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

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.

Introduction

The modern localization of brain function started in the 19th century when focal lesions identified at postmortem were related to specific neurological deficits. More recently positron emission tomography (PET) and functional magnetic resonance imaging (MRI) have been used to map neurological function in living human subjects. The relatively low signal-to-noise ratios of these imaging techniques required averaging data from many individuals, which necessitated two methodological developments, i.e., the registration of different brains into a standard stereotaxic space, and the application of statistical tests to the resultant data to generate so-called statistical parametric maps. Here we apply these computational methods to a lesional study in multiple sclerosis (MS).

MRI is commonly used for the diagnosis of MS and for measuring disease severity in clinical trials Miller et al 1998, Rovaris and Filippi 1999. Paradoxically, MRI-determined total lesion load in the brain correlates weakly with disability Miki et al 1999, Molyneux et al 1998, Nijeholt et al 1998 as assessed, for example, by Kurtzke’s Expanded Disability Status Scale (EDSS) (Kurtzke, 1983). There are several possible explanations for this poor correlation; i.e., the EDSS is nonlinear and heavily weighted toward ambulatory deficits (Willoughby and Paty, 1988); cortical reorganization could contribute to functional recovery Lee et al 2000, Reddy et al 2000; conventional T2-MRI of the brain does not detect spinal cord lesions, dysfunction of normally appearing white matter, or axonal damage, all of which may contribute to disability Filippi et al 1995, Miller et al 1998, Rovaris and Filippi 1999; and finally, these studies fail to take lesion location into account.

Unlike EDSS, cognitive impairment seems to correlate better with total lesion load Arnett et al 1994, Foong et al 1997, Rao et al 1989, Rovaris et al 1998, Sperling et al 2001, Swirsky-Sacchetti et al 1992. However, another paradox has been the lack of a relationship between the location of lesions and specific cognitive deficits. For example, Foong and colleagues (1997) showed that, while MS patients with deficits on executive tasks thought to be sensitive to frontal lobe damage had more lesions in frontal lobes than cognitively unaffected patients, this could be explained by the fact that they also had greater total lesion load. One reason for the inability to find a correlation between lesion location and cognitive impairment could be the relatively small numbers of patients in the studies reported to date.

We have developed an automatic imaging algorithm for lesion detection in MS Evans et al 1997, Zijdenbos et al 1996., Blok et al 1998. This procedure has the advantage of being fully automatic and, therefore, applicable to large numbers of MRI volumes. We applied this algorithm to the MRI scans of 452 patients with relapsing-remitting MS. By transforming each patient’s three-dimensional (3D) image into standard stereotaxic space Collins et al 1994, Evans et al 1992, Arnett et al 1994, Talairach and Tournoux 1988, we were able to perform voxelwise correlations between the likelihood of a lesion being present and the score on different functional disability scales. We describe here a new approach that emphasizes the importance of lesion location in determining the extent and type of neurological deficit in relapsing-remitting MS.