Embedded Files
To cite CONN in your work, please include CONN's RRID and at least one of the following general references:
e.g. CONN (RRID:SCR_009550) [1] was used for the analysis of fMRI data.
[1] Nieto-Castanon, A. (2020). Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Boston, MA: Hilbert Press
[1] Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). CONN: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity, 2(3), 125-141
To cite one specific version of CONN in your work, please include the specific DOI of your CONN release/version:
e.g. Functional images were preprocessed and denoised using the default options in CONN version 22a [2].
[2] Nieto-Castanon, A. & Whitfield-Gabrieli, S. (2022). CONN functional connectivity toolbox: RRID SCR_009550 release 22. doi:10.56441/hilbertpress.2246.5840
Writing your manuscript Methods section: CONN creates automated boilerplate text describing in detail the methods used to process your data during CONN's preprocessing, denoising, first- and second- level analyses, including proper attribution to the different software and methods used. Authors are welcome to modify and reuse these passages as needed. We recommend authors to copy these passages verbatim to the Methods section of their manuscript in order to foster consistent descriptions of all methodological details necessary to replicate the analyses. Ultimately, we hope this contributes towards changing current practices in ways that can help improve reproducibility and replicability in neuroimaging studies.
How to: To generate an automated boilerplate description of the data processing pipeline used in your analyses, open your CONN project and click on the 'Methods' button on the main GUI. CONN will read the details of your project and it will launch a window where you can specify what aspects of your data preprocessing, denoising, and analysis pipelines to include. The specific text generated by CONN will vary depending on the choices of preprocessing or analysis options specified in your project. The generated text can then be copied/pasted to your own text editor, or it can be exported as a Word document (.docx) or as a HTML document (.html). The section below shows one example of such text.
Example of automated text generated by CONN describing preprocessing, denoising, first- and second-level analyses performed in sample NYU dataset
Methods.pdfNote for editors and reviewers: All of the text produced by CONN's automated descriptions is distributed under a Public Domain Dedication license (CC0 1.0) and it can be used, copied, modified, and distributed freely without requiring any permission from us. We strongly believe that the specificity of these automated methodological descriptions helps the field by improving the ability to replicate the analyses reported in a manuscript, and that requiring authors to paraphrase or modify these boilerplate descriptions decreases their specificity while serving no scientific purpose (see fMRIPrep notes on this same issue). We recognize that parts of these texts may be flagged by automated plagiarism detection procedures, and will be happy to work with editors and reviewers to facilitate the automated marking of these verbatim passages to avoid such occurrences.
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