A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

Heat transfer due to electroconvulsive therapy: Influence of anisotropic thermal and electrical skull conductivity

To improve the EEG source localization in the brain, the conductivities used in the head model play a very important role. In this study, we focus on the modeling of the anisotropic conductivity of the white matter. The anisotropic conductivity profile can be derived from diffusion weighted magnetic resonance images (DW-MRI). However, deriving these anisotropic conductivities from diffusion weighted MR images of the white matter is not straightforward. In the literature, two methods can be found for calculating the conductivity from the diffusion weighted images. One method uses a fixed value for the ratio of the conductivity in different directions, while the other method uses a conductivity profile obtained from a linear scaling of the diffusion ellipsoid. We propose a model which can be used to derive the conductivity profile from the diffusion tensor images. This model is based on the variable anisotropic ratio throughout the white matter and is a combination of the linear relationship as stated in the literature, with a constraint on the magnitude of the conductivity tensor (also known as the volume constraint). This approach is stated in the paper as approach A. In our study we want to investigate dipole estimation differences due to using a more simplified model for white matter anisotropy (approach B), while the electrode potentials are derived using a head model with a more realistic approach for the white matter anisotropy (approach A). We used a realistic head model, in which the forward problem was solved using a finite difference method that can incorporate anisotropic conductivities. As error measures we considered the dipole location error and the dipole orientation error. The results show that the dipole location errors are all below 10 mm and have an average of 4 mm in gray matter regions. The dipole orientation errors ranged up to 66.4°, and had a mean of, on average, 11.6° in gray matter regions. In a qualitative manner, the results show that the orientation and location error is dependent on the orientation of the test dipole. The location error is larger when the orientation of the test dipole is similar to the orientation of the anisotropy, while the orientation error is larger when the orientation of the test dipole deviates from the orientation of the anisotropy. From these results, we can conclude that the modeling of white matter anisotropy plays an important role in EEG source localization. More specifically, accurate source localization will require an accurate modeling of the white matter conductivity profile in each voxel.

Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

Differentiation of absolute metabolite concentrations between gray and white matter in the occipital region of normal human brain was performed by in vivo localized single-voxel 1H magnetic resonance spectroscopy at 1.5 Tesla with long echo time (136 ms). With the combination of image segmentation between white and gray matter and cerebrospinal fluid, signal compensation of T1 and T2 effects, tissue water signal as the internal concentration reference, as well as compensation by different water contents in gray and white matters, it was determined that the levels of N-acetylaspartate (NAA), creatine and/or phosphocreatine (Cr), and choline-containing compounds (Cho) in gray matter were significantly higher than in white matter. The averaged NAA, Cr, and Cho concentrations in gray matter were 11.0, 9.7, and 1.9 mM/liter, respectively, in comparison with 7.5, 5.2, and 1.6 mM/liter in white matter. These results suggest that precise composition of white and gray matter and cerebrospinal fluid is necessary to avoid partial voluming effect in a single voxel and to accurately quantify the metabolite concentrations.

The real head model has been used for the accurate scalp potential modeling. The realistic shapes of head tissues were derived from a set of 2-D magnetic resonance images (MRI) by extracting surface boundaries for the major tissues such as the scalp, the skull, the cerebrospinal fluid (CSF), the white matter, and the gray matter. From boundary data a 3-D volume generic head model has been constructed and a mesh for an arbitrary complexity head shape has been generated for finite-element method (FEM) modeling. This paper first addresses the use of this realistic finite elements head model to solve the EEG forward problem. The accuracy and computational time of the potential modeling are then examined.

In this study, we investigated the impact of uncertainty in head tissue conductivities and inherent geometrical complexities including fontanels in neonates. Based on MR and CT coregistered images, we created a realistic neonatal head model consisting of scalp, skull, fontanels, cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using computer simulations, we investigated the effects of exclusion of CSF and fontanels, discrimination between GM and WM, and uncertainty in conductivity of neonatal head tissues on EEG forward modeling. We found that exclusion of CSF from the head model induced the strongest widespread effect on the EEG forward solution. Discrimination between GM and white matter also induced a strong widespread effect, but which was less intense than that of CSF exclusion. The results also showed that exclusion of the fontanels from the neonatal head model locally affected areas beneath the fontanels, but this effect was much less pronounced than those of exclusion of CSF and GM/WM discrimination. Changes in GM/WM conductivities by 25% with respect to reference values induced considerable effects in EEG forward solution, but this effect was more pronounced for GM conductivity. Similarly, changes in skull conductivity induced effects in the EEG forward modeling in areas covered by the cranial bones. The least intense effect on EEG was caused by changes in conductivity of the fontanels. Our findings clearly emphasize the impact of uncertainty in conductivity and deficiencies in head tissue compartments on modeling research and localization of brain electrical activity in neonates. Hum Brain Mapp 37:3604-3622, 2016. © 2016 Wiley Periodicals, Inc.

