Cluster analysis of behavioural and event related potentials during a contingent negative variation paradigm in remitting-relapsing and benign forms of multiple sclerosis

RESEARCH ARTICLE Open Access Cluster analysis of behavioural and event-related potentials during a contingent negative variation paradigm in remitting-relapsing and benign forms of multiple sclerosis Javier J Gonzalez-Rosa 1* , Manuel Vazquez-Marrufo 1 , Encarnacion Vaquero 1 , Pablo Duque 2 , Monica Borges 2 , Carlos M Gomez-Gonzalez 1 and Guillermo Izquierdo 2 Abstract Background: Event-related potentials (ERPs) may be used as a highly sensitive way of detecting subtle degrees of cognitive dysfunction. On the other hand, impairment of cognitive skills is increasingly recognised as a hallmark of patients suffering from multiple sclerosis (MS). We sought to determine the psychophysiological pattern of information processing among MS patients with the relapsing-remitting form of the disease and low physical disability considered as two subtypes: ‘typical relapsing-remitting’(RRMS) and ‘benign MS’(BMS). Furthermore, we subjected our data to a cluster analysis to determine whether MS patients and healthy controls could be differentiated in terms of their psychophysiological profile. Methods: We investigated MS patients with RRMS and BMS subtypes using event-related potentials (ERPs) acquired in the context of a Posner visual-spatial cueing paradigm. Specifically, our study aimed to assess ERP brain activity in response preparation (contingent negative variation -CNV) and stimuli processing in MS patients. Latency and amplitude of different ERP components (P1, eN1, N1, P2, N2, P3 and late negativity -LN) as well as behavioural responses (reaction time -RT; correct responses -CRs; and number of errors) were analyzed and then subjected to cluster analysis. Results: Both MS groups showed delayed behavioural responses and enhanced latency for long-latency ERP components (P2, N2, P3) as well as relatively preserved ERP amplitude, but BMS patients obtained more important performance deficits (lower CRs and higher RTs) and abnormalities related to the latency (N1, P3) and amplitude of ERPs (eCNV, eN1, LN). However, RRMS patients also demonstrated abnormally high amplitudes related to the preparation performance period of CNV (cCNV) and post-processing phase (LN). Cluster analyses revealed that RRMS patients appear to make up a relatively homogeneous group with moderate deficits mainly related to ERP latencies, whereas BMS patients appear to make up a rather more heterogeneous group with more severe information processing and attentional deficits. Conclusions: Our findings are suggestive of a slowing of information processing for MS patients that may be a consequence of demyelination and axonal degeneration, which also seems to occur in MS patients that show little or no progression in the physical severity of the disease over time. * Correspondence: [email protected] 1 Department of Experimental Psychology, University of Seville, Seville, Spain Full list of author information is available at the end of the article Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 © 2011 Gonzalez-Rosa et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background In most cases, the onset of multiple sclerosis (MS) is marked by relapses and remissions, termed relapsingremitting MS (RRMS), and the majority of patients with this MS subtype are characterized by a continuous accumulation of disability [1]. On the other hand, there is a minority of patients who are characterized by having few relapses at the onset of the disease and an accumulation of modest or no disability over a long period of time, so they are often described as having a benign course of MS (BMS). However, this benign prognosis is currently being discussed [2,3]. Cognitive dysfunction is a frequent finding in patients with MS [4-6]. Deficits in memory, attention, speed of information processing and executive functions are typical and about 30% to 70% of patients experience impairment in these cognitive domains during the course of the disease [4-6]. Longitudinal studies have consistently revealed a progression of cognitive impairment over time in numerous MS disease courses [7,8] even in the absence of clinical disability [9,10]. During the last few decades, cognitive assessment using scalp-recorded event-related brain potentials (ERPs) have proved to be an adequate method and an objective tool to observe cognitive-related aspects in different neurological diseases. ERPs permit a precise analysis of the time course of neural events supporting task performance and can reveal both psychophysiological correlates of poor performance, providing a unique insight into information processing. Whereas early-latency components of ERPs (N1 and P2) can be used to assess the first stages of information processing [11,12], the long-latency components of ERPs (N2, P3 and late deflections after P3) can provide a more sensitive method for assessing higher-order information processing [11-13]. Therefore long-latency components have been used more frequently than earlylatency components to evaluate the cognitive complaints of most neurological diseases. In cognitive-oriented ERPs studies, the so-called contingent negative variation (CNV) is of particular interest. The CNV is an eventrelated slow negative shift in the human scalp electroencephalogram (EEG) that occurs between two successive stimuli (a