Feilong, M., Jiahui, G., Gobbini, M.I. et al.
Nat Methods (2024).
Abstract
Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly available high-quality structural scans of 1,031 brains—25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-participant correlations based on onavg than on other templates, and onavg only needs three-quarters as much data to achieve the same performance compared with other templates. The optimized sampling also reduces CPU time across algorithms by 1.3–22.4% due to less variation in the number of vertices in each searchlight.
Here are some thoughts:
Neuroscientists face challenges in comparing brain activity data across individuals due to variations in brain shape. To address this, researchers align data to a common reference using cortical surface templates, which map brain activity onto a brain model. Traditional templates, based on 40 brains, have limitations such as uneven sampling and reliance on a spherical brain model, leading to biases and distortions in data analysis.
To improve this, a Dartmouth team developed the "onavg" template using data from 1,031 brain scans from OpenNeuro. This template better represents the human brain by accurately mapping its geometric shape and ensuring even distribution of data points, reducing biases. The onavg template was tested and found to provide more accurate and reliable data with less computational effort, outperforming older models.
Key advantages of the onavg template include:
- More accurate mapping of brain activity, especially in previously underrepresented areas.
- Increased efficiency, requiring less data for reliable results, which is beneficial for costly or rare data collection.
- Reduced computational time, facilitating quicker data analysis in large-scale studies.
- Improved replicability and reproducibility of research findings.
Despite its advancements, onavg has limitations. It is still an approximation and may not fully capture individual brain variations. It was mainly tested in specific neuroimaging contexts, and further validation is needed across diverse tasks and populations. The template's development relied on data from healthy individuals, suggesting future research should include more diverse populations.
The onavg template is freely available to the scientific community, and its developers are optimistic about its broad impact in neuroscience, particularly in studies of vision, hearing, language, and neurological disorders.
