Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis
Publication date
2025-06-17
Authors
Steele, Nick
Morey, Rajendra A
Hussain, Ahmed
Russell, Courtney
Suarez-Jimenez, Benjamin
Pozzi, Elena
Jameei, Hadis
Schmaal, Lianne
Veer, Ilya M.
Waller, Lea
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Document Type
/dk/atira/pure/researchoutput/researchoutputtypes/workingpaper/preprint
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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 produced stronger effect sizes and revealed findings in brain regions that 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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Citation
Steele, N, Morey, R A, Hussain, A, Russell, C, Suarez-Jimenez, B, Pozzi, E, Jameei, H, Schmaal, L, Veer, I M, Waller, L, Jahanshad, N, Thomopoulos, S I, Salminen, L E, Olff, M, Frijling, J L, Veltman, D J, Koch, S B J, Nawijn, L, van Zuiden, M, Wang, L, Zhu, Y, Li, G, Stein, D J, Ipser, J, Neria, Y, Zhu, X, Ravid, O, Zilcha-Mano, S, Lazarov, A, Huggins, A A, Stevens, J S, Ressler, K, Jovanovic, T, van Rooij, S J H, Fani, N, Mueller, S C, Hudson, A R, Daniels, J K, Sierk, A, Manthey, A, Walter, H, van der Wee, N J A, van der Werff, S J A, Vermeiren, R R J M, Schmahl, C, Herzog, J I, Rektor, I, Říha, P, Kaufman, M L, Lebois, L A M, Baker, J T, Rosso, I M, Olson, E A, King, A, Liberzon, I, Angstadt, M, Davenport, N D, Disner, S G, Sponheim, S R, Straube, T, Hofmann, D, Lu, G, Qi, R, Wang, X, Kunch, A, Xie, H, Quidé, Y, El-Hage, W, Lissek, S, Berg, H, Bruce, S E, Cisler, J, Ross, M, Herringa, R J, Grupe, D W, Nitschke, J B, Davidson, R J, Larson, C, deRoon-Cassini, T A, Tomas, C W, Fitzgerald, J M, Elman, J, Panizzon, M, Franz, C E, Lyons, M J, Kremen, W S, Feola, B, Blackford, J U, Olatunji, B O, May, G, Nelson, S M, Gordon, E M, Abdallah, C G, Lanius, R, Densmore, M, Théberge, J, Neufeld, R W J, Thompson, P M & Sun, D 2025 'Image-Based Meta- and Mega-Analysis (IBMMA) : A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis' bioRxiv. https://doi.org/10.1101/2025.06.16.657725