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.

Electroconvulsive therapy-induced brain plasticity determines therapeutic outcome in mood disorders

“…The anatomical distribution of ECT-associated GMV changes clarifies and extends findings from previous studies (3,4,11,12), showing spatial effects on GMV in a theoretically estimated space affected by electric field propagation in the brain from unilateral ECT (18). Our result confirms the spatial distribution of the ECT effect in that regions subjected to the highest electric field strengths show the most structural change.…”

Grey matter volume increase following electroconvulsive therapy in patients with late life depression: a longitudinal MRI study

“…52 However, we observed a marked lateralization of grey matter volume increase toward the stimulation side, making a currentrelated effect more plausible than an antidepressant effect, especially because antidepressant effects cannot be at tributed to a single hemisphere. 53 Lee and colleagues 54 pro posed a spatial distribution of ECT effects whereby regions subjected to the highest electric field strengths showed the most robust structural change, which would explain the lat eralized volume increase that we observed with RUL ECT. Furthermore, in contrast to the findings of Dukart and col leagues 11 our data do not support a correlation between changes in hippocampal volume -the area in which plasti city has been documented most convincingly -and antide pressant action of ECT, casting doubt on shortterm neuro plasticity.…”

Mapping the Brain’s electric fields with Magnetoelectric nanoparticles

“…Brain segmentation (WM, GM, and CSF) of the template was performed using BrainSuite software (Klauschen et al, 2009). The finite element (FE) mesh required for the segmented brain was generated using Computer Geometry Algorithm Library (CGAL) according to previously described method (Lee et al, 2012). The relative magnetization, M rel , defined as the ratio M/M S , where M S is the saturation magnetization, was computed using standard magnetic dynamic formalism described elsewhere (Ivanov et al, 2007;Mikhaylova et al, 2004).…”

“…In spite of significant advances in this field especially during the last two decades, the existing technologies for recording neural activity are severely limited in their capabilities. These technologies include electroencephalography (EEG) (Coenen, 1995), functional magnetic resonance imaging (fMRI) and diffusion MRI (dMRI), also known as diffusion tensor imaging (DTI), or a combination of these two (DfMRI) (Yassa et al, 2010), positron emission tomography (Lee et al, 2012;Grafton et al, 1992), magnetoencephalography (MEG) (de Pasquale et al, 2010), neuronal optogenetics (Toettcher et al, 2010), molecular recording (Zamft et al, 2012), and others. Brain imaging with a spatial resolution of 1 mm can be achieved non-invasively with the MRI approaches; however, these they mostly provide a structural map and only indirectly and with a limited accuracy reflect the electric field perturbations due to neural activity; their temporal resolutions are limited by the hemodynamic response to approximately 1 s. To detect the neural activity through the hemodynamic response, fMRI and dMRI use the blood-oxygen-level dependent (BOLD) contrast and the contrast based on the strength of the diffusion of water molecules, respectively.…”