{"id":"W2170976240","doi":"10.1109/tmi.2004.837791","title":"An information-theoretic criterion for intrasubject alignment of FMRI time series: motion corrected independent component analysis","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Independent component analysis; Artificial intelligence; Computer science; Entropy (arrow of time); Computer vision; Image registration; Preprocessor; Pattern recognition (psychology); Mathematics; Algorithm; Image (mathematics)","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":[0.004244607,0.001354976,0.001254231,0.003083652,0.0008241888,0.001563865,0.00153327,0.001680273,0.0009752909],"category_scores_gemma":[0.01267391,0.0005811013,0.00108744,0.001948179,0.002158865,0.001777538,0.001539467,0.00200913,0.000783329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000990355,"about_ca_system_score_gemma":0.001918195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167298,"about_ca_topic_score_gemma":0.001357229,"domain_scores_codex":[0.9970893,0.0009289918,0.000278893,0.0005070309,0.001095521,0.0001002287],"domain_scores_gemma":[0.9959008,0.00164992,0.0006424216,0.0005812786,0.001111553,0.0001140102],"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":[0.0003514948,0.0001397623,0.003184026,0.0006633027,0.0003926619,0.0004769441,0.0003407766,0.2689565,0.125319,0.1168762,0.004801784,0.4784976],"study_design_scores_gemma":[0.0000216339,0.0002636036,0.005714968,0.00009000597,0.00009735622,0.0006188052,0.00005467495,0.872283,0.04613335,0.06383913,0.01068424,0.0001991451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002505784,0.0002161342,0.9967033,0.00007219257,0.00002165853,0.00003153986,0.00005095849,0.0001457889,0.0002525682],"genre_scores_gemma":[0.08051349,0.0003525727,0.9172412,0.0001096653,0.000108335,0.000299109,0.0003941526,0.0002531644,0.0007282792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004244607,"threshold_uncertainty_score":0.02244794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005852320285617264,"score_gpt":0.2613787639813405,"score_spread":0.2555264436957232,"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."}}