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Cognitive impairment in a sample of adult patients with multiple sclerosis: an Egyptian study

Middle East Current Psychiatry volume 30, Article number: 80 (2023) Cite this article

Abstract

Background

Multiple sclerosis is a chronic neuro-inflammatory disease of the brain and central nervous system. We aimed at assessing the cognitive functions present in adult patients with multiple sclerosis in remission phase of the disease comparing them to healthy control group.

Results

This is a cross sectional study using Penn Computerized Neurocognitive Battery (CNB) to measure performance accuracy and speed on specific neurobehavioral domains comparing MS group to healthy group. The MS group showed lower statistically significant difference in the accuracy of spatial memory, the motor speed, Non-verbal reasoning, Spatial orientation, social cognition, and working memory. Also, MS group showed statistically significant longer reaction time in facial memory, attention, spatial orientation, and non-verbal reasoning.

Conclusions

Attention, spatial orientation, non- verbal reasoning, Facial memory, working memory, social cognition and spatial memory were affected in patients with MS during their remission phase. cognitive evaluation should constitute a major part of the clinical examination in MS, especially when impairment seems likely.

Background

Multiple sclerosis (MS) is a chronic, inflammatory neurodegenerative disease of the central nervous system. MS is characterized by the accumulation of chronic white matter de-myelination with axonal loss and diffuse inflammation. Neuro degeneration can also occur from early disease stages and worsen over time [18].

Although impairment in cognitive function occurs in different neurologic diseases and clinical syndromes, the degree of dysfunction depends on the involvement of different brain structures (cortical or subcortical), the extent of neural damage or number of affected domains, and the patient’s previous cognitive reserve and performance [12].

In addition to their physical symptoms, 40% to 65% of people with MS experience some degree of cognitive impairment. Cognition represents the function of several neural pathways involved in the processing of information in the brain. The most commonly affected cognitive domains are complex attention, information processing, executive function, processing speed, and long-term memory [10].

The relationship between MS and cognitive impairment depends on several factors, including the age of onset of the disease, disability level, environmental and lifestyle factors (smoking and alcohol), psychiatric comorbidities mainly depression and anxiety, pain and fatigue. Cognitive impairment is present in all disease subtypes; however, it tends to be prominent and more severe in SP and PPMS patients, maybe due to the extensive neurodegenerative brain process and cortical involvement [1].

In a controlled study of newly diagnosed Relapsing remitting multiple sclerosis (RRMS) patients [3], frequency of cognitive impairment was 45%. Another study included RRMS untreated with disease-modifying therapies, found Cognitive impairment in 34.9% of patients [17]. In another study RRMS patients excluding patients with mean disease duration of 75 months, the prevalence of cognitive impairment was 31% [15].

Patients with Multiple sclerosis first show deficits in verbal fluency and verbal memory, and also a decline in visuo-spatial and recall skills. Deterioration in attention, information processing speed and Verbal learning can also be detected [10]. The aim of this study is to assess cognitive function in multiple sclerosis patients during remission phase of the disease.

Methods

Patients were recruited from the out- patient clinics of the Multiple Sclerosis Unit of Neurology Department, Mansoura University Hospital during the year 2020–2022. The calculated sample size of the study was 30 participants for each group at 5% level of significance and 95% power of the study, using G*Power 3 sample size calculator [22].

Inclusion criteria were patients aged between 18 and 50 years old, clinically definite MS, according to the McDonald criteria. All participants were ambulatory (Expanded Disability Status Scale <  = 5.5) in a stable phase of the disease without relapses in the last 3 months [2] and agreed to sign informed consent. We excluded patients who had Psychiatric disorders, or drug abuse, MS relapse and receive corticosteroids. Visual acuity less than 6/18 corrected, oscillopsia or diplopia that would interfere with testing, Presence of major medical disorder as hepatic, renal or hearing problems would be excluded and if they were mentally disabled.

