Marschallinger R, Schmidt P, Hofmann P, Zimmer C, Atkinson PM, Sellner J, Trinka E, Mühlau M. A MS-lesion pattern discrimination plot based on geostatistics.
Brain Behav 2016;
6:e00430. [PMID:
26855827 PMCID:
PMC4733107 DOI:
10.1002/brb3.430]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 09/08/2015] [Accepted: 12/16/2015] [Indexed: 11/15/2022] Open
Abstract
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.
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