Share it all: challenges in reproducibility from data acquisition to management and analysis

Navigating the complexities of academia as open scientists demands more than mastering reproducible workflows and open tools; it requires resilience in the face of systemic barriers and a steadfast commitment to fostering transparency
This talk offers a pragmatic guide tailored for Early Career Researchers, addressing challenges in neuroimaging research (fMRI and EEG) and the adoption of open science practices.
From curating datasets to publishing reproducible findings, we share lessons learned and practical strategies for aligning innovation with the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data.
The talk concludes with actionable steps for integrating open standards and tools while fostering a research environment that prioritizes transparency, reliability, and collaborative progress. 

In principle, pooling functional MRI data across different sites and points in time should allow for increased statistical power, provided that the imaging experiment can be conducted in a known and reproducible way. While post-processing attempts to correct for ‘site’ variance have been somewhat successful, it would be better to control for potential differences (e.g., vendor, scanner model, scanner software) during the acquisition itself. Historically this has been a challenge, as the details of the vendor-provided fMRI acquisition and reconstruction software are generally not known.

The researcher can in principle implement the imaging protocol themselves using each vendor’s pulse sequence programming environment, but this is a technically difficult and time-consuming task. Existing multi-site studies have therefore invested significant resources into making the fMRI protocols on different systems as similar as possible and maintaining them across scanner software upgrades. However, even then the protocols can often only be ‘harmonized’ with respect to high-level sequence parameters such as FOV, matrix size, and net acceleration factor; subtle but potentially important differences in, e.g., details of sequence timing or RF and gradient waveform shapes, are often not known to the researchers or are beyond their control.

In this talk we will discuss open-source fMRI approaches, and describe a fully vendor-agnostic and portable fMRI protocol that ensures identical sequence execution and image reconstruction across different scanners, and across scanner software upgrades. The acquisition sequence is defined in simple human-readable text files, and sequence execution on real hardware is made possible by openly available sequence-agnostic interpreters. We believe this framework will enable reproducible, truly harmonized multi-site and longitudinal functional studies. 

Recently, two global trends have converged at a critical juncture, researchers’ push for open science, and policy-makers’ push for the tightening of personal data privacy (PDP) legislation. The principles underlying these movements, open science’s call for data accessibility and privacy laws’ emphasis on data protection, often stand in opposition. These conflicting movements impede progress in neuroscience, highlighting the need for solutions that balance openness with PDP. Recognizing this critical need, the International Brain Initiative recently published a call to action to develop international data governance frameworks (IDGF) for neuroscience to promote international collaboration and facilitate scientific discovery.
Data governance involves the principles, procedures, frameworks, and policies that safeguard responsible processing of data. A robust IDGF is essential to balance open science with PDP laws, yet such frameworks are lacking in neuroscience. Effective IDGFs can establish standardized protocols for anonymization, data standardization and sharing, and access permissions, thus enabling researchers to share data without compromising PDP.
BRIDGE (braindatagovernance.org), is tasked to develop IDGFs to facilitate international sharing of brain and mental health data, addressing the challenges posed by varying legal, ethical, and technical landscapes. We will present a Practical Guide to Accessing and Sharing Brain Data. This guide will outline a framework for navigating the legal, ethical, and procedural requirements involved in accessing and sharing brain data. We will cover the following stages: Project Planning & Proposal Development, Ethics Approval & Informed Consent, Data Collection, Data Processing & Analysis, Data Sharing & Collaboration, Dissemination & Public Engagement, and Data Stewardship. We will include examples where possible that apply to researchers globally. We will also highlight tools, standards, and resources supporting IDG.
 

Recent advancements in large-scale brain imaging, combined with the rapid progress in deep learning and generative AI, are transforming our understanding of the human brain.
This convergence offers a unique opportunity to enhance cognitive abilities throughout the lifespan and to develop personalized, precise treatments for neurological disorders.
In this talk, I will explore how open science approaches—such as open datasets, open- source software, and collaborative tools—are driving the development and refinement of AI methods for brain imaging. These include applications in brain lesion segmentation, vision decoding, and the creation of brain foundation models.
I will discuss how open science fosters the reusability and reproducibility of AI-driven brain imaging analyses, while addressing key challenges, such as adaptation across diverse cohorts, database harmonization, cross-validation, hyperparameter optimization, and benchmark evaluation.
Open science is crucial for advancing AI applications in neuroscience and medicine, and it plays an essential role in overcoming current barriers to progress.