Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head

The Resistivity of Human Brain Tumours In Vivo

The histological structure of tumour tissues differs from healthy brain tissues. It can therefore be assumed that there are differences also in the electrical characteristics of these tissues. The electrical characteristics of the tissues define how electric current is distributed within volume conductors, such as the human body or head. Incorrect values affect, for example, the accuracy of impedance tomography or EEG source localisation. However, no controlled experimental data for human in vivo brain tumour resistivity values have been reported thus far. We have developed a controlled method for detecting the electrical resistivities of living brain tissue and investigated different types of brain tumours. The measurements were taken during brain surgeries conducted to remove the tumours. For analysis purposes, the tumours were divided into the following categories: meningiomas, low-grade gliomas, high-grade gliomas (glioblastomas) and other tumours or lesions. The averages of the measured resistivity values were 530 Ω-cm for meningiomas, 160 Ω-cm for low-grade gliomas, and 498 Ω-cm for high-grade gliomas. The differences in high- and low-grade glioma values and meningioma and low-grade glioma values were statistically highly significant. The tumour values were also compared to surrounding healthy brain tissues, and the difference ranged from 40 to 330%. The results suggest that certain tumour types have different electronic properties and that the resistivity values could be used to distinguish tumour tissue from surrounding healthy tissue and to identify and classify certain brain tumour types.

Immediate neurophysiological effects of transcranial electrical stimulation

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.

Requirements for Coregistration Accuracy in On-Scalp MEG

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.

A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.

New Strategy for Finite Element Mesh Generation for Accurate Solutions of Electroencephalography Forward Problems

The finite element method (FEM) is a numerical method that is often used for solving electroencephalography (EEG) forward problems involving realistic head models. In this study, FEM solutions obtained using three different mesh structures, namely coarse, densely refined, and adaptively refined meshes, are compared. The simulation results showed that the accuracy of FEM solutions could be significantly enhanced by adding a small number of elements around regions with large estimated errors. Moreover, it was demonstrated that the adaptively refined regions were always near the current dipole sources, suggesting that selectively generating additional elements around the cortical surface might be a new promising strategy for more efficient FEM-based EEG forward analysis.

Neuromodulation of lower limb motor responses with transcutaneous lumbar spinal cord direct current stimulation

OBJECTIVE

Trans-spinal direct current stimulation (tsDCS) is a promising technique to modulate spinal circuits. Combining clinical with modelling studies can improve effectiveness of tsDCS protocols. The aim of this study is to measure the effects of lumbar tsDCS on motor spinal responses and observe if these are consistent with the electric field (E-field) predicted from a computational model.

METHODS

The exploratory study design was double-blind crossover and randomized. tsDCS was delivered for 15 min (anodal, cathodal, sham) at L2 vertebra level (2.5 mA, 90 C/cm²) in 14 healthy subjects. F-wave, H-reflex, cortical silent period, motor evoked potential and sympathetic skin response were analyzed. Statistical methods were applied with Bonferroni correction for multiple comparisons, a p < 0.05 was set as significant. A human volume conductor model was obtained from available databases. E-field distributions in the spinal grey matter (GM) and white matter (WM) were calculated.

RESULTS

No tsDCS effects were observed. E-field magnitude predicted in the lumbosacral spinal GM and WM was <0.15 V/m, insufficient to ensure neuromodulation, which is consistent with the absence of effects.

CONCLUSION

The tsDCS protocol applied did not change motor response to delivered stimulus, thus we observed no effect on motor spinal circuits.

SIGNIFICANCE

Future tsDCS protocols should be supported by computational models.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Transcutaneous spinal direct current stimulation of the lumbar and sacral spinal cord: a modelling study

OBJECTIVE

Our aim was to perform a computational study of the electric field (E-field) generated by transcutaneous spinal direct current stimulation (tsDCS) applied over the thoracic, lumbar and sacral spinal cord, in order to assess possible neuromodulatory effects on spinal cord circuitry related with lower limb functions.

APPROACH

A realistic volume conductor model of the human body consisting of 14 tissues was obtained from available databases. Rubber pad electrodes with a metallic connector and a conductive gel layer were modelled. The finite element (FE) method was used to calculate the E-field when a current of 2.5 mA was passed between two electrodes. The main characteristics of the E-field distributions in the spinal grey matter (spinal-GM) and spinal white matter (spinal-WM) were compared for seven montages, with the anode placed either over T10, T8 or L2 spinous processes (s.p.), and the cathode placed over right deltoid (rD), umbilicus (U) and right iliac crest (rIC) areas or T8 s.p. Anisotropic conductivity of spinal-WM and of a group of dorsal muscles near the vertebral column was considered.

