Cerebro - Advancing Openness and Reproducibility in Neuroimaging Visualization

[sina-mansour]

Authors

Sina Mansour L. sina.mansour.lakouraj@gmail.com
Annie G. Bryant abry4213@uni.sydney.edu.au
Natasha Clarke natashajayne@gmail.com
Niousha Dehestani ndehestanikolag@deakin.edu.au
Jayson Jeganathan jayson.jeganathan@gmail.com
Tristan Kuehn tkuehn@uwo.ca
Jason Kai tkai@uwo.ca
Darin Erat Sleiter darin@ae.studio
Maria Di Biase <maria.dibiase@unimelb.edu.au >
Caio Seguin caioseguin@gmail.com
B.T. Thomas Yeo thomas.yeo@nus.edu.sg
Andrew Zalesky azalesky@unimelb.edu.au

Summary

Synopsis

Openness and transparency have emerged as indispensable virtues that pave the way for high-quality, reproducible research, fostering a culture of trust and collaboration within the scientific community $^1$. In the domain of neuroimaging research, these principles have led to the creation of guidelines and best practices, establishing a platform that champions openness and reproducibility $^2$. The field has made substantial strides in embracing open science, a feat achieved through the successful integration of open-source software $^3$, public dataset sharing $^4$, standardized analysis pipelines $^5$, unified brain imaging data structures $^6$, open code sharing $^7$, and the endorsement of open publishing.

The joint efforts of these initiatives have established a robust foundation for cultivating a culture of collaboration and accountability within the neuroimaging community. Nonetheless, amidst these advancements, a crucial inquiry arises: What about the visual representations —the very tools through which we observe and make sense of neuroimaging results? It is within this context that we introduce "Cerebro" $^8$, a new Python-based software designed to create publication-quality brain visualizations (see Fig.1). Cerebro represents the next step in the quest for openness and reproducibility, where authors can share not only their code and data but also their publication figures alongside the precise scripts used to generate them. In doing so, Cerebro empowers researchers to make their visualizations fully reproducible, bridging the crucial gap between data, analysis, and representation.

Future guidelines

As Cerebro continues to evolve in its early development stages, this manuscript serves as a manifesto, articulating our dedication to advancing the cause of open and reproducible neuroimaging visualization. Cerebro is guided by a set of overarching goals, which include:

Fully Scriptable Publication-Quality Neuroimaging Visualization:

Cerebro's primary mission is to equip researchers with the means to craft impeccable brain visualizations while preserving full scriptability. Cerebro enables researchers to document and share every aspect of the visualization process, ensuring the seamless reproducibility of neuroimaging figures.

Cross-Compatibility with Different Data Formats:

The diversity of brain imaging data formats can pose a significant challenge for visualization. Many existing tools are constrained by compatibility limitations with specific formats. Drawing upon the foundations laid by tools like NiBabel $^9$, Cerebro is determined to provide robust cross-compatibility across a wide spectrum of data formats.

Integration with Open Science Neuroimaging Tools:

We wholeheartedly acknowledge that Cerebro cannot thrive in isolation. To realize its full potential, it must seamlessly integrate into the existing tapestry of open science tools, software, and standards. Through active collaboration with the neuroimaging community, particularly through open, inclusive community initiatives such as brainhacks $^{10}$, Cerebro aspires to forge cross-compatibility with established and emerging tools, standards, and pipelines. Our vision is one where the future of neuroimaging visualizations is marked by unwavering openness and reproducibility, fostered by this united effort.

