From Code to Visualization: Reproducible Pipelines for Neuroimaging Research

Brisbane Convention & Exhibition Centre 

How can neuroimaging researchers ensure their findings are not only robust but fully transparent and reproducible? This talk addresses this critical question by exploring the tools and practices needed to build reproducible pipelines, from coding to visualization. Whether you’re new to reproducible research or looking to refine your workflows, this session offers actionable insights for all experience levels. We’ll begin by outlining best practices for reproducible coding, emphasizing how transparent and shareable scripts foster collaboration, trust, and consistency in data processing. Examples will highlight the use of open notebooks hosted on public repositories to share study designs, evaluations, and findings openly with the scientific community. The session will then shift focus to an often-overlooked aspect of reproducibility: scientific visualizations. Figures are central to communicating findings but are often treated as static, unrepeatable images. This talk will demonstrate how reproducible visualizations transform figures into dynamic, script-backed content, enabling others to recreate, adjust, and validate visual representations with ease. Using Python-based tools such as Nilearn, Niivue, Cerebro, MMVT, and DIPY, alongside platforms like Blender, we’ll showcase techniques for generating reproducible visualizations across neuroimaging modalities, including 2D and 3D volumetric imaging, cortical surface renderings, brain network diagrams, and tractography. Attendees will learn how to produce publication-quality figures directly from code, eliminating reliance on manual adjustments via GUIs. This session will equip attendees with practical skills for implementing reproducible workflows, along with openly available scripts that can be adapted for their projects. By integrating these practices, researchers can ensure their findings are robust, accessible, and verifiable—contributing to greater transparency and impact in neuroimaging research while addressing key challenges in reproducibility.