MAIN RESULTS

The average E-field magnitude was predicted to be above 0.15 V m^-1 in spinal cord regions located between the electrodes. L2-T8 and T8-rIC montages resulted in the highest E-field magnitudes in lumbar and sacral spinal segments (>0.30 V m^-1). E-field longitudinal component is 3 to 6 times higher than the ventral-dorsal and right-left components in both the spinal-GM and WM. Anatomical features such as CSF narrowing due to vertebrae bony edges or disks intrusions in the spinal canal correlate with local maxima positions.

SIGNIFICANCE

Computational modelling studies can provide detailed information regarding the electric field in the spinal cord during tsDCS. They are important to guide the design of clinical tsDCS protocols that optimize stimulation of application-specific spinal targets.

The Effect of Head Model Simplification on Beamformer Source Localization

Beamformers are a widely-used tool in brain analysis with magnetoencephalography (MEG) and electroencephalography (EEG). For the construction of the beamformer filters realistic head volume conductor modeling is necessary for accurately computing the EEG and MEG leadfields, i.e., for solving the EEG and MEG forward problem. In this work, we investigate the influence of including realistic head tissue compartments into a finite element method (FEM) model on the beamformer's localization ability. Specifically, we investigate the effect of including cerebrospinal fluid, gray matter, and white matter distinction, as well as segmenting the skull bone into compacta and spongiosa, and modeling white matter anisotropy. We simulate an interictal epileptic measurement with white sensor noise. Beamformer filters are constructed with unit gain, unit array gain, and unit noise gain constraint. Beamformer source positions are determined by evaluating power and excess sample kurtosis (g2) of the source-waveforms at all source space nodes. For both modalities, we see a strong effect of modeling the cerebrospinal fluid and white and gray matter. Depending on the source position, both effects can each be in the magnitude of centimeters, rendering their modeling necessary for successful localization. Precise skull modeling mainly effected the EEG up to a few millimeters, while both modalities could profit from modeling white matter anisotropy to a smaller extent of 5-10 mm. The unit noise gain or neural activity index beamformer behaves similarly to the array gain beamformer when noise strength is sufficiently high. Variance localization seems more robust against modeling errors than kurtosis.

Influence of electrode configuration on the electric field distribution during transcutaneous spinal direct current stimulation of the cervical spine

Transcutaneous spinal direct current stimulation (tsDCS) is a recent technique with promising neuromodulatory effects on spinal neuronal circuitry. The main objective of the present study was to perform a finite element analysis of the electric field distribution in tsDCS in the cervical spine region, with varying electrode configurations and geometry. A computational model of a human trunk was generated with nine tissue meshes. Three electrode configurations were tested: A) rectangular saline-soaked sponge target and return electrodes placed over C3 and T3 spinous processes, respectively; B1) circular saline-soaked sponge target and return electrodes placed over C7 spinous process and right deltoid muscle, respectively; B2) same configuration as B1, considering circular shaped electrodes with sponge and rubber layers and a small circular connector on the top surface. The electric field distribution for cervical tsDCS predicted higher magnitude in configurations B1 and B2, reaching a maximum of 0.71 V/m in the spinal white matter and 0.43 V/m in the spinal grey matter, with values above 0.15 V/m in the region of the spinal circuits related with upper limb innervation. In configuration A, the values were found to be <; 0.15 V/m through the entire spinal cord. Electric fields with magnitude above 0.15 V/m are thought to be effective in neuromodulation of the human cerebral cortex, so the configurations B1 and B2 could be an optimal choice for cervical tsDCS protocols. Computational studies using realistic models may be a powerful tool to predict physical effects of tsDCS on the cervical spinal cord and to optimize electrode placement focused on specific neurologic patient needs related with upper limb function.

Detecting large-scale networks in the human brain using high-density electroencephalography

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

Source localization of S-cone and L/M-cone driven signals using silent substitution flash stimulation

This study aimed to analyze the neuronal sources of the visual evoked potentials after flash stimulation of the S- and the L/M-cone driven channels of the visual system. For 11 volunteers a 64-channel electroencephalography (EEG) was recorded during selective excitation of both color opponent channels. Individual and grand average data were analyzed topographically. Source localization was carried out using a realistically shaped three compartment boundary element model (BEM) and a mirrored moving dipole model. We found two main components (N1, P1) in all subjects, as well as a third late component in most subjects. For these components significant latency differences (N1=33 ms, P1=22 ms; p<0.05) between both color opponent channels were found. The results showed no differences in the topography and no differences in dipole localization between both color channels. Talairach coordinates of grand averages indicated activation in area 18. Comparison of results of separately stimulated eyes revealed no differences. Our findings showed that neural processing occurs in the same areas of the visual cortex for stimuli with different spectral properties. The signals of S- and L/M-cone driven channels are transmitted in distinct pathways to the cortex. Thus, the observed latency differences might be caused by different anatomical and functional properties of these pathways.

