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Thalamic atrophy predicts cognitive impairment in relapsing remitting multiple sclerosis. Effect on instrumental activities of daily living and employment status
Journal of the Neurological Sciences, Volume 358, Issue 1-2, November 2015, Pages 236 - 242
Cognitive impairment is an important predictor of quality of life at all stages of MS. Magnetic Resonance Imaging (MRI) markers have been used to associate tissue damage with cognitive dysfunction.
The aim of the study was to designate the MRI marker that predicts cognitive decline and explore its effect on every day activities and employment status.
50 RRMS patients and 31 healthy participants underwent neuropsychological assessment using the Trail Making Test (TMT) parts A and B, semantic and phonological verbal fluency task and a computerized cognitive screening battery (Central Nervous System Vital Signs). Everyday activities were evaluated with the instrumental activities of daily living (IADL) scale and employment status. Brain MRI was performed in all participants. We measured total lesion volume, third ventricle width, corpus callosum and thalamic atrophy.
The frequency of cognitive dysfunction for our RRMS patients was 38%. RRMS patients differed significantly from controls on the TMTA, TMTB, phonological verbal fluency task, memory, psychomotor speed, reaction time and cognitive flexibility. Neuropsychological measures had a strong correlation with all MRI atrophy measures and a weak or moderate correlation with lesion volume. Psychomotor speed was the most sensitive marker for IADL, while memory and TMTB for employment status. Thalamic area was the most sensitive MRI marker for memory, psychomotor speed and TMTB..
Thalamic atrophy predicts the clinically meaningful cognitive decline in our RRMS patients.
- The frequency of cognitive dysfunction for our RRMS patients was 38%.
- Typical pattern of impairment in processing speed, episodic memory and executive functions was observed.
- Psychomotor speed predicted IADL, while memory and TMTB predicted employment status.
- Thalamic atrophy is highly informative of clinically meaningful cognitive decline.
Keywords: Multiple sclerosis, Cognitive impairment, MRI, Thalamus, Employment status, Instrumental activities of daily living.
Cognitive impairment is an important feature of multiple sclerosis (MS) with a prevalence rate ranging from 40% to 70%  and . Deficits in memory, processing speed, attention and executive functions are the most prevalent  . Although cognitive deficits are more frequent and pronounced in chronic progressive MS and tend to worsen over time, they have also been observed in relapsing remitting MS (RRMS), including clinically isolated syndromes (CIS)  and . Cognitive impairment affects patient's quality of life  and employment status  . The underlying pathophysiology of cognitive dysfunction in MS is of great importance, because a better understanding could lead to novel markers and improved therapies  .
Magnetic Resonance Imaging (MRI) offers some insight into the associations between various types of tissue damage and cognitive impairment. Numerous MRI variables are significantly correlated with cognitive performance  and . Early studies reported modest correlations between cognitive performance and global measures of white matter lesion volume  , while different atrophy markers, such as ventricular enlargement, corpus callosum atrophy and brain volume reduction revealed stronger correlations , , and . Despite the traditional focus on white matter in MS, recent findings indicate significant gray matter involvement as an integral part in our understanding of the underlying pathology of cognitive decline as well as the overall disease pathology-worsening  and . Therefore, more recent studies focusing on cortical lesions and regional gray matter atrophy, such as thalamic atrophy, have demonstrated stronger associations with cognitive function , , and .
We embraced the hypothesis that our RRMS patients will have significant differences compared to controls, mainly in information processing speed, episodic memory and executive functions and we wanted to answer the following questions:
a) Which is the most sensitive cognitive measure in predicting employment status and impairment in everyday activities in our RRMS patients?.
b) Which MRI marker predicts this clinically meaningful cognitive decline?.
Participants were 50 patients with RRMS, diagnosed according to McDonald criteria  and . Patients were consecutively recruited from the outpatient MS clinic at University of Patras, Greece. Patients with acute relapse during the last four months, on corticosteroids or on other medications that could interfere with cognition, known learning difficulties, visual deficits, motor involvement of upper limb, major psychiatric illness, other neurological diseases and of non-Greek origin were excluded from this study. Expanded Disability Status Scale (EDSS) score  was performed on all patients. Quality of life was assessed by instrumental activities of daily living (IADL)  and employment status  .
In addition, 31 Greek healthy control participants were recruited in order to obtain a sample with similar demographic characteristics to our patients. Exclusion criteria for the control sample included non-native Greek speakers, visual deficits, learning difficulties, psychiatric or neurological disorder, history of brain injury, cardiovascular illness, medication use, drug and alcohol consumption that could interfere with cognitive performance and MRI findings. Both groups were assessed with the Beck Depression Inventory - Fast Screen  in order to exclude major depression as a concomitant factor that could interfere with cognitive performance. Written informed consent was provided by all patients and controls and the study was approved by the local ethics review board.
Demographic and clinical characteristics of RRMS patients and healthy controls are summarized in Table 1 .
|Years of education||12.93(1.09)||12.26(3.55)|
Values are mean (SD).
Duration: number of years from diagnosis.
2.2. Neuropsychological testing
All participants were assessed by an experienced clinical neuropsychologist. We administered the Greek versions of the Trail Making Test parts A and B as well as semantic and phonological verbal fluency task, measuring executive function and information processing speed  and . Moreover, we used the Central Nervous System Vital Signs (CNS VS), a computerized cognitive screening battery which also provides a Greek adapted version  . We conducted the following neuropsychological tests from the Brief Core assessment of CNS VS: verbal and visual memory, symbol digit coding, stroop, shifting attention and continuous performance test. Final results of CNS VS were automatically computed, expressing patient's performance on the following domains: composite memory, processing speed, psychomotor speed, reaction time, complex attention, cognitive flexibility and executive functions.
