{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a345b","dataset_id":"ds006576","associated_paper_doi":null,"authors":["Elizabeth A. McDevitt","Ghootae Kim","Nicholas B. Turk-Browne","Kenneth A. Norman"],"bids_version":"1.6.0","contact_info":["Elizabeth McDevitt"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006576.v1.0.5","datatypes":["eeg"],"demographics":{"subjects_count":67,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"f":37,"m":32},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006576","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIMH R01-MH069456","NIMH K99-MH126154"],"ingestion_fingerprint":"6f33af917ed84f6680abfd403a116375291c933bf2a5c1809d5c5c29e72d1cd9","license":"CC0","n_contributing_labs":null,"name":"The role of REM sleep in neural differentiation of memories in the hippocampus","readme":"This dataset contains the fMRI and EEG data for E.A. McDevitt, G. Kim, N.B. Turk-Browne, K.A. Norman (2026). The role of rapid eye movement sleep in neural differentiation of memories in the hippocampus. Journal of Cognitive Neuroscience, 10.1162/jocn.a.82\nPlease refer to the paper for detailed methods.\nThe dataset includes 69 participants with three fMRI scans and one EEG session per participant. Depending on the participant's condition, the EEG session either contains sleep data from a nap or data recorded during a quiet wake session.\nPlease contact Elizabeth McDevitt (emcdevitt@princeton.edu) if you have any questions.\nNotes about the dataset:\nThe following subjects/sessions do not include a T1w anatomical scan: sub-160 ses-00; sub-170 ses-00; sub-178 ses-01\n- sub-107/ses-02/func: There are three runs of the decision task included instead of two. During decision_run-01, the participant did not respond to 'B' trials (coded in column trial_type). Therefore, there are many trials with no response_accuracy or response_times recorded in task-decision_run-01_events.tsv. Immediately following this run, the same task was re-run as decision_run-03 to collect behavioral responses; therefore the data associated with task-decision_run-03 can be considered a \"repeat\" of task-decision_run-01. Decision_run-02 was run as expected during the second cycle of the reward prediction task.\n- sub-108_ses-02_task-reward_run-01_events.tsv: Many trials have no response_accuracy or response_time recorded. The participant misunderstood instructions and did not respond on trials where they predicted a \"neutral\" outcome.\n- sub-182_ses-01_task-study_run-01_events.tsv: There was an issue with Matlab not recording \"2\" button presses during this run of the task. The experimenter recoded all \"no response\" trials as \"2\" and used this to code response_accuracy. However, there were no response_times recorded for these trials.","recording_modality":["eeg"],"senior_author":"Kenneth A. Norman","sessions":[],"size_bytes":697755198425,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["rest"],"timestamps":{"digested_at":"2026-04-22T12:29:33.583716+00:00","dataset_created_at":"2025-08-20T17:53:27.091Z","dataset_modified_at":"2026-04-16T17:23:56.000Z"},"total_files":67,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006576","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv","task-decision_bold.json","task-decision_events.json","task-familiarization_bold.json","task-familiarization_events.json","task-localizer_bold.json","task-localizer_events.json","task-postfaces_bold.json","task-postfaces_events.json","task-postscenes_bold.json","task-postscenes_events.json","task-reward_bold.json","task-reward_events.json","task-study_bold.json","task-study_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"357fcd422af1e550","model":"openai/gpt-5.2","tagged_at":"2026-01-20T19:04:00.098442+00:00"},"tags":{"pathology":["Healthy"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm is the sleep dataset: \"Surrey cEEGrid sleep data set\" which is labeled (Pathology=Healthy, Modality=Sleep, Type=Sleep). The target dataset also includes an EEG session that \"contains sleep data from a nap\" (plus some quiet wake), aligning with the convention that sleep-recording datasets are labeled with Modality=Sleep and often Type=Sleep when the EEG is primarily for sleep physiology/staging. A secondary relevant convention is the sleep-deprivation resting-state example (Healthy, Resting State, Resting-state), which helps distinguish that quiet wake/eyes-open/closed recordings map to Resting State; however, the presence of nap sleep makes Sleep the dominant modality here.","metadata_analysis":"Key facts from the provided README:\n1) Population/clinical status is not described as clinical: \"The dataset includes 69 participants with three fMRI scans and one EEG session per participant.\" (no diagnosis/condition-based recruitment stated)\n2) EEG paradigm includes sleep (nap) vs quiet wake: \"Depending on the participant's condition, the EEG session either contains sleep data from a nap or data recorded during a quiet wake session.\"\n3) Study aim links REM sleep to memory: \"The role of rapid eye movement sleep in neural differentiation of memories in the hippocampus.\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no clinical group is mentioned; only \"69 participants\" are described.\n- Few-shot suggests: datasets without a disorder focus are labeled Healthy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: EEG includes \"sleep data from a nap\" and sometimes \"quiet wake session\".\n- Few-shot suggests: sleep recording datasets map to Modality=Sleep (e.g., Surrey cEEGrid sleep).\n- Alignment: ALIGN (sleep is explicitly present and central).\n\nType:\n- Metadata says: paper focus is REM sleep effects on \"memories\" (memory-related), but EEG session content is sleep/quiet wake.\n- Few-shot suggests: when the dataset is primarily sleep recordings, Type=Sleep is used (Surrey cEEGrid sleep). If it were purely quiet wake/rest, Type would be Resting-state (sleep-deprivation example).\n- Alignment: PARTIAL—memory is the scientific goal, but the EEG acquisition described is sleep/quiet wake; using EEGDash conventions, EEG sleep datasets are typically typed as Sleep. Metadata does not explicitly describe a behavioral memory task within the EEG session itself, so Sleep is favored.","decision_summary":"Top-2 candidates per category with head-to-head selection:\n\nPathology:\n1) Healthy — Evidence: \"69 participants\" with no stated disorder/diagnosis; no clinical recruitment language. Matches few-shot convention for normative cohorts.\n2) Unknown — Would apply if participant health status were unclear/clinical status implied.\nDecision: Healthy (metadata provides no clinical population; aligns with conventions).\n\nModality:\n1) Sleep — Evidence: \"EEG session either contains sleep data from a nap\"; paper emphasizes \"rapid eye movement sleep\".\n2) Resting State — Evidence: some participants had \"quiet wake session\".\nDecision: Sleep (sleep is explicitly part of EEG sessions and central to the study; quiet wake is secondary).\n\nType:\n1) Sleep — Evidence: EEG session includes nap sleep; study centers on REM sleep; follows sleep-dataset convention in few-shot (Surrey cEEGrid labeled Type=Sleep).\n2) Memory — Evidence: title states \"neural differentiation of memories\".\nDecision: Sleep (EEG appears to be collected to characterize sleep/REM during a nap; memory is the broader research question but not clearly an EEG task construct in the provided metadata)."}},"computed_title":"The role of REM sleep in neural differentiation of memories in the hippocampus","nchans_counts":[{"val":73,"count":67}],"sfreq_counts":[{"val":512.0,"count":67}],"stats_computed_at":"2026-04-22T23:16:00.311811+00:00","total_duration_s":420130.0,"author_year":"McDevitt2025","canonical_name":null}}