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Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recent...

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Autores principales: Salvatore, Christian, Cerasa, Antonio, Battista, Petronilla, Gilardi, Maria C., Quattrone, Aldo, Castiglioni, Isabella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555016/
https://www.ncbi.nlm.nih.gov/pubmed/26388719
http://dx.doi.org/10.3389/fnins.2015.00307
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author Salvatore, Christian
Cerasa, Antonio
Battista, Petronilla
Gilardi, Maria C.
Quattrone, Aldo
Castiglioni, Isabella
author_facet Salvatore, Christian
Cerasa, Antonio
Battista, Petronilla
Gilardi, Maria C.
Quattrone, Aldo
Castiglioni, Isabella
author_sort Salvatore, Christian
collection PubMed
description Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.
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spelling pubmed-45550162015-09-18 Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach Salvatore, Christian Cerasa, Antonio Battista, Petronilla Gilardi, Maria C. Quattrone, Aldo Castiglioni, Isabella Front Neurosci Neuroscience Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients. Frontiers Media S.A. 2015-09-01 /pmc/articles/PMC4555016/ /pubmed/26388719 http://dx.doi.org/10.3389/fnins.2015.00307 Text en Copyright © 2015 Salvatore, Cerasa, Battista, Gilardi, Quattrone and Castiglioni. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Salvatore, Christian
Cerasa, Antonio
Battista, Petronilla
Gilardi, Maria C.
Quattrone, Aldo
Castiglioni, Isabella
Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title_full Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title_fullStr Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title_full_unstemmed Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title_short Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
title_sort magnetic resonance imaging biomarkers for the early diagnosis of alzheimer's disease: a machine learning approach
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555016/
https://www.ncbi.nlm.nih.gov/pubmed/26388719
http://dx.doi.org/10.3389/fnins.2015.00307
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