{"id":"W3010096825","doi":"10.1016/j.neuroimage.2020.117126","title":"Fine-grain atlases of functional modes for fMRI analysis","year":2020,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Johnson and Johnson Pharmaceutical Research and Development; Horizon 2020; National Institute of Biomedical Imaging and Bioengineering; European Research Council; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; GE Healthcare; Canada First Research Excellence Fund; National Institutes of Health; H. Lundbeck A/S; University of Southern California; Agence Nationale de la Recherche; Janssen Alzheimer Immunotherapy Research And Development; Northern California Institute for Research and Education; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Genentech; IXICO; National Institute on Aging; Fujirebio Europe; McGill University","keywords":"Functional connectivity; Computer science; Artificial intelligence; Psychology; Pattern recognition (psychology); Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001933794,0.0009574959,0.0007687375,0.002438054,0.0006391557,0.002106417,0.001023307,0.0008192561,0.01057754],"category_scores_gemma":[0.009040916,0.0006325555,0.001778355,0.002202688,0.0008823196,0.001475439,0.002254768,0.001963557,0.00328247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007711988,"about_ca_system_score_gemma":0.001530572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002748142,"about_ca_topic_score_gemma":0.005047751,"domain_scores_codex":[0.9992912,0.0002239201,0.00007933824,0.0002210001,0.0001297364,0.00005486211],"domain_scores_gemma":[0.9975834,0.001002709,0.0002200057,0.0008823271,0.0002519414,0.00005955626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000702743,0.0001566807,0.01274542,0.001892519,0.0009789877,0.0005371361,0.001457058,0.1439712,0.1050772,0.1328748,0.07738969,0.5222167],"study_design_scores_gemma":[0.0001336132,0.0002416668,0.02670373,0.0002866707,0.0003918102,0.001056702,0.0005250706,0.3665231,0.06007023,0.3792979,0.164502,0.0002675481],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01937982,0.0007174775,0.9577943,0.0006243345,0.0002175363,0.0001176642,0.009338723,0.00901624,0.002793906],"genre_scores_gemma":[0.2340133,0.001085306,0.7398882,0.0005003674,0.000217425,0.001019877,0.01560975,0.004361505,0.00330426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057754,"threshold_uncertainty_score":0.03538537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09992526605300582,"score_gpt":0.2839973172154046,"score_spread":0.1840720511623988,"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."}}