Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Stroop event-related potentials as a bioelectrical correlate of frontal lobe dysfunction in multiple sclerosis

Stroop event-related potentials as a bioelectrical correlate of frontal lobe dysfunction in... Background: Dysfunction of higher cognitive abilities occurs in 40–60 % of people with multiple sclerosis (MS), as detected with neuropsychological testing, with predominant involvement of executive functions and processing speed. Event-related potentials to the Stroop are a bioelectrical correlate of executive function. We tested whether event-related potentials to the executive Stroop test may reflect executive dysfunction in MS. Methods: 29 MS patients (M/F:14/15; mean age 40 ± 8), and 16 healthy control subjects were included in the study (M/F:7/9; mean age 36 ± 10). Patients underwent a neuropsychological battery and, according to the performance obtained, they were divided in two groups: 13 frontal patients (F-MS; M/F:6/7; mean age: 40 ± 8) and 16 non frontal patients (NF-MS; M/F:8/8; mean age: 41 ± 7). Simple and complex reaction times to the Stroop task were measured using a computerized system. Event-Related Potentials (ERPs) to the same stimuli were obtained from 29 channel EEG, during mental discrimination between congruent and incongruent stimuli. Multivariate analysis was performed on reaction times (RTs) and ERPs latencies; topographic differences were searched with low resolution brain electromagnetic tomography (LORETA). Results: Significant group effects were found on the percentage of correct responses: F-MS subjects committed more errors than the other two groups. F-MS patients showed delayed P3 and N4 compared to NF-MS patients and delayed P2, N2, P3 and N4 compared to controls. NF-MS subjects showed significantly slower P2, N2 and P3 compared to control subjects. Moreover, frontal score correlated negatively with ERPs’ latency and with complex RTs. At source analysis F-MS patients presented significantly reduced activation predominantly over frontal, cingulate and parietal regions. Conclusions: Taken together, these findings suggest that bioelectrical activity to the Stroop test may well reflect the speed and extent of neural synchronization of frontal circuits. Further studies are needed to evaluate the usefulness of Stroop reaction times and ERPs for detecting frontal involvement early at a subclinical stage, allowing early cognitive therapy, and as a paraclinical marker for monitoring treatment outcomes. Keywords: MS, Executive function, Stroop task, ERPs, Source analysis * Correspondence: letizia.leocani@hsr.it Neurological Department, Institute of Experimental Neurology (INSPE), Scientific Institute Hospital San Raffaele, University Vita-Salute San Raffaele, Via Olgettina, 60, 20132 Milan, Italy © 2016 Amato et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 2 of 10 Background out the need for early detection of cognitive impairment Cognitive dysfunction is a common finding in multiple in MS [1, 46], possibly at the subclinical level. ERPs sclerosis (MS), being reported in 40–60 % of all patients could be particularly helpful in the early recognition of [18, 58, 62], typically consisting of deficits in attention, cognitive dysfunction and have been already successfully memory, executive functions and speed of information used to this end [43]. However, the oddball task, used to processing. This pattern of dysfunctions resembles that evoke P3, is not specifically challenging executive func- typical of subcortical dementia and is considered as tion, which is generally a key feature of cognitive mostly dependent on the disruption of connections be- involvement observed in MS [5, 16, 17, 21, 47, 57]. tween cortical associative areas, related to demyelination Among the cognitive tests which are suitable for ERPs and/or axonal loss within the white matter immediately analysis, the Stroop test [65] can be a good candidate underlying the cortex [39]. and has been already applied in the study of executive Several neuroimaging studies investigated these defi- functions in MS patients, in healthy subjects and in cits in MS patients trying to establish a relationship to other neuropsychiatric disorders [4, 16, 32, 37, 71]. Cog- lesion load as detected on MRI; some of these studies nitive control and flexibility are the most impaired in proposed that cognitive impairment is better explained MS among executive functions [16], and the Stroop task is by cortical structural abnormalities rather than subcor- particularly suitable to detect deficits in these components tical white matter lesions [13, 14, 59], other recent stud- of executive function [26]. We aimed at investigating the ies instead, which compared the role of cortical lesions electrophysiological correlates of executive dysfunction in and white matter lesions in the development of cognitive MS using ERPs to Stroop stimuli in persons with MS with impairments in MS, documented a higher role of white and without executive dysfunction. As a performance cor- matter integrity changes than previously assumed [50]. relate of the ERP task, reaction times to Stroop stimuli During performance of cognitive tasks, a greater extent were measured. of brain activation has been reported in patients com- pared to healthy subjects, [6, 44, 64] indicating cortical Methods reorganization possibly owing to compensatory mecha- Subjects nisms. Moreover, MS patients with mild cognitive Twenty-nine patients (15 females; mean age 40 ± 8) with impairment presented increased and additional activa- clinically definite multiple sclerosis according to McDonald tion during attention tasks compared to controls, while criteria [45, 55, 56], and 16 healthy controls (9 females; MS patients with severe cognitive impairments presented mean age 36 ± 10) were included in the study. Patients with no additional activation [53]. These findings suggest that Expanded disability status scale [35] higher than 6.5 or with the compensation depends on the possibility to access severe cognitive, motor or visual impairment interfering additional brain structures and the exhaustion of these with task compliance, as well as with steroid or psycho- resources would determine severe cognitive impairment. active drug treatment in the previous 3 months days were Electrophysiological studies have widely examined cog- excluded from the study. The protocol was approved by the nitive dysfunction in MS patients. Coherence analysis is Institutional Ethics Committee at the Hospital San Raffaele a useful indicator of functional connections between dif- and all subjects gave their written informed consent for ferent cortical areas [39], which are disrupted in multiple participation. sclerosis. Cognitive impaired MS patients had a signifi- Prior to the beginning of the study, patients underwent cant increase of theta power over the frontal regions a neuropsychological battery including: Stroop test [65], [39] as well as an increase in beta and gamma bends Tower of Hanoi [29], Dual task [48], Wisconsin Card [69] and a diffuse coherence decrease [19, 39]. Sorting [7, 73], semantic and fonemic verbal fluency tests. Event Related Potentials (ERPs) are among the most According to their performance on these tests, a “frontal suitable electrophysiological methods to examine pro- score” was assigned to each patient, who were subdivided cessing speed, which appears to be the most common in two groups: 13 frontal patients (F-MS; 7 females, mean cognitive deficit in MS [8, 20]. Delayed latency and age 40 ± 8 years) and 16 non frontal patients (NF-MS; 8 decreased amplitude of the main ERPs components, par- females, mean age: 41 ± 7). ticularly of the P3 to oddball paradigm, representing the discrimination of stimuli differing in some physical Computerized Stroop Performance dimension and whose latency reflects processing speed Reaction times (RTs) in the Stroop task were measured [36], have been reported in MS [38, 42]. Delayed P3 is using a computerized version implemented in commercial associated with higher EDSS scores [22, 67], disease dur- STIM software (Neuroscan, Herndon, VA, USA). Re- ation [25], low performance on attention and memory sponses were recorded using a computer mouse with two tasks and total MRI lesion burden [30, 49, 63]. Previous response buttons. Four colour words (green, red, yellow, neuroimaging and neuropsychological studies pointed and blue) written in congruent (50 %) or incongruent Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 3 of 10 (50 %) colour were randomly presented (stimulus duration, two conditions, data from the congruent and incongru- 200 ms; intertrial interval, 3.5 s) in four different series of ent trials were collapsed into a single ERP for each sub- 32 stimuli each. ject to reduce signal noise. In the first condition (simple RT - SRT), the subjects The latency of the main ERP components (i.e., N1 had to press a button for every stimulus presentation, [O1 or O2 electrode], P2, N2, P3 and N4 [Fz elec- regardless of stimulus type. The second condition (go/ trode]) was measured for each subject. The amplitude no-go RT) consisted of two series, in which a response and topographic analysis was performed at time inter- was required to either the incongruent (go/no-go I) or vals of the same components (time intervals = group congruent (go/no-go C) stimuli. In the third condition mean latency value of each component ± 30 ms) using (choice RT), the subjects had to press one button after low-resolution brain electromagnetic tomography the congruent stimuli (choice C) and the other button (LORETA; [51, 52]; see Statistical analysis section after the incongruent stimuli (choice I). For each series, below). the response latency was measured only for correct responses. Trials with latencies that exceeded 2.3 s were Statistical analysis considered omissions and excluded from the calculation The significance of group effects with regard to the of average RTs and accuracy. The latter was calculated number of correct responses (in the choice condition, in the complex