<p>Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability, including different sampling strategies for the participants, different acquisition protocols and materials, but also different analytical choices in the processing of the fMRI data. While variability across participants or acquisition instruments has been extensively studied in the neuroimaging literature, the root causes of analytical variability remain an open question. Here, we share the <i>HCP Multi-Pipeline dataset</i>, which provides the resulting statistic maps for 24 typical fMRI pipelines on 1,080 participants of the HCP Young Adult dataset. We share both individual and group results for 1,000 groups of 50 participants over 5 motor contrasts. We hope this large dataset, covering a wide range of analysis conditions, will provide new opportunities to study analytical variability in fMRI.</p>

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HCP Multi-Pipeline: a derived dataset to investigate analytical variability in fMRI

  • Elodie Germani,
  • Elisa Fromont,
  • Pierre Maurel,
  • Camille Maumet

摘要

Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability, including different sampling strategies for the participants, different acquisition protocols and materials, but also different analytical choices in the processing of the fMRI data. While variability across participants or acquisition instruments has been extensively studied in the neuroimaging literature, the root causes of analytical variability remain an open question. Here, we share the HCP Multi-Pipeline dataset, which provides the resulting statistic maps for 24 typical fMRI pipelines on 1,080 participants of the HCP Young Adult dataset. We share both individual and group results for 1,000 groups of 50 participants over 5 motor contrasts. We hope this large dataset, covering a wide range of analysis conditions, will provide new opportunities to study analytical variability in fMRI.