{"id":"W4391917577","doi":"10.1101/2024.02.16.580448","title":"Longer scans boost prediction and cut costs in brain-wide association studies","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"Medical Research Council; McDonnell Center for Systems Neuroscience; National Institutes of Health; National Medical Research Council; National Research Foundation; Temasek Foundation; National Research Foundation Singapore; Alzheimer's Disease Neuroimaging Initiative; National Supercomputing Centre Singapore; National Institute of Mental Health; Pfizer; Novartis Pharmaceuticals Corporation; Servier","keywords":"Sample size determination; Neuroimaging; Duration (music); Replicate; Logarithm; Sample (material); Statistical power; Statistics; Context (archaeology); Econometrics; Magnetic resonance imaging; Functional magnetic resonance imaging; Scale (ratio); Computer science; Psychology; Mathematics; Medicine; Neuroscience; Biology","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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001269501,0.0005598455,0.0006464035,0.0005479995,0.0002638054,0.0003008395,0.0002614991,0.0004483017,0.000008810685],"category_scores_gemma":[0.05871288,0.0006050302,0.0001131664,0.0008120719,0.000200809,0.0002481452,0.001254987,0.001244898,0.00009041958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002458064,"about_ca_system_score_gemma":0.0003429473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006788012,"about_ca_topic_score_gemma":0.0001102294,"domain_scores_codex":[0.9961209,0.0003626223,0.0005530357,0.001692151,0.0006991241,0.0005722117],"domain_scores_gemma":[0.9870988,0.01146415,0.0003509192,0.0005956314,0.0003558396,0.0001347023],"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":[0.0001379471,0.000365795,0.2864901,0.00206045,0.0007342366,0.0003764305,0.0004067845,0.0002817351,0.5653602,0.003078799,0.1406957,0.00001189861],"study_design_scores_gemma":[0.001672912,0.0002511328,0.6255732,0.00323477,0.0003961248,1.359616e-7,0.00009209067,0.001918212,0.336227,0.0002440535,0.02820383,0.002186544],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650427,0.003556128,0.00003004979,0.02448844,0.004503936,0.0010304,0.0006508374,0.0006422649,0.00005526839],"genre_scores_gemma":[0.9922307,0.001185625,0.00009662167,0.005358426,0.0005182067,0.0004087288,1.877818e-7,0.0001087384,0.00009281744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3390831,"threshold_uncertainty_score":0.9996401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256812142125504,"score_gpt":0.2524695484207842,"score_spread":0.2267883342082338,"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."}}