{"id":"W2093205032","doi":"10.1016/s0933-3657(02)00005-2","title":"A novel, direct spatio-temporal approach for analyzing fMRI experiments","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Computer science; Similarity (geometry); Concatenation (mathematics); Pattern recognition (psychology); Pixel; Voxel; Artificial intelligence; k-nearest neighbors algorithm; Flexibility (engineering); Nearest neighbor search; Set (abstract data type); Spatial analysis; Data mining; Image (mathematics); Mathematics; Statistics","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":[0.0006373045,0.0009906711,0.0006833848,0.001628649,0.00052272,0.001437428,0.001232687,0.001158338,0.004624509],"category_scores_gemma":[0.002290233,0.0006185318,0.0007675528,0.001420912,0.0008497025,0.001419247,0.001060975,0.001031712,0.00126341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296416,"about_ca_system_score_gemma":0.000674364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243699,"about_ca_topic_score_gemma":0.004980339,"domain_scores_codex":[0.9995688,0.0001092117,0.0000238238,0.0001317038,0.0001399451,0.00002665347],"domain_scores_gemma":[0.9989937,0.0004670758,0.000151724,0.0001658663,0.0001495066,0.00007215457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002500156,0.000249029,0.002096536,0.0005961412,0.0004074441,0.0008132551,0.0002462477,0.02116766,0.5879785,0.03751633,0.005717189,0.3429616],"study_design_scores_gemma":[0.0001111101,0.0004362093,0.01233368,0.00007136189,0.0004055446,0.00512798,0.0001933857,0.7926738,0.0790926,0.0856996,0.02365692,0.0001977871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003852459,0.0001910495,0.9938788,0.00009175934,0.00004452291,0.00005729364,0.0001809619,0.0004200629,0.001283071],"genre_scores_gemma":[0.07225351,0.0005129831,0.9230012,0.0002077939,0.0002274211,0.0003736315,0.000312277,0.0002692494,0.002842027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004624509,"threshold_uncertainty_score":0.01547056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2207156774239676,"score_gpt":0.3612224297061823,"score_spread":0.1405067522822148,"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."}}