2D linear measures of ventricular enlargement may be relevant markers of brain atrophy and long-term disability progression in multiple sclerosis

Third ventricular width by transcranial sonography is associated with cognitive impairment in Parkinson's disease

BACKGROUND

One-fourth of Parkinson's disease (PD) patients suffer from cognitive impairment. However, few neuroimaging markers have been identified regarding cognitive impairment in PD.

OBJECTIVE

This study aimed to explore the association between third ventricular width by transcranial sonography (TCS) and cognitive decline in PD.

METHOD

Participants with PD were recruited from one medical center in China. Third ventricular width was assessed by TCS, and cognitive function was analyzed by the Mini-Mental State Examination (MMSE). Receiver operating characteristic (ROC) analysis and Cox model analysis were utilized to determine the diagnostic and predictive accuracy of third ventricular width by TCS for cognitive decline in PD patients.

RESULT

A total of 174 PD patients were recruited. Third ventricular width was negatively correlated with MMSE scores. ROC analysis suggested that the optimal cutoff point for third ventricular width in screening for cognitive impairment in PD was 4.75 mm (sensitivity 62.7%; specificity 75.6%). After 21.5 (18.0, 26.0) months of follow-up in PD patients without cognitive impairment, it was found that those with a third ventricular width greater than 4.75 mm exhibited a 7.975 times higher risk of developing cognitive impairment [hazard ratio = 7.975, 95% CI 1.609, 39.532, p = 0.011] compared with patients with a third ventricular width less than 4.75 mm.

CONCLUSION

Third ventricular width based on TCS emerged as an independent predictor of developing cognitive impairment in PD patients.

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures

Conventional brain magnetic resonance imaging (MRI) in systemic diseases with central nervous system involvement (SDCNS) may imitate MRI findings of multiple sclerosis (MS). In order to better describe the MRI characteristics of these conditions, in our study we assessed brain volume parameters in MS (n = 58) and SDCNS (n = 41) patients using two-dimensional linear measurements (2DLMs): bicaudate ratio (BCR), corpus callosum index (CCI) and width of third ventricle (W3V). In SDCNS patients, all 2DLMs were affected by age (CCI p = 0.005, BCR p < 0.001, W3V p < 0.001, respectively), whereas in MS patients only BCR and W3V were (p = 0.001 and p = 0.015, respectively). Contrary to SDCNS, in the MS cohort BCR and W3V were associated with T1 lesion volume (T1LV) (p = 0.020, p = 0.009, respectively) and T2 lesion volume (T2LV) (p = 0.015, p = 0.009, respectively). CCI was associated with T1LV in the MS cohort only (p = 0.015). Moreover, BCR was significantly higher in the SDCNS group (p = 0.01) and CCI was significantly lower in MS patients (p = 0.01). The best predictive model to distinguish MS and SDCNS encompassed gender, BCR and T2LV as the explanatory variables (sensitivity 0.91; specificity 0.68; AUC 0.86). Implementation of 2DLMs in the brain MRI analysis of MS and SDCNS patients allowed for the identification of diverse patterns of local brain atrophy in these clinical conditions.

The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity

The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study

INTRODUCTION

Early identification of patients at high risk of progression could help with a personalised treatment strategy. Magnetic resonance imaging (MRI) measures have been proposed to predict long-term disability in multiple sclerosis (MS), but a reliable predictor that can be easily implemented clinically is still needed.

AIM

Assess MRI measures during the first 5 years of the MS disease course for the ability to predict progression at 10+ years.

METHODS

Eighty-two MS patients (53 females), with ≥10 years of clinical follow-up and having two MRI scans, were included. Clinical data were obtained at baseline, follow-up and at ≥10 years. White matter lesion (WML) counts and volumes, and four linear brain sizes were measured on T2/FLAIR 'Fluid-Attenuated-Inversion-Recovery' and T1-weighted images.

RESULTS

Baseline and follow-up inter-caudate diameter (ICD) and third ventricular width (TVW) measures correlated positively with Expanded Disability Status Scale, ≥10 or more of WMLs showed a high sensitivity in predicting progression, at ≥10 years. A steeper rate of lesion volume increase was observed in subjects converting to secondary progressive MS. The sensitivity and specificity of both ICD and TVW, to predict disability at ≥10 years were 60% and 64%, respectively.

