{"id":"W3122054437","doi":"10.1093/gigascience/giaa155","title":"Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses","year":2021,"lang":"en","type":"article","venue":"GigaScience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health; Université de Montréal; Douglas Mental Health University Institute; McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institutes of Health","keywords":"Computer science; Neuroimaging; Preprocessor; Pipeline (software); Artificial intelligence; Human Connectome Project; Software; Machine learning; Replicate; Functional magnetic resonance imaging; Univariate; Task (project management); Data mining; Pattern recognition (psychology); Multivariate statistics; Psychology; Statistics; Functional connectivity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03359483,0.001623689,0.0008503773,0.001745561,0.001555572,0.003814644,0.002095433,0.001157114,0.002651324],"category_scores_gemma":[0.1720698,0.0008818271,0.001983204,0.002346167,0.002013358,0.003132213,0.002778357,0.00216258,0.001197415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507932,"about_ca_system_score_gemma":0.00261143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004448929,"about_ca_topic_score_gemma":0.004805842,"domain_scores_codex":[0.9860653,0.005929572,0.001528431,0.003170331,0.002840592,0.0004657882],"domain_scores_gemma":[0.8538244,0.1110682,0.008340074,0.01649573,0.009378172,0.0008934772],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003636509,0.0004976768,0.1366777,0.002958846,0.00308893,0.0005056809,0.003616995,0.09169742,0.1475832,0.01844451,0.01588865,0.5754039],"study_design_scores_gemma":[0.0005437175,0.00163323,0.2911801,0.0009457499,0.002246004,0.001743224,0.0009075194,0.3226242,0.2316267,0.1095274,0.03643016,0.0005920175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3069745,0.003000308,0.6745669,0.002509729,0.0003322878,0.0004414264,0.002347036,0.006881191,0.002946554],"genre_scores_gemma":[0.6218607,0.001070247,0.3624628,0.000911889,0.0001500856,0.001203574,0.005803585,0.005657998,0.0008791524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9664052,"threshold_uncertainty_score":0.1776686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3705436409879074,"score_gpt":0.4177528521224563,"score_spread":0.0472092111345489,"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."}}