Multi-Scale Computational Models for Electrical Brain Stimulation

Seo, Hyeon; Jun, Sung Chan

doi:10.3389/fnhum.2017.00515

Cited by 28 publications

(9 citation statements)

References 112 publications

(189 reference statements)

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“…Here, we postulate that subject-specific ctDCS electric field orientation within the cerebellar lobules also needs to be optimized based on subject-specific head modeling where our CLOS pipeline can be useful (see Figure 7). The direction of the electric field vector requires investigation using multi-scale modeling (Seo and Jun, 2017) vis-à-vis Purkinje cell, climbing fiber, and parallel fiber orientations at each lobule, which is our future work. This is motivated by the differences in the electric field in the mediolateral (X) direction that may affect the parallel fibers differently between Celnik and Manto montages due to the difference in the E x direction (see Supplementary Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Cerebellar Lobules Optimal Stimulation (CLOS): A Computational Pipeline to Optimize Cerebellar Lobule-Specific Electric Field Distribution

Rezaee

Dutta

2019

Front. Neurosci.

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Objective Cerebellar transcranial direct current stimulation (ctDCS) is challenging due to the complexity of the cerebellar structure which is reflected by the well-known variability in ctDCS effects. Therefore, our objective is to present a freely available computational modeling pipeline for cerebellar lobules’ optimal stimulation (CLOS). Methods CLOS can optimize lobule-specific electric field distribution following finite element analysis (FEA) using freely available computational modeling pipelines. We modeled published ctDCS montages with 5 cm × 5 cm anode placed 3 cm lateral to inion, and the same sized cathode was placed on the: (1) contralateral supra-orbital area (called Manto montage), and (2) buccinators muscle (called Celnik montage). Also, a published (3) 4×1 HD-ctDCS electrode montage was modeled. We also investigated the effects of the subject-specific head model versus Colin 27 average head model on lobule-specific electric field distribution. Three-way analysis of variance (ANOVA) was used to determine the effects of lobules, montage, and head model on the electric field distribution. The differences in lobule-specific electric field distribution across different freely available computational pipelines were also evaluated using subject-specific head model. We also presented an application of our computational pipeline to optimize a ctDCS electrode montage to deliver peak electric field at the cerebellar lobules VII-IX related to ankle function. Results Eta-squared effect size after three-way ANOVA for electric field strength was 0.05 for lobule, 0.00 for montage, 0.04 for the head model, 0.01 for lobule ∗ montage interaction, 0.01 for lobule ∗ head model interaction, and 0.00 for montage ∗ head model interaction. The electric field strength of both the Celnik and the Manto montages affected the lobules Crus I/II, VIIb, VIII, and IX of the targeted cerebellar hemisphere where Manto montage had a spillover to the contralateral cerebellar hemisphere. The 4×1 HD-ctDCS montage primarily affected the lobules Crus I/II of the targeted cerebellar hemisphere. All three published ctDCS montages were found to be not optimal for ankle function (lobules VII-IX), so we presented a novel HD-ctDCS electrode montage. Discussion Our freely available CLOS pipeline can be leveraged to optimize electromagnetic stimulation to target cerebellar lobules related to different cognitive and motor functions.

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Section: Discussionmentioning

confidence: 99%

Cerebellar Lobules Optimal Stimulation (CLOS): A Computational Pipeline to Optimize Cerebellar Lobule-Specific Electric Field Distribution

Rezaee

Dutta

2019

Front. Neurosci.

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“…Little is known about the effects of different frequencies on neural circuitry or why specific frequencies are less effective in certain diseases [3,6,8,10]. Theoretical approaches have focused on modeling the spread of current from implanted electrodes [14,15], and empirical work on the general physiology of invasive neurostimulation has focused heavily on avoiding tissue damage or over-excitation [16,17]. There is also a large body of work exploring the physics of non-invasive stimulation, much of it focused on modeling how patient anatomy influences magnetic and electrical field propagation into cortex [18e23].…”

