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Processing, evaluating and understanding FMRI data with afni_proc.py.

Richard C Reynolds1, Daniel R Glen1, Gang Chen1

  • 1Scientific and Statistical Computing Core, National Institute of Mental Health, NIH, Bethesda, USA.

Arxiv
|October 14, 2024
PubMed
Summary
This summary is machine-generated.

AFNI's afni_proc.py script streamlines functional magnetic resonance imaging (fMRI) data processing. It enhances transparency and reproducibility by providing detailed processing scripts and automated quality control checks for fMRI data analysis.

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Area of Science:

  • Neuroimaging
  • Data Science
  • Computational Neuroscience

Background:

  • Functional magnetic resonance imaging (fMRI) data acquisition and processing are complex, often involving multiple steps.
  • Ensuring the quality and understanding each processing stage of fMRI data is challenging for researchers and clinicians.

Purpose of the Study:

  • To introduce and describe AFNI's afni_proc.py script for creating and managing fMRI data processing pipelines.
  • To highlight the script's features that promote user understanding, control, transparency, and reproducibility in fMRI analysis.

Main Methods:

  • Utilizing the afni_proc.py script to generate commented processing pipelines for fMRI data.
  • Implementing automated self-checks and quality control (QC) reports within the processing workflow.
  • Demonstrating the script's application with task-based and resting-state fMRI examples.

Main Results:

  • afni_proc.py provides fully commented scripts, enabling detailed review and understanding of each processing step.
  • The tool integrates automatic runtime checks and generates interactive QC reports for data evaluation.
  • The output includes a queryable dictionary of relevant quantities for programmatic issue detection.

Conclusions:

  • afni_proc.py significantly aids in visualizing, understanding, and evaluating fMRI data processing.
  • The script enhances the transparency and reproducibility of fMRI studies by documenting processing specifics.
  • Automated QC features empower users to confidently assess data quality throughout the analysis pipeline.