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Goetz, S. M.

Publications and source records attributed to Goetz, S. M..

At least 19 recordsLinked to original sources

Variability and Landmark Quality in Anatomical Co-Registration in Neuronavigation for Brain Stimulation

Accurate neuronavigation depends not only on the technical performance of the tracking system but also on the reproducibility with which anatomical landmarks are physically localized. We quantified operator- and landmark-specific variability in repeated facial and cranial landmark localization in 19,630 measurements from 16 participants, four operators, and 13 landmarks under marked and unmarked conditions. Within-operator precision, inter-operator agreement, and marked-unmarked displacement were analyzed separately. Median within-operator precision was 1.60 mm and median inter-operator distance was 2.91 mm, with substantial differences across both operators and landmarks. The left medial canthus showed the greatest within-operator precision (1.43 mm), whereas the pronasale showed the closest inter-operator agreement (2.05 mm). Marking the positions before the procedure did not significantly improve within-operator precision overall but reduced inter-operator distances by 29.2%, which indicates convergence on a shared spatial reference rather than improved intrinsic repeatability. Between-operator differences also contained systematic directional components; signed offsets approached 1.5 mm. Exploratory analyses did not suggest general left--right difference in precision, although the approach side produced systematic spatial shifts for midline landmarks despite comparable repeatability. These findings demonstrate that anatomical landmark localization is both operator- and landmark-specific. Standardized landmark definitions, operator calibration, consistent pointer approach, and the use of multiple well-defined registration features should therefore be considered integral components of neuronavigation quality control.

neuroscience↗

Variants of relative frequency methods for determining transcranial magnetic stimulation motor threshold do not provide accurate estimation of threshold

Relative frequency methods such as five-out-of-ten have been used for a long time to determine motor threshold of transcranial magnetic stimulation (TMS), despite known severe limitations both in accuracy and speed. Variants of relative frequency methods have been developed to improve performance, such as adaptive staircasing and the recent RMT-Finder using binary search. However, they do not solve the fundamental problem underlying the use of relative frequencies to approximate the 50% TMS response rate at threshold. We demonstrate that the probability of false decisions using relative frequency due to at least half the pulses resulting in responses at subthreshold intensities or more than half the pulses resulting in no responses at suprathreshold intensity cannot be sufficiently reduced unless an impractically high pulse count is used, which substantially increases the duration of the thresholding procedure. Simulations in a virtual population of 25,000 subjects reveal that the accuracy of relative frequency methods is not improved by the adaptive staircasing or binary search variants. Although these variants reduce the number of pulses required to reach an estimate compared to the conventional relative frequency method, they are still slower and less accurate than more advanced thresholding methods such as stochastic approximation or maximum likelihood estimation. Therefore, these faster and more accurate methods should become the standard for motor thresholding of TMS, and the use of methods based on relative frequency should be discouraged. HighlightsO_LITMS motor thresholding should not use relative-frequency methods. C_LIO_LIRelative-frequency methods have a high probability of making false decisions. C_LIO_LIVariants of relative-frequency methods do not improve threshold accuracy. C_LIO_LIStochastic approximation or maximum-likelihood estimation are faster and more accurate. C_LI

bioengineering↗

Acute Increase of Excitatory Activity in Pyramidal Neurons of Rat Motor Cortex under Static Magnetic Field

ObjectivesTranscranial application of a static magnetic field (SMF) was reported to result in subsequent modulation of neural excitability in the human motor cortex, but the acute mechanisms underlying this effect are unknown. We explore the mechanisms of this phenomenon with patch-clamp recording in rat brain slices during SMF exposure. Materials and MethodsPatch-clamp recording from layer II/III pyramidal neurons in motor cortex of acutely prepared brain slices were conducted during exposure to 0.20-0.35 T SMF or sham. ResultsDuring SMF exposure we observed an increase in the frequency of spontaneous excitatory postsynaptic currents (sEPSCs) as well as miniature excitatory post-synaptic currents (mEPSCs) recorded in the presence of TTX to suppress action potentials and thus also network effects. There was a significant acute increase in sEPSC frequency for SMF exposure duration of both 6 min and 10 min, but not for 10 min sham exposure. After SMF exposure, the sEPSC and mEPSC frequency returned to baseline. The frequency of spontaneous inhibitory postsynaptic currents (sIPSCs) was unaffected by SMF. The amplitude of the postsynaptic currents decreased with time for all recordings regardless of the condition, presumably due to the expected gradual deterioration of the patch clamp seal. ConclusionsThe acute effect of SMF exposure on sEPSC and mEPSC frequency, but not amplitude, is consistent with the assumption of a presynaptic or synaptic site mediating the effect. Furthermore, the consistency of the effect between sEPSCs and mEPSCs suggests that the effect is not related to action potential propagation in the presynaptic axon. The effect of SMF on EPSCs and not IPSCs may be related to the larger length of excitatory axons compared to inhibitory axons, or to effects on extracellular ionic gradients within the slice.

bioengineering↗

Brain-State-Resolved Consistency of Corticospinal Responses with EEG-TMS

BackgroundTranscranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits motor-evoked potentials (MEPs), a neurophysiological marker of corticospinal excitability. Ongoing brain activity at the time of stimulation, such as the phase and power of the sensorimotor mu rhythm (8-13 Hz), has a significant impact on MEP amplitudes. However, it remains unclear whether these endogenous excitability states also influence the consistency of MEP amplitudes across repeated trials. ObjectivesWe investigated whether instantaneous mu dynamics modulate not only the magnitude but also the consistency of corticospinal responses to TMS. MethodsTwenty-nine healthy participants received 1200 single TMS pulses over the left M1 during simultaneous EEG recording. Trials were stratified based on pre-stimulus mu power, phase, and interhemispheric M1-M1 functional connectivity. Brain-state-resolved MEP variability was quantified using the coefficient of variation (CV) within subsets of trials defined by similar pre-stimulus mu dynamics. ResultsTrial subsets characterized by high mu power or high M1-M1 functional connectivity were associated with reduced MEP variability, indicating more consistent corticospinal output. In contrast, the mu phase did not significantly influence response consistency. Brain-state-resolved MEP variability showed greater stability across sessions compared to MEP variability estimated from random trial subsampling. ConclusionsPre-stimulus mu dynamics shape not only magnitude but also consistency of corticospinal responses to TMS. We show that corticospinal response consistency reflects a structured, brain-state-dependent property of the sensorimotor network. These findings contribute to our mechanistic understanding of brain-state-dependent neuromodulation and may be leveraged to reduce variability and improve efficacy to TMS. HighlightsO_LIOngoing sensorimotor mu dynamics shape both magnitude and consistency of MEPs. C_LIO_LITrial subsets characterized by high mu power were associated with reduced MEP variability. C_LIO_LIMu phase modulated MEP amplitude but did not influence MEP consistency. C_LIO_LIBrain-state-resolved estimates of MEP variability were more reliable across sessions. C_LIO_LIFuture TMS protocols may reduce effect variability by targeting stable excitability states. C_LI

