Brain networks construction using Bayes FDR and average power function.
Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matr...
| Publicado en: | Statistical Methods in Medical Research Vol. 29; no. 3; pp. 866 - 879 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142337899&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142337899 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Mar2020 vid: 29 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 142337899 142337899 NLM31088219 142337899 10.1177/0962280219844288 NLM31088219 142337899 ppf: 866 ppct: 13 formats: tig: atl: Brain networks construction using Bayes FDR and average power function. aug: au: Quatto, Piero Margaritella, Nicolò Costantini, Isa Baglio, Francesca Garegnani, Massimo Nemni, Raffaello Pugnetti, Luigi Austin, Peter affil: Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy sug: subj: Brain Electroencephalography False Positive Results Magnetic Resonance Imaging Probability Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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