{"id":"W4407934492","doi":"10.1016/j.brs.2024.12.956","title":"Personalized Closed-Loop rTMS Using Reinforcement Learning to Optimize Phase-Targeted Stimulation of the SMA-M1 Network","year":2025,"lang":"en","type":"article","venue":"Brain stimulation","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"SMA*; Transcranial magnetic stimulation; Reinforcement learning; Stimulation; Closed loop; Neuroscience; Reinforcement; Phase (matter); Psychology; Computer science; Artificial intelligence; Physics; Control engineering; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002267853,0.0001634111,0.0002096846,0.0001722459,0.0002505093,0.0000271213,0.0000981787,0.00007089901,0.00005237718],"category_scores_gemma":[0.0001786476,0.0001494545,0.0001088941,0.001024312,0.00003521386,0.0001173436,0.00003884922,0.000139804,8.288397e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001139583,"about_ca_system_score_gemma":0.00002740663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002217643,"about_ca_topic_score_gemma":0.000003527667,"domain_scores_codex":[0.9989347,0.00007369638,0.0003631347,0.0001641041,0.0002224472,0.0002419111],"domain_scores_gemma":[0.9993778,0.0002118723,0.00009406939,0.0001735728,0.0001113195,0.00003138006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007804397,0.00001469718,0.002113293,0.0000369701,0.0001027132,8.862268e-8,0.0004482127,0.9660646,0.01894544,0.0005368993,0.001126074,0.010533],"study_design_scores_gemma":[0.001525125,0.00004693999,0.02790586,0.0001118748,0.0000440542,1.846921e-7,0.00007628506,0.9659224,0.001919375,0.0001420928,0.002158271,0.0001475798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5975183,0.0001829195,0.3994716,0.0002329595,0.0003290633,0.0006397243,0.000001626468,0.0001852106,0.001438531],"genre_scores_gemma":[0.9965286,0.000006483387,0.002737693,0.0002421573,0.00007498715,0.00001970531,0.00002619454,0.00001972122,0.0003444204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3990103,"threshold_uncertainty_score":0.6094579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848426872464386,"score_gpt":0.2767495964926002,"score_spread":0.2582653277679564,"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."}}