{"id":"W2588730101","doi":"10.1002/brb3.626","title":"A method for independent component graph analysis of resting‐state <scp>fMRI</scp>","year":2017,"lang":"en","type":"article","venue":"Brain and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Médicale Reine Elisabeth; Fonds Léon Fredericq; Canada Excellence Research Chairs, Government of Canada; Fonds De La Recherche Scientifique - FNRS; James S. McDonnell Foundation","keywords":"Resting state fMRI; Independent component analysis; Component (thermodynamics); Computer science; Neuroscience; Chemistry; Psychology; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006953715,0.0001557539,0.0003764011,0.0002887633,0.0006282516,0.00009870109,0.0002672817,0.00005112961,0.000004687734],"category_scores_gemma":[0.009055505,0.0001402753,0.0002071099,0.0002243681,0.000232361,0.0001308501,0.0002140477,0.0001053336,0.000001506217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001822193,"about_ca_system_score_gemma":0.00002137392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002110721,"about_ca_topic_score_gemma":0.0002822378,"domain_scores_codex":[0.9985645,0.0001241709,0.0002428112,0.0005257573,0.0002980838,0.0002446254],"domain_scores_gemma":[0.9921361,0.006976197,0.0002974338,0.0004244185,0.00008424599,0.00008163823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007143207,0.0007931712,0.1504985,0.00009329611,0.0003293176,0.00005153764,0.001243912,0.0001129735,0.8114721,0.002872394,0.005008759,0.02745265],"study_design_scores_gemma":[0.0008167058,0.0001847498,0.9187132,0.00001627136,0.0007912605,0.00001039208,0.0001269631,0.0009397937,0.07456527,0.0008461428,0.002891196,0.00009802589],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932386,0.000032255,0.00412942,0.001395782,0.0001853859,0.0004527811,0.0001896353,0.00003807127,0.0003381263],"genre_scores_gemma":[0.9942093,0.00001346884,0.003655724,0.0006984298,0.00003037756,0.0001682171,0.000004872044,0.00001574754,0.001203792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7682148,"threshold_uncertainty_score":0.9992917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07479285800398347,"score_gpt":0.3510273410500635,"score_spread":0.27623448304608,"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."}}