{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3385","dataset_id":"ds004789","associated_paper_doi":null,"authors":["Haydn G. Herrema","Michael J. 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The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalling the words from the just-presented list in any order.  The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.\n#### To Note\n* The iEEG recordings are labeled either \"monopolar\" or \"bipolar.\"  The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis.  The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables.\n* Each subject has a unique montage of electrode locations.  MNI and Talairach coordinates are provided when available, along with brain region annotations.\n* Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.\n#### Contact\nFor questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.","recording_modality":["ieeg"],"senior_author":"Michael J. Kahana","sessions":["0","1","10","100","2","3","4","5","6","7","8","9"],"size_bytes":618808962711,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["FR1"],"timestamps":{"digested_at":"2026-04-22T12:26:51.695132+00:00","dataset_created_at":"2023-10-10T20:42:35.486Z","dataset_modified_at":"2024-04-22T23:33:32.000Z"},"total_files":983,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004789","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"0d9f3cbe43cc797b","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.9},"reasoning":{"few_shot_analysis":"Closest few-shot match by paradigm/construct is the healthy digit span dataset (\"digit span task with serial recall\"), which is labeled Type=Memory and Modality=Auditory. This guides mapping a verbal list learning + recall paradigm to Type=Memory (regardless of the specific mechanics like distractor arithmetic). For modality conventions, the few-shot examples indicate modality follows stimulus presentation channel (e.g., digit span uses auditory digits; motor datasets use visual targets but are typed as Motor because movement is the research focus). Here, the primary study material is visually presented words, so Modality should be Visual.","metadata_analysis":"Key metadata facts:\n- Task/paradigm: \"participants studying a list of words, presented visually one at a time\" and later \"then freely recalling the words from the just-presented list in any order.\" Also includes a delay/distractor: \"completing simple arithmetic problems that function as a distractor\".\n- Recording/population context: \"intracranial electrophysiological recordings\" and \"data was collected at clinical sites across the country\".\n- Scale: \"Subjects: 273\" with ages \"18-65\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: \"intracranial electrophysiological recordings\" and \"collected at clinical sites\" (clinical context) but does NOT explicitly name a diagnosis (e.g., epilepsy).\n2) Few-shot pattern suggests: iEEG collected at clinical sites is commonly from epilepsy monitoring cohorts (would map to Epilepsy in EEGDash when explicitly stated).\n3) Alignment: PARTIAL (clinical context aligns with a patient cohort, but diagnosis is not explicit).\n4) Resolution: choose Epilepsy as the most plausible recruited clinical population for iEEG with moderate confidence; acknowledge lack of explicit diagnostic statement.\n\nModality:\n1) Metadata says: \"words, presented visually one at a time\".\n2) Few-shot pattern suggests: modality tracks stimulus channel (e.g., digit span auditory -> Auditory; visual discrimination -> Visual).\n3) Alignment: ALIGN (visual word presentation clearly indicates Visual modality).\n\nType:\n1) Metadata says: \"delayed free recall task\", \"studying a list of words\", \"freely recalling the words\".\n2) Few-shot pattern suggests: list learning/recall/digit span paradigms map to Memory.\n3) Alignment: ALIGN (core construct is episodic/verbal memory and recall).","decision_summary":"Top-2 comparative selection:\n\nPathology candidates:\n- Epilepsy: Supported indirectly by \"intracranial electrophysiological recordings\" and collection at \"clinical sites\" (typical context for intracranial monitoring cohorts).\n- Unknown: Supported by the absence of any explicit diagnostic label in the provided metadata.\nHead-to-head: Unknown has stronger literal support (no diagnosis stated), but Epilepsy is the most likely recruited pathology for intracranial electrophysiology datasets; select Epilepsy with conservative confidence due to inference.