Abstract
Background and Objectives:
To compare the cognitive functions and trait anxiety in patients diagnosed with relapsing–remitting multiple sclerosis (RRMS) to those of matched healthy controls (HCs). In addition, the study aims to investigate the correlations among cognitive functions, anxiety, depression, and quality of life (QOL) in this clinical population.
Methods:
The sample included RRMS patients (n = 21) and an equal number of age-, education-, and sex-matched HCs. Participants were assessed on the Indian version of the Wechsler Adult Intelligence Scale-IV, auditory and visual learning and memory, and visual–spatial construction and memory. RRMS patients were also assessed for levels of anxiety, depression, and their QOL. Comparative analyses between RRMS patients and HCs were carried out for neuropsychological assessments. Correlations among cognitive functions, anxiety, depression, and QOL in RRMS patients were examined.
Results:
RRMS patients showed significant deficits across various cognitive domains, including processing speed and verbal learning, compared to HCs (P < 0.05). In addition, they reported higher levels of trait anxiety compared to HCs (P < 0.01), along with moderate state anxiety and mild depression. A significant correlation among anxiety, depression, and QOL was observed in RRMS patients.
Conclusions:
This study highlights significant cognitive impairments and psychological distress experienced by RRMS individuals, underscoring the critical need for comprehensive care addressing both cognitive impairments and psychological distress to enhance QOL.
Keywords: Multiple sclerosis, state–trait anxiety, depression, quality of life
Introduction
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. The physical symptoms may include vertigo, blurred or double vision, numbness in limbs, tingling sensation, motor incoordination, and bowel and bladder difficulties. These can vary from being mild to severe. The most common form of MS, present in 85% of patients, is the relapsing–remitting multiple sclerosis (RRMS) type. RRMS is characterized by acute attacks of neurologic dysfunction followed by recovery of varying degree after each attack.[1]
Cognitive impairment is observed in 43%–70% of MS patients. Deficits in processing speed, working memory, verbal learning, and delayed recall have been widely reported.[2,3,4,5,6] A study on RRMS reported that 51.1% of the patients had cognitive dysfunction, including deficits in working memory, executive functions, verbal memory, and visual memory.[7] A few of the Indian studies on MS have similarly shown deficits in executive functions, working memory, visual memory, and verbal memory and language, with impact on patients’ quality of life (QOL).[8,9]
Patients with MS often undergo psychological challenges; this contributes significantly to the debilitating nature of this condition. Nearly 36% of MS patients are reported to have anxiety disorders,[10] and nearly 50% of patients have depression.[11] Nearly 21.40% of patients with RRMS have been reported to have anxiety and nearly 15.78% of patients having depression.[12] Anxiety as a predisposing trait has also been studied in RRMS.[12,13,14]
In RRMS patients, psychological distress and cognitive impairments have been reported to be positively related. Anxiety is known to be associated with poor non-verbal memory, and depression scores are negatively associated with attention/processing speed. High levels of anxiety and depression are associated with cognitive deficits such as processing speed, verbal learning, and delayed recall in MS patient.[15,16,17] It is important to note that treatment of mood symptoms may mitigate the effects on cognition and/or treatment of cognition may mitigate the effects on mood in MS.[18] RRMS patients with anxiety and depression had cognitive deficits in language, information processing, and memory and low QOL scores.[19,20,21]
Despite the intricate interplay between cognitive deficits, anxiety, depression, and their impacts on QOL among RRMS patients, a limited number of studies have been carried out so far examining these variables, of which only a handful are from the Indian subcontinent.[8,9] Therefore, this study aims to explore the nature and profile of cognitive deficits as well as trait anxiety in RRMS patients compared to matched healthy controls (HCs). The study also aims to examine the relationship between cognitive function, anxiety, depression, and QOL in RRMS patients, contributing to a more comprehensive understanding of MS management.
