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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, MD, United States.

Imaging Neuroscience (Cambridge, Mass.)
|November 22, 2024
PubMed
Summary
This summary is machine-generated.

AFNI's afni_proc.py script streamlines functional MRI (fMRI) data processing. This tool enhances transparency and reproducibility by providing detailed processing scripts and automated quality checks for fMRI analysis.

Keywords:
FMRIdata visualizationprocessingquality controlreproducibilitysoftware

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Area of Science:

  • Neuroimaging
  • Data Processing
  • Scientific Software

Background:

  • Functional MRI (fMRI) data acquisition and processing are complex and prone to noise.
  • Ensuring the success of each processing step in fMRI analysis is challenging.
  • Understanding and visualizing fMRI data throughout the processing pipeline is crucial for reliable results.

Purpose of the Study:

  • Introduce AFNI's afni_proc.py as a robust tool for creating and managing fMRI data processing pipelines.
  • Highlight the features of afni_proc.py that enhance user control, evaluation, and understanding of fMRI processing.
  • Demonstrate the utility of afni_proc.py for both task-based and resting-state fMRI studies.

Main Methods:

  • Utilizes the afni_proc.py script to generate commented processing pipelines for fMRI data.
  • Incorporates automatic self-checks and runtime problem detection within the processing pipeline.
  • Outputs detailed processing scripts, programmatic quality control (QC) dictionaries, and interactive HTML QC reports.

Main Results:

  • afni_proc.py provides fully commented scripts, offering full provenance for each processing step.
  • The tool includes automatic self-checks to identify potential issues during fMRI data processing.
  • Generates comprehensive QC reports, enabling detailed evaluation of data and processing outcomes.

Conclusions:

  • afni_proc.py significantly improves the transparency and reproducibility of fMRI data analysis.
  • The script empowers users to meticulously control, evaluate, and understand their fMRI processing.
  • This tool is valuable for researchers conducting task-based and resting-state fMRI studies, facilitating better data interpretation.