MRI pattern recognition in multiple sclerosis normal-appearing brain areas.

<h4>Objective</h4>Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR technique...

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Autores principales:	Martin Weygandt, Kerstin Hackmack, Caspar Pfüller, Judith Bellmann-Strobl, Friedemann Paul, Frauke Zipp, John-Dylan Haynes
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63cb85ffbc5748b29b4f2e424e28a977
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spelling	oai:doaj.org-article:63cb85ffbc5748b29b4f2e424e28a9772021-11-18T06:51:50ZMRI pattern recognition in multiple sclerosis normal-appearing brain areas.1932-620310.1371/journal.pone.0021138https://doaj.org/article/63cb85ffbc5748b29b4f2e424e28a9772011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21695053/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objective</h4>Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques.<h4>Methods</h4>A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information.<h4>Results</h4>Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10(-13)). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10(-7)). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10(-10)).<h4>Interpretation</h4>We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.Martin WeygandtKerstin HackmackCaspar PfüllerJudith Bellmann-StroblFriedemann PaulFrauke ZippJohn-Dylan HaynesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 6, p e21138 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Martin Weygandt Kerstin Hackmack Caspar Pfüller Judith Bellmann-Strobl Friedemann Paul Frauke Zipp John-Dylan Haynes MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
description	<h4>Objective</h4>Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques.<h4>Methods</h4>A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information.<h4>Results</h4>Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10(-13)). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10(-7)). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10(-10)).<h4>Interpretation</h4>We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.
format	article
author	Martin Weygandt Kerstin Hackmack Caspar Pfüller Judith Bellmann-Strobl Friedemann Paul Frauke Zipp John-Dylan Haynes
author_facet	Martin Weygandt Kerstin Hackmack Caspar Pfüller Judith Bellmann-Strobl Friedemann Paul Frauke Zipp John-Dylan Haynes
author_sort	Martin Weygandt
title	MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
title_short	MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
title_full	MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
title_fullStr	MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
title_full_unstemmed	MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
title_sort	mri pattern recognition in multiple sclerosis normal-appearing brain areas.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/63cb85ffbc5748b29b4f2e424e28a977
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MRI pattern recognition in multiple sclerosis normal-appearing brain areas.

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