{"id":"W4377564881","doi":"10.1523/jneurosci.1014-23.2023","title":"Neuroscience Needs Network Science","year":2023,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Institute of General Medical Sciences; National Institute for Health and Care Research; National Institute of Mental Health; National Science Foundation; Royal Society; Government of Canada; Kavli Foundation; National Institutes of Health; Wellcome Trust; National Institute of Biomedical Imaging and Bioengineering; BRAIN Initiative","keywords":"Network science; Connectome; Neuroscience; Function (biology); Network dynamics; Computational neuroscience; Data science; Brain function; Connectomics; Neuroinformatics; Computer science; Cognitive science; Complex network; Psychology; Functional connectivity; Biology; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002994189,0.0002470362,0.000339225,0.001337418,0.001555517,0.0003838927,0.002342522,0.00004049512,0.000008028565],"category_scores_gemma":[0.0467594,0.0002046672,0.0001683831,0.01646997,0.002940892,0.002195924,0.0007386467,0.000516421,0.00008823046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079646,"about_ca_system_score_gemma":0.0006453912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001756316,"about_ca_topic_score_gemma":7.026634e-7,"domain_scores_codex":[0.9948053,0.0001756595,0.0006189899,0.0007094737,0.002593592,0.001096974],"domain_scores_gemma":[0.9956915,0.002526472,0.0005803868,0.0004829878,0.0003431999,0.0003754442],"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":[0.00002964488,0.00005082432,0.001622484,0.000005754067,3.842519e-7,0.0002259034,0.0001600016,0.009389626,0.9722617,0.004324689,0.0108796,0.001049331],"study_design_scores_gemma":[0.001442713,0.002934851,0.4047596,0.0001679736,0.00003732935,0.005345047,0.0004371962,0.03276944,0.2630593,0.01287384,0.2749263,0.001246386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688251,0.00004655807,0.00109839,0.01258539,0.01323069,0.0002291387,0.000006525683,0.0002575389,0.003720678],"genre_scores_gemma":[0.9852965,0.0001651304,0.0001217983,0.01310619,0.0004873789,0.00000440726,3.30769e-8,0.00002157768,0.0007969294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7092024,"threshold_uncertainty_score":0.9997725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08165825186591226,"score_gpt":0.3092752546699081,"score_spread":0.2276170028039958,"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."}}