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Research data . Dataset . 2021

Sequence learning recodes cortical representations instead of strengthening initial ones: fMRI data

Kalm, Kristjan;
Published: 29 Apr 2021
Country: United Kingdom
fMRI data from participants performing a visual sequence recall task. The data is in BIDS 1.0.1 format but each participant's data is compressed into a single archive following a 'sub-*id*.tar.gz' pattern. For each participant there are three sub-folders: (1) 'anat' -- T1-weighted anatomical image. (2) 'func' -- (a) EPI BOLD images (multiband factor 2), (b) event timings data for contrast regressors (.tsv files). (3) 'fmap' -- EPI images in opposite phase-encoding direction to 'func' images to derive inhomogeneity field maps. For full details on the file formats included see the BIDS specification at The MRI data has been fully anonymised: all information linking the participants to the MRI scans has been removed. This included 'de-facing' where all facial features are removed from the images to ensure a greater degree of anonymity for data sharing purposes. De-facing was performed with 'pydeface' package ( The data was acquired at the Medical Research Council Cognition and Brain Sciences Unit (Cambridge, UK) on a 3T Siemens Prisma MRI scanner using a 32-channel head coil and simultaneous multi-slice data acquisition. Functional images were collected using 32 slices covering the whole brain (slice thickness 2 mm, in-plane resolution 2×2 mm) with acquisition time of 1.206 seconds, echo time of 30ms, and flip angle of 74 degrees. Each participant performed two scanning runs and 510 scans were acquired per run. The initial ten volumes from the run were discarded to allow for T1 equilibration effects. Stimulus presentation was controlled by PsychToolbox software: the trials were rear projected onto a translucent screen outside the bore of the magnet and viewed via a mirror system attached to the head coil. For full details of acquisition see 'Sequence learning recodes cortical representations instead of strengthening initial ones' by Kalm K, Norris D. PLOS Computational Biology, 2021. Analysis scripts for the data are available at:


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