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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Analytic programming with FMRI data: a quick-start guide for statisticians using R.

Ani Eloyan1, Shanshan Li2, John Muschelli1

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America.

Plos One
|March 4, 2014
PubMed
Summary
This summary is machine-generated.

This tutorial introduces statistical analysis for functional magnetic resonance imaging (fMRI) data using R. It provides R code and resources for statisticians to begin fMRI research and programming.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biostatistics

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for brain research and medical diagnostics.
  • Statistical analysis is fundamental to interpreting complex fMRI data.
  • The R programming environment offers powerful tools for data analysis.

Purpose of the Study:

  • To provide a didactic introduction to statistical analysis of fMRI data.
  • To equip statisticians with R programming skills for fMRI research.
  • To serve as a tutorial for handling and analyzing fMRI datasets.

Main Methods:

  • Utilizes the R statistical programming environment.
  • Demonstrates statistical analysis techniques specific to fMRI data.
  • Includes relevant R code examples for practical application.

Main Results:

  • A clear, code-driven introduction to fMRI data analysis in R.
  • Guidance on essential statistical methods for fMRI.
  • References for further exploration of advanced techniques.

Conclusions:

  • R is a valuable tool for statistical analysis in fMRI research.
  • This manuscript serves as a foundational resource for statisticians entering the field.
  • Accessible programming with fMRI data is achievable through R.