{"id":"W2418971041","doi":"10.1016/j.jneumeth.2016.06.005","title":"Interactions between head motion and coil sensitivity in accelerated fMRI","year":2016,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Electromagnetic coil; Computer science; Functional magnetic resonance imaging; Artificial intelligence; Head (geology); SIGNAL (programming language); Computer vision; Motion (physics); Acceleration; Finger tapping; Channel (broadcasting); Physics; Psychology; Telecommunications; Neuroscience; Electronic engineering; Medicine","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":[0.002022062,0.0004384003,0.0003065168,0.0004873184,0.0002140212,0.0006101857,0.0002508237,0.000496647,0.001957281],"category_scores_gemma":[0.01649137,0.0006073565,0.0002694416,0.0004915939,0.0004469906,0.0006344722,0.0003766706,0.0004515247,0.0002170205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158053,"about_ca_system_score_gemma":0.000233863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006589165,"about_ca_topic_score_gemma":0.001712651,"domain_scores_codex":[0.9990734,0.0006359262,0.00003378665,0.000103015,0.00009087283,0.00006297057],"domain_scores_gemma":[0.9926249,0.00636367,0.0004215472,0.0003183802,0.0001620181,0.000109465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.009998991,0.0004496588,0.08165039,0.0006125623,0.000913896,0.001377487,0.001981762,0.0216107,0.7991996,0.003754478,0.00130484,0.07714572],"study_design_scores_gemma":[0.0001666743,0.001024156,0.8904944,0.0000576934,0.0008726828,0.002595933,0.0001976432,0.05203847,0.04538208,0.005836015,0.001225464,0.0001087327],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782678,0.0006466543,0.01921724,0.0001136014,0.0000238391,0.00003256487,0.0001364643,0.00008318546,0.001478709],"genre_scores_gemma":[0.9936203,0.000173531,0.005248158,0.0000467503,0.00005363738,0.00002884384,0.0001335444,0.0001087982,0.000586357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002022062,"threshold_uncertainty_score":0.01069379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2288675584975266,"score_gpt":0.4433482484750864,"score_spread":0.2144806899775598,"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."}}