Methods
Patients and HCs
This is a cross-sectional study that included an age-, education-, and sex-matched HC group. Ethical approval was received from the Institutional Ethics Committee at the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru-29, India. Data collection was carried out between June 2019 and February 2020. Using a consecutive sampling approach, RRMS patients meeting the exclusion and inclusion criteria were included in the study. They were recruited from the inpatients and outpatients services under the department of neurology. Patients were also recruited from multiple sclerosis and neuroimmunology specialty clinics under the department of neurology in a tertiary center of South India. RRMS diagnosis was based on McDonald’s criteria[22] and was confirmed by the author NM, a neurologist specializing in demyelinating disorders. Written informed consent was obtained from all participants who were included in the study. Patients’ ages ranged from 18 to 45 years; they had normal or corrected vision, hearing sufficient to perform paper-and-pencil tests, and formal education of at least 7th standard. Patients were comfortable conversing in English or Hindi. Patients were excluded if they had any psychiatric disorder (except anxiety/depression secondary to MS), any neurologic/neurosurgical condition other than MS, a history of intellectual and developmental disability, or comorbid substance dependence. Forty-four RRMS patients were screened for recruitment, and 23 patients could not be included in the final study for various reasons [Figure 1].
Figure 1.
Screening and selection process of study participants: RRMS patients and healthy controls. RRMS: relapsing–remitting multiple sclerosis
An age-, sex-, and education-matched HC group from the community was included via snowball sampling. Participants were fluent in English or Hindi and had normal or corrected vision and hearing sufficient to perform paper-and-pencil tests. They had no history of any psychiatric or neurologic/neurosurgical disorder and no comorbid substance dependence. HCs were screened using Kessler Psychological Distress Scale (K-10) scale to rule out current significant psychological distress.[23] Thirty-six HCs were screened for recruitment, and 15 HCs could not be included in the final study for various reasons [Figure 1].
Assessment of cognitive functions
The following cognitive assessments were administered to patients and HCs: Indian version of the Wechsler Adult Intelligence Scale-IV (WAIS-IVIndia), comprising verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI), and full-scale intelligence quotient (FSIQ); auditory verbal learning test (AVLT)[24] comprising words designating familiar objects like tools, animals, vehicles, and body parts. There are two lists A and B, with different words in each list. In AVLT, the following were recorded: learning and information processing (Trial 5), interference learning (List B), immediate memory recall (IR), delayed memory recall (DR), long-term percent retention (LTPR; DR/Trial 5 score ×100), hit score (number of words correctly recognized on recognition trial), omissions (misses, on recognition trial), commissions (false alarm, on recognition trial). Visual construction, visual learning, and memory were assessed by the Complex Figure Test (CFT).[24,25] The test consists of a complex design that the participant has to recall by drawing it from memory. It consists of three aspects: copy trial to assess visual-constructive ability, IR to assess immediate visual memory, and DR to assess delayed visual memory.
Assessment of QOL
QOL was evaluated using the 54-item Multiple Sclerosis QOL (MSQOL-54) questionnaire.[26] This instrument encompasses two overarching subdomains: physical and mental QOL. Higher scores reflect better QOL, whereas lower scores signify poorer QOL.
Assessment of anxiety
The 40-item State–Trait Anxiety Inventory (STAI)[27] was administered. State anxiety comprises 20 items, reflecting transient feelings of apprehension and stress at the present moment, while trait anxiety includes 20 items, indicating enduring predispositions toward anxiety across situations. Higher scores on STAI denote greater levels of anxiety.
Assessment of depression
Depression was evaluated using the Patient Health Questionnaire (PHQ) that comprised nine items with four response categories.[28] Higher scores on PHQ denote greater levels of depression.
HCs were not assessed on the STAI-State scale, PHQ, and MSQOL-54, since they were screened and excluded from the study if K-10 showed significant current psychological distress. Adequate breaks were given in between the tests to alleviate examinee fatigue.
Statistical analysis
The univariate and multivariate normality of continuous variables was assessed using the Shapiro–Wilk test and the Doornik-Hansen test. Continuous variables were presented as mean/standard deviation (SD) or median/interquartile range (IQR), contingent upon their normality. Groupwise comparisons for each subtest were conducted using Student’s t-test for normally distributed variables or the Mann–Whitney U-test for non-normally distributed variables. Gender–group associations were examined using a chi-squared test. Pearson’s/Spearman’s rank correlation was utilized, based on variable’s normality, to explore the relationship between cognitive functions, anxiety, depression, and QOL among RRMS patients. False discovery rate adjustment was applied to P values to mitigate inflated type I error due to multiple comparisons in both comparison and correlation analyses using the Benjamini and Hochberg technique. We also used a nonparametric multivariate analysis of variance (MANOVA) test to check if the combinations of subtests for each of the tests, WAIS, AVLT, and CFT, were significantly different between patients and HCs. A P value of <0.05 was considered statistically significant. Data were analyzed using Statistical Package for the Social Sciences software (Version 29) and RStudio software (Version 2023.12.1).
