Differential effects of anodal and dual tDCS on sensorimotor functions in chronic hemiparetic stroke patients

Background and purpose Previous tDCS studies in chronic stroke patients reported highly inconsistent effects on sensorimotor functions. Underlying reasons could be the selection of different kinematic parameters across studies and for different tDCS setups. We reasoned that tDCS may not simply induce global changes in a beneficial-adverse dichotomy, but rather that different sensorimotor kinematics are differentially affected. Furthermore, the often-postulated higher efficacy of bilateral-dual (bi-tDCS) over unilateral-anodal (ua-tDCS) could not yet be demonstrated consistently either. We investigated the effects of both setups on a wider range of kinematic parameters from standardized robotic tasks in patients with chronic stroke. Methods Twenty-four patients with arm hemiparesis received tDCS (20min, 1 mA) concurrent to kinematic assessments in a sham-controlled, cross-over and double-blind clinical trial. Performance was measured on four sensorimotor tasks (reaching, proprioception, cooperative and independent bimanual coordination) from which 30 parameters were extracted. On the group-level, the patterns of changes relative to sham were assessed using paired-samples t-tests and classified as (1) performance increases, (2) decreases and (3) non-significant differences. Correlations between parametric change scores were calculated for each task to assess effects on the individual-level. Results Both setups induced complex effect patterns with varying proportions of performance increases and decreases. On the group-level, more increases were induced in the reaching and coordination tasks while proprioception and bimanual cooperation were overall negatively affected. Bi-tDCS induced more performance increases and less decreases compared to ua-tDCS. Changes across parameters occurred more homogeneously under bi-tDCS than ua-tDCS, which induced a larger proportion of performance trade-offs. Conclusions Our data demonstrate profound tDCS effects on sensorimotor functions post-stroke, lending support for more pronounced and favorable effects of bi-tDCS compared to ua-tDCS. However, no uniformly beneficial pattern was identified. Instead, the modulations varied depending on the task and electrode setup, with increases in certain parameters occurring at the expense of decreases in others

Anodal transcranial direct current stimulation over S1 differentially modulates proprioceptive accuracy in young and old adults

Background: Proprioception is a prerequisite for successful motor control but declines throughout the lifespan. Brain stimulation techniques such as anodal transcranial direct current stimulation (a-tDCS) are capable of enhancing sensorimotor performance across different tasks and age groups. Despite such growing evidence for a restorative potential of tDCS, its impact on proprioceptive accuracy has not been studied in detail yet. Objective: This study investigated online effects of a-tDCS over S1 on proprioceptive accuracy in young (YA) and old healthy adults (OA). Methods: The effect of 15 min of a-tDCS vs. sham on proprioceptive accuracy was assessed in a cross-over, double blind experiment in both age groups. Performance changes were tested using an arm position matching task in a robotic environment. Electrical field (EF) strengths in the target area S1 and control areas were assessed based on individualized simulations. Results: a-tDCS elicited differential changes in proprioceptive accuracy and EF strengths in the two groups: while YA showed a slight improvement, OA exhibited a decrease in performance during a-tDCS. Stronger EF were induced in target S1 and control areas in the YA group. However, no relationship between EF strength and performance change was found. Conclusion: a-tDCS over S1 elicits opposing effects on proprioceptive accuracy as a function of age, a result that is important for future studies investigating the restorative potential of a-tDCS in healthy aging and in the rehabilitation of neurological diseases that occur at advanced age. Modeling approaches could help elucidate the relationship between tDCS protocols, brain structure and performance modulation

Towards individualized transcranial electric stimulation therapy through computer simulation

Transcranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question

IMporter

A tool to import a InstrumentMarker file into a Localite TMSNavigator session

A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains

Simulating transcranial electric stimulation is actively researched as knowledge about the distribution of the electrical field is decisive for understanding the variability in the elicited stimulation effect. Several software pipelines comprehensively solve this task in an automated manner for standard use-cases. However, simulations for non-standard applications such as uncommon electrode shapes or the creation of head models from non-optimized T1-weighted imaging data and the inclusion of irregular structures are more difficult to accomplish. We address these limitations and suggest a comprehensive workflow to simulate transcranial electric stimulation based on open-source tools. The workflow covers the head model creation from MRI data, the electrode modeling, the modeling of anisotropic conductivity behavior of the white matter, the numerical simulation and visualization. Skin, skull, air cavities, cerebrospinal fluid, white matter, and gray matter are segmented semi-automatically from T1-weighted MR images. Electrodes of arbitrary number and shape can be modeled. The meshing of the head model is implemented in a way to preserve the feature edges of the electrodes and is free of topological restrictions of the considered structures of the head model. White matter anisotropy can be computed from diffusion-tensor imaging data. Our solver application was verified analytically and by contrasting the tDCS simulation results with that of other simulation pipelines (SimNIBS 3.0, ROAST 3.0). An agreement in both cases underlines the validity of our workflow. Our suggested solutions facilitate investigations of irregular structures in patients (e.g. lesions, implants) or new electrode types. For a coupled use of the described workflow, we provide documentation and disclose the full source code of the developed tools

Building a high resolution surface-based human head and torso model for evaluation of specific absorption rates in MRI

We built four prototypes of high resolution surface-based human head models that can be simulated in a reasonable time by a commercially available frequency domain solver-ANSYS HFSS-and evaluated the influence of cerebrospinal fluid and of the upper part of the human torso on field propagation estimates of traveling wave excitation at 297.2 MHz. Combining neighboring triangular faces located in the same plane into a single one is an approach that achieves simulations of high-resolution human models previously not accessible to tetrahedral-mesh-based solvers. If electrical contact between anatomically connected parts of CSF is correctly considered, CSF was found to partially shield brain tissues from the incident RF field