{"id":"W4366728298","doi":"10.1101/2023.04.21.537808","title":"Neuroticism and emotion regulation: An effective connectivity analysis of large-scale resting-state brain networks","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"NIH Blueprint for Neuroscience Research; Canadian Institute for Advanced Research; Medical Research Council; McDonnell Center for Systems Neuroscience; National Institutes of Health; National Health and Medical Research Council","keywords":"Neuroticism; Default mode network; Psychology; Anger; Sadness; Resting state fMRI; Anterior cingulate cortex; Posterior cingulate; Dorsolateral prefrontal cortex; Cognitive psychology; Emotion classification; Functional magnetic resonance imaging; Clinical psychology; Prefrontal cortex; Personality; Neuroscience; Cognition; Social psychology","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":[0.0006945825,0.00024735,0.0002369267,0.0009262541,0.0002140557,0.0003844174,0.0002486531,0.0002201281,0.0009476236],"category_scores_gemma":[0.00269102,0.0001158917,0.0004110571,0.0005815175,0.000210131,0.0003183833,0.0003427508,0.0002383691,0.00009126944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001661227,"about_ca_system_score_gemma":0.0001605634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002155016,"about_ca_topic_score_gemma":0.00368055,"domain_scores_codex":[0.9998251,0.00007260491,0.00000906733,0.00005694375,0.00001545904,0.00002089901],"domain_scores_gemma":[0.9993888,0.000355705,0.0001015343,0.00007002668,0.00004674847,0.00003729061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001593058,0.0004088322,0.6761169,0.0006178449,0.002456241,0.001721478,0.00148805,0.07103235,0.06593685,0.008922649,0.005757643,0.1639482],"study_design_scores_gemma":[0.00002974546,0.0001931021,0.7717963,0.00006124678,0.000367254,0.0009671897,0.000244907,0.2087166,0.002391018,0.01378401,0.001408171,0.00004045047],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650642,0.0005484636,0.0321831,0.0001634387,0.00001323011,0.00003301743,0.001210569,0.0000826074,0.0007013996],"genre_scores_gemma":[0.9954566,0.0001135734,0.003468793,0.00001336062,0.00001345039,0.00003348618,0.0007475652,0.000008138904,0.0001451255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002155016,"threshold_uncertainty_score":0.004284918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02441886802967297,"score_gpt":0.255088541285854,"score_spread":0.230669673256181,"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."}}