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Current Journal of Neurology logoLink to Current Journal of Neurology
. 2025 Apr 4;24(2):143–153. doi: 10.18502/cjn.v24i2.20691

The relationship between quantitative magnetic resonance imaging markers and clinical/cognitive assessments in patients with multiple sclerosis: A cross-sectional study

Habib Farahmand 1, Amir Moghadam-Ahmadi 2, Parvin Khalili 3, Houshang Amiri 4, Alireza Vakilian 5,6, Fariba Khatami 7, Seyed Hamid Pakzad-Moghadam 8, Fatemeh Ayoobi 9
PMCID: PMC13183249  PMID: 42158308

Abstract

Background: This study examines the relationship between quantitative magnetic resonance imaging (MRI) markers and clinical/cognitive performance in patients with multiple sclerosis (MS), exploring the impact of MRI markers on disability, clinical status, and cognitive function.

Methods: This cross-sectional study recruited patients with MS from the MS registry center of Rafsanjan University of Medical Sciences, Rafsanjan, Iran. Informed consent was obtained from all participants (8 men, 57 women). Patients with MS underwent neuropsychological and clinical assessments using a word-pair learning task, the Wisconsin Card Sorting test (WCST), Tower of London test (TOL), Paced Auditory Serial Addition Test (PASAT), Multiple Sclerosis Functional Composite (MSFC), and the Expanded Disability Status Scale (EDSS). MRI markers were assessed by the neurologist and radiologist. Statistical significance was set at P < 0.05.

Results: Patients with plaques in the basal ganglia and thalamus had significantly different MSFC (P = 0.038) and PASAT (P = 0.010) scores, while higher EDSS scores correlated with T2-weighted-fluid-attenuated inversion recovery (T2-FLAIR) hyper-intense plaques (P = 0.025). T1 black hole plaques were associated with increased depression (P = 0.015). WCST scores were significantly higher in patients with infratentorial plaques (P = 0.006) and those with T1 black hole lesions (P < 0.05). Total plaque volume positively correlated with EDSS score (r = 0.386, P = 0.002) and word-pair learning (r = 0.254, P = 0.045), and negatively correlated with PASAT scores (r = -0.299, P = 0.017). Enhanced plaques correlated positively with TOL performance (r = 0.319, P = 0.010).

Conclusion: Memory decline and increased disability in patients with MS are associated with brain volume loss, increased plaque volume, and plaque location in the infratentorial region, basal ganglia, and thalamus. Enhanced plaques or T1 black hole lesions also contribute to cognitive impairment.

Key Words: Disability, Multiple Sclerosis, Cognitive Assessment, Quantitative Magnetic Resonance Imaging Markers, Plaque Location

Introduction

Multiple sclerosis (MS) is characterized as an inflammatory disease and destruction of myelin with the formation of lesions or plaques in the central nervous system (CNS), including the brain and spinal cord.1 The disease is considered a permanent or progressive disability because it impacts the white and grey matter of the CNS.2 Patients have a wide array of symptoms, including psychiatric, motor, and cognitive symptomatology.3,4 The Expanded Disability Status Scale (EDSS) is a MS quantifying disability method.5 However, it has some shortcomings in the measurement of upper organ disability and cognition. The Multiple Sclerosis Functional Composite (MSFC) is another disability measurement method with more emphasis on limb function and mental processing speed.6

The prevalence of cognitive disorders in patients with MS is estimated from 43% to 70%.7,8 MS affects numerous cognition sites and it can occur during the course of the disease.7 Information processing speed (IPS), attention, executive function, working memory, and long-term memory are the most common cognitive deficits in patients with MS.7,9 The results of two studies have presented the association between brain lesion loads on T2-weighted images and impaired cognitive functioning in patients with MS.10,11 The results of other studies of MS utilizing neuroimaging described the relation between cognitive impairments and various measures of cerebral hemispheres, including T2 lesion burden, cerebral atrophy, third ventricle width, corpus callosum size, and cortical lesions.12-15 Brain atrophy has been found in primary progressive MS (PPMS) and secondary progressive MS (SPMS).16,17 The accumulation of hypo-intense lesions (black holes) may correlate with disease progression and disability.18-20 Recent studies have reported that normalized total and cortical volume values were associated with depression and fatigue, disease duration, and disability in patients with MS.13,19 The results of one study showed that cerebellar grey matter damage and disruption of its connections contributed to cognitive dysfunction and physical disability in MS.21 A systematic review and meta-analysis presented that cortical lesion volume, brain volume, grey matter volume, and spinal cord magnetic resonance imaging (MRI) measurements might be reliable indicators of disability in patients with MS.22 Quantitative MRI markers at MS onset, specifically paramagnetic rim lesions and spinal cord lesions linked to physical disability and cortical lesions linked to cognitive impairment, can refine patient profiling and inform treatment decisions.23

Previous MRI studies examining disability in MS have been limited by focusing on specific brain regions, lesions, and EDSS.24,25 A key limitation is the lack of separate analyses correlating diverse quantitative MRI markers with clinical assessments.26 While prior research has established correlations between various brain and spinal cord regions with alternative disability measures like the Timed 25-Foot Walk (T25FW), Nine-Hole Peg Test (9-HPT), and MSFC, these associations with quantitative MRI markers remain largely unexplored.27,28 This study aims to comprehensively investigate the relationship between quantitative MRI markers and a broader range of clinical and cognitive assessments in patients with MS.

