{"id":"W4308459649","doi":"10.1016/j.media.2022.102681","title":"From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Beijing Municipality; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Context (archaeology); Artificial intelligence; Deep learning; Graph; Pattern recognition (psychology); Machine learning; Theoretical computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001748218,0.0002934665,0.0006206139,0.001652158,0.001562316,0.000205138,0.0006410555,0.000131546,0.008546567],"category_scores_gemma":[0.129821,0.0003044457,0.0005942583,0.009897556,0.0002411254,0.0002330513,0.0008622283,0.001070953,0.0001371795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159601,"about_ca_system_score_gemma":0.0001221194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002458054,"about_ca_topic_score_gemma":0.00003901504,"domain_scores_codex":[0.9945841,0.000558562,0.0005358803,0.001347392,0.002324948,0.0006490691],"domain_scores_gemma":[0.9734105,0.02534505,0.000202406,0.0004609326,0.0001794778,0.0004016063],"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":[0.003224794,0.003915168,0.1860453,0.0002349456,0.01234248,0.002248748,0.01098643,0.3126945,0.09895599,0.01033115,0.237024,0.1219965],"study_design_scores_gemma":[0.004268758,0.001046152,0.1325088,0.00005220854,0.001779342,0.00005827393,0.006006425,0.5230739,0.006150336,0.0331821,0.2894471,0.002426656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2224317,0.00009299513,0.7351083,0.04090319,0.0005612694,0.0002847183,0.0001293294,0.0001954646,0.0002929519],"genre_scores_gemma":[0.9712492,0.000008069305,0.005664922,0.02037311,0.0006710479,0.0004634893,0.0001339234,0.00004379867,0.001392475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7488174,"threshold_uncertainty_score":0.9999408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03119687508531557,"score_gpt":0.3144274947720919,"score_spread":0.2832306196867763,"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."}}