\n\nModality candidates:\n- Visual: Supported by \"words, presented visually one at a time\"; task is primarily visually presented word lists.\n- Other: Possible if arithmetic/distractor modality were unspecified, but nothing indicates auditory/tactile.\nHead-to-head: Visual clearly wins.\n\nType candidates:\n- Memory: Supported by \"delayed free recall task\", \"studying a list of words\", and \"freely recalling the words\".\n- Attention: Could be argued due to distractor arithmetic, but it functions as delay/distractor rather than primary construct.\nHead-to-head: Memory clearly wins.\n\nConfidence justifications:\n- Pathology 0.6: inference from clinical/iEEG context only (quotes: \"intracranial electrophysiological recordings\", \"collected at clinical sites\"), no explicit diagnosis.\n- Modality 0.8: 1 strong direct quote plus consistent task description (quote: \"presented visually\"); little competition from other modalities.\n- Type 0.9: 3+ explicit memory/recall phrases (\"delayed free recall\", \"studying a list of words\", \"freely recalling\") plus strong few-shot analog (digit span labeled Memory)."}},"nemar_citation_count":3,"computed_title":"Delayed Free Recall of Word Lists","nchans_counts":[{"val":126,"count":87},{"val":108,"count":32},{"val":112,"count":32},{"val":110,"count":31},{"val":88,"count":29},{"val":128,"count":28},{"val":120,"count":28},{"val":127,"count":24},{"val":116,"count":22},{"val":124,"count":21},{"val":196,"count":20},{"val":109,"count":19},{"val":111,"count":18},{"val":106,"count":17},{"val":100,"count":17},{"val":113,"count":16},{"val":125,"count":15},{"val":86,"count":14},{"val":60,"count":13},{"val":107,"count":13},{"val":64,"count":13},{"val":158,"count":12},{"val":118,"count":12},{"val":68,"count":11},{"val":104,"count":11},{"val":76,"count":10},{"val":122,"count":10},{"val":180,"count":10},{"val":178,"count":10},{"val":121,"count":10},{"val":102,"count":9},{"val":80,"count":9},{"val":56,"count":9},{"val":142,"count":9},{"val":140,"count":8},{"val":75,"count":8},{"val":97,"count":8},{"val":153,"count":8},{"val":148,"count":7},{"val":172,"count":7},{"val":130,"count":7},{"val":90,"count":7},{"val":83,"count":7},{"val":62,"count":7},{"val":114,"count":7},{"val":188,"count":7},{"val":85,"count":7},{"val":146,"count":7},{"val":162,"count":6},{"val":134,"count":6},{"val":168,"count":6},{"val":70,"count":6},{"val":173,"count":6},{"val":92,"count":6},{"val":78,"count":6},{"val":72,"count":6},{"val":139,"count":6},{"val":206,"count":5},{"val":141,"count":5},{"val":165,"count":5},{"val":74,"count":5},{"val":93,"count":5},{"val":96,"count":5},{"val":160,"count":5},{"val":203,"count":4},{"val":119,"count":4},{"val":54,"count":4},{"val":161,"count":4},{"val":46,"count":4},{"val":136,"count":4},{"val":177,"count":4},{"val":224,"count":4},{"val":123,"count":4},{"val":200,"count":4},{"val":84,"count":4},{"val":176,"count":3},{"val":208,"count":3},{"val":154,"count":3},{"val":152,"count":3},{"val":166,"count":3},{"val":133,"count":3},{"val":99,"count":3},{"val":138,"count":3},{"val":212,"count":3},{"val":103,"count":3},{"val":94,"count":3},{"val":186,"count":3},{"val":37,"count":3},{"val":69,"count":3},{"val":50,"count":3},{"val":59,"count":3},{"val":58,"count":2},{"val":95,"count":2},{"val":36,"count":2},{"val":182,"count":2},{"val":156,"count":2},{"val":43,"count":2},{"val":26,"count":2},{"val":170,"count":2},{"val":55,"count":2},{"val":67,"count":2},{"val":151,"count":2},{"val":105,"count":2},{"val":149,"count":2},{"val":213,"count":2},{"val":184,"count":2},{"val":179,"count":2},{"val":52,"count":2},{"val":87,"count":2},{"val":218,"count":2},{"val":14,"count":1},{"val":53,"count":1},{"val":48,"count":1},{"val":101,"count":1},{"val":175,"count":1},{"val":129,"count":1},{"val":195,"count":1},{"val":229,"count":1},{"val":131,"count":1},{"val":77,"count":1},{"val":209,"count":1},{"val":65,"count":1},{"val":216,"count":1},{"val":16,"count":1},{"val":98,"count":1},{"val":202,"count":1},{"val":38,"count":1},{"val":190,"count":1},{"val":73,"count":1},{"val":63,"count":1},{"val":215,"count":1}],"sfreq_counts":[{"val":1000.0,"count":785},{"val":500.0,"count":119},{"val":1600.0,"count":32},{"val":999.0,"count":19},{"val":499.7071,"count":16},{"val":2000.0,"count":6},{"val":1024.0,"count":4},{"val":512.0,"count":2}],"stats_computed_at":"2026-04-22T23:16:00.308316+00:00","total_duration_s":2795378.4640718163,"author_year":"Herrema2023_Delayed_Free_Recall","canonical_name":null}}