Detection of motor-evoked potentials below the noise floor: rethinking the motor stimulation threshold

Examining motor evidence for the pause-then-cancel model of action-stopping: insights from motor system physiology

Stopping initiated actions is fundamental to adaptive behavior. Longstanding, single-process accounts of action-stopping have been challenged by recent, two-process, "pause-then-cancel" models. These models propose that action-stopping involves two inhibitory processes: 1) a fast Pause process, which broadly suppresses the motor system as the result of detecting any salient event, and 2) a slower Cancel process, which involves motor suppression specific to the cancelled action. A purported signature of the Pause process is global suppression, or the reduced corticospinal excitability (CSE) of task-unrelated effectors early on in action-stopping. However, unlike the Pause process, few (if any) motor system signatures of a Cancel process have been identified. Here, we used single- and paired-pulse transcranial magnetic stimulation (TMS) methods to comprehensively measure the local physiological excitation and inhibition of both responding and task-unrelated motor effector systems during action-stopping. Specifically, we measured CSE, short-interval intracortical inhibition (SICI), and the duration of the cortical silent period (CSP). Consistent with key predictions from the pause-then-cancel model, CSE measurements at the responding effector indicated that additional suppression was necessary to counteract Go-related increases in CSE during action-stopping, particularly at later timepoints. Increases in SICI on Stop-signal trials did not differ across task-related and task-unrelated effectors, or across timepoints. This suggests SICI as a potential source of global suppression. Increases in CSP duration on Stop-signal trials were more prominent at later timepoints and were related to individual differences in CSE. Our study provides further evidence from motor system physiology that multiple inhibitory processes influence action-stopping.NEW & NOTEWORTHY Current debate surrounds whether single- or dual-process models better account for human action-stopping ability. We show that motor suppression of a successfully stopped muscle follows a distinct time course compared with when that same muscle is unrelated to the stopping task. Our results further suggest that distinct local inhibitory neuron populations contribute to these unique sources of suppression. Our study provides evidence from motor system physiology that multiple inhibitory processes influence action-stopping.

Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery

Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain-computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.

Corticomotor pathway function and recovery after stroke: a look back and a way forward

Stroke is a leading cause of adult disability that results in motor deficits and reduced independence. Regaining independence relies on motor recovery, particularly regaining function of the hand and arm. This review presents evidence from human studies that have used transcranial magnetic stimulation (TMS) to identify neurophysiological mechanisms underlying upper limb motor recovery early after stroke. TMS studies undertaken at the subacute stage after stroke have identified several neurophysiological factors that can drive motor impairment, including membrane excitability, the recruitment of corticomotor neurons, and glutamatergic and GABAergic neurotransmission. However, the inherent variability and subsequent poor reliability of measures derived from motor evoked potentials (MEPs) limit the use of TMS for prognosis at the individual patient level. Currently, prediction tools that provide the most accurate information about upper limb motor outcomes for individual patients early after stroke combine clinical measures with a simple neurophysiological biomarker based on MEP presence or absence, i.e. MEP status. Here, we propose a new compositional framework to examine MEPs across several upper limb muscles within a threshold matrix. The matrix can provide a more comprehensive view of corticomotor function and recovery after stroke by quantifying the evolution of subthreshold and suprathreshold MEPs through compositional analyses. Our contention is that subthreshold responses might be the most sensitive to reduced output of corticomotor neurons, desynchronized firing of the remaining neurons, and myelination processes that occur early after stroke. Quantifying subthreshold responses might provide new insights into post-stroke neurophysiology and improve the accuracy of prediction of upper limb motor outcomes.

Through-polymer, via technology-enabled, flexible, lightweight, and integrated devices for implantable neural probes

In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions

The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.

Reducing motor evoked potential amplitude variability through normalization

Background

Transcranial Magnetic Stimulation (TMS) is used for in vivo assessment of human motor cortical excitability, with application of TMS pulses over the motor cortex resulting in muscle responses that can be recorded with electromyography (EMG) as Motor Evoked Potentials (MEPs). These have been widely explored as potential biomarkers for neuropsychiatric disorders but methodological heterogeneity in acquisition, and inherent high variability, have led to constraints in reproducibility. Normalization, consisting in scaling the signal of interest to a known and repeatable measurement, reduces variability and is standard practice for between-subject comparisons of EMG. The effect of normalization on variability of MEP amplitude has not yet been explored and was assessed here using several methods.

Methods

Three maximal voluntary isometric contractions (MVICs) and 40 MEPs were collected from the right hand in healthy volunteers, with a retest session conducted 4 to 8 weeks later. MEP amplitude was normalized using either external references (MVICs) or internal references (extreme MEPs). Iterative re-sampling of 30 normalized MEPs per subject was repeated 5,000 times to define, for each normalization method, distributions for between-subject coefficients of variation (CV) of the mean MEP amplitude. Intra-class correlation coefficients (ICC) were used to assess the impact of normalization on test–retest stability of MEP amplitude measurements.

