{"id":"W2952702384","doi":"10.1038/s41598-018-37300-4","title":"MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Network analysis; Artificial intelligence; Computational biology; Pattern recognition (psychology); Biology","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":[0.002443095,0.0008683719,0.0008108747,0.006637182,0.0007700245,0.001377439,0.001126311,0.0007388628,0.004986035],"category_scores_gemma":[0.01487054,0.0002939,0.001018371,0.002600177,0.0007070542,0.002006388,0.00175529,0.0008695878,0.0007937964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005672559,"about_ca_system_score_gemma":0.001020383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002771547,"about_ca_topic_score_gemma":0.003508736,"domain_scores_codex":[0.9986791,0.0004950818,0.00007953222,0.0003577328,0.0003034716,0.0000850666],"domain_scores_gemma":[0.995958,0.002288119,0.000561103,0.0005710097,0.0004770441,0.0001446856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001280955,0.0003268378,0.02199656,0.001003798,0.001394907,0.0006721501,0.0008804181,0.1997176,0.02858022,0.05052341,0.01777474,0.6758485],"study_design_scores_gemma":[0.00007029469,0.0001322344,0.00640321,0.00003723012,0.0001227618,0.0003840279,0.0001650235,0.9290394,0.005590382,0.05281937,0.005188101,0.00004801258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.045625,0.0002959255,0.9482229,0.0002181985,0.0000505403,0.0001554459,0.001272966,0.003101947,0.001057069],"genre_scores_gemma":[0.5172499,0.0003757094,0.4744412,0.00009653569,0.0001043485,0.0006384929,0.004952413,0.0006736906,0.001467737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006637182,"threshold_uncertainty_score":0.01668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375428493050977,"score_gpt":0.2891808249002385,"score_spread":0.2554265399697287,"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."}}