Results
Demographic and clinical features
Patients (n = 21, M:F =9:12) were compared to sex-, age-, and education-matched HCs (n = 21, M:F =7:14). Patients had a median (IQR) age of 26 (21–38) years, and HCs also had a median (IQR) age of 26 (23–38) years. The mean number of years of formal education was 14.35 years ±2.51 for the patients and 15.14 years ±2.86 for HCs. There were no statistically significant (P < 0.05) differences in sex ratio (P = 0.525), age (P = 0.331), or education (P = 0.353) between the patients and HCs. The mean age at the onset of RRMS was 24.26 years ±7.43, the mean duration of illness was 4.60 years ±3.65, and the mean number of episodes was 2.35 ± 1.46.
Cognitive and psychological functions in patients with RRMS and HCs
Table 1 presents the results of cognitive and psychological functions for both patients and HCs. RRMS patients exhibited a significantly lower FSIQ score compared to HCs (mean ± SD of RRMS patients: 75.62 ± 12.35, of HCs: 84.57 ± 10.48; P value: 0.015). In addition, the median (IQR) WAIS-PSI score differed significantly between RRMS patients and HCs (RRMS: 78, IQR: 68–86; HCs: 94, IQR: 89–100; P < 0.01). The result of the nonparametric MANOVA showed that the combination of all four indices of WAIS differed significantly between patients and HCs (F1,40 = 3.54, P = 0.029). The AVLT scores for List B (mean ± SD: 5.88 ± 2.15), IR (mean ± SD: 10 ± 3.89), LTPR [median (IQR): 85.7 (73.33–100)], and commission [median (IQR): 0 (0–2)] were significantly lower in RRMS patients compared to HCs [List B: 8.05 ± 2.38, IR: 12.81 ± 1.66, LTPR: 100 (93.33–107.14), commission: 0 (0–0)], all at P < 0.05. When the combined subscales of AVLT were compared, the difference was found to be significant in patients compared to HCs (F1,40 = 9.068, P = 0.001). While the average scores for all three subtests of CFT trended lower in RRMS cases compared to HCs, the difference was not statistically significant. Trait anxiety was significantly higher among RRMS patients (mean ± SD: 45.47 ± 11.85) compared to HCs (mean ± SD: 36.71 ± 8.02) at P < 0.01. The patients exhibited moderate state anxiety with a mean ± SD score of 43.09 ± 13.93. RRMS patients exhibited mild depression, with a median (IQR) score of 6 (3–12). The average mean ± SD scores for physical and mental QOL among RRMS patients were 53.29 ± 24.25 and 57.17 ± 28.05, respectively.
Table 1.
Comparison of cognitive and psychological functions of RRMS patients and healthy controls
| Test | RRMS (n=21) | HC (n=21) | Standardized test statistics | Mean/median difference (95% CI) | Adjusted P |
|---|---|---|---|---|---|
| WAIS | F1,40=3.54a | 0.029* | |||
| VCI | 84.09±14.26 | 84.14±10.37 | -0.01 | -0.05 (−7.82-7.73) | 0.990 |
| PRI | 83 (72–88)b | 86 (79–92)b | -1.26c | −5 (-13–3)d | 0.283 |
| WMI | 78.85±11.49 | 85.48±12.54 | -1.78 | −6.62 (-14.12–0.88) | 0.164 |
| PSI | 78 (68–86)b | 94 (89–100)b | -3.46c | -14 (-24 to−6)d | 0.001** |
| Full-Scale IQ | 75.62±12.35 | 84.57±10.48 | -2.53 | −8.32 (-15.12 to 21.53) | 0.015* |
| AVLT | F1,40=9.07a | 0.001** | |||
| Trial 5 | 13 (9–15)b | 13 (12–15)b | -0.94c | -1 (-3–1)d | 0.470 |
| List B | 5.88±2.15 | 8.05±2.38 | -3.19 | -2.24 (-3.65 to -0.82) | 0.011* |
| IR | 10±3.89 | 12.81±1.66 | -3.04 | -2.80 (−4.67 to -0.94) | 0.011* |
| DR | 11 (7–14)b | 14 (13–14)b | -1.94c | -2 (−5–0)d | 0.084 |
| LTPR | 85.7 (73.33–100)b | 100 (93.33–107.14)b | -3.05c | -14.30 (-23.73 to−6.67)d | 0.011* |
| Hits | 15 (14–15)b | 15 (15–15)b | -0.83c | 0 (0–0)d | 0.471 |
| Omissions | 0 (0–1)b | 0 (0–0)b | 0.52c | 0 (0–0)d | 0.586 |
| Commissions | 0 (0–2)b | 0 (0–0)b | 2.52c | 0 (0–1)d | 0.022* |
| CFT | F1,40=2.53a | 0.092 | |||
| Construction | 36 (34–36)b | 36 (36–36)b | -2.12c | 0 (-1–0)d | 0.102 |
