Skip to main content

P300 event-related potentials in patients with multiple sclerosis



Cognitive impairment (CI) is a common and disabling symptom during the disease even in the earliest “preclinical” phase of patients with MS (pwMS). This study aims to assess cognitive function by measuring P300 event-related potential (ERP) and to look into the relationship between P300 abnormalities with the severity of the physical disability, education level, and disease duration.


Fifty pwMS (28 females and 22 males) aged 20–54 years and fifty healthy subjects comprised of 21 females and 29 males aged 18–50 years serves as the control group was studied. All participants underwent medical history, neurological examination, cognitive functions using the Montreal Cognitive Assessment scale (MoCA) and the P300 ERP.


In this study, 48% of pwMS had CI. They had a longer P300 latency and a lower amplitude. Those with impaired cognition had a longer duration of illness and higher Expanded Disability Status Scale (EDSS), whereas those with intact cognition had a higher education level. P300 latency was correlated positively with EDSS and disease duration, but negatively with education level. P300 amplitude was found to be negatively correlated with EDSS, and disease duration but positively to the education level.


P300, as a non-invasive test, would support the presence of CI in pwMS patients and could be used for screening in daily practice. P300 has a strong relationship with illness duration, disease subtypes, EDSS, and education level.


Multiple sclerosis (MS) is a chronic, progressive, inflammatory, demyelinating autoimmune disorder caused by autoantibodies and immune cells destroying the myelin sheath [1]. MS causes a wide range of symptoms, including motor, cognitive, and neuropsychiatric issues, due to the widespread development of myelin destruction [2].

Cognitive impairment (CI) is a common and disabling symptom of all phenotypes of MS patients [3, 4], with a prevalence ranging from 34 to 65% of pwMS during the disease, even in the earliest "preclinical" phase [5, 6].

It has been established that the main risk factors for CI are greater physical disability, as measured by the Expanded Disability Status Scale (EDSS), older age, and disease duration [3, 7].

Various neuropsychological testing batteries have been developed to target the key areas of MS cognitive dysfunction in clinical practice, though their use is limited due to the long administration time, practice effect, and physical disability [8]. P300 event-related brain potentials (ERPs) have been used as neurophysiological markers in the assessment of cognition in pwMS in addition to neuropsychological tests [9, 10] and, more recently, in clinical practice [11]. Their measurement can shed light on cognitive processing stages, such as encoding, selecting, memorizing, and decision-making.

Given that cognitive disorders are among the most common problems in pwMS, the current study sought to assess the P300 based on age, gender, level of education, length of illness, EDSS, and total MoCA scores (as important individual characteristics).


Between November 2021 and November 2022, a case–control study was conducted at the MS clinic at Baghdad Teaching Hospital/Medical City. The IRB (Institute Review Board) of Al-Nahrain University's College of Medicine approved the study, and all participants provided informed consent.


Fifty pwMS were included in this study after meeting the disease’s McDonald Criteria [12]. They were made up of 28 females and 22 males ranging in age from 20 to 54. Their illness lasted anywhere from a year to more than 20 years. Patients with relapsing–remitting MS and secondary-progressive MS phenotypes were investigated. The patients had to be able to read and write to be included in the study, and none of them were taking any medications that had a significant impact on their cognitive performance. All of the patients were receiving immunomodulation therapy, such as interferon or glatiramer acetate. Patients with a history of impaired hearing function, a diagnosed psychiatric disorder, or a CI before MS diagnosis were barred from participating in the study.

Fifty healthy subjects consisting of 29 males and 21 females aged 18–50 years serve as the control group.


History and clinical examination

For all patients, a detailed history and neurological examination were performed:

Expanded disability status scale

Kurtzke's [13] Expanded Disability Status Scale (EDSS) is used to assess the level of disability in pwMS. In a 20-step scale, EDSS scores range from 0 (normal) to 10 (death due to MS) (with 0.5-unit increments). EDSS steps 1.0–4.5 refer to fully ambulatory patients, with the precise step number determined by the functional system score(s), whereas EDSS steps 5.0–9.5 are primarily defined by ambulation impairment [14].

Assessment of cognitive function

The Montreal cognitive assessment (MoCA) score was used to assess cognitive function. It evaluates multiple cognitive domains and is scored out of 30 points. A normal MoCA score range is between 0 and 30, with a score of 26 or higher considered normal [15, 16].