Inclusion criteria for the control group included healthy subjects with matched age, gender and education with the patient group. They were relatives of patients admitted in the ward of Neurology Department, Mansoura University Hospital will be recruited as a control group.

A semi-structured sheet: Including socio-demographic data, age, sex, marital status, residence, religion, years of education, past history of psychiatric disorders, family history of psychiatric disorder.

For group I: data about their illness were collected e.g. number of episodes, previous hospitalization, staging of the disease and treatment received. Diagnosis was obtained clinically and confirmed by higher staff members. For group II: healthy individuals with matched age, gender and education.

The Mini- International Neuropsychiatric Interview (M.I.N.I) is a short structured diagnostic interview, developed jointly by psychiatrists and clinicians in the United States and Europe, for DSM-IV and ICD-10 psychiatric disorders. With an administration time of approximately 15 min, it was designed to meet the need for a short but accurate structured psychiatric interview. It was used for clinical assessment to exclude presence of current psychiatric disorder [21] (Arabic version) [5].

Penn Computerized Neurocognitive Battery (CNB) (Arabic version) was designed to measure performance accuracy and speed on specific neurobehavioral domains using tests that were previously validated. Penn CNB consists of 14 tests. Motor Praxis Test (MPRAXIS) was used for assessment of sensorimotor integration speed. The Penn Continuous Performance Test–Number (PCPT-n) for assessment of attention and Face memory (immediate and delayed) with the Penn Face Memory Test (CPF) and Penn Face Memory Test–Delayed Memory (CPFd) were also used.

The Penn Conditional Exclusion Task (PCET) was used for assessment of abstraction and mental flexibility. Short Computerized Finger-Tapping Task (sCTAP) was used for assessment of spatial memory. Short Visual Object Learning Test (s VOLT) and Short Visual Object Learning Test Delayed Memory (s VOLTd) and The Penn Matrix Reasoning Test (PMAT) for assessment of nonverbal reasoning were also used.

The Short Penn Line Orientation Test (s PLOT) for spatial orientation was used. The Age Differentiation Test (ADT) for social cognition, Penn Emotion Recognition Task (ER40) and The Measured Emotion Differentiation Test (MEDF); and finally, the Short Fractal N-Back (SFNB2) for assessment of working memory were used [9].

Statistical analysis and data interpretation

Data analysis was performed by SPSS software, version 25 (SPSS Inc., PASW statistics for windows version 25. Chicago: SPSS Inc.). Qualitative data were described using number and percent. Quantitative data were described using median (minimum and maximum) for non-normally distributed data and mean ± Standard deviation for normally distributed data after testing normality using Kolmogrov-Smirnov test. Significance of the obtained results was judged at the (≤ 0.05) level. Chi-Square and Monte Carlo tests were used to compare qualitative data between groups as appropriate. Student t test and Mann Whitney U test were used to compare between 2 studied groups for normally and non-normally distributed data. The Spearman's rank-order correlation is used to determine the strength and direction of a linear relationship between two non-normally distributed continuous variables and / or ordinal variables. Binary logistic regression was used to assess the effect of combination of more than 2 independent variables on dichotomous outcome using Stepwise technique.

Results

Regarding socio-demographic variables of studied groups, there was no statistically significant difference between patients and control (Table 1). They were matched in age, gender and education. The mean age of onset of the disease was 27.97 ± 7.01. Median duration since last admission / months was 12(5–48). Number of episodes with Median min–max) were 3(1–10) (Table 2).

Table 1 Sociodemographic characteristics and clinical variables of the studied groups
Full size table
Table 2 Motor speed, spatial memory, immediate facial memory and attention among studied group
Full size table

On assessing Spatial memory, there was statistically significant difference between both group regarding true negative (p = 0.04) where control group showed more accuracy than cases and false positive (p = 0.04) where cases are more accurate than control group. Regarding motor speed, there was statistically significant difference between both group (p = 0.008) where control group showed better results than cases in accuracy.

