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

Nick 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 Ressler; Tanja Jovanovic; 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 R.J.M. Vermeiren; Christian Schmahl; Julia I. 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 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 Elman; Matthew Panizzon; Carol E. Franz; Michael J. Lyons; William S. Kremen; Brandee Feola; Jennifer U. Blackford; Bunmi O. Olatunji; Geoffrey May; Steven M. Nelson; Evan M. Gordon; Chadi G. Abdallah; Ruth Lanius; Maria Densmore; Jean Théberge; Richard W.J. Neufeld; Paul M. Thompson; Delin Sun

doi:10.1016/j.neuroimage.2025.121554

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

Nick 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 Ressler, Tanja Jovanovic, 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 R.J.M. Vermeiren, Christian Schmahl, Julia I. 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 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 Elman, Matthew Panizzon, Carol E. Franz, Michael J. Lyons, William S. Kremen, Brandee Feola, Jennifer U. Blackford, Bunmi O. Olatunji, Geoffrey May, Steven M. Nelson, Evan M. Gordon, Chadi G. Abdallah, Ruth Lanius, Maria Densmore, Jean Théberge, Richard W.J. Neufeld, Paul M. Thompson, Delin Sun

Department of Psychology

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	121554
Journal	NeuroImage
Volume	322
DOIs	https://doi.org/10.1016/j.neuroimage.2025.121554
State	Published - 15 Nov 2025

Bibliographical note

Publisher Copyright:
Copyright © 2025. Published by Elsevier Inc.

Keywords

Big data
Mega-analysis
Meta-analysis
Neuroimaging
PTSD
Resting-state fMRI

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2025.121554

Cite this

Steele, N., Huggins, A. A., 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., ... Sun, D. (2025). Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis. NeuroImage, 322, Article 121554. https://doi.org/10.1016/j.neuroimage.2025.121554

@article{d84dc59a2f394692b0b4ce921993076c,

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

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.",

keywords = "Big data, Mega-analysis, Meta-analysis, Neuroimaging, PTSD, Resting-state fMRI",

author = "Nick Steele and Huggins, \{Ashley A.\} and Morey, \{Rajendra A.\} and Ahmed Hussain and Courtney Russell and Benjamin Suarez-Jimenez and Elena Pozzi and Hadis Jameei and Lianne Schmaal and Veer, \{Ilya M.\} and Lea Waller and Neda Jahanshad and Thomopoulos, \{Sophia I.\} and Salminen, \{Lauren E.\} and Miranda Olff and Frijling, \{Jessie L.\} and Veltman, \{Dick J.\} and Koch, \{Saskia B.J.\} and Laura Nawijn and \{van Zuiden\}, Mirjam and Li Wang and Ye Zhu and Gen Li and Stein, \{Dan J.\} and Jonathan Ipser and Yuval Neria and Xi Zhu and Orren Ravid and Sigal Zilcha-Mano and Amit Lazarov and Stevens, \{Jennifer S.\} and Kerry Ressler and Tanja Jovanovic and \{van Rooij\}, \{Sanne J.H.\} and Negar Fani and Mueller, \{Sven C.\} and Hudson, \{Anna R.\} and Daniels, \{Judith K.\} and Anika Sierk and Antje Manthey and Henrik Walter and \{van der Wee\}, \{Nic J.A.\} and \{van der Werff\}, \{Steven J.A.\} and Vermeiren, \{Robert R.J.M.\} and Christian Schmahl and Herzog, \{Julia I.\} and Ivan Rektor and Pavel {\v R}{\'i}ha and Kaufman, \{Milissa L.\} and Lebois, \{Lauren A.M.\} and Baker, \{Justin T.\} and Rosso, \{Isabelle M.\} and Olson, \{Elizabeth A.\} and Anthony King and Israel Liberzon and Michael Angstadt and Davenport, \{Nicholas D.\} and Disner, \{Seth G.\} and Sponheim, \{Scott R.\} and Thomas Straube and David Hofmann and Guangming Lu and Rongfeng Qi and Xin Wang and Austin Kunch and Hong Xie and Yann Quid{\'e} and Wissam El-Hage and Shmuel Lissek and Hannah Berg and Bruce, \{Steven E.\} and Josh Cisler and Marisa Ross and Herringa, \{Ryan J.\} and Grupe, \{Daniel W.\} and Nitschke, \{Jack B.\} and Davidson, \{Richard J.\} and Christine Larson and deRoon-Cassini, \{Terri A.\} and Tomas, \{Carissa W.\} and Fitzgerald, \{Jacklynn M.\} and Jeremy Elman and Matthew Panizzon and Franz, \{Carol E.\} and Lyons, \{Michael J.\} and Kremen, \{William S.\} and Brandee Feola and Blackford, \{Jennifer U.\} and Olatunji, \{Bunmi O.\} and Geoffrey May and Nelson, \{Steven M.\} and Gordon, \{Evan M.\} and Abdallah, \{Chadi G.\} and Ruth Lanius and Maria Densmore and Jean Th{\'e}berge and Neufeld, \{Richard W.J.\} and Thompson, \{Paul M.\} and Delin Sun",

note = "Publisher Copyright: Copyright {\textcopyright} 2025. Published by Elsevier Inc.",

year = "2025",

month = nov,

day = "15",

doi = "10.1016/j.neuroimage.2025.121554",

language = "English",

volume = "322",

journal = "NeuroImage",

issn = "1053-8119",

publisher = "Academic Press Inc.",

}

Steele, N, Huggins, AA, Morey, RA, Hussain, A, Russell, C, Suarez-Jimenez, B, Pozzi, E, Jameei, H, Schmaal, L, Veer, IM, Waller, L, Jahanshad, N, Thomopoulos, SI, Salminen, LE, Olff, M, Frijling, JL, Veltman, DJ, Koch, SBJ, Nawijn, L, van Zuiden, M, Wang, L, Zhu, Y, Li, G, Stein, DJ, Ipser, J, Neria, Y, Zhu, X, Ravid, O, Zilcha-Mano, S, Lazarov, A, Stevens, JS, Ressler, K, Jovanovic, T, van Rooij, SJH, Fani, N, Mueller, SC, Hudson, AR, Daniels, JK, Sierk, A, Manthey, A, Walter, H, van der Wee, NJA, van der Werff, SJA, Vermeiren, RRJM, Schmahl, C, Herzog, JI, Rektor, I, Říha, P, Kaufman, ML, Lebois, LAM, Baker, JT, Rosso, IM, Olson, EA, King, A, Liberzon, I, Angstadt, M, Davenport, ND, Disner, SG, Sponheim, SR, Straube, T, Hofmann, D, Lu, G, Qi, R, Wang, X, Kunch, A, Xie, H, Quidé, Y, El-Hage, W, Lissek, S, Berg, H, Bruce, SE, Cisler, J, Ross, M, Herringa, RJ, Grupe, DW, Nitschke, JB, Davidson, RJ, Larson, C, deRoon-Cassini, TA, Tomas, CW, Fitzgerald, JM, Elman, J, Panizzon, M, Franz, CE, Lyons, MJ, Kremen, WS, Feola, B, Blackford, JU, Olatunji, BO, May, G, Nelson, SM, Gordon, EM, Abdallah, CG, Lanius, R, Densmore, M, Théberge, J, Neufeld, RWJ, Thompson, PM & Sun, D 2025, 'Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis', NeuroImage, vol. 322, 121554. https://doi.org/10.1016/j.neuroimage.2025.121554

TY - JOUR

T1 - Image-based meta- and mega-analysis (IBMMA)

T2 - A unified framework for large-scale, multi-site, neuroimaging data analysis

AU - Steele, Nick

AU - Huggins, Ashley A.

AU - Morey, Rajendra A.

AU - Hussain, Ahmed

AU - Russell, Courtney

AU - Suarez-Jimenez, Benjamin

AU - Pozzi, Elena

AU - Jameei, Hadis

AU - Schmaal, Lianne

AU - Veer, Ilya M.

AU - Waller, Lea

AU - Jahanshad, Neda

AU - Thomopoulos, Sophia I.

AU - Salminen, Lauren E.

AU - Olff, Miranda

AU - Frijling, Jessie L.

AU - Veltman, Dick J.

AU - Koch, Saskia B.J.

AU - Nawijn, Laura

AU - van Zuiden, Mirjam

AU - Wang, Li

AU - Zhu, Ye

AU - Li, Gen

AU - Stein, Dan J.

AU - Ipser, Jonathan

AU - Neria, Yuval

AU - Zhu, Xi

AU - Ravid, Orren

AU - Zilcha-Mano, Sigal

AU - Lazarov, Amit

AU - Stevens, Jennifer S.

AU - Ressler, Kerry

AU - Jovanovic, Tanja

AU - van Rooij, Sanne J.H.

AU - Fani, Negar

AU - Mueller, Sven C.

AU - Hudson, Anna R.

AU - Daniels, Judith K.

AU - Sierk, Anika

AU - Manthey, Antje

AU - Walter, Henrik

AU - van der Wee, Nic J.A.

AU - van der Werff, Steven J.A.

AU - Vermeiren, Robert R.J.M.

AU - Schmahl, Christian

AU - Herzog, Julia I.

AU - Rektor, Ivan

AU - Říha, Pavel

AU - Kaufman, Milissa L.

AU - Lebois, Lauren A.M.

AU - Baker, Justin T.

AU - Rosso, Isabelle M.

AU - Olson, Elizabeth A.

AU - King, Anthony

AU - Liberzon, Israel

AU - Angstadt, Michael

AU - Davenport, Nicholas D.

AU - Disner, Seth G.

AU - Sponheim, Scott R.

AU - Straube, Thomas

AU - Hofmann, David

AU - Lu, Guangming

AU - Qi, Rongfeng

AU - Wang, Xin

AU - Kunch, Austin

AU - Xie, Hong

AU - Quidé, Yann

AU - El-Hage, Wissam

AU - Lissek, Shmuel

AU - Berg, Hannah

AU - Bruce, Steven E.

AU - Cisler, Josh

AU - Ross, Marisa

AU - Herringa, Ryan J.

AU - Grupe, Daniel W.

AU - Nitschke, Jack B.

AU - Davidson, Richard J.

AU - Larson, Christine

AU - deRoon-Cassini, Terri A.

AU - Tomas, Carissa W.

AU - Fitzgerald, Jacklynn M.

AU - Elman, Jeremy

AU - Panizzon, Matthew

AU - Franz, Carol E.

AU - Lyons, Michael J.

AU - Kremen, William S.

AU - Feola, Brandee

AU - Blackford, Jennifer U.

AU - Olatunji, Bunmi O.

AU - May, Geoffrey

AU - Nelson, Steven M.

AU - Gordon, Evan M.

AU - Abdallah, Chadi G.

AU - Lanius, Ruth

AU - Densmore, Maria

AU - Théberge, Jean

AU - Neufeld, Richard W.J.

AU - Thompson, Paul M.

AU - Sun, Delin

PY - 2025/11/15

Y1 - 2025/11/15

N2 - 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.

AB - 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.

KW - Big data

KW - Mega-analysis

KW - Meta-analysis

KW - Neuroimaging

KW - PTSD

KW - Resting-state fMRI

UR - https://www.scopus.com/pages/publications/105021469590

U2 - 10.1016/j.neuroimage.2025.121554

DO - 10.1016/j.neuroimage.2025.121554

M3 - Article

C2 - 41138791

AN - SCOPUS:105021469590

SN - 1053-8119

VL - 322

JO - NeuroImage

JF - NeuroImage

M1 - 121554

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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