References (Bibtex)

References

1. Munafò, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., Percie du Sert, N., ... & Ioannidis, J. (2017). A manifesto for reproducible science. Nature human behaviour, 1(1), 1-9.
2. Niso, G., Botvinik-Nezer, R., Appelhoff, S., De La Vega, A., Esteban, O., Etzel, J. A., ... & Rieger, J. W. (2022). Open and reproducible neuroimaging: from study inception to publication. NeuroImage, 119623.
3. Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in neuroinformatics, 5, 13.
4. Markiewicz, C. J., Gorgolewski, K. J., Feingold, F., Blair, R., Halchenko, Y. O., Miller, E., ... & Poldrack, R. (2021). The OpenNeuro resource for sharing of neuroscience data. Elife, 10, e71774.
5. Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., ... & Yeo, B. T. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature neuroscience, 20(3), 299-303.
6. Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., ... & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3(1), 1-9.
7. Smout, C., Holford, D. L., Garner, K., Martinez, P. A., Campbell, M. E. J., Khormi, I., ... & Coelho, L. P. (2023). An open code pledge for the neuroscience community. Aperture Neuro, Proceedings of the OHBM Brainhack 2021.
8. Mansour L., S., Bryant, A. G., Jeganathan, J., Kai, J., Kuehn, T., Sleiter, D. E., … & Dehestani, N.. (2023). sina-mansour/Cerebro_Viewer: v0.0.10.4 (v0.0.10.4). Zenodo. https://doi.org/10.5281/zenodo.8238773
9. Brett, M., Markiewicz, C. J., Hanke, M., Côté, M. A., Cipollini, B., McCarthy, P., ... & Guidotti, R. (2020). nipy/nibabel: 3.2. 1. Zenodo.
10. Gau, R., Noble, S., Heuer, K., Bottenhorn, K. L., Bilgin, I. P., Yang, Y. F., ... & Marinazzo, D. (2021). Brainhack: Developing a culture of open, inclusive, community-driven neuroscience. Neuron, 109(11), 1769-1775.

Bibtex bibliography

@article{munafo2017manifesto,
  title={A manifesto for reproducible science},
  author={Munaf{\`o}, Marcus R and Nosek, Brian A and Bishop, Dorothy VM and Button, Katherine S and Chambers, Christopher D and Percie du Sert, Nathalie and Simonsohn, Uri and Wagenmakers, Eric-Jan and Ware, Jennifer J and Ioannidis, John},
  journal={Nature human behaviour},
  volume={1},
  number={1},
  pages={1--9},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{niso2022open,
  title={Open and reproducible neuroimaging: from study inception to publication},
  author={Niso, Guiomar and Botvinik-Nezer, Rotem and Appelhoff, Stefan and De La Vega, Alejandro and Esteban, Oscar and Etzel, Joset A and Finc, Karolina and Ganz, Melanie and Gau, Remi and Halchenko, Yaroslav O and others},
  journal={NeuroImage},
  pages={119623},
  year={2022},
  publisher={Elsevier}
}

@article{gorgolewski2011nipype,
  title={Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python},
  author={Gorgolewski, Krzysztof and Burns, Christopher D and Madison, Cindee and Clark, Dav and Halchenko, Yaroslav O and Waskom, Michael L and Ghosh, Satrajit S},
  journal={Frontiers in neuroinformatics},
  volume={5},
  pages={13},
  year={2011},
  publisher={Frontiers}
}

@article{markiewicz2021openneuro,
  title={The OpenNeuro resource for sharing of neuroscience data},
  author={Markiewicz, Christopher J and Gorgolewski, Krzysztof J and Feingold, Franklin and Blair, Ross and Halchenko, Yaroslav O and Miller, Eric and Hardcastle, Nell and Wexler, Joe and Esteban, Oscar and Goncavles, Mathias and others},
  journal={Elife},
  volume={10},
  pages={e71774},
  year={2021},
  publisher={eLife Sciences Publications Limited}
}

@article{nichols2017best,
  title={Best practices in data analysis and sharing in neuroimaging using MRI},
  author={Nichols, Thomas E and Das, Samir and Eickhoff, Simon B and Evans, Alan C and Glatard, Tristan and Hanke, Michael and Kriegeskorte, Nikolaus and Milham, Michael P and Poldrack, Russell A and Poline, Jean-Baptiste and others},
  journal={Nature neuroscience},
  volume={20},
  number={3},
  pages={299--303},
  year={2017},
  publisher={Nature Publishing Group US New York}
}