RTs (go/no-go and choice) as the per- go/no-go C condition, and go/no-go I condition), RT centage of correct responses. latency in the choice C condition, choice I condition, go/no-go C condition, go/no-go I condition, and simple Event-related potential recording RT condition, and latency of the main ERP components Twenty-nine EEG channels with binaural reference were (N1, P2, N2, P3 and N4) was tested using three separate recorded using scalp electrodes set on an elastic cap multivariate analyses of variance (MANOVAs). Post hoc (Electrocap International, Eaton, OH, USA). The EEG tests were performed using Bonferroni correction. Cor- signal was amplified (Synamps, Neuroscan, Herndon, relations between frontal score and RTs and between VA, USA), filtered (DC–50 Hz), and digitized (sampling frontal score and ERP latencies were also performed frequency, 250 Hz). The electrooculogram and electro- using Spearman’s coefficient. All of the statistical tests myogram of the right and left extensor pollicis brevis were performed using SPSS 17 software (Technologies, were also recorded to detect eye movements and relax- Chicago, IL, USA). Group differences in the amplitude ation failure. and topography of ERP waveforms were investigated A series of 120 of the same Stroop stimuli (stimulus dur- using LORETA with a statistical nonparametric voxel- ation, 200 ms; intertrial interval, 6 s) used for the RT meas- wise comparison between the F-MS, NF-MS and control urement were presented using the same computerized groups. The level of significance was set at p < 0.05. version implemented in commercial STIM software (Neuroscan, Herndon, VA, USA). The subjects were Results instructed to mentally discriminate between congruent Stroop RTs and incongruent stimuli. This condition was chosen for Significant group effects were found on the percentage ERP recording to avoid movement interference. Atten- of correct responses (Fig. 1) at MANOVA (p =0.001): in tion was monitored every 10–15 trials by randomly ask- ing subjects to verbally define the congruency of the last stimulus presented. Recordings were performed in the morning (8:30–10:00 a.m.) to reduce variability due to circadian fluctuations. Event-related potential analysis Epochs from −500 to 1200 ms from stimulus onset were obtained. Linear detrending was performed over the entire epoch to correct for DC drifts. The baseline was then corrected between −500 and 0 ms. Epochs that contained artefacts or muscle relaxation failure upon vis- ual inspection were excluded from the analysis. Initially, separate averages were obtained for congruent and Fig. 1 Percentage of correct responses in the choice condition, the go/ no-go condition and the simple reaction time condition, in controls incongruent stimuli. After a preliminary comparison (white), NF-MS (grey) and F-MS (black). F-MS vs CNT: p = 0.001; F-MS vs between and within groups, which did not show signifi- NF-MS p = 0.001. Line bars over each column indicate standard error ** cant differences between the parameters obtained in the Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 4 of 10 the choice condition F-MS patients committed signifi- groups, had a significantly reduced activation of the right cantly more errors than controls (p = 0.001) and NF-MS supramarginal gyrus, the right inferior parietal lobule, patients (p =0.001). the right middle and inferior temporal gyri and the There were no significant group effects on RTs at superior and middle frontal gyri (Figs. 5 and 6). In the MANOVA. P2 time window (time interval = group mean P2 latency value ± 20 ms), there were not significant differences be- tween groups. In the N2 time window (time interval = ERPs latency group mean N2 latency value ± 20 ms), F-MS patients Significant group effect was found (Fig. 2) at MANOVA (F showed a significantly decreased activity in the cingulate = 21.699; p = 0.000); F-MS patients showed significantly gyrus and in the parahippocampal gyrus compared to delayed P2, N2, P3 and N4 latencies compared to controls NF-MS patients (Fig. 7) but not significant differences (P2: p = 0.000; N2: p =0.001; P3: p =0.000; N4: p = 0.000) compared to control subjects; significance was reached and P3 and N4 latencies compared to NF-MS patients vs NF-MS and not vs controls, owing to a slight non sig- (P3: p = 0.015; N4: p = 0.000). NF-MS patients showed nificant increase in activation in NF-MS vs controls. In significantly delayed P2, N2 and P3 latencies compared the P3 time window (time interval = group mean P3 la- to controls (P2: p = 0.007; N2: p = 0.021; P3: p = 0.033). tency value ± 20 ms), F-MS group presented a reduced activity reaching significance vs controls in the superior Correlations and medial frontal gyri, the cingulate gyrus, the precu- There was a negative correlation between frontal score neus and the precentral lobule (Fig. 8) and vs NF-MS in and N1 latency (ρ = −0.426, p = 0.024), P2 latency (ρ = the anterior cingulate, the medial frontal gyrus and the −0.643, p = 0.000) and N4 latency (ρ = −0.566, p = 0.002). cingulate gyrus (Fig. 9). In the N4 time window (time Moreover, frontal score correlated negatively with RTs interval = group mean N4 latency value ± 20 ms), F-MS speed in the go/no-go I condition (ρ = −0.425, p = 0.022) patients showed a significant decreased activity com- and in the choice C condition (ρ = −0.381, p = 0.042) pared to healthy subjects in the cingulate gyrus, the (Fig. 3), and correlated positively with the percentage of paracentral lobule and the precuneus (Fig. 10). correct responses in the go/no-go C condition (ρ = 0.431, p = 0.019) and in the choice condition (ρ = 0.550, p = 0.002) (Fig. 4). Discussion Compared to NF-MS patients and control subjects, our sample of F-MS patients showed delayed ERPs’ latencies, ERPs amplitude and topography reduced frontoparietal activity and less accuracy in the LORETA statistical non-parametric voxel-wise analysis execution of the Stroop task. Moreover, frontal score revealed significant group differences. In the N1 time correlated negatively with ERPs’ latency and with com- window (time interval = group mean N1 latency value ± plex RTs. These findings are discussed in details below. 20 ms), the F-MS group, compared to the other two Fig. 2 N1, N2, P3, N4 and P6 latencies in CNT subjects (white), NF-MS patients (grey) and F-MS patients (black). F-MS vs CNT: p =0.000; p =0.001; * ** *** p =0.000; p =0.000. F-MS vs NF-MS: p =0.015; p = 0.000. NF-MS vs CNT: p =0.007; p =0.021; p = 0.033. Line bars over each column indicate **** # ## ° °° °°° standard error Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 5 of 10 Fig. 3 Correlation between Frontal Score and RTs in the Choice condition (ρ = −0.381, p = 0.042) RTs to errors. However, this methodological choice could not The lower level of accuracy observed in frontal patients allow us to avoid two possible confounding factors. One compared with the other two groups, but not in non is learning itself: subjects with MS-related learning im- frontal patients compared with controls, suggests an pairment could present slower learning and therefore impairment, in the first group, of conflict monitoring higher impairment in the most complex tasks because function, necessary to process competing information these were performed later, favoring the subjects with and select the adequate response, reported to be medi- faster learning. The second is cognitive fatigue, defined ated by frontal structures as the anterior cingulate cortex as performance decay with test repetition and reported [10, 12]. Moreover, accuracy and speed in the complex to affect MS patients to a greater extent than healthy tasks were correlated with frontal score obtained from controls [34, 41]. However, performance at the comput- neuropsychological assessment. Overall, these findings erized RTs was more impaired in frontal compared with suggest that computerized RTs may provide useful mea- non frontal MS patients and correlated with the frontal sures for the assessment of executive functions in these score, suggesting that this tools reflects, at least partially, patients. Although a learning effect may have certainly the severity of frontal involvement. To further interpret occurred during RTs measurements, the tasks were per- our findings more studies are needed specifically ad- formed in a sequence with increasing difficulty. This dressing the issues of whether this impairment is a direct choice was made to facilitate learning as much as pos- correlate of executive function or it is at least partly sible for the subsequent ERPs recordings, to minimize mediated by learning difficulties or cognitive fatigue. In an additional source of between-subject variability across any case, both learning difficulties and cognitive fatigue RT tasks and to limit the number of RT exclusions due may well represent other correlate of frontal dysfunction, Fig. 4 Correlation between Frontal Score and percentage of correct responses in the Choice condition (ρ = 0.550, p = 0.002 Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 6 of 10 Fig. 5 LORETA non-parametric voxel-wise comparison map between F-MS and controls in the N1 time window. Blue: regions of significant decreased activity in F-MS needing more work to disentangle the relative contribu- These latter findings point out to the possibility that tion of these factors to our findings. bioelectrical activity to the Stroop stimuli, particularly the later component, may well reflect the speed of ERPs latency neural synchronization of frontal lobe circuits, being es- ERPs latencies were significantly increased in both pa- pecially involved in patients with frontal dysfunction. tients groups compared with controls and in the F-MS group compared to NF-MS group. This finding is consist- ERPs amplitude and topography ent with previous studies widely documenting cognitive LORETA topographic ERPs analysis showed reduced ac- ERPs latencies’ delay in multiple sclerosis [3, 28, 72]. tivity in the N1, N2, P3 and N4 time windows mostly This delay was significant for all components mea- over