{"id":"W4415459694","doi":"10.1016/j.neubiorev.2025.106437","title":"Neural alterations in substance use disorders: A meta-analysis using activation network mapping across and within task domains","year":2025,"lang":"en","type":"article","venue":"Neuroscience & Biobehavioral Reviews","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"Canadian Institutes of Health Research; Eli Lilly Canada","keywords":"Task (project management); Underpinning; Substance use; Brain mapping; Artificial neural network; Nerve net; Neural activity; Functional connectivity","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":[0.02039364,0.00218807,0.005494819,0.00647328,0.0008022024,0.002704675,0.001799445,0.001339714,0.002038972],"category_scores_gemma":[0.02986393,0.000969189,0.03868558,0.006446337,0.0006473464,0.001460194,0.001765971,0.001641376,0.0001803089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189334,"about_ca_system_score_gemma":0.00152581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005335481,"about_ca_topic_score_gemma":0.008264892,"domain_scores_codex":[0.9874356,0.008266726,0.00135838,0.001989165,0.0006839873,0.0002660803],"domain_scores_gemma":[0.9713577,0.02311881,0.002335411,0.001948835,0.0009550726,0.0002841381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.001521372,0.00003101153,0.05569767,0.01380413,0.9150175,0.0001920676,0.0001331897,0.002343855,0.0008929081,0.0003248405,0.0003725688,0.009668991],"study_design_scores_gemma":[0.0003078965,0.0002521002,0.03833531,0.001663026,0.9541247,0.000169187,0.00009353791,0.002445935,0.0003889506,0.001033815,0.001155208,0.00003028798],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2145209,0.7414918,0.03564822,0.00159647,0.0004895953,0.0005416987,0.003817021,0.0004048274,0.001489425],"genre_scores_gemma":[0.9424418,0.04360875,0.01118494,0.0003446582,0.000140492,0.0005762728,0.001393288,0.0001065627,0.0002031194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02039364,"threshold_uncertainty_score":0.1078532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2873586250354503,"score_gpt":0.3969184911611842,"score_spread":0.1095598661257338,"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."}}