Reward biases spontaneous neural reactivation during sleep

uploaded by Virginie Sterpenich on 2021-03-25 - 3 months ago
last modified on 2021-06-07 - 14 days ago
authored by Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz
1125
We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-01/func/sub-01_task-game_run-1_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,634 (voxels).

/sub-01/func/sub-01_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,603 (voxels).

/sub-02/func/sub-02_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,601 (voxels).

/sub-02/func/sub-02_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,3053 (voxels).

/sub-03/func/sub-03_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,604 (voxels).

/sub-03/func/sub-03_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,2599 (voxels).

/sub-04/anat/sub-04_T1w.nii.gz

The most common set of dimensions is: 192,256,256 (voxels), This file has the dimensions: 256,256,192 (voxels).

/sub-04/func/sub-04_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,600 (voxels).

/sub-04/func/sub-04_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,2350 (voxels).

/sub-05/func/sub-05_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,603 (voxels).

and 40 more files

OpenNeuro Accession Number: ds003574
Files: 315, Size: 17.95GB, Subjects: 18, Session: 1
Available Tasks: rest, game, Face-Maze game, Rest during sleep with simultaneous EEG
Available Modalities: T1w, channels, eeg, events, bold

README

The data included 18 participants that played at 2 different games during wakefulness in the 3T MRI: the FACE and the MAZE game, intermixed with periods of REST and period of preparation of each game (game session). The tasks were manipulated and at the end of the game session, one game was won (Reward game) and the second was lost (No Reward game), randomly assigned for each participant. Next, during the sleep session, 64 electrodes were placed on the head of the participants, before they slept in the MRI with EEG for 1-2 hours (sleep session). Participants can be separated according to the won game (face or maze) and according sleep depth (whether they reached N3 sleep in the MRI or only N2 sleep). A decoding classifier was trained on the data from the game session at wake and applied to the MRI data acquired during sleep (sleep session). Finally, a memory test was performed the next day on the 2 tasks (face and maze). For any question related to the methods, please see the manuscript or contact Virginie Sterpenich (Virginie.Sterpenich@unige.ch)

Files includes are 1) 2 EPI sessions for the tasks 2) 1 EPI session during resting including wake and sleep (sleep session) 3) 1 EEG file corresponding to the sleep session (including wake and sleep in the MRI) 4) 1 T1 anatomical image

Authors

  • Virginie Sterpenich
  • Mojca KM van Schie
  • Maximilien Catsiyannis
  • Avinash Ramyead
  • Stephen Perrig
  • Hee-Deok Yang
  • Dimitri Van De Ville
  • Sophie Schwartz

Dataset DOI

10.18112/openneuro.ds003574.v1.0.2

License

CC0

Acknowledgements

This research was supported by the National Center of Competence in Research (NCCR) Affective Sciences financed by the Swiss National Science Foundation (grant number: 51NF40-104897) and hosted by the University of Geneva, and the Swiss National Science Foundation (grant numbers: 320030-159862 and 320030-135653), and the Mercier Foundation. Hee-Deok Yang work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4005305) We also thank Ben Meuleman for statistical advice.

How to Acknowledge

Please cite the paper

Funding

  • Swiss National Science Foundation (51NF40-104897 - 320030-159862 - 320030-135653)
  • Mercier Foundation
  • National Research Foundation of Korea (NRF - 2017R1A2B4005305)

References and Links

  • Reward biases spontaneous neural reactivation during sleep, Virginie Sterpenich,Mojca KM van Schie,Maximilien Catsiyannis,Avinash Ramyead,Stephen Perrig,Hee-Deok Yang,Dimitri Van De Ville,Sophie Schwartz, Nat. Comm., 2021

Ethics Approvals

How To Cite

Copy
Virginie Sterpenich and Mojca KM van Schie and Maximilien Catsiyannis and Avinash Ramyead and Stephen Perrig and Hee-Deok Yang and Dimitri Van De Ville and Sophie Schwartz (2021). Reward biases spontaneous neural reactivation during sleep. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003574.v1.0.2
More citation info

Reward biases spontaneous neural reactivation during sleep

uploaded by Virginie Sterpenich on 2021-03-25 - 3 months ago
last modified on 2021-06-07 - 14 days ago
authored by Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz
1125