CONCLUSION

Despite advances in brain imaging and computerised volumetric analysis, ICD and TVW remain relevant as they are simple, fast and have the potential in predicting long-term disability. However, in this study, despite the statistical significance of these measures, the clinical utility is still not reliable.

Predictive value of brain atrophy, serum biomarkers and information processing speed for early disease progression in multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is a chronic autoimmune-mediated demyelinating disease of the central nervous system (CNS). A clinical presentation of the disease is highly differentiated even from the earliest stages of the disease. The application of stratifying tests in clinical practice would allow for improving clinical decision-making including a proper assessment of treatment benefit/risk balance.

METHODS

This prospective study included patients with MS diagnosed up to 1 year before recruitment. We analyzed serum biomarkers such as CXCL13, CHI3L1, OPN, IL-6, and GFAP and neurofilament light chains (NfLs); brain MRI parameters of linear atrophy such as bicaudate ratio (BCR), third ventricle width (TVW); and information processing speed were measured using the Symbol Digit Modalities Test (SDMT) during the 2 years follow-up.

RESULTS

The study included a total of 50 patients recruited shortly after the diagnosis of MS diagnosis (median 0 months; range 0-11 months), and the mean time of observation was 28 months (SD = 4.75). We observed a statistically significant increase in the EDSS score (Wilcoxon test: Z = 3.06, p = 0.002), BCR (Wilcoxon test: Z = 4.66, p < 0.001), and TVW (Wilcoxon test: Z = 2.84, p = 0.005) after 2 years of disease. Patients who had a significantly higher baseline level of NfL suffered from a more severe disease course as per the EDSS score (Mann-Whitney U-test: U = 107, Z = -2,74, p = 0.006) and presence of relapse (Mann-Whitney U-test: U = 188, Z = -2.01, p = 0.044). In the logistic regression model, none of the parameters was a significant predictor for the achieving of no evidence of disease activity status (NEDA). In the model considering all assessed parameters, only the level of NfL had a significant impact on disease progression, measured as the increase in EDSS (logistic regression: β = 0.002, p = 0.017).

CONCLUSION

We confirmed that NfL levels in serum are associated with more active disease. Moreover, we found that TVW at the time of diagnosis was associated with an impairment in cognitive function measured by information processing speed at the end of the 2-year observation. The inclusion of serum NfL and TVW assessment early in the disease may be a good predictor of disease progression independent of NEDA.

Comparison of the clinical value of MRI and plasma markers for cognitive impairment in patients aged ≥75 years: a retrospective study

Background

Dementia has become the main cause of disability in older adults aged ≥75 years. Cerebral small vessel disease (CSVD) is involved in cognitive impairment (CI) and dementia and is a cause of vascular CI (VCI), which is manageable and its onset and progression can be delayed. Simple and effective markers will be beneficial to the early detection and intervention of CI. The aim of this study is to investigate the clinical application value of plasma amyloid β1-42 (Aβ42), phosphorylated tau 181 (p-tau181) and conventional structural magnetic resonance imaging (MRI) parameters for cognitive impairment (CI) in patients aged ≥75 years.

Methods

We retrospectively selected patients who visited the Affiliated Hospital of Xuzhou Medical University and were clinically diagnosed with or without cognitive dysfunction between May 2018 and November 2021. Plasma indicators (Aβ42 and p-tau181) and conventional structural MRI parameters were collected and analyzed. Multivariate logistic regression and receiver operator characteristic (ROC) curve were used to evaluate the diagnostic value.

Results

One hundred and eighty-four subjects were included, including 54 cases in CI group and 130 cases in noncognitive impairment (NCI) groups, respectively. Univariate logistic regression analysis revealed that the percentages of Aβ42+_, P-tau 181+, and Aβ42+/P-tau181+ showed no significant difference between the groups of CI and NCI (all P > 0.05). Multivariate logistic regression analysis showed that moderate/severe periventricular WMH (PVWMH) (OR 2.857, (1.365-5.983), P = 0.005), lateral ventricle body index (LVBI) (OR 0.413, (0.243-0.700), P = 0.001), and cortical atrophy (OR 1.304, (1.079-1.575), P = 0.006) were factors associated with CI. The combined model including PVWMH, LVBI, and cortical atrophy to detect CI and NCI showed an area under the ROC curve (AUROC) is 0.782, with the sensitivity and specificity 68.5% and 78.5%, respectively.

