{"id":"W4405517989","doi":"10.1162/imag_a_00434","title":"Evaluating permutation-based inference for partial least squares analysis of neuroimaging data","year":2024,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University; Douglas Mental Health University Institute","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Neuroimaging; Permutation (music); Partial least squares regression; Inference; Computer science; Resampling; Artificial intelligence; Mathematics; Algorithm; Psychology; Machine learning; Philosophy; Neuroscience","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001007902,0.0002117019,0.0002831705,0.0006863286,0.0004575151,0.0004128538,0.0009719809,0.00001562489,0.00001570796],"category_scores_gemma":[0.04615105,0.0002052734,0.000145498,0.00309052,0.0006105175,0.001093215,0.0003576168,0.0001727103,0.000005855268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003967794,"about_ca_system_score_gemma":0.0003313566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004469477,"about_ca_topic_score_gemma":0.00001039564,"domain_scores_codex":[0.9966075,0.0002006673,0.0003966816,0.001572512,0.0008027977,0.0004198021],"domain_scores_gemma":[0.9858398,0.01297186,0.000145361,0.0008214562,0.0001500757,0.00007148251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003136718,0.00006982779,0.01063228,0.000111062,0.00001460421,0.00002309907,0.0002422649,0.09288786,0.8798249,0.001712468,0.0004024659,0.01404784],"study_design_scores_gemma":[0.0001401253,0.00006328526,0.008917362,0.00004372885,0.0002355889,0.000005612434,0.00003118224,0.9570202,0.03181825,0.0002341664,0.001306412,0.0001841215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.494466,0.000355477,0.4777967,0.01979089,0.004011963,0.001014132,0.001400567,0.0007953889,0.0003688859],"genre_scores_gemma":[0.9958175,0.0000043558,0.000949415,0.00302819,0.0000704237,0.00004826689,0.00001640341,0.00002415982,0.00004125586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8641323,"threshold_uncertainty_score":0.9618836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2269568400108148,"score_gpt":0.4457584407988839,"score_spread":0.2188016007880692,"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."}}