We classified as cognitively impaired, patients who failed on at least 33% of the included measures  . We considered that patients had failed a particular test if they scored 1.5 standard deviation below (or above, if higher score corresponds to worse performance) the average performance of the control group.
2.3. MRI protocol and analysis
Brain MRI was performed on each subject using the same scanning protocol on a Philips Gyroscan unit (Philips Medical Systems, The Netherlands) at a field strength of 1.5 T. Axial T1- weighted (repetition time/echo time 500/20 ms), axial fluid-attenuated inversion recovery (FLAIR) images (repetition time/echo time/inversion time 11,000/140/2800 ms), coronal T1-weighted (repetition time/echo time 400/20 ms) and a sagittal T1-weighted image through corpus callosum (repetition time/echo time 250/20 ms) with 5 mm thick slices, 0.5 mm gaps, 256 × 256 matrix and 250 field of view were obtained. Analysis was performed with manual segmentation using Java based ImageJ software  . All image analysis was performed by a neuroradiologist who was blind to the clinical data. Total lesion volume was calculated as the sum of the areas from all lesions identified on axial FLAIR images, multiplying by slice thickness plus the slice gap ( Fig. 1 a)  . Brain atrophy measures were obtained from T1-weighted images. To obtain third ventricle width, a line was drawn through the long axis of the ventricle, parallel to the interhemispheric fissure in the section where the third ventricle was most visible. The width was then measured by drawing a second line perpendicular to the first at its midpoint ( Fig. 1 b)  . Corpus callosum atrophy was obtained as the surface area of corpus callosum on the mid-sagittal T1-weighted image in mm2 ( Fig. 2 a)  . Thalamic atrophy was measured as the surface area of both thalami in mm2 on the coronal T1-weighted image that thalamus is best visualized along with brainstem ( Fig. 2 b)  .
In order to establish the validity of our MRI measures, the neuroradiologist repeated all measures from 10 randomly chosen subjects (5 patients with RRMS and 5 controls) to determine intra-rater reliability, expressed as the coefficient of variation (COV) , , and . The COV for total FLAIR lesion volume was 3.5%, 1.1% for third ventricle width, 1.7% for thalamic atrophy and 1.5% for the surface of corpus callosum..
2.4. Statistical analysis
Statistical analysis was performed using the statistical programming language R  .
Normality hypothesis of the variables of interest was evaluated with Shapiro–Wilk test. Clinical and demographic characteristics between groups were analyzed using the Mann–Whitney test for age and education, and χ2 test for gender. Pearson correlation coefficients and Spearman rank correlation coefficients were calculated for all measures in both groups. A correlation coefficient of 0.1 represents a weak correlation, coefficient of 0.3 is considered a moderate correlation and a correlation coefficient of 0.5 and above represents a strong correlation  . Comparisons between RRMS and controls were investigated on each neuropsychological and MRI measure using t-test and Mann–Whitney U test (according to whether or not the normality assumption was met by the Shapiro–Wilk test).
Hedges' g effect size indicator was also calculated. A Hedges' g effect size of 0.2 to 0.3 is considered a “small” effect, around 0.5 a “medium” effect and above 0.8, a “large” effect  .
We conducted a stepwise regression analysis with p = .05 to enter and p = .10 to exit, using as dependent variable the IADL. On the first step we used age, gender, years of education, duration and EDSS as covariates and on the second step all neuropsychological measures. The same procedure but with a logistic regression model was performed, in order to evaluate the neuropsychological measures that predict employment status.
We then used as dependent variables the neuropsychological measures that appeared from the previous regressions to be the best predictors for IADL and employment status, and proceeded with a stepwise analysis using the same covariates as before, and all MRI measures as independent variables.
Differences were considered significant, for all analyses, when p values were less than .05*, less than .01** and less than .001***.
RRMS patients differed significantly from controls with a large effect size in TMTA, reaction time and third ventricle width. Medium effect size was noted in TMTB, composite memory, psychomotor speed, cognitive flexibility, corpus callosum and thalamic area, while a small effect size was noted in phonological verbal fluency task. Table 2 displays performance (mean scores) on all cognitive tasks and MRI atrophy measures in RRMS patients and controls.
|RRMS||Controls||P value||Hedges' g|
p < .001***, p < .01 **, p < .05 *, Hedges' g 0.2–0.3: small effect, 0.5: medium effect, 0.8 and above: large effect, TMTA: Trail Making Test A, TMTB: Trail Making Test B, VFS: Verbal Fluency Semantic, VFP: Verbal Fluency Phonological, CMEM: Composite Memory, PSYSP: Psychomotor Speed, PRSP: Processing Speed, CAT: Complex Attention, RT: Reaction Time, CFL: Cognitive Flexibility, EXF: Executive Functions, 3rd V: 3rd Ventricle width, CC: Corpus Callosum Surface, THAL: Thalamic Area, RRMS: Relapsing Remitting Multiple Sclerosis, n.s.: non significant.
The frequency of cognitive dysfunction observed for our RRMS patients was 19/50 (38.00%). Impairment in TMTA and TMTB was found in 34% of the patients. Semantic verbal fluency impairment was detected in 24%, while phonological in 30% of RRMS patients. Deficits in composite memory were noted in 16%, while psychomotor speed was impaired in 20% of our patients. Processing speed was found to be impaired in 8%, reaction time in 58%, cognitive flexibility in 28%, while complex attention and executive functions in 24% of our RRMS patients.
