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Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients

Published online by Cambridge University Press:  08 July 2010

RALPH H.B. BENEDICT ,
SARAH A. MORROW ,
BIANCA WEINSTOCK GUTTMAN ,
DIANE COOKFAIR  and
DAVID J. SCHRETLEN
RALPH H.B. BENEDICT*
Affiliation:
Department of Neurology, Division of Cognitive and Behavioral Neurosciences, State University of New York at Buffalo School of Medicine and Biomedical Sciences, Buffalo, New York Jacobs Neurological Institute, Buffalo, New York
SARAH A. MORROW
Affiliation:
Department of Neurology, Division of Cognitive and Behavioral Neurosciences, State University of New York at Buffalo School of Medicine and Biomedical Sciences, Buffalo, New York Jacobs Neurological Institute, Buffalo, New York
BIANCA WEINSTOCK GUTTMAN
Affiliation:
Department of Neurology, Division of Cognitive and Behavioral Neurosciences, State University of New York at Buffalo School of Medicine and Biomedical Sciences, Buffalo, New York Jacobs Neurological Institute, Buffalo, New York
DIANE COOKFAIR
Affiliation:
Department of Neurology, Division of Cognitive and Behavioral Neurosciences, State University of New York at Buffalo School of Medicine and Biomedical Sciences, Buffalo, New York Jacobs Neurological Institute, Buffalo, New York
DAVID J. SCHRETLEN
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
*
*Correspondence and reprint requests to: Ralph H.B. Benedict, Department of Neurology, 100 High Street (D-6), Buffalo, New York 14203. E-mail: benedict@buffalo.edu
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Abstract

Cognitive reserve is widely recognized as a moderator of cognitive decline in patients with senile dementias such as Alzheimer’s disease. The same effect may occur in multiple sclerosis (MS), an immunologic disorder affecting the central nervous system. While MS is traditionally considered an inflammatory, white matter disease, degeneration of gray matter is increasingly recognized as the primary contributor to progressive cognitive decline. Our aim was to determine if individual differences in estimated cognitive reserve protect against the progression of cognitive dysfunction in MS. Ninety-one patients assessed twice roughly 5 years apart were identified retrospectively. Cognitive testing emphasized mental processing speed. Cognitive reserve was estimated by years of education and by performance on the North American Adult Reading Test (NAART). After controlling for baseline characteristics, both years of education (p = .013) and NAART scores (p = .049) significantly improved regression models predicting cognitive decline. Symbol Digit Modalities Test (SDMT) performance showed no significant change in patients with > 14 years of education, whereas it declined significantly in patients with ≤ 14 years of education. We conclude that greater cognitive reserve as indexed by either higher premorbid intelligence or more years of education protects against the progression of cognitive dysfunction in MS. (JINS, 2010, 16, 829–835.)

Keywords

Multiple sclerosisNeuropsychologyCognitive reserveBrain

Information

Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 16 , Issue 5 , September 2010 , pp. 829 - 835
Copyright
Copyright © The International Neuropsychological Society 2010