On assessing Immediate facial memory, there was statistically significantly difference among both groups regarding true negative (p = 0.009), false positive (p = 0.009). Total accuracy (p = 0.002) &Total time response (p = 0.028) were statistically significantly difference where control group showed better results in accuracy and less time for response than cases.

On assessing attention using Penn continuous performance task-number version, there was statistically significantly difference among both groups regarding reaction time for true positive response (p = 0.02) where cases took much time than control group (Table 3).

Table 3 Non-verbal reasoning test, delayed facial memory, abstraction and mental flexibility and delayed object memory test among studied groups
Full size table

Regarding Penn Matrix Analysis test, there was statistically significant difference among control and patients’ group regarding time response (p = 0.038) and accuracy (p < 0.001) where cases took more time and showed less accuracy. On assessing delayed face memory using Penn facial memory delayed, there was statistically significantly difference among both groups regarding accuracy (p = 0.04), false positive (p = 0.004) and true negative (p = 0.028) where cases showed less accuracy than control group.

For abstraction and mental flexibility using Penn conditional exclusion test, There was no statistically significantly difference among both group regarding accuracy (p = 0.194) and reaction time (p = 0.425).On assessing delayed memory using Short visual object learning tests, There was statistically significantly difference among both group regarding true negative (p = 0.045) and false positive (p = 0.045) where cases showed less accuracy than control (Table 4).

Table 4 Spatial orientation test, Social cognition, Emotion recognition, Working memory and age differentiation between studied groups
Full size table

On assessing spatial orientation using short Penn line orientation test, there was statistically significantly difference among both group regarding accuracy (p = 0.001) and reaction time (p = 0.046) where cases showed less accuracy than control and more time than control.

Regarding social cognition using Measured emotion differentiation test, there was statistically significantly difference among both groups regarding accuracy (p = 0.005) and reaction time (p = 0.043) where cases showed less accuracy than control and more time than control. For Emotion recognition, using Penn emotion recognition test There was statistically significantly difference among both group regarding reaction time (p = 0.004) where cases showed more time than control. Also, for age differentiation test, there was statistically significantly difference among both group regarding accuracy (p = 0.049) where cases showed less accuracy than control.

Using short fractal- N-back test to assess working memory, there was statistically significantly difference among both group regarding reaction time (p = 0.018) and accuracy (p = 0.001) where cases showed more time than control and less accuracy (Table 5).

Table 5 Correlation between disease characters and cognitive battery among multiple sclerosis cases
Full size table

There was statistically significant negative correlation between Duration since last admission (months) and Motor praxis test for reaction to time. There was no statically significant correlation between disease character and delayed facial memory, Penn matrix and attention.

There was statistically significant negative correlation between Number of episodes and Sum of finger tapping dominant and non- dominant. There was no statistically significant correlation between disease characters and cognitive battery regarding spatial orientation, working memory and social cognition among multiple sclerosis cases.

Discussion

Cognitive impairment is a common expression of MS. It is a frequent cause of disability and socio-economic decline for patients with MS. Even though the ability of detecting cognitive difficulties has increased over the last years, there are still many patients remain undiagnosed and their complaints are considered to be part of comorbidities. Sometimes treating the depression, anxiety and fatigue by psychotherapeutic interventions improves the cognitive alterations [16].

Regarding attention, the MS patients showed statistically significant longer reaction time in comparison to the control group. This may be due to subtle motor deficit which may lead to taking longer duration to respond to the test. These results were consistent with results of previous studies such as [7] who showed a lower attention performance in the MS group than the control group. On the other hand, a study carried by [13] revealed a significant deficit among control group than patients group. It may be related to higher levels of depression that was reported by control group.

The patients group showed more time than control and less accuracy regarding working memory. Which may be due to deficit in Information Processing Speed (IPS), affecting primarily the encoding of information. Previous studies agreed with our results such as [20] who found that working memory was the most frequently impaired domain in all patients. However, these results were different from previous studies such as, [7] and [8] who found no significant difference between MS patients and control group in relation to working memory. This may be due to less sensitivity of the test than our battery test.