Table 3 demonstrates the correlation coefficients between neuropsychological, MRI measures and IADL.
|VFS||− 0.53***||− 0.66***|
|VFP||− 0.42**||− 0.50***||0.70***|
|RT||0.48**||0.53***||− 0.41**||− 0.52***|
|CAT||0.47**||0.56***||− 0.60***||− 0.57***||0.63***|
|CFL||− 0.57***||− 0.65***||0.63***||0.63***||− 0.69***||− 0.89***|
|PRSP||− 0.69***||− 0.73***||0.62***||0.55***||− 0.66***||− 0.66***||0.71***|
|CMEM||− 0.38**||− 0.45**||0.36**||0.48**||− 0.61***||0.49**||0.59***||0.66***|
|PSYSP||− 0.59***||− 0.50***||0.39**||0.44**||− 0.58***||− 0.43**||0.54***||0.72***||0.58***|
|EXF||− 0.60***||− 0.68***||0.69***||0.69***||− 0.72***||− 0.91***||0.95***||0.79***||0.60***||0.55***|
|LVL||0.30**||0.37**||− 0.07||− 0.34**||0.39**||0.28*||− 0.35**||− 0.33**||− 0.38**||− 0.32**||− 0.43**|
|CC||− 0.54***||− 0.56***||0.59***||0.68***||− 0.64***||− 0.61***||0.64***||0.68***||0.50***||0.54***||0.71***||− 0.35**|
|3rd V||0.64***||0.61***||− 0.58***||− 0.65***||0.67***||0.62***||− 0.66***||− 0.73***||− 0.53***||− 0.50***||− 0.73***||0.39**||− 0.90***|
|THAL||− 0.68***||− 0.72***||0.66***||0.62***||− 0.65***||− 0.67***||0.70***||0.79***||0.56***||0.55***||0.77***||0.36**||0.92***||− 0.90***|
|IADL||0.45**||0.31**||− 0.29*||− 0.29*||0.38**||0.30**||− 0.42**||− 0.55***||− 0.50***||− 0.69***||− 0.42**||0.16*||− 0.49**||0.52***||− 0.49**|
0.5 and above: strong correlation⁎⁎⁎, 0.3: moderate correlation⁎⁎, 0.1: weak correlation⁎..
TMTA: Trail Making Test A, TMTB: Trail Making Test B, VFS: Verbal Fluency Semantic, VFP: Verbal Fluency Phonological, CMEM: Composite Memory, PSYSP: Psychomotor Speed, PRSP: Processing Speed, CAT: Complex Attention, RT: Reaction Time, CFL: Cognitive Flexibility, EXF: Executive Functions, 3rd V: 3rd Ventricle Width, CC: Corpus Callosum Surface, THAL: Thalamic Area, LVL: Lesion Volume, IADL: Instrumental Activities of Daily Living.
Instrumental activities of daily living were strongly correlated with processing speed, composite memory and psychomotor speed, moderately correlated with TMTA, TMTB, reaction time, complex attention, executive functions and cognitive flexibility and weakly correlated with semantic and phonological verbal fluency tasks. IADL had a strong correlation with third ventricle width, moderate correlation with corpus callosum and thalamic area, and a weak correlation with lesion volume. Our atrophy measures (corpus callosum surface, thalamic area and third ventricle width) were strongly correlated with all cognitive indices. Lesion volume had a moderate correlation with TMTA, TMTB, phonological verbal fluency task, reaction time, cognitive flexibility, processing speed, executive functions, psychomotor speed and composite memory, weak correlation with complex attention and did not show any correlation with semantic verbal fluency task.
We also compared clinical, neuropsychological and MRI measures between employed and unemployed RRMS patients as demonstrated in Table 4 .
|Employed (N = 35)||Unemployed (N = 15)||P value||Hedges' g|
|3rd V||4.61(0.96)||6.71(1.38)||<.001***||− 0.68|
p < .001 ***, p < .01 **, p < .05 *, Hedges' g 0.2–0.3: small effect, 0.5: medium effect, 0.8 and above: large effect.
EDSS: Expanded Disability Status Scale, DUR: Duration (number of years from diagnosis), TMTA: Trail Making Test A, TMTB: Trail Making Test B, VFS: Verbal Fluency Semantic, VFP: Verbal Fluency Phonological, CAT: Complex Attention, PRSP: Processing Speed, CMEM: Composite Memory, PSYSP: Psychomotor Speed, RT: Reaction Time, CFL: Cognitive Flexibility, EXF: Executive Functions, LVL: Lesion Volume, CC: Corpus Callosum Surface, 3rd V: 3rd Ventricle Width, THAL: Thalamic Area, IADL: Instrumental Activities of Daily Living, n.s.: non-significant.
Unemployed RRMS patients scored significantly lower than employed patients in all neuropsychological measures except complex attention. Large effect size was noted in all cognitive variables except from TMTA and TMTB that had a moderate effect size. Atrophy was significantly more prominent in unemployed compared to employed RRMS patients. Corpus callosum and thalamic atrophy had a strong effect size, while third ventricle width a moderate effect between employed and unemployed patients. Total lesion volume and EDSS did not show significant difference between employed and unemployed RRMS patients.
In order to reveal the MRI marker that is the best predictor of cognitive decline in those neuropsychological measures that affect every day activities and employment status, we performed stepwise regression analysis using as dependent variable IADL and all neuropsychological measures as predictors. On the first block we used age, gender, years of education, duration and EDSS as covariates. The retained neuropsychological measure that was found to be the best predictor for IADL was psychomotor speed (R2 = 0.501, p < .001). We also conducted a logistic regression analysis using as dependent variable the employment status and after controlling for the same covariates, we found that composite memory (Nagelkerke R2 = 0.656, p < .001) and TMTB (Nagelkerke R2 = 0.722, p < .001) were the best predictors of employment status.
