[{"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 adults\n\nExclusion Criteria\n\n* Presence of any neurological disorder\n* History of seizures\n* History of autonomic dysfunction\n* Current use of seizure-threshold lowering medications\n* Presence of metal implants\n* History of prior neurosurgical interventions",true,"ALL","18 Years","90 Years",{"count":21,"type":22},10,"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","NOT_YET_RECRUITING","2026-06-16",{"date":41,"type":42},"2026-06-18","ACTUAL",{"date":44,"type":22},"2026-08-01",{"date":46,"type":22},"2027-03-31",{"name":48,"class":49},"Columbia University","OTHER"]