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Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing
Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476568/ https://www.ncbi.nlm.nih.gov/pubmed/28630492 http://dx.doi.org/10.1038/s41598-017-04204-8 |
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author | Dottori, Martin Sedeño, Lucas Martorell Caro, Miguel Alifano, Florencia Hesse, Eugenia Mikulan, Ezequiel García, Adolfo M. Ruiz-Tagle, Amparo Lillo, Patricia Slachevsky, Andrea Serrano, Cecilia Fraiman, Daniel Ibanez, Agustin |
author_facet | Dottori, Martin Sedeño, Lucas Martorell Caro, Miguel Alifano, Florencia Hesse, Eugenia Mikulan, Ezequiel García, Adolfo M. Ruiz-Tagle, Amparo Lillo, Patricia Slachevsky, Andrea Serrano, Cecilia Fraiman, Daniel Ibanez, Agustin |
author_sort | Dottori, Martin |
collection | PubMed |
description | Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings. |
format | Online Article Text |
id | pubmed-5476568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54765682017-06-23 Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing Dottori, Martin Sedeño, Lucas Martorell Caro, Miguel Alifano, Florencia Hesse, Eugenia Mikulan, Ezequiel García, Adolfo M. Ruiz-Tagle, Amparo Lillo, Patricia Slachevsky, Andrea Serrano, Cecilia Fraiman, Daniel Ibanez, Agustin Sci Rep Article Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings. Nature Publishing Group UK 2017-06-19 /pmc/articles/PMC5476568/ /pubmed/28630492 http://dx.doi.org/10.1038/s41598-017-04204-8 Text en © The Author(s) 2017 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/. |
spellingShingle | Article Dottori, Martin Sedeño, Lucas Martorell Caro, Miguel Alifano, Florencia Hesse, Eugenia Mikulan, Ezequiel García, Adolfo M. Ruiz-Tagle, Amparo Lillo, Patricia Slachevsky, Andrea Serrano, Cecilia Fraiman, Daniel Ibanez, Agustin Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title | Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title_full | Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title_fullStr | Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title_full_unstemmed | Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title_short | Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing |
title_sort | towards affordable biomarkers of frontotemporal dementia: a classification study via network’s information sharing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476568/ https://www.ncbi.nlm.nih.gov/pubmed/28630492 http://dx.doi.org/10.1038/s41598-017-04204-8 |
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