{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a32fc","dataset_id":"ds003876","associated_paper_doi":null,"authors":["Gunnarsdottir, Kristin","Li, Adam","Smith, Rachel","Kang, Joon","Korzeniewska, Anna","Crone, Nathan","Rouse, Adam","Cheng, Jennifer","Kinsman, Michael","Landazuri, Patrick","Uysal, Utku","Ulloa, Carol","Cameron, Nathaniel","Cajigas, Iahn","Jagid, Jonathan","Kanner, Andres","Elarjani, Turki","Bicchi, Manuel","Inati, Sara","Zaghloul, Kareem","Boerwinkle, Varina","Wyckoff, Sarah","Barot, Niravkumar","Gonzalez-Martinez, Jorge","Sarma, Sridevi"],"bids_version":"1.6.0","contact_info":["Adam Li","Kristín María Gunnarsdóttir"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds003876.v1.0.2","datatypes":["ieeg"],"demographics":{"subjects_count":39,"ages":[57,25,27,31,36,39,41,20,46,37,16,62,32,24,48,23,32,35,24,35,68,22,58,35,35,31,26,57,65,27,21,30,28,45,36,21,52,23,49,48,24,25,36,27,37,39,43,23,32],"age_min":16,"age_max":68,"age_mean":35.775510204081634,"species":null,"sex_distribution":{"f":24,"m":26},"handedness_distribution":{"r":25,"l":3}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003876","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIH T32 EB003383","NSF GRFP (DGE-1746891)","ARCS Scholarship","Whitaker Fellowship","Chateaubriand Fellowship","NIH R21 NS103113","US NSF Career Award 1055560","Burroughs Well CASI Award 1007274"],"ingestion_fingerprint":"670e62099d6f4d036f46e4701c6208021927a8ab3f5a086bc50babbcc2cdb72d","license":"CC0","n_contributing_labs":null,"name":"Epilepsy-iEEG-Interictal-Multicenter-Dataset","readme":"Epilepsy Interictal Dataset\n=====================\nThis dataset was updated and prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in [1] and [2].\nSummary\n-------------\nThis dataset comprises of de-identified subjects with interictal iEEG recordings possibly with sleep or awake state annotated. The subjects come from the following centers:\n- National Institute of Health (NIH)\n- Johns Hopkins Hospital (JHH)\n- University of Miami Florida Jackson Memorial Hospital (UMF)\nIn the actual study, there is also data from Kansas University Medical Center (KUMC), University of Pittsburgh Medical Center and Cleveland Clinic, whose data is not shared due to restrictions imposed by the centers there.\nSome subjects, namely with the ``rns`` prefix in their subject ID were treated with RNS rather then surgical resection/ablation.\nDerivatives\n----------------\nThe processed data corresponding to the ``source-sink`` analysis and ``hfo`` comparisons are shown in the ``derivatives/`` folder. The HFO analysis consists of two folders, one is an RMS detector and the other is a Hilbert detector. See the paper for details.\nTies to Other Datasets\n--------------------------------\nNIH ``pt1, pt2, pt3``, JHH ``jh103, jh105`` subjects are also datasets in ``https://openneuro.org/datasets/ds003029``, where the ictal snapshots are stored. These correspond to the following:\n- pt1: pt01\n- pt2: pt2\n- pt3: pt3\n- jh103: jh103\n- jh105: jh105\nMoreover, the cclinic subjects are used in that study, but not open-access due to data sharing limitations at Cleveland Clinic. Those ictal datasets were analyzed in https://www.nature.com/articles/s41593-021-00901-w.\nReferences\n----------------\n[1] Li, A., Huynh, C., Fitzgerald, Z. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 24, 1465–1474 (2021). https://doi.org/10.1038/s41593-021-00901-w\n[2] Kristin M. Gunnarsdottir, Adam Li, Rachel J. Smith, Joon-Yi Kang, Nathan E. Crone, Anna Korzeniewska, Adam Rouse, Nathaniel Cameron, Iahn Cajigas, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Nirav Barot, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone. bioRxiv 2021.10.15.464594; doi: https://doi.org/10.1101/2021.10.15.464594\n[3] 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[4] 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":"Sarma, Sridevi","sessions":["extraoperative"],"size_bytes":5368598177,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["interictal","interictalasleep","interictalawake"],"timestamps":{"digested_at":"2026-04-22T12:25:45.222138+00:00","dataset_created_at":"2021-11-09T21:38:53.750Z","dataset_modified_at":"2023-01-24T01:58:40.000Z"},"total_files":54,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003876","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"bcdea8c6ea12ca36","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:21:53.669276+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Resting State"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.9,"modality":0.75,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot is the pediatric epilepsy HFO dataset (Pathology=Epilepsy, Modality=Sleep/Resting State, Type=Clinical/Intervention), which establishes the convention that epilepsy cohorts analyzed for biomarkers (e.g., HFOs) are labeled as Pathology=Epilepsy and Type=Clinical/Intervention, even when there is no explicit behavioral task. The intraoperative SEP (Surgery) example also reinforces that clinically sourced electrophysiology datasets focused on clinical markers/localization map to Type=Clinical/Intervention rather than Perception/Attention/Motor.","metadata_analysis":"Key metadata facts from the README:\n1) Clinical population: \"Epilepsy Interictal Dataset\" and \"subjects with interictal iEEG recordings\".