Can transcranial electric stimulation with multiple electrodes reach deep targets?

Huang, Yu; Parra, Lucas C.

doi:10.1016/j.brs.2018.09.010

Cited by 106 publications

(154 citation statements)

References 32 publications

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“…This is true for cortical locations as well as deep brain areas. This appears to conflict with our previous conclusion on this topic (Huang and Parra, 2019), where we show that the two techniques were largely the same in terms of focality. One caveat of that earlier work is that the electrode arrays had not been systematically optimized, and thus we left the possibility open that IFS could be more focal than HD-TES.…”

Section: Discussioncontrasting

confidence: 99%

“…The promise of non-invasive deep brain stimulation has caused considerable excitement in the research community and in the news media Shen, 2018). Our subsequent analysis of IFS suggests that it may not differ much from other multi-electrode methods in terms of intensity of stimulation (Huang and Parra, 2019). However, this conclusion is limited to the case of IFS using only two electrodes for each of the two currents.…”

Section: Introductionmentioning

confidence: 80%

“…The purpose of this lower limit is to distribute currents over multiple locations and therefore reduce sensation while still injecting the same total current. We have done this in previous work and found the solutions 6 with numerical optimization Huang and Parra, 2019). Now we realize that these are still linear inequality constraints and thus result in a convex feasibility region.…”

Section: Closed Form Solution For Max-intensity Optimizationmentioning

confidence: 99%

“…We use the terms "modulation depth" and "intensity" interchangeably in this paper but we only optimize the modulation depth for both HD-TES and IFS (see Figures 3, 4 and 5 for the differences). For IFS, if the electric field for one frequency is E 1 and for the other frequency E 2 then the modulation depth of the interfering fields is 2 min(|E 1 |, |E 2 |) (Huang and Parra, 2019). Let's assume that these two fields are generated by the current distributions s 1 and s 2 , each oscillating at their own frequency.…”

Section: Optimization Of Intensity In Interferential Stimulationmentioning

confidence: 99%

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Optimization of interferential stimulation of the human brain with electrode arrays

Huang

Datta

Parra

2019

Preprint

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Interferential stimulation (IFS) has generated considerable interest recently because of its potential to achieve focal electrical stimulation in deep brain areas despite applying currents transcranially. Conventionally, IFS applies sinusoidal currents through two electrode pairs with close-by frequencies.Here we propose to use two arrays of scalp electrodes with current intensity through each electrode optimized to target a desired location in the brain. We formulate rigorous optimization criteria for IFS to achieve either maximal modulation depth or most focal stimulation. For max-modulation optimization we show analytically that the solution is the same for IFS as for conventional multi-electrode stimulation. We proof that the solution can be found directly from the forward model without the need for algorithmic optimization. For the max-focality criterion the solution requires numerical optimization. Once optimized, we find that IFS can achieve more focal modulation of stimulation intensity than conventional stimulation with a single frequency, both at the cortical surface and deep in the brain.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 80%

Section: Closed Form Solution For Max-intensity Optimizationmentioning

confidence: 99%

Section: Optimization Of Intensity In Interferential Stimulationmentioning

confidence: 99%

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Optimization of interferential stimulation of the human brain with electrode arrays

Huang

Datta

Parra

2019

Preprint

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“…We also note recent efforts to increase stimulation intensity up to 6mA in humans by distributing current across multiple electrodes (85), which can achieve electric fields of 3 V/m in the brain (82). Given our estimates here, this would generate effects on synaptic plasticity of ~3%, notably affecting a few synapses most strongly ( Figure 8D).…”

Section: Dose Responsementioning

confidence: 69%

Direct current stimulation boosts associative Hebbian synaptic plasticity and maintains its pathway specificity

