{"id":"W3199008037","doi":"10.1016/j.media.2021.102233","title":"BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":708,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Computer science; Functional magnetic resonance imaging; Artificial intelligence; Pooling; Connectome; Neuroimaging; Pattern recognition (psychology); Graph; Human Connectome Project; Machine learning; Psychology; Neuroscience; Functional connectivity; 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,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02012230046150534,"score_gpt":0.2959105910532257,"score_spread":0.2757882905917204,"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."},"direct_labels":[],"classifier":{"requested":true,"available":true,"version":"metacan-v1-d91a1de5be90","frame_rows_covered":4299418,"decision_targets":["metaresearch","metaepi_narrow","metaepi_broad","bibliometrics","sts","scholarly_communication","open_science","research_integrity","randomized_trial","nonrandomized_trial","observational","systematic_review","meta_analysis","case_report","qualitative","simulation_or_modeling","bench_or_experimental","theoretical_or_conceptual","not_applicable"],"score_encoding":"uint16_le_65535","score_resolution":0.000015259021896696422,"interpretation":"Scores imitate each teacher on the enriched screening sample; they are not calibrated prevalence probabilities for the full frame.","warning":null},"prediction":{"classifier_version":"metacan-v1-d91a1de5be90","candidate_union":["observational","bench_or_experimental","not_applicable"],"consensus_intersection":["bench_or_experimental","not_applicable"],"score_encoding":"uint16_le_65535","score_resolution":0.000015259021896696422,"scores":{"codex":{"metaresearch":0.00045777065690089265,"metaepi_narrow":0.000045777065690089265,"metaepi_broad":0.00018310826276035706,"bibliometrics":0.026413366903181506,"sts":0.00016784924086366064,"scholarly_communication":0.00009155413138017853,"open_science":0.00018310826276035706,"research_integrity":0.0001373311970702678,"randomized_trial":0.000015259021896696422,"nonrandomized_trial":0.000030518043793392844,"observational":0.028076600289921417,"systematic_review":0.000030518043793392844,"meta_analysis":0.003723201342793927,"case_report":0.00030518043793392844,"qualitative":0.00012207217517357137,"simulation_or_modeling":0.011718928816662852,"bench_or_experimental":0.07866025787747005,"theoretical_or_conceptual":0.00025940337224383917,"not_applicable":0.2639658197909514,"design_other":0.08070496681162738},"gemma":{"metaresearch":0.00648508430609598,"metaepi_narrow":0.000015259021896696422,"metaepi_broad":0.00016784924086366064,"bibliometrics":0.008285648889906157,"sts":0.00022888532845044633,"scholarly_communication":0.00012207217517357137,"open_science":0.000015259021896696422,"research_integrity":0.0006408789196612497,"randomized_trial":0.000015259021896696422,"nonrandomized_trial":0.0005493247882810712,"observational":0.005432211795223926,"systematic_review":0.00006103608758678569,"meta_analysis":0.0019226367589837492,"case_report":0.00018310826276035706,"qualitative":0.00012207217517357137,"simulation_or_modeling":0.009201190203707943,"bench_or_experimental":0.11032272831311513,"theoretical_or_conceptual":0.0018310826276035706,"not_applicable":0.3930266269932097}},"interpretation":"Scores imitate each teacher on the enriched screening sample; they are not calibrated prevalence probabilities for the full frame."}}