cue stimulus and an imperative stimulus) that are associated with or are ‘contingent’on each other. It is thought that the CNV is able to mirror certain aspects of motor and cognitive preparation and endogenous attention [14-16]. It has been previously shown that MS patients display enhanced latency in both early and late ERP components, whereas the amplitude of ERP may also be considerably reduced in the final stages of the disease. The main studies with MS patients have reported abnormalities in the latency or the amplitude of late ERP components, such as N2 or P3 [17-20], but also in early ERP components such as N1 or P2 [17,18,21]. These results have been interpreted, and supported by neuropsychological and neuroimaging evidence, as a general objective index of cognitive impairment in MS patients, characterized by slow information processing, as a result of disconnections between cortical-subcortical and cortico-cortical structures [17,18,22-24]. Most of these results using ERPs in MS disease have been elicited during discriminatory tasks, classically oddball tasks. However, new studies using more refined tasks may explore other impaired cognitive domains or detailed aspects in the information processing from a psychophysiological approach [24]. In particular, the study of attention mechanisms using the central cue Posner paradigm [25,26] could be a powerful tool to explore basic processes underlying more complex information processing skills in these patients. The central cue Posner paradigm allows studying the deployment of attention to certain positions in space [16,27] from a behavioural and psychophysiological point of view. The motivation for the present study was to investigate the information processing patterns and abnormalities of attentional mechanisms associated with the Posner paradigm both in benign and typical relapsingremitting forms of MS disease. To our knowledge, there are no published studies regarding the temporal dynamics of CNV associated to a Posner task in MS disease. According to previous psychophysiological studies [28,29], we suggest that patients with a benign profile of MS also manifest altered cognitive processing that may be different from that which patients with a typical relapsing-remitting course demonstrate. Furthermore, and consistent with current evidence about the natural course of MS disease [1,2], we suggest that the deterioration of different cognitive mechanisms is masked throughout the relapsing-remitting course of MS despite minimal physical disability. Additionally, and in an effort to explore natural subgroups of MS patients from a different perspective, we conducted a cluster analysis to establish particular grouping of individuals on the basis of their common psychophysiological pattern and cognitive profile and whether this particular grouping might also be related to clinical parameters. Methods A previous paper [28] incorporates subsets of the data presented here, focusing on the contribution to preliminary psychophysiological results in different groups of MS disease. The current study incorporates new in-depth psychophysiological measures as well as more refined statistical analysis in order to identify a relationship between the studied measures and the identification of natural subgroups based on psychophysiological measures. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 2 of 19 Participants Clinical, behavioural and ERP data were collected from twenty-seven MS patients, clinically defined according to the Poser criteria [30], and classified in two clinical subgroups: (1) seventeen had typical relapsing-remitting MS (RRMS), defined as a history of several relapses and remissions and with disabilityontheKurtzkeExpanded Disability Status Scale (EDSS) [31] less than 3.5; (2) ten had a profile of benign MS (BMS), defined by a relapsing-remitting onset, but a disease course of more than 8 years without relapses and remissions and with an EDSS of less than 3. Other inclusion criteria of our study were: clinical remission of the disease in the last three months prior to the study; and absence of corticosteroid treatment for at least three months. Patients were excluded if: a major depressive disorder had begun after the onset of MS, if patients showed any other CNS disorder, or metabolic, psychiatric, learning disorders; or in the presence of physical impairment (motor and visual) that might have interfered with testing. Lastly, we must highlight that overall, the MS patients included had no immunomodulation record before beginning our study. Patients were previously assessed at the Multiple Sclerosis Unit of the Neurology Unit of the Virgen Macarena Hospital (Seville, Spain) and participated voluntarily in the psychophysiological testing. All participants signed informed consent before their inclusion in the study and the protocol was approved by the ethics committees of the participating hospital and the University of Seville. Participants included in this study formed the same dataset as that used in Gonzalez et al, 2006. MS patients were matched with a healthy control group for age, sex and educational level with a healthy control group. In addition to these variables, age at disease onset, disease duration, and EDSS scores were collected for all MS patients. Table 1 details the demographic and clinical MS variables of all participants in this study. Stimuli and Procedures All stimuli were presented on a black computer screen