{"id":"W2910220482","doi":"10.3389/fnins.2018.01018","title":"Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Stavros Niarchos Foundation; Child Mind Institute; National Institute of Mental Health; Leon Levy Foundation","keywords":"Autism spectrum disorder; Computer science; Machine learning; Artificial intelligence; Support vector machine; Autism; Functional connectivity; Resting state fMRI; Pipeline (software); Graph; Centrality; Pattern recognition (psychology); Psychology; Neuroscience; Psychiatry; Theoretical computer science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00192368,0.001260825,0.0007521631,0.004026763,0.0005578405,0.001401507,0.0006446132,0.001038508,0.001415785],"category_scores_gemma":[0.0133361,0.0003280453,0.0009854612,0.0012713,0.0006036832,0.001933252,0.0008916653,0.001007912,0.0007188558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007021436,"about_ca_system_score_gemma":0.0005503414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003646651,"about_ca_topic_score_gemma":0.006151091,"domain_scores_codex":[0.9992749,0.0003040139,0.00004978962,0.0002010034,0.000104639,0.00006565639],"domain_scores_gemma":[0.9948902,0.003401982,0.0006605353,0.000440772,0.0004476785,0.000158824],"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":[0.001077972,0.0005051726,0.1664515,0.0005476709,0.0009159562,0.0005089514,0.0004852349,0.4059057,0.01527392,0.01474316,0.01175997,0.3818249],"study_design_scores_gemma":[0.00002134127,0.0001135118,0.03060572,0.00006792326,0.0001091368,0.0002977106,0.0000960638,0.9431887,0.002604228,0.02121537,0.001641193,0.00003907438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515897,0.004300117,0.429443,0.002177245,0.0001923975,0.0001542594,0.002661342,0.003181254,0.006300655],"genre_scores_gemma":[0.960964,0.0005959583,0.03515464,0.00009677547,0.0001593702,0.00004971456,0.002106559,0.0001255452,0.0007474817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004026763,"threshold_uncertainty_score":0.0101735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04341641900401118,"score_gpt":0.2370620913772856,"score_spread":0.1936456723732744,"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."}}