{"id":"W2905138852","doi":"10.1016/j.bpsc.2018.12.002","title":"Awake Mouse Imaging: From Two-Photon Microscopy to Blood Oxygen Level–Dependent Functional Magnetic Resonance Imaging","year":2018,"lang":"en","type":"article","venue":"Biological Psychiatry Cognitive Neuroscience and Neuroimaging","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Deutsche Forschungsgemeinschaft; International Headache Society","keywords":"Magnetic resonance imaging; Microscopy; Two-photon excitation microscopy; Nuclear magnetic resonance; Magnetic resonance microscopy; Blood-oxygen-level dependent; Functional imaging; Functional magnetic resonance imaging; Oxygen; Materials science; Chemistry; Medicine; Physics; Optics; Radiology; Spin echo; Fluorescence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000673135,0.0007504562,0.0004444621,0.0006144093,0.0003192995,0.001048436,0.001280151,0.001236303,0.001615048],"category_scores_gemma":[0.0006628743,0.0007524933,0.0003092796,0.0004853579,0.0008224781,0.00157009,0.001095362,0.002011936,0.0004547477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000514893,"about_ca_system_score_gemma":0.0003430657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006941428,"about_ca_topic_score_gemma":0.001942999,"domain_scores_codex":[0.9998424,0.00004113684,0.00000593517,0.00004271701,0.00004742775,0.00002029428],"domain_scores_gemma":[0.9996519,0.0001419099,0.0000698705,0.00005194143,0.00003910282,0.00004530706],"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":[0.0001641334,0.00005339135,0.0005021916,0.0002161187,0.00004190999,0.0001738762,0.00006306789,0.0007048946,0.96804,0.004893312,0.001459093,0.02368811],"study_design_scores_gemma":[0.00005521064,0.0004475861,0.006418705,0.0001050464,0.00008188329,0.00140957,0.0001480319,0.02673027,0.9326572,0.014885,0.01694945,0.0001119827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1411433,0.01200852,0.8348069,0.002324105,0.0003046932,0.0001805533,0.000764298,0.002202211,0.006265402],"genre_scores_gemma":[0.5341405,0.01611648,0.437521,0.001787848,0.0002456781,0.0004226489,0.0005332622,0.0006706528,0.008561905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001615048,"threshold_uncertainty_score":0.005402863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04468969350250009,"score_gpt":0.3290343824088302,"score_spread":0.2843446889063301,"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."}}