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

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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Publication Details

Journal
NeuroImage
Published
2025-10-23
DOI
https://doi.org/10.1016/j.neuroimage.2025.121554
Citations
1
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
1.43

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article

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

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

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

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.

NeuroImageVol. 322
Marquette University (US), South African Medical Research Council (ZA), Université de Tours (FR), Boston University (US), Northwestern University (US), Leiden University (NL), Western University (CA), Netherlands Institute for Neuroscience (NL), University of Minnesota (US), Nebraska Medical Center (US), Central European Institute of Technology (CZ), University of Wisconsin–Madison (US), University of Arizona (US), Radboud University Nijmegen (NL), Emory University (US), Inserm (FR), Baylor University (US), University of Cape Town (ZA), Tel Aviv University (IL), The University of Melbourne (AU), University of Groningen (NL), Duke University (US), Baylor College of Medicine (US), Wayne State University (US), Vanderbilt University (US), Medical College of Wisconsin (US), Washington University in St. Louis (US), University of Missouri–St. Louis (US), Masaryk University (CZ), Heidelberg University (DE), University of Münster (DE), University of Michigan (US), Columbia University Irving Medical Center (US), Nanjing General Hospital of Nanjing Military Command (CN), Leiden University Medical Center (NL), McLean Hospital (US), Ghent University Hospital (BE), University Hospital Heidelberg (DE), University of Rochester Medical Center (US), Yale University (US), New York Psychoanalytic Society and Institute (US), University of California San Diego (US), Humboldt-Universität zu Berlin (DE), Arq Psychotrauma Expert Group (NL), Orygen Youth Health (AU), VA San Diego Healthcare System (US), Central Institute of Mental Health (DE), Amsterdam Neuroscience (NL), Nanjing Drum Tower Hospital (CN), Orygen (AU), Minneapolis VA Health Care System (US), Institute on Aging (US), Durham VA Medical Center (US), Academic College of Tel Aviv-Yafo (IL), De Viersprong (NL), Amsterdam University Medical Centers (NL), Duke University Hospital (US), Center for Occupational Research and Development (US), University of Chinese Academy of Sciences (CN), Imaging Center (US), University of Wisconsin–Milwaukee (US), New York State Psychiatric Institute, University of Nebraska Medical Center (US), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE), Nanjing Medical University (CN), Neuroscience Research Australia (AU), The University of Texas at Austin (US), Nanjing University (CN), University of Amsterdam (NL), Vanderbilt University Medical Center (US), University of Toledo (US), Texas A&M University (US), University of Haifa (IL), Bryan College (US)
National Science Foundation, U.S. Department of Defense, Michael J. Fox Foundation for Parkinson's Research, McLean Hospital, Waisman Center, National Alliance for Research on Schizophrenia and Depression, South African Medical Research Council, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, Ministerstvo Školství, Mládeže a Tělovýchovy, ZonMw, Chinese Academy of Sciences, Government of Jiangsu Province, Ministerstvo Zdravotnictví Ceské Republiky, Universiteit Gent, Natural Science Foundation of Jiangsu Province, Fondation Pierre Deniker pour la Recherche et la Prévention en Santé Mentale, Agentura Pro Zdravotnický Výzkum České Republiky, National Institutes of Health, Institute for Clinical and Translational Research, University of Wisconsin, Madison, National Center for PTSD, U.S. Department of Veterans Affairs, Bijzonder Onderzoeksfonds UGent, National Social Science Fund of China, Institute of Psychology, Chinese Academy of Sciences, National Institute of Mental Health, National Institute of Child Health and Human Development, Congressionally Directed Medical Research Programs
Openalex Percentile: Top 15%
Functional Brain Connectivity Studies
1.43
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