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Resting-state fMRI (rs-fMRI) data exclusion due to motion can bias results. Participant characteristics influence exclusion, necessitating formal accounting for missing data in analyses.

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Data Science

Background:

  • Resting-state fMRI (rs-fMRI) analysis often excludes motion-affected data.
  • Data quality and motion are linked to participant characteristics, especially in pediatric studies.
  • Quality control (QC) decisions in rs-fMRI research involve balancing data integrity with potential biases.

Purpose of the Study:

  • To investigate how participant characteristics influence exclusion in rs-fMRI data.
  • To explore the impact of different dataset versions and motion scrubbing thresholds on data inclusion.
  • To inform future research design and quality control procedures in neuroimaging.

Main Methods:

  • Analysis of participant inclusion/exclusion across ABCD dataset versions (Community Collection, Release 4).
  • Examination of motion scrubbing thresholds as a QC choice.
  • Statistical investigation of relationships between participant characteristics and data exclusion.

Main Results:

  • Participant exclusion from rs-fMRI analysis was significantly associated with a wide range of behavioral, demographic, and health variables.
  • These associations indicate a high likelihood of biased results in rs-fMRI studies analyzing these variables.
  • The findings were consistent across different dataset versions and QC choices.

Conclusions:

  • Missing data in rs-fMRI analyses must be formally accounted for to avoid biased interpretations.
  • Improved data acquisition and analysis techniques are crucial to minimize motion's impact on data quality.
  • Open datasets should include comprehensive quality control information for transparency and reproducibility.