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Accurate autocorrelation modeling substantially improves fMRI reliability.

Wiktor Olszowy1,2, John Aston3, Catarina Rua4

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This study compared temporal autocorrelation modeling in functional Magnetic Resonance Imaging (fMRI) software. AFNI demonstrated superior performance over FSL and SPM, suggesting improvements for fMRI analysis reliability.

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Area of Science:

  • Neuroimaging
  • Statistical Analysis
  • Neuroscience

Background:

  • Recent controversies surround neuroimaging statistical methods.
  • Temporal autocorrelation modeling (pre-whitening) is crucial in task fMRI studies.
  • Comparing AFNI, FSL, and SPM for this process is vital.

Purpose of the Study:

  • To compare the temporal autocorrelation modeling capabilities of AFNI, FSL, and SPM.
  • To assess the impact of autocorrelation modeling on fMRI results.
  • To identify potential improvements for fMRI analysis packages.

Main Methods:

  • Utilized eleven datasets comprising 980 scans.
  • Included diverse fMRI protocols and subject populations.
  • Evaluated autocorrelation modeling performance across AFNI, FSL, and SPM.

Main Results:

  • AFNI's autocorrelation modeling outperformed FSL and SPM.
  • Residual autocorrelated noise in FSL and SPM confounded first-level results.
  • SPM's FAST method improved upon its default pre-whitening.

Conclusions:

  • Accurate autocorrelation modeling can enhance the reliability of task fMRI studies.
  • AFNI shows better performance in temporal autocorrelation modeling compared to FSL and SPM.
  • fMRI analysis packages should offer diagnostic plots for pre-whitening assessment.