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Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI.

Richard C Reynolds1, Paul A Taylor1, Daniel R Glen1

  • 1Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, United States.

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|February 16, 2023
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Summary
This summary is machine-generated.

This study details functional magnetic resonance imaging (fMRI) quality control (QC) procedures using AFNI software. It presents a hierarchical approach to ensure data integrity for resting-state and task-based fMRI datasets.

Keywords:
AFNIFMRIdata visualizationprocessingquality controlreproducibilityresting statetask-based

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Data Science

Background:

  • Functional magnetic resonance imaging (fMRI) data analysis requires rigorous quality control (QC).
  • Current QC practices are often under-appreciated, impacting the reliability of fMRI research.
  • Standardized QC procedures are essential for reproducible neuroimaging studies.

Purpose of the Study:

  • To describe a comprehensive, hierarchical quality control (QC) framework for fMRI data.
  • To provide practical guidance on assessing fMRI dataset quality using the AFNI software package.
  • To ensure the appropriateness and integrity of fMRI data for analysis.

Main Methods:

  • A sequential, hierarchical QC approach was implemented, including Getting To Know Your Data (GTKYD), quantitative assessment (APQUANT), qualitative review (APQUAL), graphical user interface (GUI) checks, and stimulus timing statistics (STIM) for task data.
  • Procedures were applied to both resting-state (139 subjects) and task-based (30 subjects) fMRI datasets.
  • Datasets were categorized as 'Include,' 'exclude,' or 'uncertain' based on QC evaluations.

Main Results:

  • The described QC procedures effectively evaluated the quality of fMRI datasets.
  • The hierarchical approach facilitates a thorough understanding of data properties and potential issues.
  • Scripts for processing and analysis are made available to the research community.

Conclusions:

  • Implementing systematic QC procedures is crucial for robust fMRI data analysis.
  • The AFNI-based hierarchical QC framework enhances data reliability and research reproducibility.
  • Researchers are encouraged to adopt these procedures to ensure the quality of their fMRI findings.