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Multiple Sclerosis http://msj.sagepub.com/

Attention Network Test reveals alerting network dysfunction in multiple sclerosis Carsten Urbanek, Nicholetta Weinges-Evers, Judith Bellmann-Strobl, Markus Bock, Jan Dörr, Eric Hahn, Andres H Neuhaus, Carolin Opgen-Rhein, Thi Minh Tam Ta, Katja Herges, Caspar F Pfueller, Helena Radbruch, Klaus D Wernecke, Stephanie Ohlraun, Frauke Zipp, Michael Dettling and Friedemann Paul Mult Scler 2010 16: 93 originally published online 7 December 2009 DOI: 10.1177/1352458509350308 The online version of this article can be found at: http://msj.sagepub.com/content/16/1/93

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Research Paper

Attention Network Test reveals alerting network dysfunction in multiple sclerosis

Multiple Sclerosis 16(1) 93–99 ! The Author(s), 2010. Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1352458509350308 msj.sagepub.com

Carsten Urbanek1,*, Nicholetta Weinges-Evers2,3,*, Judith Bellmann-Strobl2,3, Markus Bock2,3, Jan Do¨rr2,3, Eric Hahn1, Andres H Neuhaus1, Carolin Opgen-Rhein1, Thi Minh Tam Ta1, Katja Herges2, Caspar F Pfueller2,3, Helena Radbruch2, Klaus D Wernecke4, Stephanie Ohlraun3, Frauke Zipp2, Michael Dettling1 and Friedemann Paul2,3 Abstract Attention is one of the cognitive domains typically affected in multiple sclerosis. The Attention Network Test was developed to measure the function of the three distinct attentional networks, alerting, orienting, and executive control. The Attention Network Test has been performed in various neuropsychiatric conditions, but not in multiple sclerosis. Our objective was to investigate functions of attentional networks in multiple sclerosis by means of the Attention Network Test. Patients with relapsing–remitting multiple sclerosis (n ¼ 57) and healthy controls (n ¼ 57) matched for age, sex, and education performed the Attention Network Test. Significant differences between patients and controls were detected in the alerting network (p ¼ 0.003), in contrast to the orienting (p ¼ 0.696) and the conflict (p ¼ 0.114) network of visual attention. Mean reaction time in the Attention Network Test was significantly longer in multiple sclerosis patients than in controls (p ¼ 0.032), Multiple sclerosis patients benefited less from alerting cues for conflict resolution compared with healthy controls. The Attention Network Test revealed specific alterations of the attention network in multiple sclerosis patients which were not explained by an overall cognitive slowing. Keywords cognitive impairment, multiple sclerosis, outcome measurement Date received: 30th March 2009; accepted: 20th June 2009

Introduction Cognitive impairment is one of the most frequent symptoms of multiple sclerosis (MS), affecting up to 70% of patients during the course of the disease,1–4 and has a relevant negative impact on employment status and social functioning of these patients.3 Cognitive domains typically affected in MS include attention, working memory, verbal and visuospatial memory, information-processing speed, and executive functions,1,4,5 while intellectual functions and language abilities are less disturbed.6 Although impairment of attention is considered to be one of the cognitive domains predominantly affected in MS patients, studies on attentional deficits in this condition have yielded inconsistent results, presumably owing to varying definitions of attention and the application of qualitatively different instruments to measure attention.5

Recent advances in cognitive neuroscience provided evidence that the human attentional system comprises three distinct networks which carry out the functions of alerting, orienting, and executive control (¼conflict network).7,8 In this concept, alerting is defined as achieving and maintaining a state of alertness; orienting is defined

1 Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charite´ University Medicine Berlin, Berlin, Germany. 2 Cecilie Vogt Clinic, Charite´ University Medicine Berlin, Berlin, Germany. 3 NeuroCure Clinical Research Center, Charite´ University Medicine Berlin, Berlin, Germany. 4 Sostana GmbH and Charite´ University Medicine Berlin, Berlin, Germany.

