{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a334e","dataset_id":"ds004457","associated_paper_doi":null,"authors":["Harvey Huang","Nicholas M Gregg","Gabriela Ojeda Valencia","Benjamin H Brinkmann","Brian N Lundstrom","Gregory A Worrell","Kai J Miller","Dora Hermes"],"bids_version":"v 1.9.9","contact_info":["Harvey Huang"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds004457.v1.0.1","datatypes":["ieeg"],"demographics":{"subjects_count":5,"ages":[13,30,20,46,18],"age_min":13,"age_max":46,"age_mean":25.4,"species":null,"sex_distribution":{"f":3,"m":2},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004457","osf_url":null,"github_url":null,"paper_url":null},"funding":["R01 MH122258 CRCNS: Processing speed in the human connectome across the lifespan","T32 GM065841: Medical Scientist Training Program at Mayo Clinic"],"ingestion_fingerprint":"9cb41071bbd5fdc3ab9b128487d3016da5ae57cff61655cdfec05f33c9ddd258","license":"CC0","n_contributing_labs":null,"name":"Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex","readme":"# Basis Profile Curve identification in the human ventral temporal cortex\nThis dataset contains intracranial EEG recordings from five patients during single pulse electrical stimulation as described in:\n* H Huang, NM Gregg, G Ojeda Valencia, BH Brinkmann, BN Lundstrom, GA Worrell, KJ Miller, and D Hermes (2022) Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex. (Under Review)\nPlease cite this work when using the data. These data were recorded at the Mayo Clinic in Rochester, MN, as part of the NIH Brain Initiative supported project R01 MH122258 \"CRCNS: Processing speed in the human connectome across the lifespan\". Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH122258 and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM065841. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The data was collected by Harvey Huang, Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Harvey Huang, Dora Hermes and Gabriela Ojeda Valencia.\nData can be analyzed using the Matlab code at:\n* https://github.com/hharveygit/VTCBPC_JNS_Manu\n## Format\nData are formatted according to BIDS version 1.9.9\n## Single pulse stimulation\nThe patient were resting in the hospital bed, while single pulse stimulation was performed with a frequency of ~0.2 Hz. The stimulation had a duration of 200 microseconds, was biphasic and had an amplitude of 6mA.\n## Contact\nPlease contact Dora Hermes (hermes.dora@mayo.edu) for questions.","recording_modality":["ieeg"],"senior_author":"Dora Hermes","sessions":["ieeg01"],"size_bytes":11705318065,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ccep"],"timestamps":{"digested_at":"2026-04-21T23:07:09.786769+00:00","dataset_created_at":"2023-02-01T14:05:34.238Z","dataset_modified_at":"2023-06-02T19:39:03.000Z"},"total_files":5,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004457","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv","task-ccep_events.json"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"4179a5f0bd19c4f0","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Surgery"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.6,"modality":0.85,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by population/context is the pediatric epilepsy HFO dataset (Epilepsy / Resting State or Sleep / Clinical/Intervention), which shows the convention that when the dataset is composed of clinical patients and the primary purpose is biomarker/mapping/clinical neurophysiology, the Type is typically labeled Clinical/Intervention. However, unlike that example, this dataset metadata does not explicitly state a diagnosis (e.g., epilepsy), so the epilepsy-specific pathology label cannot be taken as a metadata fact and must be treated as an inference only.","metadata_analysis":"Key metadata indicating an intracranial clinical stimulation-mapping context: (1) \"This dataset contains intracranial EEG recordings from five patients during single pulse electrical stimulation\". (2) \"The patient were resting in the hospital bed, while single pulse stimulation was performed\". (3) Title: \"Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex\". (4) Task name provided: \"ccep\" (consistent with cortico-cortical evoked potentials / stimulation-evoked responses).","paper_abstract_analysis":"No useful paper information. (Only a citation/title is provided; no abstract text included in the metadata.)","evidence_alignment_check":"Pathology: Metadata SAYS: \"intracranial EEG recordings from five patients\" and \"resting in the hospital bed\" but does not name a disorder. Few-shot pattern SUGGESTS clinical iEEG patient cohorts often map to Epilepsy or Surgery depending on explicit wording. ALIGN/CONFLICT: partially aligned (clinical patients) but diagnosis missing, so we avoid epilepsy-as-fact.\n\nModality: Metadata SAYS \"single pulse electrical stimulation\" (direct brain stimulation), not an external sensory stimulus. Few-shot pattern SUGGESTS using Visual/Auditory/Tactile/Motor only when those stimuli are presented; otherwise use Other/Unknown. ALIGN: aligned → choose Other.\n\nType: Metadata SAYS stimulation is used to probe circuitry/evoked responses: \"Electrical stimulation... produces distinct responses\" and \"single pulse stimulation\" in iEEG patients. Few-shot pattern SUGGESTS that patient-based neurophysiology mapping/biomarker-style datasets are labeled Clinical/Intervention. ALIGN: aligned → choose Clinical/Intervention.","decision_summary":"Top-2 candidates:\n\nPathology:\n1) Surgery — Evidence: \"intracranial EEG recordings from five patients\"; \"resting in the hospital bed\"; single-pulse stimulation implies implanted electrodes and a clinical inpatient context.\n2) Epilepsy — Evidence: common clinical reason for intracranial EEG + stimulation (CCEP) is presurgical epilepsy evaluation, but this is not explicitly stated.\nSelection: Surgery (metadata supports inpatient implanted-electrode context, while epilepsy is only a plausible but unstated diagnosis). Confidence limited because no explicit diagnosis/surgical indication is provided.\n\nModality:\n1) Other — Evidence: \"single pulse electrical stimulation\" (not sensory-channel stimuli).\n2) Unknown — would apply if stimulation context were unclear, but it is explicit.\nSelection: Other. Confidence high due to explicit stimulation description.\n\nType:\n1) Clinical/Intervention — Evidence: invasive iEEG in \"patients\" plus \"single pulse electrical stimulation\" to characterize responses/circuitry.\n2) Other — could be basic systems/connectivity science rather than intervention, but stimulation in patients strongly fits Clinical/Intervention in EEGDash conventions.\nSelection: Clinical/Intervention. Confidence moderate-high based on multiple explicit stimulation/patient quotes."}},"nemar_citation_count":3,"computed_title":"Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex","nchans_counts":[{"val":192,"count":1},{"val":194,"count":1},{"val":135,"count":1},{"val":178,"count":1},{"val":206,"count":1}],"sfreq_counts":[{"val":2048.0,"count":5}],"stats_computed_at":"2026-04-21T23:17:03.729887+00:00","total_duration_s":20253.298828125,"canonical_name":null,"name_confidence":0.66,"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":"Huang2023"}}