| IR | 16.21±9.36 | 20.11±5.53 | -1.64 | -3.90 (−8.70–0.89) | 0.160 |
| DR | 16.71±9.69 | 20.17±5.33 | -1.43 | -3.45 (−8.33–1.43) | 0.160 |
| STAI | |||||
| Trait | 45.47±11.85 | 36.71±8.02 | 2.80 | 8.76 (2.45–15.07) | 0.008** |
| State | 43.09±13.93 | - | - | - | - |
| PHQ | 6 (3–12) | - | - | - | - |
| MSQOL-54 | 58.74±22.53 | - | - | - | - |
| Physical | 53.29±24.25 | - | - | - | - |
| Mental | 57.17±28.05 | - | - | - | - |
Continuous variables are presented as mean±SD or median (25th, 75th). aF-test statistics obtained from MANOVA. bMedian (25th, 75th). cMann–Whitney U-test. dMedian difference (95% confidence interval). *P<0.05; **P<0.01. AVLT: Auditory Verbal learning Test, CFT: Complex Figure Test, CI: confidence interval, DR: delayed recall, HC: healthy control, IQ: intelligence quotient, IR: immediate recall, List B: interference list, LTPR: long-term percent retention, MANOVA: multivariate analysis of variance, MSQOL-54: Multiple Sclerosis Quality of Life, PHQ: Patient Health Questionnaire, PRI: Perceptual Reasoning Index, PSI: Processing Speed Index, RRMS: relapsing–remitting multiple sclerosis, SD: standard deviation, STAI: State–Trait Anxiety Inventory, VCI: Verbal Comprehension Index, WAIS: Wechsler Adult Intelligence Scale, WMI: Working Memory Index
Table 2 shows the correlations among cognitive functions, anxiety, depression, and QOL in RRMS patients. Strong negative correlations were evident between trait anxiety and QOL (physical QOL: r = -0.757; mental QOL: r = -0.885), state anxiety and QOL (physical QOL: r = -0.626; mental QOL: r = -0.877), and depression and QOL (physical QOL: rho = -0.816; mental QOL: rho = -0.913), all significant at P < 0.01. In addition, strong positive correlations were found between depression and anxiety (trait: rho =0.814; state: rho = 0.894); both were statistically significant at P < 0.01. The correlations between cognitive functions and state anxiety, trait anxiety, and depression were not statistically significant. No significant correlations were found between cognitive functions and QOL.
Table 2.
Correlation among cognitive functions, anxiety, depression, and quality of life in relapsing–remitting multiple sclerosis patients (n=21)
| Test | STAI (trait) | STAI (state) | PHQ | Physical QOL | Mental QOL |
|---|---|---|---|---|---|
| WAIS-IVIndia | -0.257 | -0.401 | -0.182 | 0.092 | 0.092 |
| VCI | -0.21a | -0.297a | -0.18a | 0.171a | 0.171a |
| PRI | -0.149 | -0.243 | 0.102a | 0.044 | 0.044 |
| WMI | -0.04 | -0.162 | -0.102a | -0.157 | -0.157 |
| PSI | -0.115a | -0.241a | -0.111a | 0.143a | 0.143a |
| AVLT | |||||
| Trial 5 | -0.102a | -0.09a | -0.212a | 0.382a | 0.207a |
| List B | -0.393 | -0.452 | -0.523a | 0.539 | 0.539 |
| IR | -0.036 | -0.176 | -0.202a | 0.295 | 0.295 |
| DR | -0.17a | -0.189a | -0.394a | 0.452a | 0.452a |
| LTPR | -0.316a | -0.235a | -0.276a | 0.425a | 0.330a |
| Hits | 0.01a | -0.146a | -0.309a | 0.243a | 0.188a |
| Omissions | -0.054a | 0.054a | 0.258a | -0.158a | -0.104a |
| Commissions | 0.027a | 0.144a | 0.124a | -0.188a | -0.212a |
| CFT | |||||
| Construction | -0.096a | -0.195a | -0.06a | 0.031a | 0.131a |
| IR | -0.19 | -0.319 | -0.188a | 0.183 | 0.183 |
| DR | -0.212 | -0.352 | -0.216a | 0.212 | 0.212 |
| STAI | |||||
| Trait | 0.814**a | - | - | ||
| State | 0.853** | - | 0.894**a | - | - |
| PHQ | - | - | - | -0.816**a | -0.913**a |
| MSQOL-54 | -0.771** | -0.728** | - | - | - |
| Physical | -0.757** | -0.626** | - | - | - |
| Mental | -0.885** | -0.877** | - | - | - |
aSpearman’s rank correlation. *P<0.05; **P<0.01. AVLT: Auditory Verbal learning Test, CFT: Complex Figure Test, DR: delayed recall, IR: immediate recall, List B: interference list, LTPR: long-term percent retention, MSQOL-54: Multiple Sclerosis Quality of Life, PHQ: Patient Health Questionnaire, PRI: Perceptual Reasoning Index, PSI: Processing Speed Index, QOL: quality of life, STAI: State–Trait Anxiety Inventory, VCI: Verbal Comprehension Index, WAIS-IVIndia: Indian version of the Wechsler Adult Intelligence Scale-IV, WMI: Working Memory Index
