The mediating role of trait mental fatigue in cognitive decline among PwMS: Implications for verbal memory and information processing speed

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Kartsidis, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5515189/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Trait mental fatigue (MF) and cognitive dysfunction significantly impair the quality of life in people with multiple sclerosis (PwMS), particularly impacting information processing speed (IPS) and verbal learning-memory (VL/M). We assessed 66 PwMS and 38 healthy controls (HC) via the oral form of the Symbol Digit Modalities Test (SDMT-Of) for IPS, the Greek Verbal Learning Test (GVLT) for VL/M, and the cognitive subscale of the Modified Fatigue Impact Scale (MFIS-c) for MF. This aimed at investigating the mediating role of MF in the relationship between IPS and VL/M in PwMS. PwMS performed significantly worse than HC across all domains. Mediation analysis, controlling for age, sex, education, disease duration, and MS-type, revealed a significant effect of IPS on VL/M in PwMS. This effect became non-significant once MF was introduced, whereas the indirect effect of IPS on VL/M through MF remained significant. No significant mediation effects were observed in HC, even after controlling for age, sex, and education, underscoring the unique impact of MF on MS. This study highlights the mediating role of trait MF in cognitive deficits among PwMS, suggesting that interventions targeting MF could enhance cognitive performance. The study is registered with ClinicalTrials.gov Identifier NCT04806568 ( https://www.clinicaltrials.gov/study/NCT04806568 ). Biological sciences/Neuroscience/Diseases of the nervous system/Multiple sclerosis Biological sciences/Neuroscience/Learning and memory Biological sciences/Neuroscience/Diseases of the nervous system/Neurodegeneration Cognitive Decline Mediation Analysis Mental Fatigue Multiple Sclerosis Information Processing Speed Verbal Episodic Memory-Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Mental fatigue (MF) and cognitive dysfunction are prevalent non-motor symptoms that significantly impair the quality of life (QoL) in people with multiple sclerosis (PwMS) 1 . MS is a neurodegenerative and inflammatory disease of the central nervous system (CNS), characterized by a wide range of symptoms and neuropsychiatric implications due to its varied impact on the brain 2 . This heterogeneity leads to highly individualized clinical profiles and disease trajectories 3 . Despite this heterogeneity, cognitive impairment is highly common in MS, affecting 40–70% of PwMS 3 . Although all cognitive domains can be impacted, episodic memory and information processing speed are the most frequently affected 4 . Several factors, including disease duration, and the extent of neurological disability 5 , as well as mood-related factors such as depression 6 and fatigue 7 , influence cognitive functioning in MS. Notably, mental fatigue (MF) is a significant contributor, with 76–97% of PwMS reporting it as a symptom 8 , and 40% identifying it as the most disabling 9 . Unlike in healthy individuals, cognitive lapses and MF are more frequent and severe in PwMS due to disease-related neurodegenerative processes 1 . To fully understand the impact of MF on MS, it is crucial to conceptualize it as a trait—a persistent, subjective difficulty in concentration and clear thinking 10 . Trait MF differs from state MF, which refers to a temporary decline in performance following cognitively demanding tasks 11 . Although both types are often assessed via self-reported scales, state MF is task-specific 12 , while trait MF reflects an individual’s overall cognitive capacity across time 12 . A recent study in a Greek MS population demonstrated that trait and state MF are distinct phenomena in MS 13 . This investigation specifically focused on trait MF, considering it a symptom that consistently affects the cognitive functioning of PwMS. Trait MF exacerbates cognitive deficits, diminishing QoL and complicating disease management for PwMS 1 , 14 . It negatively affects information processing speed (IPS) 7 , and verbal learning/memory (VL/M) 15 , two of the most impacted cognitive functions in MS 1 . IPS and VL/M feature a complex interplay, with deficits in IPS influencing VL/M 15 . Although evidence is scarce, the relationship between IPS, VL/M and trait MF is unsurprising, as slower IPS hinders downstream processes 15 . Specifically, a compromised IPS impacts VL/M, with trait MF intensifying these effects and leading to more severe memory deficits 7 , 15 . The ambiguity surrounding the relationship between IPS, VL/M and trait MF arises from theoretical and methodological issues in previous research, particularly the differentiation between subjectively assessed trait and state MF. When appropriate tools are used, PwMS with elevated trait MF show significant impairments in both IPS and VL/M compared with non-fatigued PwMS and healthy individuals 7 , 15 . However, prior studies have often relied on inadequate assessment tools, such as the Fatigue Severity Scale (FSS) 16 – 19 , which lacks precision in evaluating MF in PwMS 20 . Conversely, the Modified Fatigue Impact Scale (MFIS), is more sensitive in detecting physical and mental fatigue in highly fatigued populations 21 . Given the prevalence of MF in PwMS, we used the MFIS as our assessment tool to increase accuracy in identifying trait MF. Evidence suggests that IPS and VM/L are often confounded by trait MF when properly assessed 7 , 15 . Therefore, we aimed to explore the relationships among these prominent symptoms and clarify the cognitive processes underlying MS. We conducted neuropsychological assessments across multiple cognitive domains, to establish baseline differences between PwMS and healthy controls (HC), with a focus on IPS, VL/M, and trait MF. Our primary objective was to investigate whether self-reported trait MF mediates the relationship between IPS and VL/M, proposing a model to elucidate the interplay between these frequently reported symptoms. Furthermore, we examined this model in HC to determine whether trait MF differentially impacts cognitive functioning in PwMS, beyond the effects observed in healthy individuals. Methods Participants PwMS and HC were recruited as part of MS-NEUROPLAST 22 , a randomized controlled trial registered at ClinicalTrials.gov under identifier code NCT04806568 (https://www.clinicaltrials.gov/study/NCT04806568). The study was approved by the Aristotle University of Thessaloniki Research Ethics Committee (Registration No. 254350/2020) and all procedures conformed to the principles outlined in the Helsinki Declaration of Human Rights. All participants received a participant information sheet and a consent form. The participants provided their written informed consent before enrollment and data were collected with institutional ethical approval. PwMS were aged 18-65 years with a confirmed clinically definite diagnosis according to the 2017 revised McDonald criteria 23 . MS type (relapsing or progressive) was classified based on Lublin’s criteria 24 . Further inclusion criteria included an Expanded Disability Status Scale (EDSS) score 25 at screening of up to 6.5, neurological stability for at least one month before screening, and stable disease modifying treatment (DMT) for at least 6 months. Exclusion criteria included other demyelinating or significant CNS conditions, active immune system diseases, severe psychiatric disorders affecting compliance with the study protocol, and the inability to undergo magnetic resonance imaging (MRI) and electroencephalography (EEG) measurements. HC were required to have normal hearing and normal or corrected-to-normal vision. The exclusion criteria were any neurological, mental, developmental, psychiatric, or physical disorders, unrecovered neurological disorders (e.g., stroke, traumatic brain injury), unstable medication within the last three months, use of CNS drugs (e.g., β-blockers), concurrent participation in another relevant study, and inability or unwillingness to undergo EEG measurements. Outcome measures Neuropsychological battery and patient-reported outcome measures All participants underwent an extensive neuropsychological assessment to evaluate their cognitive functioning and completed patient-reported outcome measures (PROMs) to assess their psychological state. All assessments used in this study are validated for the Greek population. Detailed descriptions of these measures are available in the MS-NEUROPLAST Clinical Trial Protocol 22 and summarized in Table 1. Tests used in the mediation model Our primary goals were to compare IPS, VL/M, and levels of MF between HC and PwMS, and to investigate whether trait MF mediates the relationship between IPS and VL/M. To achieve this, we used the following standardized tests: Greek Verbal Learning Test (GVLT): The GVLT 26 , a Greek adaptation of the California Verbal Learning Test (CVLT) 27 , assesses VL/M through word recall across 5 trials 26 . The total immediate recall score was used for comparing VL/M performance between PwMS and HC and as the independent variable in the mediation model. Symbol Digit Modalities Test-Oral form (SDMT-Of): The SDMT-Of 28 , validated in the Greek population 29 , measures IPS and is commonly used in PwMS to minimize potential motor-related confounds 29 . The total number of correct symbol-digit pairings served as the IPS measure and the dependent variable in the mediation model. Modified Fatigue Impact Scale (MFIS): The MFIS, a modified form of the Fatigue Impact Scale 30 , includes 21 items across physical, cognitive, and psychosocial 30 subscales. The Greek version includes the cognitive fatigue and physical fatigue 31 subscales. The cognitive subscale was used to assess trait MF, the mediating variable in our model. [Insert Table 1 here] Statistical analysis All data analyses were conducted via the IBM SPSS Statistics, version 22.0 (IBM Corp, Armonk, NY). Group comparisons for the neuropsychological assessments and PROMs were performed via two-sided independent t-tests or Mann-Whitney U tests, depending on data distribution, with normality assessed via the Shapiro-Wilk test. The significance level for the analyses was set at α = 0.05. To explore the potential effects of the MF on the IPS-VL/M interaction, a mediation analysis (model 4) was conducted using Hayes’ PROCESS macro 32 , which investigates the total, direct and indirect effects between the independent (X), mediator (M), and dependent (Y) variables 32 . The total effect represents a simple regression between X and Y, excluding M. The direct effect