Next Article in Journal
Multiple Head Rotations Result in Persistent Gait Alterations in Piglets
Next Article in Special Issue
Neural Signatures of Error Processing in Depressed Adolescents with Comorbid Non-Suicidal Self-Injury (NSSI)
Previous Article in Journal
Stroke-Prone SHR as Experimental Models for Cardiovascular Disease Risk Reduction in Humans
Previous Article in Special Issue
Saliva Free Light Chains in Patients with Neuro-Sjögren
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 10
Issue 11
10.3390/biomedicines10112975
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The TRACK-MS Test Battery: A Very Brief Tool to Track Multiple Sclerosis-Related Cognitive Impairment

by
Daniela Taranu
1,*,
Hayrettin Tumani
1,*,
Jill Holbrook
1,
Visal Tumani
2,
Ingo Uttner
1 and
Patrick Fissler
1,3,4
1
Department of Neurology, Faculty of Medicine, Ulm University, D-89071 Ulm, Germany
2
Department of Psychiatry, Faculty of Medicine, Ulm University, D-89071 Ulm, Germany
3
Psychiatric Services Thurgau, CH-8596 Münsterlingen, Switzerland
4
University Hospital for Psychiatry and Psychotherapy, Paracelsus Medical University, A-5020 Salzburg, Austria
*
Authors to whom correspondence should be addressed.
Biomedicines 2022, 10(11), 2975; https://doi.org/10.3390/biomedicines10112975
Submission received: 20 October 2022 / Revised: 12 November 2022 / Accepted: 16 November 2022 / Published: 18 November 2022
(This article belongs to the Special Issue Central and Peripheral Nervous Systems: Structure, Function, and Diseases)
Browse Figure
Versions Notes

Abstract

:
Tracking cognition in patients with multiple sclerosis (MS) is important for detection of disease progression but it is often not performed in routine settings due to time constraints. This exploratory cohort study aims to develop a very brief repeatable tracking tool with comparable test quality criteria to the current gold standard, the Brief International Cognitive Assessment for MS (BICAMS). The study included 88 participants (22 healthy controls, 66 MS patients) who were examined at baseline and at one-year follow-up. As a validity criterion for the six administered cognitive tests, we assessed the difference between MS patients and HC, and the correlation with MS-related disability. Combining the two tests with the highest validity—the Controlled Oral Word Association Test and Symbol Digit Modalities Test—yielded an administration time of 5 min. Comparing this new TRACK-MS test battery with the 15 min BICAMS indicated that TRACK-MS showed larger differences between MS patients and healthy controls, a higher correlation with MS-related disability, smaller practice effects, and a good test–retest reliability. We provide evidence that TRACK-MS, although faster to administer, showed at least comparable quality criteria as the BICAMS. As the study was exploratory, replication of these results is necessary.

1. Introduction

Cognitive impairment is a frequent symptom in multiple sclerosis (MS), found in 40–70% of patients [1,2,3]. Organized in multiple cerebral networks and pathways, cognition can be grouped into various domains such as executive functions, learning and memory, attention, visuospatial, perceptual and social functions [4,5]. As recent studies demonstrate, cognitive functions are interrelated with other behavior-relevant factors such as emotion and affect [6,7]. Complex metabolic and neurohormonal pathways mediate these functions and play a role in the development of various psychiatric and neurological diseases [8,9].
Jean-Martin Charcot was the first to describe cognitive problems in MS, but it was not until 1991 that S. Rao brought renewed attention to the topic, aiming to reveal the precise mechanisms of MS-related cognitive impairment in the hope of developing effective treatments [10]. Although all cognitive domains can be altered in MS [10,11,12,13,14,15,16,17], the extent and severity of cognitive deficits are quite variable, and depend on many factors, including the type of MS [18]. In general, secondary-progressive MS (SPMS) patients have more pronounced cognitive impairment [19], at least in part because the severity of symptoms is directly associated with the longer duration of disease in these patients. This contrasts with patients who have a relapsing MS course, or patients with primary progressive MS, which is often characterized by an onset late in life [20,21,22]. Cognitive function may be affected even before the first local symptom of MS occurs [23], and reliable assessment of cognition as well as regular surveillance in the course of the disease is necessary. Even subtle cognitive impairment can cause deterioration in patients’ quality of life and negatively affect their participation in work and social activities [24,25,26,27,28,29]. Moreover, monitoring changes in cognition in patients with a clinically isolated syndrome or radiologically isolated syndrome may contribute to identifying those at risk of MS [30,31,32]. Tracking cognitive change helps to predict the potential clinical course and to assess the effect of medical treatment and rehabilitation [33,34,35]. Additionally, in some patients cognitive impairment is the only symptom of MS, so assessing this impairment regularly is the only way to track disease activity [35,36,37]. However, cognitive assessment in routine clinical practice is still limited because of time constraints [38] and often also because of an unfamiliarity with and insufficient training of the testing procedures. Attempts to assess the cognitive status of MS patients with simple screening tests such as the Mini-Mental Status Examination [39] or the Montreal Cognitive Assessment [40] proved to be unsuitable, since these tools are unable to pick up subtle deficits, and the results are dependent on age and educational level [31,41,42]. A very brief cognitive tracking tool is needed that is valid, reliable, time efficient, easy to administer, and has no practice effects [43].
An international panel of experts recommended the use of the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) as a neuropsychological screening tool in order to improve the clinical care of patients with MS [44,45,46]. The test is made up of three subtests: the California Verbal Learning Test II (CVLT-II); the Symbol Digit Modalities Test (SDMT); and the Brief Visual Memory Test-Revised (BVMT-R) [46,47]. Assessment using this battery takes about 15 to 20 min, and it can also be performed in smaller facilities and practices without special neuropsychological expertise [46]. In the German language version of the BICAMS, the Verbal Learning and Memory Test (VLMT) is included instead of the CVLT II, and this version is designated as the BICAMS-M [45]. The BICAMS successfully identified impaired cognitive function in adults with MS compared with healthy controls [44]. Deficits in MS patients were observed in all three cognitive domains assessed by the BICAMS: information processing speed, immediate verbal recall memory, and immediate visual recall memory [44].
Using two of the three BICAMS tests—the SDMT and BVMT-R—provided very similar test–retest reliability and validity in comparison with the complete BICAMS in terms of identifying cognitive impairment in MS patients. This abbreviated test procedure is known as the BICAMS-short [48]. If the BICAMS-short is considered too time-intensive, yearly administration of the SDMT is recommended instead [49]. The SDMT is considered to be the most valid single test, with a specificity of 0.60 and a sensitivity of 0.91 [49]. In addition, decline in the SDMT is associated with decline in clinical status [50]. The SDMT was also shown to be the best predictor of employability in studies including multiple cognitive or motor performance tests [50,51]. However, the SDMT is not sufficient to determine impairment in other cognitive domains [52].
Overall, despite the demonstrated clinical utility of the BICAMS and its subtests, research on cognitive testing for MS patients remains sparse [53]. Data are lacking as to whether, in comparison with the BICAMS, other cognitive tests may be more valid, reliable, and time-efficient, and have smaller practice effects [54]. Combining other cognitive tests may yield a shorter battery with similar or better test quality criteria. In the current exploratory cohort study, six cognitive tests were administered (three included in the BICAMS) with the aim of identifying a shorter test battery than the BICAMS that has better or similar test quality criteria, including validity, test–retest reliability, and practice effects.

