{"id":"W4228997503","doi":"10.1007/s11517-022-02558-4","title":"Modeling the dynamic brain network representation for autism spectrum disorder diagnosis","year":2022,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Dynamic functional connectivity; Computer science; Artificial intelligence; Functional magnetic resonance imaging; Autism spectrum disorder; Graph embedding; Discriminative model; Neuroimaging; Graph; Autism; Embedding; Cluster analysis; Machine learning; Recurrent neural network; Pattern recognition (psychology); Neuroscience; Artificial neural network; Psychology; Theoretical computer science","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"],"consensus_categories":[],"category_scores_codex":[0.00111406,0.0001755915,0.0002308817,0.00004218717,0.001014553,0.00003388881,0.000430959,0.00006688935,0.0001718911],"category_scores_gemma":[0.01649315,0.0001262271,0.0001375447,0.0004727386,0.00008501689,0.00003576023,0.0007194745,0.0005381309,0.000006604303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034552,"about_ca_system_score_gemma":0.00002474135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003275741,"about_ca_topic_score_gemma":0.000007142411,"domain_scores_codex":[0.9979194,0.0002213369,0.0003043818,0.0005837149,0.000460799,0.0005104152],"domain_scores_gemma":[0.9838334,0.01583064,0.00005544258,0.0001883427,0.000008603076,0.00008353887],"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":[0.00001563205,0.00004547822,0.001171361,0.000007626443,0.00001147827,0.000007711718,0.00007303661,0.984043,0.0002556123,0.006751701,0.0005681001,0.007049287],"study_design_scores_gemma":[0.0002476487,0.000139401,0.003145735,0.0000167487,0.000005035247,0.00002567058,0.00005604635,0.9883623,0.00002219009,0.003194211,0.004619999,0.0001650745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4215136,0.0003259182,0.5158159,0.06010978,0.001377975,0.0004795324,0.00001247254,0.000313127,0.00005178298],"genre_scores_gemma":[0.9942906,0.00002052851,0.0006077025,0.004489284,0.0003035096,0.0002387329,0.000008356166,0.00001990068,0.00002136843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.572777,"threshold_uncertainty_score":0.9917914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03329517361715493,"score_gpt":0.278181208496327,"score_spread":0.2448860348791721,"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."}}