Section: Introductionmentioning

confidence: 99%

Consistent linear and non-linear responses to invasive electrical brain stimulation across individuals and primate species with implanted electrodes

et al. 2019

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a b s t r a c tBackground: Electrical neuromodulation via implanted electrodes is used in treating numerous neurological disorders, yet our knowledge of how different brain regions respond to varying stimulation parameters is sparse. Objective/Hypothesis: We hypothesized that the neural response to electrical stimulation is both regionspecific and non-linearly related to amplitude and frequency. Methods: We examined evoked neural responses following 400 ms trains of 10e400 Hz electrical stimulation ranging from 0.1 to 10 mA. We stimulated electrodes implanted in cingulate cortex (dorsal anterior cingulate and rostral anterior cingulate) and subcortical regions (nucleus accumbens, amygdala) of nonhuman primates (NHP, N ¼ 4) and patients with intractable epilepsy (N ¼ 15) being monitored via intracranial electrodes. Recordings were performed in prefrontal, subcortical, and temporal lobe locations. Results: In subcortical regions as well as dorsal and rostral anterior cingulate cortex, response waveforms depended non-linearly on frequency (Pearson's linear correlation r < 0.39), but linearly on current (r > 0.58). These relationships between location, and input-output characteristics were similar in homologous brain regions with average Pearson's linear correlation values r > 0.75 between species and linear correlation values between participants r > 0.75 across frequency and current values per brain region. Evoked waveforms could be described by three main principal components (PCs) which allowed us to successfully predict response waveforms across individuals and across frequencies using PC strengths as functions of current and frequency using brain region specific regression models. Conclusions: These results provide a framework for creation of an atlas of input-output relationships which could be used in the principled selection of stimulation parameters per brain region.

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“…Scientific computation has been trending toward multi-scale modeling, where models are developed to explore biological phenomena across multiple length or hierarchical scales (Yu and Bagheri, 2016 ; Seo and Jun, 2017 ). Neural computation spans molecular (Naoki et al, 2005 ; Bartol et al, 2015 ), cellular (Jarsky et al, 2005 ; Migliore and Migliore, 2012 ), network (Hendrickson et al, 2015 ), and cortical systems (Markram, 2006 ) hierarchical scales.…”

Section: Introductionmentioning

confidence: 99%

A Glutamatergic Spine Model to Enable Multi-Scale Modeling of Nonlinear Calcium Dynamics

Mergenthal

Bingham

et al. 2018

Front. Comput. Neurosci.

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In synapses, calcium is required for modulating synaptic transmission, plasticity, synaptogenesis, and synaptic pruning. The regulation of calcium dynamics within neurons involves cellular mechanisms such as synaptically activated channels and pumps, calcium buffers, and calcium sequestrating organelles. Many experimental studies tend to focus on only one or a small number of these mechanisms, as technical limitations make it difficult to observe all features at once. Computational modeling enables incorporation of many of these properties together, allowing for more complete and integrated studies. However, the scale of existing detailed models is often limited to synaptic and dendritic compartments as the computational burden rapidly increases when these models are integrated in cellular or network level simulations. In this article we present a computational model of calcium dynamics at the postsynaptic spine of a CA1 pyramidal neuron, as well as a methodology that enables its implementation in multi-scale, large-scale simulations. We first present a mechanistic model that includes individually validated models of various components involved in the regulation of calcium at the spine. We validated our mechanistic model by comparing simulated calcium levels to experimental data found in the literature. We performed additional simulations with the mechanistic model to determine how the simulated calcium activity varies with respect to presynaptic-postsynaptic stimulation intervals and spine distance from the soma. We then developed an input-output (IO) model that complements the mechanistic calcium model and provide a computationally efficient representation for use in larger scale modeling studies; we show the performance of the IO model compared to the mechanistic model in terms of accuracy and speed. The models presented here help achieve two objectives. First, the mechanistic model provides a comprehensive platform to describe spine calcium dynamics based on individual contributing factors. Second, the IO model is trained on the main dynamical features of the mechanistic model and enables nonlinear spine calcium modeling on the cell and network level simulation scales. Utilizing both model representations provide a multi-level perspective on calcium dynamics, originating from the molecular interactions at spines and propagating the effects to higher levels of activity involved in network behavior.

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Multi-Scale Computational Models for Electrical Brain Stimulation

Cited by 28 publications

References 112 publications

Cerebellar Lobules Optimal Stimulation (CLOS): A Computational Pipeline to Optimize Cerebellar Lobule-Specific Electric Field Distribution

Cerebellar Lobules Optimal Stimulation (CLOS): A Computational Pipeline to Optimize Cerebellar Lobule-Specific Electric Field Distribution

Consistent linear and non-linear responses to invasive electrical brain stimulation across individuals and primate species with implanted electrodes

A Glutamatergic Spine Model to Enable Multi-Scale Modeling of Nonlinear Calcium Dynamics

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