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Accurate autocorrelation modeling substantially improves fMRI reliability

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  • Wiktor Olszowy

    (University of Cambridge
    University of Lausanne)

  • John Aston

    (University of Cambridge)

  • Catarina Rua

    (University of Cambridge)

  • Guy B. Williams

    (University of Cambridge)

Abstract

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM’s alternative pre-whitening method, FAST, performed better than SPM’s default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.

Suggested Citation

  • Wiktor Olszowy & John Aston & Catarina Rua & Guy B. Williams, 2019. "Accurate autocorrelation modeling substantially improves fMRI reliability," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09230-w
    DOI: 10.1038/s41467-019-09230-w
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    Cited by:

    1. Park, Jun Young & Polzehl, Joerg & Chatterjee, Snigdhansu & Brechmann, André & Fiecas, Mark, 2020. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    2. Bingkai Wang & Xi Luo & Yi Zhao & Brian Caffo, 2021. "Semiparametric partial common principal component analysis for covariance matrices," Biometrics, The International Biometric Society, vol. 77(4), pages 1175-1186, December.
    3. Annika Garlichs & Helen Blank, 2024. "Prediction error processing and sharpening of expected information across the face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Zachary F. Fisher & Jonathan Parsons & Kathleen M. Gates & Joseph B. Hopfinger, 2023. "Blind Subgrouping of Task-based fMRI," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 434-455, June.

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