Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1038/s41598-018-23152-5 http://scihub22266oqcxt.onion/10.1038/s41598-018-23152-5 C5856783!5856783!29549369     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2018 ; 8 (ä): ä Nephropedia Template TP {{tp|p=29549369|t=2018. An ANOVA approach for statistical comparisons of brain networks |pdf=|usr=}}{{29549369}} gab.com Text An ANOVA approach for statistical comparisons of brain networks JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29549369 #FOAvip The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks. Twit Text FOAVip An ANOVA approach for statistical comparisons of brain networks ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29549369 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29549369 #FOAvip English Wikipedia <ref>{{Cite pmid|29549369}}</ref> An ANOVA approach for statistical comparisons of brain networks #MMPMID29549369 Fraiman D; Fraiman R Sci Rep 2018[]; 8 (ä): ä PMID29549369show ga The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks. äAn ANOVA approach for statistical comparisons of brain networks (epmc).pdf DeepDyve Pubget Overpricing