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  • Marschallinger_et_al-2016-Brain_and_Behavior

    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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A MS-lesion pattern discrimination plot based on geostatistics

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A MS-lesion pattern discrimination plot based on geostatistics. / Marschallinger, Robert; Schmidt, Paul; Hofmann, Peter; Zimmer, Claus; Atkinson, Peter Michael; Sellner, Johann; Trinker, Eugen; Mühlau, Mark.

In: Brain and Behavior, Vol. 6, No. 3, 03.2016.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Marschallinger, R, Schmidt, P, Hofmann, P, Zimmer, C, Atkinson, PM, Sellner, J, Trinker, E & Mühlau, M 2016, 'A MS-lesion pattern discrimination plot based on geostatistics', Brain and Behavior, vol. 6, no. 3. https://doi.org/10.1002/brb3.430

APA

Marschallinger, R., Schmidt, P., Hofmann, P., Zimmer, C., Atkinson, P. M., Sellner, J., Trinker, E., & Mühlau, M. (2016). A MS-lesion pattern discrimination plot based on geostatistics. Brain and Behavior, 6(3). https://doi.org/10.1002/brb3.430

Vancouver

Marschallinger R, Schmidt P, Hofmann P, Zimmer C, Atkinson PM, Sellner J et al. A MS-lesion pattern discrimination plot based on geostatistics. Brain and Behavior. 2016 Mar;6(3). https://doi.org/10.1002/brb3.430

Author

Marschallinger, Robert ; Schmidt, Paul ; Hofmann, Peter ; Zimmer, Claus ; Atkinson, Peter Michael ; Sellner, Johann ; Trinker, Eugen ; Mühlau, Mark. / A MS-lesion pattern discrimination plot based on geostatistics. In: Brain and Behavior. 2016 ; Vol. 6, No. 3.

Bibtex

@article{0f6ff11eb80046caad22824baaf58b4f,
title = "A MS-lesion pattern discrimination plot based on geostatistics",
abstract = "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.",
keywords = "Discrimination, geostatistics, lesion, Multiple Sclerosis, pattern",
author = "Robert Marschallinger and Paul Schmidt and Peter Hofmann and Claus Zimmer and Atkinson, {Peter Michael} and Johann Sellner and Eugen Trinker and Mark M{\"u}hlau",
note = "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.",
year = "2016",
month = mar,
doi = "10.1002/brb3.430",
language = "English",
volume = "6",
journal = "Brain and Behavior",
issn = "2162-3279",
publisher = "John Wiley & Sons, Ltd",
number = "3",

}

RIS

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 -