{"dataset":{"id":"53727","dataset_id":"on003574","name":"Reward biases spontaneous neural reactivation during sleep","description":"This dataset comprises neuroimaging and electrophysiological recordings from 18 participants who engaged in reward-based cognitive tasks during wakefulness followed by sleep monitoring in an MRI scanner. Participants played two games (FACE and MAZE) during 3T fMRI acquisition, with one game designated as rewarded and the other as non-rewarded. Subsequent sleep sessions were recorded with simultaneous 64-channel EEG and fMRI for 1-2 hours, during which participants reached either N2 or N3 sleep stages, followed by a memory test. The dataset enables investigation of how reward biases spontaneous neural reactivation during sleep through multimodal neuroimaging analysis.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003574","concept_doi":"10.82901/nemar.on003574","latest_version_doi":"10.82901/nemar.on003574.v1.0.0","created_at":"2026-06-22 06:01:24","updated_at":"2026-07-10 22:17:20","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Reward biases spontaneous neural reactivation during sleep\",\n  \"description\": \"This dataset comprises neuroimaging and electrophysiological recordings from 18 participants who engaged in reward-based cognitive tasks during wakefulness followed by sleep monitoring in an MRI scanner. Participants played two games (FACE and MAZE) during 3T fMRI acquisition, with one game designated as rewarded and the other as non-rewarded. Subsequent sleep sessions were recorded with simultaneous 64-channel EEG and fMRI for 1-2 hours, during which participants reached either N2 or N3 sleep stages, followed by a memory test. The dataset enables investigation of how reward biases spontaneous neural reactivation during sleep through multimodal neuroimaging analysis.\",\n  \"methods_description\": \"Data collection involved two phases: (1) wakefulness phase with 3T MRI-fMRI during FACE and MAZE game tasks intermixed with rest periods, with reward manipulation (one game won, one lost per participant); (2) sleep phase with simultaneous 64-channel EEG and fMRI acquisition lasting 1-2 hours in the MRI scanner. A decoding classifier trained on wakefulness fMRI data was applied to sleep session recordings. Memory testing was performed the following day.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Virginie Sterpenich\": {},\n    \"Mojca KM van Schie\": {},\n    \"Maximilien Catsiyannis\": {},\n    \"Avinash Ramyead\": {},\n    \"Stephen Perrig\": {},\n    \"Hee-Deok Yang\": {},\n    \"Dimitri Van De Ville\": {},\n    \"Sophie Schwartz\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"fMRI\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"sleep\"\n    },\n    {\n      \"term\": \"reward processing\"\n    },\n    {\n      \"term\": \"memory consolidation\"\n    },\n    {\n      \"term\": \"neural reactivation\"\n    },\n    {\n      \"term\": \"multimodal neuroimaging\"\n    },\n    {\n      \"term\": \"face recognition\"\n    },\n    {\n      \"term\": \"maze learning\"\n    },\n    {\n      \"term\": \"N2 sleep\"\n    },\n    {\n      \"term\": \"N3 sleep\"\n    },\n    {\n      \"term\": \"simultaneous EEG-fMRI\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.18112/openneuro.ds003574.v1.0.2\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsIdenticalTo\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003574\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003574\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41467-021-22202-3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Mercier Foundation\"\n    },\n    {\n      \"funder_name\": \"National Research Foundation of Korea (NRF - 2017R1A2B4005305)\"\n    },\n    {\n      \"funder_name\": \"Swiss National Science Foundation\",\n      \"award_number\": \"51NF40-104897\"\n    },\n    {\n      \"funder_name\": \"Swiss National Science Foundation\",\n      \"award_number\": \"320030-159862\"\n    },\n    {\n      \"funder_name\": \"Swiss National Science Foundation\",\n      \"award_number\": \"320030-135653\"\n    },\n    {\n      \"funder_name\": \"National Research Foundation of Korea\",\n      \"award_number\": \"NRF-2017R1A2B4005305\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"eeg\",\n    \"func\"\n  ],\n  \"sizes\": [\n    \"19.1 GB (91 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".gitattributes\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"b45b17cfe66fe93bf1600d0a29bc4463a9333f259d97d4bb8b70820417b0c776\"\n}","last_activity_at":"2026-06-22 06:01:24","source":"openneuro","source_id":"ds003574","subject_count":18,"modalities":"anat,eeg,func","age_min":18,"age_max":26,"file_size":19286081824,"total_files":358,"tasks":"game,rest","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003574-blue)](https://doi.org/10.82901/nemar.on003574)\n\nThe 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).\r\nFor any question related to the methods, please see the manuscript or contact Virginie Sterpenich (Virginie.Sterpenich@unige.ch)\r\n\r\nFiles includes are\r\n1) 2 EPI sessions for the tasks\r\n2) 1 EPI session during resting including wake and sleep (sleep session)\r\n3) 1 EEG file corresponding to the sleep session (including wake and sleep in the MRI)\r\n4) 1 T1 anatomical image\r\n\r\n","bids_version":"1.4.1","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-08 12:11:53","zarr_store_count":18,"zarr_index_etag":"c88f673533a3b603198e6f2dc1fbd172","zarr_source_commit":"6624e6e3cb24b6862779faa8146f2ce2b1012028","archive_status":"ready","archive_size":17219913813,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":2,"num_datapaper_citations":0,"n_channels":64,"electrode_system":"10-10","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":19139345106,"data_complete":0,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":108337.64,"recording_duration_min":3180,"recording_duration_max":7615,"recording_count":18,"recordings_unavailable":0,"recordings_measured":18,"channel_count_min":69,"channel_count_max":69,"sampling_frequency":500,"power_line_frequency":50,"eeg_reference":"FCz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-22 06:10:16\",\"metadata_updated_at\":\"2026-06-22 06:10:24\",\"archive_checked_at\":\"2026-07-31 03:57:30\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-22 06:13:53\",\"citations_updated_at\":\"2026-08-18 03:00:10\",\"channel_montage_checked_at\":\"2026-06-28 23:13:13\",\"hed_checked_at\":\"2026-06-30 04:44:46\",\"data_checked_at\":\"2026-07-22 22:47:23\",\"availability_report_at\":\"2026-07-23 01:02:13\",\"signal_defaults_at\":\"2026-09-02 12:01:02\"}","participants":18,"num_citations":2,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"17.96 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on003574/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}