{"id":"W1964724088","doi":"10.1016/j.neuroimage.2011.03.033","title":"Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":120,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Voxel; Dynamic functional connectivity; Resting state fMRI; Default mode network; Functional connectivity; Computer science; Artificial intelligence; Brain mapping; Neuroscience; Kalman filter; Pattern recognition (psychology); Brain activity and meditation; Functional magnetic resonance imaging; Regression; Psychology; Electroencephalography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008110934,0.0006406018,0.0005625434,0.0007437167,0.0002997747,0.00073669,0.0004865394,0.0007351972,0.0005519294],"category_scores_gemma":[0.003496304,0.000415807,0.0005482459,0.0006939235,0.0004905919,0.001449152,0.0003240652,0.0006089421,0.0001435844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004341264,"about_ca_system_score_gemma":0.0005483355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006267595,"about_ca_topic_score_gemma":0.007142826,"domain_scores_codex":[0.9997929,0.00006064101,0.00001108575,0.00007863877,0.00003342256,0.00002344276],"domain_scores_gemma":[0.9992662,0.0005286868,0.00008941522,0.00005715069,0.00004643005,0.00001199267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002161349,0.0001210171,0.01152254,0.0001943643,0.0003256281,0.0001585596,0.0002656069,0.736613,0.06388817,0.01775995,0.0006623462,0.1682727],"study_design_scores_gemma":[0.000009923075,0.00004472917,0.006280836,0.000009088158,0.00004323825,0.00007041726,0.00002995003,0.9782738,0.005784304,0.009062786,0.0003638976,0.0000269768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06810686,0.00022956,0.9309109,0.00008736961,0.00001123389,0.00002084747,0.00005719689,0.0001646974,0.0004112749],"genre_scores_gemma":[0.8212765,0.0005608991,0.1767386,0.00003532185,0.0000289128,0.0001018837,0.0002131227,0.00009073828,0.0009540238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006267595,"threshold_uncertainty_score":0.01246226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2098367592110318,"score_gpt":0.2829263401579283,"score_spread":0.07308958094689652,"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."}}