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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07824 |
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| _version_ | 1866908311738122240 |
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| author | Taylor, Paul A. Aggarwal, Himanshu Bandettini, Peter Barilari, Marco Bright, Molly Caballero-Gaudes, Cesar Calhoun, Vince Chakravarty, Mallar Devenyi, Gabriel Evans, Jennifer Garza-Villarreal, Eduardo Rasgado-Toledo, Jalil Gau, Remi Glen, Daniel Goebel, Rainer Gonzalez-Castillo, Javier Gulban, Omer Faruk Halchenko, Yaroslav Handwerker, Daniel Hanayik, Taylor Lauren, Peter Leopold, David Lerch, Jason Mathys, Christian McCarthy, Paul McLeod, Anke Mejia, Amanda Moia, Stefano Nichols, Thomas Pernet, Cyril Pessoa, Luiz Pfleiderer, Bettina Rajendra, Justin Reyes, Laura Reynolds, Richard Roopchansingh, Vinai Rorden, Chris Russ, Brian Sundermann, Benedikt Thirion, Bertrand Torrisi, Salvatore Chen, Gang |
| author_facet | Taylor, Paul A. Aggarwal, Himanshu Bandettini, Peter Barilari, Marco Bright, Molly Caballero-Gaudes, Cesar Calhoun, Vince Chakravarty, Mallar Devenyi, Gabriel Evans, Jennifer Garza-Villarreal, Eduardo Rasgado-Toledo, Jalil Gau, Remi Glen, Daniel Goebel, Rainer Gonzalez-Castillo, Javier Gulban, Omer Faruk Halchenko, Yaroslav Handwerker, Daniel Hanayik, Taylor Lauren, Peter Leopold, David Lerch, Jason Mathys, Christian McCarthy, Paul McLeod, Anke Mejia, Amanda Moia, Stefano Nichols, Thomas Pernet, Cyril Pessoa, Luiz Pfleiderer, Bettina Rajendra, Justin Reyes, Laura Reynolds, Richard Roopchansingh, Vinai Rorden, Chris Russ, Brian Sundermann, Benedikt Thirion, Bertrand Torrisi, Salvatore Chen, Gang |
| contents | Visualizations are vital for communicating scientific results. Historically, neuroimaging figures have only depicted regions that surpass a given statistical threshold. This practice substantially biases interpretation of the results and subsequent meta-analyses, particularly towards non-reproducibility. Here we advocate for a "transparent thresholding" approach that not only highlights statistically significant regions but also includes subthreshold locations, which provide key experimental context. This balances the dual needs of distilling modeling results and enabling informed interpretations for modern neuroimaging. We present four examples that demonstrate the many benefits of transparent thresholding, including: removing ambiguity, decreasing hypersensitivity to non-physiological features, catching potential artifacts, improving cross-study comparisons, reducing non-reproducibility biases, and clarifying interpretations. We also demonstrate the many software packages that implement transparent thresholding, several of which were added or streamlined recently as part of this work. A point-counterpoint discussion addresses issues with thresholding raised in real conversations with researchers in the field. We hope that by showing how transparent thresholding can drastically improve the interpretation (and reproducibility) of neuroimaging findings, more researchers will adopt this method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07824 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation Taylor, Paul A. Aggarwal, Himanshu Bandettini, Peter Barilari, Marco Bright, Molly Caballero-Gaudes, Cesar Calhoun, Vince Chakravarty, Mallar Devenyi, Gabriel Evans, Jennifer Garza-Villarreal, Eduardo Rasgado-Toledo, Jalil Gau, Remi Glen, Daniel Goebel, Rainer Gonzalez-Castillo, Javier Gulban, Omer Faruk Halchenko, Yaroslav Handwerker, Daniel Hanayik, Taylor Lauren, Peter Leopold, David Lerch, Jason Mathys, Christian McCarthy, Paul McLeod, Anke Mejia, Amanda Moia, Stefano Nichols, Thomas Pernet, Cyril Pessoa, Luiz Pfleiderer, Bettina Rajendra, Justin Reyes, Laura Reynolds, Richard Roopchansingh, Vinai Rorden, Chris Russ, Brian Sundermann, Benedikt Thirion, Bertrand Torrisi, Salvatore Chen, Gang Neurons and Cognition Visualizations are vital for communicating scientific results. Historically, neuroimaging figures have only depicted regions that surpass a given statistical threshold. This practice substantially biases interpretation of the results and subsequent meta-analyses, particularly towards non-reproducibility. Here we advocate for a "transparent thresholding" approach that not only highlights statistically significant regions but also includes subthreshold locations, which provide key experimental context. This balances the dual needs of distilling modeling results and enabling informed interpretations for modern neuroimaging. We present four examples that demonstrate the many benefits of transparent thresholding, including: removing ambiguity, decreasing hypersensitivity to non-physiological features, catching potential artifacts, improving cross-study comparisons, reducing non-reproducibility biases, and clarifying interpretations. We also demonstrate the many software packages that implement transparent thresholding, several of which were added or streamlined recently as part of this work. A point-counterpoint discussion addresses issues with thresholding raised in real conversations with researchers in the field. We hope that by showing how transparent thresholding can drastically improve the interpretation (and reproducibility) of neuroimaging findings, more researchers will adopt this method. |
| title | Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2504.07824 |