the frontal, cingulate and parietal regions evident in sured but the earliest (N1). This result suggests that in frontal MS patients compared with controls and with our sample of patients visual discrimination processes, non frontal patients. as reflected by the posterior N1 component, were not N1 is assumed to reflect selective attention to basic delayed and that the cognitive ERPs latencies’ delay stimulus characteristics, initial selection for later pattern observed cannot be explained only in term of impaired recognition, and intentional discrimination processing information processing speed since in this case we [70]. Its source is located in the inferior occipital lobe, would have observed a delay also at this earlier level of occipito-temporal junction [31], and inferior temporal information processing. lobe [9]. Since the discrimination process, reflected by Fig. 6 LORETA non-parametric voxel-wise comparison map between F-MS and NF-MS in the N1 time window. Blue: regions of significant decreased activity in NF-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 7 of 10 Fig. 7 LORETA non-parametric voxel-wise comparison map between F-MS and NF-MS in the N2 time window. Blue: regions of significant decreased activity in NF-MS occipito-temporal N1, can be modulated by top-down responses and that it may at least partially account for executive control (the greater the difficulty of stimulus conflict monitoring. N2 is especially pronounced over discrimination, the greater the need of top-down execu- the fronto-central electrodes and has been proposed to tive control modulation and therefore the greater the reflect ACC sensitivity to conflict [68]. amplitude of the N1 component, cfr. [23]), the signifi- The P3 component is elicited in tasks related to stimu- cant reduced activity observed here in the N1 time win- lus differentiation and appears when a memory repre- dow in F-MS patients compared to both the other two sentation of the recent stimulus context is updated upon groups, could be determined by executive control defi- the detection of deviance from it [66]. The frontal P300 cits in these patients: control and NF-MS groups would component in go/no-go-like tasks has been associated show a greater N1 amplitude, with respect to F-MS with an inhibitory mechanism [24]. However, in the group, as a consequence of executive control modula- present study, the subjects only had to mentally discrim- tion, which is instead lacking in frontal patients. inate between congruent and incongruent stimuli; there- The N2 component in go/no-go-like tasks has been at- fore, conflict did not arise at the response level. Thus, tributed to response inhibition mechanisms [27, 33]. the P3 component observed herein most likely reflects However, the N2 component has also been reported to the detection of conflict that arose at the level of the occur in relation to covert responses in the present study semantic encode. and in previous studies [2, 54]. This would indicate that The N4 component to the Stroop task is supposed to it is not completely attributable to the inhibition of reflect anterior cingulate activity [40], which has been Fig. 8 LORETA Non-parametric voxel-wise comparison map between F-MS and controls in the P3 time window. Blue: regions of significant decreased activity in F-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 8 of 10 Fig. 9 Non-parametric voxel-wise comparison map between F-MS and NF-MS in the P3 time window. Blue: regions of significant decreased activity in F-MS widely documented to account for conflict monitoring performance at complex tasks. Compensatory mecha- function and for triggering compensatory adjustment in nisms depend on the possibility to access additional cognitive control [11, 15, 27]. brain structures and the exhaustion of these resources Taken together these findings reflect cognitive control seems to determine severe cognitive impairment, as doc- impairment in frontal involved MS patients. umented elsewhere [53]. Previous functional neuroimaging studies to Stroop task [60, 61] showed a greater activation in MS subjects Conclusion compared with healthy controls in several areas involved Our finding of decreased accuracy in frontal involved in the task execution, which resulted hypo-activated MS group suggests that this approach may provide use- herein in cognitive impaired patients. These findings are ful objective measures for the assessment of executive only apparently inconsistent; in the studies by Rocca functions in these patients. Topographic analysis of ERPs et al., in fact the increase in activation in MS patients, components to the Stroop stimuli showed predominant which, although not significant, was reported to occur involvement of frontal, cingulate and parietal regions, also herein in NF-MS patients, seems to reflect compen- probably reflecting the executive stage of stimulus pro- satory mechanisms granting a normal performance, cessing. Also the latency of these components correlated whereas our sample of patients with frontal involvement with neuropsychological frontal score. Taken together, seems to be too compromised to compensate and just these findings suggest that bioelectrical activity to the presented reduced activation accompanied by impaired Stroop test may well reflect the speed and extent of Fig. 10 Non-parametric voxel-wise comparison map between F-MS and controls in the N4 time window. Blue: regions of significant decreased activity in F-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 9 of 10 neural synchronization of frontal circuits. Further stud- 17. Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008;7(12):1139–51. ies are needed to evaluate the usefulness of Stroop reac- 18. Comi G, Filippi M, Martinelli V, Sirabian G, Visciani A, Campi A, et al. Brain tion times and ERPs for detecting frontal involvement magnetic resonance imaging correlates of cognitive impairment in multiple early at a subclinical stage, allowing early cognitive ther- sclerosis. J Neurol Sci. 1993;115(Suppl):66–73. 19. Cover KS, Vrenken H, Geurts JJ, van Oosten BW, Jelles B, Polman CH, et al. apy, and as a paraclinical marker for monitoring treat- Multiple sclerosis patients show a highly significant decrease in alpha band ment outcomes. interhemispheric synchronization measured using MEG. Neuroimage. 2006;29(3):783–8. Competing interests 20. DeLuca J, Chelune GJ, Tulsky DS, Lengenfelder J, Chiaravalloti ND. Is speed The authors declare that they have no competing interests. of processing or working memory the primary information processing deficit in multiple sclerosis? J Clin Exp Neuropsychol. 2004;26(4):550–62. Authors’contribution 21. Drew M, Tippett LJ, Starkey NJ, Isler RB. Executive dysfunction and cognitive NA data and statistical analysis and interpretation, manuscript draft and impairment in a large community-based sample with Multiple Sclerosis from revision. MC supervision of source localization, manuscript revision. MR New Zealand: a descriptive study. Arch Clin Neuropsychol. 2008;23(1):1–19. patients recruitment and clinical assessments, manuscript revision. LM 22. Ellger T, Bethke F, Frese A, Luettmann RJ, Buchheister A, Ringelstein EB, patients recruitment and clinical assessments, manuscript revision. BC, et al. Event-related potentials in different subtypes of multiple sclerosis–a patients recruitment and clinical assessments, manuscript revision. MF cross-sectional study. J Neurol Sci. 2002;205(1):35–40. neuropsychological assessments, manuscript revision. FP neuropsychological 23. Fedota JR, McDonald CG, Roberts DM, Parasuraman R. Contextual task assessments, manuscript revision. GC supervision to clinical and difficulty modulates stimulus discrimination: electrophysiological evidence neuropsychological assessment, manuscript revision. VM supervision to for interaction between sensory and executive processes. Psychophysiology. clinical and neuropsychological assessment, manuscript revision. LL study 2012;49(10):1384–93. design, supervision to data collection and analysis, manuscript revision. 24. Gajewski, Falkenstein. Effects of task complexity on ERP components in All authors read and approved the final manuscript. Go/Nogo tasks. Int J Psychophysiology. 2013;87(3):273–8. 25. Gil R, Zai L, Neau JP, Jonveaux T, Agbo C, Rosolacci T, et al. Event-related Acknowledgements auditory evoked potentials and multiple sclerosis. Electroencephalogr Clin The authors wish to thank A. Nossa, EEG technician, for EEG recordings. Neurophysiol. 1993;88(3):182–7. 26. Goldberg E, Bougakov D. Neuropsychologic assessment of frontal lobe Received: 5 September 2015 Accepted: 18 March 2016 dysfunction. Psychiatr Clin North Am. 2005;28(3):567–80. 27. Gonzalez-Rosa JJ, Inuggi A, Blasi V, Cursi M, Annovazzi P, Comi G, et al. Response competition and response inhibition during different Choice- References discrimination tasks: Evidence from ERP measured inside MRI scanner. 1. Amato MP, Portaccio E, Goretti B, Zipoli V, Hakiki B, Giannini M, et al. Int J Psychophysiol. 2013;89(1):37–47. Cognitive impairments in early stages of multiple sclerosis. Neurol Sci. 28. Gonzalez-Rosa JJ, Vazquez-Marrufo M, Vaquero E, Duque P, Borges M, 2010;31(S2):S211–4. Gomez-Gonzalez CM, et al. Cluster analysis of behavioural and event-related 2. Amato N, Riva N, Cursi M, Martins-Silva A, Martinelli V, Comola M, et al. potentials during a contingent negative variation paradigm in remitting- Different frontal involvement in ALS and PLS revealed by Stroop event-related relapsing and benign forms of multiple sclerosis. BMC Neurol. 2011;11:64. potentials and reaction times. Front Aging Neurosci. 2013;5:82. 29. Hofstadter DR. Metamagical Themas: Questing for the Essence of Mind and 3. Aminoff JC, Goodin DS. Long-latency cerebral event-related potentials in Pattern. New York: Basic Books; 1985. multiple sclerosis. J Clin Neurophysiol. 2001;18(4):372–7. 30. Honig LS, Ramsay RE, Sheremata WA. Event-related potential P300 in 4. Annovazzi P, Colombo B, Bernasconi L, Schiatti E, Comi G, Leocani L. multiple sclerosis. Relation to magnetic resonance imaging and cognitive Cortical function abnormalities in migraine: neurophysiological and impairment. Arch Neurol. 