Regional electric field induced by electroconvulsive therapy in a realistic finite element head model: Influence of white matter anisotropic conductivity

Abstract

We present the first computational study investigating the electric field (E-field) strength generated by various electroconvulsive therapy (ECT) electrode configurations in specific brain regions of interest (ROIs) that have putative roles in the therapeutic action and/or adverse side effects of ECT. This study also characterizes the impact of the white matter (WM) conductivity anisotropy on the E-field distribution. A finite element head model incorporating tissue heterogeneity and WM anisotropic conductivity was constructed based on structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. We computed the spatial E-field distributions generated by three standard ECT electrode placements including bilateral (BL), bifrontal (BF), and right unilateral (RUL) and an investigational electrode configuration for focal electrically administered seizure therapy (FEAST). The key results are that (1) the median E-field strength over the whole brain is 3.9, 1.5, 2.3, and 2.6 V/cm for the BL, BF, RUL, and FEAST electrode configurations, respectively, which coupled with the broad spread of the BL E-field suggests a biophysical basis for observations of superior efficacy of BL ECT compared to BF and RUL ECT; (2) in the hippocampi, BL ECT produces a median E-field of 4.8 V/cm that is 1.5–2.8 times stronger than that for the other electrode configurations, consistent with the more pronounced amnestic effects of BL ECT; and (3) neglecting the WM conductivity anisotropy results in E-field strength error up to 18% overall and up to 39% in specific ROIs, motivating the inclusion of the WM conductivity anisotropy in accurate head models. This computational study demonstrates how the realistic finite element head model incorporating tissue conductivity anisotropy provides quantitative insight into the biophysics of ECT, which may shed light on the differential clinical outcomes seen with various forms of ECT, and may guide the development of novel stimulation paradigms with improved risk/benefit ratio.

Introduction

Electroconvulsive therapy (ECT) is a therapeutic intervention in which electric current is applied through scalp electrodes to induce a generalized seizure in anesthetized patients (Abrams, 2002, APA, 2001). Although ECT plays a vital role in the treatment of medication-resistant psychiatric disorders, such as major depression, the use of ECT has been limited by its cognitive side effects (particularly amnesia (Sackeim et al., 2007, Squire, 1986)), by cardiac complications (Nuttall et al., 2004), by the need for general anesthesia, as well as by the high rate of relapse (Sackeim et al., 1990). Despite the introduction of various improvements of ECT technique, there is still limited knowledge of how to optimally select electrode placement (Kellner et al., 2010b) or stimulus current parameters (Peterchev et al., 2010) for maximal efficacy and tolerability. Indeed, the therapeutic action and adverse side effects of ECT are highly dependent upon electrode placement and stimulus current parameters, but a complete mechanistic explanation for these relationships is still lacking. For instance, right unilateral (RUL) ECT leads to fewer cognitive side effects than bilateral frontotemporal (BL) ECT (Sackeim et al., 2000), but it is not known whether this is by virtue of lower electric field (E-field) strength in hippocampus and other regions crucial for memory. Furthermore, alternative ECT electrode configurations such as bifrontal (BF) (Abrams, 2002) and focal electrically administered seizure therapy (FEAST) (Sackeim, 2004) have been proposed with the goal of preferentially targeting frontal brain regions to reduce memory impairment, but the frontal E-field strength relative to the rest of the brain and relative to other electrode placements has not been quantified.

To understand the underlying biophysical mechanisms of ECT, a few early studies undertook measurements of the E-field generated by ECT in human cadavers (Lorimer et al., 1949, Smitt and Wegener, 1944) and in an electrolytic tank containing a human half-skull (Rush and Driscoll, 1968). However, the electrolytic tank measurements did not account for the geometry and conductivity properties of the scalp and the brain. The intracerebral cadaver measurements were carried out after an uncontrolled interval of time following death, potentially resulting in altered conductivity profile of the head tissues, and the tissues were damaged in the process of inserting the recording probes, potentially altering the paths of current flow generated by the scalp electrodes. Furthermore, neither of the studies produced a high-resolution map of the E-field or the current density distributions in the brain.

In order to provide more detailed field maps, a number of computational studies have simulated the distribution of the E-field or the current density field (which equals the product of E-field and conductivity) in the brain using a volume conductor model of the head. The representation of the head in computational ECT models ranges in detail from concentric spheres (Deng et al., 2009, Deng et al., 2011, Weaver et al., 1976) to low-resolution realistically-shaped representations (Bai et al., 2011, Sekino and Ueno, 2002, Sekino and Ueno, 2004) to high-resolution anatomically-accurate models (Nadeem et al., 2003, Szmurło et al., 2006). Furthermore, a substantial number of E-field/current density modeling studies have been published in the context of other transcranial electric stimulation paradigms, again ranging from simplified to realistic head representations (Datta et al., 2008, Datta et al., 2009, Grandori and Rossini, 1988, Holdefer et al., 2006, Im et al., 2008, Lee et al., 2009b, Miranda et al., 2006, Miranda et al., 2007, Nathan et al., 1993, Oostendorp et al., 2008, Parazzini et al., 2011, Rush and Driscoll, 1968, Sadleir et al., 2010, Salvador et al., 2010, Saypol et al., 1991, Stecker, 2005, Suh et al., 2009, Suh et al., 2010, Suihko, 2002, Wagner et al., 2007). However, these studies have various limitations. The spherical and simplified geometry models do not fully account for tissue inhomogeneity and anisotropy, and the complex geometries of head tissues, including orifices in the skull such as the auditory canals and the orbits. The published anatomically-accurate ECT models (Nadeem et al., 2003, Szmurło et al., 2006) consider only isotropic tissue conductivity, explore only a limited set of electrode configurations (BL and RUL), and do not perform region of interest (ROI) analysis of the field distribution in the brain. The computational models of non-ECT transcranial electric stimulation offer some insights into the biophysics of the problem, but do not provide data specific to ECT electrode configurations and stimulus current parameters.

