Modeling of Recruitment CurvesNCT07561372
Adaptive Recruitment Curve Analysis Using Bayesian Modeling
A study testing Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1) and Algorithm: hbMEP-adaptive algorithm (version 2) in people with Modeling of Recruitment Curves, run by Columbia University at 1 site in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Modeling of Recruitment Curves
- What is being tested?
- Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1) and Algorithm: hbMEP-adaptive algorithm (version 2)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 14 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2026to 31 Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Healthy adult volunteers aged 18 years and older.
- Able to understand study procedures and provide written informed consent.
Who cannot join
- 1. History of adverse reaction to Transcranial Magnetic Stimulation (TMS) or non-invasive neurostimulation.
- 2. History of seizures, epilepsy, or family history of epilepsy.
- 3. History of stroke, brain injury, or illness causing brain injury.
- 4. History of head injury or neurosurgery.
- 5. History of neurological diseases, or central nervous system lesions.
- 6. Presence of metallic implants or foreign bodies in the head (outside of dental work/fillings).
Show all 9Show fewer
- 7. Presence of implanted electronic or medical devices (e.g., cardiac pacemakers, medical pumps, implanted stimulators).
- 8. Current pregnancy or possibility of pregnancy.
- 9. Currently taking medications that alter cortical excitability or lower seizure threshold.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Columbia University Irving Medical Center
New York, New York, 10032
James R McIntosh, PhD, 9294352335, jrm2263@cumc.columbia.edu
James R McIntosh, PhD
About this study
Official title: Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling
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).
This 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.
The 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.
Read the detailed description
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, and the number of samples required to reach predefined tolerances on these parameters.
- Conditions
- Modeling of Recruitment Curves
- Being tested
- Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1), Algorithm: hbMEP-adaptive algorithm (version 2), ML-PEST, MagPro X100 Transcranial Magnetic Stimulation
- Keywords
- TMS, SCS, SCI, Hierarchical Bayesian, Motor threshold, Transcranial Magnetic Stimulation, Spinal cord stimulation, Evoked Potentials
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Algorithm: Uniform SamplingOther
Standard uniform distribution sampling used as a baseline comparison.
Algorithm: hbMEP-adaptive algorithm (version 1)Other
An active sampling algorithm for recruitment curve estimation.
Algorithm: hbMEP-adaptive algorithm (version 2)Other
An alternative active sampling algorithm for recruitment curve estimation.
ML-PESTOther
Algorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm.
MagPro X100 Transcranial Magnetic StimulationDevice
The proposed algorithms will deliver stimulation by using this magnetic stimulation methodology.
Digitimer DS8R Transcutaneous Electrical stimulationDevice
The proposed algorithms will deliver stimulation by using this electrical stimulation methodology.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of stimuli to reach a pre-defined threshold error
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
Measured: Through completion of the study visit, 2-4 hours.
Number of stimuli to reach a pre-defined predictive curve error
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
Measured: Through completion of the study visit, 2-4 hours.
Secondary outcomes (1)
Mean absolute error in a given parameter (e.g. threshold, predictive curve, slope) for a given number of stimuli
The error of the methods under comparison, with the ground truth computed from recruitment curves fitted subsequent to sampling using aggregated data.
Measured: Through completion of the study visit, 2-4 hours.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2026
- Study start1 Sep 2026 (estimated)
- Primary completion31 Mar 2027 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Modeling of Recruitment CurvesStudies of the same condition, open and under way.
- More trials run by Columbia UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 14 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Columbia University
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of Neurological Disorders and Stroke (NINDS)
- Responsible party
- Principal investigator, James McIntosh, Columbia University
- Investigators
- James R McIntosh, PhD, Principal investigator, Columbia University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07561372
- Sponsor's protocol ID
- AAAV6853
- Other IDs
- 1R03NS141040-01A1
- Status last verified
- Aug 2026
- First submitted
- 20 Apr 2026
- Last updated
- 14 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: De-identified MEP data and analysis code and algorithms.
Publications
Tyagi V, Murray LM, Asan AS, Mandigo C, Virk MS, Harel NY, Carmel JB, McIntosh JR. Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates. Brain Stimul. 2025 Nov-Dec;18(6):1855-1870. doi: 10.1016/j.brs.2025.09.008. Epub 2025 Sep 18.
PubMed 40975380
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling
- ClinicalTrials.gov ID
- NCT07561372
- Acronym / sponsor ID
- AAAV6853
- Conditions
- Modeling of Recruitment Curves
- Keywords
- TMS, SCS, SCI, Hierarchical Bayesian, Motor threshold, Transcranial Magnetic Stimulation, Spinal cord stimulation, Evoked Potentials
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 14 participants (estimated)
- Lead sponsor
- Columbia UniversityUniversity, hospital or other
- Collaborators
- National Institute of Neurological Disorders and Stroke (NINDS)
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 31 Mar 2027 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 1 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
This 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.
The 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.
Detailed description
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, and the number of samples required to reach predefined tolerances on these parameters.
Study design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| Test of developed methods | Experimental | 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 stimulationParticipants 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. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Healthy adult volunteers aged 18 years and older.
- Able to understand study procedures and provide written informed consent.
Key exclusion criteria (9 listed)
- 1. History of adverse reaction to Transcranial Magnetic Stimulation (TMS) or non-invasive neurostimulation.
- 2. History of seizures, epilepsy, or family history of epilepsy.
- 3. History of stroke, brain injury, or illness causing brain injury.
- 4. History of head injury or neurosurgery.
- 5. History of neurological diseases, or central nervous system lesions.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Algorithm: Uniform SamplingStandard uniform distribution sampling used as a baseline comparison. | Other | Test of developed methods |
| Algorithm: hbMEP-adaptive algorithm (version 1)An active sampling algorithm for recruitment curve estimation. | Other | Test of developed methods |
| Algorithm: hbMEP-adaptive algorithm (version 2)An alternative active sampling algorithm for recruitment curve estimation. | Other | Test of developed methods |
| ML-PESTAlgorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm. | Other | Test of developed methods |
| MagPro X100 Transcranial Magnetic StimulationThe proposed algorithms will deliver stimulation by using this magnetic stimulation methodology. | Device | Test of developed methods |
| Digitimer DS8R Transcutaneous Electrical stimulationThe proposed algorithms will deliver stimulation by using this electrical stimulation methodology. | Device | Test of developed methods |
Primary outcome
Number of stimuli to reach a pre-defined threshold error
Time frame: Through completion of the study visit, 2-4 hours.
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
Number of stimuli to reach a pre-defined predictive curve error
Time frame: Through completion of the study visit, 2-4 hours.
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
1 secondary outcome
Mean absolute error in a given parameter (e.g. threshold, predictive curve, slope) for a given number of stimuli
Time frame: Through completion of the study visit, 2-4 hours.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2026.
- Background: PubMed 40975380Tyagi V, Murray LM, Asan AS, Mandigo C, Virk MS, Harel NY, Carmel JB, McIntosh JR. Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates. Brain Stimul. 2025 Nov-Dec;18(6):1855-1870. doi: 10.1016/j.brs.2025.09.008. Epub 2025 Sep 18.
- Individual participant dataSharing planned: Yes. De-identified MEP data and analysis code and algorithms.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
James R McIntosh, PhD principal investigator
Columbia University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1) and Algorithm: hbMEP-adaptive algorithm (version 2) in people with Modeling of Recruitment Curves, run by Columbia University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact James R McIntosh, PhD (+19294352335, jrm2263@cumc.columbia.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of stimuli to reach a pre-defined threshold error (time frame: Through completion of the study visit, 2-4 hours.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07561372, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Aug 2026.