{"id":"W4391065381","doi":"10.1101/2024.01.20.576475","title":"Individual connectivity-based parcellations reflect functional properties of human auditory cortex","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; Massachusetts Life Sciences Center; National Institutes of Health; Canadian Institutes of Health Research; Suomen Kulttuurirahasto; Centre d'Imagerie BioMédicale; China Postdoctoral Science Foundation","keywords":"Auditory cortex; Functional magnetic resonance imaging; Human Connectome Project; Resting state fMRI; Human brain; Audiology; Auditory imagery; Cortex (anatomy); Psychology; Temporal cortex; Neuroscience; Computer science; Functional connectivity; Cognition; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005612575,0.0003567374,0.0002784772,0.001343889,0.0002379602,0.000481183,0.0002356803,0.0003141665,0.001262497],"category_scores_gemma":[0.003356417,0.0002082998,0.0003177501,0.0007908526,0.0003974252,0.0007546738,0.0004211597,0.0002604875,0.0001625684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002520401,"about_ca_system_score_gemma":0.0001352311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00167316,"about_ca_topic_score_gemma":0.003958672,"domain_scores_codex":[0.9996777,0.000074811,0.00001971909,0.0001297794,0.00006136976,0.00003672347],"domain_scores_gemma":[0.9989089,0.0006345279,0.000155546,0.0001431518,0.0001223338,0.0000356288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007744396,0.0001044143,0.2176616,0.0005297876,0.001254941,0.0006334853,0.002444052,0.05708637,0.5534589,0.002943005,0.00181124,0.1612976],"study_design_scores_gemma":[0.00001595888,0.0001367916,0.885886,0.00001042461,0.0001701218,0.0007881557,0.0002483206,0.08127312,0.02685842,0.003497754,0.001069037,0.00004604839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9553655,0.0001766386,0.04261749,0.00004213137,0.000005709501,0.00004721385,0.0004690309,0.0001537575,0.001122429],"genre_scores_gemma":[0.9885522,0.00007640511,0.01058808,0.000007329768,0.000008204294,0.00004467803,0.0005105989,0.00003828986,0.0001740883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00167316,"threshold_uncertainty_score":0.004223466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06382103250338472,"score_gpt":0.2577604616758664,"score_spread":0.1939394291724816,"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."}}