Materials and Methods

Participants: The patients were selected through the MS registry center of Rafsanjan University of Medical Sciences, Rafsanjan, Iran, from April to July 2024. The total number of patients in the registry was 250. Sampling was conducted by calling patients and obtaining their consent. Informed consents were collected from all patients (8 men and 57 women aged 18 to 50 years old) recruited before they participated in the study. The main reason for including such a high percentage of female patients was the significant preponderance of female patients with MS in comparison to male patients in our city (The ratio of female MS patients to male MS patients is more than 6 times higher), as well as the more agreement of female patients to be recruited in the study. Patients diagnosed with MS according to the revised McDonald diagnostic criteria as having relapsing-remitting MS (RRMS) or SPMS were admitted to this study.29 A trained examiner administered cognitive tests and disability assessments over two days, after which the patient underwent an MRI. The radiologist and neurologist measured the MRI indices. Their disability status was evaluated with the EDSS scale (eight functional systems ranging from 0 to 10). Higher scores indicate greater disability.5 The MSFC comprises the average of the z-scores on the T25FW, the 9-HPT, and the Paced Auditory Serial Addition Test (PASAT) with a 3-second interstimulus interval. Higher scores indicate a lesser degree of disability.30 The Ethics Committee of Rafsanjan University of Medical Sciences approved the protocol of the study (ethical code: IR.RUMS.REC.1401.137).

Excluding criteria were: (a) comorbid clinical conditions other than MS affecting the CNS, (b) physical or cognitive impeditive disabilities secondary to conditions other than MS, such as drugs or toxins, (c) an impeditive psychiatric illness, and (d) in the last six weeks, an MS attack treated with corticosteroids at high doses, or untreated.

MRI measurement and image analysis: 1.5 Tesla (1.5T) Siemens scanner (Essenza, Germany) carried out MRI experiments in the Ali Ibn Abitaleb Hospital in Rafsanjan City, located in the south of Iran. The imaging protocol comprised standard precontrast axial T2-weighted, axial T1-weighted, sagittal fluid-attenuated inversion recovery (FLAIR), and post-contrast T1-weighted images in three orthogonal planes through 0.1 mmol/kg intravenous (IV) administration of gadolinium. The number of lesions on T2-weighted scans, the total volume of gadolinium-enhancing lesions, volumes of lesions on T2-weighted scans, and brain volume were segmented by the Lesion Segmentation Tool (LST) toolbox version 2.0.15 for statistical parametric mapping (SPM).31 From magnetic resonance (MR) images, the typical locations of MS plaques were identified by a radiologist and neurologist, which included: lesions at the basal ganglia and thalamus, the periventricular and juxtacortical areas, the corpus callosum, the callososeptal interface, the infratentorial region, and the cervical cord (Figure 1).

Figure 1.

Figure 1

Typical locations of multiple sclerosis (MS) plaques. A) Enhanced plaques; B) Lesions at the basal ganglia and thalamus; C) Lesions at the periventricular and juxtacortical areas; D) Black hole; E) Involvement of the corpus callosum; F) Lesions at the callososeptal interface; G) Lesions at the infratentorial region; H) Lesion in the cervical cord

Procedures and evaluation tools: A trained assistant using the following tests conducted cognitive and neuropsychological assessments (all tests that have been evaluated for validity and reliability at Sina Institute of Cognitive Behavioral Sciences, Tehran, Iran):

(I) Word-pair learning task to measure verbal learning and working memory: There are seven pairs of unrelated words to read for participants, and then the participants must pronounce the pair of the first word. This test is repeated until the person can answer all pairs correctly. Higher scores indicate a memory performance impairment.32 The validity and reliability of this questionnaire have also been confirmed in Persian.33

(II) The Wisconsin Card Sorting Test (WCST) is able to measure attention, working memory, executive functioning, and visual processing. In the present study, six indices were analyzed: 1) the number of categories completed, 2) the number of perseverative errors (PE), 3) the number of non-PE (NPE), 4) the total number of errors (NE), 5) trials to complete the first category (trial 1st), and 6) failure to maintain set (failure).34

(III) The PASAT-3 is utilized to measure IPS and working memory. In this test, the numbers are read to a person in three seconds, and he/she must sum up both numbers. The maximum score is 60.35

(IV) The Tower of London test (TOL) is able to measure planning abilities and problem-solving.36 The patient must solve a series of 12 stages; time and error are measured at each stage. The test demonstrates acceptable construct validity for measuring planning and organization, with a correlation of 0.41 reported against the Porteus Maze test (PMT). Its reliability is established with a reported value of 0.79.37

Depressive symptomatology was detected by the Beck Depression Inventory-Second Edition (BDI-II), a self-report instrument recommended for patients with MS, with 21 items rated on a scale from 0 to 3; the maximum total score was 63.38 The validity and reliability of this questionnaire have also been confirmed in Persian.39

The State-Trait Anxiety Inventory (STAI) was used to measure adult anxiety. It is also used to make a distinction between state anxiety and trait anxiety (feelings of anxiety and depression). The STAI contains 40 questions, which take 10-20 minutes to complete. The test is available in Persian.40 The validity and reliability of this questionnaire have also been confirmed in Persian.41

We assessed fatigue using the Fatigue Severity Scale (FSS) as a self-report questionnaire consisting of 9 items with a 7-point scale.20 The reliability and validity of this questionnaire were confirmed by Krupp et al. concerning patients with MS.42

Results were presented as mean ± standard deviation (SD) for numerical data and as number (percent) for categorical variables. The correlation between MRI indices and cognitive or clinical indices in patients with MS was evaluated using Pearson’s correlation coefficient (r). The relationship between MRI indices (yes/no) and other factors was evaluated in a dependent t-test. Statistical significance was set at P ≤ 0.05. The SPSS statistical software (version 15.0, SPSS Inc., Chicago, IL, USA) was used for the statistical analysis.

Results

Demographic results: Sixty-five patients with MS enrolled in this study. Table 1 shows the participants’ clinical characteristics and demographic information. Of the 65 patients, 8 (12.3%) were men and 57 (87.7%) were women. The mean and SD of the patients’ age was 33.34 ± 8.76 years, and their ages ranged from 18 to 49 years.

Table 1.