@article{gorgolewski2016brain,
  title={The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments},
  author={Gorgolewski, Krzysztof J and Auer, Tibor and Calhoun, Vince D and Craddock, R Cameron and Das, Samir and Duff, Eugene P and Flandin, Guillaume and Ghosh, Satrajit S and Glatard, Tristan and Halchenko, Yaroslav O and others},
  journal={Scientific data},
  volume={3},
  number={1},
  pages={1--9},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{smout2021open,
  title={An open code pledge for the neuroscience community},
  author={Smout, Cooper and Holford, Dawn Liu and Garner, Kelly and Martinez, Paula Andrea and Campbell, Megan Ethel Janine and Khormi, Ibrahim and Gomes, Dylan GE and Bayer, Johanna Margarete Marianne and Bradley, Claire and Schettino, Antonio and others},
  year={2021},
  publisher={Aperture Neuro},
}

@software{sina_mansour_l_2023_8238773,
  author       = {Mansour L., Sina and Bryant, Annie G. and Jeganathan, Jayson and Kai, Jason and Kuehn, Tristan and Sleiter, Darin Erat and Clarke, Natasha and Wright, Brooklyn and Dehestani, Niousha},
  title        = {sina-mansour/Cerebro\_Viewer: v0.0.10.4},
  month        = aug,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.0.10.4},
  doi          = {10.5281/zenodo.8238773},
  url          = {https://doi.org/10.5281/zenodo.8238773}
}

@software{brett_matthew_2020_4295521,
  author       = {Brett, Matthew and Markiewicz, Christopher J. and Hanke, Michael and Côté, Marc-Alexandre and Cipollini, Ben and McCarthy, Paul and Jarecka, Dorota and Cheng, Christopher P. and Halchenko, Yaroslav O. and Cottaar, Michiel and others},
  title        = {nipy/nibabel: 3.2.1},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {3.2.1},
  doi          = {10.5281/zenodo.4295521},
  url          = {https://doi.org/10.5281/zenodo.4295521}
}

@article{gau2021brainhack,
  title={Brainhack: Developing a culture of open, inclusive, community-driven neuroscience},
  author={Gau, R{\'e}mi and Noble, Stephanie and Heuer, Katja and Bottenhorn, Katherine L and Bilgin, Isil P and Yang, Yu-Fang and Huntenburg, Julia M and Bayer, Johanna MM and Bethlehem, Richard AI and Rhoads, Shawn A and others},
  journal={Neuron},
  volume={109},
  number={11},
  pages={1769--1775},
  year={2021},
  publisher={Elsevier}
}

[sina-mansour]

Figure:

Caption:

Fig. 1 Cerebro – A Python-Based 3D Brain Visualization Tool. (a) Cerebro leverages an extensive array of open-source Python tools, forming a robust foundation for its functionality. It also integrates with these tools to enable seamless Pythonic neuroimaging visualizations. (b) Cerebro prioritizes principles of scriptability, reproducibility, visualization quality, cross-compatibility, and integration in its pursuit of providing a reliable solution for the neuroimaging community. (c) An illustrative Cerebro-generated visualization, accompanied by a scannable QR code linked to the publicly accessible reproduction scripts.

[sina-mansour]

Ping: @anniegbryant @clarkenj @Niousha-Dehestani @jaysonjeg @tkkuehn @kaitj @dsleiter

This is our submission for the OHBM Brainhack 2023 proceedings.

[clarkenj]

Awesome! I no longer have access to that email unfortunately, if it's not too late my email is natashajayne@gmail.com. But no worries :)

[anibalsolon]

hello all, could you please provide the authors information in the following link by May 15th?
https://docs.google.com/forms/d/e/1FAIpQLSckbC4F6KOtge1KOyzwj5yIbWR7tB8HrnqQ4KPZB7Mr3UcvMw/viewform