examines how variations in X impact Y with M held constant. The indirect effect explores how changes in X influence M, which then affects Y through the X → M → Y path (Figure 1). Multiple comparisons were corrected via percentile bootstrapping (10,000 iterations) to estimate 95% confidence intervals (CIs), with a coefficient considered significant if its CI (lower and upper CI levels) did not include 0. The mediation model was applied to both the PwMS and HC groups. The analysis was adjusted for age, sex, years of education, MS type and disease duration in PwMS and for age, sex and years of education in HC. [Insert Figure 1 here] Results Study sample The characteristics of the PwMS and HC groups are summarized in Table 2. The two groups were age- and sex-matched, with PwMS having significantly fewer years of education, with a median difference of 4 years. The study sample included 66 PwMS, 55 of whom were females (82.09%) The average age of PwMS was 42.00 (±11.61) years, with a median of 14 (±2) years of education. The mean EDSS score was 3.20 (±1.56). Among the PwMS, 47 had relapsing-remitting MS (RRMS), and 19 had progressive MS (PMS). The mean disease duration was 10.97 (± 9.37) years. The HC group consisted of 38 participants, 26 females (68.42%), with a mean age of 37.71 (±9.13) years, and a median of 18 (±3.12) years of education. [Insert Table 2 here] Between-group comparisons A comparison of performance on all neuropsychological tests and PROMs was conducted between the PwMS and the HC, with a focus on IPS (SDMT-Of), VL/M (GVLT) and trait MF (MFIS-c). Compared with HC, PwMS significantly underperformed on all tests ( p<0.05 ) (Table 3, Figure 2). [Insert Table 3 here] [Insert Figure 2 here] Mediation analysis results for PwMS The results of the mediation analysis involving IPS, VL/M, and trait MF are summarized in Table 4, with regression coefficients superimposed on the statistical diagram of the model in Figure 3. The total effect of IPS on VL/M, a simple regression between these two variables adjusted for age, sex, years of education, MS type and disease duration was positive and significant (c: β = 0.262, 95% CI = [.031, .493]). The direct effect of IPS on VL/M, which investigates whether this relationship is independent of MF, was positive but non-significant (c΄: β = .137, 95% CI = [-.105, .380]) (Table 4, Figure 3). [Insert Table 4 here] Interestingly, the relationship between IPS and VL/M changes in the presence of MF. The indirect effect of IPS on VL/M through the IPS→MF→VL/M path was positive and significant (ab: β = .125, 95% CI = [.015, .248]). The individual regression paths (a-path: IPS→MF and b-path: MF→VL/M) indicate that PwMS with decreased IPS had higher levels of MF (a: β = -.298, 95% CI = [-.480, -.116]) which, in turn, negatively affected verbal memory performance (b: β = -.419, 95% CI = [-.753, -.830]) (Table 4, Figure 3). The fact that the direct effect of IPS on VL/M becomes non-significant after introducing MF as the mediator, whereas the indirect effect remains significant, indicates that MF fully mediates the relationship between IPS and VL/M. A graphical representation of the mediation effects is provided in Figure 4. The figure, generated via Python, visualizes the relationships between IPS (x-axis), VL/M (y-axis), and MF (z-axis) for enhanced interpretation of the mediation model. [Insert Figure 3 here] [Insert Figure 4 here] Mediation analysis results for HC The results of the mediation analysis involving IPS, VL/M, and MF in HC are summarized in Table 5, with regression coefficients superimposed on the statistical diagram of the model in Figure 5. The total effect of IPS on VL/M, a simple regression between these two variables adjusted for age, sex and years of education, was positive but non-significant (c: β = .216, 95% CI = [-.239, .672]). Similarly, the direct effect of IPS on VL/M, which investigates whether this relationship is independent of MF, was also positive and non-significant (c΄: β = .213, 95% CI = [-.249, .675]) (Table 5, Figure 5). [Insert Table 5 here] In contrast to PwMS, the relationship between IPS and VM/L was not affected by the introduction of MF as a mediator. The indirect effect of IPS on VL/M through the IPS→MF→VL/M path remained positive but non-significant (ab: β = .032, 95% CI = [-.046, .091]). The individual regression paths (a-path: IPS→MF and b-path: MF→VL/M) indicated that HC with decreased IPS did not exhibit higher levels of MF (a: β = .079, 95% CI = [-.191, .348]) and their verbal memory performance was unaffected (b: β = -.157, 95% CI = [-.889, .573]). These findings suggest that IPS and VL/M are independent in HC and that MF does not mediate or influence this relationship or affect its direction and intensity in HC (Table 5, Figure 5). A graphical representation of the mediation effects for HC is provided in Figure 6. The figure generated via Python visualizes the relationships between IPS (x-axis), VL/M (y-axis), and MF (z-axis) for enhanced interpretation of the mediation model. [Insert Figure 5 here] [Insert Figure 6 here] Discussion Our study examined the mediating role of trait mental fatigue (MF) in the relationship between information processing speed (IPS) and verbal learning-memory (VL/M) in PwMS and healthy individuals. We found that MF mediates the relationship between IPS and VL/M in PwMS, but no such mediation was observed in HC. Significant cognitive differences were noted between the two groups, supporting our hypothesis that trait MF is a unique symptom in MS, distinct from the fatigue experienced by healthy individuals. Our findings align with previous research showing that PwMS report more persistent and debilitating fatigue symptoms than their healthy counterparts do 33 , 34 . With our research, we clarify certain ambiguities in the literature concerning MF and cognitive functioning in MS 16 – 19 , by distinguishing between state and trait fatigue. While studies have focused predominantly on state fatigue, revealing notable differences in cognitive performance between PwMS and HC 11 , classifying MF solely as a temporary state may overlook its complex manifestations. Research has underscored the distinct nature of state and trait fatigue, emphasizing the need for different approaches in studying their impact on cognitive functioning in PwMS 13 , 35 – 38 . Trait MF can stem from CNS changes, including alterations in neurotransmitter levels, neural connectivity and brain activity patterns 35 , 37 . Conversely, state MF likely reflects deficiencies in cognitive reserves, neural effectiveness, and adaptive strategies during cognitive tasks 38 . This distinction is pronounced when both types of fatigue are examined within the same cohort 36 . For example, Genova et al., (2013) 36 reported similar overall brain activation patterns during state MF induction in both PwMS and HC. However, trait MF was associated with decreased white matter integrity exclusively in PwMS 36 . Since state MF activation patterns are commonly observed in the general population 39 and other clinical conditions such as chronic fatigue syndrome 34 , it is plausible that state MF is not unique to PwMS, with observed differences likely arising from varying baseline conditions, namely, the decreased cognitive abilities of PwMS 1 . Additionally, Genova et al. (2013) 36 noted that while trait MF predicted structural brain differences, it did not directly impact cognitive performance. They acknowledged a limitation in using the FSS, stating that, while widely employed 20 , it does not specifically target MF. In contrast, they recommended using a more tailored measure, specifically designed to evaluate MF, such as the MFIS 36 , which we employed in our study. The limitations of using the FSS in effectively capturing MF have been underscored in numerous studies, complicating the accurate assessment of trait MF 16 – 19 . These challenges, exacerbated by factors such as small sample sizes and the lack of appropriate control cohorts, have obscured a clearer understanding of how trait MF interacts with cognitive functioning in PwMS. Our findings, derived from a questionnaire well-suited for effectively identifying fatigue in susceptible cohorts 21 , address the constraints of previous studies. Additionally, our study advances the understanding of the relationships between prevalent and persistent cognitive symptoms in MS, specifically IPS, VL/M and trait MF 1 . While prior studies have focused predominantly on direct one-to-one relationships, oversimplifying the complexity of the observed impairments 7 , 15 , our study introduces a more comprehensive model that integrates the most prominent symptoms experienced by PwMS. By incorporating subjective experiences of MF with objective measures of cognitive functioning, our model enables a more nuanced evaluation of the cognitive status of PwMS and clarifies how persistent fatigue can influence cognitive domains. Our study is subject to certain limitations. First, there was a difference in educational levels between PwMS and HC, with the latter exhibiting higher attainment. This aligns with existing research indicating that PwMS often have lower educational levels due to the challenges posed by disease progression 40 . Notably, PwMS with higher education (16–18 years) performed better on the administered tests, which is consistent with the notion that education plays a neuroprotective role in MS progression 40 . Second, our sample included individuals with both relapsing-remitting and progressive MS (RRMS = 47; PMS = 19). While this diversity could introduce variability, RRMS, being the most common type 41 , constituted most of the sample. We controlled for MS type, and the expected performance pattern emerged, indicating that the participants with PMS did not disproportionally influence our results. Additionally, we ensured sample stability by accounting for potential relapses, minimizing the impact of acute disease episodes on our findings. Another aspect worthy of mention is the relatively high performance of PwMS on GVLT. Given that participants had previously been assessed with this specific tool, familiarity might have influenced the outcomes. Nonetheless, this factor does not undermine the validity of our results; if anything, lower scores would have reinforced our model, as we further controlled for disease duration to minimize this potential effect. Finally, we did not control for specific medications that may influence fatigue, as the efficacy of DMTs in managing MF remains inconclusive 42 . Although some DMTs may have a beneficial effect on MF, these findings are preliminary and lack consensus 42 . If validated, the effects of DMT on