{"success":true,"database":"eegdash","data":{"_id":"696fdefaac44fa1028dc631c","dataset_id":"ds007120","associated_paper_doi":null,"authors":["Keisuke Hatano","Naoto Kuroda","Hiroshi Uda","Kazuki Sakakura","Michael J. Cools","Aimee F. Luat","Shin-Ichiro Osawa","Hitoshi Nemoto","Kazushi Ukishiro","Hidenori Endo","Nobukazu Nakasato","Yutaro Takayama","Keiya Iijima","Masaki Iwasaki","Eishi Asano"],"bids_version":"1.7.0","contact_info":["Keisuke Hatano"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007120.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":65,"ages":[12,8,10,15,5,17,6,10,11,17,13,11,14,8,19,5,13,9,12,11,4,10,16,15,16,10,14,7,17,17,30,10,4,9,22,12,11,10,16,6,19,12,10,15,4,10,12,8,14,14,6,4,8,14,37,19,21,14,5,12,11,10,19,16,13,14,8,15,5,15,4,11,18,10,17,7,14,13,19,13,17,9,41,6,12,16,8,14,14,17,5,10,16,15,7,14,5,16,13,11,10,17,14,8,9,17,12,11,13,11,5,13,4,16,11,5,5,7,16,15,8,14,5,13,11,8,3,2,2,2,2,2,7,1,1,1,3,1,2,2,1,1,3,1,1,2,9,13,11,13,20,15,6,13,11,17,13,7,8,6,3,3,3,3,3,45,3,3,3,1,2,16,12,8,35,18,35,15,26,22,16,35,32,14,12,27,20,22,37,21,34,28,19,36,14,21,39,27,21,42,44,15,14,32,15,23,12,8,18,38,31,26,7,33,25,36,25,6,45,14,36,11,19,9,20,5,21,11,12,16],"age_min":1,"age_max":45,"age_mean":13.656521739130435,"species":null,"sex_distribution":{"f":119,"m":114},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007120","osf_url":null,"github_url":null,"paper_url":null},"funding":["the National Institutes of Health (NIH; NS064033 to E.A.)","the Uehara Memorial Foundation Postdoctoral Fellowship (202441017 to K.H.; 20210301 to H.U.)","the Japan Society for the Promotion of Science (JP22J23281 and JP22KJ0323 to N.K.; 202560628 to H.U.; JP19K09494 and 22K09296 to M.I.)"],"ingestion_fingerprint":"f29baf826a0e7ffa5f7d81cb3a3084fed41d207d2ffcef0b3bbcbb0f57413241","license":"CC0","n_contributing_labs":null,"name":"iEEG_comprehensive_HFA_model_part2","readme":"This dataset contains intracranial EEG data recorded during non-REM sleep and used in Hatano et al. (in press).\nAuthors:\nKeisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, Aimee F. Luat, Shin-Ichiro Osawa, Hitoshi Nemoto, Kazushi Ukishiro, Hidenori Endo, Nobukazu Nakasato, Yutaro Takayama, Keiya Iijima, Masaki Iwasaki, Eishi Asano\nFunding:\nNational Institutes of Health (NIH; NS064033 to E.A.);\nUehara Memorial Foundation Postdoctoral Fellowship (202441017 to K.H.; 20210301 to H.U.);\nJapan Society for the Promotion of Science (JP22J23281, JP22KJ0323, and 202560576 to N.K.; 202560628 to H.U.; JP19K09494 and 22K09296 to M.I.)","recording_modality":["ieeg"],"senior_author":"Eishi Asano","sessions":["01"],"size_bytes":35448518496,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007120","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["sleep"],"timestamps":{"digested_at":"2026-04-22T12:30:02.554389+00:00","dataset_created_at":"2025-12-21T05:14:15.466Z","dataset_modified_at":"2025-12-22T04:51:05.000Z"},"total_files":70,"computed_title":"iEEG_comprehensive_HFA_model_part2","nchans_counts":[{"val":128,"count":13},{"val":112,"count":10},{"val":104,"count":5},{"val":110,"count":4},{"val":132,"count":4},{"val":118,"count":3},{"val":138,"count":2},{"val":120,"count":2},{"val":108,"count":2},{"val":140,"count":2},{"val":106,"count":2},{"val":150,"count":2},{"val":126,"count":2},{"val":130,"count":2},{"val":100,"count":2},{"val":56,"count":2},{"val":136,"count":1},{"val":124,"count":1},{"val":98,"count":1},{"val":156,"count":1},{"val":116,"count":1},{"val":34,"count":1},{"val":84,"count":1},{"val":164,"count":1},{"val":134,"count":1},{"val":144,"count":1},{"val":122,"count":1}],"sfreq_counts":[{"val":1000.0,"count":70}],"stats_computed_at":"2026-04-22T23:16:00.312363+00:00","total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"be5e6f94893664e7","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm/context is the pediatric epilepsy sleep/HFO dataset (\"Dataset of EEG recordings... pediatric patients with epilepsy... first 3 hours of sleep EEG\"), which shows the convention that sleep recordings aimed at high-frequency activity/HFO biomarkers in clinical monitoring contexts are labeled with Modality=Sleep and Type=Sleep, and Pathology=Epilepsy when the population is epilepsy patients. Another relevant style reference is the \"Surrey cEEGrid sleep data set\" example, which demonstrates that when the recording is during sleep (no active task), Modality=Sleep and Type=Sleep are used.