{"id":"W2607972699","doi":"10.1016/j.neuroimage.2017.04.011","title":"Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voxel; Decoding methods; Univariate; Multivariate statistics; Pattern recognition (psychology); Computer science; Image resolution; Artificial intelligence; Signal-to-noise ratio (imaging); Noise (video); Algorithm; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008566525,0.001264108,0.0007577946,0.0007808972,0.0003109913,0.001078453,0.0007695463,0.0008139419,0.002298754],"category_scores_gemma":[0.003334611,0.0007831918,0.0007381978,0.0008702613,0.0004421835,0.0009036878,0.0006037431,0.0008912868,0.0008990712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007936902,"about_ca_system_score_gemma":0.001757609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003679149,"about_ca_topic_score_gemma":0.006166315,"domain_scores_codex":[0.99975,0.00008022894,0.00001713018,0.00004191916,0.00006800726,0.00004269559],"domain_scores_gemma":[0.9989362,0.0006119313,0.0001290003,0.00007300459,0.0002072127,0.00004267231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005680922,0.0002282968,0.00170611,0.000536311,0.0001906165,0.0001440994,0.0001591338,0.3350317,0.3215842,0.008254158,0.002917593,0.3286797],"study_design_scores_gemma":[0.00003658591,0.0001511081,0.001865469,0.0000219774,0.00007578839,0.000204504,0.00004058797,0.9019102,0.08852556,0.00485715,0.002266097,0.00004494804],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02884763,0.0002650431,0.9687875,0.0002289888,0.00001473163,0.00005906139,0.000141702,0.0007607642,0.0008945624],"genre_scores_gemma":[0.2430611,0.0004732491,0.7536047,0.0001335085,0.00003296596,0.000237202,0.0003840911,0.0006472159,0.001425911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003679149,"threshold_uncertainty_score":0.007690072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517912310695694,"score_gpt":0.306189730629393,"score_spread":0.2810106075224361,"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."}}