2. Materials and Methods

2.1. Study Population

Participants were recruited from the MS outpatient clinic in Ulm, Germany for the study and largely came from Ulm and the surrounding area. In total, 66 MS patients (primary progressive MS: n = 22; secondary progressive MS: n = 22; relapsing-remitting MS: n = 22) and 22 healthy controls (HC) were examined at baseline and after 12 months of follow-up. We used the STROBE cohort reporting guidelines [55]. Inclusion criteria were as follows: Age between 18 and 85, clinically proven MS (primary progressive, secondary progressive, relapsing-remitting types), good communication abilities to conduct the assessment, to understand the purpose of the examination and to provide their informed consent. Exclusion criteria were as follows: Another illness, if it was expected to impair the clinical assessment of MS symptoms, or a history of severe depression or another severe psychiatric disorder.

2.2. Procedures

Participants signed a written informed consent form, and the study was approved by the Ethics Committee of Ulm University in 13 September 2016 (File number 157/16). The patients did not receive any economic compensation for their participation. All assessments were performed by a single clinical neurologist (DT).
At baseline and at 12 months, all participants underwent a comprehensive neurological status examination as well as a detailed neuropsychological assessment, and were administered the Expanded Disability Status Scale (EDSS) [56]. For the neuropsychological assessments, we included tests that assessed cognitive domains typically affected in MS: the Controlled Oral Word Association Test (COWAT) for verbal fluency [57], Verbal Learning and Memory Test (VLMT) for verbal episodic memory [45], BVMT-R for visual episodic memory [45], Symbol Digit Modalities Test (SDMT) for processing speed [45], Paced Auditory Serial Addition Test (PASAT) for working memory [58], and the Block Design Test (BDT) for visuospatial reasoning [59] (for a detailed description, please refer to the Supplementary Materials section). Although not frequently used in MS patients, the COWAT was included, because it is short and comprehensively validated and therefore qualified as a potential tracking tool. The sequence of the individual assessments was performed in the same order for all test participants and the tests for individual patients were administered at the same time of day at baseline and follow-up.

2.3. Statistical Analyses

Statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). Statistical significance was set at alpha level < 0.05. Group differences were examined with an ANCOVA at baseline. Covariates included age and education. Additionally, effect sizes (ES) for group comparisons (MS vs. HC) were defined as group differences at baseline divided by baseline standard deviation (z-standardization). Multiple regression analyses were performed to assess the relationship of MS-related disability with all outcome variables. Standardized beta (β) was reported as an effect size measure. Three composite scores were constructed by averaging z-standardized scores: (1) TRACK-MS test, composed of the COWAT and SMDT scores; (2) the BICAMS-short, composed of the SDMT and BVMT-R scores; and (3) the BICAMS, composed of the VLMT, BVMT-R, and SDMT scores.
The practice effect of each test was examined using two-tail paired t-test. Test–retest reliability over the period of 1 year was assessed using correlation analysis between pre- and posttest. The size of the practice effect was calculated and determined by Cohen’s criteria (Cohen, 1988): 0.21–0.49 indicating small; 0.50–0.79, medium; and over 0.80, large effect size.
The number of participants was calculated with a power analysis based on the primary aim of the study (assessing differences between different MS types and healthy controls in baseline and 1-year change of cerebral symptoms including cognitive impairment, fatigue and psychopathology, for details see [60]). The exploratory analysis of the study reported in this manuscript, has a power of 80% to detect (i) small to medium true effect sizes (r = 0.29) for associations (e.g., link between cognitive performance and MS-related disability), and (ii) medium to large effect size (Cohen’s d = 0.70) for differences between patients with MS and healthy controls, assuming an α-error of 0.05.

