{"id":"W4290014901","doi":"10.1109/jbhi.2022.3196689","title":"A Joint Constrained CCA Model for Network-Dependent Brain Subregion Parcellation","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; National Natural Science Foundation of China","keywords":"Functional magnetic resonance imaging; Artificial intelligence; Pattern recognition (psychology); Canonical correlation; Computer science; Putamen; Correlation; Neuroimaging; Neuroscience; Psychology; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.002559651,0.0001018888,0.0002947986,0.0001545755,0.0006537457,0.000026597,0.0001091438,0.00003965263,0.00001328269],"category_scores_gemma":[0.0008925065,0.00008354252,0.00007864107,0.0001714106,0.0001618077,0.0001700434,0.0000699917,0.0003472819,9.243365e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001593472,"about_ca_system_score_gemma":0.0004586932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003527533,"about_ca_topic_score_gemma":0.000003296297,"domain_scores_codex":[0.9978331,0.0000905487,0.001031315,0.00008305677,0.0006671085,0.0002948612],"domain_scores_gemma":[0.9974651,0.001295462,0.0008645904,0.00006939952,0.00007501779,0.0002304034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00100893,0.0006275502,0.0002219292,0.00143407,0.00009449168,0.00002492542,0.01750946,0.09726784,0.0026633,0.01340698,0.7747695,0.09097096],"study_design_scores_gemma":[0.002414878,0.002168894,0.0001135838,0.00005736922,0.00001363203,0.0007035168,0.0009433857,0.9326528,0.0001153753,0.01155027,0.04911323,0.0001531003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08399451,0.0002799355,0.6724808,0.2393107,0.002581896,0.0009209685,0.0001421101,0.00004580425,0.0002433422],"genre_scores_gemma":[0.9560537,0.0002254916,0.00434316,0.0387378,0.0004808117,0.00001925442,0.000004238348,0.00001001748,0.0001255731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8720592,"threshold_uncertainty_score":0.5028149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185094022225201,"score_gpt":0.3220049339040225,"score_spread":0.2034955316815024,"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."}}