[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100636106":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":22,"studyPopulation":13,"armGroups":23,"interventions":35,"overallOfficials":61,"centralContacts":65,"locations":70,"responsibleParty":87,"collaborators":90,"id":94,"slug":13,"hasResults":95,"nctId":96,"briefTitle":97,"officialTitle":98,"acronym":13,"eligibilityCriteria":99,"healthyVolunteers":100,"sex":101,"minAge":102,"maxAge":103,"enrollmentInfo":104,"targetDuration":13,"studyType":107,"phases":108,"briefSummary":109,"conditions":110,"keywords":112,"overallStatus":121,"whyStopped":13,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":131},{"fullName":5,"class":6},"Columbia University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"Mean absolute threshold error","The threshold error of the methods under comparison, with the ground truth computed from recruitment curves fitted subsequent to sampling using aggregated data.","Through completion of the study visit, an average of 1 hour.",null,{"allocation":15,"interventionModel":16,"interventionModelDescription":17,"primaryPurpose":18,"observationalModel":13,"timePerspective":13,"maskingInfo":19},"NA","SINGLE_GROUP","This is a single-group, within-subject methodological study designed to compare different TMS sampling algorithms. All participants undergo multiple experiments in a single sessions. A single experiment will compare multiple sampling algorithms. Specifically, the neurostimulation pulses dictated by each active algorithm are interleaved in a randomized sequence. This interleaved design ensures that any time-dependent physiological variables impact the threshold and recruitment curve estimations of all tested algorithms equally.","BASIC_SCIENCE",{"masking":20,"maskingDescription":21,"whoMasked":13},"NONE","Participants are functionally masked to the specific interventions, as the stimulation parameters generated by the different algorithms are randomly interleaved pulse-by-pulse.","Transcranial magnetic stimulation and other types of neurostimulation play a crucial role in advancing the understanding and manipulation of neural activity for both research and therapeutic purposes. The proposed approach to sampling recruitment curves in real-time promises to significantly improve the efficiency and precision of experiments that use electrical or electromagnetic stimulation techniques, reducing the experimental burden for participants as well as experimenters. By enhancing experimental efficiency in multiple experimental settings and techniques, this research directly contributes to accelerating the translation of scientific discoveries into clinical applications. This study will benchmark the relative performance of different methods against each other by testing existing and proposed algorithms using neurostimulation in people, and comparing the resultant estimates in recruitment curve parameters.",[24],{"label":25,"type":26,"description":27,"interventionNames":28},"Test of developed methods","EXPERIMENTAL","Participants undergo distinct experiments within a single session to compare different neurostimulation sampling algorithms. Each experiment involves recruitment curve sampling with different methods (e.g., Uniform, Expected Information Gain) to evaluate the accuracy and efficiency of motor threshold.",[29,30,31,32,33,34],"Other: Algorithm: Uniform Sampling","Other: Algorithm: hbMEP-adaptive algorithm (version 1)","Other: Algorithm: hbMEP-adaptive algorithm (version 2)","Other: ML-PEST","Device: MagPro X100 Transcranial Magnetic Stimulation","Device: Digitimer DS8R Transcutaneous Electrical stimulation",[36,40,44,48,52,57],{"type":6,"name":37,"description":38,"armGroupLabels":39,"otherNames":13},"Algorithm: Uniform Sampling","Standard uniform distribution sampling used as a baseline comparison.",[25],{"type":6,"name":41,"description":42,"armGroupLabels":43,"otherNames":13},"Algorithm: hbMEP-adaptive algorithm (version 1)","An active sampling algorithm for recruitment curve estimation.",[25],{"type":6,"name":45,"description":46,"armGroupLabels":47,"otherNames":13},"Algorithm: hbMEP-adaptive algorithm (version 2)","An alternative active sampling algorithm for recruitment curve estimation.",[25],{"type":6,"name":49,"description":50,"armGroupLabels":51,"otherNames":13},"ML-PEST","Algorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm.",[25],{"type":53,"name":54,"description":55,"armGroupLabels":56,"otherNames":13},"DEVICE","MagPro X100 Transcranial Magnetic Stimulation","The proposed algorithms will deliver stimulation by using this magnetic stimulation methodology.",[25],{"type":53,"name":58,"description":59,"armGroupLabels":60,"otherNames":13},"Digitimer DS8R Transcutaneous Electrical stimulation","The proposed algorithms will deliver stimulation by using this electrical stimulation methodology.",[25],[62],{"name":63,"affiliation":5,"role":64},"James R McIntosh, PhD","PRINCIPAL_INVESTIGATOR",[66],{"name":63,"role":67,"phone":68,"phoneExt":13,"email":69},"CONTACT","+19294352335","jrm2263@cumc.columbia.edu",[71],{"facility":72,"status":13,"city":73,"state":73,"zip":74,"country":75,"countryCode":76,"cosmosGeoPoint":77,"geoPoint":82,"contacts":83},"Columbia University Irving Medical Center","New York","10032","United States","US",{"type":78,"coordinates":79},"Point",[80,81],-74.00597,40.71427,{"lat":81,"lon":80},[84,86],{"name":63,"role":67,"phone":85,"phoneExt":13,"email":69},"9294352335",{"name":63,"role":64,"phone":13,"phoneExt":13,"email":13},{"type":64,"investigatorFullName":88,"investigatorTitle":89,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"James McIntosh","Associate Research Scientist",[91],{"name":92,"class":93},"National Institute of Neurological Disorders and Stroke (NINDS)","NIH","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":105,"type":106},10,"ESTIMATED","INTERVENTIONAL",[15],"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.",[111],"Modeling of Recruitment Curves",[113,114,115,116,117,118,119,120],"TMS","SCS","SCI","Hierarchical Bayesian","Motor threshold","Transcranial Magnetic Stimulation","Spinal cord stimulation","Evoked Potentials","NOT_YET_RECRUITING","2026-06-16",{"date":124,"type":125},"2026-06-18","ACTUAL",{"date":127,"type":106},"2026-08-01",{"date":129,"type":106},"2027-03-31",{"name":5,"class":6},1]