CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Data Science

Background:

• Coordinate-based meta-analysis (CBMA) is crucial in human brain imaging research.
• Manual data preparation and post hoc analyses in CBMA are time-consuming and error-prone.
• There is a need for efficient and reliable tools to support CBMA workflows.

Purpose of the Study:

• To develop an automated and user-friendly toolbox for CBMA.
• To enhance the speed, reproducibility, and reliability of CBMA data handling.
• To provide a practical example of the toolbox's application.

Main Methods:

• Development of a suite of automated MATLAB functions named Coordinate-Based Meta-Analyses Toolbox (CBMAT).
• Implementation of functions for data preparation and post hoc analyses in CBMA.
• Creation of an annotated example dataset with 34 experiments for demonstration.

Main Results:

• CBMAT offers a fast, reproducible, and reliable method for CBMA data processing.
• The toolbox automates previously manual and error-prone steps.
• An annotated example demonstrates CBMAT's utility on a real dataset.

Conclusions:

• CBMAT significantly improves data handling for coordinate-based meta-analyses.
• The toolbox facilitates more efficient and accurate neuroimaging research.
• CBMAT is available for download, promoting wider adoption in the research community.