By our bootstraps: Comparing methods for measuring auditory 40 Hz steady-state neural activity

Although the 40 Hz auditory steady-state response (ASSR) is of clinical interest, the construct validity of EEG and MEG measures of 40 Hz ASSR cortical microcircuits is unclear. This study evaluated several MEG and EEG metrics by leveraging findings of (a) an association between the 40 Hz ASSR and age in the left but not right hemisphere, and (b) right- > left-hemisphere differences in the strength of the 40 Hz ASSR. The contention is that, if an analysis method does not demonstrate a left 40 Hz ASSR and age relationship or hemisphere differences, then the obtained measures likely have low validity. Fifty-three adults were presented 500 Hz stimuli modulated at 40 Hz while MEG and EEG were collected. ASSR activity was examined as a function of phase similarity (intertrial coherence) and percent change from baseline (total power). A variety of head models (spherical and realistic) and a variety of dipole source modeling strategies (dipole source localization and dipoles fixed to Heschl's gyri) were compared. Several sensor analysis strategies were also tested. EEG sensor measures failed to detect left 40 Hz ASSR and age associations or hemisphere differences. A comparison of MEG and EEG head-source models showed similarity in the 40 Hz ASSR measures and in estimating age and left 40 Hz ASSR associations, indicating good construct validity across models. Given a goal of measuring the 40 Hz ASSR cortical microcircuits, a source-modeling approach was shown to be superior in measuring this construct versus methods that rely on EEG sensor measures.

Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity

Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power synchronization mechanisms. These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psychology.

The effect of inter-electrode distance on the electric field distribution during transcutaneous lumbar spinal cord direct current stimulation

Previous studies have indicated potential neuromodulation of the spinal circuitry by transcutaneous spinal direct current stimulation (tsDCS), such as changes in motor unit recruitment, shortening of the peripheral silent period and interference with supraspinal input to lower motor neurons. All of these effects were dependent on the polarity of the electrodes. The present study investigates how the distance between the electrodes during tsDCS influences the electric field's (E-field) spatial distribution in the lumbar and sacral spinal cord (SC). The electrodes were placed longitudinally along the SC, with the target electrode over the lumbar spine, and the return electrode above the former, considering four different distances (4, 8, 12 and 16 cm from the target). A fifth configuration was also tested with the return electrode over the right deltoid muscle. Peak values of the E-field's magnitude are found in the lumbo-sacral region of the SC for all tested configurations. Increasing the distance between the electrodes results in a wider spread of the E-field magnitude distribution along the SC, with larger maximum peak values and a smoother variation. The fifth configuration does not present the highest maximum values when compared to the other configurations. The results indicate that the choice of the return electrode position relative to the target can influence the distribution and the range of values of the E-field magnitude in the SC. Possible clinical significance of the observed effects will be discussed.

Using transcranial direct-current stimulation (tDCS) to understand cognitive processing

Noninvasive brain stimulation methods are becoming increasingly common tools in the kit of the cognitive scientist. In particular, transcranial direct-current stimulation (tDCS) is showing great promise as a tool to causally manipulate the brain and understand how information is processed. The popularity of this method of brain stimulation is based on the fact that it is safe, inexpensive, its effects are long lasting, and you can increase the likelihood that neurons will fire near one electrode and decrease the likelihood that neurons will fire near another. However, this method of manipulating the brain to draw causal inferences is not without complication. Because tDCS methods continue to be refined and are not yet standardized, there are reports in the literature that show some striking inconsistencies. Primary among the complications of the technique is that the tDCS method uses two or more electrodes to pass current and all of these electrodes will have effects on the tissue underneath them. In this tutorial, we will share what we have learned about using tDCS to manipulate how the brain perceives, attends, remembers, and responds to information from our environment. Our goal is to provide a starting point for new users of tDCS and spur discussion of the standardization of methods to enhance replicability.

Constructing a three-dimensional electrical model of a living cochlear implant user's cochlea

BACKGROUND

Hearing performance varies greatly among users of cochlear implants. Current three-dimensional cochlear models that predict the electrical fields inside a stimulated cochlea and their effect on neural excitation are generally based on a generic human or guinea pig cochlear shape that does not take inter-user morphological variations into account. This precludes prediction of user-specific performance.

AIMS

The aim of this study is to develop a model of the implanted cochlea of a specific living human individual and to assess if the inclusion of morphological variations in cochlear models affects predicted outcomes significantly.

METHODS

Five three-dimensional electric volume conduction models of the implanted cochleae of individual living users were constructed from standard CT scan data. These models were embedded in head models that include monopolar return electrodes in accurate anatomic positions. Potential distributions and neural excitation patterns were predicted for each of the models.

