[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"modeling-of-recruitment-curves\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:modeling-of-recruitment-curves":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":29,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":5},"100636106","adaptive-recruitment-curve-analysis-using-bayesian-modeling-100636106",false,"NCT07561372","Adaptive Recruitment Curve Analysis Using Bayesian Modeling","Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling","Inclusion Criteria:\n\n* Healthy adult volunteers aged 18 years and older.\n* Able to understand study procedures and provide written informed consent.\n\nExclusion Criteria:\n\n* 1\\. History of adverse reaction to Transcranial Magnetic Stimulation (TMS) or non-invasive neurostimulation.\n* 2\\. History of seizures, epilepsy, or family history of epilepsy.\n* 3\\. History of stroke, brain injury, or illness causing brain injury.\n* 4\\. History of head injury or neurosurgery.\n* 5\\. History of neurological diseases, or central nervous system lesions.\n* 6\\. Presence of metallic implants or foreign bodies in the head (outside of dental work\u002Ffillings).\n* 7\\. Presence of implanted electronic or medical devices (e.g., cardiac pacemakers, medical pumps, implanted stimulators).\n* 8\\. Current pregnancy or possibility of pregnancy.\n* 9\\. Currently taking medications that alter cortical excitability or lower seizure threshold.",true,"ALL","18 Years","90 Years",{"count":21,"type":22},14,"ESTIMATED","INTERVENTIONAL",[25],"NA","The purpose of this study is to better understand how electrical or magnetic stimulation affect the nervous system by optimizing the way researchers measure muscle responses. The relationship between stimulation intensity and muscle response is described by \"neural recruitment curves,\" which are critical for monitoring the state of the nervous system during therapies like transcranial magnetic stimulation (TMS) and spinal cord stimulation (SCS).\n\nThis study tests a new, real-time computational approach based on our previously developed methods (Hierarchical Bayesian models) to estimate these recruitment curves more efficiently. The primary goal is to use this model to dynamically guide the experiment, automatically selecting the optimal stimulation intensities to test.\n\nThe investigators hypothesize that this optimized approach will accurately estimate the entire recruitment curve, or specific targets components of it like the motor threshold, using significantly fewer samples than standard methods. By reducing the number of measurements required, this approach aims to decrease experimental time and minimize participant burden, making future TMS and SCS therapies and experiments more feasible and efficient.",[28],"Modeling of Recruitment Curves",[30,31,32,33,34,35,36,37],"TMS","SCS","SCI","Hierarchical Bayesian","Motor threshold","Transcranial Magnetic Stimulation","Spinal cord stimulation","Evoked Potentials","RECRUITING","2026-08-12",{"date":41,"type":42},"2026-08-14","ACTUAL",{"date":44,"type":22},"2026-09-01",{"date":46,"type":22},"2027-03-31",{"name":48,"class":49},"Columbia University","OTHER"]