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Functional magnetic resonance imaging (fMRI) of brain activation during transcranial electrical stimulation is used to provide insight into the mechanisms of neuromodulation and targeting of particular brain structures. However, the passage of current through the body may interfere with the concurrent detection of blood oxygen level-dependent (BOLD) signal, which is sensitive to local magnetic fields. To test whether these currents can affect concurrent fMRI recordings we performed conventional gradient echo-planar imaging (EPI) during transcranial direct current (tDCS) and alternating current stimulation (tACS) on two post-mortem subjects. tDCS induced signals in both superficial and deep structures. The signal was specific to the electrode montage, with the strongest signal near cerebrospinal fluid (CSF) and scalp. The direction of change relative to non-stimulation reversed with tDCS stimulation polarity. For tACS there was no net effect of the MRI signal. High-resolution individualized modeling of current flow and induced static magnetic fields suggested a strong coincidence of the change EPI signal with regions of large current density and magnetic fields. These initial results indicate that (1) fMRI studies of tDCS must consider this potentially confounding interference from current flow and (2) conventional MRI imaging protocols can be potentially used to measure current flow during transcranial electrical stimulation. The optimization of current measurement and artifact correction techniques, including consideration of the underlying physics, remains to be addressed., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Rational development of transcranial current stimulation (tCS) requires solving the 'forward problem': the computation of the electric field distribution in the head resulting from the application of scalp currents. Derivation of forward models has represented a major effort in brain stimulation research, with model complexity ranging from spherical shells to individualized head models based on magnetic resonance imagery. Despite such effort, an easily accessible benchmark head model is greatly needed when individualized modeling is either undesired (to observe general population trends as opposed to individual differences) or unfeasible. Here, we derive a closed-form linear system which relates the applied current to the induced electric potential. It is shown that in the spherical harmonic (Fourier) domain, a simple scalar multiplication relates the current density on the scalp to the electric potential in the brain. Equivalently, the current density in the head follows as the spherical convolution between the scalp current distribution and the point spread function of the head, which we derive. Thus, if one knows the spherical harmonic representation of the scalp current (i.e. the electrode locations and current intensity to be employed), one can easily compute the resulting electric field at any point inside the head. Conversely, one may also readily determine the scalp current distribution required to generate an arbitrary electric field in the brain (the 'backward problem' in tCS). We demonstrate the simplicity and utility of the model with a series of characteristic curves which sweep across a variety of stimulation parameters: electrode size, depth of stimulation, head size and anode-cathode separation. Finally, theoretically optimal montages for targeting an infinitesimal point in the brain are shown.

Transcranial current stimulation (tCS) is a promising noninvasive technique to elicit neuromodulation by passing weak electrical currents through scalp electrodes. While significant effort has been devoted towards designing stimulation protocols which "steer" current to regions of interest, previous work has been almost exclusively focused on the magnitude of the electric field, while ignoring the effects of direction. This is despite previous in vitro studies demonstrating that the angle between the field orientation and the cell axis of symmetry has significant effects on the resulting membrane polarization presumably underlying therapeutic effects. To that end, here we examine the impact of the desired electric field orientation on the optimal placement of electrodes for a given target region. Based on high-resolution head models derived from magnetic resonance scans of patients enrolled in a clinical trial examining the use of tCS in rehabilitation after stroke, we derive and employ an optimization algorithm which computes the montage maximizing directed current flow at the target. The results reveal a strong dependence of the optimal montage on the desired orientation; moreover, the magnitude of the induced electric field at the target region varies widely with the preferred direction. This suggests that identifying the desired electric field orientation at the region of interest is a crucial step in the development of rational electrical stimulation paradigms.

Transcranial direct current stimulation (tDCS) provides a non-invasive tool to elicit neuromodulation by delivering current through electrodes placed on the scalp. The present clinical paradigm uses two relatively large electrodes to inject current through the head resulting in electric fields that are broadly distributed over large regions of the brain. In this paper, we present a method that uses multiple small electrodes (i.e. 1.2 cm diameter) and systematically optimize the applied currents to achieve effective and targeted stimulation while ensuring safety of stimulation. We found a fundamental trade-off between achievable intensity (at the target) and focality, and algorithms to optimize both measures are presented. When compared with large pad-electrodes (approximated here by a set of small electrodes covering 25 cm(2)), the proposed approach achieves electric fields which exhibit simultaneously greater focality (80% improvement) and higher target intensity (98% improvement) at cortical targets using the same total current applied. These improvements illustrate the previously unrecognized and non-trivial dependence of the optimal electrode configuration on the desired electric field orientation and the maximum total current (due to safety). Similarly, by exploiting idiosyncratic details of brain anatomy, the optimization approach significantly improves upon prior un-optimized approaches using small electrodes. The analysis also reveals the optimal use of conventional bipolar montages: maximally intense tangential fields are attained with the two electrodes placed at a considerable distance from the target along the direction of the desired field; when radial fields are desired, the maximum-intensity configuration consists of an electrode placed directly over the target with a distant return electrode. To summarize, if a target location and stimulation orientation can be defined by the clinician, then the proposed technique is superior in terms of both focality and intensity as compared to previous solutions and is thus expected to translate into improved patient safety and increased clinical efficacy.

