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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2015
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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. |
format | Online Article Text |
id | pubmed-4555016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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