RESULTS

Modeled potential distributions and neural excitation profiles (threshold amplitudes, center frequencies, and bandwidths) are affected by user-specific cochlear morphology and electrode placement within the cochlea.

CONCLUSIONS

This work suggests that the use of user-specific models is indicated when more detailed analysis is required than what is available from generic models. Copyright © 2015 John Wiley & Sons, Ltd.

Skull Defects in Finite Element Head Models for Source Reconstruction from Magnetoencephalography Signals

Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched the known physical locations and orientations. Ignoring skull defects in the head model during reconstruction displaced sources under a skull defect away from that defect. Sources next to a defect were reoriented. When skull defects, with their physical conductivity, were incorporated in the head model, the location and orientation errors were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated the influence on MEG signals. We propose concrete guidelines for taking into account conducting skull defects during MEG coil placement and modeling. Exact finite element head models can improve localization of brain function, specifically after surgery.

The role of blood vessels in high-resolution volume conductor head modeling of EEG

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

Development and Validation of a High-Fidelity Finite-Element Model of Monopolar Stimulation in the Implanted Guinea Pig Cochlea

GOAL

To validate a new electroanatomical model of the implanted guinea pig cochlea against independently obtained in vivo voltage tomography data, and evaluate the validity of exist-ing modeling assumptions on current paths and neural excitation.

METHODS

An in silico model was generated from sTSLIM images and analyzed in COMSOL Multiphysics. Tissue resistivities and boundary conditions were varied to test model sensitivity.

RESULTS

The simulation was most sensitive to the resistivities of bone, perilymph, and nerve. Bone tissue in particular should be separated by morphology because different types of bone have different electrical properties. Despite having a strong impact on intrascalar voltages and exit pathways, most boundary conditions, including a new alternative proposed to account for the unmodeled return path, only had a weak effect on neural excitation.

CONCLUSION

The new model demonstrated a strong correlation with the in vivo voltage data.

SIGNIFICANCE

These findings address a long-standing knowledge gap about appropriate boundary conditions, and will help to promote wider acceptance of insights from computational models of the cochlea.

Combined EEG/MEG can outperform single modality EEG or MEG source reconstruction in presurgical epilepsy diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.

Montage matters: the influence of transcranial alternating current stimulation on human physiological tremor

BACKGROUND

Classically, studies adopting non-invasive transcranial electrical stimulation have placed greater importance on the position of the primary "stimulating" electrode than the secondary "reference" electrode. However, recent current density modeling suggests that ascribing a neutral role to the reference electrode may prove an inappropriate oversimplification.

HYPOTHESIS

We set out to test the hypothesis that the behavioral effects of transcranial electrical stimulation are critically dependent on the position of the return ("reference") electrode.

METHODS

We examined the effect of transcranial alternating current stimulation (sinusoidal waveform with no direct current offset at a peak-to-peak amplitude of 2000 μA and a frequency matched to each participant's peak tremor frequency) on physiological tremor in a group of healthy volunteers (N = 12). We implemented a sham-controlled experimental protocol where the position of the stimulating electrode remained fixed, overlying primary motor cortex, whilst the position of the return electrode varied between two cephalic (fronto-orbital and contralateral primary motor cortex) and two extracephalic (ipsilateral and contralateral shoulder) locations. We additionally controlled for the role of phosphenes in influencing motor output by assessing the response of tremor to photic stimulation, through self-reported phosphene ratings.

RESULTS

Altering only the position of the return electrode had a profound behavioral effect: only the montage with extracephalic return contralateral to the primary stimulating electrode significantly entrained physiological tremor (15.9% ± 6.1% increase in phase stability, 1 S.E.M.). Photic stimulation also entrained tremor (11.7% ± 5.1% increase in phase stability). Furthermore, the effects of electrical stimulation are distinct from those produced from direct phosphene induction, in that the latter were only seen with the fronto-orbital montage that did not affect the tremor.

CONCLUSION

The behavioral effects of transcranial alternating current stimulation appear to be critically dependent on the position of the reference electrode, highlighting the importance of electrode montage when designing experimental and therapeutic protocols.

Influence of the head model on EEG and MEG source connectivity analyses

The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

Changes in scalp potentials and spatial smoothing effects of inclusion of dura layer in human head models for EEG simulations