On assessing facial memory (immediate and delayed), control group showed better results in accuracy and less time for response than cases. This may be the extensive neurodegenerative brain process and cortical involvement in MS patients. These results agreed with results of previous studies such as [19] where Long-term memory and immediate memory were the most affected domains in MS, with a prevalence of 33–65% 9 in this study.

However, these results disagree with [13] who found no statistically significant differences emerged between the scores of the two groups.

On testing motor speed, there was a statistically significant difference between both groups where control group showed better results than cases in accuracy. May be this is due to to demyelination of the motor neurons. This may explain why MS patients showed longer time to respond to most of the tests used. This result is in line with a study carried by Maggio et al. [13] among 60 patients.

As for social cognition, using emotional differentiation test, age differentiation test, and emotional recognition tests, MS patients showed less accuracy than control and more time than control. This finding is in line with [6] who identified Significantly decreased performance in MS compared to control.

Regarding non- verbal reasoning, there was a significant difference among control and patients group where patients took more time and showed less accuracy. These results are consistent with [4] study where MS patients showed impaired results in non-verbal reasoning test. However, these results are different with previous study where Logical reasoning was assessed by spatial reasoning task. Results revealed no difference between control and patients’ groups. It may be related to the sample size that was large compared to our study [11].

Regarding Spatial memory, there was a statistically significant difference between both group where cases are more accurate than control group in our study. This result agrees with [23] who found there was a spatial memory decline in MS patients more than control.

On assessing spatial orientation, MS patients showed statistically significant less accuracy than control and more time than control. This may be due to damage to white matter structure leading to disconnection between the cortical and subcortical regions responsible for spatial orientation. These results are consistent with previous studies such as [14] who found less accurate spatial orientation performance is present in up to 41% of the participants with early MS.

Conclusions

Cognitive dysfunction has a remarkably negative impact on functionality, compromising employment status, social activities, treatment adherence and quality of life.

In our research we studied cognitive impairment among a representive sample of MS patients in outpatient clinics of Multiple Sclerosis Unite of Neurology Department, Mansoura University Hospital. We used Penn Computerized Neurocognitive Battery. It measured speed and accuracy of cognitive domains. Attention, spatial orientation, non- verbal reasoning, Facial memory, working memory, social cognition and spatial memory were affected in patients with MS during their remission phase. cognitive evaluation should constitute a major part of the clinical examination in MS, especially when impairment seems likely.

Availability of data and materials

Is not applicable.

Abbreviations

ADT:

The Age Differentiation Test

CNB:

Penn Computerized Neurocognitive Battery

CPF:

Penn Face Memory Test

CPFd:

Penn Face Memory Test–Delayed Memory

ER40:

Penn Emotion Recognition Task

M.I.N.I:

The Mini- International Neuropsychiatric Interview

MEDF:

The Measured Emotion Differentiation Test

MPRAXIS:

Motor Praxis Test

MS:

Multiple sclerosis

PCET:

Penn Conditional Exclusion Task

PCPT-n:

Penn Continuous Performance Test–Number

PMAT:

The Penn Matrix Reasoning Test

s PLOT:

The Short Penn Line Orientation Test

s VOLT:

Short Visual Object Learning Test

s VOLTd:

Short Visual Object Learning Test Delayed Memory

sCTAP:

Short Computerized Finger-Tapping Task

SFNB2:

Short Fractal N-Back

References

  1. Amato MP, Prestipino E, Bellinvia A (2019) Identifying risk factors for cognitive issues in multiple sclerosis. Expert Rev Neurother 19(4):333–347. https://doi.org/10.1080/14737175.2019.1590199