Measurement of P300

In a silent room, P300 was elicited using a Key point electromyography machine (Medtronic, Denmark) and an auditory "oddball" paradigm. Under the 10–20 International System, standard Ag/AgCl electrodes positioned at Cz, referenced at the mastoid process, and a forehead ground was used.

Subjects were instructed to mentally count rare tones while lying comfortably on the couch with their eyes closed, and they were then asked to report the number of rare tones counted at the end of each run. To determine performance accuracy, each patient's count was compared to the actual number of target tones provided at the end of each session. Two or three trials were conducted to ensure the consistency of the waveform, with each trial lasting until 200 artifact-free infrequent stimuli responses were recorded and averaged. The P300's latencies and amplitudes were measured.

The impedance was kept at 5K or less, the bandpass filter was set between 1 and 30 Hz, and the analysis time was set at 1024 ms with a 100 ms pre-stimulus baseline record.

Statistical analysis

SPSS statistical software, version 25, was used for all statistical analyses (IBM Corporation, USA). The quantitative variables were presented as means with standard deviations (SD) and were analyzed using an independent Student t test. The Chi-square test was used to analyze categorical variables that were expressed as counts and percentages. Pearson's correlation analysis was used to examine correlations between different quantitative variables. A statistically significant level of statistics was considered for all tests when p < 0.05.


Table 1 displays the study population's basic demographic information. There was no statistically significant difference between the studied groups in terms of age, gender, or employment. On the contrary, pwMS had significantly lower education levels and total MoCA scores than controls (p < 0.001). Table 2 shows that pwMS had significantly longer P300 latency and smaller amplitude than controls (p 0.001, respectively) (p < 0.001, respectively).

Table 1 Demographic characteristics of the study population
Table 2 P300 parameters of the study population

The duration of illness and the EDSS were significantly higher in those with impaired cognition (p = 0.001, p < 0.001, respectively) when pwMS were divided into those with intact and those with impaired cognition based on the cutoff value of 26 of the MoCA score. Those with impaired cognition had significantly lower education levels and total MoCA scores (Table 3).

Table 3 Demographic and clinical data of patients with multiple sclerosis according to MoCA score

Table 4 shows that in pwMS with impaired versus intact cognition, the P300 latency was significantly prolonged, while the amplitude was significantly lower (p < 0.001, respectively).

Table 4 P300 parameters in patients with multiple sclerosis according to their cognition

Table 5 and Figs. 1, 2 and 3 demonstrate Pearson’s correlation between P300 ERP and demographic features (age, disease duration, EDSS, education level, and MoCA scores).

Table 5 Pearson’s correlation between demographic data and P300 parameters
Fig. 1
figure 1

Scatter plot and regression line between P300 latency and age (top left); disease duration (bottom left); educational level (top right), and EDSS (bottom right)

Fig. 2
figure 2

Scatter plot and regression line between P300 amplitude and age (top left); disease duration (bottom left); educational level (top right), and EDSS (bottom right)

Fig. 3
figure 3

Scatter plot and regression line between P300 latency and total MoCA score (left); P300 amplitude and total MoCA score (right)

Age and P300 latency were found to have a significant positive correlation (r = 0.432, p < 0.001). On the other hand, there was a significant negative correlation with P300 amplitude (r = − 0.415, p < 0.001). Similarly, disease duration was found to be positively related to P300 latency and negatively related to P300 amplitude (r = 0.750, p < 0.001, and r = − 0.838; p < 0.001, respectively). Likewise, there was a significant positive correlation between EDSS and P300 latency (r = 0.861, p < 0.001) but it was negatively correlated with P300 amplitude (r = − 0.915, p < 0.001).

Educational level, on the other hand, has a negative correlation with P300 latency and a positive correlation with P300 amplitude (r = − 0.623; p = 0.002 and r = 0.555; p < 0.001, respectively). P300 wave latency was also found to be negatively related to the total MoCA score (r = − 0.963, p < 0.001) and positively related to P300 amplitude (r = 0.932, p < 0.001).


In this study, 48% of pwMS had CI, which is within the range reported elsewhere, indicating widespread focal white matter lesions that are primarily related to impaired information processing speed, implying MS-related CI as a disconnection syndrome [17].