The dura layer which covers the brain is less conductive than the CSF (cerebrospinal fluid) and also more conductive than the skull bone. This could significantly influence the flow of volume currents from cortex to the scalp surface which will also change the magnitude and spatial profiles of scalp potentials. This was examined with a 3-D finite element method (FEM) model of an adult subject constructed from 192 segmented axial magnetic resonance (MR) slices with 256×256 pixel resolution. The voxel resolution was 1×1×1 mm. The model included the dura layer. In addition, other major tissues were also identified. The electrical conductivities of various tissues were obtained from the literature. The conductivities of dura and CSF were 0.001 S/m and 0.06 S/m, respectively. The electrical activity of the cortex was represented by 144,000 distributed dipolar sources with orientations normal to the local cortical surface. The dipolar intensity was in the range of 0.0-0.4 mA meter with a uniform random distribution. Scalp potentials were simulated for two head models with an adaptive finite element solver. One model had the dura layer and in the other model, dura layer was replaced with the CSF. Spatial contour plots of potentials on the cortical surface, dural surface and the scalp surface were made. With the inclusion of the dura layer, scalp potentials decrease by about 20%. The contours of gyri and sulci structures were visible in the spatial profiles of the cortical potentials which were smoothed out on the dural surface and were not visible on the scalp surface. These results suggest that dura layer should be included for an accurate modeling of scalp and cortical potentials.

A guideline for head volume conductor modeling in EEG and MEG

For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the above-mentioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect. Finally, the distinction between skull spongiosa and compacta caused the weakest effects in both modalities when using an optimized conductivity value for the homogenized compartment. We conclude that it is highly recommendable to include the CSF and distinguish between gray and white matter in head volume conductor modeling. Especially for the MEG, the modeling of skull spongiosa and compacta might be neglected due to the weak effects; the simplification of not modeling white matter anisotropy is admissible considering the complexity and current limitations of the underlying modeling approach.

Inconsistent outcomes of transcranial direct current stimulation (tDCS) may be originated from the anatomical differences among individuals: a simulation study using individual MRI data

Transcranial direct current stimulation (tDCS) is a kind of neuromodulation protocol, which transmits small amount of DC currents through scalp electrodes to facilitate or inhibit particular areas of the brain. Although many studies have demonstrated that tDCS can effectively modulate excitability of various brain sites, the outcomes of the tDCS treatment are not consistent among subjects to whom identical electrode montages were applied. So far, no studies have clearly elucidated the main cause of this individual variability. The hypothesis of our study was that the individual variability in the tDCS effect might be originated due to the anatomical differences among subjects. To verify our hypothesis, we investigated the relationship between the current density value at dorsolateral prefrontal cortex (DLPFC) simulated using finite element method (FEM) and the behavioral outcomes of a simple working memory (WM) task. A 3-back WM task experiment was conducted with twenty-five healthy subjects before and after the DC stimulation, when the cathode and anode electrodes were attached to right supraorbital area and F3 location, respectively, for all subjects. The results showed that participants who showed enhanced WM task performance after tDCS had a significantly larger current density on DLPFC, suggesting that the inconsistent behavioral outcomes of tDCS might be partially due to the anatomical differences among subjects.

Variation in deep brain stimulation electrode impedance over years following electrode implantation

BACKGROUND

Deep brain stimulation (DBS) electrode impedance is a major determinant of current delivery to target tissues, but long-term variation in impedance has received little attention.

OBJECTIVES

To assess the relationship between electrode impedance and time in a large DBS patient population and characterize the relationship between contact activity and impedance.

METHODS

We collected retrospective impedance and programming data from 128 electrodes in 84 patients with Parkinson's disease, essential tremor or dystonia. Effects of time, contact activity, stimulation voltage and other parameters on impedance were assessed. We also examined impedance changes following contact activation and deactivation.

RESULTS

Impedance decreased by 73 Ω/year (p < 0.001), with 72% of contacts following a downward trend. Impedance was on average 163 Ω lower in active contacts (p < 0.001). Contact activation and inactivation were associated with a more (p < 0.001) and less (p = 0.016) rapid decline in impedance, respectively. Higher stimulation voltages were associated with lower impedance values (p < 0.001). Contact number and electrode model were also significant predictors of impedance.

CONCLUSIONS

Impedance decreases gradually in a stimulation-dependent manner. These trends have implications for long-term programming, the development of a closed-loop DBS device and current understanding of the electrode-tissue interface.

Comparison of three-shell and simplified volume conductor models in magnetoencephalography

Experimental MEG source imaging studies have typically been carried out with either a spherically symmetric head model or a single-shell boundary-element (BEM) model that is shaped according to the inner skull surface. The concepts and comparisons behind these simplified models have led to misunderstandings regarding the role of skull and scalp in MEG. In this work, we assess the forward-model errors due to different skull/scalp approximations and due to differences and errors in model geometries. We built five anatomical models of a volunteer using a set of T1-weighted MR scans and three common toolboxes. Three of the models represented typical models in experimental MEG, one was manually constructed, and one contained a major segmentation error at the skull base. For these anatomical models, we built forward models using four simplified approaches and a three-shell BEM approach that has been used as reference in previous studies. Our reference model contained in addition the skull fine-structure (spongy bone). We computed signal topographies for cortically constrained sources in the left hemisphere and compared the topographies using relative error and correlation metrics. The results show that the spongy bone has a minimal effect on MEG topographies, and thus the skull approximation of the three-shell model is justified. The three-shell model performed best, followed by the corrected-sphere and single-shell models, whereas the local-spheres and single-sphere models were clearly worse. The three-shell model was the most robust against the introduced segmentation error. In contrast to earlier claims, there was no noteworthy difference in the computation times between the realistically-shaped and sphere-based models, and the manual effort of building a three-shell model and a simplified model is comparable. We thus recommend the realistically-shaped three-shell model for experimental MEG work. In cases where this is not possible, we recommend a realistically-shaped corrected-sphere or single-shell model.

Magnetoencephalography signals are influenced by skull defects

OBJECTIVE

Magnetoencephalography (MEG) signals had previously been hypothesized to have negligible sensitivity to skull defects. The objective is to experimentally investigate the influence of conducting skull defects on MEG and EEG signals.

METHODS

A miniaturized electric dipole was implanted in vivo into rabbit brains. Simultaneous recording using 64-channel EEG and 16-channel MEG was conducted, first above the intact skull and then above a skull defect. Skull defects were filled with agar gels, which had been formulated to have tissue-like homogeneous conductivities. The dipole was moved beneath the skull defects, and measurements were taken at regularly spaced points.

RESULTS

The EEG signal amplitude increased 2-10 times, whereas the MEG signal amplitude reduced by as much as 20%. The EEG signal amplitude deviated more when the source was under the edge of the defect, whereas the MEG signal amplitude deviated more when the source was central under the defect. The change in MEG field-map topography (relative difference measure, RDM(∗)=0.15) was geometrically related to the skull defect edge.

CONCLUSIONS

MEG and EEG signals can be substantially affected by skull defects.

SIGNIFICANCE

MEG source modeling requires realistic volume conductor head models that incorporate skull defects.

Computational modeling of direct neuronal recruitment during intracortical microstimulation in somatosensory cortex

OBJECTIVE

Electrical stimulation of cortical tissue could be used to deliver sensory information as part of a neuroprosthetic device, but current control of the location, resolution, quality, and intensity of sensations elicited by intracortical microstimulation (ICMS) remains inadequate for this purpose. One major obstacle to resolving this problem is the poor understanding of the neural activity induced by ICMS. Even with new imaging methods, quantifying the activity of many individual neurons within cortex is difficult.

APPROACH

We used computational modeling to examine the response of somatosensory cortex to ICMS. We modeled the axonal arbors of eight distinct morphologies of interneurons and seven types of pyramidal neurons found in somatosensory cortex and identified their responses to extracellular stimulation. We then combined these axonal elements to form a multi-layered slab of simulated cortex and investigated the patterns of neural activity directly induced by ICMS. Specifically we estimated the number, location, and variety of neurons directly recruited by stimulation on a single penetrating microelectrode.

MAIN RESULTS

The population of neurons activated by ICMS was dependent on both stimulation strength and the depth of the electrode within cortex. Strikingly, stimulation recruited interneurons and pyramidal neurons in very different patterns. Interneurons are primarily recruited within a dense, continuous region around the electrode, while pyramidal neurons were recruited in a sparse fashion both near the electrode and up to several millimeters away. Thus ICMS can lead to an unexpectedly complex spatial distribution of firing neurons.

SIGNIFICANCE

These results lend new insights to the complexity and range of neural activity that can be induced by ICMS. This work also suggests mechanisms potentially responsible for the inconsistency and unnatural quality of sensations initiated by ICMS. Understanding these mechanisms will aid in the design of stimulation that can be used to generate effective sensory feedback for neuroprosthetic devices.

Subspace electrode selection methodology for EEG multiple source localization error reduction due to uncertain conductivity values

This paper proposes a modification of the subspace correlation cost function and the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) method for electroencephalography (EEG) source analysis in epilepsy. This enables to reconstruct neural source locations and orientations that are less degraded due to the uncertain knowledge of the head conductivity values. An extended linear forward model is used in the subspace correlation cost function that incorporates the sensitivity of the EEG potentials to the uncertain conductivity value parameter. More specifically, the principal vector of the subspace correlation function is used to provide relevant information for solving the EEG inverse problems. A simulation study is carried out on a simplified spherical head model with uncertain skull to soft tissue conductivity ratio. Results show an improvement in the reconstruction accuracy of source parameters compared to traditional methodology, when using conductivity ratio values that are different from the actual conductivity ratio.

Influence of blood vessel conductivity in cochlear implant stimulation using a finite element head model

It is known that the inclusion of blood vessels in finite element (FE) models can influence the current conduction results. However, there have been no studies exploring the impact of blood vessel conductivity on human head models for cochlear implant (CI) stimulation. The three-dimensional (3D) FE model presented in this paper aims to provide understanding in this regard. The electrical conductivity of blood was varied to determine the sensitivity of the 3D model. The results show that some of the current is exiting the cochlea and taking the jugular vein pathway. When compared to the case with blood vessels being omitted, the current density in the blood increased by 13.1%, 17.2% and 20.7% for low, medium and high electrical conductivity cases considered, respectively. This study suggests that blood vessels cannot be neglected from CI models as the jugular vein can provide a low impedance pathway, through which current can leave the cochlea. It also indicates the importance of using correct tissue property values for performing accurate bioelectric modeling analyses.

Comparison of spherical and realistically shaped boundary element head models for transcranial magnetic stimulation navigation

OBJECTIVE

MRI-guided real-time transcranial magnetic stimulation (TMS) navigators that apply electromagnetic modeling have improved the utility of TMS. However, their accuracy and speed depends on the assumed volume conductor geometry. Spherical models found in present navigators are computationally fast but may be inaccurate in some areas. Realistically shaped boundary-element models (BEMs) could increase accuracy at a moderate computational cost, but it is unknown which model features have the largest influence on accuracy. Thus, we compared different types of spherical models and BEMs.

METHODS

Globally and locally fitted spherical models and different BEMs with either one or three compartments and with different skull-to-brain conductivity ratios (1/1-1/80) were compared against a reference BEM.

RESULTS

The one-compartment BEM at inner skull surface was almost as accurate as the reference BEM. Skull/brain conductivity ratio in the range 1/10-1/80 had only a minor influence. BEMs were superior to spherical models especially in frontal and temporal areas (up to 20mm localization and 40% intensity improvement); in motor cortex all models provided similar results.

CONCLUSIONS

One-compartment BEMs offer a good balance between accuracy and computational cost.

SIGNIFICANCE

Realistically shaped BEMs may increase TMS navigation accuracy in several brain areas, such as in prefrontal regions often targeted in clinical applications.

A pipeline for the simulation of transcranial direct current stimulation for realistic human head models using SCIRun/BioMesh3D

The current work presents a computational pipeline to simulate transcranial direct current stimulation from image based models of the head with SCIRun [15]. The pipeline contains all the steps necessary to carry out the simulations and is supported by a complete suite of open source software tools: image visualization, segmentation, mesh generation, tDCS electrode generation and efficient tDCS forward simulation.

Stimulation strength and focality of electroconvulsive therapy with individualized current amplitude: a preclinical study

This study investigates the stimulation strength and focality of electroconvulsive therapy (ECT) with individualized current amplitude in a nonhuman primate (NHP) model. We generated an anatomically realistic finite element model of a NHP head incorporating tissue heterogeneity and white matter conductivity anisotropy based on structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. The electric field spatial distributions of three conventional ECT electrode placements (bilateral, bifrontal, and right unilateral) and an experimental frontomedial electrode configuration were simulated. We calibrated the electric field maps relative to an empirical neural activation threshold and evaluated the stimulation strength and focality of the various ECT electrode configurations with individualized current amplitudes corresponding to the motor threshold and seizure threshold assessed in the anesthetized NHP. Understanding the stimulation strength and focality of various forms of ECT could provide insight into the mechanisms of therapeutic seizure induction, and could provide support for the clinical investigation of ECT with individualized current amplitude as an intervention with potentially improved risk/benefit ratio.

Systematic approximations of neural fields through networks of neural masses in the virtual brain

Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly. This leads naturally to a description of neural fields on folded cortical sheets with local and global connectivities. The numerical approximation of neural fields in biologically realistic situations as addressed in Virtual Brain simulations (see http://thevirtualbrain.org/app/ (version 1.0)) is challenging and requires a thorough evaluation if the Virtual Brain approach is to be adapted for systematic studies of disease and disorders. Here we analyze the sampling problem of neural fields for arbitrary dimensions and provide explicit results for one, two and three dimensions relevant to realistically folded cortical surfaces. We characterize (i) the error due to sampling of spatial distribution functions; (ii) useful sampling parameter ranges in the context of encephalographic (EEG, MEG, ECoG and functional MRI) signals; (iii) guidelines for choosing the right spatial distribution function for given anatomical and geometrical constraints.

Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error

The conductivity profile of the head has a major effect on EEG signals, but unfortunately the conductivity for the most important compartment, skull, is only poorly known. In dipole modeling studies, errors in modeled skull conductivity have been considered to have a detrimental effect on EEG source estimation. However, as dipole models are very restrictive, those results cannot be generalized to other source estimation methods. In this work, we studied the sensitivity of EEG and combined MEG + EEG source estimation to errors in skull conductivity using a distributed source model and minimum-norm (MN) estimation.

We used a MEG/EEG modeling set-up that reflected state-of-the-art practices of experimental research. Cortical surfaces were segmented and realistically-shaped three-layer anatomical head models were constructed, and forward models were built with Galerkin boundary element method while varying the skull conductivity. Lead-field topographies and MN spatial filter vectors were compared across conductivities, and the localization and spatial spread of the MN estimators were assessed using intuitive resolution metrics.

The results showed that the MN estimator is robust against errors in skull conductivity: the conductivity had a moderate effect on amplitudes of lead fields and spatial filter vectors, but the effect on corresponding morphologies was small. The localization performance of the EEG or combined MEG + EEG MN estimator was only minimally affected by the conductivity error, while the spread of the estimate varied slightly. Thus, the uncertainty with respect to skull conductivity should not prevent researchers from applying minimum norm estimation to EEG or combined MEG + EEG data. Comparing our results to those obtained earlier with dipole models shows that general judgment on the performance of an imaging modality should not be based on analysis with one source estimation method only.

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We tested EEG and MEG + EEG minimum-norm estimation with errors in skull models.

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Error in skull conductivity has only a small effect on morphology.

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Error in skull conductivity has moderate effect on amplitudes.

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Quality of minimum-norm estimates is minimally affected by skull conductivity error.

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Sensitivity to conductivity error depends on chosen source estimation method.

Computational representation of a realistic head and brain volume conductor model: electroencephalography simulation and visualization study

Computational head and brain volume conductor modeling is a practical and non-invasive method to investigate neuroelectrical activity in the brain. Anatomical structures included in a model affect the flow of volume currents and the resulting scalp surface potentials. The influence of different tissues within the head on scalp surface potentials was investigated by constructing five highly detailed, realistic head models from segmented and processed Visible Human Man digital images. The models were: (1) model with 20 different tissues, that is, skin, dense connective tissue (fat), aponeurosis (muscle), outer, middle and inner tables of the scalp, dura matter, arachnoid layer (including cerebrospinal fluid), pia matter, six cortical layers, eye tissue, muscle around the eye, optic nerve, temporal muscle, white matter and internal air, (2) model with three main inhomogeneities, that is, scalp, skull, brain, (3) model with homogeneous scalp and remaining inhomogeneities, (4) model with homogeneous skull and remaining inhomogeneities, and (5) model with homogeneous brain matter and remaining inhomogeneities. Scalp potentials because of three different dipolar sources in the parietal-occipital lobe were computed for all five models. Results of a forward solution revealed that tissues included in the model and the dipole source location directly affect the simulated scalp surface potentials. The major finding indicates that significant change in the scalp surface potentials is observed when the brain's distinctions are removed. The other modifications, for example, layers of the scalp and skull are important too, but they have less effect on the overall results.

A finite difference method for solving the three-dimensional EEG forward problem

A finite difference method (FDM) has been implemented to solve the electroencephalogram (EEG) forward problem. This method has been evaluated by means of computer simulations, by comparing with analytic solutions in a three-sphere concentric head model. The effects of dipole eccentricity, spacing of finite difference model and number of grid nodes on solution accuracy are also addressed in the simulations. The simulation results suggest that the FDM can achieve satisfactory forward solution due to a dipole source.

Long-term Recordings of Local Field Potentials From Implanted Deep Brain Stimulation Electrodes

BACKGROUND:

Deep brain stimulation (DBS) of the subthalamic nucleus is an effective treatment for Parkinson disease. However, DBS is not responsive to an individual's disease state, and programming parameters, once established, do not change to reflect disease state. Local field potentials (LFPs) recorded from DBS electrodes are being investigated as potential biomarkers for the Parkinson disease state. However, no patient data exist about what happens to LFPs over the lifetime of the implant.

OBJECTIVE:

We investigated whether LFP amplitude and response to limb movement differed between patients implanted acutely with subthalamic nucleus DBS electrodes and patients implanted 2 to 7 years previously.

METHODS:

We recorded LFPs at DBS surgery time (9 subjects), 3 weeks after initial placement (9 subjects), and 2 to 7 years (median: 3.5) later during implanted programmable generator replacement (11 sides). LFP power-frequency spectra for each of 3 bipolar electrode derivations of adjacent contacts were calculated over 5-minute resting and 30-second movement epochs. Monopolar impedance data were used to evaluate trends over time.

RESULTS:

There was no significant difference in β-band LFP amplitude between initial electrode implantation (OR) and 3-week post-OR times (P = .94). However, β-band amplitude was lower at implanted programmable generator replacement times than in OR (P = .008) and post-OR recordings (P = .039). Impedance measurements declined over time (P &lt; .001).

CONCLUSION:

Postoperative LFP activity can be recorded years after DBS implantation and demonstrates a similar profile in response to movement as during acute recordings, although amplitude may decrease. These results support the feasibility of constructing a closed-loop, patient-responsive DBS device based on LFP activity.