INTRODUCTION

Multiple sclerosis (MS), like other neurological diseases affecting the brain, causes impairment on a range of neuropsychological (NP) tests. Long considered primarily a physically disabling disease, many recent studies have shown mild to severe NP deficits in MS patients, especially on tests emphasizing either memory or the speed and efficiency of mental processing (Benedict et al., Reference Benedict, Fischer, Archibald, Arnett, Beatty and Bobholz2002; Bobholz & Rao, Reference Bobholz and Rao2003; Chiaravalloti & DeLuca, Reference Chiaravalloti and DeLuca2008; Rao, Leo, Bernardin, & Unverzagt, Reference Rao, Leo, Bernardin and Unverzagt1991). Recently, many studies have found that such deficits correlate with abnormalities on structural brain magnetic resonance imaging (MRI), particularly measures of whole and regional brain atrophy (Amato et al., Reference Amato, Bartolozzi, Zipoli, Portaccio, Mortilla and Guidi2004; Benedict, Ramasamy, Munschauer, Weinstock-Guttman, & Zivadinov, Reference Benedict, Ramasamy, Munschauer, Weinstock-Guttman and Zivadinov2009; Benedict, Bruce, et al., Reference Benedict, Bruce, Dwyer, Abdelrahman, Hussein and Weinstock-Guttman2006; Christodoulou et al., Reference Christodoulou, Krupp, Liang, Huang, Melville and Roque2003; Houtchens et al., Reference Houtchens, Benedict, Killiany, Sharma, Jaisani and Singh2007; Sanfilipo, Benedict, Weinstock-Guttman, & Bakshi, Reference Sanfilipo, Benedict, Weinstock-Guttman and Bakshi2006; Tekok-Kilic et al., Reference Tekok-Kilic, Benedict, Weinstock-Guttman, Dwyer, Carone and Srinivasaraghavan2007). Most often, large individual variability can be observed with correlation coefficients falling in the vicinity of r = 0.60, thus accounting for roughly 1/3 of the variance in NP measures. This observation raises the question of why NP deficits are not consistently more severe in MS, and more strongly correlated with brain atrophy, in a disease that almost invariably affects the brain.

Cognitive reserve (CR) might explain some of this variation in the expression of MS neuropathology on NP testing. CR may be defined as individual differences in the baseline efficiency of cognitive processing, such that those with more efficient networks have greater capacity and are more flexible in coping with impairment (Stern, Reference Stern2009). Common metrics of CR are achieved education level and irregular word reading ability (Alexander et al., Reference Alexander, Furey, Grady, Pietrini, Brady and Mentis1997; Lynn & Mikk, Reference Lynn and Mikk2007; McCarthy, Sellers, & Burns, Reference McCarthy, Sellers and Burns2003), and these CR proxies can be independent predictors of neuropsychological outcomes (Albert & Teresi, Reference Albert and Teresi1999). CR, therefore, represents a potential mechanism for coping with brain damage (Jacobs et al., Reference Jacobs, Sano, Marder, Bell, Bylsma and Lafleche1994; Stern, Reference Stern2009; Stern, Gurland, Tatemichi, Tang, Wilder, & Mayeux, Reference Stern, Gurland, Tatemichi, Tang, Wilder and Mayeux1994). This model emphasizes behavioral adaptation or NP compensation, perhaps mediated by increased activation of either usual or alternate neural networks. CR could explain why a person with high intelligence or education, for example, can sustain more cerebral injury before showing a functional deficit. There is considerable evidence supporting this concept in the Alzheimer’s disease (AD) literature (Roe, Xiong, Miller, & Morris, Reference Roe, Xiong, Miller and Morris2007; Stern, Reference Stern2006). As noted by Sumowski and DeLuca (Sumowski, Chiaravalloti, Wylie, & Deluca, Reference Sumowski, Chiaravalloti, Wylie and Deluca2009), recent investigations in other neurological populations such as frontotemporal dementia (Borroni et al., Reference Borroni, Premi, Agosti, Alberici, Garibotto and Bellelli2009), stroke (Elkins, Longstreth, Manolio, Newman, Bhadelia, & Johnston, Reference Elkins, Longstreth, Manolio, Newman, Bhadelia and Johnston2006), head trauma (Ropacki & Elias, Reference Ropacki and Elias2003), Parkinson’s disease (Glatt et al., Reference Glatt, Hubble, Lyons, Paolo, Troster and Hassanein1996), and ischemic white matter disease (Dufouil, Alperovitch, & Tzourio, Reference Dufouil, Alperovitch and Tzourio2003), have also supported the CR hypothesis.

