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Updated: Sep 4, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Reproducibility and replicability in neuroimaging data analysis.

Tü Lay Adali¹, Vince D Calhoun²

¹Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland.

Current Opinion in Neurology

|July 20, 2022

Summary

This summary is machine-generated.

Reproducibility in neuroimaging (NI) data analysis is crucial but often overlooked. Ensuring consistent results with the same data and code is a vital first step before assessing replicability.

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Area of Science:

Neuroimaging analysis
Computational neuroscience
Data science

Background:

Machine learning is increasingly applied to neuroimaging data analysis.
The reproducibility and replicability of these data-driven methods are gaining importance.
However, terminology is often undefined, and computational reproducibility is frequently disregarded.

Purpose of the Study:

To review recent findings on reproducibility in neuroimaging data analysis.
To highlight the importance of computational reproducibility.
To present practical examples demonstrating the impact of reproducibility in neuroimaging software.

Main Methods:

Literature review of recent papers on reproducibility in neuroimaging.
Analysis of two case studies involving widely used neuroimaging software.
Discussion of available solutions for assessing computational reproducibility.

Main Results:

Computational reproducibility, defined as obtaining consistent results using the same data and code, is often neglected in neuroimaging research.
Lack of reproducibility can significantly bias subsequent analysis stages.
Examples illustrate the critical need for reproducibility in common neuroimaging software.

Conclusions:

Reproducibility must be prioritized as the foundational step in all neuroimaging data analyses, including those aiming for replicability.
Available tools and methods can be employed to assess and ensure reproducibility.
Addressing reproducibility is essential for the integrity and reliability of neuroimaging research findings.