Discussion
The major findings of this study are as follows. Compared to HCs, patients with RRMS had significant deficits in various cognitive functions. Patients had significantly higher trait anxiety scores (compared to HCs), a moderate level of state anxiety symptoms, and a mild level of depressive symptoms. It was observed that physical and mental QOL were affected almost equally. Furthermore, significant correlations were observed among anxiety, depression, and QOL.
Cognitive functions in RRMS
In the present study, we found that the cognitive performance of RRMS patients differed significantly (compared to HCs) for the combination of WAIS indices (VCI, PRI, WMI, PSI) as a whole and specifically in PSI and FSIQ, where the drop in IQ is a derivative effect of deficits across the four cognitive indices. Similarly, the RRMS group differed significantly (compared to HCs) in combined subscales of AVLT and individual functions for interference learning, immediate auditory memory, and inhibition of false alarms in AVLT recognition trial. Alongside these deficits, deficit was also noted in LTPR on AVLT. Parallel to other research studies, the findings of the current study suggest that RRMS patients struggle with cognitive deficits in information processing speed, learning, and memory for auditory stimuli. However, contrary to other studies, our RRMS group did not show any significant deficits in CFT for visual construction and visual learning, and memory.[4,5,7,8,9,29]
Anxiety and depression in RRMS
Trait anxiety is deemed to be a stable personal quality or temperamental trait that describes someone’s vulnerability or predisposition to anxiety. RRMS patients in this study had a mean trait anxiety score of 45.47, which was significantly higher than that of controls (36.71, which is normal). Patients may have had high trait anxiety before the onset of RRMS, and this or other associated temperamental factors contributed to the acquisition of the disease, which is consistent with studies that found peculiar personality traits in RRMS patients, like neuroticism, conditioning them to develop neuropsychiatric conditions like anxiety and depression.[13] In contrast to trait anxiety, state anxiety is a measure of anxiety at a particular moment and would be expected to go up with disease. Our patients had a state anxiety mean score of 43.09, consistent with moderate state anxiety and in line with other studies.[12,14] Our patients’ mean score indicated mild depression at the time of evaluation.
QOL IN RRMS: The role of cognitive deficits, anxiety, and depression
As observed on MSQOL, patients’ mean mental health composite QOL score (57.17) was as low as their physical health composite score (53.29). In other words, for our RRMS patients, cognitive deficits and psychological distress affected QOL as much as the physical symptoms of the disease.
Furthermore, we found significant negative correlations between each of the psychological variables (trait anxiety, state anxiety, and depression) and each of the QOL variables (physical health composite, mental health composite). Other researchers likewise found that anxiety and depression are negatively associated with QOL.[19,30] Contrary to other studies, no significant correlation was found between cognitive and psychological functions and QOL.[31] However, we speculate that the larger sample size would help elicit the combined, negative synergistic influence of cognitive and psychological deficits on the QOL score.