The course of MS differs markedly from AD in that cognitive impairment progresses much more slowly, with periods of stability in some patients and more rapid decline in others (Amato, Ponziani, Siracusa, & Sorbi, Reference Amato, Ponziani, Siracusa and Sorbi2001; Kujala, Portin, & Ruutiainen, Reference Kujala, Portin and Ruutiainen1997; Sperling et al., Reference Sperling, Guttmann, Hohol, Warfield, Jakab and Parente2001). Yet the CR hypothesis may be relevant in the presentation of neuropsychological compromise in MS. Indeed, in a recent functional MRI (fMRI) study (Sumowski, Wylie, Deluca, & Chiaravalloti, Reference Sumowski, Wylie, Deluca and Chiaravalloti2010), MS patients showed a positive correlation between higher estimated premorbid IQ and resting state activity, and a negative correlation with prefrontal recruitment which was presumably required to facilitate performance of the activation task (n-back). The authors concluded that patients with lower CR required more cerebral resources to perform the same cognitive task than patients with greater CR (Sumowski et al. Reference Sumowski, Wylie, Deluca and Chiaravalloti2010).

Sumowski and colleagues (Reference Sumowski, Chiaravalloti, Wylie and Deluca2009) also investigated whether the adverse impact of brain atrophy on information processing speed and efficiency would be moderated by CR. In 38 patients, cognitive function was assessed using the Symbol Digit Modalities Test (SDMT) and Paced Auditory Serial Addition Test (PASAT), which are widely accepted as reliable and sensitive in MS studies (Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006; Benedict et al., Reference Benedict, Fischer, Archibald, Arnett, Beatty and Bobholz2002; Rao et al., Reference Rao, Leo, Bernardin and Unverzagt1991). Sumowski et al. (Reference Sumowski, Wylie, Deluca and Chiaravalloti2010) measured brain atrophy using the third ventricle width (TVW), which correlates strongly with cognitive impairment in MS (Benedict, Bruce, et al., Reference Benedict, Bruce, Dwyer, Abdelrahman, Hussein and Weinstock-Guttman2006; Benedict, Weinstock-Guttman, Fishman, Sharma, Tjoa, & Bakshi, Reference Benedict, Weinstock-Guttman, Fishman, Sharma, Tjoa and Bakshi2004; Houtchens et al., Reference Houtchens, Benedict, Killiany, Sharma, Jaisani and Singh2007; Tekok-Kilic et al., Reference Tekok-Kilic, Benedict, Weinstock-Guttman, Dwyer, Carone and Srinivasaraghavan2007). Hierarchical regression models showed that the expected relationship between brain atrophy and cognitive processing was moderated by estimated premorbid intelligence. The data revealed a significant atrophy × CR interaction in that the impact of atrophy on cognition was attenuated at higher levels of CR. In other words, among patients with more severe brain atrophy, those with greater CR showed better cognitive performance. The same was not true of patients with minimal atrophy.

The findings from Sumowski and colleagues support the CR model and may explain some of the variance in NP outcomes not accounted for by MRI measured neuropathology. Their conclusions, however, were based on a small, cross-sectional sample, and our objective was to replicate this work using a longitudinal design emphasizing decline in cognitive function, and a larger sample size.

METHODS

Participants

We studied 91 patients with clinically definite MS (Polman et al., Reference Polman, Reingold, Edan, Filippi, Hartung and Kappos2005) registered at the MS clinic within the Jacobs Neurological Institute (JNI) in Buffalo, NY. The patients entered the study for one of three reasons: participation in research (n = 52; 57%), routine monitoring of cognitive function (n = 10; 11%), or referral for evaluation of a specified management problem related to suspected cognitive impairment (n = 29; 32%). All provided informed consent for either participation in a prospective study, or the storage and analysis of their clinical data, in accordance with Institutional Review Board guidelines. Patients were excluded from the study if any of the following criteria were met: (a) past history of a medical or psychiatric disorder that could substantially influence cognitive function or have a lasting impact on brain integrity, including but not limited to craniocerebral trauma with greater than 5-min loss of consciousness, alcohol or drug dependence, and learning disability; (b) current major depression or alcohol/substance abuse; (c) neurological impairment that might interfere with cognitive testing; (d) an MS relapse or acute corticosteroid treatment within 6 weeks of testing. As in our previous work (Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006; Houtchens et al., Reference Houtchens, Benedict, Killiany, Sharma, Jaisani and Singh2007; Parmenter, Testa, Schretlen, Weinstock-Guttman, & Benedict, Reference Parmenter, Testa, Schretlen, Weinstock-Guttman and Benedict2010; Parmenter, Zivadinov, et al., Reference Parmenter, Zivadinov, Kerenyi, Gavett, Weinstock-Guttman and Dwyer2007), major depressive episode was assessed by means of a site-specific semi-structured interview based on the DSM-IV (APA, 2000), which mirrors the DSM-IV criteria while accounting for the influence MS may have on the neurovegetative symptoms of depression.