We then performed a stepwise regression analysis using as dependent variables psychomotor speed, composite memory and TMTB and all MRI measures as predictors. After controlling for all covariates, thalamic atrophy appeared to be the best predictor of cognitive decline as demonstrated in Table 5 .
|Covariates||Retained MRI measure||Adjusted R2||Standardized β||p|
|TMTB||Age, gender, duration, education, EDSS||THAL||0.447||− 0.568||<.001***|
|CMEM||Age, gender, duration, education, EDSS||THAL||0.260||0.401||<.01**|
|PSYSP||Age, gender, duration, education, EDSS||THAL||0.329||0.426||<.001***|
p < .01**, p < .001***..
TMTB: Trail Making Test B, CMEM: Composite Memory, PSYSP: Psychomotor Speed, EDSS: Expanded Disability Status Scale, THAL: Thalamic Area.
Since the 1980s, studies of MS patients have reported cognitive impairment with prevalence rates ranging from 40 to 70%  and . Cognitive impairment has been demonstrated at all stages and in all subtypes of the disease including relapsing-remitting, secondary progressive, primary progressive, clinically isolated syndrome and even benign MS  and . Cognitive impairment tends to be more severe and pronounced in the progressive phase  and . Although MS can affect almost all aspects of cognition  , the typical profile of cognitive decline consists of impairment in episodic memory, efficiency of information processing (including working memory and processing speed) and executive functions  . Overt dementia is rare in MS, and the usual clinical presentation is one of specific and subtle cognitive deficits that vary significantly among patients  . In order to explain this variability, research has been focused on cognitive reserve  .
Current data indicate that patients with increased reading level and years of education will have a smaller degree of cognitive decline over 5 years  and also suggest that cognitive reserve reduces the negative effect of brain atrophy on cognition in MS  .
Cognitive impairment is an important predictor of health-related quality of life at all stages of MS, affecting daily activities and employment status , , and . Cognitive status is only weakly correlated with disease duration and physical disability  and  and can predict future disease progression  and . Our study showed that 38% of our RRMS patients in a district population of Western Greece were cognitively impaired, in accordance with the estimated prevalence of previous studies  and .
Performance in TMTA, TMTB, phonological verbal fluency task, cognitive flexibility, memory, psychomotor speed and reaction time were found to be significantly different between our RRMS patients and controls ( Table 2 ). Large effect size was noted in TMTA and reaction time, medium in TMTB, composite memory, psychomotor speed and cognitive flexibility, while small effect size was present in phonological verbal fluency task ( Table 2 ). These findings are consistent with the ubiquitous concession that the typical profile of cognitive decline in MS consists of impairment in memory, information processing speed and executive functions  . TMT A measures attention, visuomotor and information processing speed, while TMT B assesses cognitive flexibility and the ability to shift between two different cognitive stimuli, which is an integral part of executive functions  . Psychomotor speed and reaction time are important components of information processing speed and can be measured by traditional widely used tests such as Paced Auditory Serial Addition Test  and Symbol Digit Modalities Test  or by computerized batteries , , , and .
In the present study we administered two traditional paper and pencil neuropsychological tests (TMT parts A and B and the verbal fluency task) as well as CNS-VS, a computerized cognitive screening battery , , and . Computerized batteries are easy to use, require significantly less time to administer, produce instant scoring and have demonstrated comparable results to traditional neuropsychological batteries  . They can also accurately quantify the ‘speed factor’ via multiple parameters such as psychomotor speed, processing speed and reaction time, increasing their sensitivity in detecting subtle changes in information processing speed , , and . However, the reliance on the visual modality, the familiarity of the participant with computers, and the provision of less qualitative information compared to paper and pencil tests are the main disadvantages of computerized cognitive screening batteries  .
In order to study the effect of cognitive decline in quality of life of our RRMS patients, we used the Instrumental Activities of Daily Living (IADL) scale  . IADL are functional activities that involve use of a tool or instrument (e.g., making a telephone call, cooking, shopping, and managing finances), require more steps for completion and considered to be of a higher order compared to basic ADL (e.g., mobility, dressing, bathing and feeding)  . IADL have been clearly associated with self-awareness of functional status and reports of quality of life in multiple sclerosis patients  . In addition, we used employment status as another measure of quality of life in our RRMS patients  . We found that unemployed patients scored significantly lower than employed RRMS patients with larger effect size on verbal fluency task, memory, processing speed and reaction time, while physical disability as measured by EDSS did not show any significant difference ( Table 4 ).
Numerous MRI markers have been used and are significantly correlated with cognitive performance , , and . Seminal studies of MS patients found a modest association between total lesion volume (or in specific sites of cerebral white matter) and cognitive status  and . This supported the notion that cognitive dysfunction in MS may be caused by a functional disconnection between cortical and deep gray matter structures, secondary to white matter lesions  and . MRI atrophy measures, such as whole brain atrophy, or central atrophy (corpus callosum area, third ventricle width) have provided robust correlations with cognitive dysfunction and are thought to be markers of the most destructive aspects of MS pathology  and . More recently, gray matter involvement in MS has received increased interest. Cortical lesions and cortical atrophy are now considered to be independent predictors of cognitive dysfunction in MS  . Moreover, a growing body of data indicates that deep gray matter atrophy is related to cognitive decline in MS  and . In the present study we measured total lesion volume, third ventricle width, corpus callosum and thalamic atrophy. Between our employed and unemployed RRMS patients there was a significant difference in all MRI atrophy measures with large effect size on corpus callosum and thalamic area and moderate on third ventricle width, while there was no difference in total lesion volume ( Table 4 ). Thalamic atrophy, corpus callosum atrophy and third ventricle width were strongly inter-correlated, while total lesion volume had a moderate correlation with atrophy measures ( Table 3 ).