*These authors contributed equally. Corresponding author: Dr Friedemann Paul, NeuroCure Clinical Research Center, Charite´ University Medicine Berlin, Charite´platz 1, 10117 Berlin, Germany. Email: [email protected]

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as the selection of information from sensory input; and executive control is to be understood as resolving conflict among responses.8 Each of these three attentional functions is related to and served by anatomically distinct brain networks, and is influenced and innervated by different neuromodulatory systems.7–9 A recent approach to investigating the three attentional networks of alerting, orienting, and executive control and their interaction in healthy individuals as well as in various neuropsychological conditions is the Attention Network Test (ANT).7,10 The ANT was developed as a computer-based test, and has shown that the efficiency of each of the three specialized attentional networks is independently measurable with reasonable reliability.10 Moreover, the ANT has been validated in healthy populations,8,11 and a recent study with functional magnetic resonance imaging (fMRI) supported the concept of specific anatomical locations of the three networks.12 We have recently demonstrated significant differences in executive function between healthy individuals and schizophrenic patients by means of the ANT,13–15 and deficits of visual attention networks have been shown in a multitude of psychiatric and neuropsychiatric conditions, such as affective disorders, dementia, traumatic brain injury, and so on.16–19 In contrast, the ANT has not hitherto been applied in patients diagnosed with MS. Here, we report the results of the first cross-sectional ANT study in MS, in which we aimed to investigate whether specific changes in attentional networks can be detected in patients versus healthy control individuals matched for age, sex, and education.

Methods Study participants Sixty patients with a definite diagnosis of MS according to the current panel criteria20 were consecutively recruited from the outpatient clinic of the Cecilie Vogt Clinic, Berlin, Germany. Patients had been referred by their family physicians or neurologists for a second opinion on their MS diagnosis or to assess eligibility for participation in a clinical MS trial. Patients were required to have a relapsing–remitting disease course and a period of >30 days free of clinical relapses or corticosteroid treatment prior to enrolment. Three MS patients were excluded later because of error rates >10% in the ANT. Fifty-seven healthy individuals who were matched in a cross-sectional study design to the remaining patients with respect to sex, age, and education level served as controls. They were recruited from newspaper advertisements and proved healthy after screening with the Mini-International Neuropsychiatric Interview21 by trained psychiatrists.

Table 1. Demographic and clinical data of study participants

Sex (m/f) Age (years, mean  SD) Secondary education (yes/no) Duration of disease (months, mean  SD) EDSS (median, range) BRB-N z-score (mean  SD) MFIS (mean  SD) BDI (mean  SD) FSS (mean  SD)

MS patients (n ¼ 57)

Healthy controls (n ¼ 57)

18/39 38.12 (9.16) 35/22 73.68 (53.79)

18/39 36.58 (9.34) 35/22 n.a.

2.0 (0–5.5) 0.39 (1.13) 1.39 (0.8) 7.68 (6.56) 3.67 (1.5)

n.a. n.a. n.a. n.a. n.a.

EDSS, Expanded Disability Status Scale; BRB-N, Brief Repeatable Battery of Neuropsychological Tests; MFIS, Modified Fatigue Impact Scale; BDI, Beck Depression Inventory; FSS, Fatigue Severity Scale; SD, standard deviation.

In particular, healthy controls did not suffer from any affective disorder, implying symptoms such as fatigue or loss of energy. The clinical and demographic data of the study participants are summarized in Table 1. The study was approved by the local ethics committee, and all participants gave written informed consent.

Attention Network Test The ANT was applied as described by the authors of the test.8 Briefly, participants were required to press either a key for ‘left’ or another key for ‘right’, indicating the direction of an arrow on the screen, which was the target. The target arrow was accompanied on each side by two flankers (either arrows or lines). In the majority of trials, there was a warning cue shortly before the target presentation (see Figure 1). The detailed procedure was as follows. An IBMcompatible personal computer running MS-DOS executed the commercial experimental software Experimental Run-Time System (ERTS, Version 3.37c, BeriSoft Cooperation, Frankfurt, Germany), which presented the visual stimuli and registered the participants’ responses. After a variable fixation and cueing period (900–2100 ms), 5 lines/arrows (representing a total of 3.1 of visual angle) above or below (1.1 of visual angle) the fixation cross in the centre of a cathode ray tube display were presented, and the participant was required to indicate the direction (left/ right) of the middle arrow (¼target) by pressing the corresponding key, while disregarding the two flanker

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Attention Network Test (ANT) Fixation cross

Cue (double)