3. Results

In total, 88 people were included in this study (22 HC and 66 patients with MS; see Table 1 for demographics and clinical characteristics). The raw scores of the cognitive test results for baseline and for the follow-up after one year are presented in Table 2.
HC had more years of education (p = 0.03). In all individual cognitive tests, MS patients had lower scores than HC (p < 0.04), but only the COWAT showed a large effect size (ES = −1.03 SD, CI: −1.45 to −0.62, p < 0.001), followed by the SDMT (ES = −0.75 SD, CI: −1.14 to −0.36, p < 0.001) (see Figure 1). In addition, all scores correlated with MS-related disability (p < 0.03), but only the COWAT was associated with a large effect size (r = −0.54, CI: −0.74 to −0.35, p < 0.001), followed by the SDMT (r = −0.46, CI: −0.64 to −0.28, p < 0.001) (see Figure 1). The SDMT, BDT, and VLMT tests had a good 1-year retest reliability (r ≥ 0.80), while the BVMT, COWAT and PASAT tests showed an adequate reliability (r ≥ 0.70) (see Figure 1).
Comparing the so-called TRACK-MS that combined the two most valid tests (COWAT and SDMT) with the current gold standard of the BICAMS, TRACK-MS showed a larger difference between patients with MS and HC with a large effect size (ESTRACK-MS = −0.89, CI −1.22 to −0.56, F(1, 84) = 21.6, p < 0.001, ƞ2 = 0.21). The BICAMS demonstrated medium effect sizes (ESBICAMS = −0.65, CI: −0.97 to −0.33, F(1, 84) = 13.7, p < 0.001, ƞ2 = 0.14). Similarly, TRACK-MS showed a larger correlation with disability with a large effect size (rTRACK-MS = −0.57, CI: −0.75 to −0.39). The BICAMS demonstrated medium-sized correlations (rBICAMS = 0.39, CI: −0.57 to −0.21). Furthermore, TRACK-MS had good test–retest reliability (rTRACK-MS = 0.86, CI: 0.79 to 0.91), although the BICAMS had a better reliability (rBICAMS = 0.92; CI: 0.87 to 0.95). To assess practice effects, we calculated the change in test performance from pretest to posttest. In the HC group, there was no significant improvement from pre- to posttest in the BICAMS (d = 0.07, CI: 0.05 to 0.18) and TRACK-MS (d = 0.02, CI: −0.13 to 0.17) with neglectable effect sizes. In the total sample, there was a significant improvement from pre- to posttest for the BICAMS (d = 0.14; CI: 0.07 to 0.22) but not for TRACK-MS (d = 0.08, CI: −0.01 to 0.18).
Additional analysis regarding the correlation between sequence of test administration and sequence of validity ranking (difference MS vs. healthy controls) revealed no association (rho = 0.21; p = 0.62).

4. Discussion

In the current exploratory cohort study, we use a bundle of cognitive tests addressing the cognitive domains typically impaired in MS to identify a very brief test battery—TRACK-MS—with test quality criteria that were at least comparable to the gold standard BICAMS. We hope this very brief test battery will increase feasibility of cognitive tracking in routine clinical practice. Our results provide evidence that TRACK-MS—which takes only about 5 min to administer—is a valid and reliable cognitive tracking tool that is less prone to practice effects than the BICAMS. TRACK-MS also includes the COWAT, which demonstrated the highest single-test validity in our study, and the current gold standard single test, the SDMT [61]. To our knowledge, the COWAT has not been used in any previous study to assess MS-related cognitive impairment. Furthermore, we report for the first time that the combined test battery consisting of the COWAT and SDMT was at least as useful as the BICAMS for detecting cognitive impairment in MS patients. Both the COWAT and SDMT pose high demands on processing speed, which is strongly affected by white matter alterations (e.g., hyperintensities and volume). Hence, the favorable test characteristics of TRACK-MS may result from its demands on processing speed in the visual (SDMT) and in the verbal domain (COWAT) [62]. Our results are in line with studies showing high impairment in processing speed in MS patients [63].
In our study, the SDMT had the second-best validity after the COWAT. Our results for the SDMT are in line with previously reported results [45], which showed that the SDMT and BVMT-R reliably differentiated between MS patients and HCs [64,65]. Similarly, earlier research showed that the SDMT and BVMT-R were the most sensitive tests of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) battery and that these exhibited the largest differences between MS and HCs [11,66]. Previously, it was also reported that MS patients and HC showed differences mainly in the SDMT and BVMT-R but not in the CVLT-II [67]. Two additional validation studies reported that the SDMT and BVMT-R were able to differentiate between MS patients and HC, whereas the CVLT-II could not [68,69]. The SDMT was repeatedly reported to be better than the PASAT in discriminating between MS patients and HC [70,71]. Additionally, the SDMT has been shown to be the most sensitive test in the Brief Repeatable Neuropsychological Battery [72,73], and to have a higher correlation with the EDSS than the PASAT [74]. A moderate-to-large correlation of the EDSS with SDMT (r = 0.55), BVMT-R (r = 0.54), and CVLT (r = 0.40) has been demonstrated [75,76], corresponding with our results.

5. Limitations and Future Perspectives

A limitation of the current study is that the combination of SDMT and COWAT (TRACK-MS) was not defined a priori but was selected based on the data analysis, which demonstrated the highest validity of this test combination. Because of this exploratory approach, the study should be replicated in a validation study. Here, we could only show the lower boundary of practice effects, as the cognitive ability of the MS group probably declined over time. It would be desirable in future studies to assess the real magnitude of the practice effect, thereby allowing for longitudinal correction of scores. Practice effects are difficult to estimate: they likely depend on baseline performance as a result of the regression-to-the-mean effect and the ceiling phenomenon; i.e., individuals with higher baseline performances are likely to profit less from retesting and therefore tent to show smaller practice effects. Because patients with MS tend to have lower baseline scores, this phenomenon likely leads to larger practice effects in patients with MS. This phenomenon was seen in our study, in which larger practice effects were observed in the total sample (BICAMS: d = 0.14 vs. TRACK-MS: d = 0.08) than in the healthy control sample (BICAMS: d = 0.07 vs. TRACK-MS: d = 0.02). The development of alternative versions of the COWAT and SDMT could minimize practice effects. It should be pointed out, however, that we provide evidence that the BICAMS has larger practice effects than TRACK-MS.
In terms of the study population, the mean years of education differed in the MS and HC groups at baseline. However, we accounted for this covariate statistically. It should also be noted that the presence of dysarthria may affect the COWAT and SDMT scores [77]. Theoretically, in patients with severe dysarthria, these tests would not measure cognitive flexibility but motoric speech impairment. However, in our study, there were no patients with marked motor disabilities or speech disorders.
Validation was restricted to the difference between patients with MS and HC and the association of the tests with MS-related disability. Links between cognitive scores and brain biomarkers (e.g., inflammatory markers or extent of white matter hyperintensities) are also of interest and could be assessed in future studies. The relatively small sample size resulted in a fairly large confidence interval. Study replication within a larger sample would be expected to increase the precision of the estimates.

