The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems.

Objective. Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions.Approach. Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model.Main results. Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines.Significance. It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis.

Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation.

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal-to-noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér-Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM-based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.

Does participant's age impact on tDCS induced fields? Insights from computational simulations.

Objective: Understanding the induced current flow from transcranial direct current stimulation (tDCS) is essential for determining the optimal dose and treatment. Head tissue conductivities play a key role in the resulting electromagnetic fields. However, there exists a complicated relationship between skull conductivity and participant age, that remains unclear. We explored how variations in skull electrical conductivities, particularly as a suggested function of age, affected tDCS induced electric fields.Approach: Simulations were employed to compare tDCS outcomes for different intensities across head atlases of varying age. Three databases were chosen to demonstrate differing variability in skull conductivity with age and how this may affect induced fields. Differences in tDCS electric fields due to proposed age-dependent skull conductivity variation, as well as deviations in grey matter, white matter and scalp, were compared and the most influential tissues determined.Main results: tDCS induced peak electric fields significantly negatively correlated with age, exacerbated by employing proposed age-appropriate skull conductivity (according to all three datasets). Uncertainty in skull conductivity was the most sensitive to changes in peak fields with increasing age. These results were revealed to be directly due to changing skull conductivity, rather than head geometry alone. There was no correlation between tDCS focality and age.Significance: Accurate and individualised head anatomy andin vivoskull conductivity measurements are essential for modelling tDCS induced fields. In particular, age should be taken into account when considering stimulation dose to precisely predict outcomes.

The analytical subtraction approach for solving the forward problem in EEG.

OBJECTIVE: The subtraction approach is known for being a theoretically-rigorous and accurate technique for solving the forward problem in electroencephalography by means of the finite element method. One key aspect of this approach consists of computing integrals of singular kernels over the discretised domain, usually referred to as potential integrals. Several techniques have been proposed for dealing with such integrals, all of them approximating the results at the expense of reducing the accuracy of the solution. In this paper, we derive analytic formulas for the potential integrals, reducing approximation errors to a minimum. APPROACH: Based on volume coordinates and Gauss theorems, we obtained parametric expressions for all the element matrices needed in the formulation assuming first order basis functions defined on a tetrahedral mesh. This included solving potential integrals over triangles and tetrahedra, for which we found compact and efficient formulas. MAIN RESULTS: Comparison with numerical quadrature schemes allowed us to test the advantages of the methodology proposed, which were found of great relevance for highly-eccentric sources, as those found in the somatosensory and visual cortices. Moreover, the availability of compact formulas allowed for an efficient implementation of the technique, which resulted in similar computational cost than the simplest numerical scheme. SIGNIFICANCE: The analytical subtraction approach is the optimal subtraction-based methodology with regard to accuracy. The computational cost is similar to that obtained with the lowest order numerical integration scheme, making it a competitive option in the field. The technique is highly relevant for improving electromagnetic source imaging results utilising individualised head models and anisotropic electric conductivity fields without imposing impractical mesh requirements.

Variation in Reported Human Head Tissue Electrical Conductivity Values.

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.

Beyond episodic memory: Semantic processing as independent predictor of hippocampal/perirhinal volume in aging and mild cognitive impairment due to Alzheimer's disease.

OBJECTIVE: Given that lexical-semantic decline precedes episodic memory deficits in the Alzheimer's disease (AD) timeline, it is expected that performance on a lexical-semantic task would be associated with mediotemporal volumes independently of the association this region has with episodic memory in the early stage of AD. METHOD: Fifty patients with mild cognitive impairment due to AD and 50 healthy adults completed tests of lexical-semantic skills (category fluency test), episodic memory for semantically relevant material (prose memory test), episodic memory for non semantically relevant material (Rey-Osterrieth Figure test), lexical-executive abilities (letter fluency test), and a neurostructural MRI. Hippocampal, perirhinal, entorhinal, temporopolar, and orbitofrontal volumes were extracted. The association between test performance and volume of each region was tested using partial correlations (age-education corrected). The improvement (ΔR2) in predicting volumetric indices offered by episodic-memory/lexical-semantic processing, once accounting for their counterpart, was tested using hierarchical regressions. RESULTS: There were no significant findings for control indices. Prose memory accounted for independent portions of volumetric variability within almost all regions. Category fluency accounted for independent portions of volumetric variability of left and right hippocampus and left perirhinal cortex in addition to the predictive strength of the Rey-Osterrieth Figure, and for an independent portion of volumetric variability in the left hippocampus in addition to the predictive strength of prose memory. CONCLUSIONS: There was an association between hippocampal and perirhinal volume and lexical-semantic processing in addition to the contribution given by episodic memory. This statistical separation supports the importance of lexical-semantic processing as independent indicator of AD. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data.