Fixation cross

Target (congruent)

Key

600–4700 ms

100 ms

or

Cue (spatial)

or

Cue (central)

or

Cue (no) = fixation cross

400 ms

Reaction time

or

Target (incongruent)

or

Target (neutral)

Figure 1. Timeline of the experimental procedure: after a variable period of time, one out of four possible cue types is presented for 100 ms; after a pause of 400 ms, one out of three possible target types is presented and participants have to indicate the direction of the middle arrow by pressing the corresponding key.

lines/arrows on each side. In 75% of cases, the target presentation was preceded by one of three possible cues; in 25%, there was no warning cue (¼ no cue) before the presentation of targets and flankers. The warning cue could be either in the centre of the screen (¼ central cue), or above/below the centre exactly where the target would later appear (¼ spatial cue), or in both possible target locations (¼ double cue). All spatial cues presented were valid. The warning cues were shown for 100 ms and were followed by another fixation period of 400 ms. In the ‘no cue’ trials the previous fixation period – showing only a cross in the middle of the screen – was prolonged for 500 ms. Hereafter the target and the four flankers were presented synchronously. The four flankers could be either lines (¼ neutral condition), or arrows in the same direction as the target arrow (¼ congruent condition), or arrows in the opposite direction (¼ incongruent condition). Maximum response time was 1700 ms; the trial duration varied pseudo-randomly between 2800 ms and 5200 ms and had a mean of 4000 ms (Figure 1). Different cues and targets appeared in pseudo-random order and were equally balanced so that 4 cues  3 targets  2 (up/down)  2 (left/right)  2 (repetition) ¼

96 trials formed one test block. After a short training session with feedback, participants took part in three test blocks without feedback, resulting in a total of 288 trials. Between test blocks there was a short break.

ANT data analysis For every participant, median reaction times (RT) were computed for all 12 combinations of the 4 cue conditions (NO, CENTRAL, DOUBLE, SPATIAL) and the 3 flanker conditions (CONGRUENT, INCONGRUENT, NEUTRAL). The average of these 12 conditions resulted in the individual mean RT. Effects of the alerting, orienting, and conflict networks were defined as reaction time differences. They were calculated in each case by subtracting RTs of different conditions: alerting ¼ no cue - double cue, orienting ¼ central cue - spatial cue, conflict ¼ incongruent condition - congruent condition. Effect ratios were obtained by dividing network effects by the individual mean RT. Group results were obtained by calculating arithmetic mean  standard deviation (SD) of the respective variables.

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Multiple Sclerosis 16(1) Table 2. ANT results

Further tests To investigate a possible influence of fatigue and depressivity on ANT performance, all MS patients filled in the Beck Depression Inventory (BDI),22 the Modified Fatigue Impact Scale (MFIS),23 and the Fatigue Severity Scale (FSS)24 prior to the ANT. Neurological disability was assessed using the EDSS (Expanded Disability Status Scale).25 Results of the Brief Repeatable Battery of Neuropsychological Tests (BRB-N) in the version validated for the German language26 were also available from the MS cohort.

Statistical analysis Results are expressed as arithmetic mean  SD, and as median and range for EDSS. After proof of the (univariate) distributions for normality (Q–Q-plots, Kolmogorov–Smirnov tests modified by Lilliefors), differences between the patients and control group concerning alerting effect and ratio, orienting effect and ratio, conflict effect and ratio, mean RT, and correct answers were elucidated using the t-test. Frequencies (in percentages) were found using Fisher’s test in contingency tables. Interrelationships between ANT mean RT, alerting effect and ratio, orienting effect and ratio, conflict effect and ratio, EDSS, MFIS, FSS, and BDI were judged by Pearson’s correlation coefficient r and tested against r ¼ 0. The multivariate analysis of covariance (MANCOVA) was applied in order to test simultaneously for differences between the controls and MS patients with respect to the three effects investigated, adjusting for RT as covariate. Multivariate tests were conducted using four test statistics (Pillai’s Trace, Wilks’ Lambda, Hoteling’s Trace, Roy’s Largest Root) and completed with univariate between-subjecteffect tests. A two-tailed p-value < 0.05 was considered statistically significant. All tests should be understood as constituting exploratory data analysis, such that no adjustments for multiple testing have been made. Numerical calculations were performed with SPSS, Version 15 (SPSS GmbH Software, Munich, Germany).

