Image-based meta-and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Neuroimage |
| MU Faculty or unit | |
| Citation | |
| web | https://www.sciencedirect.com/science/article/pii/S1053811925005579?pes=vor&utm_source=clarivate&getft_integrator=clarivate |
| Doi | https://doi.org/10.1016/j.neuroimage.2025.121554 |
| Keywords | Neuroimaging; Big data; Meta-analysis; PTSD; Resting-state fMRI |
| Description | 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. |
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