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rt-me-fMRI: a task and resting state dataset for real-time, multi-echo fMRI methods development and validation

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Samenvatting

A multi-echo fMRI dataset (N=28 healthy participants) with four taskbased and two resting state runs was collected, curated and made available to the community. Its main purpose is to advance the development of methods for real-time multi-echo functional magnetic resonance imaging (rt-me-fMRI) analysis with applications in neurofeedback, real-time quality control, and adaptive paradigms, although the variety of experimental task paradigms supports a multitude of use cases. Tasks include finger tapping, emotional face and shape matching, imagined finger tapping and imagined emotion processing. This work provides a detailed description of the full dataset; methods to collect, prepare, standardize and preprocess it; quality control measures; and data validation measures. A web-based application is provided as a supplementary tool with which to interactively explore, visualize and understand the data and its derivative measures: https://rt-me-fmri.herokuapp.com/. The dataset itself can be accessed via a data use agreement on DataverseNL at https://dataverse.nl/dataverse/rt-me-fmri. Supporting information and code for reproducibility can be accessed at https://github.com/jsheunis/rt-me-fMRI.

Originele taal-2Engels
Artikelnummer70
Aantal pagina's21
TijdschriftF1000Research
Volume10
DOI's
StatusGepubliceerd - 5 aug. 2021

Bibliografische nota

Publisher Copyright:
© 2021. Heunis S et al.

Financiering

Grant information: This work was funded by the foundation Health-Holland LSH-TKI (grant LSHM16053-SGF) and supported by Philips Research. LH was supported by the European Union’s Horizon 2020 research and innovation program under the Grant Agreement No 794395. CCG was supported by the Spanish Ministry of Economy and Competitiveness (Ramon y Cajal Fellowship, RYC-2017-21845), the Basque Government (PIBA_2019_104) and the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100).

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