Demographic characteristics of the patients with multiple sclerosis (MS) (n = 65)

Characteristics Value
Age (year) (mean ± SD) 33.34 ± 8.76
Median (range) 32.00 (18.00-49.00)
Age (year) [n (%)]
     ≤ 30 26 (40.00)
     31-40 22 (33.84)
     > 40 17 (26.15)
Gender [n (%)]
     Men 8 (12.30)
     Women 57 (87.70)
Education [n (%)]
     Elementary 7 (10.76)
     Middle school 17 (26.15)
     Diploma 28 (43.70)
     College 13 (20.00)
Marital status [n (%)]
     Single 12 (18.46)
     Married 53 (81.54)
Interval since first symptoms (year) (mean ± SD) 3.15 ± 3.73
Median (range) 1.00 (1.00-15.00)
Course of disease [n (%)]
     Relapsing-remitting 46 (70.76)
     Secondary-progressive 19 (29.24)
Drug [n (%)]
     Any interferon beta 59 (90.76)
     Glatiramer acetate 6 (9.24)
EDSS score* (mean ± SD) 1.50 ± 1.31
Median (range) 1.50 (0-3.50)
MSFC# (mean ± SD) 0.00 ± 0.76
Median (range) 0.10 (-2.38, 1.25)

EDSS: Expanded Disability Status Scale; MSFC: Multiple Sclerosis Functional Composite; SD: Standard deviation

*

Scores range from 0 to 10, higher scores indicate a greater degree of disability;

#

Scores of MSFC are expressed as z-score, with higher scores indicating improvement in disability.

We demonstrated the location of plaques in all patients with MS. Figure 1 shows the anatomical localization of plaques. Due to the fact that there were lesions at the periventricular, juxtacortical, corpus callosum, and callososeptal interface areas in all patients, and also, T1 hyper plaque was present in only one patient, they were not mentioned in the results in the tables.

Neuroanatomical and clinical results: Table 2 compares the clinical indices with the characterization and anatomical localization of plaques in patients. According to the results, there was a marked difference in the MSFC score between patients who had a plaque in the basal ganglia and thalamus and those who did not, and the disability was higher in patients with plaque in the basal ganglia and thalamus (P = 0.038). Moreover, there was lower disability (EDSS score) in the patient who had T2 FLAIR hyper-intense plaques in the brain (P = 0.025).

Table 2.

The relationship between neuroanatomical measurements and clinical indices in patients with multiple sclerosis (MS) (n = 65)

Indices EDSS
(mean ± SD)
P MSFC
(mean ± SD)
P State anxiety
(mean ± SD)
P Trait anxiety
(mean ± SD)
P FSS
(mean ± SD)
P Depression
(mean ± SD)
P
Anatomical localization of plaques
Infratentorial 0.642 0.690 0.501 0.316 0.237 0.295
     Yes 1.54 ± 1.25 0.03 ± 0.65 50.06 ± 13.78 48.73 ± 12.97 39.40 ± 10.36 18.62 ± 11.65
     No 1.34 ± 1.61 -0.11 ± 1.14 47.15 ± 14.11 44.85 ± 9.65 43.54 ± 14.10 14.77 ± 12.14
Basal ganglia and thalamus 0.224 0.038* 0.862 0.306 0.382 0.139
     Yes 1.69 ± 1.34 -0.19 ± 0.62 49.76 ± 14.37 49.47 ± 13.24 39.06 ± 11.16 19.91 ± 11.51
     No 1.29 ± 1.28 0.20 ± 0.86 49.16 ± 13.33 46.29 ± 11.41 41.52 ± 11.30 15.58 ± 11.79
Cervical cord 0.525 0.880 0.046* 0.148 0.175 0.496
     Yes 1.56 ± 1.32 -0.01 ± 0.73 47.46 ± 14.02 46.63 ± 13.04 39.10 ± 10.93 17.25 ± 12.53
     No 1.32 ± 1.36 0.02 ± 0.88 55.18 ± 11.64 51.71 ± 9.79 43.41 ± 11.69 19.53 ± 9.35
Plaque characterization
T1 black hole 0.691 0.423 0.517 0.296 0.422 0.015*
     Yes 1.54 ± 1.25 -0.03 ± 0.70 49.98 ± 14.16 48.69 ± 12.86 39.72 ± 10.88 19.11 ± 12.09
     No 1.32 ± 1.69 0.17 ± 1.05 47.00 ± 12.01 44.36 ± 9.58 42.73 ± 12.99 11.64 ± 7.67
T2 FLAIR hyper intense 0.025* 0.552 0.920 0.735 0.240 0.558
     Yes 1.44 ± 1.30 0.01 ± 0.77 49.51 ± 13.87 48.05 ± 12.47 40.52 ± 11.15 18.00 ± 11.68
     No 3.25 ± 0.35 -0.32 ± 0.60 48.50 ± 14.85 45.00 ± 14.14 31.00 ± 12.73 13.00 ± 18.39
Enhanced plaque 0.706 0.788 0.611 0.627 0.244 0.902
     Yes 1.43 ± 1.24 0.03 ± 0.64 50.52 ± 13.09 48.85 ± 12.83 38.30 ± 9.59 17.63 ± 11.03
     No 1.55 ± 1.39 -0.02 ± 0.85 48.74 ± 14.37 47.32 ± 12.23 41.61 ± 12.17 18.00 ± 12.39

EDSS: Expanded Disability Status Scale; MSFC: Multiple Sclerosis Functional Composite; FSS: Fatigue Severity Scale; FLAIR: Fluid-attenuated inversion recovery; SD: Standard deviation

*

P < 0.05

In addition, there was a significantly higher depression in patients with T1 black hole plaque in the brain compared to those with no plaque (P = 0.015). Besides, there was a difference in the state anxiety in patients who had cervical cord plaques and those who did not (P = 0.046).

Neuroanatomical and neuropsychological results: Table 3 shows the relationship between neuroanatomical measurements and cognition in patients with MS.

Table 3.