6. Conclusions and Implications

The data from our study provide evidence that TRACK-MS, a very brief cognitive tracking tool for MS, has at least comparable test quality criteria to the current gold standard, the BICAMS. TRACK-MS can be administered in 5 min and showed better validity and lower practice effects than the BICAMS as well as good test–retest reliability. TRACK-MS is not intended to create a comprehensive profile of cognitive strengths and weaknesses, but to track MS-related cognitive impairment. TRACK-MS poses strong demands on processing speed in the visual and verbal domain. These demands may account for its high validity in assessing MS-related impairment. We report for the first time that the combination of the COWAT and SDMT scores is as useful as the BICAMS in detecting cognitive impairment in MS patients. A larger study to confirm our results would be useful. The TRACK-MS may prove to become a valuable tool in routine clinical practice, allowing time-efficient assessment and longitudinal tracking of cognition in MS patients. Such a reliable assessment tool will allow detection of subtle cognitive impairment, with the hope that early detection may facitilitate the use of therapeutic interventions to positively impact patients’ quality of life.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines10112975/s1; Description of the psychometric tests. The cognitive tests are described in Taranu et al, 2021 [60], as well.

Author Contributions

Conceptualization D.T., V.T., P.F., I.U. and H.T.; methodology D.T., H.T. and I.U.; formal analysis D.T. and P.F.; data curation D.T.; writing—original draft preparation D.T., J.H. and P.F.; writing—review and editing H.T., P.F., I.U., V.T. and J.H.; supervision H.T., P.F. and I.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Ulm University in 13 September 2016 (File number 157/16).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, [DT], upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. DeLuca, G.C.; Yates, R.L.; Beale, H.; Morrow, S.A. Cognitive impairment in multiple sclerosis: Clinical, radiologic and pathologic insights. Brain Pathol. 2015, 25, 79–98. [Google Scholar] [CrossRef] [PubMed]
  2. McNicholas, N.; O’Connell, K.; Yap, S.M.; Killeen, R.P.; Hutchinson, M.; McGuigan, C. Cognitive dysfunction in early multiple sclerosis: A review. QJM Mon. J. Assoc. Physicians 2018, 111, 359–364. [Google Scholar] [CrossRef] [PubMed]
  3. Oreja-Guevara, C.; Ayuso Blanco, T.; Brieva Ruiz, L.; Hernandez Perez, M.A.; Meca-Lallana, V.; Ramio-Torrenta, L. Cognitive Dysfunctions and Assessments in Multiple Sclerosis. Front. Neurol. 2019, 10, 581. [Google Scholar] [CrossRef] [PubMed]
  4. First, M.B. Diagnostic and statistical manual of mental disorders, 5th edition, and clinical utility. J. Nerv. Ment. Dis. 2013, 201, 727–729. [Google Scholar] [CrossRef] [PubMed]
  5. Woodruff, B.K. Disorders of cognition. Semin. Neurol. 2011, 31, 18–28. [Google Scholar] [CrossRef]
  6. Battaglia, S.; Cardellicchio, P.; Di Fazio, C.; Nazzi, C.; Fracasso, A.; Borgomaneri, S. The Influence of Vicarious Fear-Learning in “Infecting” Reactive Action Inhibition. Front. Behav. Neurosci. 2022, 16, 946263. [Google Scholar] [CrossRef]
  7. Battaglia, S.; Cardellicchio, P.; Di Fazio, C.; Nazzi, C.; Fracasso, A.; Borgomaneri, S. Stopping in (e)motion: Reactive action inhibition when facing valence-independent emotional stimuli. Front. Behav. Neurosci. 2022, 16, 998714. [Google Scholar] [CrossRef]
  8. Tanaka, M.; Toth, F.; Polyak, H.; Szabo, A.; Mandi, Y.; Vecsei, L. Immune Influencers in Action: Metabolites and Enzymes of the Tryptophan-Kynurenine Metabolic Pathway. Biomedicines 2021, 9, 734. [Google Scholar] [CrossRef]
  9. Tanaka, M.; Vecsei, L. Editorial of Special Issue “Crosstalk between Depression, Anxiety, and Dementia: Comorbidity in Behavioral Neurology and Neuropsychiatry”. Biomedicines 2021, 9, 517. [Google Scholar] [CrossRef]
  10. Rao, S.M.; Leo, G.J.; Bernardin, L.; Unverzagt, F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology 1991, 41, 685–691. [Google Scholar] [CrossRef]
  11. Benedict, R.H.; Cookfair, D.; Gavett, R.; Gunther, M.; Munschauer, F.; Garg, N.; Weinstock-Guttman, B. Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J. Int. Neuropsychol. Soc. JINS 2006, 12, 549–558. [Google Scholar] [CrossRef]
  12. Chiaravalloti, N.D.; DeLuca, J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008, 7, 1139–1151. [Google Scholar] [CrossRef]
  13. Dulau, C.; Deloire, M.; Diaz, H.; Saubusse, A.; Charre-Morin, J.; Prouteau, A.; Brochet, B. Social cognition according to cognitive impairment in different clinical phenotypes of multiple sclerosis. J. Neurol. 2017, 264, 740–748. [Google Scholar] [CrossRef]
  14. Cotter, J.; Vithanage, N.; Colville, S.; Lyle, D.; Cranley, D.; Cormack, F.; Barnett, J.H.; Murray, K.; Pal, S. Investigating Domain-Specific Cognitive Impairment Among Patients With Multiple Sclerosis Using Touchscreen Cognitive Testing in Routine Clinical Care. Front. Neurol. 2018, 9, 331. [Google Scholar] [CrossRef] [Green Version]
  15. Ciampi, E.; Uribe-San-Martin, R.; Vasquez, M.; Ruiz-Tagle, A.; Labbe, T.; Cruz, J.P.; Lillo, P.; Slachevsky, A.; Reyes, D.; Reyes, A.; et al. Relationship between Social Cognition and traditional cognitive impairment in Progressive Multiple Sclerosis and possible implicated neuroanatomical regions. Mult. Scler. Relat. Disord. 2018, 20, 122–128. [Google Scholar] [CrossRef]
