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As many as two-thirds of multiple sclerosis (MS) patients are unable to retain employment. Neurological and cognitive status are known to be significant predictors of unemployment, but the relationship between the two is unclear. Furthermore, the association between employment status and depression, anxiety, and personality has not been adequately explored in MS patients. This study examined the demographic, neurological, neuropsychological, and personality factors associated with unemployment in MS. We also sought to determine the utility of the Multiple Sclerosis Functional Composite (MSFC), a measure of MS-related disability incorporating physical and cognitive measures, in predicting employment status. A consecutive sample of 106 MS patients (61.3% unemployed) completed the Brief Repeatable Battery of Neuropsychological Tests (BRBN), Hospital Anxiety and Depression Scale (HADS), and NEO Five-Factor Personality Inventory. The MSFC emerged as the most robust predictor of employment status in MS patients, exceeding the predictive value of the EDSS. Together with NEO "Agreeableness" and HADS Depression subscore, the MSFC accounted for 49.8% of the variance in employment status. Unemployment was also associated with a progressive disease course, longer disease duration, and being female. While Global Cognitive Impairment did not differentiate between groups, unemployed patients scored significantly lower on three of five BRBN indices: Symbol Digit Modality Test, Paced Auditory Serial Addition Test, and Word List Generation. The findings highlight the utility of the MSFC as a predictor of unemployment in MS. Furthermore, a strong association was found between unemployment and the personality construct "Agreeableness", and severity of depression.

The structural integrity of the corticospinal tract (CST) after stroke is closely linked to the degree of motor impairment. Simple and reliable methods of assessing white matter integrity within the CST would facilitate the use of this measure in routine clinical practice. Commonly, diffusion tensor imaging is used to measure voxel-wise fractional anisotropy (FA) in a variety of regions of interest (ROIs) representing the CST. Several methods are currently in use with no consensus about which approach is best. ROIs are usually either the whole CST or the posterior limb of the internal capsule (PLIC). These are created manually on brain images or with reference to an individual's CST determined by tractography. Once the ROI has been defined, the FA can be reported as an absolute measure from the ipsilesional side or as a ratio in comparison to the contralesional side. Both corticospinal tracking and manual ROI definition in individual stroke patients are time consuming and subject to bias. Here, we investigated whether using a CST template derived from healthy volunteers was a feasible method for defining the appropriate ROI within which to measure changes in FA. We reconstructed the CST connecting the primary motor cortex to the ipsilateral pons in 23 age-matched control subjects and 21 stroke patients. An average healthy CST template was created from the 23 control subjects. For each patient, FA values were then calculated for both the template CST and for their own CST. We compared patients' FA metrics between the two tracts by considering four measures (FA in the ipsilesional side, FA in the contralesional side, FA ratio of the ipsilesional side to the contralesional side and FA asymmetry between the two sides) and in two tract-based ROIs (whole tract and tract section traversing the PLIC). There were no significant differences in FA metrics for either method, except for contralesional FA. Furthermore, we found that FA metrics relating to CST damage all correlated with motor ability post-stroke equally well. These results suggest that the healthy CST template could be a surrogate structure for defining tract-based ROIs with which to measure stroke patients' FA metrics, avoiding the necessity for CST tracking in individual patients. CST template-based automated quantification of structural integrity would greatly facilitate implementation of practical clinical applications of diffusion tensor imaging.

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The Symbol Digit Modalities Test (SDMT) is a particularly sensitive measure of cognitive dysfunction in patients with multiple sclerosis (MS). While computerized versions have been developed for use in functional magnetic resonance imaging (fMRI), none has been validated in MS patients. The aim of this study was to validate a new computerized version of the SDMT for use in MS patients. We developed a novel computerized version of the SDMT (c-SDMT) which was completed by 119 MS patients and 38 healthy controls. Our version consisted of eight timed trials of nine symbols. Both groups also underwent cognitive testing with the Brief Repeatable Battery of Neuropsychological Tests (BRB-N) which included the 90-s paper SDMT (p-SDMT) scored according to the number of correct responses per each 15-s interval. The sensitivity and specificity of the c-SDMT and p-SDMT to detect overall cognitive impairment on the BRB-N was determined. MS patients performed significantly worse than controls on both the c-SDMT (t = -6.1, p < 0.001) and p-SDMT (t = 5.7, p < 0.001). More MS patients were impaired on the c-SDMT than the p-SDMT (37% vs. 29%). The sensitivity and specificity of the SDMT was as follows: 71% and 84%, respectively, for the c-SDMT, and 67% and 95%, respectively, for the p-SDMT. Both versions detected a significant group × time effect over the course of the individual trials, although the pattern of responses differed between them. Good test-retest reliability for the c-SDMT was shown (ICC = 0.94). The results support the validity of this version of the c-SDMT as a sensitive measure of cognitive dysfunction in MS. The methodology is also fMRI compatible.

BackgroundMotor deficit after stroke is related to regional anatomical damage.ObjectiveTo examine the influence of lesion location on upper limb motor deficit in chronic patients with stroke.MethodsLesion likelihood maps were created from T1-weighted structural MRI in 33 chronic patients with stroke with either purely subcortical lesions (SC, n=19) or lesions extending to any of the cortical motor areas (CM, n=14). We estimated lesion likelihood maps over the whole brain and applied multivoxel pattern analysis to seek the contribution weight of lesion likelihood to upper limb motor deficit. Among 5 brain regions of interest, the brain region with the greatest contribution to motor deficit was determined for each subgroup.ResultsThe corticospinal tract was most likely to be damaged in both subgroups. However, while damage in the corticospinal tract was the best indicator of motor deficit in the SC patients, motor deficit in the CM patients was best explained by damage in brain areas activated during handgrip.ConclusionsQuantification of structural damage can add to models explaining motor outcome after stroke, but assessment of corticospinal tract damage alone is unlikely to be sufficient when considering patients with stroke with a wide range of lesion topography.

This is the first attempt at an Internet validation of the MSNQ. The modest sensitivity and specificity values suggest that further research is needed before either the patient or informant version of the MSNQ can be used for neuropsychological screening purposes over the Internet.