Steele Nick, Morey Rajendra A, Hussain Ahmed, Russell Courtney, Suarez-Jimenez Benjamin, Pozzi Elena, Jameei Hadis, Schmaal Lianne, Veer Ilya M, Waller Lea, Jahanshad Neda, Thomopoulos Sophia I, Salminen Lauren E, Olff Miranda, Frijling Jessie L, Veltman Dick J, Koch Saskia B J, Nawijn Laura, van Zuiden Mirjam, Wang Li, Zhu Ye, Li Gen, Stein Dan J, Ipser Jonathan, Neria Yuval, Zhu Xi, Ravid Orren, Zilcha-Mano Sigal, Lazarov Amit, Huggins Ashley A, Stevens Jennifer S, Ressler Kerry, Jovanovic Tanja, van Rooij Sanne J H, Fani Negar, Mueller Sven C, Hudson Anna R, Daniels Judith K, Sierk Anika, Manthey Antje, Walter Henrik, van der Wee Nic J A, van der Werff Steven J A, Vermeiren Robert R J M, Schmahl Christian, Herzog Julia I, Rektor Ivan, Říha Pavel, Kaufman Milissa L, Lebois Lauren A M, Baker Justin T, Rosso Isabelle M, Olson Elizabeth A, King Anthony, Liberzon Israel, Angstadt Michael, Davenport Nicholas D, Disner Seth G, Sponheim Scott R, Straube Thomas, Hofmann David, Lu Guangming, Qi Rongfeng, Wang Xin, Kunch Austin, Xie Hong, Quidé Yann, El-Hage Wissam, Lissek Shmuel, Berg Hannah, Bruce Steven E, Cisler Josh, Ross Marisa, Herringa Ryan J, Grupe Daniel W, Nitschke Jack B, Davidson Richard J, Larson Christine, deRoon-Cassini Terri A, Tomas Carissa W, Fitzgerald Jacklynn M, Elman Jeremy, Panizzon Matthew, Franz Carol E, Lyons Michael J, Kremen William S, Feola Brandee, Blackford Jennifer U, Olatunji Bunmi O, May Geoffrey, Nelson Steven M, Gordon Evan M, Abdallah Chadi G, Lanius Ruth, Densmore Maria, Théberge Jean, Neufeld Richard W J, Thompson Paul M, Sun Delin
Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA.
bioRxiv. 2025 Jun 17:2025.06.16.657725. doi: 10.1101/2025.06.16.657725.
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.
从多个研究地点汇总而来的神经影像数据集，其规模和复杂性日益增加，这带来了巨大的分析挑战。现有的统计分析工具难以处理缺失的体素数据，计算速度有限，内存分配效率低下，并且能够建模的统计设计类型也受到限制。我们引入了基于图像的元分析和大型分析（IBMMA），这是一个用R和Python实现的新型软件包，它为分析各种神经影像特征提供了一个统一的框架，通过并行处理有效地处理大规模数据集，提供灵活的统计建模选项，并妥善管理多地点研究中常见的缺失体素数据。IBMMA产生了更强的效应量，并揭示了传统软件因缺失体素数据导致脑覆盖范围出现空白而忽略的脑区中的研究结果。IBMMA有潜力加速神经科学领域的发现，并提高神经影像研究结果的临床实用性。