neuroscience↗

High-Power Dual-Channel Chamber for High-Frequency Magnetic Neuromodulation

ObjectiveSeveral novel methods, including magnetogenetics and magnetoelectric stimulation, use high frequency alternating magnetic fields to precisely manipulate neural activity. To quantify the behavioral effects of such interventions in a freely moving mouse, we developed a dual-channel magnetic chamber, specifically designed for rate-sensitive magnetothermal-genetic stimulation, and adaptable for other uses of alternating magnetic fields. ApproachThrough an optimized coil design, the system allows independent control of two spatially orthogonal uniform magnetic fields delivered at different frequencies within a 10 x 10 x 6 cm3 chamber suitable for mouse studies. The two channels have nominal frequencies of 50 and 550 kHz with peak magnetic field strengths of 88 and 12.5 mT, achieved with resonant coil drives having peak voltages of 1.6 and 1.8 kV and currents of 1.0 and 0.26 kA, respectively. Additionally, a liquid cooling system enables magnetic field generation for second-level durations, and an observation port and camera allow video capture of the animals behavior within the chamber. Main ResultsThe system generates high-amplitude magnetic fields across two widely separated frequency channels with negligible interference (< 1%). Relatively uniform magnetic field distribution ({+/-}10% across 94% of the chamber volume) is maintained throughout the chamber, and temperature increase of the inner side of the coil enclosure during the operation is limited to < 0.35 {degrees}C/s to ensure in vivo safety. Using cobalt-doped and undoped iron oxide nanoparticles, we demonstrate channel-specific heating rates of 3.5 {degrees}C/s and 1.5 {degrees}C/s, respectively, validating frequency-selectivity. Both channels can run continuously for 4 seconds stably. SignificanceWe present a novel magnetic stimulation platform that combines high-frequency, high-power capability with two independently-controlled channels generating different frequencies, along with a real-time behavioral observation system for freely moving animals. The system supports frequency-multiplexed stimulation strategies for precise modulation of neural activity, making it a versatile tool for advancing magnetogenetics, neural circuit interrogation, and noninvasive stimulation approaches in neuroscience and bioengineering.

bioengineering↗

Separating three variability and noise sources in the response fluctuation of brain stimulation

Motor-evoked potentials (MEPs) are among the few directly observable responses to suprathreshold brain stimulation and serve a variety of applications. If the MEP size is graphed over the stimulation strength, they form an input-output (IO), recruitment, or dose-response curve. Previous statistical models with two variability sources inherently consider the small MEPs at the low plateau as part of the neural recruitment properties. However, recent studies demonstrated that small MEP responses are contaminated and over-shadowed by background noise of mostly technical quality and suggested that the recruitment curve should continue below this noise level. This work intends to separate physiological variability from background noise and improve the description of recruitment behaviour. We developed a model with three variability sources and a logarithmic logistic function without a lower plateau. Compared to previous models, we incorporated an additional source for background noise from amplifiers, electrode impedance, and remote bioelectric activity, which form the obesrved low-side plateau. Compared to the dual-variability source modes, our approach better described IO characteristics, evidenced by lower Bayesian Information Criterion scores across all subjects and pulse shapes. The model independently extracted hidden variability information across the stimulated neural system and isolated it from background noise, which led to an accurate estimation of the IO curve parameters. This new model offers a robust tool to analyse brain stimulation IO curves in clinical and experimental neuroscience, reducing the risk of spurious results from inappropriate statistical methods. By providing a more accurate representation of MEP responses and variability sources, this approach advances our understanding of cortical excitability and may improve the assessment of neuromodulation effects.

neuroscience↗

Reduced Auditory Perception and Brain Response with Quiet TMS Coil

BACKGROUNDElectromagnetic forces in transcranial magnetic stimulation (TMS) coils generate a loud clicking sound that produces confounding auditory activation and is potentially hazardous to hearing. To reduce this noise while maintaining stimulation efficiency similar to conventional TMS coils, we previously developed a quiet TMS double containment coil (qTMS-DCC). OBJECTIVETo compare the stimulation strength, perceived loudness, and EEG response between qTMS-DCC and a commercial TMS coil. METHODSNine healthy volunteers participated in a within-subject study design. The resting motor thresholds (RMTs) for qTMS-DCC and MagVenture Cool-B65 were measured. Psychoacoustic titration matched the Cool-B65 loudness to qTMS-DCC pulsed at 80, 100, and 120% RMT. Event-related potentials (ERPs) were recorded for both coils. The psychoacoustic titration and ERPs were acquired with the coils both on and 6 cm off the scalp, the latter isolating the effects of airborne auditory stimulation from body sound and electromagnetic stimulation. The ERP comparisons focused on a centro-frontal region that encompassed peak responses in the global signal. RESULTSRMT did not differ significantly between the coils, with or without the EEG cap on the head. qTMS-DCC was perceived to be substantially quieter than Cool-B65. For example, qTMS-DCC at 100% coil-specific RMT sounded like Cool-B65 at 34% RMT. The general ERP waveform and topography were similar between the two coils, as were early-latency components, indicating comparable electromagnetic brain stimulation in the on-scalp condition. qTMS-DCC had a significantly smaller P180 component in both on-scalp and off-scalp conditions, supporting reduced auditory activation. CONCLUSIONSThe stimulation efficiency of qTMS-DCC matched Cool-B65, while having substantially lower perceived loudness and auditory-evoked potentials. HighlightsO_LIqTMS coil is subjectively and objectively quieter than conventional Cool-B65 coil C_LIO_LIqTMS coil at 100% motor threshold was as loud as Cool-B65 at 34% motor threshold C_LIO_LIAttenuated coil noise reduced auditory N100 and P180 evoked response components C_LIO_LIqTMS coil enables reduction of auditory activation without masking C_LI

