Introduction

 

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system affecting white and possibly grey matter. Thalamic axons convey information between multiple subcortical and specific cortical regions in the brain. That is why thalamus is considered a crucial structure, serving a wide range of neurologic functions. Extensive involvement of gray matter (GM), and particularly of the thalamus, is usually associated with a very wide range of clinical features including cognitive impairment, motor deficits, painful phenomena and fatigue in patients with MS. Both direct microstructural damage to thalamus and indirect damage caused by axonal degeneration secondary to white matter MS lesions could be intimately related to functional disability in MS¹. Diffusion tensor imaging (DTI) describes the properties of water diffusion in vivo, thus disclosing important details of fiber tract orientation and

providing information regarding microstructural integrity of the tissue². Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) are considered sensitive measures to different structural tissue characteristics. As Fractional anisotropy (FA) reflects the organization and directionality of water diffusion along axons. Therefore, higher FA values may reflect more coherent axons, or a higher degree of myelination, on the other hand, lower FA may imply loss of WM integrity and more tissue damage³. Our aim is to detect the thalamic involvement using diffusion tensor imaging (DTI) and to study its impact on cognitive impairment, clinical disability and fatigue in MS patients.

 

Subjects and MethodS

 

This is a case control study conducted on 31 patients (20 males and 11females; Mean age: 34.4±8.5 SD) with a diagnosis of relapsing remitting multiple sclerosis and secondary progressive multiple sclerosis according to Revised McDonald Criteria⁴. We recruited cases from the multiple sclerosis unit, department of Neurology at Cairo university hospitals during period from January 2014 to September 2014.We also recruited 18 age, sex and education level-matched healthy individuals as a control group. We had excluded patients whose age is < 18 and > 45 years, patients with primary progressive MS, patients with history of neurological disorder other than MS, patients with known comorbidities as HTN or DM, left-handed patients, patients with history of alcohol intake or drugs abuse, patients with severe sensory deficit preventing them from performing cognitive tests used this study, history of psychiatric disorders or use of antipsychotic drugs and patients who had a contraindication to undergo a 1.5 Tesla MR imaging.

 

Methods

All MS patients were subjected to: Evaluation of disability using the expanded disability status scale (EDSS); EDSS has been the most widely used measure of the disability in multiple sclerosis⁵, evaluation of fatigue using the fatigue severity scale (FSS)(6). Patients who obtained an FSS score of > 4 were considered fatigued, whereas those with an FSS score ≤ 4 were considered non-fatigued. All individuals in both groups were subjected to the following battery of neuropsychological and radiological assessment.

 

Neuropsychological Assessment:

a)        Mental state examination using Mini-Mental State Examination (MMSE)⁷.

b)       California Verbal Learning Test- 2nd edition (CVLT-II): This test was used to assess the verbal learning and memory⁸. The following scores were calculated: Total recall over all learning trials (CVLT-II-TR), Short-term recall after interference (CVLT-II-SR) and Recall after the delay interval (CVLT-II-DR).

c)        Brief Visuospatial Memory Test–Revised (BVMT-R): This test was used to assess the visuospatial memory and learning. The stimulus material was a matrix of six visual designs, held before the participant for 90 seconds⁹. The following measures were considered for each memory test:  Total recall over all learning trials (BVMT--TR) and Recall after the delay interval (BVMT-DR).

d)       Paced Auditory Serial Addition Task (PASAT): This test evaluates working memory and information processing. Scores are the number of correct responses¹⁰.

e)        Symbol Digit Modalities Test (SDMT): The SDMT measures concentration and sustained attention. Subjects are given 90 s to substitute numbers for symbols as part of a set code¹¹.

f)        Verbal fluency (VF) test: It assessed the phonemic and semantic fluency. As regard the phonemic fluency, the examinee must produce orally as many words as possible beginning with a specified letter (Haa (ح during a one minute. While in semantic fluency, the examinee is asked to produce as many animal names as possible within a one-minute interval¹².

 

Neuroradiological Assessment:

Diffusion Tensor Imaging (DTI): It was performed using a 1.5 Tesla unit (Philips Achieva, Netherlands). DTI consists of axial single shot spin echo echoplanar sequence in 25 encoding directions with diffusion weighting factor (b value) of 1000 s/mm2, TR= 10951, TE= 67, matrix= 128 X 128, FOV= 224 X224 mm, number of excitation= 2, slice thickness = 3 mm without gaps, flip angle= 90o. Analysis of the images was performed using Philips workstation. Directionally-encoded color DTI maps were obtained then fused with T2 weighted images or T2 3D fast field echo (3D FFE) images. Red, green and blue colors represent right-left, anterior-posterior and superior-inferior directions, respectively. FA and ADC were measured through drawing regions of interest (ROI) in the thalami of both cerebral hemispheres in three consecutive levels. FA and ADC were calculated automatically for each ROI. Mean FA and ADC were calculated for the right and left thalami and used for statistical analysis.