Neuropsychological measures had a strong correlation with all MRI atrophy measures (third ventricle width, thalamic and corpus callosum atrophy), thus we could not demonstrate which MRI atrophy metric has a better correlation with our cognitive tests. On the other hand, total lesion volume had only a weak or moderate correlation with neuropsychological measures ( Table 3 ), as previously described , , and .
Memory, psychomotor and processing speed showed a strong correlation with IADL ( Table 3 ). Therefore, speed factor appears to have the strongest correlation with IADL, as previously reported  , while in our study we also found memory to be strongly correlated with IADL. Moreover, 3rd ventricle width had a strong correlation with IADL, thalamic and corpus callosum atrophy a moderate, and total lesion volume had only a weak correlation with IADL ( Table 3 ).
To the best of our knowledge, only a few studies have explored the effect of cognitive decline in MS on actual performance of IADL , , , , and . In the present study, we performed regression analysis and found that psychomotor speed was the best predictor among all cognitive indices of impairment on instrumental activities of daily living. This finding is in accordance with a previous study, which demonstrated processing speed to be strongly correlated with timed instrumental activities of daily living  while another study  failed to show any significant correlation between objective (performance-based) and subjective (self-report) measures of everyday activities, raising the importance of validity  and  of the neuropsychological measures used for MS patients.
Further regression analysis demonstrated memory and TMTB, as the best predictors of employment status. A previous study showed that memory and information processing speed distinguished employed from unemployed MS patients  , while another study revealed information processing speed and executive functions to be significantly affected in unemployed versus employed MS patients  .
In our study, it appears that poor performance in memory, psychomotor speed and TMTB are the most clinically meaningful cognitive indices, predicting impairment in quality of life as measured by IADL and employment status.
In order to explore the association between our MRI measures and cognition, we performed another regression analysis model that revealed thalamic atrophy as the best predictor of impairment in memory, psychomotor speed and TMTB in our RRMS patients ( Table 5 ). Consistent with our results, previous studies have shown that thalamic atrophy was the best predictor of cognitive decline  and , had a strong correlation with 3rd ventricle width , , and  and a modest correlation with white matter lesion volume  .
Gray matter involvement is an integral part in our understanding of the underlying MS pathology, and of particular relevance to cognitive decline as well as overall disease progression  and . Pathology in deep gray matter structures, particularly thalamus is frequently observed including demyelinating lesions and atrophy  and . While thalamic pathology appears to be present in MS from early stage, it is a likely substrate for accumulating cognitive and motor dysfunction in the progressive stage , , and . Thalamus, as a ‘relay organ’ is involved in a variety of neurological functions including motor, sensory, integrative and cognitive functions. Thalamic axons transmit information between subcortical and cortical areas, therefore, damage to thalamic nuclei and their connections potentially can present with a wide range of symptoms, including cognitive decline  and . More recent studies, using advanced structural and functional MR techniques, demonstrated thalamic atrophy as a predictor of cognitive decline in MS patients and reported that further pathology within thalamus, detected by mean diffusivity, and functional connectivity adds incremental variance in predicting cognitive impairment in MS patients , , , and . Therefore, it seems that thalamus does not simply act as a signal relay, but is able to suppress or amplify signals in transit, therefore further work is needed, in order to establish if the changes in thalamic and cortical synchronicity are independent contributors of cognitive decline or reflect the structural damage underlying them  .
In the present study we did not correct for multiple comparisons, because our results are in accordance with the literature and we have focused more on effect sizes compared to p values. Moreover, our study is an exploratory, a hypothesis generation research study, and as already highlighted, further research is needed in order to establish the role of thalamus in cognitive decline of MS patients.
Another limitation of the present work is the lack of advanced MRI measures. On the other hand, the greatest strength of this study is the emphasis on clinically meaningful cognitive decline.
The present study demonstrated for the first time in a Greek population that thalamic atrophy is highly informative of clinically meaningful cognitive impairment in RRMS patients, affecting employment status and instrumental activities of daily living.
All authors have approved the submission of the manuscript and have no conflicts of interest or relationship to disclose. Institutional Review Board approval was obtained.
This research received no specific grant from any funding agency.