bioengineering↗

A user-friendly input-output (IO) curve analysis tool for variable brain stimulation responses, particularly evoked potentials in transcranial magnetic stimulation (TMS)

BackgroundMotor-evoked potentials (MEPs) are among the few readily observable direct responses to suprathreshold stimuli administered to the brain. They serve for a variety of applications, often in the form of dose-response curves, also called recruitment or input-output (IO) curves. However, MEPs and thus IO curves demonstrate extreme trial-to-trial variability that can exceed more than two decimal orders of magnitude. Recent studies have identified issues in previous statistical analysis of IO curves and introduced better methods, others could quantitatively separate several widely independent variability sources. However, research has failed providing the field with a user-friendly implementation of the methods for analysing such IO curves statistically sound and separating variability so that they were limited to a few research groups. ObjectiveThis work intends to provide the latest methods for analysing IO curves and extract variability information in an open-source package so that the community can easily use and adapt them to their own needs. MethodsWe implemented recent IO curve methods with a graphical user interface and provided the code as well as compiled versions for Mac, Linux, and Windows. ResultsThe application imports typical IO data of individual stimulus-response sets, guides users step by step through the analysis, and allows exporting of the results including figures for post-hoc analysis and documentation. ConclusionThis graphical application offers a user-friendly environment for analysing the variability of evoked potentials and its various contributions, catering to the needs of clinical and experimental neuroscientists.

bioinformatics↗

Quantitative Impact of Coil Misalignment and Misplacement in Transcranial Magnetic Stimulation

IntroductionTargeting in transcranial magnetic stimulation (TMS) involves the accurate placement and positioning of the stimulation coil on the head of a subject or patient. In clinical and research applications, this placement is even done manually and/or with fixed coil holders that do not compensate for motion and drift of the head. The placement involves six degrees of freedom (DOF; three position DOF: 1x contact and 2x head location; three rotational DOF: 2x alignment and 1x electric field direction/orientation), which challenge operators. This procedure is--even with an experienced user--prone to positioning errors, which can result in low treatment efficacy or high stimulation strength due to overestimating the resting motor threshold (RMT). Whereas the position and field orientation are at least widely appreciated, the coil-head alignment and its impact are often not even known. Errors involve constant errors, drift (both leading to bias and inter-individual variability), and particularly fluctuations (causing intra-individual variability). ObjectiveWe demonstrate the impact of positioning error on cortical field strength to get a better understanding of the importance of accurate positioning and compare as well as quantify the impact of position vs. alignment errors. MethodsWe simulated the impact in a realistic head anatomy to quantify various levels of position errors and misalignment, rolling-off the coil from the target. ResultsPosition and alignment errors shift the focus of the electric field and reduce the electric field in the actual target. A misalignment of 10{degrees} can exceed the loss of stimulation strength in the target associated with a shift of 10 mm, corresponding to threshold stimulation leading to no detectable electromyographic response anymore. Misalignment in the direction of the handle (pitch), with which many operators appear to struggle most, reduces the field in the actual target faster than left-right roll. ConclusionThis work highlights the importance of the coil-head alignment for intra- and interindividual variability.

