{"id":"W3190378680","doi":"10.7554/elife.62324","title":"Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability","year":2021,"lang":"en","type":"article","venue":"eLife","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Fonds de recherche du Québec – Nature et technologies; Réseau en Bio-Imagerie du Quebec","keywords":"Identifiability; Connectome; Resting state fMRI; Human Connectome Project; Functional connectivity; Computer science; Neuroscience; Preprocessor; Default mode network; Artificial intelligence; Biological system; Biology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006170863,0.0003466154,0.0004964991,0.0007389766,0.0004354018,0.0008370883,0.0006061263,0.0006553049,0.001622846],"category_scores_gemma":[0.04326461,0.0003382807,0.0005466642,0.0008114409,0.001695174,0.001289783,0.001046001,0.0009030535,0.0001823056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559806,"about_ca_system_score_gemma":0.0003939962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008636235,"about_ca_topic_score_gemma":0.001216256,"domain_scores_codex":[0.9975117,0.001053244,0.0001912268,0.0008069737,0.0002789758,0.0001578706],"domain_scores_gemma":[0.9807438,0.01330803,0.002235615,0.003131481,0.0004386987,0.0001423793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001599766,0.0003903603,0.4414705,0.001025929,0.001660433,0.001972232,0.006951537,0.05815372,0.1878556,0.06177463,0.00366366,0.2334817],"study_design_scores_gemma":[0.00007255694,0.0003933412,0.6976244,0.0001377413,0.0003699107,0.003004557,0.0009496276,0.1407477,0.02750594,0.1248329,0.004246403,0.000114926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7621069,0.0007312433,0.2331497,0.0006599756,0.00004757077,0.00008633149,0.0006061501,0.0002397574,0.002372263],"genre_scores_gemma":[0.9862776,0.0001022343,0.01261132,0.00005186968,0.0000202991,0.00007378674,0.000480012,0.00005342197,0.0003293717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006170863,"threshold_uncertainty_score":0.03263509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04086489961000864,"score_gpt":0.2582202109638083,"score_spread":0.2173553113537996,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}