\n\nThe current dataset matches these examples on the key paradigm dimension (sleep / non-REM sleep recording), and partially matches on the likely HFA/HFO biomarker context implied by the dataset name (HFA model) and intracranial recording. However, unlike the epilepsy few-shot, this dataset metadata does not explicitly state the diagnosis; so pathology must be inferred more cautiously.","metadata_analysis":"Key metadata facts:\n1) Recording state: \"This dataset contains intracranial EEG data recorded during non-REM sleep\" (README).\n2) Task label: tasks includes \"sleep\" (tasks field).\n3) Dataset title implies high-frequency activity modeling: title \"iEEG_comprehensive_HFA_model_part2\" (dataset title), suggesting a high-frequency activity (HFA) analysis/modeling focus.\n\nNotably absent: no explicit recruitment diagnosis (e.g., \"epilepsy\", \"seizure\", \"Parkinson's\") is stated in the provided description/README.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- What metadata SAYS: \"intracranial EEG\" and \"non-REM sleep\" are stated, but no diagnosis/recruitment condition is provided (no explicit pathology term present).\n- What few-shot pattern SUGGESTS: The epilepsy+sleep/HFO few-shot indicates that sleep EEG/iEEG used for high-frequency biomarkers is commonly from epilepsy monitoring cohorts.\n- ALIGN or CONFLICT: Partial alignment only (clinical iEEG context aligns, but diagnosis is missing).\n- Resolution: Since metadata lacks an explicit diagnosis, pathology is an inference; label chosen with reduced confidence.\n\nModality:\n- What metadata SAYS: \"recorded during non-REM sleep\" and task \"sleep\".\n- What few-shot pattern SUGGESTS: Sleep recordings are labeled Modality=Sleep.\n- ALIGN or CONFLICT: Align.\n\nType:\n- What metadata SAYS: Recording during \"non-REM sleep\" with task \"sleep\"; no active cognitive task described.\n- What few-shot pattern SUGGESTS: Sleep datasets are labeled Type=Sleep (even if clinical).\n- ALIGN or CONFLICT: Align.","decision_summary":"Top-2 comparative selection:\n\nPathology candidates:\n1) Epilepsy — Evidence: intracranial EEG is typically collected in presurgical epilepsy monitoring; dataset name includes \"HFA\" (high-frequency activity), often studied in epilepsy contexts; few-shot epilepsy example involves sleep EEG for HFO biomarkers.\n2) Unknown — Evidence: metadata provides no explicit diagnosis/recruitment condition beyond \"intracranial EEG\" and \"non-REM sleep\".\nHead-to-head: Because there is no explicit diagnosis in metadata, \"Unknown\" is strongly plausible; however, the combination of intracranial EEG + sleep + HFA modeling is more consistent with an epilepsy monitoring cohort than with a normative cohort. Final: Epilepsy, but with low confidence due to inference-only support.\n\nModality candidates:\n1) Sleep — Evidence quotes/features: \"recorded during non-REM sleep\"; tasks includes \"sleep\".\n2) Resting State — Alternative because sleep can sometimes be conflated with resting, but metadata explicitly specifies non-REM sleep.\nHead-to-head: Sleep is directly stated; final Sleep.\n\nType candidates:\n1) Sleep — Evidence quotes/features: \"non-REM sleep\"; task \"sleep\".\n2) Clinical/Intervention — Alternative because intracranial EEG is clinical, but the primary construct here is sleep physiology/non-REM activity rather than an intervention.\nHead-to-head: Sleep is the clearest construct indicated; final Sleep.\n\nConfidence justification:\n- Pathology=0.6: contextual inference only (iEEG + HFA name + sleep; no explicit diagnosis quote).\n- Modality=0.8: 2 explicit metadata indicators (\"non-REM sleep\" + task \"sleep\") and strong few-shot convention match.\n- Type=0.8: same 2 explicit indicators and strong few-shot convention match."}},"canonical_name":null,"name_confidence":0.74,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Hatano2025_part2"}}