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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Improving functional magnetic resonance imaging reproducibility.

Cyril Pernet¹, Jean-Baptiste Poline²

¹Centre for Clinical Brain Sciences, Neuroimaging Sciences, University of Edinburgh Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB UK.

|April 2, 2015

Summary

This summary is machine-generated.

Reproducibility in functional magnetic resonance imaging (fMRI) studies is declining due to complex methods. This article offers practical advice for fMRI researchers to enhance study reproducibility through open science principles and data sharing.

Keywords:

Code Functional MRI Open science Reproducibility Scripts Workflows

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Computational Neuroscience

Background:

Replication is fundamental to the scientific method.
Increasing complexity in functional magnetic resonance imaging (fMRI) paradigms and analysis techniques hinders study reproducibility.
Challenges in fMRI reproducibility impact the reliability of scientific findings.

Purpose of the Study:

To provide practical guidance for fMRI researchers with limited computational expertise.
To offer actionable steps for improving the reproducibility of fMRI studies.
To advocate for the adoption of open science practices in neuroimaging research.

Main Methods:

Documenting all aspects of the experimental methodology.
Sharing experimental procedures, data, and analysis workflows.
Adopting transparent and open science practices.

Main Results:

Implementing open science practices enhances the reproducibility of fMRI studies.
Sharing data, metadata, and analysis workflows is essential for scientific rigor.
Moving towards open science facilitates easier replication of fMRI experiments.

Conclusions:

Sharing experiments, data, metadata, derived data, and analysis workflows is crucial for neuroimaging.
Adopting data science principles will solidify neuroimaging's position as a robust scientific field.
Openness and comprehensive data sharing are key to advancing neuroimaging research.