Gender had no significant impact on the cognitive function of the participants, according to the current study’s findings, which are consistent with the findings of other researchers [18,19,20], but contradict the findings of Benedict et al. [21], who identified the male gender as one of the risk factors for CI in pwMS, and Shaygannejhad et al. [22] who reported more cognitive complications in women than men. The lack of consistency could be attributed to the study's small sample size, different MS disease types, disease duration, and cognitive test type.

Age of pwMS did not affect cognitive function in this study, which is consistent with the findings of Hassanshahi et al. [20] but differs from the findings of other researchers [18, 19, 23], who reported a decrease in cognitive level with aging or a significant relationship between age and learning, memory, and executive functions based on different batteries. The inconsistency with the latter results could be attributed to the study of different age groups, evaluation of older ages, or differences in cognitive tests.

In the current study, CI was significantly related to the duration of illness, as evidenced by a significant association between disease duration and prolonged P300 latency, lower P300 amplitude, and low MoCA score, all of which reflect changes in the central nervous system over time [24, 25]. Patients' cognitive abilities deteriorated over time, as measured by assessments of their short-term verbal memory, abstract reasoning, and linguistic abilities [7, 26,27,28]. In contrast to our findings, other studies demonstrate weak or no correlation between CI and disease duration [29, 30].

In this study, low educational attainment was linked to poor cognitive performance in pwMS. This finding is consistent with the findings of Shaygannejhad and colleagues [22], who discovered a significant relationship between cognitive disorders and educational level. It was reported that the higher education groups had the highest scores on the correct answer to the Judgment of Line Orientation Test [31] and a lower level of education was most important for CI even in patients without gray matter atrophy [32, 33]. In contrast to these findings, some research groups have stated that education level does not act as a predictor of cognitive dysfunction [19, 20, 34], which could be due to differences in cognitive assessment tools, sample populations, and sizes.

Our findings show a strong relationship between CI and individual physical state (as measured by the EDSS), which is consistent with other studies [7, 35,36,37].

The current study found that P300 ERP at Cz (central lobe) was significantly different in pwMS compared to the control group. The prolonged latency reflects the length of time it takes for information to be processed, whereas the reduced amplitude reflects a disruption in the activities of some centers (frontal and parietal cortex, thalamus, and temporomesial cortex) or temporal dispersion of information processing [38].

In people with MS, P300 REP was linked to disease duration, EDSS, education level, and MoCA score. Other studies have confirmed and described these findings [10, 39, 40]. However, they differed from studies that found pwMS to have normal P300 latencies [41, 42], unaffected amplitude [43], or no statistical correlations between P300 latency and/or amplitude and disease duration [44]. This difference could be attributed to the inclusion of more patients with lower physical disabilities or to the fact that the disease manifested itself in individual patients at different ages.

Total MoCA scores of pwMS were positively correlated with P300 amplitude and negatively correlated with P300 latency, which is consistent with Tag El-din and colleagues' findings [45].

The strong correlation between P300 ERP and EDSS and disease duration supports Ateş and colleagues' findings [46]. Furthermore, Triantafyllou and colleagues [47] discovered a significant relationship with EDSS but not with disease duration. Rasoulifard et al. [48] discovered a significant correlation between P300 latency but not amplitude, disease duration, or EDSS.

Patients with more severe physical disabilities (EDSS) had more cognitive dysfunctions, longer latencies, and lower amplitudes of P300 ERPs, according to the findings of this study. These findings were also reported by Kocer et al. [49].

The small sample size, the inclusion of only relapsing–remitting and secondary-progressive MS phenotypes, the difference in educational level, and the wide range of disease duration are among the limitations of our study. Consequently, longitudinal research may be required in the future for additional evaluation.


Our study concludes that (1) P300, as a non-invasive test would support the presence of CI in pwMS and can be used for screening in daily practice. (2) P300 is significantly related to illness duration, education level, and EDSS. Based on the findings, which show frequent changes in P300 in pwMS, we recommend that CI be diagnosed early to plan additional supportive treatment.

