Dipole characterization of single neurons from their extracellular action potentials

The spatial variation of the extracellular action potentials (EAP) of a single neuron contains information about the size and location of the dominant current source of its action potential generator, which is typically in the vicinity of the soma. Using this dependence in reverse in a three-component realistic probe + brain + source model, we solved the inverse problem of characterizing the equivalent current source of an isolated neuron from the EAP data sampled by an extracellular probe at multiple independent recording locations. We used a dipole for the model source because there is extensive evidence it accurately captures the spatial roll-off of the EAP amplitude, and because, as we show, dipole localization, beyond a minimum cell-probe distance, is a more accurate alternative to approaches based on monopole source models. Dipole characterization is separable into a linear dipole moment optimization where the dipole location is fixed, and a second, nonlinear, global optimization of the source location. We solved the linear optimization on a discrete grid via the lead fields of the probe, which can be calculated for any realistic probe + brain model by the finite element method. The global source location was optimized by means of Tikhonov regularization that jointly minimizes model error and dipole size. The particular strategy chosen reflects the fact that the dipole model is used in the near field, in contrast to the typical prior applications of dipole models to EKG and EEG source analysis. We applied dipole localization to data collected with stepped tetrodes whose detailed geometry was measured via scanning electron microscopy. The optimal dipole could account for 96% of the power in the spatial variation of the EAP amplitude. Among various model error contributions to the residual, we address especially the error in probe geometry, and the extent to which it biases estimates of dipole parameters. This dipole characterization method can be applied to any recording technique that has the capabilities of taking multiple independent measurements of the same single units.     

Effects of cavities on EEG dipole localization and their relations with surface electrode positions

Effects of cavities in the human head on EEG dipole localization have been investigated by computer simulation. The human head is represented by a homogeneous spherical conductor including an eccentric spherical cavity which approximates effects of actual cavities inside the head. The homogeneous sphere model is used for assessing the effects caused by neglecting the cavity in the volume conductor model in the inverse dipole fitting procedure. Four electrode configurations have been examined to investigate their relation to the EEG inverse dipole solution. After examination of 2520 dipoles in the brain, the effects of cavities in the human head are found to be negligible when the dipole is located in the cortex or in the subcortex. When the dipole is located in the brain stem, the EEG inverse dipole solution is strongly affected by the cavity and is sensitive to the electrode configuration on the scalp. The EEG inverse dipole solution in the deep brain is sensitive to inhomogeneity in the lower part of the head when a single positive or negative potential pole is observed by the electrodes on the scalp, and at the same time is sensitive to the extent of the scalp covered by the electrodes. In conclusion, the electrodes should cover as much of the upper scalp as possible for deep source localization.     

Electric Field Encephalography as a Tool for Functional Brain Research: A Modeling Study

We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2–3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.     

Presurgical functional localization of primary somatosensory cortex by dipole tracing method of scalp-skull-brain head model applied to somatosensory evoked potential

The aim of the present study was to explore the utility of dipole tracing (DT) of a scalp-skull-brain (SSB) head model in preoperative functional localization of the human brain. Nine patients who underwent surgery of mass lesions around the central sulcus (CS) were employed. By using SSB/DT, dipole source location of early cortical components of the somatosensory evoked potential (SEP) was estimated before surgery. Motor cortex, CS and primary somatosensory cortex were determined by cortical SEP during surgery. After surgery precise functional mapping was reproduced in MRI, and the accuracy of DT was evaluated by measuring the distance between estimated dipole source and the posterior bank of the CS. We defined this distance as localization error of DT. In 4 cases without structural change around the sensorimotor cortex, localization error ranged from 1 to 4 mm with an average of 2 mm. In 5 cases with structural alteration of sensorimotor cortex, localization error ranged from 6 to 10 mm with an average of 8 mm. The difference in localization error between the two groups was statistically significant, and may have been caused by changes of conductance near sensorimotor cortex in the latter group. Functional localization by DT was accurate and useful. But localization error could not be ignored in cases with structural alteration in the sensorimotor cortex.     