Purpose: To determine the effects of high-frequency electrical stimulation on electrographic seizure activity during and after stimulation (ON-effect and OFF-effect)., Methods: The modulation and suppression of epileptiform activity during (ON-effect) and after (OFF-effect) high-frequency electrical stimulation was investigated using the high-K(+) and picrotoxin hippocampal slice epilepsy models. Uniform sinusoidal fields (50 Hz) were applied with various intensity levels for 1 min across brain slices. Extracellular and intracellular activity were monitored during and after stimulation., Results: The ON-effects of high-frequency stimulation were highly variable across individual slices and models; ON-effects included modulation of activity, pacing, partial suppression, or activity resembling spreading-depression. On average, epileptic activity, measured as power in the extracellular fields, increased significantly during stimulation. Following the termination of electrical stimulation, a robust poststimulation suppression period was observed. This OFF suppression was observed even at relatively moderate stimulation intensities. The duration of OFF suppression increased with stimulation intensity, independent of ON-effects. Antagonism of GABA(A)function did not directly effect OFF suppression duration., Conclusions: The present results suggest that "rational" seizure control protocols using intermittent high-frequency electrical stimulation should control for both ON and OFF effects.

Despite compelling phenomenological evidence that small electric fields (<5 mV/mm) can affect brain function, a quantitative and experimentally verified theory is currently lacking. Here we demonstrate a novel mechanism by which the nonlinear properties of single neurons "amplify" the effect of small electric fields: when concurrent to suprathreshold synaptic input, small electric fields can have significant effects on spike timing. For low-frequency fields, our theory predicts a linear dependency of spike timing changes on field strength. For high-frequency fields (relative to the synaptic input), the theory predicts coherent firing, with mean firing phase and coherence each increasing monotonically with field strength. Importantly, in both cases, the effects of fields on spike timing are amplified with decreasing synaptic input slope and increased cell susceptibility (millivolt membrane polarization per field amplitude). We confirmed these predictions experimentally using CA1 hippocampal neurons in vitro exposed to static (direct current) and oscillating (alternating current) uniform electric fields. In addition, we develop a robust method to quantify cell susceptibility using spike timing. Our results provide a precise mechanism for a functional role of endogenous field oscillations (e.g., gamma) in brain function and introduce a framework for considering the effects of environmental fields and design of low-intensity therapeutic neurostimulation technologies.

Clinical effects of transcranial electrical stimulation with weak currents are remarkable considering the low amplitude of the electric fields acting on the brain. Elucidating the processes by which small currents affect ongoing brain activity is of paramount importance for the rational design of noninvasive electrotherapeutic strategies and to determine the relevance of endogenous fields. We propose that in active neuronal networks, weak electrical fields induce small but coherent changes in the firing rate and timing of neuronal populations that can be magnified by dynamic network activity. Specifically, we show that carbachol-induced gamma oscillations (25-35 Hz) in rat hippocampal slices have an inherent rate-limiting dynamic and timing precision that govern susceptibility to low-frequency weak electric fields (<50 Hz;10 V/m). This leads to a range of nonlinear responses, including the following: (1) asymmetric power modulation by DC fields resulting from balanced excitation and inhibition; (2) symmetric power modulation by lower frequency AC fields with a net-zero change in firing rate; and (3) half-harmonic oscillations for higher frequency AC fields resulting from increased spike timing precision. These underlying mechanisms were elucidated by slice experiments and a parsimonious computational network model of singlecompartment spiking neurons responding to electric field stimulation with small incremental polarization. Intracellular recordings confirmed model predictions on neuronal timing and rate changes, as well as spike phase-entrainment resonance at 0.2 V/m. Finally, our data and mechanistic framework provide a functional role for endogenous electric fields, specifically illustrating that modulation of gamma oscillations during theta-modulated gamma activity can result from field effects alone. [ABSTRACT FROM AUTHOR]

An abstract of the article "Long-term effects of weak electrical stimulation on active neuronal networks" by Davide Reato and colleagues is presented.

Abstract Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high-pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts - defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance – from methodology-related artifacts - arising from non-ideal experimental conditions or non-ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG-tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non-inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source-specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition- GSVD), non-stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG-tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non-stationary, physiology-specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG-tDCS stimulation artifacts. Highlights • We identify inherent physiological artifacts during concurrent (online) tDCS-EEG. • Physiological artifacts include cardiac, ocular, motion, and myogenic artifacts. • Dose response and current source independence of physiological artifacts are examined. • FEM models explain physiological artifacts based on specific impedance changes. • Typical signal processing methods cannot reliably remove physiological artifacts. [ABSTRACT FROM AUTHOR]

Transcranial electric stimulation aims to stimulate the brain by applying weak electrical currents at the scalp. However, the magnitude and spatial distribution of electric fields in the human brain are unknown. We measured electric potentials intracranially in ten epilepsy patients and estimate electric fields across the entire brain by leveraging calibrated current-flow models. When stimulating at 2 mA, cortical electric fields reach 0.4 V/m, the lower limit of effectiveness in animal studies. When individual whole-head anatomy is considered, the predicted electric field magnitudes correlate with the recorded values in cortical (r = 0:89) and depth (r = 0:84) electrodes. Accurate models require adjustment of tissue conductivity values reported in the literature, but accuracy is not improved when incorporating white matter anisotropy or different skull compartments. This is the first study to validate and calibrate current- ow models with in vivo intracranial recordings in humans, providing a solid foundation to target stimulation and interpret clinical trials. [ABSTRACT FROM AUTHOR]