MF would warrant inclusion in our model. Nonetheless, participants maintained stable medication for at least six months before and during the study. Future research could help clarify the relationship between DMTs and MF, enabling a more refined model to incorporate validated DMT effects. Our study demonstrated that trait MF mediates the relationship between IPS and VL/M in PwMS, an effect not observed in healthy individuals. This highlights trait MF as a unique and persistent symptom in MS, that significantly affects cognitive functioning. Our results emphasize the imperative for tailored interventions to enhance IPS and reduce MF, potentially improving VL/M outcomes. Cognitive training software aimed at enhancing IPS, combined with behavioral or pharmaceutical strategies to reduce MF, could effectively alleviate the memory difficulties faced by PwMS. Additionally, our model may aid in identifying vulnerability factors in PwMS, and future research could refine it by incorporating neuroimaging data and exploring differences across MS subtypes. Ultimately, our findings underscore the importance of targeted strategies to address the complex cognitive symptoms in PwMS. Declarations Competing Interests All the authors except AA, PEK, IN, and CS declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. AA, PEK, IN, and CS disclose that they were supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 314). The funder did not influence the study design, data collection, management, analysis and interpretation, writing of this protocol or the decision to submit the article for publication. All the remaining authors declare no conflict of interest. Funding This research project is supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 314). Author Contribution CS secured funding. PDB provided resources. CS, IN, PEK, NG and PDB contributed to the conception of the clinical study protocol. CS, IN, MK, PEK, AL, NG and PDB contributed to the design of the clinical study protocol. IN and NG contributed to the recruitment and medical evaluation of the patients. NT and MK performed the neuropsychological assessment. NT developed the research question, designed methodologies, performed the analysis and wrote the first draft of the manuscript. AA and PEK contributed to methodology implementation and statistical analysis. NT, AA and CS prepared Figures 1-6. NT prepared Tables 1-5. CS supervised the study. NT, AA, PEK, MK, AL, IN, NG, PDB and CS wrote sections of the manuscript. All authors reviewed the manuscript and approved the submitted version. Acknowledgement The authors extend their gratitude to the nursing staff of the Multiple Sclerosis Centre at AHEPA University General Hospital in Thessaloniki for their assistance with patient recruitment. We also thank all patients and healthy volunteers for their participation. Data Availability The datasets of this study are not publicly available owing to the sensitive nature of the data and concerns of the General Data Protection Regulation (GDPR) but are available from the corresponding author upon reasonable request. References Benedict, R. H. B. et al. Predicting quality of life in multiple sclerosis: Accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. J. Neurol. Sci. 231 , 29–34 (2005). Amato, M. P. et al. Treatment of cognitive impairment in multiple sclerosis: position paper. J. 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Brain Res. 1252 , 152–160 (2009). Bjørnevik, K. et al. Level of education and multiple sclerosis risk after adjustment for known risk factors: The EnvIMS study. Mult. Scler. 22 , 104–111 (2016). Loma, I. & Heyman, R. Multiple Sclerosis: Pathogenesis and Treatment. Curr. Neuropharmacol. 9 , 409–416 (2011). Elkhooly, M., Bao, F. & Bernitsas, E. Impact of Disease Modifying Therapy on MS-Related Fatigue: A Narrative Review. Brain Sci. 14 , 4 (2024). Tables Table 1. The neuropsychological tests and PROMs used in the current study. Neuropsychological Assessment Domain Mini-Mental State Exam (MMSE) Overall cognitive function Greek Verbal Learning Test (GVLT)* Verbal episodic memory and learning Symbol Digit Modalities Test-Oral Form (SDMT-Of)* Information processing speed Brief Visuospatial Memory Test-Revised (BVMT-R) Visuospatial memory Stroop Neuropsychological Screening Test (SNST) Executive functions Digit Span total score (DStotal) Executive functions (working memory, attention) Verbal Fluency test (XSA, AFO) Phonetic and semantic verbal processing Greek Attenuation Test (GAT) Premorbid abilities Cognitive Reserve Index questionnaire (CRIq) Cognitive reserve Clock Drawing Test (CDT)** Executive functions (spatial knowledge, visuo-constructive skills) Patient Reported Outcome Measures (PROMs) Modified Fatigue Impact Scale (MFIS) * Overall fatigue Modified Fatigue Impact Scale – Cognitive subscale (MFIS-c)* Mental/Cognitive fatigue Modified Fatigue Impact Scale – Physical subscale (MFIS-p) Physical fatigue Depression, Anxiety, Stress Scale (DASS-21) Mood Beck Depression Inventory – Fast Screen (BDI-FS) Non-health related depression European Quality of Life–5 Dimensions–5 Levels (EQ-5D-5L)** Quality of life Multiple Sclerosis Impact Scale-29 Items (MSIS-29)** Multiple sclerosis related symptoms Note: The tests marked with an asterisk (*) were included in the mediation model, while the remaining tests were used to assess the general cognitive and psychological status of PwMS in comparison to HC. The tests marked with two asterisks (**) were not included in the comparison analysis, as they were used exclusively in the assessment of PwMS. Table 2. Demographics and disease-related information of PwMS and HC. Significant differences between the groups ( p<0.05 ) are denoted with bold type. Variable PwMS (n = 66) HC (n = 38) p-value Age 42.00 (11.61) 37.71(9.13) 0.063 Education* 14(2) 18(3.12) <0.001 Sex (Females) 55 (82.09%) 26 (68.42) 0.133 EDSS 3.20 (1.56) − − Disease Duration 10.97 (9.37) − − MS Type RRMS 47 (71.21%) − − PMS 19 (28.79%) − − Note: Values are presented as the mean (SD) or n (%). The mean and standard deviation are shown for most variables, while * denotes cases where the median and the interquartile range are provided. PwMS: People with Multiple Sclerosis; HC: Healthy Controls; n: number of participants; EDSS: Expanded Disability Status Scale; RRMS: Relapsing Remitting Multiple Sclerosis; PMS: Progressive Multiple Sclerosis. Table 3 . Between-subject comparisons of cognitive performance and psychological status. Significant differences between the groups ( p<0.05 ) are denoted with bold type. PwMS HC t(df)/z Significance (two-sided p) Mean (SD) MMSE 28.56(1.45) 29.82(.46) -5.222(103) <.001 SDMT-Of 49.64(12.39) 63.82(11.22) -5.714(103) <.001 GVLT 52.76(12.77) 60.77(13.29) -3.203(103) .001 BVMT-R 23.81(7.65) 27.45(4.77) -2.245(103) .025 AFO 52.16(10.44) 66.26(9.74) -6.808(103) <.001 XSA 30.82(10.35) 41.55(8.12) -5.498(103) <.001 SNST 94.34(23.18) 122.26(18.23) -6.308(103) <.001 DStotal 24.65(4.18) 28.29(6.73) -3.416(103) <.001 GAT 39.43(5.49) 43.09(3.91) -4.111(103) <.001 DASS21 14.36(13.19) 9.60(9.32) -2.076(103) .036 BDI-FS 3.66(3.32) 2.47(2.95) -2.120(103) .034 CRIq 99.08(8.16) 108.20(8.16) -3.791(103) <.001 MFIS 32.60(18.23) 22.89(13.57) 2.829(102) .006 MFIS-c 16.49(9.72) 12.18(8.06) 2.063(102) .016 MFIS-p 16.38(9.97) 10.24(7.58) 3.378(102) .001 Note: For the MMSE, GVLT, BVMT-R, DASS-21, BDI-FS, GAT and CRIq, comparisons were made via the non-parametric Mann-Witney U test due to non-normal distribution. For the SDMT-Of, AFO, XSA, SNST, DStotal, MFIS, MFIS-c, and MFIS-p Independent Sample T-tests were applied. PwMS: People with Multiple Sclerosis; HC: Healthy Controls; t(df)/z: t-value (degrees of freedom) / z-value; SD: Standard Deviation. Table 4 . Summary of mediation analysis for PwMS. Significance, indicated by the absence of 0 in the CI, is denoted with bold type. Effect Path Coeff. (β) SE t 95% CI Lower Upper IPS on MF IPS → MF a -.298 .099 -.320 -.480 -.116 MF on VL/M MF → VL/M b -.419 .158 -2.120 -.753 -.830 Total IPS → VL/M c .262 .116 2.266 .030 .493 Indirect IPS → MF → VL/M ab .125 .059 — .015 .248 Direct IPS → VL/M c΄ .137 .121 1.136 -.106 .380 Note: IPS: Information Processing Speed; MF: Mental Fatigue; VL/M: Verbal Learning and Memory; Coeff.: Regression Coefficient; SE: Standard Error; t: t-value; CI: Confidence Interval. Table 5 . Summary of the mediation analysis for HC. Significance, indicated by the absence of 0 in the CI, is denoted with bold type. Effect Path Coeff. (β) SE t 95% CI Lower Upper IPS on MF IPS → MF a .079 .132 .682 -.191 .348 MF on VL/M MF → VL/M b -.157 .307 -.909 -.889 .573 Total IPS → VL/M c .216 .224 .965 -.239 .672 Indirect IPS → MF → VLM ab .003 .032 — -.046 .091 Direct IPS → VL/M c΄ .213 .227 0.938 -.249 .675 Note: IPS: Information Processing Speed; MF: Mental Fatigue; VL/M: Verbal Learning and Memory; Coeff.: Regression Coefficient; SE: Standard Error; t: t-value; CI: Confidence Interval. Additional Declarations Competing interest reported. All the authors except AA, PEK, IN, and CS declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. AA, PEK, IN, and CS disclose that they were supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 314). The funder did not influence the study design, data collection, management, analysis and interpretation, writing of this protocol or the decision to submit the article for publication. All the remaining authors declare no conflict of interest. Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 15 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers invited by journal 13 Mar, 2025 Editor assigned by journal 04 Mar, 2025 Editor invited by journal 09 Dec, 2024 Submission checks completed at journal 06 Dec, 2024 First submitted to journal 24 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5515189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388043192,"identity":"6a8a4df5-48bd-4385-ab14-2d65ca8b4e64","order_by":0,"name":"Nefeli Tsoukaki","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Nefeli","middleName":"","lastName":"Tsoukaki","suffix":""},{"id":388043194,"identity":"317eb1de-4211-4956-b120-d504f37e7a02","order_by":1,"name":"Alexandra Anagnostopoulou","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Anagnostopoulou","suffix":""},{"id":388043197,"identity":"0c1b2c43-6cf9-4746-87ae-b918f18d6859","order_by":2,"name":"Panagiotis E. Kartsidis","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"E.","lastName":"Kartsidis","suffix":""},{"id":388043199,"identity":"0da6fb73-206d-4a56-86eb-8ac91db3f8dd","order_by":3,"name":"Maria Karagianni","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Karagianni","suffix":""},{"id":388043201,"identity":"ff0a42e1-1eaa-4a13-9994-b63f21c8ad9a","order_by":4,"name":"Athanasia Liozidou","email":"","orcid":"","institution":"Scientific College of Greece","correspondingAuthor":false,"prefix":"","firstName":"Athanasia","middleName":"","lastName":"Liozidou","suffix":""},{"id":388043204,"identity":"c62bc5b1-ca70-4199-992f-2d9de8eb3cc6","order_by":5,"name":"Ioannis Nikolaidis","email":"","orcid":"","institution":"Hippokration General Hospital of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Nikolaidis","suffix":""},{"id":388043206,"identity":"c317fd71-0494-41b0-8346-80031da62ae1","order_by":6,"name":"Nikolaos Grigoriadis","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Nikolaos","middleName":"","lastName":"Grigoriadis","suffix":""},{"id":388043207,"identity":"e001728a-21f5-45ae-ad08-354ee4dc2272","order_by":7,"name":"Panagiotis D. Bamidis","email":"","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"D.","lastName":"Bamidis","suffix":""},{"id":388043208,"identity":"a82d3375-c959-41f8-921d-ccb674dc5f30","order_by":8,"name":"Charis Styliadis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLCCBwYMjG0MzAeAzAPEqGdmYEgAa2FLALKI1sLAwNjAwGNAnBb+/vPHJBIK7GT7+M98/Mz74w6DfDQBbRI3ktkkEgySjdskcjdL8yQ8YzA8l4Bfi4EEM0gLc2KbBO8GoJbDDIY9BBxmwH8YpKU+sY3/zOPfxGlhADvscGIbQw4b2BZ5HgJagH4xtkgwOA70S5qZ5Zy0wzwGhLTw9x98eOPDn2rZ+f2HH994Y3NYTp6Qw4CARQKZx2NwgLAW5g8oXPkGwlpGwSgYBaNgZAEABRs/tLZeWUsAAAAASUVORK5CYII=","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":true,"prefix":"","firstName":"Charis","middleName":"","lastName":"Styliadis","suffix":""}],"badges":[],"createdAt":"2024-11-24 17:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5515189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5515189/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-38027-3","type":"published","date":"2026-02-06T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71762995,"identity":"c7526b28-f5e9-4a7d-b22f-a7a7a41ccf01","added_by":"auto","created_at":"2024-12-18 11:12:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1033464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlock diagram of the mediation model for PwMS\u003c/strong\u003e. The\u003cstrong\u003e \u003c/strong\u003eblock diagram of the mediation model included IPS as the independent variable; VL/M as the dependent variable/outcome; MF as the mediator; and age, sex, years of education, MS type and disease duration as covariates. The arrows illustrate the influence of each variable on the other in the model, showing the direct effect of IPS on VL/M, the indirect effect through MF, and the total effect combining both paths (i.e., the direct and indirect effects). The covariates marked with an asterisk (*), C4 (MS Type) and C5 (Disease Duration) were used only in the mediation analysis for PwMS.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/5b65f8b310d77d9af2dd9966.png"},{"id":71763002,"identity":"18176d2d-6437-4cea-b5c4-87217256f62e","added_by":"auto","created_at":"2024-12-18 11:12:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":531092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of VL/M, IPS and MF between PwMS and HC\u003c/strong\u003e. Asterisks (*) indicate significant differences between the two groups with * denoting p\u0026lt;0.05 and ** denoting p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/ef94dbaad22e7e8d6c2f99eb.png"},{"id":71763004,"identity":"d13a48b4-d2d9-4d31-8c1e-493ee3da747d","added_by":"auto","created_at":"2024-12-18 11:12:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2054118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatistical diagram depicting the mediation model for PwMS\u003c/strong\u003e. The statistical diagram illustrates the main effects of the mediation model. The total effect (path c) represents the significant impact of IPS on VL/M. Path a depicts the effect of IPS on MF, whereas path b represents the effect of MF on VL/M. The direct effect (path c’) shows the effect of IPS on VL/M in the presence of the mediator MF. In this case, the direct effect is no longer significant, indicating that MF mediates the relationship between IPS and VL/M in PwMS. The significant indirect effect (path ab) confirms the presence of full mediation after correction for multiple comparisons. The analysis controlled for age (C1), sex (C2), years of education (C3), MS type (C4) and disease duration (C5).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/e4ad78f3fdaf410aa4dca9d0.png"},{"id":71763000,"identity":"dd781868-6a5a-497d-9107-d02ba75f263b","added_by":"auto","created_at":"2024-12-18 11:12:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3506727,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical presentation of the mediation effect for PwMS\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eThe red line illustrates the regression of IPS on VL/M, with MF as the mediator. The blue dashed lines and grey boundaries represent the 95% prediction intervals, indicating the expected range of VL/M outcomes for varying levels of IPS and MF, with a specified confidence level. As MF increases, both IPS and VL/M tend to decrease, highlighting a negative association where higher levels of MF are associated with poorer cognitive performance in both the IPS and VL/M domains, consistent with the expected pattern.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/674dcfcf6ed335a196963010.png"},{"id":71763005,"identity":"4753cd4b-3b0f-4e2e-b0e8-73223309f8f0","added_by":"auto","created_at":"2024-12-18 11:12:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2032869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatistical diagram depicting the mediation model for HC\u003c/strong\u003e. The statistical diagram illustrates the main effects of the mediation model. The total effect (path c) represents the non-significant relationship between IPS and VL/M. Path a illustrates the effect of the IPS on MF, whereas path b shows how MF influences VL/M. The direct effect (path c’) reflects the relationship between IPS and VL/M when MF is included as a mediator, but remains non-significant, indicating that MF does not mediate the relationship between IPS and VL/M in healthy individuals. The analysis controlled for the effects of age (C1), sex (C2), and years of education (C3).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/76dcc91561e3b7200c21246e.png"},{"id":71762997,"identity":"f7bc8d97-7758-4404-917c-cc953669231d","added_by":"auto","created_at":"2024-12-18 11:12:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2977700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical presentation of the mediation effect for HC\u003c/strong\u003e. The red line illustrates the regression of IPS on VL/M when MF is included as a mediator. The blue dashed lines and grey boundaries represent the 95% prediction intervals, indicating the expected range of VL/M outcomes for varying levels of IPS and MF with a specified confidence level. HC are predominantly concentrated at low to moderate levels of MF, whereas their IPS and VL/M remain relatively high. The performance distribution is closely aligned with the regression line, indicating that IPS and VL/M scores in HC are higher and largely independent of MF levels, further supporting the non-significant mediation effect.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/ec486baed900b9ca7a05f8bc.png"},{"id":102234265,"identity":"7296b197-cb1e-4182-b005-d7faa458c99a","added_by":"auto","created_at":"2026-02-09 16:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13188545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5515189/v1/bc7cefe8-fa70-4185-82b8-fac458796757.pdf"}],"financialInterests":"Competing interest reported. All the authors except AA, PEK, IN, and CS declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. AA, PEK, IN, and CS disclose that they were supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 314). The funder did not influence the study design, data collection, management, analysis and interpretation, writing of this protocol or the decision to submit the article for publication. All the remaining authors declare no conflict of interest.","formattedTitle":"The mediating role of trait mental fatigue in cognitive decline among PwMS: Implications for verbal memory and information processing speed","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental fatigue (MF) and cognitive dysfunction are prevalent non-motor symptoms that significantly impair the quality of life (QoL) in people with multiple sclerosis (PwMS)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. MS is a neurodegenerative and inflammatory disease of the central nervous system (CNS), characterized by a wide range of symptoms and neuropsychiatric implications due to its varied impact on the brain\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This heterogeneity leads to highly individualized clinical profiles and disease trajectories\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite this heterogeneity, cognitive impairment is highly common in MS, affecting 40\u0026ndash;70% of PwMS\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although all cognitive domains can be impacted, episodic memory and information processing speed are the most frequently affected\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral factors, including disease duration, and the extent of neurological disability\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, as well as mood-related factors such as depression\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and fatigue\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, influence cognitive functioning in MS. Notably, mental fatigue (MF) is a significant contributor, with 76\u0026ndash;97% of PwMS reporting it as a symptom\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and 40% identifying it as the most disabling\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Unlike in healthy individuals, cognitive lapses and MF are more frequent and severe in PwMS due to disease-related neurodegenerative processes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo fully understand the impact of MF on MS, it is crucial to conceptualize it as a trait\u0026mdash;a persistent, subjective difficulty in concentration and clear thinking\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Trait MF differs from state MF, which refers to a temporary decline in performance following cognitively demanding tasks\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although both types are often assessed via self-reported scales, state MF is task-specific\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, while trait MF reflects an individual\u0026rsquo;s overall cognitive capacity across time\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A recent study in a Greek MS population demonstrated that trait and state MF are distinct phenomena in MS\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This investigation specifically focused on trait MF, considering it a symptom that consistently affects the cognitive functioning of PwMS.