 

Statistical Analysis:

Data was analyzed using IBM SPSS advanced statistics version 20 (SPSS Inc., Chicago, IL). Numerical data were expressed as mean and standard deviation or median and range as appropriate. For not normally distributed quantitative data, comparison between two groups was done using Mann-Whitney test (non-parametric t-test). Spearman-rho method was used to test correlation between numerical variables. All tests were two-tailed. A p-value < 0.05 was considered significant and less than 0.01 were considered highly significant.

 

ResultS

 

I.             Comparative Analysis

*    Clinical data:

The duration of illness among MS patients ranged from 2-20 years with mean disease duration 6.9±4.9 SD. Number of attacks ranged from 3-10 attacks with mean number of attacks 5.8±2.6 SD and total EDSS ranged from 2- 6.5 with mean total EDSS 3.9±1.6 SD. FSS scores in patient group ranged from 1.1 – 5.5 mean score 3.5±1.2 SD. 10 patients were considered fatigued (FSS score > 4) and 21 were non-fatigued (FSS ≤ 4). In the fatigued group six patients had SPMS and four patients had RRMS with statistically significant difference between two groups (SPMS patients are more fatigued than RRMS patients) (P-value = 0.003).

Although no statistical significant difference was found between patients and control in MMSE; however, the patients showed significant worse performance than controls in all neuropsychological assessment tests used (Table 1). On comparing between RRMS patients to SPMS patients in performance of neuropsychological tests; SPMS patients showed worse performance in BVMT-R total recall (P-value = 0.034) and VF-animal (P-value=0.038). Otherwise, no statistically significant difference was found in other cognitive tests.

*    Radiological data:

Compared to healthy controls, MS patients had higher thalamic FA and ADC values than controls (p value <0.001) as shown in table 2. Moreover, the same finding was detected also in RRMS and PPMS subgroups (P-value = <0.001, <0.001 respectively). On the other hand, RRMS and SPMS patients showed no statistically significant difference in mean thalamic FA or ADC values; which means that there is affected diffusivity over thalamic tissue in patients with MS regardless its type.

 

II.            Correlative Analysis:

*    Neuropsychological Assessment:

Age was negatively correlated with CVLT-II-SR (r = -0.49, P-value = 0.005), CVLT-II-DR (r= -0.61, P-value = <0.001), BVMT-TR (r= -0.52, p value =0.003), BVMT-DR (r= -0.54, 0.002), SDMT (r= -0.57, P-value = 0.001). While all neuropsychological tests were correlated with the years of education (p value = <0.001).

Cognitive domains measured; with different neuropsychological tests, were correlated with duration of illness, number of attacks, degree of disability but not with fatigue severity (p value > 0.05) (Table 3).

*    Radiological data:

No correlation was found between FA and ADC in both thalami and age or education; as for clinical data both FA and ADC were correlated with number of attacks and total EDSS (Rt. Thalami FA) but not with duration of illness and degree of disability. In addition, no correlation was found between thalamus diffusivity and different cognitive domains (Table 4).

 

Table 1. Comparison between neuropsychological tests in MS patients and control.

 

BVMT-DR Brief Visuospatial Memory Test–Revised, Delayed Recall, BVMT-TR Brief Visuospatial Memory Test–Revised, Total Recall, CVLT-II-DR California Verbal Learning Test- 2nd edition- Delayed Recall, CVLT-II-SR California Verbal Learning Test- 2nd edition- Short Term Recall, CVLT-II-TR California Verbal Learning Test- 2nd edition-Total  Recall, MS Multiple sclerosis, MMSE Mini-Mental State Examination, PASAT Paced Auditory Serial Addition Task, SDMT Symbol Digit Modalities Test, VF Verbal Fluency.

*Significant at P<0.05 ** Significant at P<0.01

 

 

Table 2. Comparison between radiological results in MS patients and control.

 

ADC Apparent Diffusion Coefficient, FA Fractional anisotropy, MS Multiple sclerosis

*Significant at P<0.01

Table 3. Correlations between clinical data and neuropsychological tests.

 

	Duration of illness		Number of attacks		EDSS
	r	p	r	p	r	p
CVLT-II-TR	-0.33	0.07	-0.23	0.21	-0.44	0.01**
CVLT-II-SR	-0.28	0.13	-0.17	0.35	-0.49	0.005**
CVLT-II-DR	-0.465	0.008**	-0.37	0.04*	-0.55	0.001**
BVMT-TR	-0.43	0.017*	-0.39	0.03*	-0.6	<0.001**
BVMT-DR	-0.44	0.013*	-0.38	0.034*	-0.5	0.004**
PASAT	-0.3	0.1	-0.31	0.1	-0.4	0.028*
SDMT	-0.41	0.021*	-0.22	0.23	-0.47	0.007**
VF-Letter	-0.2	0.29	-0.2	0.3	-0.43	0.02*
VF-animal	-0.29	0.12	-0.22	0.23	-0.46	0.01**

BVMT-DR Brief Visuospatial Memory Test–Revised, Delayed Recall, BVMT-TR Brief Visuospatial Memory Test–Revised, Total Recall, CVLT-II-DR California Verbal Learning Test- 2nd edition- Delayed Recall, CVLT-II-SR California Verbal Learning Test- 2nd edition- Short Term Recall, CVLT-II-TR California Verbal Learning Test- 2nd edition-Total Recall, EDSS Expanded Disability Status Scale, PASAT  Paced Auditory Serial Addition Task, SDMT Symbol Digit Modalities Test, VF Verbal Fluency

*Significant at P<0.05 ** Significant at P<0.01

 

Table 4. Correlations of radiological data.

