{"success":true,"database":"eegdash","data":{"_id":"69a33a3b897a7725c66f3eee","dataset_id":"ds007427","associated_paper_doi":null,"authors":["Verónica Henao Isaza","Carlos Andrés Tobón Quintero","John Fredy Ochoa Gómez"],"bids_version":"1.8.0","contact_info":["Verónica Henao Isaza"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds007427.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":44,"ages":[55,52,46,42,55,52,48,46,62,59,41,57,55,63,52,44,65,49,49,46,43,51,41,46,47,23,30,28,28,30,29,23,21,26,27,26,27,26,28,29,23,30,44,38,33,41,33,37,34,36,36,30,34,30,43,24,31,28,29,25,23,28,21,25,26,28,23,33,39,46,31,29,30,29,34,42,34,34,39,35,35,34,31,30,41,42,25,45,32,40,38,30],"age_min":21,"age_max":65,"age_mean":36.71739130434783,"species":null,"sex_distribution":{"m":37,"f":55},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007427","osf_url":null,"github_url":null,"paper_url":null},"funding":["Comité para el Desarrollo de la Investigación - CODI, Universidad de Antioquia"],"ingestion_fingerprint":"1f242a7472d1c24853ce83e7477194bd4fc2b9aa27a3ad9207d39a7989ba1198","license":"CC0","n_contributing_labs":null,"name":"Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification","readme":"References\n----------\nHenao Isaza, V., Aguillon, D., Tobón Quintero, C. A., Lopera, F., & Ochoa Gómez, J. F. (2026). Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer's risk classification. PLOS ONE. https://doi.org/10.1371/journal.pone.0343722","recording_modality":["eeg"],"senior_author":"John Fredy Ochoa Gómez","sessions":["V0"],"size_bytes":3379868887,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007427","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["CE"],"timestamps":{"digested_at":"2026-04-22T12:30:16.749912+00:00","dataset_created_at":"2026-02-15T13:49:59.563Z","dataset_modified_at":"2026-04-03T09:25:28.000Z"},"total_files":44,"computed_title":"Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification","nchans_counts":[{"val":60,"count":44}],"sfreq_counts":[{"val":1000.0,"count":44}],"stats_computed_at":"2026-04-22T23:16:00.312648+00:00","total_duration_s":14081.556,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"c4fe53facd692509","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Dementia"],"modality":["Resting State"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.6,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by research aim and keywords is the dementia resting-state dataset: \"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects\" (Pathology=Dementia, Modality=Resting State, Type=Clinical/Intervention). It shows the convention that Alzheimer’s-focused EEG biomarker datasets are labeled under Dementia and typically get Type=Clinical/Intervention when the main purpose is diagnosis/biomarkers. A secondary convention match is the resting-state datasets that encode eyes-open/closed in metadata (e.g., sleep-deprivation resting-state example labeled Modality=Resting State, Type=Resting-state), which guides interpreting an abbreviated task label like \"CE\" as likely \"(eyes) closed\" resting.","metadata_analysis":"Key facts available are sparse but point to an Alzheimer’s biomarker/clinical focus and a likely eyes-closed resting recording.\n\nQuoted metadata:\n1) Title: \"Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification\".\n2) README reference: \"Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer's risk classification\".\n3) Tasks field: \"tasks\": [\"CE\"].\n\nParticipant info is present but internally inconsistent: \"Subjects: 44\" yet \"Sex: {'m': 37, 'f': 55}\" (sums to 92), suggesting incomplete/erroneous summary and no explicit group/diagnosis fields to directly confirm Alzheimer’s disease vs at-risk vs controls.","paper_abstract_analysis":"No useful paper information. (Only a citation is provided; no abstract text is included in the metadata.)","evidence_alignment_check":"Pathology:\n- Metadata says: Alzheimer’s-focused clinical aim via title: \"Alzheimer’s risk classification\".\n- Few-shot pattern suggests: Alzheimer’s-related EEG biomarker datasets map to Pathology=\"Dementia\" (see Alzheimer’s/FTD resting dataset labeled Dementia).\n- Alignment: PARTIAL. Both indicate an Alzheimer’s/dementia theme, but metadata does NOT explicitly state participants were diagnosed with Alzheimer’s/dementia (could be at-risk individuals).\n\nModality:\n- Metadata says: task label only: \"CE\".\n- Few-shot pattern suggests: eyes-open/closed paradigms are labeled Modality=\"Resting State\" (e.g., eyes-closed resting datasets).\n- Alignment: WEAK/PARTIAL. \"CE\" plausibly means (eyes) closed, consistent with resting-state acquisition, but it is not explicitly defined.\n\nType:\n- Metadata says: \"EEG biomarker studies\" and \"Alzheimer’s risk classification\" (clinical classification purpose).\n- Few-shot pattern suggests: when the main focus is clinical biomarkers/diagnosis in a dementia context, Type=\"Clinical/Intervention\" (Alzheimer’s/FTD example).\n- Alignment: ALIGN. Both point to a clinical biomarker/classification purpose rather than a basic cognitive construct task.","decision_summary":"Top-2 comparative selections:\n\n1) Pathology\n- Candidate A: Dementia\n  Evidence: (i) Title explicitly targets Alzheimer’s: \"Alzheimer’s risk classification\"; (ii) few-shot Alzheimer’s dataset is labeled Dementia.\n- Candidate B: Unknown\n  Evidence: No explicit participant diagnosis/groups are stated anywhere (only \"Subjects: 44\"), so recruitment condition is not confirmed.\n- Decision: Dementia (winner by thematic specificity, but not explicitly confirmed).\n- Evidence alignment: Partial (theme aligns; recruitment diagnosis not stated).\n\n2) Modality\n- Candidate A: Resting State\n  Evidence: task label \"CE\" is commonly used for \"(eyes) closed\" resting EEG; matches few-shot conventions for eyes-closed resting paradigms.\n- Candidate B: Unknown\n  Evidence: \"CE\" is undefined in provided metadata; no stimulus/task description.\n- Decision: Resting State (winner by plausible task-code interpretation).\n- Evidence alignment: Partial/weak (inference from abbreviation).\n\n3) Type\n- Candidate A: Clinical/Intervention\n  Evidence: (i) \"EEG biomarker studies\"; (ii) \"Alzheimer’s risk classification\" indicates clinical classification/biomarker development.\n- Candidate B: Resting-state\n  Evidence: If CE is eyes-closed resting, the data could be used for resting-state neuroscience.\n- Decision: Clinical/Intervention (winner because stated purpose is biomarker risk classification).\n- Evidence alignment: Align (purpose clearly clinical/biomarker)."}},"canonical_name":null,"name_confidence":0.74,"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":"Isaza2026_Comprehensive"}}