\n2) Task/stimulus context: \"interictal iEEG recordings possibly with sleep or awake state annotated\" (suggesting no explicit external stimulus paradigm).\n3) Clinical/biomarker focus: \"processed data corresponding to the \\\"source-sink\\\" analysis and \\\"hfo\\\" comparisons\" and referenced work: \"Neural fragility as an EEG marker of the seizure onset zone\" and \"Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone\".\n4) Intervention/treatment context: \"Some subjects... were treated with RNS rather then surgical resection/ablation.\"","paper_abstract_analysis":"No useful paper information. (Only references are provided; no abstract text is included in the metadata.)","evidence_alignment_check":"Pathology:\n- Metadata says: \"Epilepsy Interictal Dataset\"; \"subjects with interictal iEEG recordings\".\n- Few-shot suggests: epilepsy biomarker datasets (HFO-focused) -> Pathology=Epilepsy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"interictal iEEG recordings possibly with sleep or awake state annotated\" and reference to \"resting-state EEG marker\".\n- Few-shot suggests: when no explicit stimulus/task and/or resting/sleep is the condition, use Modality=Resting State (or Sleep if explicitly sleep recordings dominate).\n- Alignment: PARTIAL ALIGN; metadata mentions sleep/awake annotation but does not describe a sleep experiment per se. Resting-state appears explicitly in the reference title, supporting Resting State as dominant.\n\nType:\n- Metadata says: \"marker of the seizure onset zone\"; \"resting-state EEG marker of the epileptogenic zone\"; HFO comparisons; RNS vs resection/ablation context.\n- Few-shot suggests: biomarker/localization in a clinical epilepsy cohort -> Type=Clinical/Intervention.\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates per category (with head-to-head comparison):\n\nPathology:\n1) Epilepsy — Supported by \"Epilepsy Interictal Dataset\" and \"interictal iEEG recordings\" plus seizure-onset-zone/epileptogenic-zone biomarker references.\n2) Surgery — Some subjects had \"surgical resection/ablation\" or \"RNS\", but recruitment focus is epilepsy, not post-surgical-only cohort.\nWinner: Epilepsy (clear recruitment condition).\nConfidence basis: 3+ explicit epilepsy-related phrases.\n\nModality:\n1) Resting State — Supported by \"interictal iEEG recordings\" (no task described) and the explicit reference phrase \"resting-state EEG marker\".\n2) Sleep — Mentioned as \"possibly with sleep or awake state annotated\", but not described as a sleep study/staging dataset.\nWinner: Resting State (stronger explicit 'resting-state marker' + lack of stimulus paradigm).\nConfidence basis: 1 strong direct cue (\"resting-state\") + contextual inference from interictal recordings.\n\nType:\n1) Clinical/Intervention — Supported by clinical localization/biomarker framing: \"marker of the seizure onset zone\", \"epileptogenic zone\", HFO comparisons, and treatment context (\"RNS\" vs \"surgical resection/ablation\").\n2) Resting-state — Could be argued because analyses are on resting interictal data, but the primary research purpose is clinical localization/biomarkers rather than resting-state cognition.\nWinner: Clinical/Intervention.\nConfidence basis: multiple explicit biomarker/clinical-localization phrases plus intervention context."}},"nemar_citation_count":3,"computed_title":"Epilepsy-iEEG-Interictal-Multicenter-Dataset","nchans_counts":[{"val":128,"count":10},{"val":129,"count":8},{"val":86,"count":4},{"val":135,"count":4},{"val":98,"count":4},{"val":47,"count":2},{"val":101,"count":2},{"val":111,"count":2},{"val":110,"count":2},{"val":147,"count":1},{"val":146,"count":1},{"val":168,"count":1},{"val":118,"count":1},{"val":46,"count":1},{"val":190,"count":1},{"val":125,"count":1},{"val":186,"count":1},{"val":121,"count":1},{"val":134,"count":1},{"val":193,"count":1},{"val":107,"count":1},{"val":95,"count":1},{"val":170,"count":1},{"val":114,"count":1},{"val":182,"count":1}],"sfreq_counts":[{"val":1000.0,"count":25},{"val":2000.0,"count":7},{"val":999.4121105232217,"count":6},{"val":1024.0,"count":5},{"val":999.9999999999999,"count":4},{"val":499.7071044492829,"count":2},{"val":500.0,"count":2},{"val":1024.5997950800408,"count":2},{"val":512.0,"count":1}],"stats_computed_at":"2026-04-22T23:16:00.306580+00:00","total_duration_s":20727.7554762069,"author_year":"Gunnarsdottir2021","canonical_name":null}}