Health Effects of Lesion Localization in Multiple Sclerosis: Spatial Registration and Confounding Adjustment

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individu...

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Published in:	PloS one Vol. 9; no. 9; p. e107263
Main Authors:	Eloyan, Ani, Shou, Haochang, Shinohara, Russell T., Sweeney, Elizabeth M., Nebel, Mary Beth, Cuzzocreo, Jennifer L., Calabresi, Peter A., Reich, Daniel S., Lindquist, Martin A., Crainiceanu, Ciprian M.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 18.09.2014 Public Library of Science (PLoS)
Subjects:	Adult Aged Algorithms Autoimmune diseases Biology and Life Sciences Brain Brain - diagnostic imaging Brain - pathology Brain damage Brain research Data Interpretation, Statistical Disability Epidemiology Female Health aspects Health risks Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted Lesions Localization Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medicine Medicine and Health Sciences Middle Aged Multiple sclerosis Multiple Sclerosis - pathology Neurological disorders Neurology NMR Nuclear magnetic resonance Physical Sciences Population (statistical) Public health Radiography Registration Regression analysis Spatial distribution Statistical analysis Studies Young Adult
ISSN:	1932-6203, 1932-6203
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Abstract	Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.
AbstractList	Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map. Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.
Audience	Academic
Author	Lindquist, Martin A. Sweeney, Elizabeth M. Nebel, Mary Beth Reich, Daniel S. Shinohara, Russell T. Shou, Haochang Cuzzocreo, Jennifer L. Crainiceanu, Ciprian M. Eloyan, Ani Calabresi, Peter A.
AuthorAffiliation	3 Translational Neurology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America 6 Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America University Medical Center Göttingen, Germany 5 Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America 1 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America 4 Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
AuthorAffiliation_xml	– name: 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America – name: 6 Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America – name: 1 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America – name: 4 Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland, United States of America – name: University Medical Center Göttingen, Germany – name: 5 Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America – name: 3 Translational Neurology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
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Copyright	COPYRIGHT 2014 Public Library of Science 2014 Eloyan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Eloyan et al 2014 Eloyan et al
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Analyzed the data: AE HS EMS MBN. Contributed reagents/materials/analysis tools: PAC. Wrote the paper: AE CMC DSR MBN. Discussed the analysis and results: RTS DSR MAL CMC HS EMS MBN. Hand-segmented lesions: JLC.
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SubjectTerms	Adult Aged Algorithms Autoimmune diseases Biology and Life Sciences Brain Brain - diagnostic imaging Brain - pathology Brain damage Brain research Data Interpretation, Statistical Disability Epidemiology Female Health aspects Health risks Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted Lesions Localization Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medicine Medicine and Health Sciences Middle Aged Multiple sclerosis Multiple Sclerosis - pathology Neurological disorders Neurology NMR Nuclear magnetic resonance Physical Sciences Population (statistical) Public health Radiography Registration Regression analysis Spatial distribution Statistical analysis Studies Young Adult
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Title	Health Effects of Lesion Localization in Multiple Sclerosis: Spatial Registration and Confounding Adjustment
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