{"id":"W2911495580","doi":"10.1016/j.mri.2019.01.019","title":"Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Uehara Memorial Foundation","keywords":"Communication noise; Computer science; Voxel; Noise (video); Pattern recognition (psychology); Resting state fMRI; Artificial intelligence; Resampling; Connectome; Set (abstract data type); Neuroscience; Functional connectivity; Psychology","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":[0.001169652,0.001120299,0.001063384,0.0015795,0.0006834281,0.001318306,0.0009034907,0.001248973,0.001930579],"category_scores_gemma":[0.003957903,0.0004593256,0.001041536,0.001079861,0.0003929044,0.0009139099,0.0008387959,0.0009385644,0.001638449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196143,"about_ca_system_score_gemma":0.001069872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00220679,"about_ca_topic_score_gemma":0.006724718,"domain_scores_codex":[0.9994142,0.0001026717,0.0000426786,0.0001787254,0.0001654889,0.0000963175],"domain_scores_gemma":[0.9990895,0.0003324833,0.00008046426,0.0001853289,0.0002830217,0.00002915874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005312829,0.0002858737,0.003534528,0.0003467558,0.0001443043,0.0002754005,0.0002193897,0.008256391,0.3259583,0.003540957,0.006361526,0.6505452],"study_design_scores_gemma":[0.00007717929,0.000357618,0.05466802,0.0001042978,0.000445404,0.001542706,0.0002074088,0.6818082,0.2295928,0.01287711,0.01816916,0.0001501344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06155716,0.0006032346,0.9335925,0.000189628,0.0001171014,0.0001688978,0.0007118365,0.002038742,0.001020946],"genre_scores_gemma":[0.3165102,0.0006711668,0.6735892,0.0001710967,0.0001566754,0.0005211369,0.003183853,0.0009362589,0.004260455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00220679,"threshold_uncertainty_score":0.006458402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299868012840086,"score_gpt":0.2665254233211114,"score_spread":0.2335267431927106,"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."}}