Results Following Scherer et al.,26 who suggested that a BRB-N z-score of –1.68.

Alerting (ms) Alerting ratio Orienting (ms) Orienting ratio Conflict (ms) Conflict ratio Mean RT (ms) Correct answers (%)

MS patients

Healthy controls

Mean SD

Mean

SD

26 0.047 43 0.074 102 0.171 599 98.3

41 0.076 41 0.077 89 0.159 559 98.8

21 0.041 28 0.053 34 0.054 87 1.3

30 0.051 37 0.061 46 0.069 107 1.7

p-value (t-test) 0.003 0.001 0.696 0.759 0.114 0.279 0.032 0.073

SD, standard deviation. Bold numbers indicate significant differences between groups.

Mean RT was significantly correlated with age in both groups (r ¼ 0.465, p < 0.001; and r ¼ 0.396, p ¼ 0.002, respectively); however, there was no significant correlation between age and ANT effects or ANT ratios in either group. We carried out a MANCOVA between control and patient groups with mean RT as covariate. Multivariate tests resulted in significant differences between the groups (p ¼ 0.048 for all tests) and a significant impact of RT (p < 0.001, all tests). Completing univariate between-subject-effects tests confirmed that only the alerting effect differed significantly between healthy controls and MS patients (p ¼ 0.012), in contrast to orienting (p ¼ 0.512) and conflict effect (p ¼ 0.325); impact of RT was significant for alerting and conflict effect (p ¼ 0.011 and p ¼ 0.002), but not for orienting effect (p ¼ 0.178).

Alerting and conflict resolution To examine the influence of alerting on conflict resolution, we performed a mixed analysis of variance (as carried out by, among others, Fernandez-Duque and Black18) with conflict effect flanker types (congruent, incongruent) and alerting effect cues (double cue, no cue) as within-subject factors, and group (patients, controls) as a between-subject factor. We found a significant interaction of alerting cues and conflict flankers F(1,112) ¼ 14.1, p < 0.001. In a further analysis which investigated the groups separately, the alerting RT effect interacted with the flanker type in MS patients, F(1,112) ¼ 11.3, p ¼ 0.001. In contrast, the interaction of alerting and conflict was not significant in healthy controls, F(1,112) ¼ 3.0, p ¼ 0.09 (Figure 2).

ANT

Influence of fatigue and depressivity, EDSS, and disease duration on ANT performance

Network effects and ratios, mean RT, and accuracy data for the two groups are shown in Table 2.

In MS patients, there was no correlation of mean RT, alerting and orienting effects/ratios on the one hand

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RT (ms) 700 MS patients

650 Healthy controls

600

No cue

550

Double cue

500 Congruent

Incongruent

Figure 2. Interaction of alerting (no cue–double cue) and conflict (incongruent flankers–congruent flankers) effects for patients and controls. RT, mean reaction time.

and EDSS, duration of disease, and MFIS, FSS, and BDI scores on the other. Only conflict effect (r ¼ –0.28, p ¼ 0.037) and conflict ratio (r ¼ –0.36, p ¼ 0.006) were correlated with duration of disease.

Discussion Our study is the first to investigate attentional networks by means of the ANT in a large cohort of patients with relapsing–remitting MS and to compare the test results with a group of healthy individuals matched for age, sex, and education. The most important finding of this work is a specific alteration of the alerting network in MS patients, as indicated by a significantly lower alerting effect in the ANT, while also showing that there was no difference between patients and control group with respect to the orienting and the conflict effect. Interestingly, the vast majority of patients were not cognitively impaired according to the results of the BRB-N. The lower alerting effect demonstrated in MS patients as compared with control individuals reflects a smaller RT difference between trials with and without warning cue. This may either indicate an impairment in using the warning cue to accelerate response times, or the ability to maintain alertness without a cue. Given the fact that even in the trials without warning cue healthy controls had a faster mean RT than MS patients in trials with warning cue (594 ms vs. 601 ms), we assume that MS patients lack – beyond slower overall RTs – the capacity to fully use the additional helpful information of the warning cue to improve their