  16. Ntoskou, K.; Messinis, L.; Nasios, G.; Martzoukou, M.; Makris, G.; Panagiotopoulos, E.; Papathanasopoulos, P. Cognitive and Language Deficits in Multiple Sclerosis: Comparison of Relapsing Remitting and Secondary Progressive Subtypes. Open Neurol. J. 2018, 12, 19–30. [Google Scholar] [CrossRef]
  17. Macias Islas, M.A.; Ciampi, E. Assessment and Impact of Cognitive Impairment in Multiple Sclerosis: An Overview. Biomedicines 2019, 7, 22. [Google Scholar] [CrossRef] [Green Version]
  18. Brochet, B.; Ruet, A. Cognitive Impairment in Multiple Sclerosis With Regards to Disease Duration and Clinical Phenotypes. Front. Neurol. 2019, 10, 261. [Google Scholar] [CrossRef] [Green Version]
  19. Greim, B.; Zettl, U.K. Neuropsychological disorders in multiple sclerosis. Fortschr. Der Neurol.-Psychiatr. 2009, 77 (Suppl. 1), S28–S31. [Google Scholar] [CrossRef]
  20. Amato, M.P.; Zipoli, V.; Portaccio, E. Multiple sclerosis-related cognitive changes: A review of cross-sectional and longitudinal studies. J. Neurol. Sci. 2006, 245, 41–46. [Google Scholar] [CrossRef]
  21. Brochet, B.; Clavelou, P.; Defer, G.; De Seze, J.; Louapre, C.; Magnin, E.; Ruet, A.; Thomas-Anterion, C.; Vermersch, P. Cognitive Impairment in Secondary Progressive Multiple Sclerosis: Effect of Disease Duration, Age, and Progressive Phenotype. Brain Sci. 2022, 12, 183. [Google Scholar] [CrossRef] [PubMed]
  22. Ruano, L.; Portaccio, E.; Goretti, B.; Niccolai, C.; Severo, M.; Patti, F.; Cilia, S.; Gallo, P.; Grossi, P.; Ghezzi, A.; et al. Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult. Scler. 2017, 23, 1258–1267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Cortese, M.; Riise, T.; Bjornevik, K.; Bhan, A.; Farbu, E.; Grytten, N.; Hogenesch, I.; Midgard, R.; Smith Simonsen, C.; Telstad, W.; et al. Preclinical disease activity in multiple sclerosis: A prospective study of cognitive performance prior to first symptom. Ann. Neurol. 2016, 80, 616–624. [Google Scholar] [CrossRef] [PubMed]
  24. Arnett, P.A.; Strober, L.B. Cognitive and neurobehavioral features in multiple sclerosis. Expert Rev. Neurother. 2011, 11, 411–424. [Google Scholar] [CrossRef] [PubMed]
  25. Goverover, Y.; Chiaravalloti, N.; DeLuca, J. The influence of executive functions and memory on self-generation benefit in persons with multiple sclerosis. J. Clin. Exp. Neuropsychol. 2013, 35, 775–783. [Google Scholar] [CrossRef]
  26. Kalmar, J.H.; Gaudino, E.A.; Moore, N.B.; Halper, J.; Deluca, J. The relationship between cognitive deficits and everyday functional activities in multiple sclerosis. Neuropsychology 2008, 22, 442–449. [Google Scholar] [CrossRef]
  27. Kavaliunas, A.; Tinghog, P.; Friberg, E.; Olsson, T.; Alexanderson, K.; Hillert, J.; Karrenbauer, V.D. Cognitive function predicts work disability among multiple sclerosis patients. Mult. Scler. J.–Exp. Transl. Clin. 2019, 5, 2055217318822134. [Google Scholar] [CrossRef] [Green Version]
  28. Morrow, S.A.; Drake, A.; Zivadinov, R.; Munschauer, F.; Weinstock-Guttman, B.; Benedict, R.H. Predicting loss of employment over three years in multiple sclerosis: Clinically meaningful cognitive decline. Clin. Neuropsychol. 2010, 24, 1131–1145. [Google Scholar] [CrossRef]
  29. van Gorp, D.A.M.; van der Hiele, K.; Heerings, M.A.P.; Jongen, P.J.; van der Klink, J.J.L.; Reneman, M.F.; Arnoldus, E.P.J.; Beenakker, E.A.C.; van Eijk, J.J.J.; Frequin, S.; et al. Cognitive functioning as a predictor of employment status in relapsing-remitting multiple sclerosis: A 2-year longitudinal study. Neurol. Sci. Off. J. Ital. Neurol. Soc. Ital. Soc. Clin. Neurophysiol. 2019, 40, 2555–2564. [Google Scholar] [CrossRef] [Green Version]
  30. Menascu, S.; Stern, M.; Aloni, R.; Kalron, A.; Magalshvili, D.; Achiron, A. Assessing cognitive performance in radiologically isolated syndrome. Mult. Scler. Relat. Disord. 2019, 32, 70–73. [Google Scholar] [CrossRef]
  31. Rosca, E.C.; Simu, M. Montreal cognitive assessment for evaluating cognitive impairment in multiple sclerosis: A systematic review. Acta Neurol. Belg. 2020, 120, 1307–1321. [Google Scholar] [CrossRef]
  32. Reuter, F.; Zaaraoui, W.; Crespy, L.; Faivre, A.; Rico, A.; Malikova, I.; Soulier, E.; Viout, P.; Ranjeva, J.P.; Pelletier, J.; et al. Frequency of cognitive impairment dramatically increases during the first 5 years of multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 2011, 82, 1157–1159. [Google Scholar] [CrossRef]
  33. Goretti, B.; Portaccio, E.; Zipoli, V.; Hakiki, B.; Siracusa, G.; Sorbi, S.; Amato, M.P. Coping strategies, psychological variables and their relationship with quality of life in multiple sclerosis. Neurol. Sci. Off. J. Ital. Neurol. Soc. Ital. Soc. Clin. Neurophysiol. 2009, 30, 15–20. [Google Scholar] [CrossRef]
  34. Cerqueira, J.J.; Compston, D.A.S.; Geraldes, R.; Rosa, M.M.; Schmierer, K.; Thompson, A.; Tinelli, M.; Palace, J. Time matters in multiple sclerosis: Can early treatment and long-term follow-up ensure everyone benefits from the latest advances in multiple sclerosis? J. Neurol. Neurosurg. Psychiatry 2018, 89, 844–850. [Google Scholar] [CrossRef]
