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Related Experiment Video

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Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat
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Quality control procedures and metrics for resting-state functional MRI.

Rasmus M Birn1,2

  • 1Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.

Frontiers in Neuroimaging
|August 9, 2023
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Summary

This study introduces a comprehensive approach to functional MRI (fMRI) data quality control. It combines visual and statistical methods to ensure reliable analysis of large fMRI datasets.

Keywords:
artifactsconnectivityfMRImotionquality control

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Area of Science:

  • Neuroimaging
  • Data Science

Background:

  • Functional MRI (fMRI) data quality is crucial for accurate analysis.
  • Monitoring data quality during acquisition and processing is essential, especially for large, multi-site studies.
  • Inconsistent acquisition parameters and artifacts can significantly impact results.

Purpose of the Study:

  • To present a combined qualitative and quantitative framework for assessing fMRI data quality.
  • To identify and mitigate potential sources of error in fMRI data processing pipelines.
  • To guide subject exclusion and further processing steps in large-scale fMRI studies.

Main Methods:

  • Utilized the AFNI software package for fMRI data processing and quality assessment.
  • Employed qualitative visual inspections of structural and functional images, connectivity maps, and signal-to-noise ratio (SNR) maps.
  • Incorporated quantitative metrics including acquisition parameters, head motion, temporal SNR, data smoothness, and functional connectivity strength.

Main Results:

  • Evaluated metrics at multiple processing stages to detect abnormalities and deviations.
  • Assessed the impact of motion censoring thresholds and bandpass filtering on data quality.
  • Demonstrated how qualitative and quantitative assessments inform subject selection for group analyses.

Conclusions:

  • A robust quality control strategy is vital for reliable fMRI research.
  • Combining qualitative and quantitative methods provides a thorough evaluation of fMRI data integrity.
  • This approach aids in managing large datasets and ensuring the validity of neuroimaging findings.