reaction time in the cued trials. Moreover, an analysis of interaction between alerting effect and conflict effect revealed that in contrast to healthy controls there is a significant interaction between these two effects in MS patients. While healthy controls benefited from cues for trials with congruent flankers as well as for trials with incongruent flankers, MS patients benefited significantly less from cues before the incongruent flanker trials than from cues before the congruent flanker trials. This phenomenon occurs similarly in patients suffering from Alzheimer’s disease.18 The alerting network can effectuate a wakeful state to prepare for sensory input.7 In an fMRI study using the ANT, the alerting contrast revealed a strong thalamic involvement and activation of anterior and cortical sites.12 Considering brain oscillations, the alerting network showed a specific decrease in theta-, alpha-, and betaband activity 200–450 ms after the warning signal.27 Animal models provided evidence for norepinephrine (NE) involvement in modulating the alerting network, in contrast to the orienting and the executive control network, which are mainly influenced by the neuromodulators acetylcholine and dopamine.28 Interestingly, studies with healthy individuals showed that the three networks work independently.8,10 This might explain why the three networks are not necessarily equally affected in various neuropsychiatric diseases, but may be specifically altered depending on the respective condition and its specific pathogenesis. For example, patients with Alzheimers’s disease showed a deficit in the alerting effect which was explained by decreased NE levels.16 By contrast, alerting is not affected in mild traumatic brain injury,19 in deafness,29 or, in the majority of studies, in schizophrenia.13,30,31 Against the background of these findings, our results point to a specific alteration of the alerting network in MS. Thus, we may assume that global impairment of attention, which is a prominent and frequent feature in cognitively altered MS patients,5,32 may result primarily from a dysfunction of the alerting network. The reasons for this selective affection of the alerting network, as detected by the ANT in our patients, and the fact that the orienting and executive control networks did not differ from controls, require further investigation. It is intriguing to hypothesize that thalamic involvement in the MS disease process, which has recently been demonstrated by various neuroimaging techniques including perfusion studies and quantification of iron accumulation33–38 and by histopathology,39,40 may contribute to alterations of the alerting network. Of the three attentional networks, only the alerting network was associated with a strong activation of the thalamus during performance of the ANT in a recent fMRI study in healthy volunteers.12 The important role of the thalamus for arousal, attention, and alertness has been

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appreciated for decades.41 The possible contribution of altered NE levels to changes in the alerting effect in MS, as has been shown for Alzheimer’s disease,16 remains to be clarified. Beta-adrenergic agonists were reported to be involved in immunological processes and to influence the course of experimental autoimmune encephalomyelitis;42 however, data on the role of NE in MS are contradictory.43–45 Not surprisingly, mean RT in our study was significantly longer in patients as compared with controls, and this was not explained by depressivity or fatigue. We considered the possibility that mean RT in patients may have been negatively influenced by visual dysfunction, visual field defects, or upper extremity dysfunction caused by ataxia, spasticity, tremor, or muscle weakness, which may have hampered the patients in pressing the keys. Following the analysis algorithm for the ANT, the mean RT is likely to influence the results of the alerting, the orienting, and the conflict condition, which would impede the interpretation of group differences in the three networks detected by the univariate t-test. Therefore, we performed a MANCOVA with mean RT as covariate and could thus clearly demonstrate that the significant differences in the alerting effect between patients and controls were not a consequence of the longer mean RT in MS patients, but represent a distinct disease-related difference. Interestingly, we found a significant correlation between conflict effect and conflict ratio on the one hand and duration of disease in MS patients on the other hand, although the parameters of the conflict network showed no association with age or disease severity (EDSS). This might reflect hitherto covert changes in the conflict network of visual attention, increasing with the duration of the pathologic processes underlying the disease. This time course may possibly represent alterations in compensatory cortical adaptive responses.46 Regarding practicability, the acceptance of the ANT, which may cause certain fatigability effects during a performance time of about 20 minutes, was generally high, and none of the patients refused test completion. In addition, patients reported feeling challenged by the task and made every possible effort to perform the test at maximum accuracy. In summary, our study shows that the ANT can be performed in MS patients and is capable of detecting specific disease-related alterations of the alerting network. The ANT may provide an easily performable, well accepted, language-independent and novel method for testing functions of attentional networks in MS. It will be interesting to investigate further the activation of attentional networks in MS by fMRI while performing the ANT. Further studies should also address the interplay of attentional networks and the neuroendocrine system in MS.