Results

In the absence of normalization, MEPs collected from the right hand of 47 healthy volunteers were within reported values regarding between-subject variability (95% confidence intervals for the CV: [1.0567,1.0577]) and showed good temporal stability (ICC = 0.77). Internal reference normalization substantially reduced between-subject variability, by values of up to 64%, while external reference normalization had no impact or increased between-subject variability. Normalization with the smallest references reduced test–retest stability, with use of the largest references resulting in slight reduction or improvement of ICCs. Internal reference normalization using the largest MEPs was found to be robust to several sensitivity analyses.

Conclusion

Internal, but not external, reference normalization reduces between-subject variability of MEP amplitude, and has a minimal impact on within-subject variability when conducted with the largest references. Additional research is necessary to further validate these normalization methods toward potential use of MEPs as biomarkers of neuropsychiatric disorders.

Reliability of a TMS-derived threshold matrix of corticomotor function

Transcranial magnetic stimulation (TMS) studies typically focus on suprathreshold motor evoked potentials (MEPs), overlooking small MEPs representing subthreshold corticomotor pathway activation. Assessing subthreshold excitability could provide insights into corticomotor pathway integrity and function, particularly in neurological conditions like stroke. The aim of the study was to examine the test-retest reliability of metrics derived from a novel compositional analysis of MEP data from older adults. The study also compared the composition between the dominant (D) and non-dominant (ND) sides and explored the association between subthreshold responses and resting motor threshold. In this proof-of-concept study, 23 healthy older adults participated in two identical experimental sessions. Stimulus-response (S-R) curves and threshold matrices were constructed using single-pulse TMS across intensities to obtain MEPs in four upper limb muscles. S-R curves had reliable slopes for every muscle (Intraclass Correlation Coefficient range = 0.58-0.88). Subliminal and suprathreshold elements of the threshold matrix showed good-excellent reliability (D subliminal ICC = 0.83; ND subliminal ICC = 0.79; D suprathreshold ICC = 0.92; ND suprathreshold ICC = 0.94). By contrast, subthreshold elements of the matrix showed poor reliability, presumably due to a floor effect (D subthreshold ICC = 0.39; ND subthreshold ICC = 0.05). No composition differences were found between D and ND sides (suprathreshold BF01 = 3.85; subthreshold BF01 = 1.68; subliminal BF01 = 3.49). The threshold matrix reliably assesses subliminal and suprathreshold MEPs in older adults. Further studies are warranted to evaluate the utility of compositional analyses for assessing recovery of corticomotor pathway function after neurological injury.

Three novel methods for determining motor threshold with transcranial magnetic stimulation outperform conventional procedures

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.

Identifiability Analysis and Noninvasive Online Estimation of the First-Order Neural Activation Dynamics in the Brain With Closed-Loop Transcranial Magnetic Stimulation

BACKGROUND

Neurons 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.

OBJECTIVE

To develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS.

METHOD

First, 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.

RESULTS

The 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.

CONCLUSIONS

SPE 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.

SIGNIFICANCE

The proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks

BACKGROUND

Transcranial magnetic stimulation (TMS) can modulate neural activity by evoking action potentials in cortical neurons. TMS neural activation can be predicted by coupling subject-specific head models of the TMS-induced electric field (E-field) to populations of biophysically realistic neuron models; however, the significant computational cost associated with these models limits their utility and eventual translation to clinically relevant applications.

OBJECTIVE

To develop computationally efficient estimators of the activation thresholds of multi-compartmental cortical neuron models in response to TMS-induced E-field distributions.

METHODS

Multi-scale models combining anatomically accurate finite element method (FEM) simulations of the TMS E-field with layer-specific representations of cortical neurons were used to generate a large dataset of activation thresholds. 3D convolutional neural networks (CNNs) were trained on these data to predict thresholds of model neurons given their local E-field distribution. The CNN estimator was compared to an approach using the uniform E-field approximation to estimate thresholds in the non-uniform TMS-induced E-field.

RESULTS

The 3D CNNs estimated thresholds with mean absolute percent error (MAPE) on the test dataset below 2.5% and strong correlation between the CNN predicted and actual thresholds for all cell types (R2 > 0.96). The CNNs estimated thresholds with a 2-4 orders of magnitude reduction in the computational cost of the multi-compartmental neuron models. The CNNs were also trained to predict the median threshold of populations of neurons, speeding up computation further.

CONCLUSION

3D CNNs can estimate rapidly and accurately the TMS activation thresholds of biophysically realistic neuron models using sparse samples of the local E-field, enabling simulating responses of large neuron populations or parameter space exploration on a personal computer.

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

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.