Image-based meta- and mega-analysis (IBMMA): a unified framework for large-scale, multi-site, neuroimaging data analysis - Steele, Nick; Huggins, Ashley A.; Morey, Rajendra A.; Hussain, Ahmed; Russell, Courtney; Suarez-Jimenez, Benjamin; Pozzi, Elena; Jameei, Hadis; Schmaal, Lianne; Veer, Ilya M.; Waller, Lea; Jahanshad, Neda; et al. (NeuroImage, 2025)
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.
Online verfügbar: 23. Oktober 2025, Artiklelversion: 25. Oktober 2025
Gesehen am 11.12.2025