Availability of data and materials

All data generated or analyzed during this study are included in this published article. The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Cognitive impairment


Expanded disability status scale


Event-related potentials


Montreal Cognitive Assessment test


Multiple sclerosis


Patients with multiple sclerosis


Relapsing–remitting multiple sclerosis


Secondary progressive multiple sclerosis


  1. Szilasiová J, Rosenberger J, Mikula P, Vitková M, Fedičová M, Gdovinová Z. Cognitive event-related potentials—the P300 wave is a prognostic factor of long-term disability progression in patients with multiple sclerosis. J Clin Neurophysiol. 2022;39(5):390–6.

    PubMed  Google Scholar 

  2. Shah A, Panchal V, Patel K, Alimohamed Z, Kaka N, Sethi Y, et al. Pathogenesis and management of multiple sclerosis revisited. Dis Mon. 2022; 101497.

  3. Brochet B, Ruet A. Cognitive impairment in multiple sclerosis with regards to disease duration and clinical phenotypes. Front Neurol. 2019;10:261.

    PubMed  PubMed Central  Google Scholar 

  4. Paolicelli D, Manni A, Iaffaldano A, Tancredi G, Ricci K, Gentile E, et al. Magnetoencephalography and high-density electroencephalography study of acoustic event related potentials in early stage of multiple sclerosis: a pilot study on cognitive impairment and fatigue. Brain Sci. 2021;11(4):481.

    PubMed  PubMed Central  Google Scholar 

  5. Benedict RHB, Amato MP, DeLuca J, Geurts JJG. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol. 2020;19:860–71.

    PubMed  PubMed Central  Google Scholar 

  6. DeLuca J, Chiaravalloti ND, Sandroff BM. Treatment and management of cognitive dysfunction in patients with multiple sclerosis. Nat Rev Neurol. 2020;16:319–32.

    PubMed  Google Scholar 

  7. Ruano L, Portaccio E, Goretti B, Niccolai C, Severo M, Patti F, et al. Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult Scler. 2017;23:1258–67.

    PubMed  Google Scholar 

  8. Sumowski JF, Benedict R, Enzinger C, Filippi M, Geurts JJ, Hamalainen P, et al. Cognition in multiple sclerosis: state of the field and priorities for the future. Neurology. 2018;90:278–88.

    PubMed  PubMed Central  Google Scholar 

  9. Chinnadurai SA, Venkatesan SA, Shankar G, Samivel B, Ranganathan LN. A study of cognitive fatigue in multiple sclerosis with novel clinical and electrophysiological parameters utilizing the event related potential P300. Mult Scler Relat Disord. 2016;10:1–6.

    PubMed  Google Scholar 

  10. Pokryszko-Dragan A, Zagrajek M, Slotwinski K, Bilinska M, Gruszka E, Podemski R. Event-related potentials and cognitive performance in multiple sclerosis patients with fatigue. Neurol Sci. 2016;37:1545–56.

    PubMed  PubMed Central  Google Scholar 

  11. Lazarevic S, AzanjacArsic A, Aleksic D, Toncev G, Miletic-Drakulic S. Depression and fatigue in patients with multiple sclerosis have no influence on the parameters of cognitive evoked potentials. J Clin Neurophysiol. 2021;38:36–42.

    PubMed  Google Scholar 

  12. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73.

    PubMed  Google Scholar 

  13. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444–5218.

    CAS  PubMed  Google Scholar 

  14. Hatipoglu H, Kabay SC, Hatipoglu MG, Ozden H. Expanded Disability Status Scale-Based Disability and dental-periodontal conditions in patients with multiple sclerosis. Med Princ Pract. 2016;25:49–55.

    PubMed  Google Scholar 

  15. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriat Soc. 2005;53(4):695–9.

    PubMed  Google Scholar 

  16. Guo QH, Cao XY, Zhou Y, Zhao QH, Ding D, Hong Z. Application study of quick cognitive screening test in identifying mild cognitive impairment. Neurosci Bull. 2010;26(1):47–54.

    PubMed  PubMed Central  Google Scholar 

  17. Rocca MA, Pravatà E, Valsasina P, Radaelli M, Colombo B, Vacchi L, et al. Hippocampal-DMN disconnectivity in MS is related to WM lesions and depression. Hum Brain Mapp. 2015;36:5051–63.

