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dc.contributor.author | Díaz-Parra, Antonio | es_ES |
dc.contributor.author | Pérez-Ramírez, María Úrsula | es_ES |
dc.contributor.author | Pacheco-Torres, J. | es_ES |
dc.contributor.author | Pfarr, S. | es_ES |
dc.contributor.author | Sommer, W.H. | es_ES |
dc.contributor.author | Moratal, David | es_ES |
dc.contributor.author | Canals, S. | es_ES |
dc.date.accessioned | 2018-12-06T21:04:00Z | |
dc.date.available | 2018-12-06T21:04:00Z | |
dc.date.issued | 2017 | es_ES |
dc.identifier.isbn | 978-1-5090-2809-2 | |
dc.identifier.issn | 1557-170X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/113523 | |
dc.description.abstract | [EN] The use of functional magnetic resonance imaging (fMRI) to measure spontaneous fluctuations in blood oxygen level dependent (BOLD) signals has become an indispensable tool to investigate how brain regions interact and form longrange networks. Statistical dependency measures between brain regions obtained from BOLD signals can inform about brain functional states in longitudinal studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements in clinical and animal studies, providing excellent translational capabilities. In the present study, we apply Network-Based Statistic (NBS) to investigate alterations in the functional connectivity (FC) of the rat brain in a post-dependent (PD) state, an established animal model of clinical relevant features in alcoholism. In contrast to mass-univariate tests, in which comparisons are performed at single link-level, NBS enhances the statistical power by assuming that the connections comprising the effect of interest are interconnected. Brain-wide resting-state fMRI signals were collected in 14 controls and 13 PD rats, and Pearson correlations computed between 47 brain regions of interest (ROIs). The NBS analysis revealed statistically significant differences in a connected network of structures including hippocampus, amygdala, lateral hypothalamus and the raphe nucleus, all regions with known relevance for addictive behaviors. In contrast, no individual connection could be found significant by univariate comparisons with false discovery rate (FDR) correction. Correlations between the structures in the identified subnetwork tend to decrease or become negative (anti-correlated) in the PD state compared to controls. We interpret this result as evidence for a disconnected subnetwork in the PD state. | es_ES |
dc.description.sponsorship | Research supported by the Spanish Ministerio de Economia y Competitividad (MINECO) under grants BFU2015-64380-C2-1-R and BFU2015-64380-C2-2R and European Union's Horizon 2020 research and innovation programme under grant agreement No 668863 (SyBil-AA). Santiago Canals acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). Antonio Diaz-Parra and Ursula Perez-Ramirez are funded by the Spanish Ministerio de Educacion, Cultura y Deporte (MECD) under grant FPU13/01475 and FPU13/03537, respectively. | |
dc.language | Inglés | es_ES |
dc.publisher | IEEE Engineering in Medicine and Biology Society | es_ES |
dc.relation.ispartof | Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic | es_ES |
dc.type | Artículo | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.1109/EMBC.2017.8036879 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/668863/EU/Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//BFU2015-64380-C2-2-R/ES/ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDAD/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU13%2F01475/ES/FPU13%2F01475/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU13%2F03537/ES/FPU13%2F03537/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica | es_ES |
dc.description.bibliographicCitation | Díaz-Parra, A.; Pérez-Ramírez, MÚ.; Pacheco-Torres, J.; Pfarr, S.; Sommer, W.; Moratal, D.; Canals, S. (2017). Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 533-536. https://doi.org/10.1109/EMBC.2017.8036879 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017) | es_ES |
dc.relation.conferencedate | Julio 11-15,2017 | es_ES |
dc.relation.conferenceplace | Jeju Island, South Korea | es_ES |
dc.relation.publisherversion | http://doi.org/10.1109/EMBC.2017.8036879 | es_ES |
dc.description.upvformatpinicio | 533 | es_ES |
dc.description.upvformatpfin | 536 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.pmid | 29059927 | |
dc.relation.pasarela | S\353078 | es_ES |
dc.contributor.funder | Ministerio de Educación | es_ES |
dc.contributor.funder | Ministerio de Economía, Industria y Competitividad | es_ES |