The relationship between neuroanatomical measurements and cognitive indices in patients with multiple sclerosis (MS) (n = 65)

Indices Word-pair learning
task
(mean ± SD)
P PASAT
(mean ± SD)
P TOL
(mean ± SD)
P WCST indices
PE
(mean ± SD)
P Trial 1 st # 
(mean ± SD)
P
     No 4.79 ± 2.99 28.82 ± 13.94 30.21 ± 2.85 5.89 ± 4.12 6.47 ± 0.73
Anatomical localization of plaques
Infratentorial 0.510 0.752 0.291 0.808 0.006*
     Yes 4.92 ± 3.05 28.02 ± 13.15 30.25 ± 2.80 5.40 ± 3.81 6.44 ± 0.73
     No 4.31 ± 2.72 26.69 ± 14.88 29.38 ± 1.66 5.69 ± 3.77 6.08 ± 0.28
Basal ganglia and thalamus 0.372 0.010* 0.666 0.446 0.207
     Yes 5.12 ± 3.20 23.74 ± 11.72 29.94 ± 2.79 5.12 ± 3.93 6.47 ± 0.75
     No 4.45 ± 2.73 32.16 ± 13.91 30.23 ± 2.47 5.84 ± 3.63 6.26 ± 0.58
Cervical cord 0.955 0.100 0.974 0.114 0.345
     Yes 1.56 ± 1.32 26.13 ± 14.05 30.08 ± 2.74 5.02 ± 3.51 6.42 ± 0.71
     No 1.32 ± 1.36 32.35 ± 10.38 30.06 ± 2.33 6.71 ± 4.31 6.24 ± 0.56
Plaque characterization
T1 black hole 0.598 0.149 0.818 0.044* 0.016*
     Yes 4.89 ± 3.02 26.67 ± 13.86 30.11 ± 2.76 5.04 ± 3.65 6.43 ± 0.72
     No 4.36 ± 2.87 33.09 ± 9.64 29.91 ± 1.92 7.55 ± 3.86 6.09 ± 0.30
T2 FLAIR hyper intense 0.124 0.473 0.296 0.353 0.783
     Yes 4.70 ± 2.95 27.97 ± 13.54 30.02 ± 2.64 5.54 ± 3.77 6.37 ± 0.68
     No 8.00 ± 2.83 21.00 ± 5.66 32.00 ± 1.41 3.00 ± 4.24 6.50 ± 0.71
Enhanced plaque 0.973 0.453 0.630 0.256 0.125
     Yes 4.81 ± 3.01 26.26 ± 12.72 29.89 ± 2.31 4.85 ± 3.21 6.22 ± 0.58

PASAT: Paced Auditory Serial Addition Test; TOL: Tower of London test; WCST: Wisconsin Card Sorting Test; PE: Perseverative errors; FLAIR: Fluid-attenuated inversion recovery; SD: Standard deviation

*

P < 0.05;

#

Trials to complete the first category

Results showed that the number of trials to complete the first category (trial 1st) in WCST was significantly higher in patients who had plaques in the infratentorial (P = 0.006) area. Moreover, the score of PASAT was lower in patients who had plaque in the basal ganglia and thalamus (P = 0.010). Patients with T1 black hole lesions had high scores in trial 1st (P = 0.016) and low PE score (P = 0.044) in WCST, and other WCST indices had no significant differences.

MRI indices and neuropsychological/clinical results: Table 4 shows the correlation between cognitive and clinical measures with MRI indices in patients with MS. A positive correlation was found between the total volume of plaques with EDSS score (r = 0.386, P = 0.002) and word-pair learning (r = 0.254, P = 0.045) and a negative correlation with PASAT (r = -0.299, P = 0.017). Cognitive assessment and MRI measurements showed a positive correlation between brain volume with PASAT (r = 0.272, P = 0.031). The total of enhanced plaques in the brain [total Enhanced plaques in the brain (EPB)] showed a correlation with TOL (r = 0.319, P = 0.010).

Table 4.

Correlation (r) between cognitive and clinical measures with magnetic resonance imaging (MRI) indices in patients with multiple sclerosis (MS) (n = 65)

Indices Brain volume P Total VP P Total EPB P Total EPC P Total VEP P Total NP P
EDSS -0.079 0.540 0.386 0.002* 0.204 0.103 0.157 0.211 -0.081 0.529 -0.058 0.649
MSFC 0.156 0.221 -0.238 0.060 0.135 0.285 0.021 0.867 0.109 0.395 0.032 0.802
State anxiety 0.060 0.639 0.012 0.927 0.001 0.991 0.057 0.654 0.070 0.586 0.081 0.520
Trait anxiety 0.075 0.561 0.101 0.432 0.032 0.797 0.043 0.736 0.075 0.562 0.035 0.780
FSS 0.127 0.321 0.110 0.390 -0.176 0.161 -0.031 0.809 0.020 0.877 -0.134 0.286
Depression 0.038 0.768 0.152 0.234 0.065 0.605 0.072 0.570 0.092 0.471 0.079 0.529
Word-pair learning task -0.216 0.890 0.254 0.045* -0.219 0.079 0.039 0.758 -0.048 0.711 0.065 0.607
PASAT 0.272 0.031* -0.299 0.017* 0.015 0.907 -0.025 0.845 0.070 0.588 -0.241 0.054
TOL -0.128 0.318 0.234 0.064 0.319 0.010* 0.066 0.603 0.066 0.605 0.105 0.406
WCST, PE 0.085 0.506 -0.058 0.654 -0.173 0.169 -0.179 0.154 0.026 0.840 -0.075 0.555
WCST, trial 1st# -0.091 0.479 0.049 0.701 0.037 0.768 0.107 0.397 -0.033 0.796 -0.033 0.793

VP: Volume of plaques; EPB: Enhanced plaques in the brain; EPC: Enhanced plaques in the cervical cord; VEP: Volume of enhanced plaques; NP: Number of plaques; EDSS: Expanded Disability Status Scale; MSFC: Multiple Sclerosis Functional Composite; FSS: Fatigue Severity Scale; PASAT: Paced Auditory Serial Addition Test; TOL: Tower of London test; WCST: Wisconsin Card Sorting Test; PE: Perseverative errors

*

P < 0.05;

#

Trials to complete the first category

Discussion

This study focused on the relationship between the radiological indices with clinical variables and cognitive status in a sample of patients suffering from MS in Rafsanjan City. The main result of this study is that both cognitive and clinical measurements had a correlation with the neuroanatomical measurement of plaque in the brain and MRI indices in patients with MS.

