Towards precise brain stimulation: Is electric field simulation related to neuromodulation?

Antonenko, Daria; Thielscher, Axel; Saturnino, Guilherme B.; Aydın, Semiha; Ittermann, Bernd; Grittner, Ulrike; Flöel, Agnes

doi:10.1016/j.brs.2019.03.072

Cited by 125 publications

(143 citation statements)

References 61 publications

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“…However, the induced electric field also extended to neighboring regions, such as the OFC and the medial PFC for rFPC-tDCS and premotor and somatosensory areas for lM1-tDCS. The latter resemble recent findings in lM1-tDCS with SimNIBS [40]. Notwithstanding the certain degree of diffusion of the induced electric field, we could establish that the spatial effects of tDCS for both protocols were consistent with the target area.…”

supporting

confidence: 88%

“…Further, both active tDCS protocols led to an increase in the simulated electric field strength, corresponding with enhanced local plasticity as shown in a recent study assessing the neurophysiological effects of tDCS with SimNIBS and magnetic resonance spectroscopy [40]. Accordingly, a mechanism by which rFPC-tDCS may regulate exploration comes about through an enhanced recruitment of the local neuronal populations in the rFPC region.…”

Section: Discussionmentioning

confidence: 65%

“…To mitigate that limitation, we carried out electric field simulations in the individual anatomy and assessed the strength and focality of the induced electric field for each tDCS target. The simulations demonstrated an enhanced focal activation in the targeted area, corresponding with increases in activation (excitatory effects [40]) and with a focality and peak value not significantly different between both active stimulation protocols. However, the induced electric field also extended to neighboring regions, such as the OFC and the medial PFC for rFPC-tDCS and premotor and somatosensory areas for lM1-tDCS.…”

mentioning

confidence: 85%

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Modulation of neural activity in frontopolar cortex drives reward-based motor learning

Ruiz

Maudrich

Kalloch

et al. 2020

Preprint

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Decision-making is increasingly being recognised to play a role in learning motor skills.Understanding the neural processes regulating motor decision-making is therefore essential to identify mechanisms that contribute to motor skill learning. In decision-making tasks, the frontopolar cortex (FPC) is involved in tracking the reward of different alternative choices, as well as their reliability. Whether this FPC function extends to reward landscapes associated with a continuous movement dimension remains unknown. Here we used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants completed a motor sequence learning task using trial-wise reward feedback to discover a hidden performance goal along a continuous dimension: timing. As a control condition, we modulated contralateral motor cortex (left M1) activity with tDCS, which has been shown to benefit motor skill learning but less consistently reward-based motor learning. Each active tDCS condition was contrasted to sham stimulation. Right FPC-tDCS led to faster learning primarily through a regulation of exploration, without concurrent modulation of motor noise. A Bayesian computational model revealed that following rFPC-tDCS, participants had a higher expectation of reward, consistent with their faster learning. These higher reward estimates were inferred to be less volatile, and thus participants under rFPC-tDCS deemed the mapping between movement and reward to be more stable. Relative to sham, lM1-tDCS did not significantly modulate main behavioral outcomes. The results indicate that brain regions previously linked to decision-making, such as the FPC, are relevant for motor skill learning.

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supporting

confidence: 88%

Section: Discussionmentioning

confidence: 65%

mentioning

confidence: 85%

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Modulation of neural activity in frontopolar cortex drives reward-based motor learning

Ruiz

Maudrich

Kalloch

et al. 2020

Preprint

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“…In this work, the EF strength was adopted as metric of neuromodulation to demonstrate the accuracy of the proposed segmentation. Although the most suitable metric is still to be resolved (Antonenko et al, 2019;Laakso et al, 2019).…”

Section: Analysis Methodsmentioning

confidence: 99%

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

Rashed¹,

Gómez-Tames²,

Hirata³

2020

Neural Networks

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Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode montage for accurate targeting. This procedure is guided by computational head models generated from anatomical images such as MRI. Distribution of the EF in segmented head models can be calculated through simulation studies. Therefore, fast, accurate, and feasible segmentation of different brain structures would lead to a better adjustment for customized tDCS studies.In this study, a single-encoder multi-decoders convolutional neural network is proposed for deep brain segmentation. The proposed architecture is trained to segment seven deep brain structures using T1-weighted MRI. Network generated models are compared with a reference model constructed using a semi-automatic method, and it presents a high matching especially in Thalamus (Dice Coeffi-cient (DC) = 94.70%), Caudate (DC = 91.98%) and Putamen (DC = 90.31%) structures. Electric field distribution during tDCS in generated and reference models matched well each other, suggesting its potential usefulness in clinical practice.

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“…, subarachnoid CSF, the ventricles, GM, WM, and the air cavities in the skull, as 524 demonstrated using an exemplarily head image from a local, large-scale imaging study(47) 2). 525Combining image-based meshing with the surface-based meshing preserves the feature edges of the 526 electrodes while avoiding any restrictions concerning the topology of tissue structures of the head The image-based meshing holds two advantages.…”

mentioning

confidence: 99%

A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains

Kalloch

Bazin

Villringer

et al. 2020

Preprint

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AbstractSimulating transcranial electric stimulation is actively researched as knowledge about the distribution of the electrical field is decisive for understanding the variability in the elicited stimulation effect. Several software pipelines comprehensively solve this task in an automated manner for standard use-cases. However, simulations for non-standard applications such as uncommon electrode shapes or the creation of head models from non-optimized T1-weighted imaging data and the inclusion of irregular structures are more difficult to accomplish.We address these limitations and suggest a comprehensive workflow to simulate transcranial electric stimulation based on open-source tools. The workflow covers the head model creation from MRI data, the electrode modeling, the modeling of anisotropic conductivity behavior of the white matter, the numerical simulation and visualization.Skin, skull, air cavities, cerebrospinal fluid, white matter, and gray matter are segmented semi-automatically from T1-weighted MR images. Electrodes of arbitrary number and shape can be modeled. The meshing of the head model is implemented in a way to preserve feature edges of the electrodes and is free of topological restrictions of the considered structures of the head model. White matter anisotropy can be computed from diffusion-tensor imaging data.Our solver application was verified analytically and by contrasting tDCS simulation results with another simulation pipeline (SimNIBS 3.0). An agreement in both cases underlines the validity of our workflow.Our suggested solutions facilitate investigations of irregular structures in patients (e.g. lesions, implants) or of new electrode types. For a coupled use of the described workflow, we provide documentation and disclose the full source code of the developed tools.

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Towards precise brain stimulation: Is electric field simulation related to neuromodulation?

Cited by 125 publications

References 61 publications

Modulation of neural activity in frontopolar cortex drives reward-based motor learning

Modulation of neural activity in frontopolar cortex drives reward-based motor learning

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains

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