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Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an i...

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Autores principales: Lee, Garam, Nho, Kwangsik, Kang, Byungkon, Sohn, Kyung-Ah, Kim, Dokyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374429/
https://www.ncbi.nlm.nih.gov/pubmed/30760848
http://dx.doi.org/10.1038/s41598-018-37769-z
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author Lee, Garam
Nho, Kwangsik
Kang, Byungkon
Sohn, Kyung-Ah
Kim, Dokyoon
author_facet Lee, Garam
Nho, Kwangsik
Kang, Byungkon
Sohn, Kyung-Ah
Kim, Dokyoon
author_sort Lee, Garam
collection PubMed
description Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
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spelling pubmed-63744292019-02-19 Predicting Alzheimer’s disease progression using multi-modal deep learning approach Lee, Garam Nho, Kwangsik Kang, Byungkon Sohn, Kyung-Ah Kim, Dokyoon Sci Rep Article Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials. Nature Publishing Group UK 2019-02-13 /pmc/articles/PMC6374429/ /pubmed/30760848 http://dx.doi.org/10.1038/s41598-018-37769-z Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lee, Garam
Nho, Kwangsik
Kang, Byungkon
Sohn, Kyung-Ah
Kim, Dokyoon
Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title_full Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title_fullStr Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title_full_unstemmed Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title_short Predicting Alzheimer’s disease progression using multi-modal deep learning approach
title_sort predicting alzheimer’s disease progression using multi-modal deep learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374429/
https://www.ncbi.nlm.nih.gov/pubmed/30760848
http://dx.doi.org/10.1038/s41598-018-37769-z
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