A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation.

BACKGROUND: Transcranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed. NEW METHOD: The pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers. RESULTS: Models of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ. COMPARISON WITH EXISTING METHODS: Current methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers. CONCLUSION: The pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

Quasistatic approximation in neuromodulation.

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

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.

Amplitude-determined seizure-threshold, electric field modeling, and electroconvulsive therapy antidepressant and cognitive outcomes.

Electroconvulsive therapy (ECT) pulse amplitude, which dictates the induced electric field (E-field) magnitude in the brain, is presently fixed at 800 or 900 milliamperes (mA) without clinical or scientific rationale. We have previously demonstrated that increased E-field strength improves ECT's antidepressant effect but worsens cognitive outcomes. Amplitude-determined seizure titration may reduce the E-field variability relative to fixed amplitude ECT. In this investigation, we assessed the relationships among amplitude-determined seizure-threshold (STa), E-field magnitude, and clinical outcomes in older adults (age range 50 to 80 years) with depression. Subjects received brain imaging, depression assessment, and neuropsychological assessment pre-, mid-, and post-ECT. STa was determined during the first treatment with a Soterix Medical 4×1 High Definition ECT Multi-channel Stimulation Interface (Investigation Device Exemption: G200123). Subsequent treatments were completed with right unilateral electrode placement (RUL) and 800 mA. We calculated Ebrain defined as the 90th percentile of E-field magnitude in the whole brain for RUL electrode placement. Twenty-nine subjects were included in the final analyses. Ebrain per unit electrode current, Ebrain/I, was associated with STa. STa was associated with antidepressant outcomes at the mid-ECT assessment and bitemporal electrode placement switch. Ebrain/I was associated with changes in category fluency with a large effect size. The relationship between STa and Ebrain/I extends work from preclinical models and provides a validation step for ECT E-field modeling. ECT with individualized amplitude based on E-field modeling or STa has the potential to enhance neuroscience-based ECT parameter selection and improve clinical outcomes.

Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation modality that can alter cortical excitability. However, it remains unclear how the subcellular elements of different neuron types are polarized by specific electric field (E-field) distributions. OBJECTIVE: To quantify neuronal polarization generated by tDCS in a multi-scale computational model. METHODS: We embedded layer-specific, morphologically-realistic cortical neuron models in a finite element model of the E-field in a human head and simulated steady-state polarization generated by conventional primary-motor-cortex-supraorbital (M1-SO) and 4 × 1 high-definition (HD) tDCS. We quantified somatic, axonal, and dendritic polarization of excitatory pyramidal cells in layers 2/3, 5, and 6, as well as inhibitory interneurons in layers 1 and 4 of the hand knob. RESULTS: Axonal and dendritic terminals were polarized more than the soma in all neurons, with peak axonal and dendritic polarization of 0.92 mV and 0.21 mV, respectively, compared to peak somatic polarization of 0.07 mV for 1.8 mA M1-SO stimulation. Both montages generated regions of depolarization and hyperpolarization beneath the M1 anode; M1-SO produced slightly stronger, more diffuse polarization peaking in the central sulcus, while 4 × 1 HD produced higher peak polarization in the gyral crown. The E-field component normal to the cortical surface correlated strongly with pyramidal neuron somatic polarization (R2>0.9), but exhibited weaker correlations with peak pyramidal axonal and dendritic polarization (R2:0.5-0.9) and peak polarization in all subcellular regions of interneurons (R2:0.3-0.6). Simulating polarization by uniform local E-field extracted at the soma approximated the spatial distribution of tDCS polarization but produced large errors in some regions (median absolute percent error: 7.9 %). CONCLUSIONS: Polarization of pre- and postsynaptic compartments of excitatory and inhibitory cortical neurons may play a significant role in tDCS neuromodulation. These effects cannot be predicted from the E-field distribution alone but rather require calculation of the neuronal response.

Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.

The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10-20 System's neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.

Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry.

Background: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation modality that can alter cortical excitability. However, it remains unclear how the subcellular elements of different neuron types are polarized by specific electric field (E-field) distributions. Objective: To quantify neuronal polarization generated by tDCS in a multi-scale computational model. Methods: We embedded layer-specific, morphologically-realistic cortical neuron models in a finite element model of the E-field in a human head and simulated steady-state polarization generated by conventional primary-motor-cortex-supraorbital (M1-SO) and 4×1 high-definition (HD) tDCS. We quantified somatic, axonal, and dendritic polarization of excitatory pyramidal cells in layers 2/3, 5, and 6, as well as inhibitory interneurons in layers 1 and 4 of the hand knob. Results: Axonal and dendritic terminals were polarized more than the soma in all neurons, with peak axonal and dendritic polarization of 0.92 mV and 0.21 mV, respectively, compared to peak somatic polarization of 0.07 mV for 1.8 mA M1-SO stimulation. Both montages generated regions of depolarization and hyperpolarization beneath the M1 anode; M1-SO produced slightly stronger, more diffuse polarization peaking in the central sulcus, while 4×1 HD produced higher peak polarization in the gyral crown. Simulating polarization by uniform local E-field approximated the spatial distribution of tDCS polarization but produced large errors in some regions. Conclusions: Polarization of pre- and postsynaptic compartments of excitatory and inhibitory cortical neurons may play a significant role in tDCS neuromodulation. These effects cannot be predicted from the E-field distribution alone but rather require calculation of the neuronal response.