that was viewed from a 70-cm distance. Subjects sat in a chair in front of the video monitor displaying the visual stimuli in a sound-attenuated room with dimmed lights. They were asked to fix their eyes on the central fixation (a white cross) and to remain as quiet as possible. Participants were asked to detect and respond to a visual target stimulus (a black and red checkerboard) and to ignore and not respond to a standard visual stimulus (a black and white checkerboard). Both stimuli were preceded by a central directional cue. A detailed description of the experimental paradigm can be found in references 26 and 28. For a basic description please see the footnote in Figure 1. ERP recording procedures and measurements EEG recordings were obtained using 13 scalp sites from an electrode cap (Electrocap™) according to the International 10-20 electrode system. Blinks and saccades were controlled using vertical and horizontal electrooculogram (VEOG and HEOG) and were recorded with bipolar recording by means of electrodes situated in the external canthi of the ocular orbits and in the inferior and superior positions of the left orbit. All electrodes were referenced to the left mastoid and re-referenced off-line to the linked mastoid and the ground electrode was placed on the mid-forehead. The amplification gain was 20,000 (Neuroscan, Grass) and data were filtered using a band-pass of 0.01-100 Hz. EEG was digitised at a frequency rate of 500 Hz and the impedance was kept below 5 kOhm. Previous to average calculation, each EEG epoch was visually inspected and epochs containing excessive movement or EEG artefacts were rejected. Additionally, trials in which HEOG artefacts were higher than ± 60 μV were rejected. The period of 100 ms previous to standard/target stimuli was considered as the baseline for standard/target ERP analysis. A new baseline of 100 ms prior to the cue stimuli was computed for the CNV analysis. The different experimental conditions were averaged independently. Behavioural measures Various measures were recorded to assess behavioural performance for each participant. Reaction times (RTs) Table 1 Demographic and MS clinical variables of all participants Groups CONTROLS RRMS BMS p N18 17 10 - - - Sex(female) 15 (83%) 12 (80%) 6 (60%) 0.716 Education (years) 10.67 ± 2.3 9.9 ± 3.9 9.2 ± 3.1 0.537 Age 36.54 ± 8.73 [26-54] 38.88 ± 9.04 [24-63] 42.30 ± 7.21 [28-56] 0.247 Age at onset - - - 34.12 ± 6.5 [23-44] 30.90 ± 6.9 [21-42] 0.414 EDSS - - - 2.1 ± 1.3 [0-3,5] 1.6 ± 0.9 [1-3] 0.609 Disease Duration - - - 4.67 ± 4,13 [1-18] 12.09 ± 4.77 [8-16] 0.001 Mean, standard deviation, and range are shown for age, age at onset, disease duration and EDSS score. Sex (% female) and years of education are also shown. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 3 of 19 to each target presentation and the percentage of correct responses (% CRs) were obtained. The percentage of error (% Errors) was also analysed. An error was considered if: no response to the target occurred, or the response occurred 700 ms after target stimulus (missed); when responses to the targets occurred with the wrong finger (target error); and when the response was to standard stimuli (false positive). For the percentage Error analysis (due to great ratio of valid stimuli in comparison to invalid stimuli), the difference between number of stimuli of each condition were computed using a ratio-adjusted percentage. ERP measures ERP components were functional and operationally defined on the basis of previous research, in terms of experimental task effects in a specified latency range and in specific scalp locations. For all ERP components the variables considered were the baseline-to-peak amplitudes and the stimulus-to-peak latencies. These measured time windows were determined on the basis of inspection of the grand average waveforms in the control group. The amplitude of each ERP component was measured within latency windows centred on the peak latency of the grand average ERPs. In order to determine relevant time windows of amplitudes and latencies of the ERP components, peak analysis was separately performed for the standard and target stimuli and during the CNV period. Thus, for standard stimuli, early N1 (eN1), P1, N1, P3 and late negativity (LN) components were measured. Different ERP components were only analyzed when they appeared. In the case of target stimuli, it was not possible to analyze P1 and N1, probably due to the lack of an adequate signal- to-noise ratio, given the low number of target stimuli. However, P2, N2, P3 and LN were measured. Each ERP component was obtained for each subject as the greatest Figure 1 Experimental paradigm. A modified version of the central-cue Posner’s paradigm. 75% standard stimuli, 25% target trials. Five blocks with 200 trials each were presented. All stimuli last for 300 milliseconds (ms). The presentation of stimuli was random (left or the right side). Target and Standards were presented at 2.46 degrees to the left or the right of the central fixation point. Cues could point towards the position where the stimulus appears (valid trials, 80%) or the opposite side (invalid trials, 20%). The subject’s task was to indicate the appearance of the target stimulus in the left or right visual field by pressing the left button or right button. Therefore, left valid, left invalid, right valid and right invalid trials appeared both for standard and target trials. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 4 of 19 positive (P1, P2, P3) or negative (N1, N2, LN) deflection on typical electrodes in the appropriate time window (for an example of procedure, see Gonzalez-Rosa et al, 2006). LN component was defined as the highest negative deflection between 400 and 500 ms immediately after the P3 wave on the ERP, and amplitude was measured at frontal, central and parietal electrodes from difference waveforms obtained by subtracting invalid minus valid trials of average ERPs both in the target and standard condition. For the measurement of CNV, three areas of interest were defined at three different time windows and their amplitudes were calculated with respect to a baseline (100 ms prior to the cue stimulus). Immediately after the sensory ERPs induced by the cue stimulus, an acute negativity between 400 and 600 ms was presented. This early or initial phase of the CNV was termed eCNV and was defined as the highest negative deflection at this time window on central and parietal scalp locations. The central phase of the CNV (cCNV) was defined as the mean negative amplitude on fronto-central electrodes between 600-1000 ms after the cue stimulus. On the late part of the CNV, a slow negative potential appeared during the time preceding the imperative stimuli. This late or terminal period of the CNV (tCNV) was defined as the mean negative amplitude in the time window 500 ms preceding the imperative stimuli on occipital scalp locations. Statistical analysis A mixed design, factorial, and repeated measures analysis of variance (ANOVA) was used to analyse the behavioural performance by means of RTs, %CRs and Errors data. For RTs and %CRs analysis, the within-subject factors were the condition validity of the cue (valid vs. invalid) and side of presentation of the target (left vs. right), and the between-subject factor was the subject’s group (RRMSvs.BMSvs.Controls).Inthecaseof% Errors, we used the same statistical design although side of presentation was replaced with type of error (misses vs. target error vs. false positive). For all ERP comparisons, voltage measurements in different electrodes were taken for each ERP component and introduced in the ANOVA. Therefore, electrode factors could be represented by frontal, central, parietal, occipital or temporal localizations according to definitions of each ERP component, whereas hemispheric factor could be constituted by left, right or midline hemispheric localizations. Moreover the factors, side of presentation of the stimuli (left vs. right) and validity of the cue (valid vs. invalid) were introduced into the ANOVA as within-subject factors when appropriate. The between-subjects factor was always grouped in three levels (RRMS vs. BMS vs. Controls). In the case of CNV, separate repeated-measures ANOVAs for each CNV period were performed with a new within-subject factor cued side (left vs. right) as well as electrode (frontal vs. central vs. parietal vs. occipital) and hemispheric (left vs. midline vs. right hemispheric) factors. For LN wave, the same statistical analyses were conducted for both standard and target stimuli using frontal, central and parietal electrodes, but the side of presentation factor was collapsed to maximize effects in order to achieve adequate signal-to-noise ratio. Since ERP latencies followed a normal distribution, parametric tests were applied. When appropriate, the Geisser-Greenhouse (G-G) procedure was applied to correct degrees of freedom. Post hoc comparisons (t tests and Bonferroni test) were performed when ANOVA yielded significant results. A level of p≤0.05 was accepted as statistically significant. Correlations Behavioural (RTs, %CR and % errors) and psychophysiological data (amplitude and latency) of different ERP components were correlated with clinical variables (EDSS and disease duration) in MS patients using Pearson’sandSpearman’s correlation coefficients where necessary. Previously, partial correlations adjusted for age were used to screen psychophysiological variables for their relationship with EDSS and disease duration. For amplitude and latency measures, if an ERP component obtained several good correlations in different electrodes, we chose the maximum values for each ERP component. Cluster analysis Cluster analysis was performed in order to identify the natural occurrence of subgroups of patients with similar properties according to their behavioural and psychophysiological characteristics. A 2-stage cluster analysis [32] was used to build up the cluster solution, i.e. hierarchical clustering followed by non-hierarchical clustering. Initially, the behavioural and psychophysiological data from the MS patients were converted to Z-scores based on the data from the control group in order to ensure equal weighting of different symptoms in the clustering procedure. The variables introduced in the cluster analysis were those which most clearly discriminated between groups in terms of statistical significance (p > 0.035) after performing ANOVA analysis. The first step of this cluster analysis employed a Ward’smethodofminimum-variance (hierarchical) to explore and to determine the number of clusters and thus to provide the initial solution for the second step. Since outliers might severely distort the results of cluster analysis, in this first step some participants were identified as outliers Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 5 of 19 (those showed highest square Euclidean distances