Various studies have presented converging evidence that indicates the role of deep grey matter structures in the pathogenesis of IPS deficiency during MS. Some cross-sectional studies have presented a strong link between thalamic atrophy and PASAT,43-45 while diffusion tensor imaging and fractional anisotropy (FA) measures have illustrated an inverse correlation between IPS performance and microstructural alterations.44,46 Furthermore, there is some evidence claiming that damage to specific corticothalamic tracts is related to the pathology of direct thalamic atrophy, as well as the location of brain white matter lesions.46,47 However, only a limited number of studies have investigated the relationship between the anatomical location of the plaques and disability or cognition. Furthermore, we found that the anatomical location of plaques was important in cognition and disability status. Our results also showed that PASAT was affected by the dispersion of plaques in the basal ganglia and thalamus. It is noteworthy that our results showed that in addition to causing memory impairment, the presence of plaque in the basal ganglia and thalamus increased disability in patients and reduced the score of MSFC.

MS can affect all cognitive processes. In this regard, a study in 1991 identified characteristics of psychological impairment in adults with progressive forms of MS as well as in RRMS, including memory and learning, attention, and executive function.48 Recently, other studies demonstrated that damage of the cerebellum could be associated with MS-related cognitive dysfunction especially impaired executive functioning49 as cerebellar cortex circuits can also play an important role in specific cognitive functions.50,51

Consistently, posterior lobule atrophy of the cerebellum can be associated with executive functioning impairment in patients with MS.52,53 We observed a decrease in executive functioning according to WCST performance in patients who had a plaque in the infratentorial region and T1 black hole plaque. On the other hand, our results showed that patients with T1 black hole plaque were more depressed, which could lead to impaired executive function. A high level of depression could also predict decreased executive functioning and ability.54

Brain volume can affect cognitive function. Findings are very different from previous clinical trials on increasing brain volume and improving cognition with an anaerobic exercise.55,56 In addition, intellectual stimulation through social interaction was associated with increased brain volume as well as some cognitive enhancements.57 These findings show that in addition to the rate of atrophy, the volume of the smaller regional grey matter, even 10 years earlier, is associated with an increase in cognitive decline.58 Our study demonstrated a positive relationship between brain volume and cognitive performance, as tested by PASAT.

Neurological disability in SPMS has been shown to correlate with brain volume atrophy and plaques.59 Additionally, volume of plaques correlated with neurological disability.60 A strong relationship was observed between the number, size, volume, and site of MS plaques and neurological disability.61-63 In accordance with the results of previous studies, our results showed that the increase of total volume of plaques was associated with the rate of disability (MSFC) and increased it.

Previous studies investigated the association between cognitive impairment and the burden of lesions in MS.64,65 The mean volume of T2-weighted plaques showed a tendency to associate with the total regional functional scoring scale.66 Overall, our findings indicated that in patients with MS, increased plaque volume correlated with greater memory impairment and reduced performance on both the word-pair test and the PASAT.

Limitations: A limitation of this study was its lack of evaluation of the lesion load. Another study limitation was three-dimensional (3D)-FLAIR sequences with 3-millimeter cuts and 1.5T MRI. The modest sample size for clinically recruited participants, without follow-up evaluation, was the third limitation. These findings have considerable clinical relevance. It is recommended that in future research, researchers and physicians select appropriate treatment methods after diagnosing the location of plaques in patients with MS and examine the effects of memory-enhancing drugs and disability reduction according to MRI indices. Despite limitations, our findings emphasize the importance of considering the coexistence of psychiatric, clinical, and cognitive disorders with MRI findings in MS.

Conclusion

Brain volume loss and increased plaque volume correlate with memory decline. Furthermore, plaque location in the infratentorial region, basal ganglia, and thalamus is associated with memory decline and increased disability. Enhanced plaques or T1 black hole lesions also contribute to cognitive impairment. These findings highlight the need for systematic clinical and cognitive screening to enable early pharmacological and rehabilitative interventions for managing disability, psychiatric issues, and cognitive impairment in a larger population of patients with MS.

Acknowledgments

We thank all patients with MS participating in this study and Rafsanjan University of Medical Sciences.

Notes:

How to cite this article: Farahmand H, Moghadam-Ahmadi A, Khalili P, Amiri H, Vakilian A, Khatami F, et al. The relationship between quantitative magnetic resonance imaging markers and clinical/cognitive assessments in patients with multiple sclerosis: A cross-sectional study. Curr J Neurol 2025; 24(2): 143-53.

Conflict of Interests

The authors declare no conflict of interest in this study.

