Neuroinformatics plays a crucial role in organizing, analyzing, and sharing neuroscience data collected through imaging techniques such as Magnetic Resonance Imaging (MRI), functional MRI, Electroencephalography (EEG), Positron Emission Tomography (PET), and Magnetoencephalography (MEG). However, reproducibility remains a critical challenge due to variability in experimental planning, data processing pipelines, and analytic methodologies. Based on pioneering research and recent advances in the field, this session explores the intersection of reproducibility, optimal experimental planning, and computational practices of multimodal imaging in the age of big data. 

Participants will learn about effective strategies to standardize and optimize complex neuroinformatics workflows, with particular attention to state-of-the-art machine learning and biostatistical techniques. Special emphasis will also be placed on practical guidelines for managing neuroimaging databases and analytics infrastructure, exemplified by platforms such as OpenNeuro. Interactive activities will encourage participants to apply these principles directly to their own research scenarios, improving transparency, replicability, and collaborative potential.

Program

Introduction to Neuroinformatics and Reproducibility (10 min)

Case study: Lessons learned from OpenNeuro and the NeuroImaging PREProcessing toolS (NiPreps) initiative (10 min)

Interactive group activity: Optimizing Neuroimaging Pipelines (20 min)

Practical session: Neuroinformatics Tools & Database Management (20 min)

Discussion & wrap-up

Organizers

• Martin Nørgaard, Assistant Professor, University of Copenhagen
• Ruben Dörfel, PhD Student, Rigshospitalet, Karolinska Institutet
• Llucia Coll, Postdoc, University of Copenhagen

Level

Introductory: Suitable for beginners with little to no prior knowledge of neuroinformatics or reproducibility practices.