with regard to the others clusters) and removed from the following statistical step. The second step employed a K- means cluster analysis (non-hierarchical) using the nearest neighbour joining method computing Euclidean distances among all participants. K-means procedure separates subjects into a pre-spe- cified number of cluster groups. The procedure allows some flexibility in terms of choosing different indicator variables and numbers of clusters to be formed. The present study also followed some recommendations [32,33] to validate the proposed clustering solution: partitioning of clustering variables; validating the clustering solution, and profiling the clustering solution with variables other than those in the clustering procedure. Finally, in order to verify whether the clusters were really different from one another, we subsequently performed a new descriptive analysis and an ANOVA with the clusters as between-subjects factors and the new Z- scores as within-subjects. Post-hoc analysis to compare each cluster with the other for every cognitive Z-score of behavioural and psychophysiological measures was carried out taking into account a significant difference of p< 0.05. Results Behavioural data Table 2 summarizes the behavioural results after application of the Posner task for both control and MS groups. These behavioural data were partially reported in a previous study [28] although new analyses were computed in this study. MS groups obtained a worse performance than the control group with higher RTs (F (2,42) = 14.947, p< 0.001) and a lower CRs percentage (F (2,42) = 6.790, p = 0.003). Between MS groups, BMS patients showed poorerresultsthanRRMSpatientsaswellasalackof validity effect (RTs invalid minus RTs valid condition). Furthermore, the analysis of Errors percentage (missed,target error and false positive), after partialweight correction between valid and invalid trials, revealed that there was a significant error x group effect (F (4,86) = 5.780, p= 0.005) caused by the fact that MS patients obtained a higher amount of errors. Post-hoc tests proved that there was a significantly higher error rate in the BMS group in comparison with the other two groups for missed-error type (RRMS: p= 0.040; Controls, p= 0.005), which largely represents the lack of accuracy in the benign patients. In general, and for all groups during the task performance, the most important error type was the missed-error for invalid trials (validity of the cue xtype of error effect: (F (2,86) = 11.279, p< 0.001), whereas the false positive-error type was practically absent in all groups. Event-related potentials TheCNVwaveformsareshowninFigure2andthe grand-mean ERPs elicited by the visual standard and target stimuli and the difference waveforms are displayed in Figure 3. After the cue stimulus, three CNV periods were clearly identified on the grand average, whereas with the arrival of standard or target stimuli, typical ERP components were developed. The standard stimuli elicited an early inflection previous to the P1 component which we termed as frontal eN1 (mean latency = 120 ± 9 ms), P1 component (mean = 133 ± 5 ms), N1 component (mean = 174 ± 11 ms) and P3 component (mean = 352 ± 12 ms) components. On the other hand, the target stimuli elicited a frontal and central P2 component (mean = 222 ± 10 ms), N2 component (mean = 257 ± 11 ms) and P3 component (mean = 368 ± 19) components. Moreover, both standard (mean = 410 ± 21 ms) and target (mean = 443 ± 19 ms) stimuli elicited a negative late negativity (LN) component over midline after the P3 component. The new factors and levels introduced in the statistical analysis in comparison with that of our preliminary study using only standard stimuli and some ERPs components [29] did not show any new group effects for this type of stimuli. CNV period and ERP Amplitude Table 3 summarizes some results related to amplitude. In relation to the CNV period, a typical increased Table 2 Mean reactions times (RTs), percentage of correct responses (% CRs) and percentage of errors (% Errors) for different groups Groups Behavioural Controls * RRMS * BMS p RTs (ms) Valid 433 ± 50 492 ± 47 549 ± 63 - Invalid 467 ± 56 510 ± 43 560 ± 45 – Total 450 ± 53 501 ± 43 555 ± 51 <0.001 a,b,c,d % CRs Valid 96 ± 4 94 ± 5 81 ± 23 – Invalid 97 ± 5 92 ± 7 79 ± 21 – Total 96 ± 5 93 ± 6 80 ± 23 0.003 a % Errors Valid 1 3 8 Invalid 3 4 12 Total 4 7 20 0.005 a,b,c a= between control and MS groups but not differences between both MS groups; b= between RRMS and BMS groups, after post-hoc analysis; c= between control and BMS groups, after post-hoc analysis; d= between control and RRMS groups, after post-hoc analysis; * = Groups who showed statistical differences for validity of the cue (invalid > valid) Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 6 of 19 negative deflection was shown after cue stimulus. The landscape of both periods was highly similar for eCNV, cCNV and tCNV periods between control and MS groups. However, there was a significant interaction group xhemispheric xelectrodes (F (2,42)= 2.962, p= 0.035) for eCNV. Post-hoc analyses revealed that BMS group had a smaller amplitude (i.e., most positive-going) in comparison with the control group for central electrodes of (p= 0.031) and parietal electrodes of right hemispheric (p= 0.045). Post-hoc analyses also showed that there were no differences between RRMS and controls, respectively, or between the two MS