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Ma K, Goetz SM. A user-friendly input-output curve analysis tool for variable direct responses to brain stimulation. Brain Stimul 2024; 17:134-136. [PMID: 38244772 DOI: 10.1016/j.brs.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Affiliation(s)
- Ke Ma
- Department of Engineering, School of Technology, University of Cambridge, Cambridge, United Kingdom
| | - Stephan M Goetz
- Department of Engineering, School of Technology, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry and Behavioural Sciences, School of Medicine, Duke University, Durham, NC, United States of America; Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, United States of America; Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, United States of America; Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America.
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Wang B, Peterchev AV, Goetz SM. Three novel methods for determining motor threshold with transcranial magnetic stimulation outperform conventional procedures. J Neural Eng 2023; 20:10.1088/1741-2552/acf1cc. [PMID: 37595573 PMCID: PMC10516469 DOI: 10.1088/1741-2552/acf1cc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
Objective. Thresholding of neural responses is central to many applications of transcranial magnetic stimulation (TMS), but the stochastic aspect of neuronal activity and motor evoked potentials (MEPs) challenges thresholding techniques. We analyzed existing methods for obtaining TMS motor threshold and their variations, introduced new methods from other fields, and compared their accuracy and speed.Approach. In addition to existing relative-frequency methods, such as the five-out-of-ten method, we examined adaptive methods based on a probabilistic motor threshold model using maximum-likelihood (ML) or maximuma-posteriori(MAP) estimation. To improve the performance of these adaptive estimation methods, we explored variations in the estimation procedure and inclusion of population-level prior information. We adapted a Bayesian estimation method which iteratively incorporated information of the TMS responses into the probability density function. A family of non-parametric stochastic root-finding methods with different convergence criteria and stepping rules were explored as well. The performance of the thresholding methods was evaluated with an independent stochastic MEP model.Main Results. The conventional relative-frequency methods required a large number of stimuli, were inherently biased on the population level, and had wide error distributions for individual subjects. The parametric estimation methods obtained the thresholds much faster and their accuracy depended on the estimation method, with performance significantly improved when population-level prior information was included. Stochastic root-finding methods were comparable to adaptive estimation methods but were much simpler to implement and did not rely on a potentially inaccurate underlying estimation model.Significance. Two-parameter MAP estimation, Bayesian estimation, and stochastic root-finding methods have better error convergence compared to conventional single-parameter ML estimation, and all these methods require significantly fewer TMS pulses for accurate estimation than conventional relative-frequency methods. Stochastic root-finding appears particularly attractive due to the low computational requirements, simplicity of the algorithmic implementation, and independence from potential model flaws in the parametric estimators.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Angel V. Peterchev
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, USA
| | - Stefan M. Goetz
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, USA
- Department of Engineering, School of Technology, University of Cambridge, Cambridge, UK
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Spampinato DA, Ibanez J, Rocchi L, Rothwell J. Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system. J Physiol 2023; 601:2827-2851. [PMID: 37254441 PMCID: PMC10952180 DOI: 10.1113/jp281885] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 05/18/2023] [Indexed: 06/01/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) is a non-invasive technique that is increasingly used to study the human brain. One of the principal outcome measures is the motor-evoked potential (MEP) elicited in a muscle following TMS over the primary motor cortex (M1), where it is used to estimate changes in corticospinal excitability. However, multiple elements play a role in MEP generation, so even apparently simple measures such as peak-to-peak amplitude have a complex interpretation. Here, we summarize what is currently known regarding the neural pathways and circuits that contribute to the MEP and discuss the factors that should be considered when interpreting MEP amplitude measured at rest in the context of motor processing and patients with neurological conditions. In the last part of this work, we also discuss how emerging technological approaches can be combined with TMS to improve our understanding of neural substrates that can influence MEPs. Overall, this review aims to highlight the capabilities and limitations of TMS that are important to recognize when attempting to disentangle sources that contribute to the physiological state-related changes in corticomotor excitability.
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Affiliation(s)
- Danny Adrian Spampinato
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- Department of Human NeurosciencesSapienza University of RomeRomeItaly
- Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Jaime Ibanez
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- BSICoS group, I3A Institute and IIS AragónUniversity of ZaragozaZaragozaSpain
- Department of Bioengineering, Centre for NeurotechnologiesImperial College LondonLondonUK
| | - Lorenzo Rocchi
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
| | - John Rothwell
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
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Alavi SMM, Vila-Rodriguez F, Mahdi A, Goetz SM. Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation. Biomed Eng Lett 2023; 13:119-127. [PMID: 37124104 PMCID: PMC10130260 DOI: 10.1007/s13534-022-00259-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/26/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023] Open
Abstract
This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.