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student's t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer's disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM).

Sensitivity of the Projected Subtraction Approach to Mesh Degeneracies and Its Impact on the Forward Problem in EEG.

OBJECTIVE: Subtraction-based techniques are known for being theoretically rigorous and accurate methods for solving the forward problem in electroencephalography (EEG-FP) by means of the finite-element method. Within them, the projected subtraction (PS) approach is generally adopted because of its computational efficiency. Although this technique received the attention of the community, its sensitivity to degenerated elements is still poorly understood. In this paper, we investigate the impact of low-quality tetrahedra on the results computed with the PS approach. METHODS: We derived upper bounds on the relative error of the element source vector as a function of geometrical features describing the tetrahedral discretization of the domain. These error bounds were then utilized for showing the instability of the PS method with regards to the mesh quality. To overcome this issue, we proposed an alternative technique, coined projected gradient subtraction (PGS) approach, that exploits the stability of the corresponding bounds. RESULTS: Computer simulations showed that the PS method is extremely sensitive to the mesh shape and size, leading to unacceptable solutions of the EEG-FP in case of using suboptimal tessellations. This was not the case of the PGS approach, which led to stable and accurate results in a comparable amount of time. CONCLUSION: Solutions of the EEG-FP computed with the PS method are highly sensitive to degenerated elements. Such errors can be mitigated by the PGS approach, which showed better performance than the PS technique. SIGNIFICANCE: The PGS is an efficient method for computing high-quality lead field matrices even in the presence of degenerated elements.

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging.

PURPOSE: An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM-DTI) model was proposed, which accounts for both anisotropic pseudo-diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM-DTI approach for simultaneous diffusion and pseudo-diffusion tensor imaging. METHODS: Conventional IVIM estimation methods can be broadly divided into two-step (diffusion and pseudo-diffusion estimated separately) and one-step (diffusion and pseudo-diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM-DTI model. An improved one-step method based on damped Gauss-Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data. RESULTS: The one-step damped Gauss-Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM-DTI parameters compared to the other methods. CONCLUSION: One-step estimation using damped Gauss-Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo-diffusion tensor imaging using IVIM-DTI model. Magn Reson Med 79:2367-2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments.

BACKGROUND: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting-state functional magnetic resonance imaging (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients. METHODS: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers. RESULTS: The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions. CONCLUSION: Feature selection was profoundly driven by statistical independence. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically relevant brain alterations typical of MCI might be subtle and not inferable from group analysis.

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations.

PURPOSE: MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element-based simulations were performed to explore this possibility. METHODS: The natural frequencies of a series of cranial models were calculated, and MRE-associated vibration was simulated for different wave delivery methods at varying frequency, using simple isotropic viscoelastic material models for the brain. Displacement fields and the corresponding brain constitutive properties estimated by standard inversion techniques were compared across delivery methods and frequencies. RESULTS: The delivery methods produced widely different MRE displacement fields and inversions. Furthermore, resonances at natural frequencies influenced the displacement patterns. Consequently, some delivery methods led to lower inversion errors than others, and the error on the storage modulus varied by up to 11% between methods. CONCLUSION: Wave delivery has a considerable impact on brain MRE reliability. Assuming small variations in brain biomechanics, as recently reported to accompany neurodegenerative disease (e.g., 7% for Alzheimer's disease), the effect of wave delivery is important. Hence, a consensus should be established on a consistent methodology to ensure diagnostic and prognostic consistency. Magn Reson Med 78:341-356, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

A parametric finite element solution of the generalised Bloch-Torrey equation for arbitrary domains.

