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Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease

We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our app...

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Detalles Bibliográficos
Autores principales: Glozman, Tanya, Solomon, Justin, Pestilli, Franco, Guibas, Leonidas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240557/
https://www.ncbi.nlm.nih.gov/pubmed/27911322
http://dx.doi.org/10.3233/JAD-160900
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author Glozman, Tanya
Solomon, Justin
Pestilli, Franco
Guibas, Leonidas
author_facet Glozman, Tanya
Solomon, Justin
Pestilli, Franco
Guibas, Leonidas
author_sort Glozman, Tanya
collection PubMed
description We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.
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spelling pubmed-52405572017-01-23 Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease Glozman, Tanya Solomon, Justin Pestilli, Franco Guibas, Leonidas J Alzheimers Dis Research Article We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods. IOS Press 2017-01-12 /pmc/articles/PMC5240557/ /pubmed/27911322 http://dx.doi.org/10.3233/JAD-160900 Text en IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Glozman, Tanya
Solomon, Justin
Pestilli, Franco
Guibas, Leonidas
Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title_full Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title_fullStr Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title_full_unstemmed Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title_short Shape-Attributes of Brain Structures as Biomarkers for Alzheimer’s Disease
title_sort shape-attributes of brain structures as biomarkers for alzheimer’s disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240557/
https://www.ncbi.nlm.nih.gov/pubmed/27911322
http://dx.doi.org/10.3233/JAD-160900
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