{"id":"W4407935063","doi":"10.1016/j.brs.2024.12.148","title":"Enhancing precision in language mapping: Integrating MEG-informed TMS with fMRI for improved non-invasive identification of language-critical brain areas","year":2025,"lang":"en","type":"article","venue":"Brain stimulation","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Identification (biology); Psychology; Magnetoencephalography; Brain mapping; Neuroscience; Computer science; Electroencephalography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006335633,0.0001783567,0.0002724181,0.0003649747,0.0001122477,0.00005036284,0.0002032079,0.0001301616,0.000009558811],"category_scores_gemma":[0.01922683,0.0001498975,0.0000706573,0.0005217562,0.0001161409,0.0002694341,0.0000542683,0.0001681014,0.000002582809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006928826,"about_ca_system_score_gemma":0.0001643952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000705672,"about_ca_topic_score_gemma":0.0004290107,"domain_scores_codex":[0.9983421,0.0001405131,0.0006254008,0.0004707679,0.0001460372,0.000275204],"domain_scores_gemma":[0.9932967,0.006023816,0.0002354536,0.0002895789,0.0001166116,0.00003785952],"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":[0.0001885071,0.0000582281,0.0002541635,0.0001617147,0.000004692063,0.00001665151,0.008093654,0.0001849063,0.9835333,0.0002969138,0.00007073001,0.007136509],"study_design_scores_gemma":[0.001465733,0.0001785464,0.003556479,0.0004431396,0.0000152736,0.00001463421,0.004177661,0.02078999,0.9687071,0.0004341216,0.0000335529,0.0001837843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330801,0.00003243903,0.06395167,0.001411933,0.0001720474,0.001030285,0.00003217708,0.00006173743,0.0002275583],"genre_scores_gemma":[0.9955273,0.000001348978,0.002690929,0.001025338,0.00005845133,0.00006843537,0.00007358902,0.00001668491,0.0005379441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06244713,"threshold_uncertainty_score":0.9890347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745834560256852,"score_gpt":0.3329963698057573,"score_spread":0.3155380242031888,"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."}}