Nuclear magnetic resonance (NMR) has proven of enormous value in the investigation of porous media. Its use allows to study pore size distributions, tortuosity, and permeability as a function of the relaxation time, diffusivity, and flow. This information plays an important role in plenty of applications, ranging from oil industry to medical diagnosis. A complete NMR analysis involves the solution of the Bloch-Torrey (BT) equation. However, solving this equation analytically becomes intractable for all but the simplest geometries. We present an efficient numerical framework for solving the complete BT equation in arbitrarily complex domains. In addition to the standard BT equation, the generalised BT formulation takes into account the flow and relaxation terms, allowing a better representation of the phenomena under scope. The presented framework is flexible enough to deal parametrically with any order of convergence in the spatial domain. The major advantage of such approach is to allow both faster computations and sensitivity analyses over realistic geometries. Moreover, we developed a second-order implicit scheme for the temporal discretisation with similar computational demands as the existing explicit methods. This represents a huge step forward for obtaining reliable results with few iterations. Comparisons with analytical solutions and real data show the flexibility and accuracy of the proposed methodology.

Integration of Cognitive Tests and Resting State fMRI for the Individual Identification of Mild Cognitive Impairment.

BACKGROUND: Resting-state functional magnetic resonance imaging (RS-fMRI) appears as a promising imaging technique to identify early biomarkers of Alzheimer type neurodegeneration, which can be more sensitive to detect the earliest stages of this disease than structural alterations. Recent findings have highlighted interesting patterns of alteration in resting-state activity at the mild cognitive impairment (MCI) prodromal stage of Alzheimer's disease. However, it has not been established whether RS-fMRI alterations may be of any diagnostic use at the individual patient level and whether parameters derived from RS-fMRI images add any quantitative predictive/classificatory value to standard cognitive tests (CTs). METHODS: We computed a set of 444 features based on RS-fMRI and used 21 variables obtained from a neuropsychological assessment battery of tests in 29 MCI patients and 21 healthy controls. We used these indices to evaluate their impact on MCI/healthy control classification using machine learning algorithms and a 10-fold cross validation analysis. RESULTS: A classification accuracy (sensitivity/ specificity/area under curve/positive predictive value/negative predictive value) of 0.9559 (0.9620/0.9470/ 0.9517/0.9720/0.9628) was achieved when using both sets of indices. There was a statistically significant improvement over the use of CTs only, highlighting the superior classificatory role of RS-fMRI. CONCLUSIONS: RS-fMRI provides complementary information to CTs for MCI-patient/healthy control individual classification.

Extent of cortical generators visible on the scalp: effect of a subdural grid.

The effect of the non-conducting substrate of a subdural grid on the scalp electric potential distribution is studied through simulations. Using a detailed head model and the finite element method we show that the governing physics equations predict an important attenuation in the scalp potential for generators located under the grid, and an amplification for generators located under holes in the skull filled with conductive media. These effects are spatially localized and do not cancel each other. A 4 × 8 cm grid can produce attenuations of 2 to 3 times, and an 8 × 8 cm grid attenuation of up to 8 times. As a consequence, when there is no subdural grid, generators of 4 to 8 cm(2) produce scalp potentials of the same maximum amplitude as generators of 10 to 20 cm(2) under the center of a subdural grid. This means that the minimum cortical extents necessary to produce visible scalp activity determined from simultaneous scalp and subdural recordings can be overestimations.

Size of cortical generators of epileptic interictal events and visibility on scalp EEG.

