{"id":"W4385791367","doi":"10.1101/2023.08.08.552504","title":"Investigating the impact of motion in the scanner on brain age predictions","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"NIH Blueprint for Neuroscience Research; Natural Sciences and Engineering Research Council of Canada; Medical Research Council; Directorate for Biological Sciences; National Institutes of Health; Biotechnology and Biological Sciences Research Council; Fonds de Recherche du Québec - Santé; Compute Canada","keywords":"Motion (physics); Brain aging; Voxel; Scanner; Magnetic resonance imaging; Neuroimaging; Psychology; Functional magnetic resonance imaging; Computer science; Artificial intelligence; Medicine; Neuroscience; Cognition; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003158501,0.0008570245,0.0003877623,0.0007653419,0.0001898452,0.0006883879,0.0007265228,0.0007723899,0.001639031],"category_scores_gemma":[0.008960738,0.000360982,0.0006352503,0.0003394299,0.0002799181,0.0005878522,0.0006043458,0.0005662096,0.00070427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666654,"about_ca_system_score_gemma":0.0005229606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00865633,"about_ca_topic_score_gemma":0.00837709,"domain_scores_codex":[0.9992994,0.0002520947,0.00005135276,0.0002672145,0.00007072873,0.00005921391],"domain_scores_gemma":[0.9964977,0.002486408,0.0003130272,0.0003146326,0.0002981937,0.00009013807],"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":[0.003120433,0.0003955425,0.3154038,0.0007027509,0.001408769,0.0004505414,0.0004362631,0.4052148,0.02969325,0.001372568,0.006421587,0.2353797],"study_design_scores_gemma":[0.00004766139,0.000505218,0.08781254,0.0001030924,0.0002860166,0.0004060576,0.00009488412,0.8880559,0.01782816,0.002054655,0.002759011,0.00004683404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343191,0.001592022,0.0577812,0.0003652455,0.0001051343,0.0001146156,0.003137735,0.001309844,0.001275214],"genre_scores_gemma":[0.9699699,0.0002422602,0.02328761,0.00009132762,0.00003994133,0.00005279995,0.005413765,0.0001813348,0.0007211032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00865633,"threshold_uncertainty_score":0.01721185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0590630355787814,"score_gpt":0.2785515482052974,"score_spread":0.219488512626516,"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."}}