Conclusion

For individuals ≥75 years, plasma Aβ42 and P-tau181 might not be associated with cognitive impairment, and MRI parameters, including PVWMH, LVBI and cortical atrophy, are related to CI. The cognitive statuses of people over 75 years old were used as the endpoint event in this study. Therefore, it can be considered that these MRI markers might have more important clinical significance for early assessment and dynamic observation, but more studies are still needed to verify this hypothesis.

Unmet needs and gaps in the identification of secondary progression in multiple sclerosis: a Southern Italy healthcare professionals' perspective

OBJECTIVE

Multiple sclerosis (MS) is a chronic disease with different clinical courses and a tendency to worsening. The relapsing-remitting MS presents acute onset and relapses of neurological symptoms, followed by their remission. This form can convert to secondary progressive MS (SPMS) with irreversible neurological worsening and disability. The identification of signs, symptoms, markers of progression, and strategies to manage MS patients is mandatory to allow early identification of those at higher risk of conversion to SPMS, for prompt intervention to cope with the progression of the disease.

METHODS

A panel of Italian experts from Southern Italy have reviewed the current knowledge on MS and its management and identified the crucial tools for SPMS recognition.

RESULTS

More effective communication between patients and clinicians should be established, with the support of digital tools. Moreover, the improvement in the clinical use of biomarkers for progression (cellular structures and tissue organization, such as neurofilaments and chitinase 3-like 1, axonal and neurons density) and of instrumental analyses for recognition of whole-brain atrophy, chronic active lesions, spinal cord lesions and atrophy, and the improvement the combination of the Expanded Disability Status Scale and the evaluation of cognitive dysfunction are discussed.

CONCLUSION

Given the availability of a pharmacological option, adequate education both for patients, regarding the evolution of the disease and the specific treatment, and for professionals, to allow more effective and sensitive communication and the best use of diagnostic and management tools, could represent a strategy to improve patient management and their quality of life.

Cerebellar pathology and disability worsening in relapsing-remitting multiple sclerosis: A retrospective analysis from the CombiRx trial

BACKGROUND AND PURPOSE

Cerebellar damage is a valuable predictor of disability, particularly in progressive multiple sclerosis. It is not clear if it could be an equally useful predictor of motor disability worsening in the relapsing-remitting phenotype.

AIM

We aimed to determine whether cerebellar damage is an equally useful predictor of motor disability worsening in the relapsing-remitting phenotype.

METHODS

Cerebellar lesion loads and volumes were estimated using baseline magnetic resonance imaging from the CombiRx trial (n = 838). The relationship between cerebellar damage and time to disability worsening (confirmed disability progression [CDP], timed 25-foot walk test [T25FWT] score worsening, nine-hole peg test [9HPT] score worsening) was tested in stagewise and stepwise Cox proportional hazards models, accounting for demographics and supratentorial damage.

RESULTS

Shorter time to 9HPT score worsening was associated with higher baseline Expanded Disability Status Scale (EDSS) score (hazard ratio [HR] 1.408, p = 0.0042) and higher volume of supratentorial and cerebellar T2 lesions (HR 1.005 p = 0.0196 and HR 2.211, p = 0.0002, respectively). Shorter time to T25FWT score worsening was associated with higher baseline EDSS (HR 1.232, p = 0.0006). Shorter time to CDP was associated with older age (HR 1.026, p = 0.0010), lower baseline EDSS score (HR 0.428, p < 0.0001) and higher volume of supratentorial T2 lesions (HR 1.024, p < 0.0001).

CONCLUSION

Among the explored outcomes, single time-point evaluation of cerebellar damage only allows the prediction of manual dexterity worsening. In clinical studies the selection of imaging biomarkers should be informed by the outcome of interest.

Two-dimensional measurements with cut-off values are useful for assessing brain volume, physical disability, and processing speed in multiple sclerosis

BACKGROUND

Two-dimensional (2D) measures have been proposed as potential proxy measures for whole-brain volume in multiple sclerosis (MS); however, cut-off values that determine the degree of brain volume loss (BVL) have not been established. Since we had previously developed a system to categorize MS patients into clusters with significantly different degrees of BVL, we tried to identify cut-off values for 2D measurements that can discriminate MS patients on the basis of disease severity associated with brain atrophy.