neuroscience↗

Exploring the Dose-Dependency of After-Effects: A Computational Model for Theta-Burst Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation and neuromodulation technique that is widely applied in brain research and clinical applications. However, the optimal parameters of neuromodulating TMS protocols describing the specific rhythms, such as number of pulses, frequency, and stimulation strength, are widely unknown. Improving previous rather limited and ad-hoc models, we aimed to investigate the dose-dependency of theta-burst stimulation (TBS) protocols with a more elaborate but still parsimonious quantitative model representing the non-linearities of the mechanisms of synaptic plasticity and metaplasticity during repetitive magnetic stimulation. Our model, which considers the interaction between facilitatory and inhibitory processes, successfully reproduced results from TBS experiments and provide testable predictions for prolonged TBS protocols. Moreover, we suggested that the activation of kinases and phosphatases could be potential candidates for later TMS modelling research. Although this model still simplifies the complex dynamics of cellular and molecular processes, it offers a starting basis for future studies to incorporate more intricate mechanisms. By further refining our understanding of the underlying mechanisms and improving the accuracy of prediction models, we can advance the efficacy and clinical application of TBS protocols in various neurological and psychiatric conditions.

neuroscience↗

Optimisation of asymmetric field pulses for transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a widely-used noninvasive brain stimulation technique through electromagnetic induction. Nowadays commercial TMS devices routinely use conventional biphasic pulses for repetitive TMS protocols and monophasic pulses for single-pulse stimulation. They respectively generate underdamped or damped cosinusoidal electric field pulses that have been proven to be power-inefficient. Recently, symmetric field pulses with near-rectangular electric fields show great potential in terms of energy loss and coil heating, but only limited studies have investigated asymmetric field pulses with different asymmetry levels for the induced electric field waveforms. Thus, this paper optimises and searches a wide range of potential current waveforms with the goal of minimising energy loss and coil heating. The optimised results demonstrated that asymmetric field pulses with near-rectangular electric fields have significantly lower power consumption than conventional ones. These optimised waveforms commonly consist of an initial falling phase followed by rapidly rising and falling phases, trailed by a slow decay to zero. Interestingly, the initial phase has a decay time constant around 260 {micro}s and introduces a pulse-duration-dependent negative bias for the current baseline to minimise the energy loss and coil heating. Thus, it is possible to directly design asymmetric field pulses with various asymmetry ratios by using several prediction equations rather than running optimisation. These results also suggest that introducing such an initial current phase could likely significantly reduce the coil heating of most TMS pulse shapes to improve their power efficiencies.

neuroscience↗

Optimized Monophasic Pulses with Equivalent Electric Field for Rapid-Rate Transcranial Magnetic Stimulation

ObjectiveTranscranial magnetic stimulation (TMS) with monophasic pulses achieves greater changes in neuronal excitability but requires higher energy and generates more coil heating than TMS with biphasic pulses, and this limits the use of monophasic pulses in rapid-rate protocols. We sought to design a stimulation waveform that retains the characteristics of monophasic TMS but significantly reduces coil heating, thereby enabling higher pulse rates and increased neuromodulation effectiveness. ApproachA two-step optimization method was developed that uses the temporal relationship between the electric field (E-field) and coil current waveforms. The model-free optimization step reduced the ohmic losses of the coil current and constrained the error of the E-field waveform compared to a template monophasic pulse, with pulse duration as a second constraint. The second, amplitude adjustment step scaled the candidate waveforms based on simulated neural activation to account for differences in stimulation thresholds. The optimized waveforms were implemented to validate the changes in coil heating. Main resultsDepending on the pulse duration and E-field matching constraints, the optimized waveforms produced 12% to 75% less heating than the original monophasic pulse. The reduction in coil heating was robust across a range of neural models. The changes in the measured ohmic losses of the optimized pulses compared to the original pulse agreed with numeric predictions. SignificanceThe first step of the optimization approach was independent of any potentially inaccurate or incorrect model and exhibited robust performance by avoiding the highly nonlinear behavior of neural responses, whereas neural simulations were only run once for amplitude scaling in the second step. This significantly reduced computational cost compared to iterative methods using large populations of candidate solutions and more importantly reduced the sensitivity to the choice of neural model. The reduced coil heating and power losses of the optimized pulses can enable rapid-rate monophasic TMS protocols.

