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Brain Behav. 2016 Jan 30;6(3):e00430. doi: 10.1002/brb3.430. eCollection 2016 Mar.

A MS-lesion pattern discrimination plot based on geostatistics.

Author information

1
Interfaculty Department of Geoinformatics Z_GIS Univ. Salzburg Schillerstr. 305020 Salzburg Austria; Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 795020 Salzburg Austria.
2
Department of Neurology Klinikum rechts der Isar Technische Universität München Munich Germany; TUM-Neuroimaging Center Klinikum rechts der Isar Technische Universität München Munich Germany; Department of Statistics Ludwig-Maximilians-University München Munich Germany.
3
Department of Neuroradiology Klinikum rechts der Isar Technische Universität München Munich Germany.
4
Faculty of Science and Technology Lancaster University Engineering Building Lancaster LA1 4YR UK; Faculty of Geosciences University of Utrecht Heidelberglaan 23584 CS Utrecht The Netherlands; School of Geography, Archaeology and Palaeoecology Queen's University Belfast Belfast BT7 1NN Northern Ireland UK.
5
Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 795020 Salzburg Austria; Department of Neurology Klinikum rechts der IsarTechnische Universität München Munich Germany.
6
Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 79 5020 Salzburg Austria.
7
Department of Neurology Klinikum rechts der Isar Technische Universität München Munich Germany; TUM-Neuroimaging Center Klinikum rechts der Isar Technische Universität München Munich Germany; Munich Cluster for Systems Neurology (SyNergy) Munich Germany.

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.

KEYWORDS:

Discrimination; Multiple Sclerosis; geostatistics; lesion; pattern

PMID:
26855827
PMCID:
PMC4733107
DOI:
10.1002/brb3.430
[Indexed for MEDLINE]
Free PMC Article

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