groups. RegardingcCNVperiod,therewasamaineffectof group (F (2,42) = 3.385, p = 0.043), showing that RRMS patients had a significant increase (more negative) of mean cCNV amplitudes on frontal electrodes in contrast to the control group (p= 0.045). However, there were no significant differences in relation to tCNV amplitudes between different groups. There was a main electrode effect (F (2,42) = 12.589, p< 0.001), indicating that the component showed typical modulations and it was significantly higher (i.e. most negative-going) in posterior sites than anterior sites of the scalp. Visual inspection of grand-average waves suggested that amplitude of ERP components to standard stimuli (P1, N1 and P3) and to target stimuli (P2, N2 and P3) were fairly well preserved in both groups of MS patients. However, this did not occur for the frontal eN1 component elicited by standard stimuli, which had a more negative amplitude in the control group, whereas a positive deflection appeared for the MS groups. Statistical analysis confirmed a significant main group effect on this component on middle line and right hemispheric over frontal regions for controls compared with MS patients (F (4,84) = 11.303, p< 0.001), whereas there was no differences between MS groups. Although the grand means data of P1 showed typical modulations of attention effect with Posner tasks (enhanced amplitude of P1 for valid and attended condition in comparison with invalid and not attended condition), this effect was not statistically confirmed (F (1,42) = 1.523, p< 0.224). There was an interaction effect of Figure 2 Event-Related Potentials (ERP) elicited by cues and the subsequent CNV period for the three groups in different positions of the scalp. The black arrow indicates when the cue stimulus is presented and the black arrow with an asterisk when the imperative stimulus is presented. The marked black lines represent: eCNV, cCNV and tCNV periods. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 7 of 19 group xvalidity of the cue on the P1 amplitude (F (2,42) = 4.295, p= 0.020). Control and RRMS groups showed higher amplitudes for valid trials compared to invalid trials, which was not observed in the BMS group who showed the highest amplitude of all groups for invalid trials, however, post-hoc comparisons did not confirm this group effect. Both N1 and P2 components did not show group effects for amplitude. However, the P2 component showed in all groups, that stimuli preceded by invalid trials had higher amplitude respect to stimuli preceded by valid trials (validity of the cue effect: (F (1,42) = 12.847, p< 0.001) and over frontal electrodes in comparison with central electrodes (electrode xhemispheric effect: (F (2,84) = 3.536, p= 0.034). Equally, although the control group clearly exhibited a larger average of N2 amplitude than both MS groups for target stimuli (mean amplitude = Controls: -0.41 ± 3.5 μV; RRMS: 1.39 ± 3.7 μV; BMS: 2.18 ± 4.4 μV), this was not statistically significant (F (2,42) = 1.857, p= 0.169), probably caused by a high intersubject variability in all groups. In general,ahighernegativeamplitudewasobservedin central regions in comparison with frontal regions (electrode effect: F (1,42) = 14.743, p < 0.001), and for right hemispheric compared to left hemispheric positions (hemispheric effect: (F (2,84) = 3.814, p= 0.028). Significant between-group differences in the amplitude P3 component were not observed in either the standard or target stimuli. All groups obtained higher amplitudes for invalid vs. valid trials for target stimuli in the amplitude P3 component with maximum effect in parietal regionsofmeanlineincomparisonwithotherscalp positions analyzed (F (6,252) = 2.734, p= 0.034). However, for standard stimuli, and in spite of the BMS group not obtaining higher amplitudes for invalid vs. valid trials with standard stimulation (in comparison to RMSS or control groups), statistical differences were not found with respect to the validity of the cue between groups for the amplitude of the P3 component. The LN amplitude after subtraction (invalid minus valid) showed a central-parietal dominant LN at latency of 400-500 ms (mean = 447 ± 19 ms) in all groups Figure 3 Event-Related Potentials for different groups associated to: a) standard stimuli, b) target stimuli, and c) valid and invalid cues for target stimuli period for three groups in different scalp positions. In the right column the period (black lines) where the LN was analyzed and the voltage maps for difference waveform are showed. The black arrow demonstrates when the imperative stimulus is presented. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 8 of 19 which was also clearly higher to target stimuli compared with standard stimuli (F (5,210) = 4.217, p= 0.006). Only ANOVA for target stimuli of the LN component revealed a significant group effect (F (2,42) = 3.773, p= 0.031) caused by the fact that, over the central midline, the LN component was larger in amplitude (most negative-going)intheRRMSgroupthanintheBMSgroup (p= 0.028) but there were no differences compared with the control group (p= 0.399). Finally, LN amplitude showed that, both for standard (hemispheric effect: (F (2,84) = 6.321, p= 0.007) and target stimuli (hemispheric xelectrode effect: (F (4,168) = 3.446, p= 0.024), central regions had higher amplitude with respect to frontal or parietal regions. ERP Latency Table 3 summarizes the results related to latency. The ERPs to standard stimuli were P1, N1 and