{"success":true,"database":"eegdash","data":{"_id":"69a33a3b897a7725c66f3eef","dataset_id":"ds007445","associated_paper_doi":null,"authors":["Saarang Panchavati","Atsuro Daida","Sotaro Kanai","Shingo Oana","Hiroya Ono","Masaki Izumi","Kikuko Kaneko","Aria Fallah","Joe X Qiao","Noriko Salamon","Raman Sankar","Corey Arnold","William Speier","Hiroki Nariai"],"bids_version":"1.9.0","contact_info":["Hiroki Nariai","Saarang Panchavati"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds007445.v1.0.2","datatypes":["ieeg"],"demographics":{"subjects_count":19,"ages":[73],"age_min":73,"age_max":73,"age_mean":73.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007445","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIH/NINDS K23NS128318"],"ingestion_fingerprint":"a30ede383c7da4b7bdb0845ead972b7c8b537553d4d9779917b317097b775974","license":"CC0","n_contributing_labs":null,"name":"Thalamocortical ictal iEEG dataset","readme":"We investigated thalamocortical network dynamics using intracranial EEG (iEEG) recordings with thalamic sampling from 19 patients with focal epilepsy (1). The iEEG dataset analyzed in this study is publicly shared here.\nBIDS converstion was performed according to references (2) and (3).\nReferences\n(1) Panchavati S, Daida A, Kanai S, Oana S, Ono H, Izumi M, Kaneko K, Fallah A, Qiao JX, Salamon N, Sankar R, Arnold C, Speier W, Nariai H (2026). Distinct Spectral and Directional Thalamocortical Network Dynamics Define Focal Seizure Evolution. medRxiv, 2026 Feb 4:2026.02.03.26345480. doi: 10.64898/2026.02.03.26345480.\n(2) Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896\n(3) Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7","recording_modality":["ieeg"],"senior_author":"Hiroki Nariai","sessions":["sz1","sz10","sz11","sz12","sz1a","sz1b","sz2","sz3","sz4","sz5","sz6","sz7","sz8","sz9"],"size_bytes":54201403733,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007445","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["seizure"],"timestamps":{"digested_at":"2026-02-28T18:51:30.081352+00:00","dataset_created_at":"2026-02-20T22:33:40.621Z","dataset_modified_at":"2026-02-21T05:03:36.000Z"},"total_files":66,"computed_title":"Thalamocortical ictal iEEG dataset","nchans_counts":[{"val":140,"count":10},{"val":138,"count":10},{"val":83,"count":6},{"val":265,"count":6},{"val":202,"count":5},{"val":216,"count":5},{"val":162,"count":4},{"val":203,"count":3},{"val":112,"count":3},{"val":68,"count":2},{"val":49,"count":2},{"val":81,"count":2},{"val":263,"count":1},{"val":120,"count":1},{"val":201,"count":1},{"val":139,"count":1},{"val":111,"count":1},{"val":124,"count":1},{"val":261,"count":1},{"val":137,"count":1}],"sfreq_counts":[{"val":200.0,"count":42},{"val":2000.0,"count":17},{"val":200.00000000000003,"count":6},{"val":1999.9999999999998,"count":1}],"stats_computed_at":"2026-04-04T21:29:34.904289+00:00","total_duration_s":265353.4035,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"6a191a74e1def3d5","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.9,"modality":0.6,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot match is the pediatric epilepsy HFO dataset (Epilepsy + Resting State + Clinical/Intervention). That example shows the catalog convention that epilepsy-recruited intracranial/scalp EEG datasets aimed at characterizing epileptiform/epileptogenic activity are labeled as Pathology=Epilepsy and often Type=Clinical/Intervention. However, unlike that few-shot (sleep/rest recordings with HFO detection), the current dataset is explicitly ictal (seizure) iEEG and does not describe a sensory stimulus modality; therefore Modality should not be forced to Resting State or Sleep just because it is epilepsy-related.","metadata_analysis":"Population/pathology is explicitly clinical epilepsy: \"iEEG recordings with thalamic sampling from 19 patients with focal epilepsy\". The recording context is seizure/ictal: title \"Thalamocortical ictal iEEG dataset\" and task list includes \"seizure\". Clinical/research aim focuses on seizure evolution/network dynamics: \"Distinct Spectral and Directional Thalamocortical Network Dynamics Define Focal Seizure Evolution\" (referenced preprint title) and \"We investigated thalamocortical network dynamics\".","paper_abstract_analysis":"No useful paper information (no abstract provided in the metadata; only a referenced medRxiv citation/title).","evidence_alignment_check":"Pathology: Metadata says \"19 patients with focal epilepsy\" (explicit). Few-shot pattern suggests epilepsy datasets recruiting epilepsy patients map to Pathology=Epilepsy. ALIGN.\n\nModality: Metadata says \"ictal iEEG\" and task \"seizure\" but gives no external sensory stimulus (no auditory/visual/tactile paradigms described). Few-shot pattern for epilepsy examples sometimes uses Resting State or Sleep when recordings are eyes-closed rest or sleep. CONFLICT if we tried to infer Resting State/Sleep from epilepsy alone; metadata wins, so we choose a non-stimulus modality label.\n\nType: Metadata emphasizes seizure evolution and thalamocortical network dynamics in a clinical epilepsy cohort (e.g., \"ictal\" and \"focal seizure evolution\"). Few-shot convention for epilepsy clinical datasets emphasizes Type=Clinical/Intervention when pathology is central. ALIGN.","decision_summary":"Top-2 candidates per category:\n\nPathology candidates: (1) Epilepsy vs (2) Other. Evidence for Epilepsy: \"19 patients with focal epilepsy\"; task \"seizure\"; title includes \"ictal\". Winner: Epilepsy (explicit recruitment diagnosis). Alignment: aligned with few-shot epilepsy example. Confidence=0.9 (3+ explicit cues: readme + title + task).\n\nModality candidates: (1) Other vs (2) Unknown. Evidence for Other: dataset is \"ictal iEEG\" with task \"seizure\" and no described sensory stimulus channel; this is not a stimulus-driven auditory/visual/tactile/motor paradigm nor rest/sleep. Evidence for Unknown: modality is not explicitly stated. Winner: Other (context indicates seizure recording rather than an unspecified stimulus modality). Alignment: differs from epilepsy few-shot that used Resting State/Sleep because those had explicit rest/sleep context; here metadata indicates ictal. Confidence=0.6 (inference from context; no direct stimulus description).\n\nType candidates: (1) Clinical/Intervention vs (2) Other. Evidence for Clinical/Intervention: clinical cohort \"patients with focal epilepsy\"; focus on \"focal seizure evolution\" and \"thalamocortical network dynamics\" during seizures; task \"seizure\". Evidence for Other: could be framed as basic network dynamics, but still clinically anchored. Winner: Clinical/Intervention. Alignment: consistent with few-shot epilepsy clinical dataset convention. Confidence=0.8 (2+ explicit cues: epilepsy patient cohort + ictal/seizure-evolution focus)."}},"canonical_name":[],"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":"Panchavati2026"}}