Baseline mean (±SD) age was 44.8 ± 8.8 years. The sample was 70% female and 92% Caucasian. Patients completed on average 14.3 ± 2.0 years of education. All patients were characterized according to their current disease course: relapsing-remitting (n = 71), secondary-progressive (n = 17), primary progressive (n = 3); thus 78% of the sample had relapsing-remitting course, which is consistent with population studies of MS (Jacobs et al., Reference Jacobs, Wende, Brownscheidle, Apatoff, Coyle and Goodman1999). Mean disease duration was 11.0 ± 8.3 years. Expanded Disability Status Scale (EDSS) (Kurtzke, Reference Kurtzke1983) within 6 months of testing was available in 82 patients, and the median was 2.5 (range, 0–7.5).

Tests and Procedures

Following Sumowski et al. (Reference Sumowski, Wylie, Deluca and Chiaravalloti2010), we investigated information processing speed and efficiency using the SDMT and the PASAT. The SDMT (Rao, Reference Rao1991a,Reference Raob; Smith, Reference Smith1982) was used as a measure of processing speed in the visual modality. Participants were presented a series of nine symbols, each paired with a single digit number in a key at the top of an 8.5 × 11-inch sheet of paper. The remainder of the page included a pseudo-randomized sequence of symbols for which the participant was instructed to express orally the digit associated with each corresponding symbol as quickly as possible. The dependent measure was the number of correct responses in 90 s. We used the 3.0 inter-stimulus interval version of the PASAT (Gronwall, Reference Gronwall1977; Rao, Reference Rao1991a,Reference Raob) to measure of auditory processing speed and working memory. Participants were presented 60 single-digit numbers, at 3-s intervals, and asked to add each consecutive digit to the one immediately preceding it. The dependent measure was the number of correct responses across 60 trials. The 3-s version of the PASAT was selected because it is a gold standard measure of auditory processing speed in the MS literature and is included in the MS Function Composite (MSFC), a widely accepted measure of general neurological disability (Cutter et al., Reference Cutter, Baier, Rudick, Cookfair, Fischer and Petkau1999; Rudick et al., Reference Rudick, Cutter, Baier, Fisher, Dougherty and Weinstock-Guttman2001). For the present study we followed Sumowski and colleagues by calculating an information processing (IP) efficiency index, which is the mean Z score of the SDMT and PASAT based on previously published normative values (Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006). Each evaluation also included the North American Adult Reading Test (NAART) (Blair & Spreen, Reference Blair and Spreen1989; Friend & Grattan, Reference Friend and Grattan2000).

All participants were evaluated in an outpatient clinical setting housed within an urban hospital in Buffalo, NY. A trained technician or graduate student, under the supervision of a single board-certified neuropsychologist, administered all tests. Board certified neurologists reported the EDSS scores. A trained student, blinded to clinical data and presentation, entered data into an SPSS database accounting for all of the NP variables.