References

  • 1.Siva A. The spectrum of multiple sclerosis and treatment decisions. Clin Neurol Neurosurg. 2006;108(3):333–8. doi: 10.1016/j.clineuro.2005.11.010. [DOI] [PubMed] [Google Scholar]
  • 2.Ferguson B, Matyszak MK, Esiri MM, Perry VH. Axonal damage in acute multiple sclerosis lesions. Brain. 1997;120(Pt 3):393–9. doi: 10.1093/brain/120.3.393. [DOI] [PubMed] [Google Scholar]
  • 3.Cavallari M, Ceccarelli A, Wang G-Y, Moscufo N, Hannoun S, Matulis CR, et al. Microstructural changes in the striatum and their impact on motor and neuropsychological performance in patients with multiple sclerosis. PloS one. 2014;9(7):e101199. doi: 10.1371/journal.pone.0101199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Schmitt MM, Goverover Y, DeLuca J, Chiaravalloti N. Self-efficacy as a predictor of self-reported physical, cognitive, and social functioning in multiple sclerosis. Rehabil Psychol. 2014;59(1):27–34. doi: 10.1037/a0035288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS) Neurology. 1983;33(11):1444–52. doi: 10.1212/wnl.33.11.1444. [DOI] [PubMed] [Google Scholar]
  • 6.Rudick RA, Cutter G, Reingold S. The multiple sclerosis functional composite: A new clinical outcome measure for multiple sderosis trials. Mult Scler. 2002;8(5):359–65. doi: 10.1191/1352458502ms845oa. [DOI] [PubMed] [Google Scholar]
  • 7.Meijer KA, Eijlers AJC, Douw L, Uitdehaag BMJ, Barkhof F, Geurts JJG, et al. Increased connectivity of hub networks and cognitive impairment in multiple sclerosis. Neurology. 2017;88(22):2107–14. doi: 10.1212/WNL.0000000000003982. [DOI] [PubMed] [Google Scholar]
  • 8.Petracca M, Saiote C, Bender HA, Arias F, Farrell C, Magioncalda P, et al. Synchronization and variability imbalance underlie cognitive impairment in primary-progressive multiple sclerosis. Sci Rep. 2017;7(1):46411. doi: 10.1038/srep46411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Gerstenecker A, Myers T, Lowry K, Martin RC, Triebel KL, Bashir K, et al. Financial Capacity and its Cognitive Predictors in Progressive Multiple Sclerosis. Arch Clin Neuropsychol. 2017;32(8):943–50. doi: 10.1093/arclin/acx039. [DOI] [PubMed] [Google Scholar]
  • 10.Di Filippo M, Portaccio E, Mancini A, Calabresi P. Multiple sclerosis and cognition: synaptic failure and network dysfunction. Nat Rev Neurosci. 2018;19(10):599–609. doi: 10.1038/s41583-018-0053-9. [DOI] [PubMed] [Google Scholar]
  • 11.Dineen RA, Vilisaar J, Hlinka J, Bradshaw CM, Morgan PS, Constantinescu CS, et al. Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain. 2009;132(Pt 1):239–49. doi: 10.1093/brain/awn275. [DOI] [PubMed] [Google Scholar]
  • 12.Koenig KA, Sakaie KE, Lowe MJ, Lin J, Stone L, Bermel RA, et al. The relationship between cognitive function and high-resolution diffusion tensor MRI of the cingulum bundle in multiple sclerosis. Mult Scler. 2015;21(14):1794–801. doi: 10.1177/1352458515576983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Jongen PJ, Ter Horst AT, Brands AM. Cognitive impairment in multiple sclerosis. Minerva Med. 2012;103(2):73–96. [PubMed] [Google Scholar]
  • 14.Covey TJ, Zivadinov R, Shucard JL, Shucard DW. Information processing speed, neural efficiency, and working memory performance in multiple sclerosis: Differential relationships with structural magnetic resonance imaging. J Clin Exp Neuropsychol. 2011;33(10):1129–45. doi: 10.1080/13803395.2011.614597. [DOI] [PubMed] [Google Scholar]
  • 15.Rao A, Richert N, Howard T, Lewis B, Bash C, McFarland H, et al. Methylprednisolone effect on brain volume and enhancing lesions in MS before and during IFNβ-1b. Neurology. 2002;59(5):688–94. doi: 10.1212/wnl.59.5.688. [DOI] [PubMed] [Google Scholar]
  • 16.Sastre-Garriga J, Ingle GT, Chard DT, Cercignani M, Ramió-Torrentà L, Miller DH, et al. Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study. Brain. 2005;128(Pt 6):1454–60. doi: 10.1093/brain/awh498. [DOI] [PubMed] [Google Scholar]
  • 17.Dalton CM, Chard DT, Davies GR, Miszkiel KA, Altmann DR, Fernando K, et al. Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain. 2004;127(Pt 5):1101–7. doi: 10.1093/brain/awh126. [DOI] [PubMed] [Google Scholar]
  • 18.Truyen L, van Waesberghe JH, van Walderveen MA, van Oosten BW, Polman CH, Hommes OR, et al. Accumulation of hypointense lesions ("black holes") on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology. 1996;47(6):1469–76. doi: 10.1212/wnl.47.6.1469. [DOI] [PubMed] [Google Scholar]
  • 19.Mattioli F, Bellomi F, Stampatori C, Parrinello G, Capra R. Depression, disability and cognitive impairment in multiple sclerosis: A cross sectional Italian study. Neurol Sci. 2011;32(5):825–32. doi: 10.1007/s10072-011-0624-2. [DOI] [PubMed] [Google Scholar]
  • 20.Krupp LB, Alvarez LA, LaRocca NG, Scheinberg LC. Fatigue in multiple sclerosis. Arch Neurol. 1988;45(4):435–7. doi: 10.1001/archneur.1988.00520280085020. [DOI] [PubMed] [Google Scholar]
  • 21.Bonacchi R, Meani A, Pagani E, Marchesi O, Filippi M, Rocca MA. The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: A multiparametric MRI study. J Neurol. 2022;269(7):3841–57. doi: 10.1007/s00415-022-11021-1. [DOI] [PubMed] [Google Scholar]
