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A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962344/ https://www.ncbi.nlm.nih.gov/pubmed/31941972 http://dx.doi.org/10.1038/s41598-019-56923-9 |
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author | Guggenmos, Matthias Schmack, Katharina Veer, Ilya M. Lett, Tristram Sekutowicz, Maria Sebold, Miriam Garbusow, Maria Sommer, Christian Wittchen, Hans-Ulrich Zimmermann, Ulrich S. Smolka, Michael N. Walter, Henrik Heinz, Andreas Sterzer, Philipp |
author_facet | Guggenmos, Matthias Schmack, Katharina Veer, Ilya M. Lett, Tristram Sekutowicz, Maria Sebold, Miriam Garbusow, Maria Sommer, Christian Wittchen, Hans-Ulrich Zimmermann, Ulrich S. Smolka, Michael N. Walter, Henrik Heinz, Andreas Sterzer, Philipp |
author_sort | Guggenmos, Matthias |
collection | PubMed |
description | With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence. |
format | Online Article Text |
id | pubmed-6962344 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69623442020-01-23 A multimodal neuroimaging classifier for alcohol dependence Guggenmos, Matthias Schmack, Katharina Veer, Ilya M. Lett, Tristram Sekutowicz, Maria Sebold, Miriam Garbusow, Maria Sommer, Christian Wittchen, Hans-Ulrich Zimmermann, Ulrich S. Smolka, Michael N. Walter, Henrik Heinz, Andreas Sterzer, Philipp Sci Rep Article With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence. Nature Publishing Group UK 2020-01-15 /pmc/articles/PMC6962344/ /pubmed/31941972 http://dx.doi.org/10.1038/s41598-019-56923-9 Text en © The Author(s) 2020 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 Guggenmos, Matthias Schmack, Katharina Veer, Ilya M. Lett, Tristram Sekutowicz, Maria Sebold, Miriam Garbusow, Maria Sommer, Christian Wittchen, Hans-Ulrich Zimmermann, Ulrich S. Smolka, Michael N. Walter, Henrik Heinz, Andreas Sterzer, Philipp A multimodal neuroimaging classifier for alcohol dependence |
title | A multimodal neuroimaging classifier for alcohol dependence |
title_full | A multimodal neuroimaging classifier for alcohol dependence |
title_fullStr | A multimodal neuroimaging classifier for alcohol dependence |
title_full_unstemmed | A multimodal neuroimaging classifier for alcohol dependence |
title_short | A multimodal neuroimaging classifier for alcohol dependence |
title_sort | multimodal neuroimaging classifier for alcohol dependence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962344/ https://www.ncbi.nlm.nih.gov/pubmed/31941972 http://dx.doi.org/10.1038/s41598-019-56923-9 |
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