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Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets.

Authors :
Charil A
Zijdenbos AP
Taylor J
Boelman C
Worsley KJ
Evans AC
Dagher A
Source :
NeuroImage [Neuroimage] 2003 Jul; Vol. 19 (3), pp. 532-44.
Publication Year :
2003

Abstract

In multiple sclerosis (MS), the correlation between disability and the volume of white matter lesions on magnetic resonance imaging (MRI) is usually weak. This may be because lesion location also influences the extent and type of functional disability. We applied an automatic lesion-detection algorithm to 452 MRI scans of patients with relapsing-remitting MS to identify the regions preferentially responsible for different types of clinical deficits. Statistical parametric maps were generated by performing voxel-wise linear regressions between lesion probability and different clinical disability scores. There was a clear distinction between lesion locations causing physical and cognitive disability. Lesion likelihood correlated with the Expanded Disability Status Scale (EDSS) in the left internal capsule and in periventricular white matter mostly in the left hemisphere. Pyramidal deficits correlated with only one area in the left internal capsule that was also present in the EDSS correlation. Cognitive dysfunction correlated with lesion location at the grey-white junction of associative, limbic, and prefrontal cortex. Coordination impairment correlated with areas in interhemispheric and pyramidal periventricular white matter tracts, and in the inferior and superior longitudinal fascicles. Bowel and bladder scores correlated with lesions in the medial frontal lobes, cerebellum, insula, dorsal midbrain, and pons, areas known to be involved in the control of micturition. This study demonstrates for the first time a relationship between the site of lesions and the type of disability in large scale MRI data set in MS.

Details

Language :
English
ISSN :
1053-8119
Volume :
19
Issue :
3
Database :
MEDLINE
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
12880785
Full Text :
https://doi.org/10.1016/s1053-8119(03)00117-4