-  S.M. Rao, G.J. Leo, L. Bernardin, F. Unverzagt. Cognitive dysfunction in multiple sclerosis I: frequency, patterns, and prediction. Neurology. 1991;41:685-691 Crossref
-  N.D. Chiaravalloti, J. DeLuca. Cognitive impairment in multiple sclerosis. Lancet Neurol.. 2008;7:1139-1151 Crossref
-  S. Hoffmann, M. Tittgemeyer, D.Y. von Cramon. Cognitive impairment in multiple sclerosis. Curr. Opin. Neurol.. 2007;20:275-280
-  M. Deloire, A. Ruet, D. Hamel, M. Bonnet, V. Dousset, B. Brochet. MRI predictors of cognitive outcome in early multiple sclerosis. Neurology. 2011;76:1161-1167 Crossref
-  C. Potagas, E. Giogkaraki, G. Koutsis, D. Mandellos, E. Tsirempolou, C. Sfagos, et al. Cognitive impairment in different MS subtypes and clinically isolated syndromes. J. Neurol. Sci.. 2008;267:100-106 Crossref
-  S.M. Rao, G.J. Leo, L. Ellington, T. Nauertz, L. Bernardin, F. Unverzagt. Cognitive dysfunction in multiple sclerosis II: impact on employment and social functioning. Neurology. 1991;41:692-696 Crossref
-  K. Honarmand, N. Akbar, N. Kou, A. Feinstein. Predicting employment status in multiple sclerosis patients: the utility of the MS functional composite. J. Neurol.. 2011;258:244-249 Crossref
-  D.W. Langdon. Cognition in multiple sclerosis. Curr. Opin. Neurol.. 2011;24:244-249 Crossref
-  M. Filippi, M.A. Rocca, R.H. Benedict, J. DeLuca, J.G. Geurts, S.A. Rombouts, et al. The contribution of MRI in assessing cognitive impairment in multiple sclerosis. Neurology. 2010;75:2121-2128 Crossref
-  B. Grassiot, B. Desgranges, F. Eustache, G. Defer. Quantification and clinical relevance of brain atrophy in multiple sclerosis: a review. J. Neurol.. 2009;256:1397-1412 Crossref
-  M. Calabrese, F. Agosta, F. Rinaldi, I. Mattisi, P. Grossi, A. Favaretto, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch. Neurol.. 2009;66:1144-1150
-  A. Kutzelnigg, H. Lassmann. Cortical lesions and brain atrophy in MS. J. Neurol. Sci.. 2005;233:55-59 Crossref
-  N. Mistry, R. Abdel-Fahim, O. Mougin, C. Tench, P. Gowland, N. Evangelou. Cortical lesion load correlates with diffuse injury of multiple sclerosis normal appearing white matter. Mult. Scler.. 2014;20(2):227-233 Crossref
-  M.K. Houtchens, R.H. Benedict, R. Killiany, J. Sharma, Z. Jaisani, B. Singh, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology. 2007;69:1213-1223 Crossref
-  S. Batista, R. Zivadinov, M. Hoogs, N. Bergsland, M. Heininen-Brown, M.G. Dwyer, et al. Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J. Neurol.. 2012; 259-139-146
-  R.H. Bendict, H.E. Hulst, N. Bergsland, M.M. Schoonheim, M.G. Dwyer, B. Weinstock-Guttman, et al. Clinical significance of atrophy and white matter mean diffusivity within the thalamus of multiple sclerosis patients. Mult. Scler.. 2013;19(11):1478-1484
-  M.D. WI, A. Compston, G. Edan, D. Goodkin, H.P. Hartung, F.D. Lublin, et al. Recommended diagnostic criteria for multiple sclerosis : guidelines from the international panel on the diagnosis of multiple sclerosis. Ann. Neurol.. 2001;:50121-50127
-  C.H. Polman, S.C. Reingold, B. Banwell, M. Clanet, J.A. Cohen, M. Filippi, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald Criteria. Ann. Neurol.. 2011;69:292-302 Crossref
-  J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis : an Expanded Disability Status Scale (EDSS). Neurology. 1983;33:1444-1452
-  M.P. Lawton, E.M. Brody. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179-186 Crossref
-  T.G. Pack, G.M. Szirony, J.D. Kushner, J.R. Bellaw. Quality of life and employment in persons with multiple sclerosis. Work. 2014;49(2):281-287
-  R.H. Benedict, I. Fishman, M.M. McClellan, R. Bakshi, B. Weinstock-Guttman. Validity of the Beck Depression Inventory-Fast Screen in multiple sclerosis. Mult. Scler.. 2003;9(4):393-396 Crossref
-  I. Zalonis, E. Kararizou, N.I. Triantafyllou, E. Kapaki, S. Papageorgiou, P. Sgouropoulos, et al. A normative study of the trail making test A and B in Greek adults. Clin. Neuropsychol.. 2008;22(5):842-850 Crossref
-  M.H. Kosmidis, C.H. Vlahou, P. Panagiotaki, G. Kiosseoglou. The verbal fluency task in the Greek population: normative data and clustering and switching strategies. J. Int. Neuropsychol. Soc.. 2004;10(2):164-172
-  T.C. Gualtieri, L.G. Johnson. Reliability and validity of a computerized neurocognitive test batteryCNS vital signs. Arch. Clin. Neuropsychol.. 2006;21(7):623-643
-  M.R. Basso, S. Beason-Hazen, J. Lynn, K. Rammohan, R.A. Bornstein. Screening for cognitive dysfunction in multiple sclerosis. Arch. Neurol.. 1996;53:980-984
-  C.A. Schneider, W.S. Rasband, K.W. Eliceiri. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods. 2012;9(7):671-675 Crossref
-  M.P. Sanchez, A. Nieto, J. Barroso, V. Martin, M.A. Hernandez. Brain atrophy as a marker of cognitive impairment in mildly disabling relapsing-remitting multiple sclerosis. Eur. J. Neurol.. 2008;15:1091-1099 Crossref
