Stefan M. Goetz

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.

Effect of coil orientation on strength-duration time constant and I-wave activation with controllable pulse parameter transcranial magnetic stimulation

Highlights • S–D time constants are longer for anterior–posterior than posterior–anterior induced currents.• Brief (30 μs) anterior-posterior currents evoke the longest latency MEP.• Selective stimulation of neural elements may be achieved by manipulating pulse width and orientation.

Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation

Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses.

Circuit topology and control principle for a first magnetic stimulator with fully controllable waveform

Magnetic stimulation pulse sources are very inflexible high-power devices. The incorporated circuit topology is usually limited to a single pulse type. However, experimental and theoretical work shows that more freedom in choosing or even designing waveforms could notably enhance existing methods. Beyond that, it even allows entering new fields of application. We propose a technology that can solve the problem. Even in very high frequency ranges, the circuitry is very flexible and is able generate almost every waveform with unrivaled accuracy. This technology can dynamically change between different pulse shapes without any reconfiguration, recharging or other changes; thus the waveform can be modified also during a high-frequency repetitive pulse train. In addition to the option of online design and generation of still unknown waveforms, it amalgamates all existing device types with their specific pulse shapes, which have been leading an independent existence in the past years. These advantages were achieved by giving up the common basis of all magnetic stimulation devices so far, i.e., the high-voltage oscillator. Distributed electronics handle the high power dividing the high voltage and the required switching rate into small portions.

A Novel Model Incorporating Two Variability Sources for Describing Motor Evoked Potentials

OBJECTIVE Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input-output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data. METHODS A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison. RESULTS The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve. CONCLUSIONS MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability-information that has previously been treated as noise.

Comparison of coil designs for peripheral magnetic muscle stimulation

The recent application of magnetic stimulation in rehabilitation is often said to solve key drawbacks of the established electrical method. Magnetic fields cause less pain, allow principally a better penetration of inhomogeneous biologic tissue and do not require skin contact. However, in most studies the evoked muscle force has been disappointing. In this paper, a comparison of a classical round circular geometry, a commercial muscle-stimulation coil and a novel design is presented, with special emphasis on the physical field properties. These systems show markedly different force responses for the same magnetic energy and highlight the enormous potential of different coil geometries. The new design resulted in a slope of the force recruiting curve being more than two and a half times higher than the other coils. The data were analyzed with respect to the underlying physical causes and field conditions. After a parameter-extraction approach, the results for the three coils span a two-dimensional space with clearly distinguishable degrees of freedom, which can be manipulated nearly separately and reflect the two main features of a field; the peak amplitude and its decay with the distance.

A model of variability in brain stimulation evoked responses

The input-output (IO) curve of cortical neuron populations is a key measure of neural excitability and is related to other response measures including the motor threshold which is widely used for individualization of neurostimulation techniques, such as transcranial magnetic stimulation (TMS). The IO curve parameters provide biomarkers for changes in the state of the target neural population that could result from neurostimulation, pharmacological interventions, or neurological and psychiatric conditions. Conventional analyses of IO data assume a sigmoidal shape with additive Gaussian scattering that allows simple regression modeling. However, careful study of the IO curve characteristics reveals that simple additive noise does not account for the observed IO variability. We propose a consistent model that adds a second source of intrinsic variability on the input side of the IO response. We develop an appropriate mathematical method for calibrating this new nonlinear model. Finally, the modeling framework is applied to a representative IO data set. With this modeling approach, previously inexplicable stochastic behavior becomes obvious. This work could lead to improved algorithms for estimation of various excitability parameters including established measures such as the motor threshold and the IO slope, as well as novel measures relating to the variability characteristics of the IO response that could provide additional insight into the state of the targeted neural population.

Muscle force development after low‐frequency magnetic burst stimulation in dogs

Magnetic stimulation allows for painless and non‐invasive extrinsic motor nerve stimulation. Despite several advantages, the limited coupling to the target reduces the application of magnetic pulses in rehabilitation. According to experience with electrical stimulation, magnetic bursts could remove this constraint.

Transcranial Magnetic Stimulation Device With Reduced Acoustic Noise

Transcranial magnetic stimulation (TMS) is widely used for noninvasive activation of neurons in the brain for research and clinical applications. The strong, brief magnetic pulse generated in TMS is associated with a loud (>100 dB) clicking sound that can impair hearing and that activates auditory circuits in the brain. We introduce a two-pronged solution to reduce TMS noise by redesigning both the pulse waveform and the coil structure. First, the coil current pulse duration is reduced which shifts a substantial portion of the pulse acoustic spectrum above audible frequencies. Second, the mechanical structure of the stimulation coil is designed to suppress the emergence of the sound at the source, diminish down-mixing of high-frequency sound into the audible range, and impede the transmission of residual sound to the coil surface but dissipate it away from the casing. A prototype coil driven with ultrabrief current pulses (down to 45-μs biphasic duration) is demonstrated to reduce the peak sound pressure level by more than 25 dB compared to a conventional TMS configuration, resulting in loudness reduction by more than 14-fold. These results motivate improved mechanical design of TMS coils as well as design of TMS pulse generators with shorter pulse durations and increased voltage limits with the objective of reducing TMS acoustic noise while retaining the neurostimulation strength.

Impulse Noise of Transcranial Magnetic Stimulation: Measurement, Safety, and Auditory Neuromodulation

[1] Koran LM, Hanna GL, Hollander E, Nestadt G, Simpson HB, American Psychiatric Association. Practice guidelines for the treatment of patients with obsessivecompulsive disorder. Am J Psychiatry 2007;164:5e53. [2] Fitzgeral PJ, Seemann JR, Maren S. Can fear extinction be enhanced? A review of pharmacological and behavioural findings. Brain Res Bull 2013; 105C:46e60. [3] Zaghloul A,Wieraszko A. Modulation of learning and hyppocampal, neuronal plasticity by ripetitive transcranial magnetic stimulation (rTMS). Bioelectromagnetics 2006;27:288e94. [4] Goodman W, Price L, Rasmussen S, et al. The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), part I: development, use, and reliability. Arch Gen Psychiatry 1989;46:1006e11. [5] Goodman W, Price L, Rasmussen S, et al. The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), part II: validity. Arch Gen Psychiatry 1989;46:1012e6. [6] Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56e62. [7] Hall RC. Global assessment of functioning: a modified scale. Psychosomatics 1995;36:267e75. [8] Pallanti S, Hollander E, Bienstock C, et al. Treatment non-response in OCD: methodological issues and operational definitions. Int J Neuropsychopharmacol 2002;5:181e91. [9] BerlimMT, Neufeld NH, Van den Eynde F. Repetitive transcranial magnetic stimulation (rTMS) for obsessive-compulsive disorder (OCD): an exploratory metaanalysis of randomized and sham-controlled trials. J Psychiatr Res 2013;47: 999e1006.