{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:50:29Z","timestamp":1782280229037,"version":"3.54.5"},"posted":{"date-parts":[[2025,6,17]]},"group-title":"Neuroscience","reference-count":20,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>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- &amp; 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.<\/jats:p>","DOI":"10.1101\/2025.06.16.657725","type":"posted-content","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:05:36Z","timestamp":1750183536000},"source":"Crossref","is-referenced-by-count":1,"title":["Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2308-163X","authenticated-orcid":false,"given":"Nick","family":"Steele","sequence":"first","affiliation":[{"name":"Brain Imaging and Analysis Center, Duke University, Durham, NC, USA,"},{"name":"Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajendra 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