    PubMed  PubMed Central  Google Scholar 

  18. Vanotti S, Smerbeck A, Eizaguirre MB, Saladino ML, Benedict RRH, Caceres FJ. BICAMS in the Argentine population: relationship with clinical and sociodemographic variables. Appl Neuropsychol Adult. 2018;25:424–33.

    PubMed  Google Scholar 

  19. Pouramiri M, Azimian M, Akbarfahimi Z, Pishyareh E, Hossienzadeh S. Investigating the relationship between individual and clinical characteristics and executive dysfunction of multiple sclerosis individuals. Arch Rehabil. 2019;20:114–23.

    Google Scholar 

  20. Hassanshahi E, Asadollahi Z, Azin H, Hassanshahi J, Hassanshahi A, Azin M. Cognitive function in multiple sclerosis patients based on age, gender, and education level. Acta Med Iran. 2020;58(10):500–7.

    Google Scholar 

  21. Benedict RH, Zivadinov R. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol. 2011;7:332–42.

    PubMed  Google Scholar 

  22. Shaygannejad V, Afshar H. The frequency of cognitive dysfunction among multiple sclerosis patients with mild physical disability. J Isfahan Med Sch. 2012;29:167.

    Google Scholar 

  23. Tam JW, Schmitter-Edgecombe M. The role of processing speed in the Brief Visuospatial Memory Test—revised. Clin Neuropsychol. 2013;27:962–72.

    PubMed  PubMed Central  Google Scholar 

  24. Daams M, Steenwijk MD, Schoonheim MM, Wattjes MP, Balk LJ, Tewarie PK, et al. Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis. Mult Scler. 2016;22:608–19.

    PubMed  Google Scholar 

  25. Ouellette R, Bergendal Å, Shams S, Martola J, Mainero C, KristoffersenWiberg M, et al. Lesion accumulation is predictive of long-term cognitive decline in multiple sclerosis. Mult Scler Relat Disord. 2018;21:110–6.

    PubMed  Google Scholar 

  26. Amato MP, Ponziani G, Pracucci G, Bracco L, Siracusa G, Amaducci L. Cognitive impairment in early-onset multiple sclerosis. Pattern, predictors, and impact on everyday life in a 4-year follow-up. Arch Neurol. 1995;52:168–72.

    CAS  PubMed  Google Scholar 

  27. Amato MP, Ponziani G, Siracusa G, Sorbi S. Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol. 2001;58:1602–6.

    CAS  PubMed  Google Scholar 

  28. Achiron A, Chapman J, Magalashvili D, Dolev M, Lavie M, Bercovich E, et al. Modeling of cognitive impairment by disease duration in multiple sclerosis: a cross-sectional study. PLoS ONE. 2013;8: e71058.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Rogers JM, Panegyres PK. Cognitive impairment in multiple sclerosis: evidence-based analysis and recommendations. J Clin Neurosci. 2007;14:919–27.

    PubMed  Google Scholar 

  30. Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008;7:1139–51.

    PubMed  Google Scholar 

  31. Caparelli-Dáquer EM, Oliveira-Souza R, Moreira Filho PF. Judgment of line orientation depends on gender, education, and type of error. Brain Cogn. 2009;69:116–20.

    PubMed  Google Scholar 

  32. Rimkus CM, Avolio IMB, Miotto EC, Pereira SA, Mendes MF, Callegaro D, et al. The protective effects of high-education levels on cognition in different stages of multiple sclerosis. Mult Scler Relat Disord. 2018;22:41–8.

    PubMed  Google Scholar 

  33. Eijlers AJC, Meijer KA, van Geest Q, Geurts JJG, Schoonheim MM. Determinants of cognitive impairment in patients with multiple sclerosis with and without atrophy. Neuroradiology. 2018;288:544–51.

    Google Scholar 

  34. Maloni H. Cognitive impairment in multiple sclerosis. J Nurse Pract. 2018;14:172–7.

    Google Scholar 

  35. Patti F, Nicoletti A, Messina S, Bruno E, Fermo SL, Quattrocchi G, et al. Prevalence and incidence of cognitive impairment in multiple sclerosis: a population-based survey in Catania. Sicily J Neurol. 2015;262(4):923–30.