\u003c/p\u003e \u003cp\u003eTrait MF exacerbates cognitive deficits, diminishing QoL and complicating disease management for PwMS\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. It negatively affects information processing speed (IPS)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and verbal learning/memory (VL/M)\u003csup\u003e15\u003c/sup\u003e, two of the most impacted cognitive functions in MS\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. IPS and VL/M feature a complex interplay, with deficits in IPS influencing VL/M\u003csup\u003e15\u003c/sup\u003e. Although evidence is scarce, the relationship between IPS, VL/M and trait MF is unsurprising, as slower IPS hinders downstream processes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Specifically, a compromised IPS impacts VL/M, with trait MF intensifying these effects and leading to more severe memory deficits\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ambiguity surrounding the relationship between IPS, VL/M and trait MF arises from theoretical and methodological issues in previous research, particularly the differentiation between subjectively assessed trait and state MF. When appropriate tools are used, PwMS with elevated trait MF show significant impairments in both IPS and VL/M compared with non-fatigued PwMS and healthy individuals\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, prior studies have often relied on inadequate assessment tools, such as the Fatigue Severity Scale (FSS)\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, which lacks precision in evaluating MF in PwMS\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Conversely, the Modified Fatigue Impact Scale (MFIS), is more sensitive in detecting physical and mental fatigue in highly fatigued populations\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Given the prevalence of MF in PwMS, we used the MFIS as our assessment tool to increase accuracy in identifying trait MF.\u003c/p\u003e \u003cp\u003eEvidence suggests that IPS and VM/L are often confounded by trait MF when properly assessed\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Therefore, we aimed to explore the relationships among these prominent symptoms and clarify the cognitive processes underlying MS. We conducted neuropsychological assessments across multiple cognitive domains, to establish baseline differences between PwMS and healthy controls (HC), with a focus on IPS, VL/M, and trait MF. Our primary objective was to investigate whether self-reported trait MF mediates the relationship between IPS and VL/M, proposing a model to elucidate the interplay between these frequently reported symptoms. Furthermore, we examined this model in HC to determine whether trait MF differentially impacts cognitive functioning in PwMS, beyond the effects observed in healthy individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003ePwMS and HC were recruited as part of MS-NEUROPLAST\u003csup\u003e22\u003c/sup\u003e, a randomized controlled trial registered at ClinicalTrials.gov under identifier code NCT04806568 (https://www.clinicaltrials.gov/study/NCT04806568). The study was approved by the Aristotle University of Thessaloniki Research Ethics Committee (Registration No. 254350/2020) and all procedures conformed to the principles outlined in the Helsinki Declaration of Human Rights. All participants received a participant information sheet and a consent form. The participants provided their written informed consent before enrollment and data were collected with institutional ethical approval.\u003c/p\u003e\n\u003cp\u003ePwMS were aged 18-65 years with a confirmed clinically definite diagnosis according to the 2017 revised McDonald criteria\u003csup\u003e23\u003c/sup\u003e. MS type (relapsing or progressive) was classified based on Lublin\u0026rsquo;s criteria\u003csup\u003e24\u003c/sup\u003e. Further inclusion criteria included an Expanded Disability Status Scale (EDSS) score\u003csup\u003e25\u003c/sup\u003e at screening of up to 6.5, neurological stability for at least one month before screening, and stable disease modifying treatment (DMT) for at least 6 months.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included other demyelinating or significant CNS conditions, active immune system diseases, severe psychiatric disorders affecting compliance with the study protocol, and the inability to undergo magnetic resonance imaging (MRI) and electroencephalography (EEG) measurements.\u003c/p\u003e\n\u003cp\u003eHC were required to have normal hearing and normal or corrected-to-normal vision. The exclusion criteria were any neurological, mental, developmental, psychiatric, or physical disorders, unrecovered neurological disorders (e.g., stroke, traumatic brain injury), unstable medication within the last three months, use of CNS drugs (e.g., \u0026beta;-blockers), concurrent participation in another relevant study, and inability or unwillingness to undergo EEG measurements.\u003c/p\u003e\n\u003ch2\u003eOutcome measures\u003c/h2\u003e\n\u003ch3\u003eNeuropsychological battery and patient-reported outcome measures\u003c/h3\u003e\n\u003cp\u003eAll participants underwent an extensive neuropsychological assessment to evaluate their cognitive functioning and completed patient-reported outcome measures (PROMs) to assess their psychological state. All assessments used in this study are validated for the Greek population. Detailed descriptions of these measures are available in the MS-NEUROPLAST Clinical Trial Protocol\u003csup\u003e22\u003c/sup\u003e and summarized in Table 1.\u003c/p\u003e\n\u003ch3\u003eTests used in the mediation model\u003c/h3\u003e\n\u003cp\u003eOur primary goals were to compare IPS, VL/M, and levels of MF between HC and PwMS, and to investigate whether trait MF mediates the relationship between IPS and VL/M. To achieve this, we used the following standardized tests:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eGreek Verbal Learning Test (GVLT):\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe GVLT\u003csup\u003e26\u003c/sup\u003e, a Greek adaptation of the California Verbal Learning Test (CVLT)\u003csup\u003e27\u003c/sup\u003e, assesses VL/M through word recall across 5 trials\u003csup\u003e26\u003c/sup\u003e. The total immediate recall score was used for comparing VL/M performance between PwMS and HC and as the independent variable in the mediation model.\u003c/li\u003e\n \u003cli\u003eSymbol Digit Modalities Test-Oral form (SDMT-Of):\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe SDMT-Of\u003csup\u003e28\u003c/sup\u003e, validated in the Greek population\u003csup\u003e29\u003c/sup\u003e, measures IPS and is commonly used in PwMS to minimize potential motor-related confounds\u003csup\u003e29\u003c/sup\u003e. The total number of correct symbol-digit pairings served as the IPS measure and the dependent variable in the mediation model.\u003c/li\u003e\n \u003cli\u003eModified Fatigue Impact Scale (MFIS):\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe MFIS, a modified form of the Fatigue Impact Scale\u003csup\u003e30\u003c/sup\u003e, includes 21 items across physical, cognitive, and psychosocial\u003csup\u003e30\u0026nbsp;\u003c/sup\u003esubscales. The Greek version includes the cognitive fatigue and physical fatigue\u003csup\u003e31\u0026nbsp;\u003c/sup\u003esubscales. The cognitive subscale was used to assess trait MF, the mediating variable in our model.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 1 here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAll data analyses were conducted via the IBM SPSS Statistics, version 22.0 (IBM Corp, Armonk, NY). Group comparisons for the neuropsychological assessments and PROMs were performed via two-sided independent t-tests or Mann-Whitney U tests, depending on data distribution, with normality assessed via the Shapiro-Wilk test. The significance level for the analyses was set at \u0026alpha; = 0.05.\u003c/p\u003e\n\u003cp\u003eTo explore the potential effects of the MF on the IPS-VL/M interaction, a mediation analysis (model 4) was conducted using Hayes\u0026rsquo; PROCESS macro\u003csup\u003e32\u003c/sup\u003e, which investigates the total, direct and indirect effects between the independent (X), mediator (M), and dependent (Y) variables\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe total effect represents a simple regression between X and Y, excluding M. The direct effect examines how variations in X impact Y with M held constant. The indirect effect explores how changes in X influence M, which then affects Y through the X \u0026rarr; M \u0026rarr; Y path (Figure 1). Multiple comparisons were corrected via percentile bootstrapping (10,000 iterations) to estimate 95% confidence intervals (CIs), with a coefficient considered significant if its CI (lower and upper CI levels) did not include 0.\u003c/p\u003e\n\u003cp\u003eThe mediation model was applied to both the PwMS and HC groups. The analysis was adjusted for age, sex, years of education, MS type and disease duration in PwMS and for age, sex and years of education in HC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 1 here]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStudy sample\u003c/h2\u003e\n\u003cp\u003eThe characteristics of the PwMS and HC groups are summarized in Table 2. The two groups were age- and sex-matched, with PwMS having significantly fewer years of education, with a median difference of 4 years. The study sample included 66 PwMS, 55 of whom were females (82.09%) The average age of PwMS was 42.00 (\u0026plusmn;11.61) years, with a median of 14 (\u0026plusmn;2) years of education. The mean EDSS score was 3.20 (\u0026plusmn;1.56). Among the PwMS, 47 had relapsing-remitting MS (RRMS), and 19 had progressive MS (PMS). The mean disease duration was 10.97 (\u0026plusmn; 9.37) years. The HC group consisted of 38 participants, 26 females (68.42%), with a mean age of 37.71 (\u0026plusmn;9.13) years, and a median of 18 (\u0026plusmn;3.12) years of education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 2 here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eBetween-group comparisons\u003c/h2\u003e\n\u003cp\u003eA comparison of performance on all neuropsychological tests and PROMs was conducted between the PwMS and the HC, with a focus on IPS (SDMT-Of), VL/M (GVLT) and trait MF (MFIS-c). Compared with HC, PwMS significantly underperformed on all tests (\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e) (Table 3, Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 3 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 2 here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eMediation analysis results for PwMS\u003c/h2\u003e\n\u003cp\u003eThe results of the mediation analysis involving IPS, VL/M, and trait MF are summarized in Table 4, with regression coefficients superimposed on the statistical diagram of the model in Figure 3.