Acknowledgements We thank our study nurses Cordula Rudolph and Franziska Lipske for their dedicated work and all patients who contributed to this study. This work was supported by the DFG (Exc 257).

References 1. Hoffmann S, Tittgemeyer M, von Cramon DY. Cognitive impairment in multiple sclerosis. Curr Opin Neurol 2007; 20: 275–280. 2. Amato MP, Ponziani G, Siracusa G, Sorbi S. Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol 2001; 58: 1602–1606. 3. Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. frequency, patterns, and prediction. Neurology 1991; 41: 685–691. 4. Prakash RS, Snook EM, Lewis JM, Motl RW, Kramer AF. Cognitive impairments in relapsing–remitting multiple sclerosis: a meta-analysis. Mult Scler 2008; 14: 1250–1261. 5. Paul RH, Beatty WW, Schneider R, Blanco C, Hames K. Impairments of attention in individuals with multiple sclerosis. Mult Scler 1998; 4: 433–439. 6. Bobholz JA, Rao SM. Cognitive dysfunction in multiple sclerosis: a review of recent developments. Curr Opin Neurol 2003; 16: 283–288. 7. Posner MI, Petersen SE. The attention system of the human brain. Annu Rev Neurosci 1990; 13: 25–42. 8. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci 2002; 14: 340–347. 9. Fan J, Gu X, Guise KG, et al. Testing the behavioral interaction and integration of attentional networks. Brain Cogn 2009; 70: 209–220. 10. Posner MI. Measuring alertness. Ann N Y Acad Sci 2008; 1129: 193–199. 11. Neuhaus AH, Opgen-Rhein C, Urbanek C, et al. Spatiotemporal mapping of sex differences during attentional processing. Hum Brain Mapp 2009; 30: 2997–3008. 12. Fan J, McCandliss BD, Fossella J, Flombaum JI, Posner MI. The activation of attentional networks. Neuroimage 2005; 26: 471–479. 13. Urbanek C, Neuhaus AHM, Opgen-Rhein C, et al. Attention network test (ANT) reveals gender-specific alterations of executive function in schizophrenia. Psychiatry Res 2009; 168: 102–109. 14. Opgen-Rhein C, Neuhaus AH, Urbanek C, Hahn E, Sander T, Dettling M. Executive attention in schizophrenic males and the impact of comt val108/158met genotype on performance on the attention network test. Schizophr Bull 2008; 34: 1231–1239. 15. Neuhaus AH, Koehler S, Opgen-Rhein C, Urbanek C, Hahn E, Dettling M. Selective anterior cingulate cortex deficit during conflict solution in schizophrenia: an eventrelated potential study. J Psychiatr Res 2007; 41: 635–644. 16. Tales A, Muir JL, Bayer A, Jones R, Snowden RJ. Phasic visual alertness in alzheimer’s disease and ageing. Neuroreport 2002; 13: 2557–2560.

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Urbanek et al. 17. Gruber S, Rathgeber K, Bra¨unig P, Gauggel S. Stability and course of neuropsychological deficits in manic and depressed bipolar patients compared to patients with major depression. J Affect Disord 2007; 104: 61–71. 18. Fernandez-Duque D, Black SE. Attentional networks in normal aging and alzheimer’s disease. Neuropsychology 2006; 20: 133–143. 19. Halterman CI, Langan J, Drew A, et al. Tracking the recovery of visuospatial attention deficits in mild traumatic brain injury. Brain 2006; 129: 747–753. 20. Polman CH, Reingold SC, Edan G, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘McDonald Criteria’. Ann Neurol 2005; 58: 840–846. 21. Sheehan DV, Lecrubier Y, Sheehan KH, et al. The MiniInternational Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998; 59(Suppl 20): 22–33; quiz 34–57. 22. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961; 4: 561–571. 23. Fisk JD, Pontefract A, Ritvo PG, Archibald CJ, Murray TJ. The impact of fatigue on patients with multiple sclerosis. Can J Neurol Sci 1994; 21: 9–14. 24. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989; 46: 1121–1123. 25. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 1983; 33: 1444–1452. 26. Scherer P, Baum K, Bauer H, Go¨hler H, Miltenburger C. Normalization of the brief repeatable battery of neuropsychological tests (BRB-N) for German-speaking regions. application in relapsing–remitting and secondary progressive multiple sclerosis patients. Nervenarzt 2004; 75: 984–990. 27. Fan J, Byrne J, Worden MS, et al. The relation of brain oscillations to attentional networks. J Neurosci 2007; 27: 6197–6206. 28. Marrocco RT, Witte EA, Davidson MC. Arousal systems. Curr Opin Neurobiol 1994; 4: 166–170. 29. Dye MWG, Baril DE, Bavelier D. Which aspects of visual attention are changed by deafness? The case of the Attentional Network Test. Neuropsychologia 2007; 45: 1801–1811. 30. Wang K, Fan J, Dong Y, Wang CQ, Lee TMC, Posner MI. Selective impairment of attentional networks of orienting and executive control in schizophrenia. Schizophr Res 2005; 78: 235–241. 31. Gooding DC, Braun JG, Studer JA. Attentional network task performance in patients with schizophrenia-spectrum disorders: evidence of a specific deficit. Schizophr Res 2006; 88: 169–178.