METHODS

In this cross-sectional analysis, ninety-one consecutive Japanese MS patients-clinically isolated syndrome (5%), relapsing-remitting MS (78%) and progressive MS (17%)-were categorized into two clusters (CL1 and CL2) with a significantly different degree of BVL using the method described in our previous study. MS patients were also evaluated for 2D measurements, namely, third ventricle width, lateral ventricle width (LVW), bicaudate ratio (BCR), and corpus callosum index (CCI). Thereafter, we performed receiver operating characteristic analysis to determine the cut-off values of the 2D measurements for categorizing the MS patients into two clusters.

RESULTS

We identified optimal cut-off values for each 2D measure with high specificity and sensitivity. The cut-off values for LVW, BCR, and CCI divided the MS patients into two subgroups, in which whole-brain and grey matter volume, EDSS, and processing speed were significantly different.

CONCLUSION

LVW, BCR, and CCI with particular cut-off values are useful to discriminate MS patients with decreased brain volume, physical disability, and processing speed.

Usefulness of two-dimensional measurements for the evaluation of brain volume and disability in multiple sclerosis

Background

Two-dimensional (2D) measures have been proposed as potential proxies for whole-brain volume in multiple sclerosis (MS).

Objective

To verify whether 2D measurements by routine MRI are useful in predicting brain volume or disability in MS.

Methods

In this cross-sectional analysis, eighty-five consecutive Japanese MS patients—relapsing-remitting MS (81%) and progressive MS (19%)—underwent 1.5 Tesla T1-weighted 3D MRI examinations to measure whole-brain and grey matter volume. 2D measurements, namely, third ventricle width, lateral ventricle width (LVW), brain width, bicaudate ratio, and corpus callosum index (CCI), were obtained from each scan. Correlations between 2D measurements and 3D measurements, the Expanded Disability Status Scale (EDSS), or processing speed were analysed.

Results

The third and lateral ventricle widths were well-correlated with the whole-brain volume (p < 0.0001), grey matter volume (p < 0.0001), and EDSS scores (p = 0.0001, p = .0004, respectively).The least squares regression model revealed that 78% of the variation in whole-brain volume could be explained using five explanatory variables, namely, LVW, CCI, age, sex, and disease duration. By contrast, the partial correlation coefficient excluding the effect of age showed that the CCI was significantly correlated with the EDSS and processing speed (p < 0.0001).

Conclusion

Ventricle width correlated well with brain volumes, while the CCI correlated well with age-independent (i.e. disease-induced) disability.

Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach

OBJECTIVES

To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning.

METHODS

The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses.

RESULTS

We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as "deep gray matter (DGM)-first" subtype (N = 238) and "cortex-first" subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005).

CONCLUSIONS

Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS.

KEY POINTS

• The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course.

Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis

(1) Although guidelines about the use of MRI sequences for Multiple Sclerosis (MS) diagnosis and follow-up are available, variability in acquisition protocols is not uncommon in everyday clinical practice. The aim of this study was to evaluate the real-world application of MS imaging guidelines in different settings to clarify the level of adherence to these guidelines. (2) Via an on-line anonymous survey, neuroradiologists (NR) were asked about MRI protocols and parameters routinely acquired when MS patients are evaluated in their center, both at diagnosis and follow-up. Furthermore, data about report content and personal opinions about emerging neuroimaging markers were also retrieved. (3) A total of 46 participants were included, mostly working in a hospital or university hospital (80.4%) and with more than 10 years of experience (47.9%). We found a relatively good adherence to the suggested MRI protocols regarding the use of T2-weighted sequences, although almost 10% of the participants routinely acquired 2D sequences with a slice thickness superior to 3 mm. On the other hand, a wider degree of heterogeneity was found regarding gadolinium administration, almost routinely performed at follow-up examination (87.0% of cases) in contrast with the current guidelines, as well as a low use of a standardized reporting system (17.4% of cases). (4) Although the MS community is getting closer to a standardization of MRI protocols, there is still a relatively wide heterogeneity among NR, with particular reference to contrast administration, which must be overcome to guarantee an adequate quality of patients’ care in MS.

Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis

Objectives

To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.

Methods

We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 years, with disability progression defined according to the expanded disability status scale (EDSS) increment at follow-up. T2-weighted lesion load (T2LL), thalamic and cerebellar gray matter (GM) volumes, fractional anisotropy of the normal appearing white matter were calculated at baseline and included in supervised machine learning classifiers. Age, sex, phenotype, EDSS at baseline, therapy and time to follow-up period were also included. Classes were labeled as stable or progressed disability. Participants were randomly chosen from both sites to build a sample including 50% patients showing disability progression and 50% patients being stable. One-thousand machine learning classifiers were applied to the resulting sample, and after testing for overfitting, classifier confusion matrix, relative metrics and feature importance were evaluated.