Human brain consists of grey and white matter. Grey matter largely forms the outer layer of the brain, and is responsible for decision making and functioning of the human body. Complementary, the white matter (WM) contains the communication pathways between the grey matter areas. Diffusion-weighted magnetic resonance imaging (dMRI) is an imaging modality allowing to model the brain’s white matter structures by making the MRI acquisition sensitive to diffusion processes. When brain structure is altered due to pathology, it can be assessed and quantified by analysing dMRI data...

Cranial Ultrasonography

Significance of diffusion-weighted imaging and apparent diffusion coefficient maps for the evaluation of pyogenic ventriculitis

Commentary on “Diagnosis of Alzheimer's disease: Two decades of progress.” Central role of technology in the treatment and prevention of Alzheimer's disease

The influence of head tissue conductivity on magnetoencephalography (MEG) was investigated by comparing the normal component of the magnetic field calculated at 61 detectors and the localization accuracy of realistic head finite element method (FEM) models using dipolar sources and containing altered scalp, skull, cerebrospinal fluid, gray, and white matter conductivities to the results obtained using a FEM realistic head model with the same dipolar sources but containing published baseline conductivity values. In the models containing altered conductivity values, the tissue conductivity values were varied, one at a time, between 10% and 200% of their baseline values, and then varied simultaneously. Although changes in conductivity values for a single tissue layer often altered the calculated magnetic field and source localization accuracy only slightly, varying multiple conductivity layers simultaneously caused significant discrepancies in calculated results. The conductivity of scalp, and to a lesser extent that of white and gray matter, appears especially influential in determining the magnetic field. Comparing the results obtained from models containing the baseline conductivity values to the results obtained using other published conductivity values suggests that inaccuracies can occur depending upon which tissue conductivity values are employed. We show the importance of accurate head tissue conductivities for MEG source localization in human brain, especially for deep dipole sources or when an accuracy greater than 1.4 cm is needed.

Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

Background: Structural brain damage, especially to white matter, is well documented in chronic alcohol abuse. There is also evidence for brain metabolic abnormalities in this condition. It is unknown, however, to what extent these structural and metabolic changes are present in treated alcohol abusers who achieve long-term abstinence versus treatment-naïve, heavily drinking individuals. Methods: This study compared 12 recovering alcoholics with 8 actively heavily drinking subjects. Participants underwent magnetic resonance (MR) imaging and proton MR spectroscopic imaging of the brain. Semiautomated image segmentation techniques yielded volumes for gray matter, white matter, white matter lesions, and cerebral spinal fluid in multiple brain regions defined by Talairach stereotaxic coordinates. Automated spectral processing methods yielded gray and white matter concentrations of the metabolites N-acetylaspartate, creatine, and choline for the same regions. Results: Recovering alcoholics had greater volumes of frontal white matter, but the opposite was true for white matter in a “remainder” region encompassing the basal frontal and temporal lobes, the cerebellum, and the brainstem. Recovering alcoholics also had smaller volumes of white matter lesions in whole brain, in occipital and mesial parietal regions, and in the remainder region. Recovering alcoholics had greater gray matter volumes in the orbital frontal pole and postcentral gyrus, but smaller gray matter volumes in the anterior cingulate. Whole-brain and regional metabolite concentrations did not differ significantly between the two groups. Conclusions: White and gray matter volumes in different regions of the brain were greater or smaller in recovering, treated alcoholics. The findings suggest region-specific structural recovery from chronic alcohol–induced brain injury, but also region-specific long-term structural damage in abstinent alcoholics. White matter lesions were widespread in active drinkers and may partly resolve during long-term abstinence. Proton MR spectroscopic measures, as applied in this cross-sectional study, were largely ineffective in revealing metabolic effects of abstinence on the alcohol-damaged brain.

Patients with spina bifida (SB) have variable intellectual outcomes. The authors used diffusion tensor (DT) imaging to quantify whole-brain volumes of gray matter, white matter, and cerebrospinal fluid (CSF), and perform regional quantitative microstructural assessments of gray matter nuclei and white matter tracts in relation to intellectual outcomes in patients with SB. Twenty-nine children with myelomeningoceles and 20 age- and sex-matched children with normal neural tube development underwent MR imaging with DT image acquisition and assessments of intelligence. The DT imaging-derived metrics were the fractional anisotropy (FA), axial (parallel), and transverse (perpendicular) diffusivities. These metrics were also used to segment the brain into white matter, gray matter, and CSF. A region-of-interest analysis was conducted of the white and gray matter structures implicated in hydrocephalus. The amount of whole-brain gray matter was decreased in patients with SB, with a corresponding increase in CSF (p < 0.0001). Regional transverse diffusivity in the caudate nucleus was decreased (p < 0.0001), and the corresponding FA was increased (p < 0.0001), suggesting reduced dendritic branching and connectivity. Fractional anisotropy in the posterior limb of the internal capsule increased in the myelomeningocele group (p = 0.02), suggesting elimination of some divergent fascicles; in contrast, the FA in several white matter structures (such as the corpus callosum genu [p < 0.001] and arcuate fasciculus) was reduced, suggesting disruption of myelination. Diffusion tensor imaging-metrics involving gray matter volume and the caudate nucleus, but not other structures, predicted variations in IQ (r = 0.37-0.50; p < 0.05). Diffusion tensor imaging-derived metrics provide noninvasive neuronal surrogate markers of the pathogenesis of SB and predict variations in general intellectual outcomes in children with this condition.