Analysis Plan

The primary cognitive outcomes were based on the work of Sumowski et al. (Reference Sumowski, Chiaravalloti, Wylie and Deluca2009) and included the PASAT and SDMT, as well as an IP composite index, as noted above. The cognitive data were normalized in accordance with previously published norms (Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006). Baseline and follow-up data were compared using analysis of variance (ANOVA) and correlations were examined using the Pearson calculation. We accepted a p value of < .05 as significant. Linear regression analysis (forward stepwise with p to enter 0.10 and to exit 0.05) was pursued to determine significant predictors of follow-up cognitive test scores, first as measured by the IP index, and then SDMT alone as this test proved to be more sensitive to decline in cognitive capacity. Baseline score was entered and retained in Block 1. Then age, sex, disease course, and cognitive reserve measures were entered in Block 2 using a forward step procedure.

RESULTS

The mean test–retest interval was 1743.6 ± 440.6 days (or roughly 5 years), and there was no significant correlation between test–retest interval and CR measures (r values −0.01 for education and 0.14 for NAART). The correlation between education and NAART was r = 0.62 (p < .001).

For descriptive purposes the cognitive test data were compared with a demographically matched control sample from prior research (Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006). The mean IP efficiency Z score was −1.5 ± 1.5. The mean SDMT Z score was −1.9 ± 1.4, and the mean PASAT z score was −0.6 ± 1.2. These tests were correlated at r = 0.59 (p < .001). This degree of impairment observed is consistent with our prior research (Benedict, Bruce, et al., Reference Benedict, Bruce, Dwyer, Abdelrahman, Hussein and Weinstock-Guttman2006; Benedict, Cookfair, et al., Reference Benedict, Cookfair, Gavett, Gunther, Munschauer and Garg2006; Parmenter, Weinstock-Guttman, Garg, Munschauer, & Benedict, Reference Parmenter, Weinstock-Guttman, Garg, Munschauer and Benedict2007; Strober, Englert, Munschauer, Weinstock-Guttman, Rao, & Benedict, Reference Strober, Englert, Munschauer, Weinstock-Guttman, Rao and Benedict2009).

Table 1 shows the baseline and follow-up data for the disease characteristics and cognitive testing in the 91 MS patients. There was significant progression in EDSS from a median value of 2.5 to 3.5 over the 5-year course of the study. Likewise, there was significant worsening on the SDMT (p < .003) and a trend for worsening on the PASAT (p = .189). The SDMT had marginally superior test–retest reliability than PASAT (r = 0.86 vs. 0.75), and the IP index reliability was r = 0.85 (all p values < .001).

Table 1.

Baseline and follow-up clinical data

Note

Course and EDSS effects tested using Wilcoxon signed ranks test and cognitive test effects by paired-sample t test. All samples sizes are n = 91 except EDSS where the sample size was n = 78. EDSS = Expanded Disability Status Scale; SDMT = Symbol Digit Modalities Test; PASAT = Paced Auditory Serial Addition Test.

Cognitive reserve was estimated using years of completed education and the NAART. Correlations between these estimates and clinical measures are presented in Table 2. There were no statistically significant correlations between cognitive reserve and baseline clinical measures.

Table 2.

Correlation coefficients between cognitive reserve and clinical measures

Note

No correlation reaches threshold for statistical significance at p < .05. EDSS = Expanded Disability Status Scale; IP = information processing; SDMT = Symbol Digit Modalities Test; PASAT = Paced Auditory Serial Addition Test; NAART = North American Adult Reading Test.

Linear regression models were calculated predicting follow-up cognitive capacity as measured by the IP index. Baseline score was entered and retained in Block 1. Then the demographic and cognitive reserve measures were entered in Block 2 using a forward step procedure. The results are presented in Table 3. In the second step, education entered the model increasing the R 2 by a small (0.02) but statistically significant amount (p = .013). When disease course and disease duration were included in Block 2, both NAART and course were retained, increasing the R 2 by 0.03 (p = .049).

Table 3.

Results of linear regression models

Note

IP = information processing; NAART = North American Adult Reading Test; SDMT = Symbol Digit Modalities Test; PASAT = Paced Auditory Serial Addition Test.