    PubMed  Google Scholar 

  36. Amato MP, Prestipino E, Bellinvia A, Niccolai C, Razzolini L, Pastò L, et al. Cognitive impairment in multiple sclerosis: an exploratory analysis of environmental and lifestyle risk factors. PLoS ONE. 2019;14(10): e0222929.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Carotenuto A, Moccia M, Costabile T, Signoriello E, Paolicelli D, Simone M, et al. Associations between cognitive impairment at onset and disability accrual in young people with multiple sclerosis. Sci Rep. 2019;9:18074.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Fang C, Zhang Y, Zhang M, Fang Q. P300 measures and drive-related risks: a systematic review and meta-analysis. Int J Environ Res Public Health. 2020;17(15):5266.

    PubMed  PubMed Central  Google Scholar 

  39. Montaser IA, Rashad MH, Abd El-Aziz MA, Mashaal AG. Cortical lesions in a sample of Egyptian multiple sclerosis patients. Egyp J Hospital Med. 2018;72(11):5604–8.

    Google Scholar 

  40. Kiiski H, Reilly RB, Lonergan R, Kelly S, O’Brien MC, Kinsella K, et al. Detection of cognitive impairment in multiple sclerosis based on P300 event-related potential. Int J Phys Med Rehabil. 2018;6:479.

    Google Scholar 

  41. Newton MR, Barrett G, Callanan MM, Towell AD. Cognitive event-related potentials in multiple sclerosis. Brain. 1989;112:1637–60.

    PubMed  Google Scholar 

  42. Ruchkin DS, Grafman J, Krauss GL, Johnson R Jr, Canoune H, Ritter W. Event-related brain potential evidence for a verbal working memory deficit in multiple sclerosis. Brain. 1994;117:289–305.

    PubMed  Google Scholar 

  43. Gonzalez-Rosa JJ, Vazquez-Marrufo M, Vaquero E, Duque P, Borges M, Gomez-Gonzalez CM, et al. Cluster analysis of behavioral and event-related potentials during a contingent negative variation paradigm in remitting-relapsing and benign forms of multiple sclerosis. BMC Neurol. 2011;11:1–19.

    Google Scholar 

  44. Magnano I, Aiello I, Piras MRJ. Cognitive impairment and neurophysiological correlates in MS. Neurol Sci. 2006;245(1–2):117–22.

    CAS  Google Scholar 

  45. Tag El-din EA, Bahnasy WS, Rashed KH, Abd El-Samad ER, Tea AH. Cognitive functions in multiple sclerosis patients. Egyptian J Neurol Psychiatr Neurosurg. 2016;53:168–73.

    Google Scholar 

  46. Ateş H, Tunalı G, Aras L. The contribution of P300 test for cognitive evaluation in multiple sclerosis. Ondokuz Mayis Univ Tip Dergisi. 2001;18(2):87–96.

    Google Scholar 

  47. Triantafyllou NI, Voumvourakis K, Zalonis I, Sfagos K, Mantouvalos V, Malliara S, et al. Cognition in relapsing–remitting multiple sclerosis: a multichannel event-related potential (P300) study. Acta Neurol Scand. 1992;85(1):10–3.

    CAS  PubMed  Google Scholar 

  48. Rasoulifard P, Mohammadkhani G, Farahani S, Sahraiyan M, Jalaie S, Shushtary SS. The effect of duration of multiple sclerosis and expanded disability status scale on P300. Audiology. 2013;22(2):55–62.

    Google Scholar 

  49. Kocer B, Unal T, Nazliel B, Biyikli Z, Yesilbudak Z, Karakas S, et al. Evaluating sub-clinical cognitive dysfunction and event-related potentials (P300) in clinically isolated syndrome. Neurol Sci. 2008;29:435–44.

    PubMed  Google Scholar 

Download references


I thank Dr. Sarmad A. Al-Mashta from the MS clinic/Medical City for the clinical assessment of patient and assistant professor Dr. Qasim Al-Mayah from the Research Medical Unit/College of Medicine/Al-Nahrain University for helping in statistical analysis.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations



I am the author for preparation of this manuscript, I did the neurophysiological tests, made the design of the study, and I approved the final version for submission.

Corresponding author

Correspondence to Hussein Ghani Kaddoori.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the IRB (Institute Review Board) and written consent for participation from all subjects was ensured.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaddoori, H.G. P300 event-related potentials in patients with multiple sclerosis. Egypt J Neurol Psychiatry Neurosurg 59, 126 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: