| Title: |
Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis |
| Authors: |
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; Quide, Yann |
| Source: |
urn:ISSN:1053-8119 ; urn:ISSN:1095-9572 ; Neuroimage, 322, 121554 |
| Publisher Information: |
Elsevier |
| Publication Year: |
2025 |
| Collection: |
UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: |
32 Biomedical and Clinical Sciences; 42 Health Sciences; Networking and Information Technology R&D (NITRD); Neurosciences; Biomedical Imaging; Data Science; Bioengineering; 2.5 Research design and methodologies (aetiology); Neurological; Humans; Neuroimaging; Brain; Software; Image Processing; Computer-Assisted; Meta-Analysis as Topic; Big data; Mega-analysis; Meta-analysis; PTSD; Resting-state fMRI; anzsrc-for: 32 Biomedical and Clinical Sciences; anzsrc-for: 42 Health Sciences; anzsrc-for: 11 Medical and Health Sciences; anzsrc-for: 17 Psychology and Cognitive Sciences |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://hdl.handle.net/1959.4/107192; https://doi.org/10.1016/j.neuroimage.2025.121554 |
| DOI: |
10.1016/j.neuroimage.2025.121554 |
| Availability: |
https://hdl.handle.net/1959.4/107192; https://unsworks.unsw.edu.au/bitstreams/7322daf3-0974-43f0-9ff9-f0fca02b5fc9/download; https://doi.org/10.1016/j.neuroimage.2025.121554 |
| Rights: |
open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY ; https://creativecommons.org/licenses/by/4.0/ ; free_to_read |
| Accession Number: |
edsbas.21EAC848 |
| Database: |
BASE |