{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32d8","dataset_id":"ds003568","associated_paper_doi":null,"authors":["Lucrezia Liuzzi","Katharine Chang","Hanna Keren","Charles Zheng","Dipta Saha","Dylan Nielson","Argyris Stringaris"],"bids_version":"1.2.0","contact_info":["Lucrezia Liuzzi"],"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds003568.v1.0.2","datatypes":["meg"],"demographics":{"subjects_count":51,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"m":19,"f":32},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003568","osf_url":null,"github_url":null,"paper_url":null},"funding":["Intramural Research Program of the National Institute of Mental Health National Institutes of Health (NIH) (Grant No. ZIA-MH002957-01 [to AS])"],"ingestion_fingerprint":"7622ba5f61525e1989795c78834ef6e4515fc17291c614f7bf207fa0ddf01713","license":"CC0","n_contributing_labs":null,"name":"Mood induction in MDD and healthy adolescents","readme":"This dataset contains the MEG and structural MRI data from the \"Electrophysiological correlates of mood and reward dynamics in human adolescents\" pre-registered analysis (https://www.biorxiv.org/content/10.1101/2021.03.04.433969v1).\nThe task-mmi3 data corresponds to the monetary gambling mood induction task described in the paper. Task-mmi3 data has been pre-processed marking bad channels and bad segments (motion > 5mm or/and noise artifacts).\nTask-rest data is unprocessed 10 minutes resting state scan acquired during the same scanning session.\nAnatomical MRIs have been defaced and co-registered fiducial coordinates are available in the anatomical json files.\nData from four confirmatory subjects are not made available because of missing data sharing consent.\nsub-22658 and sub-24247 do not have an available structural scan.","recording_modality":["meg"],"senior_author":"Argyris Stringaris","sessions":[],"size_bytes":132520000612,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["mmi3","rest"],"timestamps":{"digested_at":"2026-04-22T12:25:35.452623+00:00","dataset_created_at":"2021-03-16T15:16:54.652Z","dataset_modified_at":"2023-07-19T20:01:43.000Z"},"total_files":118,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003568","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"6c7c3403d4d17c92","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:16:53.457778+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Affect"],"confidence":{"pathology":0.7,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by task paradigm and domain is the labeled dataset \"EEG: Three armed bandit gambling task\" (Healthy / Visual / Affect). It shares a gambling/reward paradigm and an explicit focus on reward processing dynamics, which in EEGDash conventions maps the study purpose to Affect (mood/reward/valence processing) rather than purely Decision-making. This convention guides mapping the present dataset’s \"monetary gambling mood induction\" to Affect, with likely Visual stimulus presentation.","metadata_analysis":"Key extracted facts from the provided README include: (1) population age group: \"Electrophysiological correlates of mood and reward dynamics in human adolescents\"; (2) task: \"task-mmi3 data corresponds to the monetary gambling mood induction task\"; (3) additional recording condition: \"Task-rest data is unprocessed 10 minutes resting state scan\"; (4) modality recorded is MEG (but Modality label here refers to stimulus/input channel, not recording technique): \"This dataset contains the MEG and structural MRI data\".","paper_abstract_analysis":"No useful paper information. (Only a link to a preregistered biorxiv analysis is provided; no abstract text is included in the metadata snippet.)","evidence_alignment_check":"Pathology: Metadata says participants are \"human adolescents\" but does not state a disorder/diagnosis; few-shot conventions treat non-clinical cohorts as Healthy. ALIGN (no clinical recruitment stated).\nModality: Metadata does not explicitly state stimulus channel (visual/auditory/etc.); few-shot gambling examples are typically Visual, but this is an inference. PARTIAL CONFLICT/UNCERTAINTY (few-shot suggests Visual; metadata is silent), so inference is used with lower confidence.\nType: Metadata explicitly highlights \"mood and reward dynamics\" and a \"mood induction\" gambling task; few-shot gambling example maps similar reward-focused paradigms to Affect. ALIGN (both indicate affective/reward focus).","decision_summary":"Pathology top-2: (1) Healthy — supported by absence of any diagnosis and general-population phrasing \"human adolescents\" (no clinical recruitment); (2) Development — plausible because cohort is adolescents, but Pathology is defined as clinical condition used to recruit participants, which is not stated. Winner: Healthy. Confidence: 0.7 (1 explicit quote about adolescents, plus absence-of-diagnosis inference).\nModality top-2: (1) Visual — inferred from \"monetary gambling mood induction task\" consistent with common visually-presented gambling tasks and supported by close few-shot analog (three-armed bandit labeled Visual); (2) Unknown — because metadata snippet never states stimulus channel. Winner: Visual. Confidence: 0.6 (contextual inference + few-shot analog, no direct quote).\nType top-2: (1) Affect — supported by explicit \"mood and reward dynamics\" and \"mood induction\" wording; (2) Decision-making — plausible due to \"monetary gambling\" framing. Winner: Affect, consistent with few-shot gambling convention. Confidence: 0.7 (2 explicit mood-related quotes, but task details are brief)."}},"nemar_citation_count":4,"computed_title":"Mood induction in MDD and healthy adolescents","nchans_counts":[{"val":340,"count":48},{"val":339,"count":29},{"val":335,"count":11},{"val":336,"count":6},{"val":343,"count":5},{"val":342,"count":5},{"val":312,"count":3},{"val":309,"count":3},{"val":338,"count":3},{"val":305,"count":2},{"val":310,"count":1},{"val":313,"count":1},{"val":348,"count":1}],"sfreq_counts":[{"val":1200.0,"count":118}],"stats_computed_at":"2026-04-22T23:16:00.222227+00:00","total_duration_s":81150.0,"author_year":"Liuzzi2021","canonical_name":null}}