For realistic models of the E-field generated by ECT, the inclusion of anisotropic conductivity of the white matter (WM) may be of particular importance since the E-field induced by ECT is typically widespread and reaches deep brain regions (Deng et al., 2011, Nadeem et al., 2003), and since depression itself is associated with regionally specific abnormalities of the WM fractional anisotropy (Korgaonkar et al., 2011, Wu et al., 2011). Our and other groups have previously incorporated tissue anisotropic conductivity in models of electroencephalography and magnetoencephalography (Gullmar et al., 2006, Gullmar et al., 2010, Hallez et al., 2008, Hallez et al., 2009, Haueisen et al., 2002, Kim et al., 2003, Lee et al., 2008, Lee et al., 2009a, Marin et al., 1998, Rullmann et al., 2009, Wolters et al., 2006), transcranial direct current stimulation (tDCS) (Lee et al., 2009b, Oostendorp et al., 2008, Suh et al., 2009, Suh et al., 2010), deep brain stimulation (Butson et al., 2007), transcranial magnetic stimulation (TMS) (De Lucia et al., 2007, Thielscher et al., 2011), and electrical impedance tomography (Abascal et al., 2008). These studies demonstrate that anisotropic conductivity of the brain tissue can have a non-negligible effect on the electromagnetic field solutions. However, computational models of ECT have not incorporated tissue conductivity anisotropy to date.

No direct and non-invasive in vivo measurement of brain conductivity anisotropy is available, but the similarity between the transportation processes of electrical charge carriers and water molecules enables estimation of the effective electrical conductivity tensors from the water self-diffusion tensors which can be non-invasively acquired with diffusion tensor magnetic resonance imaging (DT-MRI) (Basser et al., 1994b). Several methods have been proposed to derive the WM anisotropic conductivity from the measured diffusion tensors. In the effective medium approach (Tuch et al., 1999, Tuch et al., 2001), the WM anisotropic conductivity tensors were directly calculated by a linear scaling of the diffusion tensors using an empirically determined scaling factor (Haueisen et al., 2002, Tuch et al., 1999, Tuch et al., 2001). However, Rullmann et al., 2009, Gullmar et al., 2010 have pointed out that using this linear scaling approach may lead to extremely large anisotropic ratios in the resulting conductivity tensors. An alternative is the volume constraint approach where the WM anisotropic conductivity tensors are computed with a fixed anisotropic ratio in each WM voxel, under the assumption that the shape of the WM diffusion tensors is prolate (cigar-shaped), rotationally symmetric ellipsoid (Shimony et al., 1999, Wolters et al., 2006). With this method, the fixed anisotropic conductivity ratio of the WM tissue can be obtained from direct measurements, e.g., 10:1 for parallel:normal orientation relative to the nerve fibers (Nicholson, 1965). Another anisotropy modeling technique is based on the linear conductivity-to-diffusivity relationship in combination with a constraint on the magnitude of the electrical conductivity tensor (Hallez et al., 2008, Hallez et al., 2009). A “volume fraction algorithm” considering the partial volume effects of the cerebrospinal fluid (CSF) and the intravoxel fiber crossing structure has also been suggested (Wang et al., 2008), but no further studies using this approach have been reported.

In summary, existing studies of the E-field or current density resulting from ECT have investigated few electrode configurations in realistic-geometry head models, have not incorporated tissue conductivity anisotropy, and have not carried out analysis of the E-field strength in specific brain ROIs. Addressing these limitations, in the present study we develop an anatomically-accurate finite element (FE) model of the human head incorporating tissue heterogeneity and WM anisotropic conductivity, based on individual structural MRI and DT-MRI scans. We use the head model to simulate the E-field generated in the brain by the BL, BF, RUL, and FEAST ECT electrode configurations. We quantify the differences in E-field strength among the various ECT electrode configurations in brain ROIs that have putative role in the therapeutic action and/or adverse side effects of ECT. This analysis enables us, for example, to explore whether forms of ECT associated with fewer cognitive side effects induce lower E-field strengths in hippocampus, and to evaluate the degree to which frontal electrode configurations (BF and FEAST) achieve focal frontal stimulation. We also investigate how the WM conductivity anisotropy affects the E-field distribution in the brain. This study demonstrates the utility of anatomically-realistic computational models to provide clinically salient analysis and recommendations for the optimization of ECT. Preliminary results from this study were previously presented in part in conference proceedings (Lee et al., 2010, Lee et al., 2011).