-  R.H. Benedict, B. Weinstock-Guttman, I. Fishman, J. Sharma, C.W. Tjoa, R. Bakshi. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch. Neurol.. 2004;61:226-230 Crossref
-  R.A. Bermel, J. Sharma, C.W. Tjoa, S.R. Puli, R. Bakshi. A semiautomated measure of whole-brain atrophy in multiple sclerosis. J. Neurol. Sci.. 2003;208:57-65 Crossref
-  S. Tippmann. Programming tools: adventures with R. Nature. 2015;517(7532):109-110
-  J. Cohen. A power primer. Psychol. Bull.. 1992;112(1):155-159 Crossref
-  L.V. Hedges. Distribution theory for glass' estimator of effect size and related estimators. J. Educ. Behav. Stat.. 1981;6:107-128
-  J.M. Peyser, S.M. Rao, N.G. LaRocca, E. Kaplan. Guidelines for neuropsychological research in multiple sclerosis. Arch. Neurol.. 1990;47:94-97
-  E. Portaccio, M.L. Stromillo, B. Goretti, V. Zipoli, G. Siracusa, M. Battaglini, et al. Neuropsychological and MRI measures predict short-term evolution in benign multiple sclerosis. Neurology. 2009;73:498-503 Crossref
-  D.R. Denney, L.A. Sworowski, S.G. Lynch. Cognitive impairment in three subtypes of multiple sclerosis. Arch. Clin. Neuropsychol.. 2005;20:967-981 Crossref
-  G. Bergendal, S. Fredrikson, O. Almkvist. Selective decline in information processing in subgroups of multiple sclerosis: an 8-year longitudinal study. Eur. Neurol.. 2007;57:193-202 Crossref
-  R.S. Prakash, E.M. Snook, J.M. Lewis, R.W. Motl, A.F. Kramer. Cognitive impairments in relapsing-remitting multiple sclerosis: a meta-analysis. Mult. Scler.. 2008;14:1250-1261 Crossref
-  R.H. Benedict, S.A. Morrow, B. Weinstock Guttman, D. Cookfair, D.J. Schretlen. Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients. J. Int. Neuropsychol. Soc.. 2010;16:829-835 Crossref
-  J.F. Sumowski, N. Chiaravalloti, G. Wylie, J. DeLuca. Cognitive reserve moderates the negative effect of brain atrophy on cognitive efficiency in multiple sclerosis. J. Int. Neuropsychol. Soc.. 2009;15:606-612 Crossref
-  A.J. Mitchell, J. Benito-Leon, J.M. Gonzalez, J. Rivera-Navarro. Quality of life and its assessment in multiple sclerosis: integrating physical and psychological components of wellbeing. Lancet Neurol.. 2005;4:556-566 Crossref
-  Y. Goverover, H.M. Genova, F.G. Hillary, J. DeLuca. The relationship between neuropsychological measures and the timed instrumental activities of daily living task in multiple sclerosis. Mult. Scler.. 2007;13:636-644 Crossref
-  S.A. Morrow, A. Drake, R. Zivadinov, F. Munschauer, B. Weinstock-Guttman, R.H. Benedict. Predicting loss of employment over three years in multiple sclerosis: clinically meaningful cognitive decline. Clin. Neuropsychol.. 2010;24:1131-1145 Crossref
-  C. Smestad, L. Sandvik, N.I. Landro, E.G. Celius. Cognitive impairment after three decades of multiple sclerosis. Eur. J. Neurol.. 2010;17:499-505 Crossref
-  M.P. Amato, E. Portaccio, B. Goretti, V. Zipoli, A. Iudice, D. Della Pina, et al. Relevance of cognitive deterioration in early relapsing-remitting MS: a 3-year follow-up study. Mult. Scler.. 2010;16:1474-1482 Crossref
-  V. Zipoli, B. Goretti, B. Hakiki, G. Siracusa, S. Sorbi, E. Portaccio, et al. Cognitive impairment predicts conversion to multiple sclerosis in clinically isolated syndromes. Mult. Scler.. 2010;16:62-67 Crossref
-  M. Deloire, A. Ruet, D. Hamel, M. Bonnet, B. Brochet. Early cognitive impairment in multiple sclerosis predicts disability outcome several years later. Mult. Scler.. 2010;16:581-587 Crossref
-  M. Filippi, A. Rocca. MRI and cognition in multiple sclerosis. Neurol. Sci.. 2010;31(Suppl. 2):S231-S234
-  D.M.A. Gronwall. Paced Auditory Serial Addition Task: a measure of recovery from concussion. Percept. Mot. Skills. 1977;44:367-373 Crossref
-  A. Smith. Symbol Digit Modalities Test (SDMT). Manual (revised) (Western Psychological Services, Los Angeles, 1982)
-  A. Papathanasiou, L. Messinis, V.L. Georgiou, P. Papathanasopoulos. Cognitive impairment in relapsing remitting and secondary progressive multiple sclerosis patients: efficacy of a computerized cognitive screening battery. ISRN Neurol.. 2014;13:151379
-  H. Lapshin, K.L. Lanctot, P. O'Connor, A. Feinstein. Assessing the validity of a computer-generated cognitive screening instrument for patients with multiple sclerosis. Mult. Scler.. 2013;19(14):1905-1912 Crossref
-  A. Ruet, M.S. Deloire, J. Charre-Morin, D. Hamel, B. Brochet. A new computerized cognitive test for the detection of information processing speed impairment in multiple sclerosis. Mult. Scler.. 2013;19(12):1665-1672 Crossref
-  H. Lapshin, B. Audet, A. Feinstein. Detecting cognitive dysfunction in a busy multiple sclerosis clinical setting: a computer generated approach. Eur. J. Neurol.. 2014;21(2):281-286 Crossref
-  E. Woo. Computerized neuropsychological assessments. CNS Spectr.. 2008;13(10 Suppl. 16):14-17
-  J.F. Fitzgerald, D.M. Smith, D.K. Martin, J.A. Freedman, F.D. Wolinsky. Replications of the multidimensionality of activities of daily living. J. Gerontol.. 1993;48(1):S28-S31