    Article  CAS  PubMed  Google Scholar 

  2. Bovis F, Signori A, Carmisciano L, Maietta I, Steinerman J R, Li T et al (2018). Expanded disability status scale progression assessment heterogeneity in multiple sclerosis according to geographical areas. Ann Neurol 84(4):621–625. https://doi.org/10.1002/ana.25323

  3. Deloire MS, Salort E, Bonnet M, Arimone Y, Boudineau M, Amieva H et al (2005). Cognitive impairment as marker of diffuse brain abnormalities in early relapsing remitting multiple sclerosis. J Neurol Neurosurg Psychiatry 76(4):519–526. https://doi.org/10.1136/jnnp.2004.045872

  4. Drew M, Tippett LJ, Starkey NJ, Isler RB (2008) Executive dysfunction and cognitive impairment in a large community-based sample with Multiple Sclerosis from New Zealand: a descriptive study. Arch Clin Neuropsychol 23(1):1–19. https://doi.org/10.1016/j.acn.2007.09.005

    Article  PubMed  Google Scholar 

  5. Ghanem M, Ibrahim M, Elbehery Aa, & Elmerghany H (2000). The translation group of the Arabic version of the Mini International Neuropsychiatric Interview for Children (MINI-kid) by Sheehan DV et al.,1998. Department of Neuropsychiatry Ain Shams University, Cairo

  6. Golde S, Heine J, Pottgen J, Mantwill M, Lau S, Wingenfeld K et al. (2020). Distinct functional connectivity signatures of impaired social cognition in multiple sclerosis. Front Neurol 11:507. https://doi.org/10.3389/fneur.2020.00507

  7. Guillemin C, Lommers E, Delrue G, Gester E, Maquet P, Collette F (2022) The complex interplay between trait fatigue and cognition in multiple sclerosis. Psychol Belg 62(1):108–122. https://doi.org/10.5334/pb.1125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Henry A, Lannoy S, Chaunu MP, Tourbah A, Montreuil M (2022) Social cognition and executive functioning in multiple sclerosis: a cluster-analytic approach. J Neuropsychol 16(1):97–115. https://doi.org/10.1111/jnp.12248

    Article  PubMed  Google Scholar 

  9. Ibrahim I, Tobar S, Elassy M, Mansour H, Chen K, Wood J et al. (2015). Practice effects distort translational validity estimates for a Neurocognitive Battery. J Clin Exp Neuropsychol 37(5):530–537. https://doi.org/10.1080/13803395.2015.1037253

  10. Korakas N, Tsolaki M (2016) Cognitive impairment in multiple sclerosis: a review of neuropsychological assessments. Cogn Behav Neurol 29(2):55–67. https://doi.org/10.1097/WNN.0000000000000097

    Article  PubMed  Google Scholar 

  11. Lincoln NB, Dent A, Harding J, Weyman N, Nicholl C, Blumhardt LD, Playford ED (2002) Evaluation of cognitive assessment and cognitive intervention for people with multiple sclerosis. J Neurol Neurosurg Psychiatry 72(1):93–98. https://doi.org/10.1136/jnnp.72.1.93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Macias Islas MA, & Ciampi E (2019). Assessment and impact of cognitive impairment in multiple sclerosis: an overview. Biomedicines 7(1). https://doi.org/10.3390/biomedicines7010022

  13. Maggio MG, Russo M, Cuzzola MF, Destro M, La Rosa G, Molonia F et al (2019). Virtual reality in multiple sclerosis rehabilitation: a review on cognitive and motor outcomes. J Clin Neurosci 65:106–111. https://doi.org/10.1016/j.jocn.2019.03.017

  14. Nema E, Kalina A, Nikolai T, Vyhnalek M, Meluzinova E, Laczo J (2021) Spatial navigation in early multiple sclerosis: a neglected cognitive marker of the disease? J Neurol 268(1):77–89. https://doi.org/10.1007/s00415-020-10079-z