bioengineering↗

Identifiability analysis and noninvasive online estimation of the first-order neural activation dynamics in the brain with transcranial magnetic stimulation

BackgroundNeurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. ObjectiveTo develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. MethodFirst, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. ResultsThe results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. ConclusionsSPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. SignificanceThe proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

neuroscience↗

Analysis and Comparison of Methods for Determining Transcranial Magnetic Stimulation Thresholds

ObjectiveThresholding 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 methods. We analyzed existing methods for obtaining TMS motor threshold and their variations, introduced new methods from other fields, and compared their accuracy and speed. ApproachIn 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 maximum a- 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 ResultsThe 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. SignificanceTwo-parameter MAP estimation, Bayesian estimation, and stochastic root-finding methods have better error convergence compared to conventional single-parameter ML estimation, and they all 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.

bioengineering↗

Closed-loop and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation (cTMS)

This paper proposes a tool for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the membrane time constant and input-output curve in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). A normalized depolarization factor is defined which separates the optimization of the pulse amplitude and width. Then, the pulse amplitude is chosen by the maximization of the Fisher information matrix (FIM), while the pulse width is chosen by the maximization of the normalized depolarization factor. The simulation results confirm satisfactory estimation. The results show that the normalized depolarization factor maximization can identify the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane.

neuroscience↗

A revised calcium-dependent model of theta-burst transcranial magnetic stimulation

Calcium dependency is presently an essential assumption in modelling the neuromodulatory effects of transcranial magnetic stimulation (TMS). Among the various neuromodulatory TMS protocols, theta-burst stimulation (TBS) at present is the fastest intervention to generate strong effects. A decade ago, Y.Z. Huang et al. developed a first neuromodulation model to explain the bidirectional effects of TBS based on postsynaptic intracellular calcium concentration elevation. We discover, however, that the published computer code is not consistent with the model formulations in the corresponding paper. Further analysis confirms that the computer model with an index confusion was used for fitting the experimental results, running the simulation, and plotting the corresponding figures in the original publication. This paper intends to fix the computer code and additionally create a non-convex optimisation solution for re-calibrating the model. After re-calibration, the revised model outperforms the initial model in accuracy describing the MEP amplitudes of TBS-induced after-effects under specific situations.

neuroscience↗

Formalism for the sequential estimation of neural membrane time constant and input-output curve towards selective and closed-loop transcranial magnetic stimulation

ObjectiveTo obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input-output (IO) curve with transcranial magnetic stimulation (TMS). ApproachFirst, 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 resultsThe 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. SignificanceThis 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.

neuroscience↗

Force Recruitment of Neuromuscular Magnetic Stimulation Predicted Through High-Resolution Anatomical 3D Models

Neuromuscular magnetic stimulation is a promising tool in neurorehabilitation due to its deeper penetration, notably lower distress, and respectable force levels compared to electrical stimulation. However, this method faces great challenges from a technological perspective. The systematic design of better equipment and the incorporation into modern training setups requires better understanding of the mechanisms and predictive quantitative models of the recruited forces. This article proposes a model for simulating the force recruitment in isometric muscle stimulation of the thigh extensors based on previous theoretical and experimental findings. The model couples a 3D field model for the physics with a parametric recruitment model, which is identified with a mixed-effects design to learn the most likely model based on available experimental data with a wide range of field conditions. This approach intentionally keeps the model as mathematically simple and statistically parsimonious as possible in order to avoid over-fitting. The coupled model is able to accurately predict key phenomena observed so far, such as a threshold shift for different distances between coil and body, the different recruiting performance of various coils with available measurement data in the literature, and the saturation behaviour with its onset amplitude. The presented recruitment model could also be readily incorporated into dynamic models for biomechanics as soon as sufficient experimental data are available for calibration.

biophysics↗