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

Stephan Heunis; Marcel Breeuwer; César Caballero-Gaudes; Lydia Hellrung; Willem Huijbers; Jacobus F.A. Jansen; Rolf Lamerichs; Svitlana Zinger; Albert P. Aldenkamp

doi:10.12688/F1000RESEARCH.29988.1

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

Stephan Heunis (Corresponding author), Marcel Breeuwer, César Caballero-Gaudes, Lydia Hellrung, Willem Huijbers, Jacobus F.A. Jansen, Rolf Lamerichs, Svitlana Zinger, Albert P. Aldenkamp

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Abstract

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.

Original language	English
Article number	70
Number of pages	21
Journal	F1000Research
Volume	10
DOIs	https://doi.org/10.12688/F1000RESEARCH.29988.1
Publication status	Published - 5 Aug 2021

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2021. Heunis S et al.

Funding

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).

Keywords

Amygdala
Emotion processing
Finger tapping
Functional magnetic resonance imaging
Methods development
Motor
Multi-echo fMRI
Neurofeedback
Real-time fMRI
Resting state
Task

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10.12688/F1000RESEARCH.29988.1

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

abstract = "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.",

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note = "Funding Information: 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{\textquoteright}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). Publisher Copyright: {\textcopyright} 2021. Heunis S et al.",

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AU - Hellrung, Lydia

AU - Huijbers, Willem

AU - Jansen, Jacobus F.A.

AU - Lamerichs, Rolf

AU - Zinger, Svitlana

AU - Aldenkamp, Albert P.

N1 - Funding Information: 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). Publisher Copyright: © 2021. Heunis S et al.

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KW - Functional magnetic resonance imaging

KW - Methods development

KW - Motor

KW - Multi-echo fMRI

KW - Neurofeedback

KW - Real-time fMRI

KW - Resting state

KW - Task

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DO - 10.12688/F1000RESEARCH.29988.1

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VL - 10

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JF - F1000Research

M1 - 70

ER -

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

Abstract

Bibliographical note

Funding

Keywords

Access to Document

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