 

ADC Apparent Diffusion Coefficient, BVMT-DR Brief Visuospatial Memory Test–Revised, Delayed Recall,BVMT-TR Brief Visuospatial Memory Test–Revised, Total Recall, CVLT-II-DR California Verbal Learning Test- 2nd edition- Delayed Recall, CVLT-II-SR California Verbal Learning Test- 2nd edition- Short Term Recall, CVLT-II-TR California Verbal Learning Test- 2nd edition-Total Recall, EDSS Expanded Disability Status Scale, FA Fractional anisotropy, FSS Fatigue Severity Scale, Lt Left, MMSE= Mini-Mental State Examination, PASAT Paced Auditory Serial Addition Task, Rt Right, SDMT Symbol Digit Modalities Test, VF Verbal Fluency

 

*Significant at P<0.05

 

 

 

 

DISCUSSION

 

Cortical and subcortical demyelination are observed during the course of MS, several grey matter structures including the thalamus, hippocampus, caudate, putamen and globus pallidus were involved in MS^13,14. Our results showed that there was a significant thalamic involvement in MS patients (even in normally appearing thalamic tissue on conventional MRI) as demonstrated using DTI. In concordance with our results, other studies found significantly increased anisotropy in the normal appearing basal ganglia including the thalamus^15,16.

In our study there was also significant difference between the subgroup of patients with RRMS and controls, indicating that subtle thalamic damage can be present in early in the course of the disease. This goes with the results in previous studies that showed early thalamic involvement^16-19. In contrary to our results, another study found that there was no change in diffusivity (ADC) between MS patients and controls^20,21.

Cognitive impairment in MS patient is well proven in several previous studies yet pattern of cognitive affection was variable; not only between different studies but also varying considerably among MS patients in same studies^22-25. Different studies were directed to find the anatomical associations with different cognitive profiles. Thalamic damage can potentially trigger a cognitive dysfunction in patients with MS²⁶. In our study, we used a battery of neuropsychological tests that focused mainly on memory (verbal and visuospatial), learning, attention, planning, information processing and verbal fluency. These cognitive domains are related greatly to thalamic role in cognitive process. Our results showed that there was a significant worse performance in MS patients when compared to healthy controls in all of the neuropsychological tests. Surprisingly, there was no correlation between cognitive domains and diffusivity over the thalamus, despite other studies which support the role of thalamus in cognitive impairment in MS^27-31. This could be due to different imaging modalities used in these studies and the use of 1.5 T DTI in our study instead of 3T DTI, which can more sensitive to diffusivity changes. The most likely cause of the increased sensitivity of 3.0 T is improved detection of small lesions missed by the conventional MRI and also affection of cognitive functions could be more complex and related more to cortical–subcortical circuits disconnection³².

Multiple factors could contribute to the differences in learning, memory and executive functions profiles among the different MS types, including age, level of education, disease duration, number of attacks, coexisting clinical disability and affective disorders^22,24,33,34. This supports our results that showed the presence of significant correlations between age, level of education, duration of the disease, number of attacks, EDSS and performance in neuropsychological tests.

We also found significant correlations between the duration of disease, number of attacks and the mean FA values over the right and left thalami; which means that the more the thalamic involvement, the more the clinical disability in patients with MS. Total EDSS was also correlated with changes of FA over right thalami. Our results regarding affection of the thalamus and its contribution to disability in MS, are matching with Tover-Moll et al. (2009), who found that there was correlation between motor EDSS and diffusivity over the thalamus¹⁶. On the contrary, to our results, they found no correlation between disease duration and changes in FA or MD in patients with MS, except in the SPMS group.

Fatigue is another common symptom of MS and affects up to 90% of patients with the disease³⁵. There was no correlation between fatigue severity and diffusivity changes over the thalamus. This goes with the results of Codella and colleagues (2002), who found no significant difference in FA especially over WM between fatigued and non-fatigued individuals³⁶.

Our results support the concept of thalamic involvement in MS. It is not only the white matter that is affected; also we have affection of subcortical grey matter that contributes to pathophysiology of MS. This thalamic involvement contributes greatly to cognitive impairment and clinical disability in patients with MS. The use of 1.5 T DTI helps us to detect microstructural lesions that could affect anisotropy and diffusivity of tissues and are not visible in conventional MRI.

 

[Disclosure: Authors report no conflict of interest]

 

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