Because patients showed greater decline on the SDMT, separate models were calculated for each cognitive measure separately. The PASAT model included no demographic and cognitive reserve measures after accounting for baseline performance. The SDMT models both included only education which raised the R 2 from 0.73 to 0.76 (p = .001).

The mean change in SDMT raw score was −3.0 ± 8.4. K-means cluster analysis was used to dichotomize subjects into high and low educational reserve groups, the resulting low group (10–14 years) corresponding to 2 years of college or less, the high group (15–20 years) corresponding to 3 years of college or more. A mixed factor general linear model (GLM) was performed to evaluate the effects of educational reserve on change in SDMT score over time and assess for interaction between time and educational reserve (high vs. low). This analysis revealed a significant interaction (F(1,94) = 7.50; p = .007) with low educational reserve subjects showing significant reduction in SDMT and the high sub-group showing little change. To illustrate the effect the data are presented in Figure 1, where high education patients show no significant change on SDMT (paired T test not significant, p > .05). In contrast, low reserve patients decline by roughly 4 points, from 47.6 ± 12.8 to 43.0 ± 16.8, significant at p < .001. ANOVA showed that the difference between the sub-groups on SDMT at baseline was not significant (p > .05).

Fig. 1.

Baseline and follow-up Symbol Digit Modalities Test (SDMT) performance in high (education > 14 years, n = 38) and low (< or = 14 years, n = 53) cognitive reserve subgroups. Paired t tests show no significant change in the high reserve group. Low reserve patients decline from 47.6 ± 12.8 to 43.0 ± 16.8, significant at p < .001.

DISCUSSION

The data described in this study support the CR hypothesis in MS patients and to some extent replicate or support the work of Sumowski et al. (Reference Sumowski, Chiaravalloti, Wylie and Deluca2009) using different methods. In their cross-sectional study, Sumowski et al. (Reference Sumowski, Wylie, Deluca and Chiaravalloti2010) found that the relationship between brain atrophy and IP efficiency was moderated by cognitive reserve. While patients with high and low reserve performed similarly on NP tests when atrophy was minimal, those with low reserve showed greater IP deficiency when brain atrophy was more severe. The present study used a longitudinal design to determine if CR moderates the degree of decline on neuropsychological testing over time. We used two measures of CR, a reading test of estimated premorbid IQ and years of education, which had differential effects on cognitive outcomes. Our primary finding was that CR measures moderated the degree of decline on NP tests assessing IP efficiency. Patients with low reserve were likely to show decline over time, especially on the SDMT, whereas those with high CR did not.

Brain reserve (BR) capacity is a hypothetical construct which proposes that the amount of cerebral injury that can be sustained before reaching a threshold of clinical expression is dependent upon an individual’s baseline neural foundation (Satz et al., Reference Satz, Morgenstern, Miller, Selnes, McArthur and Cohen1993). Individual differences in brain size, synapse count, or some other indicator of neural complexity may explain why neuropathology is more detrimental in one person than another with the same level of pathology. BR is a passive concept in that the clinical presentation is not dependent on a person’s behavioral or psychological adjustment; a cerebral lesion of a particular size may cause clinical deficits in a person with relatively low BR, and not in another with higher BR (Stern, Reference Stern2002; Tabert et al., Reference Tabert, Albert, Borukhova-Milov, Camacho, Pelton and Liu2002). BR and CR are interrelated constructs. High baseline cognitive ability likely has a biological substrate such as high dendritic density. Patients with greater BR are, therefore, better equipped to compensate for brain injury mediated by increased activation of either usual or alternate neural networks. The study by Sumowski et al. (Reference Sumowski, Wylie, Deluca and Chiaravalloti2010) shows that BR or CR either help patients with brain atrophy function normally, and our data show that such patients are less likely to evidence decline on cognitive tests over time.

