Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

作者：N. C. Steele, Ashley A. Huggins, Rajendra A. Morey, Ahmed Hussain, Courtney Russell, Benjamin Suarez‐Jimenez, Elena Pozzi, Hadis Jameei, Lianne Schmaal, Ilya M. Veer, Lea Waller, Neda Jahanshad, Sophia I. Thomopoulos, Lauren E. Salminen, Miranda Olff, Jessie L. Frijling, Dick J. Veltman, Saskia B.J. Koch, Laura Nawijn, Mirjam van Zuiden, Li Wang, Ye Zhu, Gen Li, Dan J. Stein, Jonathan Ipser, Yuval Neria, Xi Zhu, Orren Ravid, Sigal Zilcha‐Mano, Amit Lazarov, Jennifer S. Stevens, Kerry J. Ressler, Tanja Jovanović, Sanne J.H. van Rooij, Negar Fani, Sven C. Mueller, Anna R. Hudson, Judith K. Daniels, Anika Sierk, Antje Manthey, Henrik Walter, Nic J.A. van der Wee, Steven J.A. van der Werff, Robert Vermeiren, Christian Schmahl, Julia Herzog, Ivan Rektor, Pavel Říha, Milissa L. Kaufman, Lauren A. M. Lebois, Justin T. Baker, Isabelle M. Rosso, Elizabeth A. Olson, Anthony King, Israel Liberzon, Michael Angstadt, Nicholas D. Davenport, Seth G. Disner, Scott R. Sponheim, Thomas Straube, David Hofmann, Guangming Lu, Rongfeng Qi, Xin Wang, Austin Kunch, Hong Xie, Yann Quidé, Wissam El‐Hage, Shmuel Lissek, Hannah Berg, Steven E. Bruce, Josh M. Cisler, Marisa Ross, Ryan J. Herringa, Daniel W. Grupe, Jack B. Nitschke, Richard J. Davidson, Christine Larson, Terri A. deRoon‐Cassini, Carissa W. Tomas, Jacklynn M. Fitzgerald, Jeremy A. Elman, Matthew S. Panizzon, Carol E. Franz, Michael J. Lyons, William S. Kremen, Brandee Feola, Jennifer Urbano Blackford, Bunmi O. Olatunji, Geoffrey May, Steven M. Nelson, Evan M. Gordon, Chadi G. Abdallah, Ruth A. Lanius, Maria Densmore, Jean Théberge, Richard W. J. Neufeld, Paul M. Thompson, Delin Sun · 发表于：NeuroImage · 年份：2025 · DOI：10.1016/j.neuroimage.2025.121554 · 被引用次数：3 · 研究领域：Functional Brain Connectivity Studies、Advanced Neuroimaging Techniques and Applications、Advanced MRI Techniques and Applications

• IBMMA efficiently handles large-scale datasets with parallel processing. • Streamlines meta- and mega-analysis workflows through an automated pipeline. • Robustly handles missing voxel-data common in multi-site neuroimaging datasets. • Enables diverse statistical designs beyond the constraints of traditional software. 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.