{"id":"W1973735135","doi":"10.1109/ciisp.2007.369316","title":"Intensity / Correlation Thresholding of FMRI Data: Data-driven Regions of Interest using Bridge Voxels","year":2007,"lang":"en","type":"article","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Thresholding; Voxel; Correlation; Functional magnetic resonance imaging; Artificial intelligence; Intensity (physics); Region of interest; Computer science; Pattern recognition (psychology); Spatial correlation; Computer vision; Image (mathematics); Mathematics; Physics; Psychology; Neuroscience; Optics","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":[0.002361447,0.0006136432,0.0008070659,0.001167138,0.0003872553,0.001012941,0.0009302336,0.0007307144,0.001262038],"category_scores_gemma":[0.006068137,0.000445642,0.0006773517,0.0008303402,0.0008194006,0.0008124916,0.0009948807,0.001064862,0.0003768913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002863521,"about_ca_system_score_gemma":0.0007590906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004766413,"about_ca_topic_score_gemma":0.001141479,"domain_scores_codex":[0.9994588,0.0001283204,0.0000469611,0.0001337418,0.0001775801,0.00005460055],"domain_scores_gemma":[0.998582,0.0007193554,0.0001750466,0.0002530295,0.0002180982,0.00005249835],"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":[0.0005945764,0.0002266055,0.004899524,0.0004188438,0.000231251,0.0004233702,0.0007273643,0.02962561,0.3942122,0.01953772,0.001551599,0.5475514],"study_design_scores_gemma":[0.0001045911,0.0004780792,0.02046242,0.00005469135,0.0001606215,0.001240405,0.0001900707,0.5664566,0.3740143,0.02877216,0.007952125,0.0001139302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02847533,0.00005691126,0.9703227,0.00003636624,0.000008722149,0.00008845164,0.00005133375,0.0006112509,0.0003489662],"genre_scores_gemma":[0.1183491,0.00007625682,0.8804271,0.00003088685,0.00001418547,0.0002030054,0.0002097994,0.0003535526,0.000336094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002361447,"threshold_uncertainty_score":0.01248866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4472889309397088,"score_gpt":0.3789497823099054,"score_spread":0.06833914862980334,"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."}}