P3 components. The ANOVA on the latency of the standard- Table 3 Amplitude and latency of ERP components for target and standard stimuli and for the CNV periods Amplitude Latency Controls RRMS BMS pControls RRMS BMS p ERPs Mean ± SD ERPs Mean ± SD (○) eN1 0.001 a Valid -0.85 ± 2.4 0.47 ± 1.2 0.97 ± 1.2 Invalid -0.63 ± 0.9 0.33 ± 1.5 0.96 ± 2.1 (○)P1 0.020 a,b,c (○) P1 0.558 Valid 1.59 ± 1.8 1.86 ± 1.3 1.59 ± 1.0 132 ± 6 135 ± 6 136 ± 7 Invalid 1.17 ± 1.5 1.19 ± 1.2 2.09 ± 2.1 133 ± 7 133 ± 5 133 ± 6 (○) N1 0.419 (○)N1 < 0.001 a,c Valid - 0.95 ± 1.2 - 1.41 ± 1.1 - 1.01 ± 1.7 174 ± 11 179 ± 10 188 ± 8 Invalid -0.98 ± 1.5 -1.46 ± 1.1 -0.81 ± 1.5 173 ± 11 181 ± 10 188 ± 9 (○) P3 0.294 (○)P3 0.003 a,b,c,d Valid 4.99 ± 2.4 4.38 ± 4.8 4.56 ± 2.7 352 ± 1 371 ± 10 381 ± 18 Invalid 5.67 ± 1.4 5.18 ± 1.5 4.61 ± 2.5 353 ± 13 375 ± 17 390 ± 12 (⊙) P2 0.232 (⊙)P2 < 0.001 a Valid 5.40 ± 2.8 5.37 ± 3.3 5.26 ± 3.7 219 ± 12 240 ± 13 241 ± 17 Invalid 5.99 ± 3.7 6.25 ± 2.7 7.25 ± 1.5 226 ± 11 242 ± 12 249 ± 13 (⊙) N2 0.169 (⊙)N2 0.002 a Valid -0.30 ± 1.7 -0.16 ± 1.8 -0.52 ± 3.8 252 ± 13 268 ± 10 271 ± 17 Invalid -0.10 ± 2.6 -0.81 ± 2.4 -0.40 ± 3.1 262 ± 11 270 ± 12 269 ± 12 (⊙) P3 0.239 (⊙)P3 < 0.001 a Valid 8.95 ± 3.7 7.63 ± 4.3 7.13 ± 4.4 372 ± 19 397 ± 15 399 ± 20 Invalid 9.81 ± 3.9 8.36 ± 4.1 9.22 ± 4.2 363 ± 20 381 ± 20 390 ± 17 (○) LN -1.72 ± 1.1 -1.80 ± 1.5 -1.68 ± 1.5 0.718 (⊙) LN -2.23 ± 2.1 -3.24 ± 2.0 -1.13 ± 1.5 0.031 b eCNV -1.82 ± 1.7 -0.87 ± 1.1 -0.18 ± 2.2 0.035 a, c cCNV 1.13 ± 0.8 -0.57 ± 0.8 0.89 ± 1.0 0.043 a, d tCNV 0.57 ± 0.6 0.96 ± 0.7 0.74 ± 0.9 0.632 Means and standard deviations (SD) of electrodes and regions of interest used for each ERP-component ANOVA analysis are shown. P values show main statistical effects involving the group factor. [⊙= target stimuli; ○= standard stimuli]. a=p< 0.05 between control and both MS groups; b=p< 0.05 between RRMS and BMS groups; c=p= < 0.05 between control and BMS groups; d=p< 0.05 between control and RRMS groups. Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 9 of 19 and BMS patients. Cluster 3 had a normal Posner task performance in terms of CRs percentage and delay in RTs, although they were also characterized by a moderate-high increase of latency-scores in analyzed ERPs (N1, P2 and P3) with respect to Cluster 1 and 2, showing a mild delay in information processing and a nonstandard increase of neural activity in high-order process (LN component) associated with periods after the P3 component. However, Cluster 4 demonstrated a global and severely altered cognitive processing, characterized by mean performances in the range of two or more standard deviations outside those of the control clusters, in all scores measured. They were characterized by a general cognitive delay, both from a behavioural perspective, with high RTs in their responses and an important increase in numbers of errors (missed), as a widespread slowing in information processing and abnormalities in high-order processing. Finally, a specific analysis for outlier cases removed from the clustering procedure revealed that these outliers only belonged to BMS patients and they showed severe impairment in terms of behavioural performance (RTs and %CRs) and psychophysiological parameters (latencies and amplitude) in the range of four or more standard deviations from the controls. For a more detailed description of cluster features, please see the footnote in Figure 4. Cluster analysis studies on ERP data are extremely scarce, particularly for MS patients. To our knowledge only one study has been published using cluster analysis to describe cognitive subgroups in MS, but with a neuropsychological approach [77]. Consistent with the findings of this study, attentional and information processing deficits may be reliable, early markers that could be used to differentiate the unaffected from the mildly cognitively impaired RRMS patients. On the other hand, there seem to be some variables that are more relevant than others for clustering the participants of our study. The new ANOVA performed with the clusters as between-subjects factors and psychophysiological scores as within-subjects factors revealed that, in general, latency parameters (N1, P2 and particularly P3 components) were the strongest scores for clustering patients. However, amplitude parameters, probably due to the higher variability within them, seem to be sensitive only to cluster splits formed mostly by patients. The behavioural performance in our attentional task was also a sensitive variable for clustering patients, with more than 2 standard deviations in the case of RTs, and more than 3 for accuracy. Studies of attention in patients with MS have observed reduced speed of information processing, although accuracy is usually similar to that of healthy subjects, indicating that differences in accuracy between MS patients and controls are much smaller than differences in speed [4,37,39,78]. Our results confirm these results at least with RRMS patients. However, when accuracy is in part determined by time required for response, differences in accuracy between MS patients and controls could be as important as differences in speed for patients with many years of disease and a benign MS profile. Limitations and implications Firstly, it should be noted that the cognitive abnormalities assessed in the present study using the Posner task might not be completely representative of the great variety and intensity of cognitive problems