The findings of this study underscore the importance of carrying out cognitive and psychological assessments of RRMS patients. The cognitive and psychological limitations of these patients are often overlooked by clinicians confronted with the significant physical devastation of the disease. Yet these nonphysical burdens are widespread in RRMS patients and undermine QOL almost as much as the physical burdens. Ameliorating the former burdens would be salutary. The greater understanding we have gained in recent years about the nature of these nonphysical burdens in RRMS patients can aid in the design of psychological intervention to help alleviate psychological burden and simultaneously improve cognitive underperformance and vice versa, with a consequent even greater boost to QOL.[32]
Strengths of the study
This study provides a comprehensive detailed investigation of how cognitive impairments and psychological deficits affect QOL in people with RRMS in the Indian population. This study contributes to the expansion of our knowledge regarding cognitive and psychological well-being as well as QOL in individuals with RRMS. The use of consecutive sampling provided a relatively unbiased sample. In addition, the study included a control group that matched the patients in terms of age, sex, and education, which makes the study findings more reliable. Moreover, the use of the entire WAIS-IVIndia battery provided robust information.
Limitations of the study
This study was conducted within a limited timeframe. As it was part of a nonfunded student’s time-bound dissertation, we could not look into the magnetic resonance imaging (MRI) data and examine the correlates. The study did not correlate the Expanded Disability Status Scale[33] scores, an important measure to assess how much a person is affected by MS, with the cognitive profile of the patient cohort. This is one of the key aspects that future studies must look into. Although this study successfully provided valuable insights, future studies with a larger sample size can further enhance the statistical power and broaden the generalizability of the findings. Due to the limited sample size, certain advanced statistical analyses, such as structural equation modeling, were not feasible. Incorporating such methods could provide deeper insights into the intricate pathways linking cognitive deficits, QOL, anxiety, and depression. Participants were recruited from a population seeking medical care, which may limit the generalizability of our results to broader community populations. In addition, the study’s cross-sectional design inherently restricts our ability to infer causality. Future studies could benefit from a longitudinal approach, including MRI data, allowing for a more comprehensive understanding of how these complex relationships evolve.
Conclusions
MS, being a neurologic condition, with significant and challenging physical symptoms (blurring or loss of vision, burning sensations in the body), may often lead to overlooking of psychological manifestations and neurocognitive deficits. Given that limited studies are available in the area of neurocognitive and psychological correlates in MS and that even fewer Indian studies are available in the given area, this comprehensive neuropsychological evaluation of an MS population gives insight into the neuropsychological difficulties that are encountered by these patients. Future studies could expand the investigation of these phenomena to other forms of MS Primary progressive multiple sclerosis (PPMS) and Secondary progressive multiple sclerosis (SPMS).
Author contributions
Vinayaka Yadav: data acquisition, analysis, and preparation of the first draft of the manuscript; Shantala Hegde: designed and conceptualized the study, interpreted the data, and revised the manuscript for intellectual content; Netravathi M: provided direction for sample acquisition, co-conceptualized the study, and revised the manuscript for intellectual content; Mariamma Phillip: provided consultation for statistical analysis; Lee Cranberg: provided inputs in interpretation of the data and preparation of the manuscript
Data availability statement
Research sample of patients was collected from the neurologic services of the wards and clinics (including the multiple sclerosis and neuroimmunology clinics) of the neurology department at our university hospital from South India catering to neurologic and neuropsychiatric services after the diagnosis was confirmed by the author NM, a neurologist specializing in demyelinating disorders. Healthy participants were screened and recruited from the general population. Due to confidentiality concerns, the datasets generated during the current study are not publicly available. They are available from the corresponding author based upon a formal data sharing agreement with NIMHANS, Bengaluru, India.
Financial support and sponsorship
The research was not funded by any commercial or nonprofit organization. SH is a Clinical and Public Health- 2018 Intermediate Fellow of the Welcome Trust-Department of Bio Technology India Alliance (IA/CPHI/17 / 1/503348).
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
Authors express their gratitude to all the participants of the study for their invaluable time.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Research sample of patients was collected from the neurologic services of the wards and clinics (including the multiple sclerosis and neuroimmunology clinics) of the neurology department at our university hospital from South India catering to neurologic and neuropsychiatric services after the diagnosis was confirmed by the author NM, a neurologist specializing in demyelinating disorders. Healthy participants were screened and recruited from the general population. Due to confidentiality concerns, the datasets generated during the current study are not publicly available. They are available from the corresponding author based upon a formal data sharing agreement with NIMHANS, Bengaluru, India.