-  Y. Goverover, N. Chiaravalloti, E. Gaudino-Goering, N. Moore, J. DeLuca. The relationship among performance of instrumental activities of daily living, self-report of quality of life, and self-awareness of functional status in individuals with multiple sclerosis. Rehabil. Psychol.. 2009;54(1):60-68 Crossref
-  M. Rovaris, G. Comi, M. Filippi. MRI markers of destructive pathology in multiple sclerosis-related cognitive dysfunction. J. Neurol. Sci.. 2006;245:111-116 Crossref
-  S.M. Rao, G.J. Leo, V.M. Haughton, P.S. Aubin-Faubert, L. Bernardin. Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis. Neurology. 1989;39:161-166
-  R.H. Benedict, D.A. Carone, R. Bakshi. Correlating brain atrophy with cognitive dysfunction, mood disturbances, and personality disorder in multiple sclerosis. J. Neuroimaging. 2004;14(Suppl. 3):36S-45S Crossref
-  S.E. Doble, J.D. Fisk, A.G. Fisher, P.G. Ritvo, T.J. Murray. Functional competence of community-dwelling persons with multiple sclerosis using the Assessment of Motor and Process Skills. Arch. Phys. Med. Rehabil.. 1994;75:843-851 Crossref
-  E. Mansson, J. Lexell. Performance of activities of daily living in multiple sclerosis. Disabil. Rehabil.. 2004;26:576-585 Crossref
-  Y. Goverover, J. Kalmar, E. Gaudino-Goering, M. Shawaryn, N.B. Moore, J. Halper, et al. The relationship between subjective and objective measures of everyday life activities in persons with multiple sclerosis. Arch. Phys. Med. Rehabil.. 2005;86:2303-2308 Crossref
-  R.H. Benedict, R. Zivadinov. Reliability and validity of neuropsychological screening and assessment strategies in MS. J. Neurol.. 2007;254(Suppl. 2):II22-II25 Crossref
-  C.I. Higginson, P.A. Arnett, W.D. Voss. The ecological validity of clinical tests of memory and attention in multiple sclerosis. Arch. Clin. Neuropsychol.. 2000;15(3):185-204
-  M.M. Schoonheim, V. Popescu, F.C. Rueda Lopes, O.T. Wiebenga, H. Vrenken, L. Douw, et al. Subcortical atrophy and cognition: sex effects in multiple sclerosis. Neurology. 2012;79(17):1754-1761 Crossref
-  A. Cifelli, M. Arridge, P. Jezzard, M.M. Esiri, J. Palace, P.M. Matthews. Thalamic neurodegeneration in multiple sclerosis. Ann. Neurol.. 2002;52(5):650-653 Crossref
-  M. Wylezinska, A. Cifelli, P. Jazzard, J. Palace, M. Alecci, P.M. Matthews. Thalamic neurodegeneration in relapsing–remitting multiple sclerosis. Neurology. 2003;60(12):1949-1954 Crossref
-  A. Minagar, M.H. Barnett, R.H. Benedict, D. Pelletier, I. Pirko, M.A. Sahraian, et al. The thalamus and multiple sclerosis. Modern views on pathologic, imaging, and clinical aspects. Neurology. 2013;80(2):210-219 Crossref
-  M. Vercellino, S. Masera, M. Lorenzatti, C. Condello, A. Merola, A. Mattioda, et al. Demyelination, inflammation and neurodegeneration in multiple sclerosis deep gray matter. J. Neuropathol. Exp. Neurol.. 2009;68:489-502 Crossref
-  C.P. Gilmore, I. Donaldson, L. Bo, T. Owens, J. Lowe, N. Evangelou. Regional variations in the extend and pattern of grey matter demyelination in multiple sclerosis: a comparison between the cerebral cortex, cerebellar cortex, deep grey matter nuclei and the spinal cord. J. Neurol. Neurosurg. Psychiatry. 2009;80:182-187 Crossref
-  R.G. Henry, M. Shieh, B. Amirbekian, S. Chung, D.T. Okuda, D. Pelletier. Connecting white matter injury and thalamic atrophy in clinically isolated syndromes. J. Neurol. Sci.. 2009;282:61-66 Crossref
-  M.A. Rocca, S. Mesaros, E. Pagani, M.P. Sormani, G. Comi, M. Filippi. Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology. 2010;257:463-469 Crossref
-  Y.D. Van Der Werf, D.J. Tisserand, P.J. Visser, P.A. Hofman, E. Vuurman, H.B. Uylings, et al. Thalamic volume predicts performance on tests of cognitive speed and decreases in healthy aging: a magnetic resonance imaging-based volumetric analysis. Brain Res. Cogn. Brain Res.. 2001;11:377-385 Crossref
-  M.M. Schoonheim, H.E. Hulst, R.B. Brandt, M. Strik, A.M. Wink, B.M.J. Uitdehaag, et al. Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology. 2015;84:1-8
-  D.T. Chard, R.H. Benedict. Paradoxes in thalamic function and cognition in people with multiple sclerosis: less is more. Neurology. 2015;84(8):764-765 Crossref
a Department of Neurology,University of Nottingham,Queen's Medical Centre,Derby Road,NottinghamNG7 2UH,UK
b Neuropsychology Section,Department of Neurology,University of Patras Medical School,Patras265 04,Greece
c Department of Radiology,University of Patras Medical School,Patras265 04,Greece
d Department of Medical Physics,University of Patras Medical School,Patras265 04,Greece
e Department of Psychiatry,University of Patras Medical School,Patras265 04,Greece
f Hellenic Open University,Patras265 04,Greece
⁎ Corresponding author at: University of Nottingham, Queen's Medical Centre, Derby Road, Nottingham NG7 2UH, UK.
© 2015 Elsevier B.V., All rights reserved.