Transcranial magnetic stimulation input-output curve slope differences suggest variation in recruitment across muscle representations in primary motor cortex

Measurement of the input-output (IO) curves of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) can be used to assess corticospinal excitability and motor recruitment. While IO curves have been used to study disease and pharmacology, few studies have compared the IO curves across the body. This study sought to characterize IO curve parameters across the dominant and non-dominant sides of upper and lower limbs in healthy participants. Laterality preferences were assessed in eight healthy participants and IO curves were measured bilaterally for the first dorsal interosseous (FDI), biceps brachii (BB), and tibialis anterior (TA) muscles. Results show that FDI has lower motor threshold than BB which is, in turn, lower than TA. In addition, both BB and TA have markedly shallower logarithmic IO curve slopes from small to large MEP responses than FDI. After normalizing these slopes by their midpoints to account for differences in motor thresholds, which could result from geometric factors such as the target depth, large differences in logarithmic slopes remain present between all three muscles. The differences in slopes between the muscles could not be explained by differences in normalized IO curve spreads, which relate to the extent of the cortical representation and were comparable across the muscles. The IO curve differences therefore suggest muscle-dependent variations in TMS-evoked recruitment across the primary motor cortex, which should be considered when utilizing TMS-evoked MEPs to study disease states and treatment effects.

Cortical excitability in patients with REM sleep behavior disorder with abnormal TRODAT-1 SPECT scan: an insight into prodromal Parkinson's disease

Introduction

REM Sleep Behavior Disorder (RBD) has been highlighted to identify a patient with prodromal Parkinson's disease (PD). Although many studies focus on biomarkers to predict an RBD patient's evolution from prodromal PD to clinical PD, the neurophysiological perturbation of cortical excitability has not yet been well elucidated. Moreover, no study describes the difference between RBD with and without abnormal TRODAT-1 SPECT.

Methods

By measuring the amplitude of motor evoked potentials (MEP), the cortical excitability changes after transcranial magnetic stimulation (TMS) were evaluated in 14 patients with RBD and eight healthy controls (HC). Seven of the 14 patients with RBD showed abnormal TRODAT-1 (TRA-RBD), and seven were normal (TRN-RBD). The tested parameters of cortical excitability include resting motor threshold (RMT), active motor threshold (AMT), short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), contralateral silence period (CSP), and input-output recruitment curve.

Results

The RMT and AMT showed no difference among the three studied groups. There was only SICI at inter-stimuli-interval 3 ms revealing group differences. The TRA-RBD demonstrated significant differences to HC in these aspects: decreased SICI, increased ICF, shortening of CSP, and augmented MEP amplitude at 100% RMT. Moreover, the TRA-RBD had a smaller MEP facilitation ratio at 50% and 100% of maximal voluntary contraction when compared to TRN-RBD. The TRN-RBD did not present any difference to HC.

Conclusion

We showed that TRA-RBD shared similar cortical excitability changes with clinical PD. These findings would provide further insight into the concept that RBD is the highly prevalent entity in prodromal PD.

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

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.

Input-output slope curve estimation in neural stimulation based on optimal sampling principles. J Neural Eng 2021;18:10.1088/1741-2552/abffe5. [PMID: 33975287 PMCID: PMC8384062 DOI: 10.1088/1741-2552/abffe5]

This paper discusses some of the practical limitations and issues, which exist for the input-output (IO) slope curve estimation (SCE) in neural, brain and spinal, stimulation techniques. The drawbacks of the SCE techniques by using existing uniform sampling and Fisher-information-based optimal IO curve estimation (FO-IOCE) methods are elaborated. A novel IO SCE technique is proposed with a modified sampling strategy and stopping rule which improve the SCE performance compared to these methods. The effectiveness of the proposed IO SCE is tested on 1000 simulation runs in transcranial magnetic stimulation (TMS), with a realistic model of motor evoked potentials. The results show that the proposed IO SCE method successfully satisfies the stopping rule, before reaching the maximum number of TMS pulses in 79.5% of runs, while the estimation based on the uniform sampling technique never converges and satisfies the stopping rule. At the time of successful termination, the proposed IO SCE method decreases the 95th percentile (mean value in the parentheses) of the absolute relative estimation errors (AREs) of the slope curve parameters up to 7.45% (2.2%), with only 18 additional pulses on average compared to that of the FO-IOCE technique. It also decreases the 95th percentile (mean value in the parentheses) of the AREs of the IO slope curve parameters up to 59.33% (16.71%), compared to that of the uniform sampling method. The proposed IO SCE also identifies the peak slope with higher accuracy, with the 95th percentile (mean value in the parentheses) of AREs reduced by up to 9.96% (2.01%) compared to that of the FO-IOCE method, and by up to 46.29% (13.13%) compared to that of the uniform sampling method.

Optimal Estimation of Neural Recruitment Curves Using Fisher Information: Application to Transcranial Magnetic Stimulation

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.

Pulse width dependence of motor threshold and input-output curve characterized with controllable pulse parameter transcranial magnetic stimulation

OBJECTIVE

To demonstrate the use of a novel controllable pulse parameter TMS (cTMS) device to characterize human corticospinal tract physiology.

METHODS

Motor threshold and input-output (IO) curve of right first dorsal interosseus were determined in 26 and 12 healthy volunteers, respectively, at pulse widths of 30, 60, and 120 μs using a custom-built cTMS device. Strength-duration curve rheobase and time constant were estimated from the motor thresholds. IO slope was estimated from sigmoid functions fitted to the IO data.

RESULTS

All procedures were well tolerated with no seizures or other serious adverse events. Increasing pulse width decreased the motor threshold and increased the pulse energy and IO slope. The average strength-duration curve time constant is estimated to be 196 μs, 95% CI [181 μs, 210 μs]. IO slope is inversely correlated with motor threshold both across and within pulse width. A simple quantitative model explains these dependencies.

CONCLUSIONS

Our strength-duration time constant estimate compares well to published values and may be more accurate given increased sample size and enhanced methodology. Multiplying the IO slope by the motor threshold may provide a sensitive measure of individual differences in corticospinal tract physiology.

SIGNIFICANCE

Pulse parameter control offered by cTMS provides enhanced flexibility that can contribute novel insights in TMS studies.