{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33d9","dataset_id":"ds005356","associated_paper_doi":null,"authors":["[Unspecified]"],"bids_version":"1.7.0","contact_info":["James F Cavanagh"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005356.v1.5.0","datatypes":["meg"],"demographics":{"subjects_count":85,"ages":[19,20,20,20,22,22,22,20,22,19,22,22,21,20,20,21,19,21,19,20,22,20,19,21,19,19,22,20,19,19,22,21,22,21,21,22,22,19,22,22,20,22,21,22,20,21,21,21,20,21,21,19,20,19,22,22,19,22,19,19,19,19,19,20,21,19,19,19,21,20,22,22,19,21,19,20,21,21,19,21,20,20,22,19],"age_min":19,"age_max":22,"age_mean":20.452380952380953,"species":null,"sex_distribution":{"m":84,"f":1},"handedness_distribution":{"r":85}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005356","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"bd8daf87c3d69223b4a90a2da2c31df5ada75d241b613a96de486218e5e472bc","license":"CC0","n_contributing_labs":null,"name":"MEG: Major Depression & Probabilistic Learning Task","readme":"Howdy y'all.   Here's data from:\nPirrung, C.J.H., Singh G., Hogeveen, J., Quinn, D. & Cavanagh, J.F.  (2025) Hypoactivation of ventromedial frontal cortex in major depressive disorder: an MEG study of the Reward Positivity.   Biological Psychiatry: Cognitive Neuroscience and Neuroimaging\nAn MEG study (306-sensor Elekta Neuromag System) of the Reward Positivity during reinforcement learning.  Participants were all SCID interviewed to meet either control (CTL, non-depressed, n=38) or major depressive disorder (MDD, n=52) group criteria.   Task was an MEG-compatible probabilistic selection task.  We'll upload their T1s and resting state soon.   <jcavanagh@unm.edu>","recording_modality":["meg"],"senior_author":"[Unspecified]","sessions":["01"],"size_bytes":173488425070,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["pst"],"timestamps":{"digested_at":"2026-04-22T12:27:42.172146+00:00","dataset_created_at":"2024-07-17T17:45:08.266Z","dataset_modified_at":"2025-03-06T16:10:24.000Z"},"total_files":116,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005356","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"0330e648b367cddc","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:49:25.650847+00:00"},"tags":{"pathology":["Depression"],"modality":["Visual"],"type":["Learning"],"confidence":{"pathology":0.9,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot examples by paradigm are the reinforcement learning / reward tasks: (1) 'EEG: Probabilistic Learning with Affective Feedback: Exp #2' labeled Type=Learning and Modality=Visual, which aligns with a probabilistic learning task studying reward-related ERPs (RewP/Reward Positivity). (2) 'EEG: Reinforcement Learning in Parkinson's' shows the convention that probabilistic selection / reinforcement learning tasks are treated as visually presented choice paradigms (Modality=Visual) and are labeled as Decision-making or Learning depending on emphasis. Given this dataset explicitly frames the construct as 'Reward Positivity during reinforcement learning', the Learning convention is the closest match.","metadata_analysis":"Key quoted facts from the dataset README: (1) Clinical population recruitment is explicit: \"major depressive disorder (MDD, n=52)\" and \"control (CTL, non-depressed, n=38)\". (2) Diagnostic ascertainment is explicit: \"Participants were all SCID interviewed\" to meet group criteria. (3) Task/construct is explicit: \"An MEG study ... of the Reward Positivity during reinforcement learning\" and \"Task was an MEG-compatible probabilistic selection task.\"","paper_abstract_analysis":"No useful paper information (only a citation; no abstract text provided in metadata).","evidence_alignment_check":"Pathology: Metadata says \"major depressive disorder (MDD, n=52)\" with SCID-based grouping; few-shot patterns also treat explicitly named disorders as the Pathology label. ALIGN. Modality: Metadata does not explicitly state stimulus modality (only \"probabilistic selection task\"), while few-shot conventions for probabilistic learning/reinforcement learning tasks (e.g., 'Probabilistic Learning with Affective Feedback' and 'Reinforcement Learning in Parkinson's') indicate Visual presentation. PARTIAL (inference guided by few-shot). Type: Metadata says \"Reward Positivity during reinforcement learning\" and \"probabilistic selection task\"; few-shot conventions label similar paradigms as Learning (probabilistic learning) or sometimes Decision-making (reinforcement learning). Mostly ALIGN with Learning given explicit reinforcement-learning framing.","decision_summary":"Top-2 candidates per category:\n- Pathology: (A) Depression vs (B) Healthy. Evidence for Depression: \"major depressive disorder (MDD, n=52)\"; \"meet either control (CTL... ) or ... (MDD) group criteria\"; \"Participants were all SCID interviewed\". Winner: Depression. Alignment: aligned.\n- Modality: (A) Visual vs (B) Multisensory/Other. Evidence for Visual: task is a \"probabilistic selection task\" (typically visually cued) and few-shot RL/probabilistic learning examples use Modality=Visual; no evidence for auditory/tactile stimuli in metadata. Winner: Visual. Alignment: inferred from few-shot + task genre (no explicit modality quote).\n- Type: (A) Learning vs (B) Decision-making. Evidence for Learning: \"Reward Positivity during reinforcement learning\"; \"probabilistic selection task\"; close few-shot match to 'Probabilistic Learning with Affective Feedback' labeled Learning. Evidence for Decision-making: selection/choice component implied by \"selection task\" and RL. Winner: Learning because reinforcement learning is explicitly the stated construct and matches few-shot Learning convention for probabilistic learning/RewP-focused datasets."}},"computed_title":"MEG: Major Depression & Probabilistic Learning Task","nchans_counts":[{"val":396,"count":113},{"val":450,"count":2}],"sfreq_counts":[{"val":1000.0,"count":55}],"stats_computed_at":"2026-04-22T23:16:00.309403+00:00","total_duration_s":65668.945,"author_year":"DS5356_MajorDepression","canonical_name":null,"name_source":"canonical"}}