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.

Electroconvulsive Therapy: Mechanisms of Action, Clinical Considerations, and Future Directions.

LEARNING OBJECTIVES: • Outline and discuss the fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes• Summarize the overview of ECT, its efficacy in treating depression, the known effects on cognition, evidence of mechanisms, and future directions. ABSTRACT: Electroconvulsive therapy (ECT) is the most effective treatment for a variety of psychiatric illnesses, including treatment-resistant depression, bipolar depression, mania, catatonia, and clozapine-resistant schizophrenia. ECT is a medical and psychiatric procedure whereby electrical current is delivered to the brain under general anesthesia to induce a generalized seizure. ECT has evolved a great deal since the 1930s. Though it has been optimized for safety and to reduce adverse effects on cognition, issues persist. There is a need to understand fundamental physiologic, cellular, and molecular mechanisms of ECT to devise strategies to optimize therapeutic outcomes. Clinical trials that set out to adjust parameters, electrode placement, adjunctive medications, and patient selection are critical steps towards the goal of improving outcomes with ECT. This narrative review provides an overview of ECT, its efficacy in treating depression, its known effects on cognition, evidence of its mechanisms, and future directions.

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.

Optimized monophasic pulses with equivalent electric field for rapid-rate transcranial magnetic stimulation.

Objective.Transcranial 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.Approach.A 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 results.Depending on the pulse duration and E-field matching constraints, the optimized waveforms produced 12%-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.Significance.The 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.

Dependence of cortical neuronal strength-duration properties on TMS pulse shape.

OBJECTIVE: The aim of present study was to explore the effects of different combinations of transcranial magnetic stimulation (TMS) pulse width and pulse shape on cortical strength-duration time constant (SDTC) and rheobase measurements. METHODS: Resting motor thresholds (RMT) at pulse widths (PW) of 30, 45, 60, 90 and 120 µs and M-ratios of 0.2, 0.1 and 0.025 were determined using figure-of-eight coil with initial posterior-to-anterior induced current. The M-ratio indicates the relative phases of the induced current with lower values signifying a more unidirectional stimulus. Strength-duration time constant (SDTC) and rheobase were estimated for each M-ratio and various PW combinations. Simulations of biophysically realistic cortical neuron models assessed underlying neuronal populations and physiological mechanisms mediating pulse shape effects on strength-duration properties. RESULTS: The M-ratio exerted significant effect on SDTC (F(2,44) = 4.386, P = 0.021), which was longer for M-ratio of 0.2 (243.4 ± 61.2 µs) compared to 0.025 (186.7 ± 52.5 µs, P = 0.034). Rheobase was significantly smaller when assessed with M-ratio 0.2 compared to 0.025 (P = 0.026). SDTC and rheobase values were most consistent with pulse width sets of 30/45/60/90/120 µs, 30/60/90/120 µs, and 30/60/120 µs. Simulation studies indicated that isolated pyramidal neurons in layers 2/3, 5, and large basket-cells in layer 4 exhibited SDTCs comparable to experimental results. Further, simulation studies indicated that reducing transient Na+ channel conductance increased SDTC with larger increases for higher M-ratios. CONCLUSIONS: Cortical strength-duration curve properties vary with pulse shape, and the modulating effect of the hyperpolarising pulse phase on cortical axonal transient Na+ conductances could account for these changes, although a shift in the recruited neuronal populations may contribute as well. SIGNIFICANCE: The dependence of the cortical strength-duration curve properties on the TMS pulse shape and pulse width selection underscores the need for consistent measurement methods across studies and the potential to extract information about pathophysiological processes.

Responses of model cortical neurons to temporal interference stimulation and related transcranial alternating current stimulation modalities.

Objective.Temporal interference stimulation (TIS) was proposed as a non-invasive, focal, and steerable deep brain stimulation method. However, the mechanisms underlying experimentally-observed suprathreshold TIS effects are unknown, and prior simulation studies had limitations in the representations of the TIS electric field (E-field) and cerebral neurons. We examined the E-field and neural response characteristics for TIS and related transcranial alternating current stimulation modalities.Approach.Using the uniform-field approximation, we simulated a range of stimulation parameters in biophysically realistic model cortical neurons, including different orientations, frequencies, amplitude ratios, amplitude modulation, and phase difference of the E-fields, and obtained thresholds for both activation and conduction block.Main results. For two E-fields with similar amplitudes (representative of E-field distributions at the target region), TIS generated an amplitude-modulated (AM) total E-field. Due to the phase difference of the individual E-fields, the total TIS E-field vector also exhibited rotation where the orientations of the two E-fields were not aligned (generally also at the target region). TIS activation thresholds (75-230 V m-1) were similar to those of high-frequency stimulation with or without modulation and/or rotation. For E-field dominated by the high-frequency carrier and with minimal amplitude modulation and/or rotation (typically outside the target region), TIS was less effective at activation and more effective at block. Unlike AM high-frequency stimulation, TIS generated conduction block with some orientations and amplitude ratios of individual E-fields at very high amplitudes of the total E-field (>1700 V m-1).Significance. The complex 3D properties of the TIS E-fields should be accounted for in computational and experimental studies. The mechanisms of suprathreshold cortical TIS appear to involve neural activity block and periodic activation or onset response, consistent with computational studies of peripheral axons. These phenomena occur at E-field strengths too high to be delivered tolerably through scalp electrodes and may inhibit endogenous activity in off-target regions, suggesting limited significance of suprathreshold TIS.

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.

Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper.

Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.

Subsecond multichannel magnetic control of select neural circuits in freely moving flies.

Precisely timed activation of genetically targeted cells is a powerful tool for the study of neural circuits and control of cell-based therapies. Magnetic control of cell activity, or 'magnetogenetics', using magnetic nanoparticle heating of temperature-sensitive ion channels enables remote, non-invasive activation of neurons for deep-tissue applications and freely behaving animal studies. However, the in vivo response time of thermal magnetogenetics is currently tens of seconds, which prevents precise temporal modulation of neural activity. Moreover, magnetogenetics has yet to achieve in vivo multiplexed stimulation of different groups of neurons. Here we produce subsecond behavioural responses in Drosophila melanogaster by combining magnetic nanoparticles with a rate-sensitive thermoreceptor (TRPA1-A). Furthermore, by tuning magnetic nanoparticles to respond to different magnetic field strengths and frequencies, we achieve subsecond, multichannel stimulation. These results bring magnetogenetics closer to the temporal resolution and multiplexed stimulation possible with optogenetics while maintaining the minimal invasiveness and deep-tissue stimulation possible only by magnetic control.

A wireless millimetric magnetoelectric implant for the endovascular stimulation of peripheral nerves.

Implantable bioelectronic devices for the simulation of peripheral nerves could be used to treat disorders that are resistant to traditional pharmacological therapies. However, for many nerve targets, this requires invasive surgeries and the implantation of bulky devices (about a few centimetres in at least one dimension). Here we report the design and in vivo proof-of-concept testing of an endovascular wireless and battery-free millimetric implant for the stimulation of specific peripheral nerves that are difficult to reach via traditional surgeries. The device can be delivered through a percutaneous catheter and leverages magnetoelectric materials to receive data and power through tissue via a digitally programmable 1 mm × 0.8 mm system-on-a-chip. Implantation of the device directly on top of the sciatic nerve in rats and near a femoral artery in pigs (with a stimulation lead introduced into a blood vessel through a catheter) allowed for wireless stimulation of the animals' sciatic and femoral nerves. Minimally invasive magnetoelectric implants may allow for the stimulation of nerves without the need for open surgery or the implantation of battery-powered pulse generators.

Isolating two sources of variability of subcortical stimulation to quantify fluctuations of corticospinal tract excitability.

OBJECTIVE: Investigate the variability previously found with cortical stimulation and handheld transcranial magnetic stimulation (TMS) coils, criticized for its high potential of coil position fluctuations, bypassing the cortex using deep brain electrical stimulation (DBS) of the corticospinal tract with fixed electrodes where both latent variations of the coil position of TMS are eliminated and cortical excitation fluctuations should be absent. METHODS: Ten input-output curves were recorded from five anesthetized cats with implanted DBS electrodes targeting the corticospinal tract. Goodness of fit of regressions with a conventional single variability source as well as a dual variability source model was quantified using a Schwarz Bayesian Information approach to avoid overfitting. RESULTS: Motor evoked potentials (MEPs) through DBS of the corticospinal tract revealed short-term fluctuations in excitability of the targeted neuron pathway reflecting endogenous input-side variability at similar magnitude as TMS despite bypassing cortical networks. CONCLUSION: Input-side variability, i.e., variability resulting in changing MEP amplitudes as if the stimulation strength was modulated, also emerges in electrical stimulation at a similar degree and is not primarily a result of varying stimulation, such as minor coil movements in TMS. More importantly, this variability component is present, although the cortex is bypassed. Thus, it may be of spinal origin, which can include cortical input from spinal projections. Further, the nonlinearity of the compound variability entails complex heteroscedastic non-Gaussian distributions and typically does not allow simple linear averages in statistical analysis of MEPs. As the average is dominated by outliers, it risks bias. With appropriate regression, the net effects of excitatory and inhibitory inputs to the targeted neuron pathways become noninvasively observable and quantifiable. SIGNIFICANCE: The neural responses evoked by artificial stimulation in the cerebral cortex are variable. For example, MEPs in response to repeated presentations of the same stimulus can vary from no response to saturation across trials. Several sources of such variability have been suggested, and most of them may be technical in nature, but localization is missing.