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Clinical correlations of brain lesion distribution in multiple sclerosis

✍ Scribed by M.M. Vellinga; J.J.G. Geurts; E. Rostrup; B.M.J. Uitdehaag; C.H. Polman; F. Barkhof; H. Vrenken


Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
220 KB
Volume
29
Category
Article
ISSN
1053-1807

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✦ Synopsis


Abstract

Purpose

To explore relations between spatial distribution of multiple sclerosis (MS) lesions, and disability. In MS, the presence of asymptomatic brain lesions challenges the prediction of disability based on conventional brain MRI. Hypothesizing that symptomatology may partly be determined by lesion location, this retrospective study explored relations between lesion location and disability using voxelwise analyses in standard space.

Materials and Methods

Using nonparametric permutation‐based statistics, voxelwise lesion probability on T2 lesion masks was related to expanded disability status scale (EDSS) and MS functional composite (MSFC) subdomain scores and demographic characteristics of 325 MS patients. To identify statistically significant locations, a cluster‐forming threshold of 3.1 was used.

Results

In clusters in the periventricular region, lesion probability correlated significantly (P < 0.001) with disability and disease duration, and was higher in progressive than in relapsing disease. When controlled for lesion load (LL), no significant clusters survived. Presence and number of spinal cord lesions did not correlate with lesion probability in any location, and did not influence correlations with disability when included in its analyses.

Conclusion

Periventricular lesions were related to disability. LL influenced relations between disability and lesion probability throughout the brain, suggesting interplay between lesional burden and its location in determining disability in MS. J. Magn. Reson. Imaging 2009;29:768–773. Β© 2009 Wiley‐Liss, Inc.


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## Abstract Morphometric studies of medical images often include a nonrigid registration step from a subject to a common reference. The presence of white matter multiple sclerosis lesions will distort and bias the output of the registration. In this article, we present a method to remove this bias