A new preprint is available about improving results reporting with transparent thresholding, both for interpreting a given study (= understanding) and for comparing across studies (= reproducibility).
Importantly, this provides a hopeful message about how we can improve evaluations and reproducibility across neuroimaging right now and in a straightforward way. Transparent thresholding is now available in a very large number of software packages, with many being added/streamlined as part of this project.
Please check out:
- Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation
Paul A. Taylor, Himanshu Aggarwal, Peter Bandettini, Marco Barilari, Molly Bright, Cesar Caballero-Gaudes, Vince Calhoun, Mallar Chakravarty, Gabriel Devenyi, Jennifer Evans, Eduardo Garza-Villarreal, Jalil Rasgado-Toledo, Remi Gau, Daniel Glen, Rainer Goebel, Javier Gonzalez-Castillo, Omer Faruk Gulban, Yaroslav Halchenko, Daniel Handwerker, Taylor Hanayik, Peter Lauren, David Leopold, Jason Lerch, Christian Mathys, Paul McCarthy, Anke McLeod, Amanda Mejia, Stefano Moia, Thomas Nichols, Cyril Pernet, Luiz Pessoa, Bettina Pfleiderer, Justin Rajendra, Laura Reyes, Richard Reynolds, Vinai Roopchansingh, Chris Rorden, Brian Russ, Benedikt Sundermann, Bertrand Thirion, Salvatore Torrisi, Gang Chen.
Preprint available on the arXiv. - A short video on the AFNI Academy going over some of the examples and major points.
As readily apparent from the long author list, this was a major collaboration of ideas and inputs. We hope neuroimagers everywhere find it compelling. Please share widely.
--pt