Bayesian estimation of directed functional coupling from brain recordings
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| Publicado en: | PLoS One vol. 12, no. 5 (May 2017), p. e0177359 |
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| Autor principal: | |
| Otros Autores: | , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 1900214016 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0177359 |2 doi | |
| 035 | |a 1900214016 | ||
| 045 | 2 | |b d20170501 |b d20170531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Benozzo, Danilo | |
| 245 | 1 | |a Bayesian estimation of directed functional coupling from brain recordings | |
| 260 | |b Public Library of Science |c May 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance. A family of measures have been developed from the parametric implementation of the Granger criteria of causality based on the linear autoregressive modelling of the signals. We propose a new Bayesian method for linear model identification with a structured prior (GMEP) aiming to apply it as linear regression method in the context of the parametric Granger causal inference. GMEP assumes a Gaussian scale mixture distribution for the group sparsity prior and it enables flexible definition of the coefficient groups. Approximate posterior inference is achieved using Expectation Propagation for both the linear coefficients and the hyperparameters. GMEP is investigated both on simulated data and on empirical fMRI data in which we show how adding information on the sparsity structure of the coefficients positively improves the inference process. In the same simulation framework, GMEP is compared with others standard linear regression methods. Moreover, the causal inferences derived from GMEP estimates and from a standard Granger method are compared across simulated datasets of different dimensionality, density connection and level of noise. GMEP allows a better model identification and consequent causal inference when prior knowledge on the sparsity structure are integrated in the structured prior. | |
| 610 | 4 | |a University of Trento | |
| 651 | 4 | |a Italy | |
| 653 | |a Functional magnetic resonance imaging | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Signal processing | ||
| 653 | |a Cognition | ||
| 653 | |a Brain | ||
| 653 | |a Image processing | ||
| 653 | |a Electroencephalography | ||
| 653 | |a Asymptotic properties | ||
| 653 | |a Machine learning | ||
| 653 | |a Entropy | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Propagation | ||
| 653 | |a Asymptotic methods | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a EEG | ||
| 653 | |a Mean square values | ||
| 653 | |a Classification | ||
| 653 | |a Autoregressive processes | ||
| 653 | |a Terminology | ||
| 653 | |a Inference | ||
| 653 | |a Regression analysis | ||
| 653 | |a Methods | ||
| 653 | |a Information processing | ||
| 653 | |a Autoregressive models | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Uniqueness | ||
| 653 | |a Visual perception | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Data processing | ||
| 653 | |a Matrices (mathematics) | ||
| 653 | |a Information systems | ||
| 653 | |a Independent variables | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Normality | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Stochastic processes | ||
| 653 | |a Coefficients | ||
| 653 | |a Brain mapping | ||
| 653 | |a Probability theory | ||
| 653 | |a Nervous system | ||
| 653 | |a Mathematical models | ||
| 653 | |a Neural networks | ||
| 653 | |a Neural coding | ||
| 653 | |a Noise levels | ||
| 700 | 1 | |a Jylänki, Pasi | |
| 700 | 1 | |a Olivetti, Emanuele | |
| 700 | 1 | |a Avesani, Paolo | |
| 700 | 1 | |a Marcel A J van Gerven | |
| 773 | 0 | |t PLoS One |g vol. 12, no. 5 (May 2017), p. e0177359 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1900214016/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1900214016/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1900214016/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |