Human in-vivo brain magnetic resonance current density imaging (MRCDI)

Volumetric measurements of weak current-induced magnetic fields in the human brain at high resolution

PURPOSE

Clinical use of transcranial electrical stimulation (TES) requires accurate knowledge of the injected current distribution in the brain. MR current density imaging (MRCDI) uses measurements of the TES-induced magnetic fields to provide this information. However, sufficient sensitivity and image quality in humans in vivo has only been documented for single-slice imaging.

METHODS

A recently developed, optimally spoiled, acquisition-weighted, gradient echo-based 2D-MRCDI method has now been advanced for volume coverage with densely or sparsely distributed slices: The 3D rectilinear sampling (3D-DENSE) and simultaneous multislice acquisition (SMS-SPARSE) were optimized and verified by cable-loop experiments and tested with 1-mA TES experiments for two common electrode montages.

RESULTS

Comparisons between the volumetric methods against the 2D-MRCDI showed that relatively long acquisition times of 3D-DENSE using a single slab with six slices hindered the expected sensitivity improvement in the current-induced field measurements but improved sensitivity by 61% in the Laplacian of the field, on which some MRCDI reconstruction methods rely. Also, SMS-SPARSE acquisition of three slices, with a factor 2 CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) acceleration, performed best against the 2D-MRCDI with sensitivity improvements for the∆ B z , c $$ \Delta {B}_{z,c} $$ and Laplacian noise floors of 56% and 78% (baseline without current flow) as well as 43% and 55% (current injection into head). SMS-SPARSE reached a sensitivity of 67 pT for three distant slices at 2 × 2 × 3 mm3 resolution in 10 min of total scan time, and consistently improved image quality.

CONCLUSION

Volumetric MRCDI measurements with high sensitivity and image quality are well suited to characterize the TES field distribution in the human brain.

Inconsistencies in mapping current distribution in transcranial direct current stimulation

INTRODUCTION

tDCS is a non-invasive neuromodulation technique that has been widely studied both as a therapy for neuropsychiatric diseases and for cognitive enhancement. However, recent meta-analyses have reported significant inconsistencies amongst tDCS studies. Enhancing empirical understanding of current flow in the brain may help elucidate some of these inconsistencies.

METHODS

We investigated tDCS-induced current distribution by injecting a low frequency current waveform in a phantom and in vivo. MR phase images were collected during the stimulation and a time-series analysis was used to reconstruct the magnetic field. A current distribution map was derived from the field map using Ampere's law.

RESULTS

The current distribution map in the phantom showed a clear path of current flow between the two electrodes, with more than 75% of the injected current accounted for. However, in brain, the results did evidence a current path between the two target electrodes but only some portion ( 25%) of injected current reached the cortex demonstrating that a significant fraction of the current is bypassing the brain and traveling from one electrode to the other external to the brain, probably due to conductivity differences in brain tissue types. Substantial inter-subject and intra-subject (across consecutive scans) variability in current distribution maps were also observed in human but not in phantom scans.

DISCUSSIONS

An in-vivo current mapping technique proposed in this study demonstrated that much of the injected current in tDCS was not accounted for in human brain and deviated to the edge of the brain. These findings would have ramifications in the use of tDCS as a neuromodulator and may help explain some of the inconsistencies reported in other studies.

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility.

SNR-Enhanced, Rapid Electrical Conductivity Mapping Using Echo-Shifted MRI

Magnetic resonance electrical impedance tomography (MREIT) permits high-spatial resolution electrical conductivity mapping of biological tissues, and its quantification accuracy hinges on the signal-to-noise ratio (SNR) of the current-induced magnetic flux density (B_z). The purpose of this work was to achieve B_z SNR-enhanced rapid conductivity imaging by developing an echo-shifted steady-state incoherent imaging-based MREIT technique. In the proposed pulse sequence, the free-induction-decay signal is shifted in time over multiple imaging slices, and as a result is exposed to a plurality of injecting current pulses before forming an echo. Thus, the proposed multi-slice echo-shifting strategy allows a high SNR for B_z for a given number of current injections. However, with increasing the time of echo formation, the B_z SNR will also be compromised by T₂*-related signal loss. Hence, numerical simulations were performed to evaluate the relationship between the echo-shifting and the B_z SNR, and subsequently to determine the optimal imaging parameters. Experimental studies were conducted to evaluate the effectiveness of the proposed method over conventional spin-echo-based MREIT. Compared with the reference spin-echo MREIT, the proposed echo-shifting-based method improves the efficiency in both data acquisition and current injection while retaining the accuracy of conductivity quantification. The results suggest the feasibility of the proposed MREIT method as a practical means for conductivity mapping.

Phantom Construction and Equipment Configurations for Characterizing Electrical Properties Using MRI

Phantom objects are commonly employed in MRI systems as stable substitutes for biological tissues to ensure systems for measuring images are operating correctly and safely. For magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance electrical property tomography (MREPT), conductivity or permittivity phantoms play an important role in checking MRI pulse sequences, MREIT equipment performance, and algorithm validation. The construction of these phantoms is explained in this chapter. In the first part, materials used for phantom construction are introduced. Ingredients for modifying the electromagnetic properties and relaxation times are presented, and the advantages and disadvantages of aqueous, gel, and hybrid conductivity phantoms are explained. The devices and methods used to confirm phantom electromagnetic properties are explained. Next, different types of MREIT electrode materials and the constant current sources used for MREIT studies are discussed. In the last section, we present the results of previous MREIT and MREPT studies.

Four-channel current switching device to enable multi-electrode magnetic resonance current density imaging

Neurostimulation with multiple scalp electrodes has shown enhanced effects in recent studies. However, visualizations of stimulation-induced internal current distributions in brain is only possible through simulated current distributions obtained from computer model of human head. While magnetic resonance current density imaging (MRCDI) has a potential for direct in-vivo measurement of currents induced in brain with multi-electrode stimulation, existing MRCDI methods are only developed for two-electrode neurostimulation. A major bottleneck is the lack of a current switching device which is typically used to convert the DC current of neurostimulation devices into user-defined waveforms of positive and negative polarity with delays between them. In this work, we present a design of a four-electrode current switching device to enable simultaneous switching of current flowing through multiple scalp electrodes.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data. We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Sensitivity and resolution improvement for in vivo magnetic resonance current-density imaging of the human brain

PURPOSE

Magnetic resonance current-density imaging (MRCDI) combines MRI with low-intensity transcranial electrical stimulation (TES; 1-2 mA) to map current flow in the brain. However, usage of MRCDI is still hampered by low measurement sensitivity and image quality.

METHODS

Recently, a multigradient-echo-based MRCDI approach has been introduced that presently has the best-documented efficiency. This MRCDI approach has now been advanced in three directions and has been validated by phantom and in vivo experiments. First, the importance of optimum spoiling for brain imaging was verified. Second, the sensitivity and spatial resolution were improved by using acquisition weighting. Third, navigators were added as a quality control measure for tracking physiological noise. Combining these advancements, the optimized MRCDI method was tested by using 1 mA TES for two different injection profiles.

RESULTS

For a session duration of 4:20 min, the new MRCDI method was able to detect TES-induced magnetic fields at a sensitivity level of 84 picotesla, representing a twofold efficiency increase against our original method. A comparison between measurements and simulations based on personalized head models showed a consistent increase in the coefficient of determination of ΔR² = 0.12 for the current-induced magnetic fields and ΔR² = 0.22 for the current flow reconstructions. Interestingly, some of the simulations still clearly deviated from the measurements despite the strongly improved measurement quality. This highlights the utility of MRCDI to improve head models for TES simulations.

CONCLUSION

The achieved sensitivity improvement is an important step from proof-of-concept studies toward a broader application of MRCDI in clinical and basic neuroscience research.

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.

Toward integrative approaches to study the causal role of neural oscillations via transcranial electrical stimulation

Diverse transcranial electrical stimulation (tES) techniques have recently been developed to elucidate the role of neural oscillations, but critically, it remains questionable whether neural entrainment genuinely occurs and is causally related to the resulting behavior. Here, we provide a perspective on an emerging integrative research program across systems, species, theoretical and experimental frameworks to elucidate the potential of tES to induce neural entrainment. We argue that such an integrative agenda is a requirement to establish tES as a tool to test the causal role of neural oscillations and highlight critical issues that should be considered when adopting a translational approach.

Safety evaluation of a new setup for transcranial electric stimulation during magnetic resonance imaging

BACKGROUND

Transcranial electric stimulation during MR imaging can introduce safety issues due to coupling of the RF field with the stimulation electrodes and leads.

OBJECTIVE

To optimize the stimulation setup for MR current density imaging (MRCDI) and increase maximum stimulation current, a new low-conductivity (σ = 29.4 S/m) lead wire is designed and tested.

METHOD

The antenna effect was simulated to investigate the effect of lead conductivity. Subsequently, specific absorption rate (SAR) simulations for realistic lead configurations with low-conductivity leads and two electrode types were performed at 128 MHz and 298 MHz being the Larmor frequencies of protons at 3T and 7T. Temperature measurements were performed during MRI using high power deposition sequences to ensure that the electrodes comply with MRI temperature regulations.

RESULTS

The antenna effect was found for copper leads at ¼ RF wavelength and could be reliably eliminated using low-conductivity leads. Realistic lead configurations increased the head SAR and the local head SAR at the electrodes only minimally. The highest temperatures were measured on the rings of center-surround electrodes, while circular electrodes showed little heating. No temperature increase above the safety limit of 39 °C was observed.

CONCLUSION

Coupling to the RF field can be reliably prevented by low-conductivity leads, enabling cable paths optimal for MRCDI. Compared to commercial copper leads with safety resistors, the low-conductivity leads had lower total impedance, enabling the application of higher currents without changing stimulator design. Attention must be paid to electrode pads.

In-vivo imaging of targeting and modulation of depression-relevant circuitry by transcranial direct current stimulation: a randomized clinical trial

Recent clinical trials of transcranial direct current stimulation (tDCS) in depression have shown contrasting results. Consequently, we used in-vivo neuroimaging to confirm targeting and modulation of depression-relevant neural circuitry by tDCS. Depressed participants (N = 66, Baseline Hamilton Depression Rating Scale (HDRS) 17-item scores ≥14 and <24) were randomized into Active/Sham and High-definition (HD)/Conventional (Conv) tDCS groups using a double-blind, parallel design, and received tDCS individually targeted at the left dorsolateral prefrontal cortex (DLPFC). In accordance with Ampere's Law, tDCS currents were hypothesized to induce magnetic fields at the stimulation-target, measured in real-time using dual-echo echo-planar-imaging (DE-EPI) MRI. Additionally, the tDCS treatment trial (consisting of 12 daily 20-min sessions) was hypothesized to induce cerebral blood flow (CBF) changes post-treatment at the DLPFC target and in the reciprocally connected anterior cingulate cortex (ACC), measured using pseudo-continuous arterial spin labeling (pCASL) MRI. Significant tDCS current-induced magnetic fields were observed at the left DLPFC target for both active stimulation montages (Brodmann's area (BA) 46: p_HD = 0.048, Cohen's d_HD = 0.73; p_Conv = 0.018, d_Conv = 0.86; BA 9: p_HD = 0.011, d_HD = 0.92; p_Conv = 0.022, d_Conv = 0.83). Significant longitudinal CBF increases were observed (a) at the left DLPFC stimulation-target for both active montages (p_HD = 3.5E-3, d_HD = 0.98; p_Conv = 2.8E-3, d_Conv = 1.08), and (b) at ACC for the HD-montage only (p_HD = 2.4E-3, d_HD = 1.06; p_Conv = 0.075, d_Conv = 0.64). These results confirm that tDCS-treatment (a) engages the stimulation-target, and (b) modulates depression-relevant neural circuitry in depressed participants, with stronger network-modulations induced by the HD-montage. Although not primary outcomes, active HD-tDCS showed significant improvements of anhedonia relative to sham, though HDRS scores did not differ significantly between montages post-treatment.

Machine learning and individual variability in electric field characteristics predict tDCS treatment response

BACKGROUND

Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Quantification of this variability could enable person-specific optimization of tDCS outcomes. This pilot study used machine learning and MRI-derived electric field models to predict working memory improvements as a proof of concept for precision cognitive intervention.

METHODS

Fourteen healthy older adults received 20 minutes of 2 mA tDCS stimulation (F3/F4) during a two-week cognitive training intervention. Participants performed an N-back working memory task pre-/post-intervention. MRI-derived current models were passed through a linear Support Vector Machine (SVM) learning algorithm to characterize crucial tDCS current components (intensity and direction) that induced working memory improvements in tDCS responders versus non-responders.

MAIN RESULTS

SVM models of tDCS current components had 86% overall accuracy in classifying treatment responders vs. non-responders, with current intensity producing the best overall model differentiating changes in working memory performance. Median current intensity and direction in brain regions near the electrodes were positively related to intervention responses (r=0.811,p<0.001 and r=0.774,p=0.001).

CONCLUSIONS

This study provides the first evidence that pattern recognition analyses of MRI-derived tDCS current models can provide individual prognostic classification of tDCS treatment response with 86% accuracy. Individual differences in current intensity and direction play important roles in determining treatment response to tDCS. These findings provide important insights into mechanisms of tDCS response as well as proof of concept for future precision dosing models of tDCS intervention.

Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling

Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Methodology for tDCS integration with fMRI

Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

Concurrent Imaging of Markers of Current Flow and Neurophysiological Changes During tDCS

Despite being a popular neuromodulation technique, clinical translation of transcranial direct current stimulation (tDCS) is hampered by variable responses observed within treatment cohorts. Addressing this challenge has been difficult due to the lack of an effective means of mapping the neuromodulatory electromagnetic fields together with the brain's response. In this study, we present a novel imaging technique that provides the capability of concurrently mapping markers of tDCS currents, as well as the brain's response to tDCS. A dual-echo echo-planar imaging (DE-EPI) sequence is used, wherein the phase of the acquired MRI-signal encodes the tDCS current induced magnetic field, while the magnitude encodes the blood oxygenation level dependent (BOLD) contrast. The proposed technique was first validated in a custom designed phantom. Subsequent test-retest experiments in human participants showed that tDCS-induced magnetic fields can be detected reliably in vivo. The concurrently acquired BOLD data revealed large-scale networks characteristic of a brain in resting-state as well as a 'cathodal' and an 'anodal' resting-state component under each electrode. Moreover, 'cathodal's BOLD-signal was observed to significantly decrease with the applied current at the group level in all datasets. With its ability to image markers of electromagnetic cause as well as neurophysiological changes, the proposed technique may provide an effective means to visualize neural engagement in tDCS at the group level. Our technique also contributes to addressing confounding factors in applying BOLD fMRI concurrently with tDCS.

Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline

Objective.

Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject’s MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool—realistic volumetric-approach to simulate transcranial electric simulation (ROAST)—is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement.

Approach.

ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline.

Main results.

The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS.

Significance.

We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

Investigating the vestibular system using modern imaging techniques-A review on the available stimulation and imaging methods

The vestibular organs, located in the inner ear, sense linear and rotational acceleration of the head and its position relative to the gravitational field of the earth. These signals are essential for many fundamental skills such as the coordination of eye and head movements in the three-dimensional space or the bipedal locomotion of humans. Furthermore, the vestibular signals have been shown to contribute to higher cognitive functions such as navigation. As the main aim of the vestibular system is the sensation of motion it is a challenging system to be studied in combination with modern imaging methods. Over the last years various different methods were used for stimulating the vestibular system. These methods range from artificial approaches like galvanic or caloric vestibular stimulation to passive full body accelerations using hexapod motion platforms, or rotatory chairs. In the first section of this review we provide an overview over all methods used in vestibular stimulation in combination with imaging methods (fMRI, PET, E/MEG, fNIRS). The advantages and disadvantages of every method are discussed, and we summarize typical settings and parameters used in previous studies. In the second section the role of the four imaging techniques are discussed in the context of vestibular research and their potential strengths and interactions with the presented stimulation methods are outlined.

Can Transcranial Electrical Stimulation Localize Brain Function?

Transcranial electrical stimulation (TES) uses constant (TDCS) or alternating currents (TACS) to modulate brain activity. Most TES studies apply low-intensity currents through scalp electrodes (≤2 mA) using bipolar electrode arrangements, producing weak electrical fields in the brain (<1 V/m). Low-intensity TES has been employed in humans to induce changes in task performance during or after stimulation. In analogy to focal transcranial magnetic stimulation, TES-induced behavioral effects have often been taken as evidence for a causal involvement of the brain region underlying one of the two stimulation electrodes, often referred to as the active electrode. Here, we critically review the utility of bipolar low-intensity TES to localize human brain function. We summarize physiological substrates that constitute peripheral targets for TES and may mediate subliminal or overtly perceived peripheral stimulation during TES. We argue that peripheral co-stimulation may contribute to the behavioral effects of TES and should be controlled for by "sham" TES. We discuss biophysical properties of TES, which need to be considered, if one wishes to make realistic assumptions about which brain regions were preferentially targeted by TES. Using results from electric field calculations, we evaluate the validity of different strategies that have been used for selective spatial targeting. Finally, we comment on the challenge of adjusting the dose of TES considering dose-response relationships between the weak tissue currents and the physiological effects in targeted cortical areas. These considerations call for caution when attributing behavioral effects during or after low-intensity TES studies to a specific brain region and may facilitate the selection of best practices for future TES studies.

Anisotropic conductivity tensor imaging for transcranial direct current stimulation (tDCS) using magnetic resonance diffusion tensor imaging (MR-DTI)

Transcranial direct current stimulation (tDCS) is a widely used non-invasive brain stimulation technique by applying low-frequency weak direct current via electrodes attached on the head. The tDCS using a fixed current between 1 and 2 mA has relied on computational modelings to achieve optimal stimulation effects. Recently, by measuring the tDCS current induced magnetic field using an MRI scanner, the internal current pathway has been successfully recovered. However, up to now, there is no technique to visualize electrical properties including the electrical anisotropic conductivity, effective extracellular ion-concentration, and electric field using only the tDCS current in-vivo. By measuring the apparent diffusion coefficient (ADC) and the magnetic flux density induced by the tDCS, we propose a method to visualize the electrical properties. We reconstruct the scale parameter, which connects the anisotropic conductivity tensor to the diffusion tensor of water molecules, by introducing a repetitive scheme called the diffusion tensor J-substitution algorithm using the recovered current density and the measured ADCs. We investigate the proposed method to explain why the iterative scheme converges to the internal conductivity. We verified the proposed method with an anesthetized canine brain to visualize electrical properties including the electrical properties by tDCS current.