It is now widely agreed that MS is both a neurodegenerative and inflammatory demyelinating disease (Trapp & Nave, Reference Trapp and Nave2008). Gray matter atrophy and cortical lesions are correlated with cognitive performance, and these lesions contain fewer inflammatory cells but many activated microglia (Bo, Vedeler, Nyland, Trapp, & Mork, Reference Bo, Vedeler, Nyland, Trapp and Mork2003a,Reference Bo, Vedeler, Nyland, Trapp and Morkb). Similar microglial activation patterns, leading to oxidative stress and excitotoxicity, have been found in MS and Alzheimer’s disease (Dal Bianco, Bradl, Frischer, Kutzelnigg, Jellinger, & Lassmann, Reference Dal Bianco, Bradl, Frischer, Kutzelnigg, Jellinger and Lassmann2008). A slow loss of dendritic density as demonstrated by gray matter atrophy, due to ongoing neurodegenerative processes, is the more likely explanation of cognitive impairment in MS patients (Zivadinov et al., Reference Zivadinov, Sepcic, Nasuelli, De Masi, Bragadin and Tommasi2001). MS also affects the brain in younger individuals and sometimes may affect children before full cognitive development is achieved. Recent data from our group show that patients with an early onset MS (before age of 18) have slower decline in neurological disability and better brain tissue preservation as measured per MRI, than their adult counterparts (Yeh et al., Reference Yeh, Weinstock-Guttman, Ramanathan, Ramasamy, Willis and Cox2009). Marked differences in clinical MRI and NP outcomes can also be seen across disease course in adult MS patients (Benedict, Bruce, et al., Reference Benedict, Bruce, Dwyer, Abdelrahman, Hussein and Weinstock-Guttman2006), and recent work suggests that compensatory fMRI changes are more apparent in patients with higher baseline CR (Sumowski et al. Reference Sumowski, Wylie, Deluca and Chiaravalloti2010). Future longitudinal studies using both repeat MRI and cognitive evaluations, in patients with and without pediatric onset or with progressive versus nonprogressive course, will hopefully shed more light on the many factors that facilitate the effects of BR and CR in MS.

Our study is limited by the lack of MRI data which would have permitted a more direct replication of the work by Sumowski et al. (Reference Sumowski, Wylie, Deluca and Chiaravalloti2010) Our study was retrospective, and the education sub-groups described in Figure 1 were not entirely matched on processing speed measures at the baseline cognitive assessment. In addition, the lack of correlation between the IP and CR measures in this sample at baseline is of some concern, adding to the importance of replication. Our study did not include a normal control group which would have enabled us to determine the extent to which our measure of decline in cognition was confounded by practice effects. The strength of our study is that it presents a longitudinal analysis supporting the importance of CR in preserving cognitive function.

In summary, we have noted the report of Sumowski et al. (Reference Sumowski, Chiaravalloti, Wylie and Deluca2009) showing that BR or CR help MS patients with brain atrophy function normally on cognitive processing tasks. Our longitudinal data support this hypothesis in that MS patients with higher CR are less likely to evidence decline in IP over time. This is a novel area of research in MS that requires replication. Future prospective studies using MRI measures as well as parallel disease onset age groups are under way.

ACKNOWLEDGMENTS

We report that the information in this manuscript and the manuscript itself has never been published either electronically or in print. There are no financial or other relationships that could be interpreted as a conflict of interest affecting this manuscript. There were no sources of financial support for this project.

References

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Figure 0

Table 1. Baseline and follow-up clinical data

Figure 1

Table 2. Correlation coefficients between cognitive reserve and clinical measures

Figure 2

Table 3. Results of linear regression models

Figure 3

Fig. 1. Baseline and follow-up Symbol Digit Modalities Test (SDMT) performance in high (education > 14 years, n = 38) and low (< or = 14 years, n = 53) cognitive reserve subgroups. Paired t tests show no significant change in the high reserve group. Low reserve patients decline from 47.6 ± 12.8 to 43.0 ± 16.8, significant at p < .001.