in MS. However, the main purpose of including the attentional Posner task is justified since attentional deficit and slowing of speed mental processing appears between the most impaired cognitive domains in this disease. Some variations of our experimental design may be taken into account in order to improve the sensitivity of our task, such as number, proportion and type of stimuli as intertrial time, or response time. Further studies on new Posner version tasks may also help clarify this issue [79]. In addition, future studies employing a standard neuropsychological assessment could help to elucidate differences between specific cognitive domains. Conventional MRI measures are also required in order to obtain a more accurate interpretation of the relationship between MRI abnormalities and neuropsychological and neurophysiological cognitive function. Another limitation in our study certainly comes from the small sample size examined in the two subgroups of MS, particularly in the benign group. Our patients may not be fully representative of the overall group of BMS subjects, and further studies including more patient groups apart from the MS spectrum (e.g. clinically isolated syndrome, secondary-progressive or primary forms of MS) are needed to verify the exact functional role of attentional and informationprocessinginERPsparameters. Despite this, our findings are in line with other studies that confirm the variability of the benign course of MS. Finally, in order to assess our results from a more global point of view, it would be particularly interesting to perform the cluster analysis including a larger set of clinical and paraclinical data in order to better establish the ecological validity of a cognitive profilebased classification of MS patients, as well as the relationship between ERP abnormalities and clinical evidence. Conclusions The present study shows a number of significant differences between patients suffering from MS and age- Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 16 of 19 matched control subjects, during completion of the visual-spatial Posner task with central cues. Our findings revealed varying levels of attentional and information processing in MS patients. When combined with behavioural methods, ERPs can help to elucidate the neuropsychological mechanisms underlying the cognitive impairments observed in this disease. Furthermore, after adjusting for subject age, disease duration in MS patients seems to be more closely related to alterations of short-latency of the ERP (N1 and P2) and early stage information processing. Abnormalities in the shortlatency of the ERP may therefore be a good marker of specific information processing dysfunction in MS patients with low disability but a long disease duration. Our findings indicate that both MS groups of our study have differentially altered patterns of cognitive processing, and that these patients may develop diverse degrees of altered cognitive processing. The characteristics of the groups including BMS patients provide empirical evidence about the heterogeneity of this disease course already suggested by electrophysiological [28,29], neuroimaging [80] and follow-up studies [7,81]. In terms of psychophysiological abnormalities, RRMS patients appear to form a more homogeneous clinical entity whereas BMS patients appear to form a more heterogeneous one. RRMS patients show mild/moderate alterations of cognitive processing, although some patients could even develop several deficits particularly related to slowing in information processing. The greater degree of abnormalities such as delays in information processing, orienting, and behavioural accuracy for BMS patients, may be explained by a longer disease duration and by a silent but continuous progression of disease. In addition, the present study indicates the importance of differentiating MS groups according to cognitive capacities. Whether there is a benign form of MS continues to be a controversial issue [2,10,81]. Our findings also suggest, supported by evidence from neuroimaging and neuropathology studies, that some patients diagnosed with BMS could have a less benign course, therefore our results are in line with clinical evidence which suggests that the benign entity of MS can often be temporary and later become disabling. List of abbreviations BMS: benign multiple sclerosis; CNV: contingent negative variation; CRs: correct responses; EDSS: expanded disability status scale; EEG: electroencephalography; ERPs: event-related potentials; MS: multiple sclerosis; RRMS: relapsing-remitting multiple sclerosis; RTs: reaction times. Acknowledgements This project has been supported by Asociación Sanitaria Virgen Macarena, MMA Foundation and Neuroinvest. The authors wish to thank all the patients and healthy subjects for their participation in this study. The authors also thank Dr. Lafina Diamandis for the constructive comments and suggestions as well as her careful reading of the manuscript. Author details 1 Department of Experimental Psychology, University of Seville, Seville, Spain. 2 Multiple Sclerosis Unit, Virgen Macarena Hospital, Seville, Spain. Authors’contributions MVM, JJGR, PD, and GI contributed to the planning and preparation of the current study. 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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Gonzalez-Rosa et al.BMC Neurology 2011, 11:64 http://www.biomedcentral.com/1471-2377/11/64 Page 19 of 19