Structural MRI findings of abnormalities in the prefrontal cortex in schizophrenia and affective disorder have been inconsistent likely due to small, heterogeneous samples, the evaluation of prefrontal gray and white matter combined, and the fact that prefrontal cortex is typically not delineated into separate gyri (e.g., Shenton et al., 2001; Strakowski et al., 2002). We previously reported smaller prefrontal gray matter in first-episode schizophrenia relative to first-episode affective psychosis and controls (Hirayasu et al., 2001). One unresolved question in the literature is whether or not further volume reduction will be observed over time, the focus of this report. Prefrontal gray and white matter volumes were measured (see Fig. 1) in patients at the time of first hospitalization for schizophrenia (n=12, 3 females) or affective psychosis (n=10, 1 female, 9 bipolar, 1 unipolar), and psychiatrically well subjects (n=15, 1 female). Subjects were rescanned approximately 1.5 years later. Seven schizophrenia, six affective, and four comparison subjects were previously described solely at first scan (Hirayasu et al., 2001). Samples did not differ in age (28.1±8.4; 22.9±2.8; 25.4±4.5; F(2,34)=2.2, p=0.12) or WAIS Information scores (11.8±3.4; 12.2±3.1, 11.8±2.0; F(2.32)=0.6, p=0.9), nor in parental socioeconomic status (F(2.34)=2.32, p=0.11) or handedness (F(2.33)=1.6, p=0.22). BPRS scores were higher in schizophrenics (41.2±13.2) than affectives (33.8±8.0) (F(1,20)=5.71, p=0.027). Similar neuroleptics were prescribed for the patient groups (schizophrenics: 7 typical, 4 atypical, 1 none; affectives: 6 typical, 3 atypical, 1 none). Fig. 1 Prefrontal ROI definitions and volumes. (a) Coronal slice, (b) axial slice showing prefrontal gray and white matter, and (c) three. Three-dimensional reconstruction of the extent of gray and white matter boundaries. MR images were acquired with a 1.5-T ... Relative volumes [i.e., absolute volumes divided by intracranial contents (ICC)] were used in the analyses. Repeated-measures ANOVA was performed with diagnosis as the between-subjects factor and time and side as the within-subjects factors for gray and white matter separately. Prefrontal gray matter at time 1 was significantly different among groups (F2,30=5.56, p=0.009), with schizophrenics smaller than controls and affectives, who did not differ from each other. There was no interaction for group by time by side for prefrontal gray matter (F2,34=0.65, p=0.53). All groups showed larger gray matter on the left (F2,30=8.72, p=0.006). White matter volumes did not differ among groups (p>0.87), and all groups showed more white matter on the right (F1,30=39.20, p<0.001) and a reduction over time (F1,30=10.902, p=0.002), there was a trend for this effect to be larger on the right (F1,30=3.14, p=0.086) (%change over time for right white matter: schizophrenics 2.8%; affectives 6.5%; controls 2.1%). Neither removing the unipolar depression subject nor removing the females altered the results. There was no significant correlation between ROI volume change and medication dosage at time 1 for either patient group (Time 2 data came from out patients based on self-report and was for this reason not used). These data suggest that prefrontal cortical gray matter is selectively smaller at first hospitalization for schizophrenia relative to affective psychosis and controls, but this volume difference did not change over the relatively short post-first hospitalization time examined. Small gray matter volumes may be due to reduced dendritic arborization or increased neural density in prefrontal cortex in schizophrenia (Benes et al., 1992; Selemon et al., 1998). White matter, which was not different among groups, showed a decline with time in all groups, possibly consistent with normal aging (Good et al., 2001, although Bartzokis et al., 2001). Potential reasons for our not observing selective gray matter reductions point to several limitations of this study including the gender distribution, as females may have greater dorsolateral and orbitofrontal lobe involvement and males greater dorsomedial changes in schizophrenia (Gur et al., 2000); the use of large ROI, as sub-regions within the prefrontal cortex may change in volume at different rates (Gur et al., 1998; DeLisi et al., 1997); and lack of exact medication dosages and compliance histories during the intrascan interval cannot be ruled out as possible confounds. Although the sample size was small, the effect size was also small (Fig. 1) suggesting that even enlarging the sample would not result in a significant change in volume over time for either gray or white matter.