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Affiliation(s)
- S. M. Mahdi Alavi
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Fidel Vila-Rodriguez
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Adam Mahdi
- Surrey Institute for People-Centred AI, University of Surrey, Surrey, UK
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Stefan M. Goetz
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC USA
- Department of Neurosurgery, Duke University, Durham, NC USA
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Wilson MT, Goldsworthy MR, Vallence AM, Fornito A, Rogasch NC. Finding synaptic couplings from a biophysical model of motor evoked potentials after theta-burst transcranial magnetic stimulation. Brain Res 2023; 1801:148205. [PMID: 36563834 DOI: 10.1016/j.brainres.2022.148205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE We aimed to use measured input-output (IO) data to identify the best fitting model for motor evoked potentials. METHODS We analyzed existing IO data before and after intermittent and continuous theta-burst stimulation (iTBS & cTBS) from a small group of subjects (18 for each). We fitted individual synaptic couplings and sensitivity parameters using variations of a biophysical model. A best performing model was selected and analyzed. RESULTS cTBS gives a broad reduction in MEPs for amplitudes larger than resting motor threshold (RMT). Close to threshold, iTBS gives strong potentiation. The model captures individual IO curves. There is no change to the population average synaptic weights post TBS but the change in excitatory-to-excitatory synaptic coupling is strongly correlated with the experimental post-TBS response relative to baseline. CONCLUSIONS The model describes population-averaged and individual IO curves, and their post-TBS change. Variation among individuals is accounted for with variation in synaptic couplings, and variation in sensitivity of neural response to stimulation. SIGNIFICANCE The best fitting model could be applied more broadly and validation studies could elucidate underlying biophysical meaning of parameters.
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Affiliation(s)
- Marcus T Wilson
- Te Aka Mātuatua-School of Science, University of Waikato, Hamilton, New Zealand.
| | - Mitchell R Goldsworthy
- Lifespan Human Neurophysiology Group, Adelaide Medical School, University of Adelaide, Adelaide, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia; Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia
| | - Ann-Maree Vallence
- Discipline of Psychology, College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia; Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Nigel C Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; South Australian Health and Medical Research Institute, Australia
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Li Z, Peterchev AV, Rothwell JC, Goetz SM. Detection of motor-evoked potentials below the noise floor: rethinking the motor stimulation threshold. J Neural Eng 2022; 19:10.1088/1741-2552/ac7dfc. [PMID: 35785762 PMCID: PMC10155352 DOI: 10.1088/1741-2552/ac7dfc] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 07/04/2022] [Indexed: 12/24/2022]
Abstract
Objective. Motor-evoked potentials (MEPs) are among the most prominent responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation and electrical stimulation. Understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smaller responses, e.g. from single motor units. However, available detection and quantization methods suffer from a large noise floor. This paper develops a detection method that extracts MEPs hidden below the noise floor. With this method, we aim to estimate excitatory activations of the corticospinal pathways well below the conventional detection level.Approach. The presented MEP detection method presents a self-learning matched-filter approach for improved robustness against noise. The filter is adaptively generated per subject through iterative learning. For responses that are reliably detected by conventional detection, the new approach is fully compatible with established peak-to-peak readings and provides the same results but extends the dynamic range below the conventional noise floor.Main results. In contrast to the conventional peak-to-peak measure, the proposed method increases the signal-to-noise ratio by more than a factor of 5. The first detectable responses appear to be substantially lower than the conventional threshold definition of 50µV median peak-to-peak amplitude.Significance. The proposed method shows that stimuli well below the conventional 50µV threshold definition can consistently and repeatably evoke muscular responses and thus activate excitable neuron populations in the brain. As a consequence, the input-output (IO) curve is extended at the lower end, and the noise cut-off is shifted. Importantly, the IO curve extends so far that the 50µV point turns out to be closer to the center of the logarithmic sigmoid curve rather than close to the first detectable responses. The underlying method is applicable to a wide range of evoked potentials and other biosignals, such as in electroencephalography.
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Affiliation(s)
- Zhongxi Li
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
| | - Angel V. Peterchev
- Departments of Psychiatry & Behavioral Sciences, Neurosurgery, Biomedical Engineering, and Electrical & Computer Engineering, Duke University, Durham, USA
| | | | - Stefan M. Goetz
- (Corresponding author) Department of Engineering, University of Cambridge, Cambridge, UK () and Departments of Psychiatry & Behavioral Sciences, Neurosurgery, and Electrical & Computer Engineering, Duke University, Durham, USA ()
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Alavi SMM, Vila-Rodriguez F, Mahdi A, Goetz SM. A formalism for sequential estimation of neural membrane time constant and input--output curve towards selective and closed-loop transcranial magnetic stimulation. J Neural Eng 2022; 19. [PMID: 36055218 DOI: 10.1088/1741-2552/ac8ed5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 09/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input--output (IO) curve with transcranial magnetic stimulation (TMS). APPROACH First, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input--output curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes. MAIN RESULTS The results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations. SIGNIFICANCE This paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.
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Affiliation(s)
- S M Mahdi Alavi
- Department of Psychiatry , The University of British Columbia, 2255 Wesbrook Mall, Vancouver, British Columbia, V6T 2A1, CANADA
| | - Fidel Vila-Rodriguez
- Department of Psychiatry , The University of British Columbia Faculty of Medicine, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, British Columbia, V6T 2A1, CANADA
| | - Adam Mahdi
- University of Oxford, Oxford Internet Institute, 1 St Giles, Oxford, Oxfordshire, OX1 2JD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Stefan M Goetz
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 200 Trent Drive, Duke University Medical Center, Durham, North Carolina, 27710, UNITED STATES
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