Interpretable, reproducible and creative neuroimaging data visualization

COEX 

“A picture is worth a thousand words.” Effective data visualization is crucial for precisely interpreting and compellingly communicating scientific findings. However, it is challenging to transform scientific results into interpretable, reproducible and creative visualization. This talk aims to review issues, principles, tools and examples for enriching interpretability, reproducibility, and creativity of neuroimaging data visualization. First, I will discuss common pitfalls hindering interpretability and reproducibility. Then, I will present guidelines for evaluating the clarity and completeness of figures (Allen et al., 2012), alongside practices to improve interpretability and reproducibility, such as highlighting results instead of hiding all but the most significant ones (Taylor et al., 2023). Next, I will introduce a comprehensive array of software toolboxes for brain visualization, and highlight an emerging tool Brain-Code (Chopra et al., 2023) which generates R and/or Python code templates to visualize brain imaging data. Finally, I will illustrate innovative visualization examples from recent literature, including graphic workflows (Bethlehem et al., 2022) and icon representations (Tian et al., 2023). During the presentation, we will include brief quiz questions for the audience on visualization and accessibility.