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10.1111/biom.12713 http://scihub22266oqcxt.onion/10.1111/biom.12713 C5682245!5682245!28498564     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2018 ; 74 (1): 342-53 Nephropedia Template TP {{tp|p=28498564|t=2018. Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data |pdf=|usr=}}{{28498564}} gab.com Text Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=28498564 #FOAvip Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. Twit Text FOAVip Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=28498564 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28498564 #FOAvip English Wikipedia <ref>{{Cite pmid|28498564}}</ref> Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data #MMPMID28498564 Montagna S; Wager T; Barrett LF; Johnson TD; Nichols TE Biometrics 2018[Mar]; 74 (1): 342-53 PMID28498564show ga Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. äSpatial Bayesian Latent Factor Regression Modeling of Coordinate-based(epmc).pdf DeepDyve Pubget Overpricing