Enhanced tES and tDCS computational models by meninges emulation

Abstract

Objective: Understanding how current reaches the brain during transcranial Electrical Stimulation (tES) underpins efforts to rationalize outcomes and optimize interventions. To this end, computational models of current flow relate applied dose to brain electric field. Conventional tES modeling considers distinct tissues like scalp, skull, cerebrospinal fluid (CSF), gray matter and white matter. The properties of highly conductive CSF are especially important. However, modeling the space between skull and brain as entirely CSF is not an accurate representation of anatomy. The space conventionally modeled as CSF is approximately half meninges (dura, arachnoid, and pia) with lower conductivity. However, the resolution required to describe individual meningeal layers is computationally restrictive in an MRI-derived head model. Emulating the effect of meninges through CSF conductivity modification could improve accuracy with minimal cost. Approach: Models with meningeal layers were developed in a concentric sphere head model. Then, in a model with only CSF between skull and brain, CSF conductivity was optimized to emulate the effect of meningeal layers on cortical electric field for multiple electrode positions. This emulated conductivity was applied to MRI-derived models. Main results: Compared to a model with conventional CSF conductivity (1.65 S/m), emulated CSF conductivity (0.85 S/m) produced voltage fields better correlated with intracranial recordings from epilepsy patients. Significance: Conventional tES mpodels have been validated using intracranial recording. Residual errors may nonetheless impact model utility. Because CSF is so conductive to current flow, misrepresentation of the skull-brain interface as entirely CSF is not realistic for tES After the embargo period, everyone is permitted to use copy and redistribute this article for non-commercial purposes only, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by-nc-nd/3.0

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

“…Current passage through the scalp (skin) depends on numerous layers and ultra-structures, each with a complex (non-linear, time-dependent) impedance to current flow [16,[38][39][40][41][42] which is computationally intractable for tES head models [21]. The pipeline developed here to simulate adaptive-scalp conductivity during tES represent two scalp layers, with just two respective associated subject-specific parameters: a deep-scalp layer with fixed conductivity (σ #! )…”

“…Additional manual segmentation was applied to correct for noise, aliasing artifacts, and to separate superficial scalp and deep scalp layers. Unless otherwise indicated, segmented tissues were assigned fixed electrical conductivity [16]: skull (σ = 0.01 S/m), gray matter (σ = 0.276 S/m), white matter (σ = 0.126 S/m), meninges/cerebrospinal fluid (σ = 0.85 S/m), and air (σ = 1e -15 S/m).…”

“…It is intractable to model <0.1 mm thick tissue layers at full head-scale, necessitating approximation [21]. Adjacent tissue layers with mismatched resistivity can especially impact on current flow patterns [22,23].…”

“…Unless otherwise indicated, segmented tissues were assigned fixed electrical conductivity [21]: skull (σ = 0.01 S/m), gray matter (σ = 0.276 S/m), white matter (σ = 0.126 S/m), meninges/cerebrospinal fluid (σ = 0.85 S/m), and air (σ = 1e -15 S/m). Deep-scalp layer was assigned a subject specific conductivity (σ DS) between 4.5e -4 S/m and 0.008 S/m.…”

Neurovascular-modulation

“…However, it is the macroscopic changes that are conventionally assumed to drive neuronal stimulation for many modalities. We thus, contrasted Activating Functions generated by conventional macroscopic tissue changes (values derived from literature; [35,61,67,68,70,73,[81][82][83][84][85]) with the BBB ultra-structure generated Activating Function derived here. This comparison is subject to a range of assumptions (e.g.…”