1992;49(1):44–50. neuropsychological evidence from reaction times and event-related 31. Hopf JM, Vogel E, Woodman G, Heinze HJ, Luck S. Localizing visual potentials to the Stroop test. Neurol Sci. 2004;25 Suppl 3:S285–7. discrimination processes in time and space. J Neurophysiol. 2002;88:2088–95. 5. Arnett PA, Rao SM, Bernardin L, Grafman J, Yetkin FZ, Lobeck L. Relationship 32. Hsieh YH, Chen KJ, Wang CC, Lai CL. Cognitive and motor components of between frontal lobe lesions and Wisconsin Card Sorting Test performance response speed in the stroop test in Parkinson's disease patients. Kaohsiung in patients with multiple sclerosis. Neurology. 1994;44(3 Pt 1):420–5. J Med Sci. 2008;24(4):197–203. 6. Audoin B, Ibarrola D, Ranjeva JP, Confort-Gouny S, Malikova I, Ali-Chérif A, 33. Jodo E, Kayama Y. Relation of a negative ERP component to response et al. Compensatory cortical activation observed by fMRI during a cognitive inhibition in a Go/NoGo task. Electroencephalogr Clin Neurophysiol. task at the earliest stage of MS. Hum Brain Mapp. 2003;20(2):51–8. 1992;82:477–82. 7. Berg EA. A simple objective technique for measuring flexibility in thinking. 34. Krupp LB, Serafin DJ, Christodoulou C. Multiple sclerosis-associated fatigue. J Gen Psychol. 1948;39:15–22. Expert Rev Neurother. 2010;10(9):1437–47. 8. Bergendal G, Fredrikson S, Almkvist O. Selective decline in information 35. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded processing in subgroups of multiple sclerosis: an 8-year longitudinal study. disability status scale (EDSS). Neurology. 1983;33(11):1444–52. Eur Neurol. 2007;57(4):193–202. 36. Kutas M, McCarthy G, Donchin E. Augmenting mental chronometry: the P300 9. Bokura H, Yamaguchi S, Kobayashi S. Electrophysiological correlates for as a measure of stimulus evaluation time. Science. 1977;197(4305):792–5. response inhibition in a Go/NoGo task. Clin Neurophysiol. 2001;112:2224–32. 37. Lapshin H, Audet B, Feinstein A. Detecting cognitive dysfunction in a busy 10. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. Conflict multiple sclerosis clinical setting: a computer generated approach. Eur J monitoring and cognitive control. Psychol Rev. 2001;108(3):624–52. Neurol. 2014;21:281–6. 11. Botvinick MM, Cohen JD, Carter CS. Conflict monitoring and anterior 38. Leocani L, Gonzalez-Rosa JJ, Comi G. Neurophysiological correlates of cognitive cingulate cortex: an update. Trends Cogn Sci. 2004;8(12):539–46. disturbances in multiple sclerosis. Neurol Sci. 2010;31 Suppl 2:S249–53. 12. Botvinick M, Nystrom LE, Fissell K, Carter CS, Cohen JD. Conflict monitoring versus 39. Leocani L, Locatelli T, Martinelli V, Rovaris M, Falautano M, Filippi M, et al. selection-for-action in anterior cingulate cortex. Nature. 1999;402(6758):179–81. Electroencephalographic coherence analysis in multiple sclerosis: correlation 13. Calabrese M, Agosta F, Rinaldi F, Mattisi I, Grossi P, Favaretto A, et al. with clinical, neuropsychological, and MRI findings. J Neurol Neurosurg Cortical lesions and atrophy associated with cognitive impairment in Psychiatry. 2000;69(2):192–8. relapsing-remitting multiple sclerosis. Arch Neurol. 2009;66(9):1144–50. 40. Liotti M, Woldorff MG, Perez R, et al. An ERP study of the temporal course of 14. Calabrese M, Rinaldi F, Grossi P, Gallo P. Cortical pathology and cognitive the Stroop color-word interference effect. Neuropsychologia. 2000;38:701–11. impairment in multiple sclerosis. Expert Rev Neurother. 2011;11(3):425–32. 15. Carter CS, van Veen V. Anterior cingulate cortex and conflict detection: an 41. MacAllister WS, Krupp LB. Multiple sclerosis-related fatigue. Phys Med update of theory and data. Cogn Affect Behav Neurosci. 2007;7(4):367–79. Rehabil Clin N Am. 2005;16(2):483–502. 16. Cerezo García M, Martín Plasencia P, Aladro BY. Alteration profile of executive 42. Magnano I, Aiello I, Piras MR. Cognitive impairment and neurophysiological functions in multiple sclerosis. Acta Neurol Scand. 2015;131(5):313–20. correlates in MS. J Neurol Sci. 2006;245(1-2):117–22. Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 10 of 10 43. Magniè MN, Bensa C, Laloux L, Bertogliati C, Faure S, Lebrun C. Contribution 69. Vazquez-Marrufo M, Gonzalez-Rosa JJ, Vaquero E, Duque P, Borges M, of cognitive evoked potentials for detecting early cognitive disorders in Gomez C, et al. Quantitative electroencephalography reveals different multiple sclerosis. Rev Neurol. 2007;163(11):1065–74. physiological profiles between benign and remitting-relapsing multiple 44. Mainero C, Pantano P, Caramia F, Pozzilli C. Brain reorganization during sclerosis patients. BMC Neurol. 2008;8:44. attention and memory tasks in multiple sclerosis: insights from functional 70. Vogel EK, Luck SJ. The visual N1 component as an index of a discrimination MRI studies. J Neurol Sci. 2006;245(1-2):93–8. process. Psychophysiology. 2000;37:190–203. 45. McDonald WI, Compston A, Edan G, et al. Recommended diagnostic criteria 71. West R, Alain C. Effects of task context and fluctuations of attention on for multiple sclerosis: guidelines from the International Panel on the neural activity supporting performance of the Stroop Task. Brain Res. diagnosis of multiple sclerosis. Ann Neurol. 2001;50(1):121–7. 2000;873:102–11. 72. Whelan R, Lonergan R, Kiiski H, Nolan H, Kinsella K, Hutchinson M, et al. 46. Moccia M, Lanzillo R, Palladino R, Chang KC, Costabile T, Russo C, et al. Impaired information processing speed and attention allocation in multiple Cognitive impairment at diagnosis predicts 10-year multiple sclerosis sclerosis patients versus controls: a high-density EEG study. J Neurol Sci. progression. Mult Scler. 2015;11:1–9. 2010;293(1-2):45–50. 47. Muhlert N, Sethi V, Schneider T, Daga P, Cipolotti L, Haroon HA, et al. Diffusion 73. Weigl E. On the psychology of so-called processes of abstraction. The MRI-based cortical complexity alterations associated with executive function in Journal of Abnormal and Social Psychology. 1941;36(1):3–33. multiple sclerosis. J Magn Reson Imaging. 2013;38(1):54–63. 48. Navon D, Gopher D. On the economy of the human-processing system. Psychol Rev. 1979;86:214–55. 49. Newton MR, Barrett G, Callanan MM, Towell AD. Cognitive event-related potentials in multiple sclerosis. Brain. 1989;112(Pt 6):1637–60. 50. Papadopoulou A, Müller-Lenke N, Naegelin Y, Kalt G, Bendfeldt K, Kuster P, et al. Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Mult Scler. 2013;19(10):1290–6. 51. Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D. Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol. 2002;24(Suppl C):91–5. 52. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol. 1994;18(1):49–65. 53. Penner IK, Rausch M, Kappos L, Opwis K, Radü EW. Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. J Neurol. 2003;250(4):461–72. 54. Pfefferbaum A, Ford JM, Weller BJ, Kopell BS. ERPs to response production and inhibition. Electroencephalogr Clin Neurophysiol. 1985;60:423–34. 55. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292–302. 56. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol. 2005;58(6):840–6. 57. Rao SM, Hammeke TA, Speech TJ. Wisconsin Card Sorting Test performance in relapsing-remitting and chronic-progressive multiple sclerosis. J Consult Clin Psychol. 1987;55(2):263–5. 58. Rao SM, Leo GJ, Bemardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns and prediction. Neurology. 1991;41(5):685–91. 59. Rinaldi F, Calabrese M, Grossi P, Puthenparampil M, Perini P, Gallo P. Cortical lesions and cognitive impairment in multiple sclerosis. Neurol Sci. 2010;31(2):S235–7. 60. Rocca MA, Bonnet MC, Meani A, Valsasina P, Colombo B, Comi G, et al. Differential cerebellar functional interactions during an interference task across multiple sclerosis phenotypes. Radiology. 2012;265(3):864–73. 61. Rocca MA, Valsasina P, Ceccarelli A, Absinta M, Ghezzi A, Riccitelli G, et al. Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Mapp. 2009;30(1):276–90. 62. Ron MA, Callanan MM, Warrington EK. Cognitive abnormalities in multiple sclerosis: a psychometric and MRI study. Psychol Med. 1991;21(1):59–68. 63. Sailer M, Heinze HJ, Tendolkar I, Decker U, Kreye O, v Rolbicki U, et al. Influence of cerebral lesion volume and lesion distribution on event-related Submit your next manuscript to BioMed Central brain potentials in multiple sclerosis. J Neurol. 2001;248(12):1049–55. 64. Staffen W, Mair A, Zauner H, Unterrainer J, Niederhofer H, Kutzelnigg A, and we will help you at every step: et al. Cognitive function and fMRI in patients with multiple sclerosis: • We accept pre-submission inquiries evidence for compensatory cortical activation during an attention task. Brain. 2002;125(6):1275–82. � Our selector tool helps you to find the most relevant journal 65. Stroop JR. 2Studies of interference in serial verbal reactions. J Exp Psychol. � We provide round the clock customer support 1935;18:643–62. � Convenient online submission 66. Sutton S, Tueting P, Zubin J, John ER. Evoked potential correlates of stimulus uncertainty. Science. 1965;150:1187–8. � Thorough peer review 67. Triantafyllou NI, Voumvourakis K, Zalonis I, Sfagos K, Mantouvalos V, Malliara � Inclusion in PubMed and all major indexing services S, et al. Cognition in relapsing-remitting multiple sclerosis: a multichannel � Maximum visibility for your research event-related potential (P300) study. Acta Neurol Scand. 1992;85(1):10–3. 68. van Veen V, Carter CS. The anterior cingulate as a conflict monitor: fMRI and Submit your manuscript at ERP studies. Physiol Behav. 2002;77:477–4820. www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multiple Sclerosis and Demyelinating Disorders Springer Journals

Stroop event-related potentials as a bioelectrical correlate of frontal lobe dysfunction in multiple sclerosis

Loading next page...
 
/lp/springer-journals/stroop-event-related-potentials-as-a-bioelectrical-correlate-of-Iq0g0N45iv

References (81)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Amato et al.