    Article  PubMed  Google Scholar 

  15. Nocentini U, Pasqualetti P, Bonavita S, Buccafusca M, De Caro M F, Farina D et al (2006). Cognitive dysfunction in patients with relapsing-remitting multiple sclerosis. Mult Scler 12(1):77–87. https://doi.org/10.1191/135248506ms1227oa

  16. Oreja-Guevara C, Ayuso Blanco T, Brieva Ruiz L, Hernandez Perez MA, Meca-Lallana V, Ramio-Torrenta L (2019) Cognitive dysfunctions and assessments in multiple sclerosis. Front Neurol 10:581. https://doi.org/10.3389/fneur.2019.00581

    Article  PubMed  PubMed Central  Google Scholar 

  17. Patti, F., Amato, M. P., Trojano, M., Bastianello, S., Tola, M. R., Goretti, B., et al (2009) Cognitive impairment and its relation with disease measures in mildly disabled patients with relapsing-remitting multiple sclerosis: baseline results from the Cognitive Impairment in Multiple Sclerosis (COGIMUS) study. Mult Scler 15(7):779–788. https://doi.org/10.1177/1352458509105544

    Article  Google Scholar 

  18. Pitteri M, Magliozzi R, Nicholas R, Ziccardi S, Pisani AI, Pezzini F et al (2022). Cerebrospinal fluid inflammatory profile of cognitive impairment in newly diagnosed multiple sclerosis patients. Mult Scler 28(5):768–777. https://doi.org/10.1177/13524585211032510

  19. Podda J, Ponzio M, Pedulla L, Monti Bragadin, M, Battaglia MA, Zaratin P, et al (2021). Predominant cognitive phenotypes in multiple sclerosis: Insights from patient-centered outcomes. Mult Scler Relat Disord 51:102919. https://doi.org/10.1016/j.msard.2021.102919

  20. Sadigh-Eteghad S, AbbasiGarravnd N, Feizollahi M, Talebi M (2021) The expanded disability status scale score and demographic indexes are correlated with the severity of cognitive impairment in multiple sclerosis patients. J Clin Neurol 17(1):113–120. https://doi.org/10.3988/jcn.2021.17.1.113

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E et al. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 59(Suppl 20):22–33;quiz 34–57. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9881538

  22. Smith AD 3rd, Duffy C, Goodman AD (2018) Novel computer-based testing shows multi-domain cognitive dysfunction in patients with multiple sclerosis. Mult Scler J Exp Transl Clin 4(2):2055217318767458. https://doi.org/10.1177/2055217318767458

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sumowski JF, Benedict R, Enzinger C, Filippi M, Geurts JJ, Hamalainen P et al (2018). Cognition in multiple sclerosis: State of the field and priorities for the future. Neurology 90(6):278–288. https://doi.org/10.1212/WNL.0000000000004977

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Authors and Affiliations

  1. Tanta Hospital for Psychiatric Health and Addiction, Tanta, Egypt

    Sara Elbasuony Elshehawy

  2. Department of Psychiatry, Faculty of Medicine, Mansoura University, Mansoura, Egypt

    Ibtihal M. A. Ibrahim & Mohamed El-Husseini Khater

  3. Department of Neurology, Faculty of Medicine, Mansoura University, Mansoura, Egypt

    Ayman Mohamed Abdel-Naby

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  1. Sara Elbasuony Elshehawy
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Contributions

S. E.: interviewed the patients and applied CNB battery to them and helped in writing the manuscript. I I: analyzed and interpreted the patient data and was a major contributor in writing the manuscript. A. A.: helped in diagnosing and recruiting MS patients. M. K.: helped in the design of the work. All authors revised and approved the final manuscript.

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Correspondence to Ibtihal M. A. Ibrahim.

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Elshehawy, S.E., Ibrahim, I.M.A., Abdel-Naby, A.M. et al. Cognitive impairment in a sample of adult patients with multiple sclerosis: an Egyptian study. Middle East Curr Psychiatry 30, 80 (2023). https://doi.org/10.1186/s43045-023-00341-1

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