Rights statement: c 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A MS-lesion pattern discrimination plot based on geostatistics
AU - Marschallinger, Robert
AU - Schmidt, Paul
AU - Hofmann, Peter
AU - Zimmer, Claus
AU - Atkinson, Peter Michael
AU - Sellner, Johann
AU - Trinker, Eugen
AU - Mühlau, Mark
N1 - c 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PY - 2016/3
Y1 - 2016/3
N2 - IntroductionA geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented.MethodsA 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.ResultsParameters 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.ConclusionsThe geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.
AB - IntroductionA geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented.MethodsA 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.ResultsParameters 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.ConclusionsThe geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.
KW - Discrimination
KW - geostatistics
KW - lesion
KW - Multiple Sclerosis
KW - pattern
U2 - 10.1002/brb3.430
DO - 10.1002/brb3.430
M3 - Journal article
VL - 6
JO - Brain and Behavior
JF - Brain and Behavior
SN - 2162-3279
IS - 3
ER -