Subject
Psychology; Neuropsychology; Clinical Psychology
eISSN
2056-6115
DOI
10.1186/s40893-016-0007-x
Publisher site
See Article on Publisher Site

Abstract

Background: Dysfunction of higher cognitive abilities occurs in 40–60 % of people with multiple sclerosis (MS), as detected with neuropsychological testing, with predominant involvement of executive functions and processing speed. Event-related potentials to the Stroop are a bioelectrical correlate of executive function. We tested whether event-related potentials to the executive Stroop test may reflect executive dysfunction in MS. Methods: 29 MS patients (M/F:14/15; mean age 40 ± 8), and 16 healthy control subjects were included in the study (M/F:7/9; mean age 36 ± 10). Patients underwent a neuropsychological battery and, according to the performance obtained, they were divided in two groups: 13 frontal patients (F-MS; M/F:6/7; mean age: 40 ± 8) and 16 non frontal patients (NF-MS; M/F:8/8; mean age: 41 ± 7). Simple and complex reaction times to the Stroop task were measured using a computerized system. Event-Related Potentials (ERPs) to the same stimuli were obtained from 29 channel EEG, during mental discrimination between congruent and incongruent stimuli. Multivariate analysis was performed on reaction times (RTs) and ERPs latencies; topographic differences were searched with low resolution brain electromagnetic tomography (LORETA). Results: Significant group effects were found on the percentage of correct responses: F-MS subjects committed more errors than the other two groups. F-MS patients showed delayed P3 and N4 compared to NF-MS patients and delayed P2, N2, P3 and N4 compared to controls. NF-MS subjects showed significantly slower P2, N2 and P3 compared to control subjects. Moreover, frontal score correlated negatively with ERPs’ latency and with complex RTs. At source analysis F-MS patients presented significantly reduced activation predominantly over frontal, cingulate and parietal regions. Conclusions: Taken together, these findings suggest that bioelectrical activity to the Stroop test may well reflect the speed and extent of neural synchronization of frontal circuits. Further studies are needed to evaluate the usefulness of Stroop reaction times and ERPs for detecting frontal involvement early at a subclinical stage, allowing early cognitive therapy, and as a paraclinical marker for monitoring treatment outcomes. Keywords: MS, Executive function, Stroop task, ERPs, Source analysis * Correspondence: letizia.leocani@hsr.it Neurological Department, Institute of Experimental Neurology (INSPE), Scientific Institute Hospital San Raffaele, University Vita-Salute San Raffaele, Via Olgettina, 60, 20132 Milan, Italy © 2016 Amato et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 2 of 10 Background out the need for early detection of cognitive impairment Cognitive dysfunction is a common finding in multiple in MS [1, 46], possibly at the subclinical level. ERPs sclerosis (MS), being reported in 40–60 % of all patients could be particularly helpful in the early recognition of [18, 58, 62], typically consisting of deficits in attention, cognitive dysfunction and have been already successfully memory, executive functions and speed of information used to this end [43]. However, the oddball task, used to processing. This pattern of dysfunctions resembles that evoke P3, is not specifically challenging executive func- typical of subcortical dementia and is considered as tion, which is generally a key feature of cognitive mostly dependent on the disruption of connections be- involvement observed in MS [5, 16, 17, 21, 47, 57]. tween cortical associative areas, related to demyelination Among the cognitive tests which are suitable for ERPs and/or axonal loss within the white matter immediately analysis, the Stroop test [65] can be a good candidate underlying the cortex [39]. and has been already applied in the study of executive Several neuroimaging studies investigated these defi- functions in MS patients, in healthy subjects and in cits in MS patients trying to establish a relationship to other neuropsychiatric disorders [4, 16, 32, 37, 71]. Cog- lesion load as detected on MRI; some of these studies nitive control and flexibility are the most impaired in proposed that cognitive impairment is better explained MS among executive functions [16], and the Stroop task is by cortical structural abnormalities rather than subcor- particularly suitable to detect deficits in these components tical white matter lesions [13, 14, 59], other recent stud- of executive function [26]. We aimed at investigating the ies instead, which compared the role of cortical lesions electrophysiological correlates of executive dysfunction in and white matter lesions in the development of cognitive MS using ERPs to Stroop stimuli in persons with MS with impairments in MS, documented a higher role of white and without executive dysfunction. As a performance cor- matter integrity changes than previously assumed [50]. relate of the ERP task, reaction times to Stroop stimuli During performance of cognitive tasks, a greater extent were measured. of brain activation has been reported in patients com- pared to healthy subjects, [6, 44, 64] indicating cortical Methods reorganization possibly owing to compensatory mecha- Subjects nisms. Moreover, MS patients with mild cognitive Twenty-nine patients (15 females; mean age 40 ± 8) with impairment presented increased and additional activa- clinically definite multiple sclerosis according to McDonald tion during attention tasks compared to controls, while criteria [45, 55, 56], and 16 healthy controls (9 females; MS patients with severe cognitive impairments presented mean age 36 ± 10) were included in the study. Patients with no additional activation [53]. These findings suggest that Expanded disability status scale [35] higher than 6.5 or with the compensation depends on the possibility to access severe cognitive, motor or visual impairment interfering additional brain structures and the exhaustion of these with task compliance, as well as with steroid or psycho- resources would determine severe cognitive impairment. active drug treatment in the previous 3 months days were Electrophysiological studies have widely examined cog- excluded from the study. The protocol was approved by the nitive dysfunction in MS patients. Coherence analysis is Institutional Ethics Committee at the Hospital San Raffaele a useful indicator of functional connections between dif- and all subjects gave their written informed consent for ferent cortical areas [39], which are disrupted in multiple participation. sclerosis. Cognitive impaired MS patients had a signifi- Prior to the beginning of the study, patients underwent cant increase of theta power over the frontal regions a neuropsychological battery including: Stroop test [65], [39] as well as an increase in beta and gamma bends Tower of Hanoi [29], Dual task [48], Wisconsin Card [69] and a diffuse coherence decrease [19, 39]. Sorting [7, 73], semantic and fonemic verbal fluency tests. Event Related Potentials (ERPs) are among the most According to their performance on these tests, a “frontal suitable electrophysiological methods to examine pro- score” was assigned to each patient, who were subdivided cessing speed, which appears to be the most common in two groups: 13 frontal patients (F-MS; 7 females, mean cognitive deficit in MS [8, 20]. Delayed latency and age 40 ± 8 years) and 16 non frontal patients (NF-MS; 8 decreased amplitude of the main ERPs components, par- females, mean age: 41 ± 7). ticularly of the P3 to oddball paradigm, representing the discrimination of stimuli differing in some physical Computerized Stroop Performance dimension and whose latency reflects processing speed Reaction times (RTs) in the Stroop task were measured [36], have been reported in MS [38, 42]. Delayed P3 is using a computerized version implemented in commercial associated with higher EDSS scores [22, 67], disease dur- STIM software (Neuroscan, Herndon, VA, USA). Re- ation [25], low performance on attention and memory sponses were recorded using a computer mouse with two tasks and total MRI lesion burden [30, 49, 63]. Previous response buttons. Four colour words (green, red, yellow, neuroimaging and neuropsychological studies pointed and blue) written in congruent (50 %) or incongruent Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 3 of 10 (50 %) colour were randomly presented (stimulus duration, two conditions, data from the congruent and incongru- 200 ms; intertrial interval, 3.5 s) in four different series of ent trials were collapsed into a single ERP for each sub- 32 stimuli each. ject to reduce signal noise. In the first condition (simple RT - SRT), the subjects The latency of the main ERP components (i.e., N1 had to press a button for every stimulus presentation, [O1 or O2 electrode], P2, N2, P3 and N4 [Fz elec- regardless of stimulus type. The second condition (go/ trode]) was measured for each subject. The amplitude no-go RT) consisted of two series, in which a response and topographic analysis was performed at time inter- was required to either the incongruent (go/no-go I) or vals of the same components (time intervals = group congruent (go/no-go C) stimuli. In the third condition mean latency value of each component ± 30 ms) using (choice RT), the subjects had to press one button after low-resolution brain electromagnetic tomography the congruent stimuli (choice C) and the other button (LORETA; [51, 52]; see Statistical analysis section after the incongruent stimuli (choice I). For each series, below). the response latency was measured only for correct responses. Trials with latencies that exceeded 2.3 s were Statistical analysis considered omissions and excluded from the calculation The significance of group effects with regard to the of average RTs and accuracy. The latter was calculated number of correct responses (in the choice condition, in the complex RTs (go/no-go and