{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a344f","dataset_id":"ds006460","associated_paper_doi":"10.1117/1.nph.12.3.035010","authors":["Jessica E. Anderson","Laura Carlton","Sreekanth Kura","Walker J. O'Brien","De'Ja Rogers","Parisa Rahimi","Parya Y. Farzam","Muhammad H. Zaman","David A. Boas","Meryem A. Yücel"],"bids_version":"1.8.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006460.v1.0.0","datatypes":["fnirs"],"demographics":{"subjects_count":17,"ages":[24,26,24,27,23,31,21,19,23,22,36,23,29,23,30,30,27],"age_min":19,"age_max":36,"age_mean":25.764705882352942,"species":null,"sex_distribution":{"f":8,"m":9},"handedness_distribution":{"l":2,"r":14}},"experimental_modalities":null,"external_links":{"paper_url":"https://www.spiedigitallibrary.org/journals/neurophotonics/volume-12/issue-3/035010/High-density-multidistance-fNIRS-enhances-detection-of-brain-activity-during/10.1117/1.NPh.12.3.035010.pdf"},"funding":["This project was supported by NIH NEW grant U01-EB 029856 and Boston University research funds."],"ingestion_fingerprint":"20ac61d9615642692b5d330b8b074cc7a2a391d15160fd247fe905e954d33e6f","license":"CC0","n_contributing_labs":null,"name":"High-DensityvSparsefNIRS_WordColorStroop_HD_Anderson_2025","readme":null,"recording_modality":["fnirs"],"senior_author":null,"sessions":["1"],"size_bytes":482171742,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["WordStroop"],"timestamps":{"digested_at":"2026-05-31T16:24:41.950062+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":17,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006460","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"f88ff66a5016b2fb","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot by cognitive construct is the TBI DPX cognitive control dataset (\"EEG: DPX Cog Ctl Task in Acute Mild TBI\"), which is labeled Type=Attention because it targets cognitive control/attention via a classic interference/control-demanding paradigm. A Word-Color Stroop task is likewise a canonical interference/cognitive control paradigm, so the few-shot convention suggests Type=Attention (rather than Perception or Memory). For Modality, multiple few-shots map stimulus categories directly to modality (e.g., visual discrimination -> Visual; auditory oddball -> Auditory), guiding us to label Stroop (word/color) as Visual. For Pathology, several few-shots label normative/non-clinical recruitment as Healthy when no diagnosis is described; here no diagnosis terms appear, so the convention suggests Healthy.","metadata_analysis":"Key metadata indicates a Stroop paradigm and no clinical recruitment. Evidence: title includes \"WordColorStroop\" (\"High-DensityvSparsefNIRS_WordColorStroop_HD_Anderson_2025\"). Task list explicitly includes \"WordStroop\" (\"tasks\": [\"WordStroop\"]). Participants overview lists only demographics with no disorder groups (\"Subjects: 17\"; \"Age range: 19-36\"; \"Sex: {'f': 8, 'm': 9}\").","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says only demographics (\"Subjects: 17\"; \"Age range: 19-36\") and provides no diagnosis/group labels; few-shot convention maps such non-clinical datasets to Healthy. ALIGN.\nModality: Metadata indicates Stroop with \"WordColorStroop\" / \"WordStroop\", which implies visually presented words/colors; few-shot convention maps visually presented discrimination/control tasks to Visual. ALIGN.\nType: Metadata indicates a Stroop task (\"WordColorStroop\" / \"WordStroop\"), a cognitive control/attention interference paradigm; few-shot DPX cognitive control example maps control-demanding interference tasks to Attention. ALIGN.","decision_summary":"Pathology top-2: (1) Healthy — supported by lack of any clinical terms and only demographics (\"Subjects: 17\", \"Age range: 19-36\"); (2) Unknown — possible because recruitment criteria are not explicitly stated. Winner: Healthy (few-shot convention + no contrary metadata). Confidence supported by 1 clear absence-of-pathology context + demographic-only description.\nModality top-2: (1) Visual — implied by \"WordColorStroop\" and \"WordStroop\" (words/colors are visual stimuli); (2) Other — if task delivery were non-visual (unlikely). Winner: Visual. Confidence supported by 2 explicit metadata strings naming word/color Stroop.\nType top-2: (1) Attention — Stroop is primarily used for selective attention/interference control; (2) Decision-making — could be framed as response selection under conflict, but less direct than attention/cognitive control. Winner: Attention, guided by the cognitive-control few-shot convention (DPX task labeled Attention) and the Stroop paradigm named in metadata."}},"computed_title":"High-DensityvSparsefNIRS_WordColorStroop_HD_Anderson_2025","nchans_counts":[{"val":428,"count":17}],"sfreq_counts":[{"val":17.438616071428573,"count":15},{"val":17.438616071428577,"count":2}],"stats_computed_at":"2026-05-31T19:34:32.602591+00:00","total_duration_s":9696.526335999999,"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":"Anderson2025_HD","bad_channels_info":null,"acknowledgements":"We thank Yuanyuan Gao, Antonio Ortega, Sudan Duwadi, Darash Desai, Alexander Von Lühmann, Jack Giblin, Xiaojun Cheng, Byungchan (Kenny) Kim, Chantal Stern, Alice Cronin-Golomb, Rini Kaplan, and Neila Gross for helpful lab training and insightful discussions and feedback. This project was supported by NIH NEW grant U01-EB 029856 and Boston University research funds.","ethics_approvals":["Boston University Charles River Campus (RCR) IRB. Functional Near-Infrared Spectroscopy of the Brain: 4502"],"references_and_links":[""],"associated_paper_meta":{"channel":"crossref-biblio","confidence":"high","author_overlap":10,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}