99 32. Rao SM. Neuropsychology of multiple sclerosis. Curr Opin Neurol 1995; 8: 216–220. 33. Houtchens MK, Benedict RHB, Killiany R, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology 2007; 69: 1213–1223. 34. Davies GR, Altmann DR, Rashid W, et al. Emergence of thalamic magnetization transfer ratio abnormality in early relapsing–remitting multiple sclerosis. Mult Scler 2005; 11: 276–281. 35. Ceccarelli A, Filippi M, Neema M, et al. T2 hypointensity in the deep gray matter of patients with benign multiple sclerosis. Mult Scler 2009; 15: 678–686. 36. Geurts JJG, Reuling IEW, Vrenken H, et al. MR spectroscopic evidence for thalamic and hippocampal, but not cortical, damage in multiple sclerosis. Magn Reson Med 2006; 55: 478–483. 37. Inglese M, Park SJ, Johnson G, et al. Deep gray matter perfusion in multiple sclerosis: dynamic susceptibility contrast perfusion magnetic resonance imaging at 3 t. Arch Neurol 2007; 64: 196–202. 38. Ge Y, Jensen JH, Lu H, et al. Quantitative assessment of iron accumulation in the deep gray matter of multiple sclerosis by magnetic field correlation imaging. AJNR Am J Neuroradiol 2007; 28: 1639–1644. 39. Cifelli A, Arridge M, Jezzard P, Esiri MM, Palace J, Matthews PM. Thalamic neurodegeneration in multiple sclerosis. Ann Neurol 2002; 52: 650–653. 40. Vercellino M, Masera S, Lorenzatti M, et al. Demyelination, inflammation, and neurodegeneration in multiple sclerosis deep gray matter. J Neuropathol Exp Neurol 2009; 68: 489–502. 41. Schiff ND. Central thalamic contributions to arousal regulation and neurological disorders of consciousness. Ann N Y Acad Sci 2008; 1129: 105–118. 42. Wiegmann K, Muthyala S, Kim DH, Arnason BG, Chelmicka-Schorr E. Beta-adrenergic agonists suppress chronic/relapsing experimental allergic encephalomyelitis (CREAE) in Lewis rats. J Neuroimmunol 1995; 56: 201–206. 43. Rajda C, Bencsik K, Ve´csei LL, Bergquist J. Catecholamine levels in peripheral blood lymphocytes from multiple sclerosis patients. J Neuroimmunol 2002; 124: 93–100. 44. Rajda C, Bencsik K, Fu¨vesi J, Seres E, Ve´csei L, Bergquist J. The norepinephrine level is decreased in the lymphocytes of long-term interferon-betatreated multiple sclerosis patients. Mult Scler 2006; 12: 265–270. 45. Flachenecker P, Reiners K, Krauser M, Wolf A, Toyka KV. Autonomic dysfunction in multiple sclerosis is related to disease activity and progression of disability. Mult Scler 2001; 7: 327–334. 46. Pantano P, Mainero C, Caramia F. Functional brain reorganization in multiple sclerosis: evidence from FMRI studies. J Neuroimaging 2006; 16: 104–114.