{"id":"W4205572065","doi":"10.1002/brb3.2491","title":"Modulating intrinsic functional connectivity with visual cortex using low‐frequency repetitive transcranial magnetic stimulation","year":2022,"lang":"en","type":"article","venue":"Brain and Behavior","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Neuroscience; Transcranial magnetic stimulation; Functional magnetic resonance imaging; Default mode network; Visual cortex; Resting state fMRI; Glutamate receptor; Inhibitory postsynaptic potential; Stimulation; Psychology; Medicine; Internal medicine; Receptor","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":[],"consensus_categories":[],"category_scores_codex":[0.0001607734,0.0002064067,0.0002178491,0.0001364889,0.0008753927,0.00005287151,0.00007082237,0.00003958285,0.0008849557],"category_scores_gemma":[0.0001201908,0.0002059004,0.00005227192,0.000420247,0.0002235378,0.0001790436,0.00005003425,0.0002333384,0.000003209563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000791793,"about_ca_system_score_gemma":0.00006758344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001086002,"about_ca_topic_score_gemma":0.00002874281,"domain_scores_codex":[0.9981379,0.0002331069,0.0002701907,0.0005825364,0.0005137464,0.0002625193],"domain_scores_gemma":[0.9991875,0.0004567583,0.00009832002,0.0001217153,0.00005955794,0.00007611681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004007684,0.0004578412,0.03257843,0.00001926981,0.000004516478,0.00008679803,0.0006991815,0.003130861,0.949325,0.0007870169,0.000005448879,0.01250485],"study_design_scores_gemma":[0.00302303,0.001066078,0.9820457,0.00001585358,0.0001249817,0.000205374,0.0002509262,0.01186325,0.0009201806,0.0001431401,0.00002072455,0.0003208339],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954986,0.00003644147,0.002993739,0.000159164,0.0002537575,0.0006948538,0.00006511848,0.0001110247,0.0001872972],"genre_scores_gemma":[0.9988897,0.000001365879,0.000496576,0.0002779102,0.00008619227,0.0001109486,0.000008203336,0.00002878078,0.0001003225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9494672,"threshold_uncertainty_score":0.9689644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03652567643809907,"score_gpt":0.2783147126855723,"score_spread":0.2417890362474733,"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."}}