\u003c/p\u003e\n\u003cp\u003eThe total effect of IPS on VL/M, a simple regression between these two variables adjusted for age, sex, years of education, MS type and disease duration was positive and significant (c: \u0026beta; = 0.262, 95% CI = [.031, .493]). The direct effect of IPS on VL/M, which investigates whether this relationship is independent of MF, was positive but non-significant (c΄: \u0026beta; = .137, 95% CI = [-.105, .380]) (Table 4, Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 4 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterestingly, the relationship between IPS and VL/M changes in the presence of MF. The indirect effect of IPS on VL/M through the IPS\u0026rarr;MF\u0026rarr;VL/M path was positive and significant (ab: \u0026beta; = .125, 95% CI = [.015, .248]). The individual regression paths (a-path: IPS\u0026rarr;MF and b-path: MF\u0026rarr;VL/M) indicate that PwMS with decreased IPS had higher levels of MF (a: \u0026beta; = -.298, 95% CI = [-.480, -.116]) which, in turn, negatively affected verbal memory performance (b: \u0026beta; = -.419, 95% CI = [-.753, -.830]) (Table 4, Figure 3). The fact that the direct effect of IPS on VL/M becomes non-significant after introducing MF as the mediator, whereas the indirect effect remains significant, indicates that MF fully mediates the relationship between IPS and VL/M. A graphical representation of the mediation effects is provided in Figure 4. The figure, generated via Python, visualizes the relationships between IPS (x-axis), VL/M (y-axis), and MF (z-axis) for enhanced interpretation of the mediation model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 3 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 4 here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eMediation analysis results for HC\u003c/h2\u003e\n\u003cp\u003eThe results of the mediation analysis involving IPS, VL/M, and MF in HC are summarized in Table 5, with regression coefficients superimposed on the statistical diagram of the model in Figure 5.\u003c/p\u003e\n\u003cp\u003eThe total effect of IPS on VL/M, a simple regression between these two variables adjusted for age, sex and years of education, was positive but non-significant (c: \u0026beta; = .216, 95% CI = [-.239, .672]). Similarly, the direct effect of IPS on VL/M, which investigates whether this relationship is independent of MF, was also positive and non-significant (c΄:\u0026nbsp;\u0026beta; = .213, 95% CI = [-.249, .675]) (Table 5, Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 5 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to PwMS, the relationship between IPS and VM/L was not affected by the introduction of MF as a mediator. The indirect effect of IPS on VL/M through the IPS\u0026rarr;MF\u0026rarr;VL/M path remained positive but non-significant (ab: \u0026beta; = .032, 95% CI = [-.046, .091]). The individual regression paths (a-path: IPS\u0026rarr;MF and b-path: MF\u0026rarr;VL/M) indicated that HC with decreased IPS did not exhibit higher levels of MF (a: \u0026beta; = .079, 95% CI = [-.191, .348]) and their verbal memory performance was unaffected (b: \u0026beta; = -.157, 95% CI = [-.889, .573]). These findings suggest that IPS and VL/M are independent in HC and that MF does not mediate or influence this relationship or affect its direction and intensity in HC (Table 5, Figure 5). A graphical representation of the mediation effects for HC is provided in Figure 6. The figure generated via Python visualizes the relationships between IPS (x-axis), VL/M (y-axis), and MF (z-axis) for enhanced interpretation of the mediation model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 5 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 6 here]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e Our study examined the mediating role of trait mental fatigue (MF) in the relationship between information processing speed (IPS) and verbal learning-memory (VL/M) in PwMS and healthy individuals. We found that MF mediates the relationship between IPS and VL/M in PwMS, but no such mediation was observed in HC. Significant cognitive differences were noted between the two groups, supporting our hypothesis that trait MF is a unique symptom in MS, distinct from the fatigue experienced by healthy individuals. Our findings align with previous research showing that PwMS report more persistent and debilitating fatigue symptoms than their healthy counterparts do\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith our research, we clarify certain ambiguities in the literature concerning MF and cognitive functioning in MS\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, by distinguishing between state and trait fatigue. While studies have focused predominantly on state fatigue, revealing notable differences in cognitive performance between PwMS and HC\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, classifying MF solely as a temporary state may overlook its complex manifestations.\u003c/p\u003e \u003cp\u003eResearch has underscored the distinct nature of state and trait fatigue, emphasizing the need for different approaches in studying their impact on cognitive functioning in PwMS\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Trait MF can stem from CNS changes, including alterations in neurotransmitter levels, neural connectivity and brain activity patterns\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Conversely, state MF likely reflects deficiencies in cognitive reserves, neural effectiveness, and adaptive strategies during cognitive tasks\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis distinction is pronounced when both types of fatigue are examined within the same cohort\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. For example, Genova et al., (2013)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e reported similar overall brain activation patterns during state MF induction in both PwMS and HC. However, trait MF was associated with decreased white matter integrity exclusively in PwMS\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince state MF activation patterns are commonly observed in the general population\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and other clinical conditions such as chronic fatigue syndrome\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, it is plausible that state MF is not unique to PwMS, with observed differences likely arising from varying baseline conditions, namely, the decreased cognitive abilities of PwMS\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, Genova et al. (2013)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e noted that while trait MF predicted structural brain differences, it did not directly impact cognitive performance. They acknowledged a limitation in using the FSS, stating that, while widely employed\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, it does not specifically target MF. In contrast, they recommended using a more tailored measure, specifically designed to evaluate MF, such as the MFIS\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which we employed in our study.\u003c/p\u003e \u003cp\u003eThe limitations of using the FSS in effectively capturing MF have been underscored in numerous studies, complicating the accurate assessment of trait MF\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These challenges, exacerbated by factors such as small sample sizes and the lack of appropriate control cohorts, have obscured a clearer understanding of how trait MF interacts with cognitive functioning in PwMS.\u003c/p\u003e \u003cp\u003eOur findings, derived from a questionnaire well-suited for effectively identifying fatigue in susceptible cohorts\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, address the constraints of previous studies. Additionally, our study advances the understanding of the relationships between prevalent and persistent cognitive symptoms in MS, specifically IPS, VL/M and trait MF\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While prior studies have focused predominantly on direct one-to-one relationships, oversimplifying the complexity of the observed impairments\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, our study introduces a more comprehensive model that integrates the most prominent symptoms experienced by PwMS. By incorporating subjective experiences of MF with objective measures of cognitive functioning, our model enables a more nuanced evaluation of the cognitive status of PwMS and clarifies how persistent fatigue can influence cognitive domains.\u003c/p\u003e \u003cp\u003eOur study is subject to certain limitations. First, there was a difference in educational levels between PwMS and HC, with the latter exhibiting higher attainment. This aligns with existing research indicating that PwMS often have lower educational levels due to the challenges posed by disease progression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Notably, PwMS with higher education (16\u0026ndash;18 years) performed better on the administered tests, which is consistent with the notion that education plays a neuroprotective role in MS progression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSecond, our sample included individuals with both relapsing-remitting and progressive MS (RRMS\u0026thinsp;=\u0026thinsp;47; PMS\u0026thinsp;=\u0026thinsp;19). While this diversity could introduce variability, RRMS, being the most common type\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, constituted most of the sample. We controlled for MS type, and the expected performance pattern emerged, indicating that the participants with PMS did not disproportionally influence our results. Additionally, we ensured sample stability by accounting for potential relapses, minimizing the impact of acute disease episodes on our findings.