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Related Concept Videos

Introduction to MATLAB01:24

Introduction to MATLAB

MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing, communications,...

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Related Experiment Video

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Integrated MATLAB Toolbox for fMRI Visualization and Data Conversion.

Hussain A Jaber1,2,3, Hadeel K Aljobouri1,3, Orhan M Koçak4

  • 1Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.

Basic and Clinical Neuroscience
|June 1, 2026
PubMed
Summary

This study introduces the visualization and conversion fMRI (VCfMRI) MATLAB toolbox, a user-friendly platform that simplifies the conversion and visualization of functional magnetic resonance imaging data across multiple file formats.

Keywords:
Brain imaging, Information systemsFunctional magnetic resonance imaging (fMRI)Medical informatics computing

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Computer Science

Background:

  • Functional magnetic resonance imaging (fMRI) analysis often requires handling diverse file formats and complex visualization tools.
  • Existing workflows for fMRI data processing can be fragmented and inefficient, posing challenges for researchers and clinicians.

Purpose of the Study:

  • To develop a unified MATLAB toolbox, named visualization and conversion fMRI (VCfMRI), for streamlined fMRI data conversion and visualization.
  • To provide a user-friendly platform that integrates multiple functionalities for handling various fMRI data formats.

Main Methods:

  • The VCfMRI toolbox was developed with 62 functions organized into seven GUI modules.
  • It supports reading and writing 3D fMRI volumes in DICOM, NIfTI, ANALYZE, and MAT formats.
  • Functionalities include batch processing, orthogonal viewing (sagittal, coronal, horizontal), and overlaying statistical maps on anatomical images with adjustable thresholds.

Main Results:

  • VCfMRI successfully converted and visualized fMRI data across all supported formats within a single environment.
  • The toolbox enables synchronized 3D views and interactive functional overlays, significantly simplifying data processing.
  • Testing was performed using real fMRI datasets acquired from a 3T Siemens scanner.

Conclusions:

  • The VCfMRI toolbox offers an efficient and integrated solution for fMRI data conversion and visualization.
  • It addresses the challenges faced by physicians, healthcare specialists, and researchers in managing multi-format neuroimaging data.
  • This tool enhances the accessibility and usability of fMRI data analysis.