{"id":"W2786868232","doi":"10.1109/cibcb.2018.8404973","title":"On the generalizability of linear and non-linear region of interest-based multivariate regression models for fMRI data","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Western Canada Research Grid; Compute Canada","keywords":"Overfitting; Multivariate statistics; Univariate; General linear model; Bayesian multivariate linear regression; Generalizability theory; Linear regression; Functional magnetic resonance imaging; Linear model; Artificial intelligence; Computer science; Proper linear model; Regression; Regression analysis; Pattern recognition (psychology); Statistics; Machine learning; Mathematics; Psychology; Artificial neural network","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":[0.1416006,0.00275286,0.002117503,0.002623654,0.001108449,0.003865293,0.002862123,0.003618148,0.002953262],"category_scores_gemma":[0.3526244,0.0009871442,0.004275084,0.002482697,0.006110061,0.006478478,0.005425029,0.004950393,0.001106874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539569,"about_ca_system_score_gemma":0.001809268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446344,"about_ca_topic_score_gemma":0.003721703,"domain_scores_codex":[0.9319813,0.04900647,0.002934332,0.01015227,0.005160784,0.0007649445],"domain_scores_gemma":[0.6537877,0.2898624,0.01028088,0.03809628,0.007128057,0.0008445989],"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":[0.002563308,0.0004274856,0.06586622,0.001824982,0.005783789,0.001145021,0.004150044,0.5006465,0.01128125,0.1202337,0.004394764,0.281683],"study_design_scores_gemma":[0.0002713573,0.00167471,0.03754665,0.0007088152,0.001072378,0.001092535,0.0006885762,0.7588218,0.004138832,0.1882207,0.005511138,0.0002524427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0587662,0.001912231,0.9315551,0.0024862,0.0001757027,0.0005787673,0.0003361056,0.001092561,0.00309709],"genre_scores_gemma":[0.731697,0.00315988,0.2561767,0.002045506,0.000657775,0.001290399,0.001334648,0.001357035,0.002281063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1416006,"threshold_uncertainty_score":0.7488647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.265895420979409,"score_gpt":0.358867039736353,"score_spread":0.09297161875694399,"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."}}