dc.contributor.author |
Sanz Perl, Yonatan |
|
dc.contributor.author |
Pallavicini, Carla |
|
dc.contributor.author |
Perez Ipiña, Ignacio |
|
dc.contributor.author |
Kringelbach, Morten |
|
dc.contributor.author |
Deco, Gustavo |
|
dc.contributor.author |
Laufs, Helmut |
|
dc.contributor.author |
Tagliazucchi, Enzo |
|
dc.date.accessioned |
2021-05-03T14:30:49Z |
|
dc.date.available |
2021-05-03T14:30:49Z |
|
dc.date.issued |
2020-07-10 |
|
dc.identifier.citation |
Perl, Y.S., Pallavicini, C., Ipiña, I.P., Kringelbach, M., Deco, G., Laufs, H., Tagliazucchi, E., 2020. Data augmentation based on dynamical systems for the classification of brain states. Chaos, Solitons and Fractals 139. https://doi.org/10.1016/j.chaos.2020.110069 |
es_ES |
dc.identifier.uri |
https://repositorio.fleni.org.ar/xmlui/handle/123456789/451 |
|
dc.identifier.uri |
https://doi.org/10.1016/j.chaos.2020.110069 |
|
dc.description.abstract |
The application of machine learning algorithms to neuroimaging data shows great promise for the classification of physiological and pathological brain states. However, classifiers trained on high dimensional data are prone to overfitting, especially for a low number of training samples. We describe the use of whole-brain computational models for data augmentation in brain state classification. Our low dimensional model is based on nonlinear oscillators coupled by the empirical structural connectivity of the brain. We use this model to enhance a dataset consisting of functional magnetic resonance imaging recordings acquired during all stages of the human wake-sleep cycle. After fitting the model to the average functional connectivity of each state, we show that the synthetic data generated by the model yields classification accuracies comparable to those obtained from the empirical data. We also show that models fitted to individual subjects generate surrogates with enough information to train classifiers that present significant transfer learning accuracy to the whole sample. Whole-brain computational modeling represents a useful tool to produce large synthetic datasets for data augmentation in the classification of certain brain states, with potential applications to computer-assisted diagnosis and prognosis of neuropsychiatric disorders. |
es_ES |
dc.language.iso |
eng |
es_ES |
dc.publisher |
Elsevier |
es_ES |
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/2.5/ar/ |
|
dc.subject |
Neuroimaging |
es_ES |
dc.subject |
Neuroimagen |
es_ES |
dc.subject |
Machine Learning |
es_ES |
dc.subject |
Aprendizaje Automático |
es_ES |
dc.title |
Data augmentation based on dynamical systems for the classification of brain states |
es_ES |
dc.type |
info:eu-repo/semantics/article |
es_ES |
dc.type |
info:eu-repo/semantics/publishedVersion |
|
dc.description.fil |
Fil: Pallavicini, Carla. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fleni; Argentina. |
|
dc.description.fil |
Fil: Sanz Perl, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. |
|
dc.description.fil |
Fil: Perez Ipiña, Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. |
|
dc.description.fil |
Fil: Kringelbach, Morten. University of Oxford. Department of Psychiatry; Reino Unido. Aarhus University. Dept. of Clinical Medicine. Center for Music in the Brain; Dinamarca. |
|
dc.description.fil |
Fil: Deco, Gustavo. Universitat Pompeu Fabra. Department of Information and Communication Technologies. Computational Neuroscience Group. Center for Brain and Cognition; España. Universitat Pompeu Fabra. Institució Catalana de la Recerca i Estudis Avançats (ICREA); España. |
|
dc.description.fil |
Fil: Laufs, Helmut. University of Kiel. Department of Neurology; Alemania. |
|
dc.description.fil |
Fil: Tagliazucchi, Enzo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. |
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dc.relation.ispartofVOLUME |
139 |
|
dc.relation.ispartofPAGINATION |
110069 |
|
dc.relation.ispartofCOUNTRY |
Estados Unidos |
|
dc.relation.ispartofCITY |
Nueva York |
|
dc.relation.ispartofTITLE |
Chaos, Solitons & Fractals |
|
dc.type.snrd |
info:ar-repo/semantics/artículo |
es_ES |