OpenNeuro Accession Number: ds003574
Files: 315, Size: 17.95GB, Subjects: 18, Session: 1
Available Tasks: rest, game, Face-Maze game, Rest during sleep with simultaneous EEG
Available Modalities: T1w, channels, eeg, events, bold

README

The data included 18 participants that played at 2 different games during wakefulness in the 3T MRI: the FACE and the MAZE game, intermixed with periods of REST and period of preparation of each game (game session). The tasks were manipulated and at the end of the game session, one game was won (Reward game) and the second was lost (No Reward game), randomly assigned for each participant. Next, during the sleep session, 64 electrodes were placed on the head of the participants, before they slept in the MRI with EEG for 1-2 hours (sleep session). Participants can be separated according to the won game (face or maze) and according sleep depth (whether they reached N3 sleep in the MRI or only N2 sleep). A decoding classifier was trained on the data from the game session at wake and applied to the MRI data acquired during sleep (sleep session). Finally, a memory test was performed the next day on the 2 tasks (face and maze). For any question related to the methods, please see the manuscript or contact Virginie Sterpenich (Virginie.Sterpenich@unige.ch)

Files includes are 1) 2 EPI sessions for the tasks 2) 1 EPI session during resting including wake and sleep (sleep session) 3) 1 EEG file corresponding to the sleep session (including wake and sleep in the MRI) 4) 1 T1 anatomical image

Authors

  • Virginie Sterpenich
  • Mojca KM van Schie
  • Maximilien Catsiyannis
  • Avinash Ramyead
  • Stephen Perrig
  • Hee-Deok Yang
  • Dimitri Van De Ville
  • Sophie Schwartz

Dataset DOI

10.18112/openneuro.ds003574.v1.0.2

License

CC0

Acknowledgements

This research was supported by the National Center of Competence in Research (NCCR) Affective Sciences financed by the Swiss National Science Foundation (grant number: 51NF40-104897) and hosted by the University of Geneva, and the Swiss National Science Foundation (grant numbers: 320030-159862 and 320030-135653), and the Mercier Foundation. Hee-Deok Yang work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4005305) We also thank Ben Meuleman for statistical advice.

How to Acknowledge

Please cite the paper

Funding

  • Swiss National Science Foundation (51NF40-104897 - 320030-159862 - 320030-135653)
  • Mercier Foundation
  • National Research Foundation of Korea (NRF - 2017R1A2B4005305)

References and Links

  • Reward biases spontaneous neural reactivation during sleep, Virginie Sterpenich,Mojca KM van Schie,Maximilien Catsiyannis,Avinash Ramyead,Stephen Perrig,Hee-Deok Yang,Dimitri Van De Ville,Sophie Schwartz, Nat. Comm., 2021

Ethics Approvals

How To Cite

Copy
Virginie Sterpenich and Mojca KM van Schie and Maximilien Catsiyannis and Avinash Ramyead and Stephen Perrig and Hee-Deok Yang and Dimitri Van De Ville and Sophie Schwartz (2021). Reward biases spontaneous neural reactivation during sleep. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003574.v1.0.2
More citation info

Dataset File Tree

Git Hash: 9734a7a 

BIDS Validation

We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-01/func/sub-01_task-game_run-1_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,634 (voxels).

/sub-01/func/sub-01_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,603 (voxels).

/sub-02/func/sub-02_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,601 (voxels).

/sub-02/func/sub-02_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,3053 (voxels).

/sub-03/func/sub-03_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,604 (voxels).

/sub-03/func/sub-03_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,2599 (voxels).

/sub-04/anat/sub-04_T1w.nii.gz

The most common set of dimensions is: 192,256,256 (voxels), This file has the dimensions: 256,256,192 (voxels).

/sub-04/func/sub-04_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,600 (voxels).

/sub-04/func/sub-04_task-rest_bold.nii.gz

The most common set of dimensions is: 64,64,36,3143 (voxels), This file has the dimensions: 64,64,36,2350 (voxels).

/sub-05/func/sub-05_task-game_run-2_bold.nii.gz

The most common set of dimensions is: 64,64,36,614 (voxels), This file has the dimensions: 64,64,36,603 (voxels).

and 40 more files

Dataset File Tree

Git Hash: 9734a7a 

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