Growing evidence indicates that fast oscillations (>80 Hz) can be recorded interictally in the scalp EEG of patients with epilepsy, and that they may point to the seizure-onset zone. However, mechanisms underpinning the emergence of scalp fast oscillations, and whether they differ from those of interictal epileptic discharges (IEDs), are yet to be understood. The visibility of cortical electric activity on scalp EEG recordings is dependent on two factors: the characteristics of the cortical generator and the background level. We studied this issue using scalp EEG recordings and detailed simulations, with a finite element model including 8 million elements and 8 different tissues. We observed an almost linear relationship between the amplitude of scalp electric potential and the extent of the generator on the cortex. However, this relationship is subject to substantial variability, with variations in factors greater than 3 occurring simply by changing the location on the cortex of generators of fixed extent. In addition, we showed that the background power in scalp EEG recordings decreases at higher frequency bands, being inversely proportional to a power of 2.5 of the frequency. In the specific case of fast oscillations, they can be detected within the lower noise level of the ripple band (80-200 Hz) even though their median amplitude on scalp EEG recordings is more than 10 times smaller than IEDs and consistent with cortical generators of approximately 1 cm(2). In conclusion, the physics governing the propagation of electrical activity from the brain to the scalp are consistent with the hypothesis that scalp fast oscillations and intracranial high-frequency oscillations (HFOs, 80-500 Hz) are expressions of common generators. Given the potential role of HFOs as biomarkers in epilepsy, the possibility to obtain some of the associated information from scalp EEG is of high clinical significance.

Error bounds in diffusion tensor estimation using multiple-coil acquisition systems.

We extend the diffusion tensor (DT) signal model for multiple-coil acquisition systems. Considering the sum-of-squares reconstruction method, we compute the Cramér-Rao bound (CRB) assuming the widely accepted noncentral chi distribution. Within this framework, we assess the effect of noise in DT estimation and other measures derived from it, as a function of the number of acquisition coils, as well as other system parameters. We show the applications of CRB in many actual problems related to DT estimation: we compare different gradient field setup schemes proposed in the literature and show how the CRB can be used to choose a convenient one; we show that for fiber-type anisotropy tensors the ellipsoidal area ratio (EAR) can be estimated with less error than other scalar factors such as the fractional anisotropy (FA) or the relative anisotropy (RA), and that for this type of anisotropy tensors, increasing the number of coils is equivalent to increasing the signal-to-noise ratio, i.e., the information of the different coils can be regarded as independent. Also, we present results showing the CRB of several parameters for actual DT-MRI data. We conclude that the CRB is a valuable tool to optimal experiment design in DT-related studies.

Neural processing of emotional facial and semantic expressions in euthymic bipolar disorder (BD) and its association with theory of mind (ToM).

BACKGROUND: Adults with bipolar disorder (BD) have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. METHODOLOGY/PRINCIPAL FINDINGS: We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP) assessment of a dual valence task (DVT), in which faces (angry and happy), words (pleasant and unpleasant), and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word) and valence (positive vs. negative). All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word). BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG), including the face fusiform area (FFA). Neural generators of N170 for faces (FG and FFA) were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind). CONCLUSIONS/SIGNIFICANCE: This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.

Electrode and brain modeling in stereo-EEG.

OBJECTIVE: To quantify the perturbation due to the presence of a measuring depth electrode on the intracranial electric potential distribution, and to study the effect of the heterogeneity and anisotropy of the brain tissues' electric conductivity. METHODS: The governing differential equations are solved with the Boundary Elements Method to compute the perturbation on the electric potential distribution caused by the presence of the measuring electrode, and with the Finite Elements Method to simulate measurements in an heterogeneous anisotropic brain model. RESULTS: The perturbation on the measured electric potential is negligible if the source of electric activity is located more than approximately 1mm away from the electrode. The error induced by this perturbation in the estimation of the source position is below 1mm in all tested situations. The results hold for different sizes of the electrode's contacts. The effect of the brain's heterogeneity and anisotropy is more important. In a particular example simulated dipolar sources in the gray matter show localization differences of up to 5mm between homogeneous isotropic and heterogeneous anisotropic brain models. CONCLUSIONS: It is not necessary to include detailed electrode models in order to solve the stereo-EEG (sEEG) forward and inverse problems. The heterogeneity and anisotropy of the brain electric conductivity should be modeled if possible. The effect of using an homogeneous isotropic brain model approximation should be studied in a case by case basis, since it depends on the electrode positions, the subject's electric conductivity map, and the source configuration. SIGNIFICANCE: This simulation study is helpful for interpreting the sEEG measurements, and for choosing appropriate electrode and brain models; a necessary first step in any attempt to solve the sEEG inverse problem.