{"id":"W3131390801","doi":"10.1101/2021.02.19.432024","title":"Structural connectome fingerprinting and age prediction in pediatric development: assessing voxel- and surface-based white matter connectivity","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; McGill University; Montreal Neurological Institute and Hospital","funders":"Canada First Research Excellence Fund; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Connectome; Voxel; Connectomics; Computer science; Tractography; White matter; Human Connectome Project; Artificial intelligence; Pattern recognition (psychology); Representation (politics); Functional connectivity; Neuroscience; Psychology; Magnetic resonance imaging; Medicine","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":[0.003475985,0.0006588356,0.0003938997,0.001725829,0.0003151342,0.001155378,0.0005996659,0.0005682757,0.002349701],"category_scores_gemma":[0.01301713,0.0002715701,0.0004951141,0.0009922585,0.0006814373,0.0008501565,0.0009067174,0.0009561285,0.0005483007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568029,"about_ca_system_score_gemma":0.0006195924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002819899,"about_ca_topic_score_gemma":0.005307626,"domain_scores_codex":[0.9988625,0.0003375703,0.00007268001,0.0004152645,0.0002414674,0.00007054547],"domain_scores_gemma":[0.9933689,0.002580494,0.001344473,0.001253734,0.001160953,0.0002913059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006559119,0.0001407077,0.8002678,0.0003651556,0.000798785,0.0004131422,0.001231389,0.009415317,0.04819757,0.002708704,0.002210844,0.1335946],"study_design_scores_gemma":[0.0000260516,0.0004887687,0.9195422,0.0001381151,0.0002378181,0.001691364,0.0004511238,0.02988377,0.03955093,0.004816926,0.003106279,0.00006677846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167573,0.0005751078,0.07734934,0.0001911302,0.00005457245,0.00006444929,0.00260908,0.000476897,0.001922165],"genre_scores_gemma":[0.9562411,0.0002176968,0.04151894,0.00004099521,0.00001807748,0.00009673698,0.001033917,0.000178287,0.0006542123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003475985,"threshold_uncertainty_score":0.01838297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163175354442627,"score_gpt":0.2729594350340803,"score_spread":0.241327681489654,"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."}}