  35. Ross, A.P.; Halper, J.; Harris, C.J. Assessing relapses and response to relapse treatment in patients with multiple sclerosis: A nursing perspective. Int. J. MS Care 2012, 14, 148–159. [Google Scholar] [CrossRef] [Green Version]
  36. Benedict, R.H.; Morrow, S.; Rodgers, J.; Hojnacki, D.; Bucello, M.A.; Zivadinov, R.; Weinstock-Guttman, B. Characterizing cognitive function during relapse in multiple sclerosis. Mult. Scler. 2014, 20, 1745–1752. [Google Scholar] [CrossRef]
  37. Sumowski, J.F.; Benedict, R.; Enzinger, C.; Filippi, M.; Geurts, J.J.; Hamalainen, P.; Hulst, H.; Inglese, M.; Leavitt, V.M.; Rocca, M.A.; et al. Cognition in multiple sclerosis: State of the field and priorities for the future. Neurology 2018, 90, 278–288. [Google Scholar] [CrossRef] [Green Version]
  38. Baetge, S.J.; Filser, M.; Renner, A.; Ullrich, S.; Lassek, C.; Penner, I.K. On the validity of single tests, two-test combinations and the full Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) in detecting patients with cognitive impairment. Mult. Scler. 2020, 26, 1919–1928. [Google Scholar] [CrossRef]
  39. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12, 189–198. [Google Scholar] [CrossRef]
  40. Nasreddine, Z.S.; Phillips, N.A.; Bedirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005, 53, 695–699. [Google Scholar] [CrossRef]
  41. Beatty, W.W.; Goodkin, D.E. Screening for cognitive impairment in multiple sclerosis. An evaluation of the Mini-Mental State Examination. Arch. Neurol. 1990, 47, 297–301. [Google Scholar] [CrossRef] [PubMed]
  42. Kalb, R.; Beier, M.; Benedict, R.H.; Charvet, L.; Costello, K.; Feinstein, A.; Gingold, J.; Goverover, Y.; Halper, J.; Harris, C.; et al. Recommendations for cognitive screening and management in multiple sclerosis care. Mult. Scler. 2018, 24, 1665–1680. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Bakirtzis, C.; Ioannidis, P.; Messinis, L.; Nasios, G.; Konstantinopoulou, E.; Papathanasopoulos, P.; Grigoriadis, N. The Rationale for Monitoring Cognitive Function in Multiple Sclerosis: Practical Issues for Clinicians. Open Neurol. J. 2018, 12, 31–40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Corfield, F.; Langdon, D. A Systematic Review and Meta-Analysis of the Brief Cognitive Assessment for Multiple Sclerosis (BICAMS). Neurol. Ther. 2018, 7, 287–306. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Filser, M.; Schreiber, H.; Pottgen, J.; Ullrich, S.; Lang, M.; Penner, I.K. The Brief International Cognitive Assessment in Multiple Sclerosis (BICAMS): Results from the German validation study. J. Neurol. 2018, 265, 2587–2593. [Google Scholar] [CrossRef] [PubMed]
  46. Langdon, D.W.; Amato, M.P.; Boringa, J.; Brochet, B.; Foley, F.; Fredrikson, S.; Hamalainen, P.; Hartung, H.P.; Krupp, L.; Penner, I.K.; et al. Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Mult. Scler. 2012, 18, 891–898. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Benedict, R.H.; Amato, M.P.; Boringa, J.; Brochet, B.; Foley, F.; Fredrikson, S.; Hamalainen, P.; Hartung, H.; Krupp, L.; Penner, I.; et al. Brief International Cognitive Assessment for MS (BICAMS): International standards for validation. BMC Neurol. 2012, 12, 55. [Google Scholar] [CrossRef] [Green Version]
  48. Penner, I.-K.; Filser, M.; Renner, A.; Ullrich, S.; Lassek, C.; Baetge, S. Screening of cognitive impairment in patients with multiple sclerosis (MS): The BICAMS short version for patient-centered care in small neurological institutions (P2.420). Neurology 2018, 90, P2.420. [Google Scholar]
  49. Van Schependom, J.; D’Hooghe, M.B.; Cleynhens, K.; D’Hooge, M.; Haelewyck, M.C.; De Keyser, J.; Nagels, G. The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis. Eur. J. Neurol. Off. J. Eur. Fed. Neurol. Soc. 2014, 21, 1219–1225. [Google Scholar] [CrossRef]
  50. Benedict, R.H.; DeLuca, J.; Phillips, G.; LaRocca, N.; Hudson, L.D.; Rudick, R.; Multiple Sclerosis Outcome Assessments, C. Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. Mult. Scler. 2017, 23, 721–733. [Google Scholar] [CrossRef]
  51. Strober, L.; Chiaravalloti, N.; Moore, N.; DeLuca, J. Unemployment in multiple sclerosis (MS): Utility of the MS Functional Composite and cognitive testing. Mult. Scler. 2014, 20, 112–115. [Google Scholar] [CrossRef] [PubMed]
  52. Leach, J.M.; Cutter, G.; Golan, D.; Doniger, G.; Zarif, M.; Bumstead, B.; Buhse, M.; Kaczmarek, O.; Sethi, A.; Covey, T.; et al. Measuring cognitive function by the SDMT across functional domains: Useful but not sufficient. Mult. Scler. Relat. Disord. 2022, 60, 103704. [Google Scholar] [CrossRef] [PubMed]
  53. Dong, X.; Xu, G.; Wang, J.; Yin, N.; Meng, N. Clinical and MRI predictors of cognitive decline in patients with relapsing-remitting multiple sclerosis: A 2-year longitudinal study. Mult. Scler. Relat. Disord. 2022, 65, 103838. [Google Scholar] [CrossRef]
  54. Oset, M.; Stasiolek, M.; Matysiak, M. Cognitive Dysfunction in the Early Stages of Multiple Sclerosis-How Much and How Important? Curr. Neurol. Neurosci. Rep. 2020, 20, 22. [Google Scholar] [CrossRef] [PubMed]
  55. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gotzsche, P.C.; Vandenbroucke, J.P.; Initiative, S. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann. Intern. Med. 2007, 147, 573–577. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Kurtzke, J.F. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983, 33, 1444–1452. [Google Scholar] [CrossRef] [Green Version]