Results

At follow-up, 36% of participants showed disability progression. The classifier with the highest resulting metrics had accuracy of 0.79, area under the true positive versus false positive rates curve of 0.81, sensitivity of 0.90 and specificity of 0.71. T2LL, thalamic volume, disability at baseline and administered therapy were identified as important features in predicting disability progression. Classifiers built on radiological features had higher accuracy than those built on clinical features.

Conclusions

Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10605-7.

Linear brain atrophy measures in multiple sclerosis and clinically isolated syndromes: a 30-year follow-up

OBJECTIVE

To determine 30-year brain atrophy rates following clinically isolated syndromes and the relationship of atrophy in the first 5 years and clinical outcomes 25 years later.

METHODS

A cohort of 132 people who presented with a clinically isolated syndrome suggestive of multiple sclerosis (MS) were recruited between 1984-1987. Clinical and MRI data were collected prospectively over 30 years. Widths of the third ventricle and the medulla oblongata were used as linear atrophy measures.

RESULTS

At 30 years, 27 participants remained classified as having had a clinically isolated syndrome, 34 converted to relapsing remitting MS, 26 to secondary progressive MS and 16 had died due to MS. The mean age at baseline was 31.7 years (SD 7.5) and the mean disease duration was 30.8 years (SD 0.9). Change in medullary and third ventricular width within the first 5 years, allowing for white matter lesion accrual and Expanded Disability Status Scale increases over the same period, predicted clinical outcome measures at 30 years. 1 mm of medullary atrophy within the first 5 years increased the risk for secondary progressive MS or MS related death by 30 years by 583% (OR 5.83, 95% CI 1.74 to 19.61, p<0.005), using logistic regression.

CONCLUSIONS

Our findings show that brain regional atrophy within 5 years of a clinically isolated syndrome predicts progressive MS or a related death, and disability 25 years later.

Neuroimaging Correlates of Cognitive Dysfunction in Adults with Multiple Sclerosis

Cognitive impairment is a frequent and meaningful symptom in multiple sclerosis (MS), caused by the accrual of brain structural damage only partially counteracted by effective functional reorganization. As both these aspects can be successfully investigated through the application of advanced neuroimaging, here, we offer an up-to-date overview of the latest findings on structural, functional and metabolic correlates of cognitive impairment in adults with MS, focusing on the mechanisms sustaining damage accrual and on the identification of useful imaging markers of cognitive decline.

Structural changes in the brain of patients with relapsing-remitting multiple sclerosis compared to controls: a MRI-based stereological study

BACKGROUND

Multiple sclerosis (MS) is an inflammatory autoimmune disorder of the central nervous system characterized by demyelination, inflammation, gliosis, and axonal loss. Nowadays, increasing scientific reports have focused on neurodegenerative processes and structural changes of the disease underlying pathogenesis.

AIM

The aim of this study is a structural analysis of brain magnetic resonance images (MRIs) in patients with relapsing-remitting multiple sclerosis (RRMS) comparing with normal individuals.

METHODS

This case-control study was carried out on MRIs of 20 patients with RRMS and 20 healthy controls in Zahedan, Iran. MR images with 4-mm slice thickness and 0.5-mm intervals in three anatomical planes (coronal, sagittal, axial) were acquired. Then, stereological parameters, including volume and volume density of different parts of the brain, based on Cavalries' point counting method were measured in both groups. Data analyses were performed using Mann-Whitney U and Pearson's correlation tests.

RESULTS

The results of the study showed that there were no significant differences in total brain, hemispheres, gray matter, and basal nuclei volume and volume density between the two groups (p ˃ 0.05). However, the left hemisphere, cerebellum, lateral ventricles, brainstem, corpus callosum, and white matter volume in RRMS patients were significantly lower than those in controls (p ˂ 0.05).

CONCLUSION

The findings showed that quantitative assessments based on stereological method on brain MRIs facilitate clarifying neuropathology of the disease. Also, it can be helpful as a simple index for following up the clinical situation and assessing therapeutic efficiency in MS patients. It may provide a precise treatment approach and justification of symptoms in patients with MS.