Kronberg

Rahman

Lafon³

et al. 2019

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Background: There is evidence that transcranial direct current stimulation (tDCS) can improve learning performance. Arguably, this effect is related to long term potentiation (LTP), but the precise biophysical mechanisms remain unknown. Hypothesis: We propose that direct current stimulation (DCS) causes small changes in postsynaptic membrane potential during ongoing endogenous synaptic activity. The altered voltage dynamics in the postsynaptic neuron then modify synaptic strength via the machinery of endogenous voltage-dependent Hebbian plasticity. This hypothesis predicts that DCS should exhibit Hebbian properties, namely pathway specificity and associativity. Methods: We studied the effects of DCS applied during the induction of LTP in the CA1 region of rat hippocampal slices and using a biophysical computational model. Results: DCS enhanced LTP, but only at synapses that were undergoing plasticity, confirming that DCS respects Hebbian pathway specificity. When different synaptic pathways cooperated to produce LTP, DCS enhanced this cooperation, boosting Hebbian associativity. Further slice experiments and computer simulations support a model where polarization of postsynaptic pyramidal neurons drives these plasticity effects through endogenous Hebbian mechanisms. The model is able to reconcile several experimental results by capturing the complex interaction between the induced electric field, neuron morphology, and endogenous neural activity. Conclusions: These results suggest that tDCS can enhance associative learning. We propose that clinical tDCS should be applied during tasks that induce Hebbian plasticity to harness this phenomenon, and that the effects should be task specific through their interaction with endogenous plasticity mechanisms. Models that incorporate brain state and plasticity mechanisms may help to improve prediction of tDCS outcomes.Abbreviations: tDCS (transcranial direct current stimulation); DCS (direct current stimulation); LTP (long term potentiation); TBS (theta burst stimulation); STDP (spike-timing dependent plasticity) Effect of DCS on LTP is pathway specificHebbian synaptic plasticity is classically characterized as a pathway specific process, i.e. only pathways that are coactive with the postsynaptic neuron are strengthened (35). Our proposal that DCS enhances LTP through membrane potential implies that the effects of DCS should follow this pathway specificity. We tested this by monitoring two independent synaptic pathways in CA1 (Figure 2A). During induction, the strong pathway received TBS while the other inactive pathway was not stimulated. As expected, LTP was observed in the strong pathway ( Figure 2B black; 1.377+-0.052, N=16, p=2.8E-6), but not the inactive pathway ( Figure 2B gray; 0.986+-0.031, N=14, p=0.657), demonstrating the wellestablished pathway specificity of LTP (35). When this induction protocol was paired with anodal DCS, LTP was enhanced only in the strong pathway ( Figure 2B red; 1.613+-0.071, N=14, p=0.011 vs. control), while the inactive pathway was unaff...

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Nonlinearities and timescales in neural models of temporal interference stimulation

Plovie,

Schoeters,

Tarnaud

et al. 2024

Bioelectromagnetics

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In temporal interference (TI) stimulation, neuronal cells react to two interfering sinusoidal electric fields with a slightly different frequency (, in the range of about 1–4 kHz, in the range of about 1–100 Hz). It has been previously observed that for the same input intensity, the neurons do not react to a purely sinusoidal signal at or . This study seeks a better understanding of the largely unknown mechanisms underlying TI neuromodulation. To this end, single‐compartment models are used to simulate computationally the response of neurons to the sinusoidal and TI waveform. This study compares five different neuron models: Hodgkin‐Huxley (HH), Frankenhaeuser–Huxley (FH), along with leaky, exponential, and adaptive‐exponential integrate‐and‐fire (IF). It was found that IF models do not entirely reflect the experimental behavior while the HH and FH model did qualitatively replicate the observed neural responses. Changing the time constants and steady state values of the ion gates in the FH model alters the response to both the sinusoidal and TI signal, possibly reducing the firing threshold of the sinusoidal input below that of the TI input. The results show that in the modified (simplified) model, TI stimulation is not qualitatively impacted by nonlinearities in the current–voltage relation. In contrast, ion channels have a significant impact on the neuronal response. This paper offers insights into neuronal biophysics and computational models of TI stimulation.

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Can transcranial electric stimulation with multiple electrodes reach deep targets?

Cited by 106 publications

References 32 publications

Optimization of interferential stimulation of the human brain with electrode arrays

Optimization of interferential stimulation of the human brain with electrode arrays

Direct current stimulation boosts associative Hebbian synaptic plasticity and maintains its pathway specificity

Nonlinearities and timescales in neural models of temporal interference stimulation

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