  57. Ross, T.P.; Calhoun, E.; Cox, T.; Wenner, C.; Kono, W.; Pleasant, M. The reliability and validity of qualitative scores for the Controlled Oral Word Association Test. Arch. Clin. Neuropsychol. Off. J. Natl. Acad. Neuropsychol. 2007, 22, 475–488. [Google Scholar] [CrossRef] [Green Version]
  58. Rosti-Otajarvi, E.; Hamalainen, P.; Koivisto, K.; Hokkanen, L. The reliability of the MSFC and its components. Acta Neurol. Scand. 2008, 117, 421–427. [Google Scholar] [CrossRef]
  59. Grote, C.; Salmon, P. Spatial complexity and hand usage on the Block Design Test. Percept. Mot. Ski. 1986, 62, 59–67. [Google Scholar] [CrossRef]
  60. Taranu, D. Cerebral symptoms and their trajectory in different multiple sclerosis types: A 1-year longitudinal observational study of cognitive impairment, psychopathology and fatigue. Dissertation Thesis, University of Ulm, Ulm, Germany, July 2021. [Google Scholar]
  61. Sandry, J.; Simonet, D.V.; Brandstadter, R.; Krieger, S.; Katz Sand, I.; Graney, R.A.; Buchanan, A.V.; Lall, S.; Sumowski, J.F. The Symbol Digit Modalities Test (SDMT) is sensitive but non-specific in MS: Lexical access speed, memory, and information processing speed independently contribute to SDMT performance. Mult. Scler. Relat. Disord. 2021, 51, 102950. [Google Scholar] [CrossRef]
  62. Archibald, C.J.; Fisk, J.D. Information processing efficiency in patients with multiple sclerosis. J. Clin. Exp. Neuropsychol. 2000, 22, 686–701. [Google Scholar] [CrossRef]
  63. Papp, K.V.; Kaplan, R.F.; Springate, B.; Moscufo, N.; Wakefield, D.B.; Guttmann, C.R.; Wolfson, L. Processing speed in normal aging: Effects of white matter hyperintensities and hippocampal volume loss. Neuropsychol. Dev. Cogn. Sect. B Aging Neuropsychol. Cogn. 2014, 21, 197–213. [Google Scholar] [CrossRef]
  64. Estiasari, R.; Fajrina, Y.; Lastri, D.N.; Melani, S.; Maharani, K.; Imran, D.; Pangeran, D.; Sitorus, F. Validity and Reliability of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) in Indonesia and the Correlation with Quality of Life. Neurol. Res. Int. 2019, 2019, 4290352. [Google Scholar] [CrossRef] [Green Version]
  65. Sousa, C.; Rigueiro-Neves, M.; Miranda, T.; Alegria, P.; Vale, J.; Passos, A.M.; Langdon, D.; Sa, M.J. Validation of the brief international cognitive assessment for multiple sclerosis (BICAMS) in the Portuguese population with multiple sclerosis. BMC Neurol. 2018, 18, 172. [Google Scholar] [CrossRef]
  66. Dusankova, J.B.; Kalincik, T.; Havrdova, E.; Benedict, R.H. Cross cultural validation of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Clin. Neuropsychol. 2012, 26, 1186–1200. [Google Scholar] [CrossRef]
  67. Costers, L.; Gielen, J.; Eelen, P.L.; Schependom, J.V.; Laton, J.; Remoortel, A.V.; Vanzeir, E.; Wijmeersch, B.V.; Seeldrayers, P.; Haelewyck, M.C.; et al. Does including the full CVLT-II and BVMT-R improve BICAMS? Evidence from a Belgian (Dutch) validation study. Mult. Scler. Relat. Disord. 2017, 18, 33–40. [Google Scholar] [CrossRef] [Green Version]
  68. Walker, L.A.; Osman, L.; Berard, J.A.; Rees, L.M.; Freedman, M.S.; MacLean, H.; Cousineau, D. Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS): Canadian contribution to the international validation project. J. Neurol. Sci. 2016, 362, 147–152. [Google Scholar] [CrossRef]
  69. Goretti, B.; Niccolai, C.; Hakiki, B.; Sturchio, A.; Falautano, M.; Minacapelli, E.; Martinelli, V.; Incerti, C.; Nocentini, U.; Murgia, M.; et al. The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS): Normative values with gender, age and education corrections in the Italian population. BMC Neurol. 2014, 14, 171. [Google Scholar] [CrossRef] [Green Version]
  70. Sonder, J.M.; Burggraaff, J.; Knol, D.L.; Polman, C.H.; Uitdehaag, B.M. Comparing long-term results of PASAT and SDMT scores in relation to neuropsychological testing in multiple sclerosis. Mult. Scler. 2014, 20, 481–488. [Google Scholar] [CrossRef]
  71. Lopez-Gongora, M.; Querol, L.; Escartin, A. A one-year follow-up study of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT) in relapsing-remitting multiple sclerosis: An appraisal of comparative longitudinal sensitivity. BMC Neurol. 2015, 15, 40. [Google Scholar] [CrossRef] [Green Version]
  72. Strober, L.; Englert, J.; Munschauer, F.; Weinstock-Guttman, B.; Rao, S.; Benedict, R.H. Sensitivity of conventional memory tests in multiple sclerosis: Comparing the Rao Brief Repeatable Neuropsychological Battery and the Minimal Assessment of Cognitive Function in MS. Mult. Scler. 2009, 15, 1077–1084. [Google Scholar] [CrossRef]
  73. Portaccio, E.; Goretti, B.; Zipoli, V.; Siracusa, G.; Sorbi, S.; Amato, M.P. A short version of Rao’s Brief Repeatable Battery as a screening tool for cognitive impairment in multiple sclerosis. Clin. Neuropsychol. 2009, 23, 268–275. [Google Scholar] [CrossRef] [PubMed]
  74. Brochet, B.; Deloire, M.S.; Bonnet, M.; Salort-Campana, E.; Ouallet, J.C.; Petry, K.G.; Dousset, V. Should SDMT substitute for PASAT in MSFC? A 5-year longitudinal study. Mult. Scler. 2008, 14, 1242–1249. [Google Scholar] [CrossRef] [PubMed]