  • 22.Mirmosayyeb O, Yazdan Panah M, Mokary Y, Mohammadi M, Moases Ghaffary E, Shaygannejad V, et al. Neuroimaging markers and disability scales in multiple sclerosis: A systematic review and meta-analysis. PLoS One. 2024;19(12):e0312421. doi: 10.1371/journal.pone.0312421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Miscioscia A, Mainero C, Treaba CA, Silvestri E, Scialpi G, Berardi A, et al. The contribution of paramagnetic rim and cortical lesions to physical and cognitive disability at multiple sclerosis clinical onset: evaluating the power of MRI and OCT biomarkers. J Neurol. 2024;271(10):6702–14. doi: 10.1007/s00415-024-12622-8. [DOI] [PubMed] [Google Scholar]
  • 24.Nabizadeh F, Zafari R, Mohamadi M, Maleki T, Fallahi MS, Rafiei N. MRI features and disability in multiple sclerosis: A systematic review and meta-analysis. J Neuroradiol. 2024;51(1):24–37. doi: 10.1016/j.neurad.2023.11.007. [DOI] [PubMed] [Google Scholar]
  • 25.Valizadeh A, Moassefi M, Barati E, Ali Sahraian M, Aghajani F, Fattahi MR. Correlation between the clinical disability and T1 hypointense lesions’ volume in cerebral magnetic resonance imaging of multiple sclerosis patients: A systematic review and meta‐analysis. CNS Neurosci Ther. 2021;27(11):1268–80. doi: 10.1111/cns.13734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Rahn AC, Koepke S, Stellmann JP, Schiffmann I, Lukas C, Chard D, et al. Magnetic resonance imaging as a prognostic disability marker in clinically isolated syndrome: a systematic review. Acta Neurol Scand. 2019;139(1):18–32. doi: 10.1111/ane.13010. [DOI] [PubMed] [Google Scholar]
  • 27.Kolind S, Seddigh A, Combes A, Russell-Schulz B, Tam R, Yogendrakumar V, et al. Brain and cord myelin water imaging: a progressive multiple sclerosis biomarker. Neuroimage Clin. 2015;9:574–80. doi: 10.1016/j.nicl.2015.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Maghzi AH, Revirajan N, Julian L, Spain R, Mowry E, Marcus J, et al. Magnetic Resonance Imaging Correlates Of Clinical Outcomes In Early Multiple Sclerosis (S13.005) Neurology. 2014;82(10_supplement):S13.005. doi: 10.1016/j.msard.2014.07.003. [DOI] [PubMed] [Google Scholar]
  • 29.Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292–302. doi: 10.1002/ana.22366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Polman CH, Rudick RA. The multiple sclerosis functional composite: a clinically meaningful measure of disability. Neurology. 2010;74(17_supplement_3):S8–S15. doi: 10.1212/WNL.0b013e3181dbb571. [DOI] [PubMed] [Google Scholar]
  • 31.Schmidt P, Wink L. LST: A lesion segmentation tool for SPM. Manual/Documentation for version. 2017;2:15–26. [Google Scholar]
  • 32.Milgram S. Behavioral Study of Obedience. J Abnorm Psychol. 1963;67:371–8. doi: 10.1037/h0040525. [DOI] [PubMed] [Google Scholar]
  • 33.Malekian M, Kazemi Y, Zarifian T. Psychometric Properties of the Persian Word Pairs Task to Evaluate Declarative Memory. Basic Clin Neurosci. 2022;13(4):511–8. doi: 10.32598/bcn.2021.2585.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Puente A. Wisconsin card sorting test. Test critiques. 1985;4:677–82. [Google Scholar]
  • 35.Deary IJ, Langan SJ, Hepburn DA, Frier BM. Which abilities does the PASAT test? Pers Individ Differ. 1991;12(10):983–7. [Google Scholar]
  • 36.Krikorian R, Bartok J, Gay N. Tower of London procedure: a standard method and developmental data. J Clin Exp Neuropsychol. 1994;16(6):840–50. doi: 10.1080/01688639408402697. [DOI] [PubMed] [Google Scholar]
  • 37.Berg WK, Byrd DL, McNamara JP, Case K. Deconstructing the tower: Parameters and predictors of problem difficulty on the Tower of London task. Brain Cogn. 2010;72(3):472–82. doi: 10.1016/j.bandc.2010.01.002. [DOI] [PubMed] [Google Scholar]
  • 38.Beck AT, Steer RA, Carbin MG. Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clin Psychol Rev. 1988;8(1):77–100. [Google Scholar]
  • 39.Ghassemzadeh H, Mojtabai R, Karamghadiri N, Ebrahimkhani N. Psychometric properties of a persian‐language version of the beck depression inventory‐second edition: BDI‐II‐PERSIAN. Depression anxiety. 2005;21(4):185–92. doi: 10.1002/da.20070. [DOI] [PubMed] [Google Scholar]
  • 40.Spielberger CD, Gorsuch RL, Lushene RE. Manual for the State-trait Anxiety Inventory. 1st ed. Palo Alto, CA: Consulting Psychologists Press; 1970. [Google Scholar]
  • 41.Abdoli N, Farnia V, Salemi S, Davarinejad O, Ahmadi Jouybari T, Khanegi M, et al. Reliability and Validity of Persian Version of State-Trait Anxiety Inventory among High School Students. East Asian Arch Psychiatry. 2020;30(2):44–7. doi: 10.12809/eaap1870. [DOI] [PubMed] [Google Scholar]
  • 42.Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46(10):1121–3. doi: 10.1001/archneur.1989.00520460115022. [DOI] [PubMed] [Google Scholar]
  • 43.Steenwijk MD, Geurts JJ, Daams M, Tijms BM, Wink AM, Balk LJ, et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain. 2016;139(Pt 1):115–26. doi: 10.1093/brain/awv337. [DOI] [PubMed] [Google Scholar]
  • 44.Benedict RH, Hulst HE, Bergsland N, Schoonheim MM, Dwyer MG, Weinstock-Guttman B, et al. Clinical significance of atrophy and white matter mean diffusivity within the thalamus of multiple sclerosis patients. Mult Scler. 2013;19(11):1478–84. doi: 10.1177/1352458513478675. [DOI] [PubMed] [Google Scholar]
  • 45.Bisecco A, Stamenova S, Caiazzo G, d'Ambrosio A, Sacco R, Docimo R, et al. Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume. Brain Imaging Behav. 2018;12(1):20–8. doi: 10.1007/s11682-016-9667-6. [DOI] [PubMed] [Google Scholar]
