especially executive function, when affected by the disease. Our data indicate that this reserve operates to attenuate cognitive impairment in RRMS patients.

Our definition of a physiological reserve in the present patient sample has similarities with the neural reserve as defined by Stern [41] . Neural reserve represents a natural inter-individual variability in brain network efficiency and capacity to perform a task. An individual with more efficient brain networks might thus be more capable of coping with brain pathology. Neural compensation refers to the process by which individuals suffering from brain pathology use different brain networks to compensate for the disruption imposed by the disease [15] and [41]. Furthermore, in our previous study [21] we described a small but significant increase in frontal P300 amplitude in patients suggesting neural compensation also being present. In patients with mild cognitive impairment, commonly regarded to have an early stage of AD, the correlation between RT and corpus callosum size is stronger than in a large and matched control group, despite no difference in mean corpus callosum size between groups [42] . It was suggested that biological limits on reserve capacity must occur in mild cognitive disorders that result in a stronger brain–behavior relationship.

In the present patient population, physiological reserve explained much of the variance in global cognitive functioning (34%) and in the domains of executive functions, attention and processing speed (23–29%). The added explanatory effect of physical disability (EDSS) and depression (BDI-NS) was low, r2 change 6.8 and 5.4%, respectively. For comparison, many previous studies have shown that brain MRI indices of disease burden (such as atrophy and lesion load) correlate with cognitive performance in MS and this association generally explains one third to one half of the variance [10] .

In this context it is relevant that P300 and RT were uncorrelated to EDSS and BDI-NS. P300 amplitude has been reported to be more often abnormal when physical disability is greater (EDSS > 3.0) [43] and in progressive states of MS [44] . In the present study a large majority of patients had mild disability (mean EDSS 2.7) which may explain the lack of correlation with P300. A similar pattern is seen for RT in MS. In a sample of patients with greater physical disability including both RRMS and SPMS patients, visual RT showed a correlation with EDSS [38] . Our finding that RT to a visual two-target stimulus is unrelated to EDSS and disease duration in RRMS, is in agreement with a previous observation [40] . Similar to the previous findings [38] and [40] we found no correlation between visual RT and level of depressive symptoms.

A limitation of the present study is the lack of other potential proxies of cognitive reserve such as IQ, occupational attainment and assessment of premorbid cognitive leisure activities. Another limitation of the present study is that the memory domain was only represented by one cognitive test (BVRT). This may explain why this only domain was not significantly affected in patients. Also, memory was the only domain function that did not correlate to the neurophysiological parameters. Furthermore, the absence of MRI data is a limitation. In future studies, assessment of P300 and RT might be considered in parallel with MRI measures when exploring cognitive function in RRMS.

The present study did not rely on published norm values but included a large and randomly selected control group [3] . Another advantage is the post hoc analysis of the cognitive reserve hypothesis because it excludes a possible population selection bias. Furthermore, the cognitive reserve hypothesis was tested using several cognitive tests and domains, and variability due to different MS subtypes was avoided.

5. Conclusions

The variance of cognitive deficit in RRMS may be due to the presence of a physiological reserve that attenuates the effect of the disease. Approximately one third of the variance is explained by this mechanism. In comparison, the present study did not find a protective effect of years of education or vocabulary knowledge on cognitive performance in RRMS. The combined effect of physiological reserve, physical disability and depression explained 44% of the variance in global cognitive functioning. We believe that our findings of a physiological reserve are important since they point at general factors such as brain signal amplitude and response speed as main predictors of cognitive dysfunction in RRMS. Additionally, measures of P300 amplitude and RT could be of help in identifying RRMS patients at increased risk of cognitive impairment.

Conflict of interest

The authors declare no conflict of interest.


This study was supported by research grants from Stockholm County Council.


