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One significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r , a small sample data set and associated graphics for comparison.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin. The development of lesions within the gray matter and white matter, which are highly variable with respect to number, total volume, morphology and spatial evolution and which only show a limited correlation with clinical disability, is a hallmark of the disease. Population-based studies indicate a distinct outcome depending on gender. Here, we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods. Within a 3 years observation period, a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging, accompanied by neurological examination. MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis, yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV). Through the MS-lesion pattern discrimination plot, the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline, two female subgroups (F1, F2) and two male subgroups (M1, M2) are discerned that follow a distinct MS-WML pattern evolution in space and time. F1 and M1 start with medium-level MS-WML pattern smoothness and TLV, both behave longitudinally quasi-static. By contrast, F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV. F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV, i.e., continued shrinking and break-up of MS-WML. As compared to the male subgroup M2, the female subgroup F2 shows continued, increased MS-WML pattern smoothness and TLV. Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS. Our results justify detailed studies on gender-related differences.

Introduction: A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented., Methods: A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill., Results: Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot., Conclusions: The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.