  75. Sadigh-Eteghad, S.; Abbasi Garravnd, N.; Feizollahi, M.; Talebi, M. The Expanded Disability Status Scale Score and Demographic Indexes Are Correlated with the Severity of Cognitive Impairment in Multiple Sclerosis Patients. J. Clin. Neurol. 2021, 17, 113–120. [Google Scholar] [CrossRef] [PubMed]
  76. Caneda, M.A.; Vecino, M.C. The correlation between EDSS and cognitive impairment in MS patients. Assessment of a Brazilian population using a BICAMS version. Arq. Neuro-Psiquiatr. 2016, 74, 974–981. [Google Scholar] [CrossRef] [Green Version]
  77. Smith, M.M.; Arnett, P.A. Dysarthria predicts poorer performance on cognitive tasks requiring a speeded oral response in an MS population. J. Clin. Exp. Neuropsychol. 2007, 29, 804–812. [Google Scholar] [CrossRef]
Biomedicines 10 02975 g001 550
Figure 1. Reliability and validity: Association of baseline cognitive test scores to its re-test scores (test-retest reliability) and to MS-related disability (EDSS, validity). Group difference in baseline cognitive test scores between MS patients and healthy controls (validity). ■ Comparison MS patients vs. healthy controls ▲ Correlation with EDSS ● Test–retest reliability. Abbreviations: BICAMS: Brief International Cognitive Assessment in Multiple Sclerosis; COWAT: Controlled Oral Word Association Test; SDMT: Symbol Digit Modalities Test; VLMT: Verbal Learning Memory Test; BVMT-r: Brief Visual Memory Test-revised; BDT: Block Design Test; PASAT: Paced Auditory Serial Addition Test.
Figure 1. Reliability and validity: Association of baseline cognitive test scores to its re-test scores (test-retest reliability) and to MS-related disability (EDSS, validity). Group difference in baseline cognitive test scores between MS patients and healthy controls (validity). ■ Comparison MS patients vs. healthy controls ▲ Correlation with EDSS ● Test–retest reliability. Abbreviations: BICAMS: Brief International Cognitive Assessment in Multiple Sclerosis; COWAT: Controlled Oral Word Association Test; SDMT: Symbol Digit Modalities Test; VLMT: Verbal Learning Memory Test; BVMT-r: Brief Visual Memory Test-revised; BDT: Block Design Test; PASAT: Paced Auditory Serial Addition Test.
Biomedicines 10 02975 g001
Table
Table 1. Demographics and clinical characteristics of both groups (MS; healthy controls).
Table 1. Demographics and clinical characteristics of both groups (MS; healthy controls).
Descriptive VariablesPatients with MS
(n = 66)		Healthy Controls (n = 22)p-Value
Age in years (Mean, SD)48.3 (10.3)49.8 (14.8)0.60 a
Sex (female/male)41/2511/110.31 b
Education years (Mean, SD)10.5 (2.4)11.8 (2.2)0.03 a
EDSS 0–3.0 (n)
EDSS 3–6.0 (n)
EDSS > 6.5 (n)		22
24
20
No therapy (n)26
First line therapy (n)18
Second line therapy (n)7
Third line therapy (n)9
Biotin (n)6
at-test for comparing independent means; b chi-square test of independence. SD: standard deviation; N: number; EDSS: Expanded Disability Status Scale; MS: multiple sclerosis; First line therapies: interferons, glatiramer acetate, teriflunomide, dimethyl fumarate; Second line therapies: fingolimod, siponimod, cladribine; Third line therapies: natalizumab, ocrelizumab.
Table
Table 2. Baseline and one year follow up cognitive tests results of MS patients and healthy controls.
Table 2. Baseline and one year follow up cognitive tests results of MS patients and healthy controls.
VariablesPatients with MS (n = 66)Healthy Controls (n = 22)
Baseline1 YearBaseline1 Year
Verbal episodic memory
 VLMT total correct, mean (SD)
 VLMT delayed recall, mean (SD)
51.3 (10.6)54.4 (10.4)59.3 (8.4)63.1 (7.9)
10.3 (3.3)11.0 (3.1)12.8(1.9)13.5 (1.8)
Visual episodic memory
 BVMT-R total correct, mean (SD)
 BVMT-R delayed recall, mean (SD)
21.1 (7.5)22.0 (5.3)26.4 (5.7)25.9 (6.5)
8.3 (2.6)8.2 (2.0)10.5 (1.6)9.6 (1.7)
Verbal attention-executive functions
 COWAT, mean (SD)
 PASAT-3 total correct, mean (SD)
28.7 (9.4)29.7 (9.1)39.5 (7.3)40.5 (7.2)
41.3 (13.8)43.5 (12.2)47.9 (6.3)47.8 (7.4)
Visual attention-executive functions
 SDMT total correct, mean (SD)
 BDT, mean (SD)
46.0 (13.6)48.1 (14.0)56.4 (8.2)55.5 (9.3)
43.1 (11.4)45.9 (11.4)52.0(10.2)52.4 (9.4)
SD: Standard Deviation; MS: multiple sclerosis, VLMT: Verbal Learning and Memory Test; BVMT-R: Brief Visual Memory Test-Revised; COWAT: Controlled Oral Word Association Test; SDMT: Symbol Digit Modalities Test; PASAT-3: Paced Auditory Serial Addition Test-3 s Version; BDT: Block Design Test.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Taranu, D.; Tumani, H.; Holbrook, J.; Tumani, V.; Uttner, I.; Fissler, P. The TRACK-MS Test Battery: A Very Brief Tool to Track Multiple Sclerosis-Related Cognitive Impairment. Biomedicines 2022, 10, 2975. https://doi.org/10.3390/biomedicines10112975

AMA Style

Taranu D, Tumani H, Holbrook J, Tumani V, Uttner I, Fissler P. The TRACK-MS Test Battery: A Very Brief Tool to Track Multiple Sclerosis-Related Cognitive Impairment. Biomedicines. 2022; 10(11):2975. https://doi.org/10.3390/biomedicines10112975

Chicago/Turabian Style

Taranu, Daniela, Hayrettin Tumani, Jill Holbrook, Visal Tumani, Ingo Uttner, and Patrick Fissler. 2022. "The TRACK-MS Test Battery: A Very Brief Tool to Track Multiple Sclerosis-Related Cognitive Impairment" Biomedicines 10, no. 11: 2975. https://doi.org/10.3390/biomedicines10112975

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop