ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2019
Optimal Estimation of Neural Recruitment Curves Using Fisher Information: Application to Transcranial Magnetic Stimulation.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Multispecies initial numerical validation of an efficient algorithm prototype for auditory brainstem response hearing threshold estimation.The Journal of the Acoustical Society of America · 2024Article
- Transcranial magnetic stimulation input-output curve slope differences suggest variation in recruitment across muscle representations in primary motor cortex.Frontiers in human neuroscience · 2024Article
- Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation.Biomedical engineering letters · 2023Article
- Detection of motor-evoked potentials below the noise floor: rethinking the motor stimulation threshold.Journal of neural engineering · 2022Article
- Input-output slope curve estimation in neural stimulation based on optimal sampling principlesJournal of neural engineering · 2021Article
- Erratum to: "Optimal Estimation of Neural Recruitment Curves Using Fisher Information: Application to Transcranial Magnetic Stimulation".IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
- Statistical Model of Motor-Evoked Potentials.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
- Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates.Brain stimulationArticle
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
This paper presents a novel method for fast and optimal determination of recruitment (input-output, IO) curve parameters in neural stimulation. A sequential parameter estimation (SPE) method was developed based on the Fisher information matrix (FIM), with a stopping rule based on successively satisfying a specified estimation tolerance. Simulated motor responses evoked by transcranial magnetic stimulation (TMS) were used as a test bed. Performance of FIM-SPE was characterized in 10 177 simulation runs for various IO parameter values corresponding to different virtual subjects, compared with uniform sampling. Unlike uniform sampling, FIM-SPE identifies and samples the areas of the IO curve that contain maximum information about the curve parameters. For the most relaxed stopping rule, the median number of samples required for convergence was only 17 for FIM-SPE versus 294 for uniform sampling. For the highest reliability stopping rule, more than 92% of the FIM-SPE runs converged, with a median of 88 samples, whereas all uniform sampling runs reached 1000 samples without converging. Compared to uniform sampling, FIM-SPE reduced estimation errors up to two-fold and required ten times fewer stimuli. FIM-SPE could improve the speed and accuracy of determination of IO curves for neural stimulation. A software implementation of the algorithm is provided online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.