  • [1] C. Potagas, E. Giogkaraki, G. Koutsis, D. Mandellos, E. Tsirempolou, C. Sfagos, et al. Cognitive impairment in different MS subtypes and clinically isolated syndromes. J. Neurol. Sci.. 2008;267:100-106
  • [2] 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;15:779-788
  • [3] M. Sundgren, L. Maurex, A. Wahlin, F. Piehl, T. Brismar. Cognitive impairment has a strong relation to nonsomatic symptoms of depression in relapsing–remitting multiple sclerosis. Arch. Clin. Neuropsychol.. 2013;28:144-155
  • [4] S.G. Lynch, B.A. Parmenter, D.R. Denney. The association between cognitive impairment and physical disability in multiple sclerosis. Mult. Scler.. 2005;11:469-476
  • [5] P.A. Arnett, F.H. Barwick, J.E. Beeney. Depression in multiple sclerosis: review and theoretical proposal. J. Int. Neuropsychol. Soc.. 2008;14:691-724
  • [6] A. Feinstein. Mood disorders in multiple sclerosis and the effects on cognition. J. Neurol. Sci.. 2006;245:63-66
  • [7] R.J. Siegert, D.A. Abernethy. Depression in multiple sclerosis: a review. J. Neurol. Neurosurg. Psychiatry. 2005;76:469-475
  • [8] Y. Bol, A.A. Duits, R.M.M. Hupperts, J.W.S. Vlaeyen, F.R.J. Verhey. The psychology of fatigue in patients with multiple sclerosis: a review. J. Psychosom. Res.. 2009;66:3-11
  • [9] S.A. Morrow, B. Weinstock-Guttman, F.E. Munschauer, D. Hojnacki, R.H.B. Benedict. Subjective fatigue is not associated with cognitive impairment in multiple sclerosis: cross-sectional and longitudinal analysis. Mult. Scler.. 2009;15:998-1005
  • [10] R.H.B. Benedict, R. Zivadinov. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat. Rev. Neurol.. 2011;7:332-342
  • [11] Y. Stern. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol.. 2012;11:1006-1012
  • [12] D. Pinter, J. Sumowski, J. DeLuca, F. Fazekas, A. Pichler, M. Khalil, et al. Higher education moderates the effect of T2 lesion load and third ventricle width on cognition in multiple sclerosis. PLoS One. 2014;9
  • [13] J.F. Sumowski, N. Chiaravalloti, G. Wylie, J. DeLuca. Cognitive reserve moderates the negative effect of brain atrophy on cognitive efficiency in multiple sclerosis. J. Int. Neuropsychol. Soc.. 2009;15:606-612
  • [14] J.F. Sumowski, G.R. Wylie, N. Chiaravalloti, J. DeLuca. Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis. Neurology. 2010;74:1942-1945
  • [15] D. Barulli, Y. Stern. Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends Cogn. Sci.. 2013;17:502-509
  • [16] Y. Stern. Cognitive reserve. Neuropsychologia. 2009;47:2015-2028
  • [17] M.D. Lezak. Neuropsychological Assessment. (Oxford University Press, Oxford, 2004)
  • [18] Y. Stern. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc.. 2002;8:448-460
  • [19] J.F. Sumowski, N. Chiaravalloti, J. DeLuca. Cognitive reserve protects against cognitive dysfunction in multiple sclerosis. J. Clin. Exp. Neuropsychol.. 2009;31:913-926
  • [20] J.F. Sumowski, N. Chiaravalloti, V.M. Leavitt, J. DeLuca. Cognitive reserve in secondary progressive multiple sclerosis. Mult. Scler.. 2012;18:1454-1458
  • [21] M. Sundgren, V.V. Nikulin, L. Maurex, A. Wahlin, F. Piehl, T. Brismar. P300 amplitude and response speed relate to preserved cognitive function in relapsing–remitting multiple sclerosis. Clin. Neurophysiol.. 2015;126:689-697
  • [22] I. Dureman, H. Sälde. Psykometriska och experimentalpsykologiska metoder för klinisk tillämpning. Studies in psychometric and experimental methods for the clinical evaluation of mental functioning. (Almqvist & Wiksell, Stockholm, 1959)
  • [23] Psykologiförlaget. Manual for the Dureman–Sälde Tests. (Psykologiförlaget AB, Stockholm, 1971)
  • [24] J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis — an expanded disability status scale (EDSS). Neurology. 1983;33:1444-1452
  • [25] R. Roxburgh, S.R. Seaman, T. Masterman, A.E. Hensiek, S.J. Sawcer, S. Vukusic, et al. Multiple sclerosis severity score — using disability and disease duration to rate disease severity. Neurology. 2005;64:1144-1151
  • [26] A.T. Beck, R.A. Steer, M.G. Garbin. Psychometric properties of the Beck Depression Inventory — 25 years of evaluation. Clin. Psychol. Rev.. 1988;8:77-100
  • [27] M.M. Plumb, J. Holland. Comparative studies of psychological function in patients with advanced cancer. 1. Self-reported depressive symptoms. Psychosom. Med.. 1977;39:264-276
  • [28] L.B. Krupp, N.G. Larocca, J. Muirnash, A.D. Steinberg. The Fatigue Severity Scale — application to patients with multiple sclerosis and systemic lupus erythematosus. Arch. Neurol.. 1989;46:1121-1123
  • [29] A. Feinstein, H. Lapshin, P. O'Connor, K.L. Lanctot. Sub-threshold cognitive impairment in multiple sclerosis: the association with cognitive reserve. J. Neurol.. 2013;260:2256-2261
  • [30] O. Ghaffar, M. Fiati, A. Feinstein. Occupational attainment as a marker of cognitive reserve in multiple sclerosis. PLoS One. 2012;7
  • [31] R.N. Jones, J. Manly, M.M. Glymour, D.M. Rentz, A.L. Jefferson, Y. Stern. Conceptual and measurement challenges in research on cognitive reserve. J. Int. Neuropsychol. Soc.. 2011;17:593-601
  • [32] R.H.B. Benedict, S.A. Morrow, B.W. Guttman, D. Cookfair, D.J. Schretlen. Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients. J. Int. Neuropsychol. Soc.. 2010;16:829-835
  • [33] J. Polich. Updating p300: an integrative theory of P3a and P3b. Clin. Neurophysiol.. 2007;118:2128-2148
  • [34] A. Pfefferbaum, J.M. Ford, B.G. Wenegrat, W.T. Roth, B.S. Kopell. Clinical application of the P3 component of event-related potentials. 1. Normal aging. Electroencephalogr. Clin. Neurophysiol.. 1984;59:85-103
  • [35] L. Leocani, J.J. Gonzalez-Rosa, G. Comi. Neurophysiological correlates of cognitive disturbances in multiple sclerosis. Neurol. Sci.. 2010;31:249-253
  • [36] I. Magnano, I. Aiello, M.R. Piras. Cognitive impairment and neurophysiological correlates in MS. J. Neurol. Sci.. 2006;245:117-122
  • [37] H. Kiiski, R.B. Reilly, R. Lonergan, S. Kelly, M. O'Brien, K. Kinsella, et al. Change in PASAT performance correlates with change in P3 ERP amplitude over a 12-month period in multiple sclerosis patients. J. Neurol. Sci.. 2011;305:45-52
  • [38] A.J. Hughes, D.R. Denney, S.G. Lynch. Reaction time and rapid serial processing measures of information processing speed in multiple sclerosis: complexity, compounding, and augmentation. J. Int. Neuropsychol. Soc.. 2011;17:1113-1121
  • [39] P. Kujala, R. Portin, A. Revonsuo, J. Ruutiainen. Automatic and controlled information-processing in multiple sclerosis. Brain. 1994;117:1115-1126
  • [40] T.N. Tombaugh, L.I. Berrigan, L.A.S. Walker, M.S. Freedman. The Computerized Test of Information Processing (CTIP) offers an alternative to the PASAT for assessing cognitive processing speed in individuals with multiple sclerosis. Cogn. Behav. Neurol.. 2010;23:192-198
  • [41] Y. Stern, C. Habeck, J. Moeller, N. Scarmeas, K.E. Anderson, H.J. Hilton, et al. Brain networks associated with cognitive reserve in healthy young and old adults. Cereb. Cortex. 2005;15:394-402
  • [42] K.J. Anstey, H.A. Mack, H. Christensen, S.C. Li, C. Reglade-Meslin, J. Maller, et al. Corpus callosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample. Neuropsychologia. 2007;45:1911-1920
  • [43] N.I. Triantafyllou, K. Voumvourakis, I. Zalonis, K. Sfagos, V. Mantouvalos, S. Malliara, et al. Cognition in relapsing–remitting multiple sclerosis — a multichannel event-related potential (P300) study. Acta Neurol. Scand.. 1992;85:10-13
  • [44] T. Ellger, F. Bethke, A. Frese, R.J. Luettmann, A. Buchheister, E.B. Ringelstein, et al. Event-related potentials in different subtypes of multiple sclerosis — a cross-sectional study. J. Neurol. Sci.. 2002;205:35-40
  • [45] A.B. Sivan. Benton Visual Retention Test. Fifth edition (The Psychological Corporation, San Antonio, TX, 1992)
  • [46] D.C. Delis, E. Kaplan, J.H. Kramer. Delis–Kaplan Executive Function System (D-KEFS). (The Psychological Corporation, San Antonio, TX, 2001)
  • [47] D. Wechsler. Wechsler Adult Intelligence Scale. Third edition (The Psychological Corporation, San Antonio, TX, 1997)


a Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

b Institute of Gerontology, School of Health Sciences, Jönköping University, Jönköping, Sweden

Corresponding author at: Department of Neurology, Karolinska University Hospital (Solna), 171 76 Stockholm, Sweden.

Search this site

Stay up-to-date with our monthly e-alert

If you want to regularly receive information on what is happening in MS research sign up to our e-alert.

Subscribe »

About the Editors

  • Prof Timothy Vartanian

    dsc_0787_400x400.jpg Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College,...
  • Dr Claire S. Riley

    headshotcsr1_185x250.jpg Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia...
  • Dr Rebecca Farber

    picforelsevier.jpg Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in...

This online Resource Centre has been made possible by a donation from EMD Serono, Inc., a business of Merck KGaA, Darmstadt, Germany.

Note that EMD Serono, Inc., has no editorial control or influence over the content of this Resource Centre. The Resource Centre and all content therein are subject to an independent editorial review.

The Grant for Multiple Sclerosis Innovation
supports promising translational research projects by academic researchers to improve understanding of multiple sclerosis (MS) for the ultimate benefit of patients.  For full information and application details, please click here

Journal Editor's choice

Recommended by Prof. Brenda Banwell

Causes of death among persons with multiple sclerosis

Gary R. Cutter, Jeffrey Zimmerman, Amber R. Salter, et al.

Multiple Sclerosis and Related Disorders, September 2015, Vol 4 Issue 5