  • 46.Bisecco A, Rocca MA, Pagani E, Mancini L, Enzinger C, Gallo A, et al. Connectivity-based parcellation of the thalamus in multiple sclerosis and its implications for cognitive impairment: A multicenter study. Hum Brain Mapp. 2015;36(7):2809–25. doi: 10.1002/hbm.22809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Preziosa P, Rocca MA, Pagani E, Stromillo ML, Enzinger C, Gallo A, et al. Structural MRI correlates of cognitive impairment in patients with multiple sclerosis: A multicenter study. Hum Brain Mapp. 2016;37(4):1627–44. doi: 10.1002/hbm.23125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology. 1991;41(5):685–91. doi: 10.1212/wnl.41.5.685. [DOI] [PubMed] [Google Scholar]
  • 49.Ruet A, Hamel D, Deloire MS, Charré-Morin J, Saubusse A, Brochet B. Information processing speed impairment and cerebellar dysfunction in relapsing–remitting multiple sclerosis. J Neurol Sci. 2014;347(1-2):246–50. doi: 10.1016/j.jns.2014.10.008. [DOI] [PubMed] [Google Scholar]
  • 50.Allen G, Buxton RB, Wong EC, Courchesne E. Attentional activation of the cerebellum independent of motor involvement. Science. 1997;275(5308):1940–3. doi: 10.1126/science.275.5308.1940. [DOI] [PubMed] [Google Scholar]
  • 51.Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(5):2322–45. doi: 10.1152/jn.00339.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.D’Ambrosio A, Pagani E, Riccitelli GC, Colombo B, Rodegher M, Falini A, et al. Cerebellar contribution to motor and cognitive performance in multiple sclerosis: an MRI sub-regional volumetric analysis. Mult Scler. 2017;23(9):1194–203. doi: 10.1177/1352458516674567. [DOI] [PubMed] [Google Scholar]
  • 53.Moroso A, Ruet A, Lamargue-Hamel D, Munsch F, Deloire M, Coupé P, et al. Posterior lobules of the cerebellum and information processing speed at various stages of multiple sclerosis. J Neurol Neurosurg Psychiatry. 2017;88(2):146–51. doi: 10.1136/jnnp-2016-313867. [DOI] [PubMed] [Google Scholar]
  • 54.Janssens AC, van Doorn PA, de Boer JB, Kalkers NF, van der Meche FG, Passchier J, et al. Anxiety and depression influence the relation between disability status and quality of life in multiple sclerosis. Mult Scler. 2003;9(4):397–403. doi: 10.1191/1352458503ms930oa. [DOI] [PubMed] [Google Scholar]
  • 55.Chimowitz MI, Estes ML, Furlan AJ, Awad IA. Further observations on the pathology of subcortical lesions identified on magnetic resonance imaging. Arch Neurol. 1992;49(7):747–52. doi: 10.1001/archneur.1992.00530310095018. [DOI] [PubMed] [Google Scholar]
  • 56.Milner B. Effects of different brain lesions on card sorting: The role of the frontal lobes. Arch Neurol. 1963;9(1):90–100. [Google Scholar]
  • 57.Mortimer JA, Ding D, Borenstein AR, DeCarli C, Guo Q, Wu Y, et al. Changes in brain volume and cognition in a randomized trial of exercise and social interaction in a community-based sample of non-demented Chinese elders. J Alzheimers Dis. 2012;30(4):757–66. doi: 10.3233/JAD-2012-120079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Aljondi R, Szoeke C, Steward C, Yates P, Desmond P. A decade of changes in brain volume and cognition. Brain Imaging Behav. 2019;13(2):554–63. doi: 10.1007/s11682-018-9887-z. [DOI] [PubMed] [Google Scholar]
  • 59.Dastidar P, Heinonen T, Lehtimäki T, Ukkonen M, Peltola J, Erilä T, et al. Volumes of brain atrophy and plaques correlated with neurological disability in secondary progressive multiple sclerosis. J Neurol Sci. 1999;165(1):36–42. doi: 10.1016/s0022-510x(99)00071-4. [DOI] [PubMed] [Google Scholar]
  • 60.Ukkonen M, Dastidar P, Heinonen T, Laasonen E, Elovaara I. Volumetric quantitation by MRI in primary progressive multiple sclerosis: volumes of plaques and atrophy correlated with neurological disability. Eur J Neurol. 2003;10(6):663–9. doi: 10.1046/j.1468-1331.2003.00617.x. [DOI] [PubMed] [Google Scholar]
  • 61.Filippi M, Paty DW, Kappos L, Barkhof F, Compston DA, Thompson AJ, et al. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis: a follow-up study. Neurology. 1995;45(2):255–60. doi: 10.1212/wnl.45.2.255. [DOI] [PubMed] [Google Scholar]
  • 62.Gasperini C, Horsfield MA, Thorpe JW, Kidd D, Barker GJ, Tofts PS, et al. Macroscopic and microscopic assessments of disease burden by MRI in multiple sclerosis: relationship to clinical parameters. J Magn Reson Imaging. 1996;6(4):580–4. doi: 10.1002/jmri.1880060404. [DOI] [PubMed] [Google Scholar]
  • 63.Gass A, Barker GJ, Kidd D, Thorpe JW, MacManus D, Brennan A, et al. Correlation of magnetization transfer ratio with clinical disability in multiple sclerosis. Ann Neurol. 1994;36(1):62–7. doi: 10.1002/ana.410360113. [DOI] [PubMed] [Google Scholar]
  • 64.Lynch SG, Parmenter BA, Denney DR. The association between cognitive impairment and physical disability in multiple sclerosis. Mult Scler. 2005;11(4):469–76. doi: 10.1191/1352458505ms1182oa. [DOI] [PubMed] [Google Scholar]
  • 65.Clouston SA, Brewster P, Kuh D, Richards M, Cooper R, Hardy R, et al. The dynamic relationship between physical function and cognition in longitudinal aging cohorts. Epidemiol Rev. 2013;35(1):33–50. doi: 10.1093/epirev/mxs004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Megna R, Alfano B, Lanzillo R, Costabile T, Comerci M, Vacca G, et al. Brain tissue volumes and relaxation rates in multiple sclerosis: implications for cognitive impairment. J Neurol. 2019;266(2):361–8. doi: 10.1007/s00415-018-9139-6. [DOI] [PubMed] [Google Scholar]

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