\u003c/p\u003e \u003cp\u003eAnother aspect worthy of mention is the relatively high performance of PwMS on GVLT. Given that participants had previously been assessed with this specific tool, familiarity might have influenced the outcomes. Nonetheless, this factor does not undermine the validity of our results; if anything, lower scores would have reinforced our model, as we further controlled for disease duration to minimize this potential effect.\u003c/p\u003e \u003cp\u003eFinally, we did not control for specific medications that may influence fatigue, as the efficacy of DMTs in managing MF remains inconclusive\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Although some DMTs may have a beneficial effect on MF, these findings are preliminary and lack consensus\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. If validated, the effects of DMT on MF would warrant inclusion in our model. Nonetheless, participants maintained stable medication for at least six months before and during the study. Future research could help clarify the relationship between DMTs and MF, enabling a more refined model to incorporate validated DMT effects.\u003c/p\u003e \u003cp\u003eOur study demonstrated that trait MF mediates the relationship between IPS and VL/M in PwMS, an effect not observed in healthy individuals. This highlights trait MF as a unique and persistent symptom in MS, that significantly affects cognitive functioning. Our results emphasize the imperative for tailored interventions to enhance IPS and reduce MF, potentially improving VL/M outcomes. Cognitive training software aimed at enhancing IPS, combined with behavioral or pharmaceutical strategies to reduce MF, could effectively alleviate the memory difficulties faced by PwMS. Additionally, our model may aid in identifying vulnerability factors in PwMS, and future research could refine it by incorporating neuroimaging data and exploring differences across MS subtypes. Ultimately, our findings underscore the importance of targeted strategies to address the complex cognitive symptoms in PwMS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eAll the authors except AA, PEK, IN, and CS declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. AA, PEK, IN, and CS disclose that they were supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \u0026ldquo;2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers\u0026rdquo; (Project Number: 314). The funder did not influence the study design, data collection, management, analysis and interpretation, writing of this protocol or the decision to submit the article for publication. All the remaining authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research project is supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \u0026ldquo;2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers\u0026rdquo; (Project Number: 314).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCS secured funding. PDB provided resources. CS, IN, PEK, NG and PDB contributed to the conception of the clinical study protocol. CS, IN, MK, PEK, AL, NG and PDB contributed to the design of the clinical study protocol. IN and NG contributed to the recruitment and medical evaluation of the patients. NT and MK performed the neuropsychological assessment. NT developed the research question, designed methodologies, performed the analysis and wrote the first draft of the manuscript. AA and PEK contributed to methodology implementation and statistical analysis. NT, AA and CS prepared Figures 1-6. NT prepared Tables 1-5. CS supervised the study. NT, AA, PEK, MK, AL, IN, NG, PDB and CS wrote sections of the manuscript. All authors reviewed the manuscript and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to the nursing staff of the Multiple Sclerosis Centre at AHEPA University General Hospital in Thessaloniki for their assistance with patient recruitment. We also thank all patients and healthy volunteers for their participation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets of this study are not publicly available owing to the sensitive nature of the data and concerns of the General Data Protection Regulation (GDPR) but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenedict, R. H. B. et al. Predicting quality of life in multiple sclerosis: Accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. \u003cem\u003eJ. Neurol. Sci.\u003c/em\u003e \u003cb\u003e231\u003c/b\u003e, 29\u0026ndash;34 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmato, M. P. et al. Treatment of cognitive impairment in multiple sclerosis: position paper. \u003cem\u003eJ. 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Subjective feeling of psychological fatigue is related to decreased reactivity in ventrolateral prefrontal cortex. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cb\u003e1252\u003c/b\u003e, 152\u0026ndash;160 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026oslash;rnevik, K. et al. Level of education and multiple sclerosis risk after adjustment for known risk factors: The EnvIMS study. \u003cem\u003eMult. Scler.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 104\u0026ndash;111 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoma, I. \u0026amp; Heyman, R. Multiple Sclerosis: Pathogenesis and Treatment. \u003cem\u003eCurr. Neuropharmacol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 409\u0026ndash;416 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkhooly, M., Bao, F. \u0026amp; Bernitsas, E. Impact of Disease Modifying Therapy on MS-Related Fatigue: A Narrative Review. \u003cem\u003eBrain Sci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 4 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eThe neuropsychological tests and PROMs used in the current study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuropsychological Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMini-Mental State Exam (MMSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall cognitive function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreek Verbal Learning Test (GVLT)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVerbal episodic memory and learning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSymbol Digit Modalities Test-Oral Form (SDMT-Of)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInformation processing speed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrief Visuospatial Memory Test-Revised (BVMT-R)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVisuospatial memory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStroop Neuropsychological Screening Test (SNST)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExecutive functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDigit Span total score (DStotal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExecutive functions (working memory, attention)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVerbal Fluency test (XSA, AFO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhonetic and semantic verbal processing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGreek Attenuation Test (GAT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePremorbid abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive Reserve Index questionnaire (CRIq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClock Drawing Test (CDT)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExecutive functions (spatial knowledge, visuo-constructive skills)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Reported Outcome Measures (PROMs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModified Fatigue Impact Scale (MFIS) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModified Fatigue Impact Scale \u0026ndash; Cognitive subscale (MFIS-c)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental/Cognitive fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModified Fatigue Impact Scale \u0026ndash; Physical subscale (MFIS-p)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDepression, Anxiety, Stress Scale (DASS-21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeck Depression Inventory \u0026ndash; Fast Screen (BDI-FS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-health related depression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEuropean Quality of Life\u0026ndash;5 Dimensions\u0026ndash;5 Levels (EQ-5D-5L)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuality of life\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple Sclerosis Impact Scale-29 Items (MSIS-29)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple sclerosis related symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: The tests marked with an asterisk (*) were included in the mediation model, while the remaining tests were used to assess the general cognitive and psychological status of PwMS in comparison to HC. The tests marked with two asterisks (**) were not included in the comparison analysis, as they were used exclusively in the assessment of PwMS.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eDemographics and disease-related information of PwMS and HC. Significant differences between the groups (\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e) are denoted with bold type.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePwMS (n = 66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (n = 38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e42.00 (11.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e37.71(9.13)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.063\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eEducation*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e14(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e18(3.12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSex (Females)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e55 (82.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e26 (68.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.133\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eEDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.20 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eDisease Duration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e10.97 (9.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eMS Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eRRMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e47 (71.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003ePMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e19 (28.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Values are presented as the mean (SD) or n (%). The mean and standard deviation are shown for most variables, while * denotes cases where the median and the interquartile range are provided. PwMS: People with Multiple Sclerosis; HC: Healthy Controls; n: number of participants; EDSS: Expanded Disability Status Scale; RRMS: Relapsing Remitting Multiple Sclerosis; PMS: Progressive Multiple Sclerosis.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eBetween-subject comparisons of cognitive performance and psychological status. Significant differences between the groups (\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e) are denoted with bold type.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePwMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et(df)/z\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance (two-sided p)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e28.56(1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e29.82(.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-5.222(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eSDMT-Of\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e49.64(12.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e63.82(11.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-5.714(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eGVLT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e52.76(12.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e60.77(13.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-3.203(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eBVMT-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e23.81(7.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e27.45(4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-2.245(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eAFO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e52.16(10.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e66.26(9.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-6.808(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eXSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e30.82(10.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e41.55(8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-5.498(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eSNST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e94.34(23.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e122.26(18.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-6.308(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eDStotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e24.65(4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e28.29(6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-3.416(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e39.43(5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e43.09(3.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-4.111(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eDASS21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e14.36(13.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.60(9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-2.076(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eBDI-FS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e3.66(3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.47(2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-2.120(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCRIq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e99.08(8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e108.20(8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-3.791(103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMFIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e32.60(18.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e22.89(13.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2.829(102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMFIS-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16.49(9.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e12.18(8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2.063(102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMFIS-p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16.38(9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e10.24(7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3.378(102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: For the MMSE, GVLT, BVMT-R, DASS-21, BDI-FS, GAT and CRIq, comparisons were made via the non-parametric Mann-Witney U test due to non-normal distribution. For the SDMT-Of, AFO, XSA, SNST, DStotal, MFIS, MFIS-c, and MFIS-p Independent Sample T-tests were applied. PwMS: People with Multiple Sclerosis; HC: Healthy Controls; t(df)/z: t-value (degrees of freedom) / z-value; SD: Standard Deviation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSummary of mediation analysis for PwMS. Significance, indicated by the absence of 0 in the CI, is denoted with bold type.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoeff. (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eIPS on MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.480\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.116\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eMF on VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMF \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e-2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.753\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-.830\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.493\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; MF \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.248\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eDirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;IPS \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003ec΄\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e-.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: IPS: Information Processing Speed; MF: Mental Fatigue; VL/M: Verbal Learning and Memory; Coeff.: Regression Coefficient; SE: Standard Error; t: t-value; CI: Confidence Interval.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;5\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSummary of the mediation analysis for HC. Significance, indicated by the absence of 0 in the CI, is denoted with bold type.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoeff. (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eIPS on MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMF on VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eMF \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eIndirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eIPS \u0026rarr; MF \u0026rarr; VLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDirect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;IPS \u0026rarr; VL/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003ec΄\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: IPS: Information Processing Speed; MF: Mental Fatigue; VL/M: Verbal Learning and Memory; Coeff.: Regression Coefficient; SE: Standard Error; t: t-value; CI: Confidence Interval.\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Decline, Mediation Analysis, Mental Fatigue, Multiple Sclerosis, Information Processing Speed, Verbal Episodic Memory-Learning","lastPublishedDoi":"10.21203/rs.3.rs-5515189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5515189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrait mental fatigue (MF) and cognitive dysfunction significantly impair the quality of life in people with multiple sclerosis (PwMS), particularly impacting information processing speed (IPS) and verbal learning-memory (VL/M). We assessed 66 PwMS and 38 healthy controls (HC) via the oral form of the Symbol Digit Modalities Test (SDMT-Of) for IPS, the Greek Verbal Learning Test (GVLT) for VL/M, and the cognitive subscale of the Modified Fatigue Impact Scale (MFIS-c) for MF. This aimed at investigating the mediating role of MF in the relationship between IPS and VL/M in PwMS. PwMS performed significantly worse than HC across all domains. Mediation analysis, controlling for age, sex, education, disease duration, and MS-type, revealed a significant effect of IPS on VL/M in PwMS. This effect became non-significant once MF was introduced, whereas the indirect effect of IPS on VL/M through MF remained significant. No significant mediation effects were observed in HC, even after controlling for age, sex, and education, underscoring the unique impact of MF on MS. This study highlights the mediating role of trait MF in cognitive deficits among PwMS, suggesting that interventions targeting MF could enhance cognitive performance. The study is registered with ClinicalTrials.gov Identifier NCT04806568 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.clinicaltrials.gov/study/NCT04806568\u003c/span\u003e\u003cspan address=\"https://www.clinicaltrials.gov/study/NCT04806568\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e","manuscriptTitle":"The mediating role of trait mental fatigue in cognitive decline among PwMS: Implications for verbal memory and information processing speed","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 11:04:03","doi":"10.21203/rs.3.rs-5515189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-04T07:10:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T20:47:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T11:37:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265610162039861357414145969116846552248","date":"2025-03-15T10:29:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103809577263896851577947598821672207319","date":"2025-03-13T14:10:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-13T09:50:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-05T04:20:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-12-09T13:21:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-06T11:54:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-24T17:30:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de318788-c654-4613-a00c-c158f1c95b66","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41350195,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Multiple sclerosis"},{"id":41350197,"name":"Biological sciences/Neuroscience/Learning and memory"},{"id":41350199,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Neurodegeneration"}],"tags":[],"updatedAt":"2026-02-09T16:06:02+00:00","versionOfRecord":{"articleIdentity":"rs-5515189","link":"https://doi.org/10.1038/s41598-026-38027-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-06 15:57:33","publishedOnDateReadable":"February 6th, 2026"},"versionCreatedAt":"2024-12-18 11:04:03","video":"","vorDoi":"10.1038/s41598-026-38027-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38027-3","workflowStages":[]},"version":"v1","identity":"rs-5515189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5515189","identity":"rs-5515189","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}