choice) as the per- go/no-go C condition, and go/no-go I condition), RT centage of correct responses. latency in the choice C condition, choice I condition, go/no-go C condition, go/no-go I condition, and simple Event-related potential recording RT condition, and latency of the main ERP components Twenty-nine EEG channels with binaural reference were (N1, P2, N2, P3 and N4) was tested using three separate recorded using scalp electrodes set on an elastic cap multivariate analyses of variance (MANOVAs). Post hoc (Electrocap International, Eaton, OH, USA). The EEG tests were performed using Bonferroni correction. Cor- signal was amplified (Synamps, Neuroscan, Herndon, relations between frontal score and RTs and between VA, USA), filtered (DC–50 Hz), and digitized (sampling frontal score and ERP latencies were also performed frequency, 250 Hz). The electrooculogram and electro- using Spearman’s coefficient. All of the statistical tests myogram of the right and left extensor pollicis brevis were performed using SPSS 17 software (Technologies, were also recorded to detect eye movements and relax- Chicago, IL, USA). Group differences in the amplitude ation failure. and topography of ERP waveforms were investigated A series of 120 of the same Stroop stimuli (stimulus dur- using LORETA with a statistical nonparametric voxel- ation, 200 ms; intertrial interval, 6 s) used for the RT meas- wise comparison between the F-MS, NF-MS and control urement were presented using the same computerized groups. The level of significance was set at p < 0.05. version implemented in commercial STIM software (Neuroscan, Herndon, VA, USA). The subjects were Results instructed to mentally discriminate between congruent Stroop RTs and incongruent stimuli. This condition was chosen for Significant group effects were found on the percentage ERP recording to avoid movement interference. Atten- of correct responses (Fig. 1) at MANOVA (p =0.001): in tion was monitored every 10–15 trials by randomly ask- ing subjects to verbally define the congruency of the last stimulus presented. Recordings were performed in the morning (8:30–10:00 a.m.) to reduce variability due to circadian fluctuations. Event-related potential analysis Epochs from −500 to 1200 ms from stimulus onset were obtained. Linear detrending was performed over the entire epoch to correct for DC drifts. The baseline was then corrected between −500 and 0 ms. Epochs that contained artefacts or muscle relaxation failure upon vis- ual inspection were excluded from the analysis. Initially, separate averages were obtained for congruent and Fig. 1 Percentage of correct responses in the choice condition, the go/ no-go condition and the simple reaction time condition, in controls incongruent stimuli. After a preliminary comparison (white), NF-MS (grey) and F-MS (black). F-MS vs CNT: p = 0.001; F-MS vs between and within groups, which did not show signifi- NF-MS p = 0.001. Line bars over each column indicate standard error ** cant differences between the parameters obtained in the Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 4 of 10 the choice condition F-MS patients committed signifi- groups, had a significantly reduced activation of the right cantly more errors than controls (p = 0.001) and NF-MS supramarginal gyrus, the right inferior parietal lobule, patients (p =0.001). the right middle and inferior temporal gyri and the There were no significant group effects on RTs at superior and middle frontal gyri (Figs. 5 and 6). In the MANOVA. P2 time window (time interval = group mean P2 latency value ± 20 ms), there were not significant differences be- tween groups. In the N2 time window (time interval = ERPs latency group mean N2 latency value ± 20 ms), F-MS patients Significant group effect was found (Fig. 2) at MANOVA (F showed a significantly decreased activity in the cingulate = 21.699; p = 0.000); F-MS patients showed significantly gyrus and in the parahippocampal gyrus compared to delayed P2, N2, P3 and N4 latencies compared to controls NF-MS patients (Fig. 7) but not significant differences (P2: p = 0.000; N2: p =0.001; P3: p =0.000; N4: p = 0.000) compared to control subjects; significance was reached and P3 and N4 latencies compared to NF-MS patients vs NF-MS and not vs controls, owing to a slight non sig- (P3: p = 0.015; N4: p = 0.000). NF-MS patients showed nificant increase in activation in NF-MS vs controls. In significantly delayed P2, N2 and P3 latencies compared the P3 time window (time interval = group mean P3 la- to controls (P2: p = 0.007; N2: p = 0.021; P3: p = 0.033). tency value ± 20 ms), F-MS group presented a reduced activity reaching significance vs controls in the superior Correlations and medial frontal gyri, the cingulate gyrus, the precu- There was a negative correlation between frontal score neus and the precentral lobule (Fig. 8) and vs NF-MS in and N1 latency (ρ = −0.426, p = 0.024), P2 latency (ρ = the anterior cingulate, the medial frontal gyrus and the −0.643, p = 0.000) and N4 latency (ρ = −0.566, p = 0.002). cingulate gyrus (Fig. 9). In the N4 time window (time Moreover, frontal score correlated negatively with RTs interval = group mean N4 latency value ± 20 ms), F-MS speed in the go/no-go I condition (ρ = −0.425, p = 0.022) patients showed a significant decreased activity com- and in the choice C condition (ρ = −0.381, p = 0.042) pared to healthy subjects in the cingulate gyrus, the (Fig. 3), and correlated positively with the percentage of paracentral lobule and the precuneus (Fig. 10). correct responses in the go/no-go C condition (ρ = 0.431, p = 0.019) and in the choice condition (ρ = 0.550, p = 0.002) (Fig. 4). Discussion Compared to NF-MS patients and control subjects, our sample of F-MS patients showed delayed ERPs’ latencies, ERPs amplitude and topography reduced frontoparietal activity and less accuracy in the LORETA statistical non-parametric voxel-wise analysis execution of the Stroop task. Moreover, frontal score revealed significant group differences. In the N1 time correlated negatively with ERPs’ latency and with com- window (time interval = group mean N1 latency value ± plex RTs. These findings are discussed in details below. 20 ms), the F-MS group, compared to the other two Fig. 2 N1, N2, P3, N4 and P6 latencies in CNT subjects (white), NF-MS patients (grey) and F-MS patients (black). F-MS vs CNT: p =0.000; p =0.001; * ** *** p =0.000; p =0.000. F-MS vs NF-MS: p =0.015; p = 0.000. NF-MS vs CNT: p =0.007; p =0.021; p = 0.033. Line bars over each column indicate **** # ## ° °° °°° standard error Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 5 of 10 Fig. 3 Correlation between Frontal Score and RTs in the Choice condition (ρ = −0.381, p = 0.042) RTs to errors. However, this methodological choice could not The lower level of accuracy observed in frontal patients allow us to avoid two possible confounding factors. One compared with the other two groups, but not in non is learning itself: subjects with MS-related learning im- frontal patients compared with controls, suggests an pairment could present slower learning and therefore impairment, in the first group, of conflict monitoring higher impairment in the most complex tasks because function, necessary to process competing information these were performed later, favoring the subjects with and select the adequate response, reported to be medi- faster learning. The second is cognitive fatigue, defined ated by frontal structures as the anterior cingulate cortex as performance decay with test repetition and reported [10, 12]. Moreover, accuracy and speed in the complex to affect MS patients to a greater extent than healthy tasks were correlated with frontal score obtained from controls [34, 41]. However, performance at the comput- neuropsychological assessment. Overall, these findings erized RTs was more impaired in frontal compared with suggest that computerized RTs may provide useful mea- non frontal MS patients and correlated with the frontal sures for the assessment of executive functions in these score, suggesting that this tools reflects, at least partially, patients. Although a learning effect may have certainly the severity of frontal involvement. To further interpret occurred during RTs measurements, the tasks were per- our findings more studies are needed specifically ad- formed in a sequence with increasing difficulty. This dressing the issues of whether this impairment is a direct choice was made to facilitate learning as much as pos- correlate of executive function or it is at least partly sible for the subsequent ERPs recordings, to minimize mediated by learning difficulties or cognitive fatigue. In an additional source of between-subject variability across any case, both learning difficulties and cognitive fatigue RT tasks and to limit the number of RT exclusions due may well represent other correlate of frontal dysfunction, Fig. 4 Correlation between Frontal Score and percentage of correct responses in the Choice condition (ρ = 0.550, p = 0.002 Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 6 of 10 Fig. 5 LORETA non-parametric voxel-wise comparison map between F-MS and controls in the N1 time window. Blue: regions of significant decreased activity in F-MS needing more work to disentangle the relative contribu- These latter findings point out to the possibility that tion of these factors to our findings. bioelectrical activity to the Stroop stimuli, particularly the later component, may well reflect the speed of ERPs latency neural synchronization of frontal lobe circuits, being es- ERPs latencies were significantly increased in both pa- pecially involved in patients with frontal dysfunction. tients groups compared with controls and in the F-MS group compared to NF-MS group. This finding is consist- ERPs amplitude and topography ent with previous studies widely documenting cognitive LORETA topographic ERPs analysis showed reduced ac- ERPs latencies’ delay in multiple sclerosis [3, 28, 72]. tivity in the N1, N2, P3 and N4 time windows mostly This delay was significant for all components mea- over the frontal, cingulate and parietal regions evident in sured but the earliest (N1). This result suggests that in frontal MS patients compared with controls and with our sample of patients visual discrimination processes, non frontal patients. as reflected by the posterior N1 component, were not N1 is assumed to reflect selective attention to basic delayed and that the cognitive ERPs latencies’ delay stimulus characteristics, initial selection for later pattern observed cannot be explained only in term of impaired recognition, and intentional discrimination processing information processing speed since in this case we [70]. Its source is located in the inferior occipital lobe, would have observed a delay also at this earlier level of occipito-temporal junction [31], and inferior temporal information processing. lobe [9]. Since the discrimination process, reflected by Fig. 6 LORETA non-parametric voxel-wise comparison map between F-MS and NF-MS in the N1 time window. Blue: regions of significant decreased activity in NF-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 7 of 10 Fig. 7 LORETA non-parametric voxel-wise comparison map between F-MS and NF-MS in the N2 time window. Blue: regions of significant decreased activity in NF-MS occipito-temporal N1, can be modulated by top-down responses and that it may at least partially account for executive control (the greater the difficulty of stimulus conflict monitoring. N2 is especially pronounced over discrimination, the greater the need of top-down execu- the fronto-central electrodes and has been proposed to tive control modulation and therefore the greater the reflect ACC sensitivity to conflict [68]. amplitude of the N1 component, cfr. [23]), the signifi- The P3 component is elicited in tasks related to stimu- cant reduced activity observed here in the N1 time win- lus differentiation and appears when a memory repre- dow in F-MS patients compared to both the other two sentation of the recent stimulus context is updated upon groups, could be determined by executive control defi- the detection of deviance from it [66]. The frontal P300 cits in these patients: control and NF-MS groups would component in go/no-go-like tasks has been associated show a greater N1 amplitude, with respect to F-MS with an inhibitory mechanism [24]. However, in the group, as a consequence of executive control modula- present study, the subjects only had to mentally discrim- tion, which is instead lacking in frontal patients. inate between congruent and incongruent stimuli; there- The N2 component in go/no-go-like tasks has been at- fore, conflict did not arise at the response level. Thus, tributed to response inhibition mechanisms [27, 33]. the P3 component observed herein most likely reflects However, the N2 component has also been reported to the detection of conflict that arose at the level of the occur in relation to covert responses in the present study semantic encode. and in previous studies [2, 54]. This would indicate that The N4 component to the Stroop task is supposed to it is not completely attributable to the inhibition of reflect anterior cingulate activity [40], which has been Fig. 8 LORETA Non-parametric voxel-wise comparison map between F-MS and controls in the P3 time window. Blue: regions of significant decreased activity in F-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 8 of 10 Fig. 9 Non-parametric voxel-wise comparison map between F-MS and NF-MS in the P3 time window. Blue: regions of significant decreased activity in F-MS widely documented to account for conflict monitoring performance at complex tasks. Compensatory mecha- function and for triggering compensatory adjustment in nisms depend on the possibility to access additional cognitive control [11, 15, 27]. brain structures and the exhaustion of these resources Taken together these findings reflect cognitive control seems to determine severe cognitive impairment, as doc- impairment in frontal involved MS patients. umented elsewhere [53]. Previous functional neuroimaging studies to Stroop task [60, 61] showed a greater activation in MS subjects Conclusion compared with healthy controls in several areas involved Our finding of decreased accuracy in frontal involved in the task execution, which resulted hypo-activated MS group suggests that this approach may provide use- herein in cognitive impaired patients. These findings are ful objective measures for the assessment of executive only apparently inconsistent; in the studies by Rocca functions in these patients. Topographic analysis of ERPs et al., in fact the increase in activation in MS patients, components to the Stroop stimuli showed predominant which, although not significant, was reported to occur involvement of frontal, cingulate and parietal regions, also herein in NF-MS patients, seems to reflect compen- probably reflecting the executive stage of stimulus pro- satory mechanisms granting a normal performance, cessing. Also the latency of these components correlated whereas our sample of patients with frontal involvement with neuropsychological frontal score. Taken together, seems to be too compromised to compensate and just these findings suggest that bioelectrical activity to the presented reduced activation accompanied by impaired Stroop test may well reflect the speed and extent of Fig. 10 Non-parametric voxel-wise comparison map between F-MS and controls in the N4 time window. Blue: regions of significant decreased activity in F-MS Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 9 of 10 neural synchronization of frontal circuits. Further stud- 17. Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008;7(12):1139–51. ies are needed to evaluate the usefulness of Stroop reac- 18. Comi G, Filippi M, Martinelli V, Sirabian G, Visciani A, Campi A, et al. Brain tion times and ERPs for detecting frontal involvement magnetic resonance imaging correlates of cognitive impairment in multiple early at a subclinical stage, allowing early cognitive ther- sclerosis. J Neurol Sci. 1993;115(Suppl):66–73. 19. Cover KS, Vrenken H, Geurts JJ, van Oosten BW, Jelles B, Polman CH, et al. apy, and as a paraclinical marker for monitoring treat- Multiple sclerosis patients show a highly significant decrease in alpha band ment outcomes. interhemispheric synchronization measured using MEG. Neuroimage. 2006;29(3):783–8. Competing interests 20. DeLuca J, Chelune GJ, Tulsky DS, Lengenfelder J, Chiaravalloti ND. Is speed The authors declare that they have no competing interests. of processing or working memory the primary information processing deficit in multiple sclerosis? J Clin Exp Neuropsychol. 2004;26(4):550–62. Authors’contribution 21. Drew M, Tippett LJ, Starkey NJ, Isler RB. Executive dysfunction and cognitive NA data and statistical analysis and interpretation, manuscript draft and impairment in a large community-based sample with Multiple Sclerosis from revision. MC supervision of source localization, manuscript revision. MR New Zealand: a descriptive study. Arch Clin Neuropsychol. 2008;23(1):1–19. patients recruitment and clinical assessments, manuscript revision. LM 22. Ellger T, Bethke F, Frese A, Luettmann RJ, Buchheister A, Ringelstein EB, patients recruitment and clinical assessments, manuscript revision. BC, et al. Event-related potentials in different subtypes of multiple sclerosis–a patients recruitment and clinical assessments, manuscript revision. MF cross-sectional study. J Neurol Sci. 2002;205(1):35–40. neuropsychological assessments, manuscript revision. FP neuropsychological 23. Fedota JR, McDonald CG, Roberts DM, Parasuraman R. Contextual task assessments, manuscript revision. GC supervision to clinical and difficulty modulates stimulus discrimination: electrophysiological evidence neuropsychological assessment, manuscript revision. VM supervision to for interaction between sensory and executive processes. Psychophysiology. clinical and neuropsychological assessment, manuscript revision. LL study 2012;49(10):1384–93. design, supervision to data collection and analysis, manuscript revision. 24. Gajewski, Falkenstein. Effects of task complexity on ERP components in All authors read and approved the final manuscript. Go/Nogo tasks. Int J Psychophysiology. 2013;87(3):273–8. 25. Gil R, Zai L, Neau JP, Jonveaux T, Agbo C, Rosolacci T, et al. Event-related Acknowledgements auditory evoked potentials and multiple sclerosis. Electroencephalogr Clin The authors wish to thank A. Nossa, EEG technician, for EEG recordings. Neurophysiol. 1993;88(3):182–7. 26. Goldberg E, Bougakov D. Neuropsychologic assessment of frontal lobe Received: 5 September 2015 Accepted: 18 March 2016 dysfunction. Psychiatr Clin North Am. 2005;28(3):567–80. 27. Gonzalez-Rosa JJ, Inuggi A, Blasi V, Cursi M, Annovazzi P, Comi G, et al. Response competition and response inhibition during different Choice- References discrimination tasks: Evidence from ERP measured inside MRI scanner. 1. Amato MP, Portaccio E, Goretti B, Zipoli V, Hakiki B, Giannini M, et al. Int J Psychophysiol. 2013;89(1):37–47. Cognitive impairments in early stages of multiple sclerosis. Neurol Sci. 28. Gonzalez-Rosa JJ, Vazquez-Marrufo M, Vaquero E, Duque P, Borges M, 2010;31(S2):S211–4. Gomez-Gonzalez CM, et al. Cluster analysis of behavioural and event-related 2. Amato N, Riva N, Cursi M, Martins-Silva A, Martinelli V, Comola M, et al. potentials during a contingent negative variation paradigm in remitting- Different frontal involvement in ALS and PLS revealed by Stroop event-related relapsing and benign forms of multiple sclerosis. BMC Neurol. 2011;11:64. potentials and reaction times. Front Aging Neurosci. 2013;5:82. 29. Hofstadter DR. Metamagical Themas: Questing for the Essence of Mind and 3. Aminoff JC, Goodin DS. Long-latency cerebral event-related potentials in Pattern. New York: Basic Books; 1985. multiple sclerosis. J Clin Neurophysiol. 2001;18(4):372–7. 30. Honig LS, Ramsay RE, Sheremata WA. Event-related potential P300 in 4. Annovazzi P, Colombo B, Bernasconi L, Schiatti E, Comi G, Leocani L. multiple sclerosis. Relation to magnetic resonance imaging and cognitive Cortical function abnormalities in migraine: neurophysiological and impairment. Arch Neurol. 1992;49(1):44–50. neuropsychological evidence from reaction times and event-related 31. Hopf JM, Vogel E, Woodman G, Heinze HJ, Luck S. Localizing visual potentials to the Stroop test. Neurol Sci. 2004;25 Suppl 3:S285–7. discrimination processes in time and space. J Neurophysiol. 2002;88:2088–95. 5. Arnett PA, Rao SM, Bernardin L, Grafman J, Yetkin FZ, Lobeck L. Relationship 32. Hsieh YH, Chen KJ, Wang CC, Lai CL. Cognitive and motor components of between frontal lobe lesions and Wisconsin Card Sorting Test performance response speed in the stroop test in Parkinson's disease patients. Kaohsiung in patients with multiple sclerosis. Neurology. 1994;44(3 Pt 1):420–5. J Med Sci. 2008;24(4):197–203. 6. Audoin B, Ibarrola D, Ranjeva JP, Confort-Gouny S, Malikova I, Ali-Chérif A, 33. Jodo E, Kayama Y. Relation of a negative ERP component to response et al. Compensatory cortical activation observed by fMRI during a cognitive inhibition in a Go/NoGo task. Electroencephalogr Clin Neurophysiol. task at the earliest stage of MS. Hum Brain Mapp. 2003;20(2):51–8. 1992;82:477–82. 7. Berg EA. A simple objective technique for measuring flexibility in thinking. 34. Krupp LB, Serafin DJ, Christodoulou C. Multiple sclerosis-associated fatigue. J Gen Psychol. 1948;39:15–22. Expert Rev Neurother. 2010;10(9):1437–47. 8. Bergendal G, Fredrikson S, Almkvist O. Selective decline in information 35. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded processing in subgroups of multiple sclerosis: an 8-year longitudinal study. disability status scale (EDSS). Neurology. 1983;33(11):1444–52. Eur Neurol. 2007;57(4):193–202. 36. Kutas M, McCarthy G, Donchin E. Augmenting mental chronometry: the P300 9. Bokura H, Yamaguchi S, Kobayashi S. Electrophysiological correlates for as a measure of stimulus evaluation time. Science. 1977;197(4305):792–5. response inhibition in a Go/NoGo task. Clin Neurophysiol. 2001;112:2224–32. 37. Lapshin H, Audet B, Feinstein A. Detecting cognitive dysfunction in a busy 10. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. Conflict multiple sclerosis clinical setting: a computer generated approach. Eur J monitoring and cognitive control. Psychol Rev. 2001;108(3):624–52. Neurol. 2014;21:281–6. 11. Botvinick MM, Cohen JD, Carter CS. Conflict monitoring and anterior 38. Leocani L, Gonzalez-Rosa JJ, Comi G. Neurophysiological correlates of cognitive cingulate cortex: an update. Trends Cogn Sci. 2004;8(12):539–46. disturbances in multiple sclerosis. Neurol Sci. 2010;31 Suppl 2:S249–53. 12. Botvinick M, Nystrom LE, Fissell K, Carter CS, Cohen JD. Conflict monitoring versus 39. Leocani L, Locatelli T, Martinelli V, Rovaris M, Falautano M, Filippi M, et al. selection-for-action in anterior cingulate cortex. Nature. 1999;402(6758):179–81. Electroencephalographic coherence analysis in multiple sclerosis: correlation 13. Calabrese M, Agosta F, Rinaldi F, Mattisi I, Grossi P, Favaretto A, et al. with clinical, neuropsychological, and MRI findings. J Neurol Neurosurg Cortical lesions and atrophy associated with cognitive impairment in Psychiatry. 2000;69(2):192–8. relapsing-remitting multiple sclerosis. Arch Neurol. 2009;66(9):1144–50. 40. Liotti M, Woldorff MG, Perez R, et al. An ERP study of the temporal course of 14. Calabrese M, Rinaldi F, Grossi P, Gallo P. Cortical pathology and cognitive the Stroop color-word interference effect. Neuropsychologia. 2000;38:701–11. impairment in multiple sclerosis. Expert Rev Neurother. 2011;11(3):425–32. 15. Carter CS, van Veen V. Anterior cingulate cortex and conflict detection: an 41. MacAllister WS, Krupp LB. Multiple sclerosis-related fatigue. Phys Med update of theory and data. Cogn Affect Behav Neurosci. 2007;7(4):367–79. Rehabil Clin N Am. 2005;16(2):483–502. 16. Cerezo García M, Martín Plasencia P, Aladro BY. Alteration profile of executive 42. Magnano I, Aiello I, Piras MR. Cognitive impairment and neurophysiological functions in multiple sclerosis. Acta Neurol Scand. 2015;131(5):313–20. correlates in MS. J Neurol Sci. 2006;245(1-2):117–22. Amato et al. Multiple Sclerosis and Demyelinating Disorders (2016) 1:8 Page 10 of 10 43. Magniè MN, Bensa C, Laloux L, Bertogliati C, Faure S, Lebrun C. Contribution 69. Vazquez-Marrufo M, Gonzalez-Rosa JJ, Vaquero E, Duque P, Borges M, of cognitive evoked potentials for detecting early cognitive disorders in Gomez C, et al. Quantitative electroencephalography reveals different multiple sclerosis. Rev Neurol. 2007;163(11):1065–74. physiological profiles between benign and remitting-relapsing multiple 44. Mainero C, Pantano P, Caramia F, Pozzilli C. Brain reorganization during sclerosis patients. BMC Neurol. 2008;8:44. attention and memory tasks in multiple sclerosis: insights from functional 70. Vogel EK, Luck SJ. The visual N1 component as an index of a discrimination MRI studies. J Neurol Sci. 2006;245(1-2):93–8. process. Psychophysiology. 2000;37:190–203. 45. McDonald WI, Compston A, Edan G, et al. Recommended diagnostic criteria 71. West R, Alain C. Effects of task context and fluctuations of attention on for multiple sclerosis: guidelines from the International Panel on the neural activity supporting performance of the Stroop Task. Brain Res. diagnosis of multiple sclerosis. Ann Neurol. 2001;50(1):121–7. 2000;873:102–11. 72. Whelan R, Lonergan R, Kiiski H, Nolan H, Kinsella K, Hutchinson M, et al. 46. Moccia M, Lanzillo R, Palladino R, Chang KC, Costabile T, Russo C, et al. Impaired information processing speed and attention allocation in multiple Cognitive impairment at diagnosis predicts 10-year multiple sclerosis sclerosis patients versus controls: a high-density EEG study. J Neurol Sci. progression. Mult Scler. 2015;11:1–9. 2010;293(1-2):45–50. 47. Muhlert N, Sethi V, Schneider T, Daga P, Cipolotti L, Haroon HA, et al. Diffusion 73. Weigl E. On the psychology of so-called processes of abstraction. The MRI-based cortical complexity alterations associated with executive function in Journal of Abnormal and Social Psychology. 1941;36(1):3–33. multiple sclerosis. J Magn Reson Imaging. 2013;38(1):54–63. 48. Navon D, Gopher D. On the economy of the human-processing system. Psychol Rev. 1979;86:214–55. 49. Newton MR, Barrett G, Callanan MM, Towell AD. Cognitive event-related potentials in multiple sclerosis. Brain. 1989;112(Pt 6):1637–60. 50. Papadopoulou A, Müller-Lenke N, Naegelin Y, Kalt G, Bendfeldt K, Kuster P, et al. Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Mult Scler. 2013;19(10):1290–6. 51. Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D. Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol. 2002;24(Suppl C):91–5. 52. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol. 1994;18(1):49–65. 53. Penner IK, Rausch M, Kappos L, Opwis K, Radü EW. Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. J Neurol. 2003;250(4):461–72. 54. Pfefferbaum A, Ford JM, Weller BJ, Kopell BS. ERPs to response production and inhibition. Electroencephalogr Clin Neurophysiol. 1985;60:423–34. 55. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292–302. 56. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol. 2005;58(6):840–6. 57. Rao SM, Hammeke TA, Speech TJ. Wisconsin Card Sorting Test performance in relapsing-remitting and chronic-progressive multiple sclerosis. J Consult Clin Psychol. 1987;55(2):263–5. 58. Rao SM, Leo GJ, Bemardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns and prediction. Neurology. 1991;41(5):685–91. 59. Rinaldi F, Calabrese M, Grossi P, Puthenparampil M, Perini P, Gallo P. Cortical lesions and cognitive impairment in multiple sclerosis. Neurol Sci. 2010;31(2):S235–7. 60. Rocca MA, Bonnet MC, Meani A, Valsasina P, Colombo B, Comi G, et al. Differential cerebellar functional interactions during an interference task across multiple sclerosis phenotypes. Radiology. 2012;265(3):864–73. 61. Rocca MA, Valsasina P, Ceccarelli A, Absinta M, Ghezzi A, Riccitelli G, et al. Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Mapp. 2009;30(1):276–90. 62. Ron MA, Callanan MM, Warrington EK. Cognitive abnormalities in multiple sclerosis: a psychometric and MRI study. Psychol Med. 1991;21(1):59–68. 63. Sailer M, Heinze HJ, Tendolkar I, Decker U, Kreye O, v Rolbicki U, et al. Influence of cerebral lesion volume and lesion distribution on event-related Submit your next manuscript to BioMed Central brain potentials in multiple sclerosis. J Neurol. 2001;248(12):1049–55. 64. Staffen W, Mair A, Zauner H, Unterrainer J, Niederhofer H, Kutzelnigg A, and we will help you at every step: et al. Cognitive function and fMRI in patients with multiple sclerosis: • We accept pre-submission inquiries evidence for compensatory cortical activation during an attention task. Brain. 2002;125(6):1275–82. � Our selector tool helps you to find the most relevant journal 65. Stroop JR. 2Studies of interference in serial verbal reactions. J Exp Psychol. � We provide round the clock customer support 1935;18:643–62. � Convenient online submission 66. Sutton S, Tueting P, Zubin J, John ER. Evoked potential correlates of stimulus uncertainty. Science. 1965;150:1187–8. � Thorough peer review 67. Triantafyllou NI, Voumvourakis K, Zalonis I, Sfagos K, Mantouvalos V, Malliara � Inclusion in PubMed and all major indexing services S, et al. Cognition in relapsing-remitting multiple sclerosis: a multichannel � Maximum visibility for your research event-related potential (P300) study. Acta Neurol Scand. 1992;85(1):10–3. 68. van Veen V, Carter CS. The anterior cingulate as a conflict monitor: fMRI and Submit your manuscript at ERP studies. Physiol Behav. 2002;77:477–4820. www.biomedcentral.com/submit

Journal

Multiple Sclerosis and Demyelinating DisordersSpringer Journals

Published: May 1, 2016

There are no references for this article.