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Publisher Correction: Accurate autocorrelation modeling substantially improves fMRI reliability

Author

Listed:
  • 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

The original HTML version of this Article had an incorrect Published online date of 25 December 2019; it should have been 21 March 2019. This has been corrected in the HTML version of the Article. The PDF version was correct from the time of publication.

Suggested Citation

  • Wiktor Olszowy & John Aston & Catarina Rua & Guy B. Williams, 2019. "Publisher Correction: Accurate autocorrelation modeling substantially improves fMRI reliability," Nature Communications, Nature, vol. 10(1), pages 1-1, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09619-7
    DOI: 10.1038/s41467-019-09619-7
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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. 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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