Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates
By improving accuracy on sparse data, our method minimizes the number of stimuli needed to probe each individual's neuromuscular parameters across multiple muscles simultaneously, thereby reducing session duration and the risk of inadvertent neuromodulation. Our approach provides a more statistically powerful and conclusive framework for inferring changes in threshold, and therefore corticospinal excitability. The hbMEP library streamlines and unifies the analysis of RCs across stimulation modalities and experimental paradigms.
Authors
- Lynda M. Murray (ORCID: https://orcid.org/0000-0001-7467-5607)
- Christopher E. Mandigo (ORCID: https://orcid.org/0000-0002-9676-5205)
- Jason B. Carmel (ORCID: https://orcid.org/0000-0002-3933-3692)
- Ahmet S. Asan (ORCID: https://orcid.org/0000-0003-1461-4209)
- Michael S. Virk (ORCID: https://orcid.org/0000-0002-8511-5602)
- James R. McIntosh (ORCID: https://orcid.org/0000-0002-3970-1619)
- Noam Y. Harel (ORCID: https://orcid.org/0000-0003-3085-2194)
- Vishweshwar Tyagi (ORCID: https://orcid.org/0009-0000-5305-7789)
Institutions
- Broad Institute (US)
- NewYork–Presbyterian Hospital (US)
- Cornell University (US)
- Columbia University Irving Medical Center (US)
- James J. Peters VA Medical Center (US)
- Columbia University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Brain stimulation
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.brs.2025.09.008
- Citations
- 3
- Primary Topic
- Transcranial Magnetic Stimulation Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.68