Keynote series Dr. Lucina Uddin - Mapping the Brain: Why Standardization Matters
By Alejandra Lopez-Castro
Imagine trying to navigate a city without a standardized map. Some maps divide the city by neighborhoods, others by landmarks, and each uses different names for the same locations. Confusing, right? Well, that’s exactly the challenge neuroscientists face when studying the brain.
To understand how different brain regions interact, researchers rely on brain atlases—specialized maps that divide the brain based on either structure (anatomy) or function (what different areas do). These atlases help scientists make sense of brain activity, but there’s a problem: not all atlases agree on how to define these regions. One study might label a particular area as responsible for memory, while another categorizes it under decision-making. With so many different brain maps, comparing results across studies becomes a puzzle.
This lack of standardization makes it difficult to build a universal understanding of brain networks. That’s where the Network Correspondence Toolbox (NCT) comes in. Developed by Dr. Lucina Q. Uddin’s research group, this tool helps neuroscientists compare their brain imaging results with widely used brain atlases, ensuring greater consistency in research.
The Network Correspondence Toolbox was created by a team of neuroscientists led by Dr Thomas Yeo and Dr. Lucina Q. Uddin, the latter a prominent researcher in cognitive and network neuroscience. Her group focuses on understanding functional brain networks, particularly in cognition and neurodevelopmental disorders. The study involved contributions from multiple experts in neuroimaging, including researchers specializing in functional connectivity, brain mapping, and statistical modeling.
Dr. Uddin’s team has been instrumental in advancing methods for analyzing brain networks, and the NCT represents a significant step toward standardizing neuroimaging research. Their work aligns with broader efforts by organizations like the Organization for Human Brain Mapping (OHBM), which aims to establish best practices for brain network nomenclature.
The study, led by first author Dr. Ruby Kong and collaborators, introduces the Network Correspondence Toolbox (NCT) as a solution to inconsistencies in brain network labeling. The research highlights several key findings:
Variability in Brain Network Definitions. Neuroscientists often describe brain activity in terms of networks rather than individual regions. However, different atlases define these networks in varying ways, leading to inconsistencies in scientific reporting. For example, the default mode network, associated with introspection and memory, may be labeled differently across atlases, making cross-study comparisons difficult.
Quantitative Evaluation of Brain Network Correspondence. The NCT provides a quantitative method to assess how well new neuroimaging results align with existing atlases. It uses the Dice coefficient, a statistical measure that quantifies the overlap between brain regions in new data and those in established atlases. This helps researchers determine how closely their findings match widely accepted brain maps.
Statistical Validation for Robust Comparisons. To ensure that observed overlaps are meaningful and not due to chance, the NCT incorporates spin tests—a statistical method that evaluates the significance of spatial correspondences. This adds rigor to neuroimaging analyses, making results more reliable.
Cross-Atlas Comparisons for Improved Accuracy. The toolbox enables comparisons across multiple atlases, preventing biases that may arise from relying on a single brain map. This approach ensures findings are not skewed by the limitations of any one atlas, promoting more reproducible results.
User-Friendly Design for Broad Accessibility. The NCT is designed to be compatible with various neuroimaging formats, making it accessible to researchers working with different types of brain imaging data. This flexibility enhances collaboration across neuroscience disciplines.
Advancing Standardization in Neuroscience. By providing a standardized framework for reporting brain network findings, the NCT improves communication among neuroscientists. The study emphasizes the importance of adopting common terminology and methodologies to enhance the reproducibility of neuroimaging studies.
The introduction of the Network Correspondence Toolbox marks a major advancement in neuroimaging research. By improving transparency, reproducibility, and consistency, the NCT helps neuroscientists move toward a more unified understanding of brain networks. With better tools for comparing brain maps, researchers can make more reliable discoveries, bringing us closer to unlocking the mysteries of the human brain.
Don't miss the interview with Dr. Uddin that you'll find in the blog post. And don't miss the keynote talk this June at the OHBM Annual Meeting 2025
Source
Kong, R., Spreng, R.N., Xue, A. et al. A network correspondence toolbox for quantitative evaluation of novel neuroimaging results. Nat Commun 16, 2930 (2025). https://doi.org/10.1038/s41467-025-58176-9
