Document Type
Article
Publication Date
11-15-2025
Publisher
Elsevier
Source Publication
NeuroImage
Source ISSN
1053-8119
Original Item ID
DOI: 10.1016/j.neuroimage.2025.121554
Abstract
The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Fitzgerald, Jacklynn M., "Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis" (2025). Psychology Faculty Research and Publications. 641.
https://epublications.marquette.edu/psych_fac/641
Comments
Published version. NeuroImage, Vol. 322 (November 15, 2025). DOI. © The Author(s). Published by Elsevier Inc. Used with permission.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Please see article for full author list.