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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
Published on: November 27, 2019
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Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data
Nick Steele1, Ashley A Huggins2, Rajendra A Morey1
1Brain 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.
Neuroimage
|October 25, 2025
Summary
A new software package, Image-Based Meta- & Mega-Analysis (IBMMA), addresses challenges in analyzing large neuroimaging datasets. IBMMA efficiently handles missing data and complex models, accelerating neuroscience discoveries.
Area of Science:
- Neuroscience
- Biostatistics
- Medical Informatics
Background:
- Neuroimaging datasets are growing in scale and complexity, posing significant analytical challenges.
- Existing statistical tools struggle with missing data, computational speed, memory allocation, and limited statistical design options for multi-site studies.
Purpose of the Study:
- Introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package for analyzing diverse neuroimaging features.
- Provide a unified framework that efficiently handles large-scale datasets, offers flexible statistical modeling, and manages missing voxel-data.
Main Methods:
- Developed IBMMA using R and Python, incorporating parallel processing for efficient large-scale data handling.
- Implemented robust methods for managing missing voxel-data common in multi-site neuroimaging studies.
- Enabled flexible statistical modeling for complex neuroimaging research designs.
Main Results:
- Successfully analyzed a large-scale dataset comprising several thousand participants.
- Identified findings in brain regions previously overlooked by traditional software due to missing data.
- Demonstrated IBMMA's capability to overcome limitations of existing neuroimaging analysis tools.
Conclusions:
- IBMMA offers a unified, efficient, and flexible framework for neuroimaging meta-analysis and mega-analysis.
- The software effectively handles missing voxel-data and large datasets, accelerating scientific discovery.
- IBMMA has the potential to enhance the clinical utility of neuroimaging findings.
