{"id":"W2952939467","doi":"10.1016/j.neuroimage.2019.06.020","title":"Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain","year":2019,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust","keywords":"Dimensionality reduction; Diffusion MRI; Diffusion; Diffusion map; Reduction (mathematics); Human brain; Multifactor dimensionality reduction; Curse of dimensionality; Computer science; Artificial intelligence; Pattern recognition (psychology); Neuroscience; Chemistry; Psychology; Mathematics; Magnetic resonance imaging; Medicine; Physics; Nonlinear dimensionality reduction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001431448,0.00008257003,0.0001909626,0.00005273839,0.00005837776,0.000002567269,0.0001066967,0.000036667,0.00001424904],"category_scores_gemma":[0.0001237746,0.00005853474,0.000136093,0.0001834182,0.00008901527,0.00004356327,0.00005131266,0.0001371212,6.955524e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001321446,"about_ca_system_score_gemma":0.00002098447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001093901,"about_ca_topic_score_gemma":3.072709e-7,"domain_scores_codex":[0.9992672,0.00002867187,0.0002408346,0.0002167467,0.0001495096,0.00009705337],"domain_scores_gemma":[0.9990306,0.00008317331,0.000189037,0.0005421109,0.0001265662,0.00002844726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004505521,0.0001618065,0.002380397,0.00007528072,0.000003990598,9.50658e-8,0.00001458078,0.000001985015,0.9942217,0.0008664938,0.000962874,0.001265781],"study_design_scores_gemma":[0.0009472201,0.0002935696,0.2543808,0.00006759406,0.00004734706,0.00002092858,0.00001724646,0.0003313457,0.7328081,0.002660756,0.008345671,0.00007945486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915621,0.00001813209,0.002705596,0.003967653,0.00008173097,0.001102314,0.00003366312,0.0000537273,0.0004751052],"genre_scores_gemma":[0.9959686,0.000007734353,0.003033543,0.0002366555,0.00003150249,0.00002192867,0.00001084981,0.00001960702,0.0006695317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2614136,"threshold_uncertainty_score":0.2386978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0538105318690795,"score_gpt":0.3601763245149703,"score_spread":0.3063657926458908,"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."}}