Ten simple rules for dynamic causal modeling

Ten simple rules for dynamic causal modeling

Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of n...

Ten Simple Rules for Dynamic Causal Modelling

Ten Simple Rules for Reviewers

Last summer, the Student Council of the International Society for Computational Biology prompted an Editorial, ‘‘Ten Simple Rules for Getting Published’’ [1]. The interest in that piece (it has been downloaded 14,880 times thus far) prompted ‘‘Ten Simple Rules for Writing a Grant’’ [2]. With this third contribution, the ‘‘Ten Rules’’ series would seem to be established, and more rules for diffe...

Ten Simple Rules for Writing a PLOS Ten Simple Rules Article

1 Bioinformatics, Murdoch Childrens Research Institute, Parkville, Victoria, Australia, 2 Life Science Computation Centre, Victorian Life Sciences Computation Initiative, Carlton, Victoria, Australia, 3 The University of Melbourne, Parkville, Victoria, Australia, 4 The Australian Research Council Centre of Excellence in Plant Cell Walls, School of Botany, University of Melbourne, Parkville, Vic...

Correction: Ten simple rules for structuring papers

[This corrects the article DOI: 10.1371/journal.pcbi.1005619.].