Adaptive current-flow models of ECT: Explaining individual static impedance, dynamic impedance, and brain current density

Ünal, Gözde; Swami, Jaiti; Canela, Carliza; Cohen, Samantha L.; Khadka, Niranjan; FallahRad, Mohamad; Short, Baron; Sackeïm, Harold A.; Bikson, Marom

doi:10.1016/j.brs.2021.07.012

Cited by 17 publications

(36 citation statements)

References 58 publications

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“…Currents used in ECT are 2–3 orders of magnitude higher, specifically 600–800 mA in this case. It is a well-known phenomenon in physics that much lower impedances are measured at higher currents (a so called “electrical breakdown” of impedance) [ 4 ]. Indeed this effect can also be observed with each individual ECT, because the so-called “static” impedance, which is measured before the stimulus current application, typically lies around 1000–2000 ohms, whereas during the current application the impedance is typically between 200 and 250 ohms.…”

Section: To the Editormentioning

confidence: 99%

“…sine wave vs. short rectangular pulses)) would have to be known as accurately as possible for each individual voxel of the brain to enable correct finite element modeling. Moreover, it is highly likely that strong direction-dependent effects, for example within the white matter tracts, play an additional role in the final modeling of the electric field [ 4 , 5 ].…”

Section: To the Editormentioning

confidence: 99%

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Electric field distribution models in ECT research

Sartorius

2022

Mol Psychiatry

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Section: To the Editormentioning

confidence: 99%

Section: To the Editormentioning

confidence: 99%

Electric field distribution models in ECT research

Sartorius

2022

Mol Psychiatry

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“…Our results indicate that, as a rule of thumb, any implanted metal in the head can be electrically modeled as a perfect insulator in the context of tES or TMS. In ECT the electric field reaches higher values (above 4500 V/m at the scalp [29]). In this case, the choice of the correct electrical model for an implant may not be straightforward as it will depend on its geometry and how strong the electric field is expected to be in its location.…”

Section: Discussionmentioning

confidence: 99%

Modeling implanted metals in electrical stimulation applications

Mercadal¹,

Salvador²,

Biagi³

et al. 2021

Preprint

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Background: Metal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive the electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario. Objective/Hypothesis: The aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants using purely electrical models but considering the electrochemical nature of the problem in the technology of interest. Methods: We built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes to represent both weak (e.g., tDCS), medium (TMS) or strong field stimulation. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate. Results: Metal implants generally act as electric insulators when exposed to electric fields up to around 100 V/m (tES and TMS range) and they only resemble a perfect conductor for fields in the order of 1000 V/m and above. The results are independent of the implant's metal, but they depend on its geometry. Conclusion(s): Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. In particular, tES modeling with implants incorrectly treated as conductors can lead to errors of 50% in induced fields or more. Our results can be used as a guide to select the correct model in each scenario.

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“…This, in turn, could give information about possible therapeutic targets. Researchers could also create models for explaining and understanding mechanisms associated with the therapeutic response, like neuromodulation strategies such as ECT ( 138 ).…”

Section: Clinical Applicationsmentioning

confidence: 99%

Understanding mental health through computers: An introduction to computational psychiatry

Martinez

Santamaría-García²

2023

Front. Psychiatry

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Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

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Adaptive current-flow models of ECT: Explaining individual static impedance, dynamic impedance, and brain current density

Cited by 17 publications

References 58 publications

Electric field distribution models in ECT research

Electric field distribution models in ECT research

Modeling implanted metals in electrical stimulation applications

Understanding mental health through computers: An introduction to computational psychiatry

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