Effects of dipole position,orientation and noise on the accuracy of EEG source localization

Background  

The electroencephalogram (EEG) reflects the electrical activity in the brain on the surface of scalp. A major challenge in this field is the localization of sources in the brain responsible for eliciting the EEG signal measured at the scalp. In order to estimate the location of these sources, one must correctly model the sources, i.e., dipoles, as well as the volume conductor in which the resulting currents flow. In this study, we investigate the effects of dipole depth and orientation on source localization with varying sets of simulated random noise in 4 realistic head models.     

Analysis of parametric estimation of head tissue conductivities using Electrical Impedance Tomography

We study the theoretical performance of using Electrical Impedance Tomography (EIT) to measure the conductivity of the main tissues of the head. The governing equations are solved using the Finite Element Method for realistically shaped head models with isotropic and anisotropic electrical conductivities. We focus on the Electroencephalography (EEG) signal frequency range since EEG source localization is the assumed application. We obtain the Cramér-Rao Lower Bound (CRLB) to find the minimum conductivity estimation error expected with EIT measurements. The more convenient electrode pairs selected for current injection from a typical EEG array are determined from the CRLB. Moreover, using simulated data, the Maximum Likelihood Estimator of the conductivity parameters is shown to be close to the CRLB for a relatively low number of measurements. The results support the idea of using EIT as a low-cost and practical tool for individually measure the conductivity of the head tissues, and to use them when solving the EEG source localization. Even when the conductivity of the soft tissues of the head is available from Diffusion Tensor Imaging, EIT can complement the electrical model with the estimation of the skull and scalp conductivities.     

Wave III of brain-stem auditory evoked potentials analysed both with 3-channel Lissajous' trajectory and dipole localization method

Wave III of the BAEP was analysed both with 3-channel Lissajous' trajectory (3-CLT) and a dipole localization method. The experiments were performed on 5 normally hearing subjects. The dipole analysis used an iterative algorithm assuming a spherical head model and homogeneous media. 3-CLT planar analysis was performed with a laboratory system. The parameters of plane C (azimuth and elevation) corresponding to wave III and those of the equivalent dipole showed a similar orientation of the plane and the dipole. This result is in agreement with previous investigations and confirms the interest of 3-CLT in far-field analysis and, at the same time, validates the dipole localization model used in this study, at least for BAEP analysis.     

Seizure source localization using a hybrid second order blind identification and extended rival penalized competitive learning algorithm

Localization of seizure sources prior to neurosurgery is crucial. In this paper, a new method is proposed to localize the seizure sources from multi-channel electroencephalogram (EEG) signals. Blind source separation based on second order blind identification (SOBI) is primarily applied to estimate the brain source signals in each window of the EEG signals. A new clustering method based on rival penalized competitive learning (RPCL) is then developed to cluster the rows of the estimated unmixing matrices in all the windows. The algorithm also includes pre and post-processing stages. By multiplying each cluster center to the EEG signals, the brain signal sources are approximated. According to a complexity value measure, the main seizure source signal is separated from the others. This signal is projected back to the electrodes’ space and is subjected to the dipole source localization using a single dipole model. The simulation results verify the accuracy of the system. In addition, correct localization of the seizure source is consistent with the clinical tests derived using the simultaneous intracranial recordings.     

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.     

EEG and implanted sources in the brain

Localisation procedures are based on models of the EEG that are relatively simple. The models are based on assumptions and choices of parameters that can be mistaken. Thus, it is crucial to validate the localisation procedures used in EEG. One of the options is to use the data obtained with electrodes that are implanted within the brain of an epileptic patient as part of the pre-surgical evaluation. When one of two neighbouring electrodes is used as a current source and the other as a current sink this can be regarded as a current dipole. The current injected has to be below the threshold for activation of cells. The position of this dipole can be deduced from magnetic resonance or X-ray images. The current dipole gives rise to a potential distribution at the scalp that can be measured by EEG. The measurements can be compared with the potential distribution that is calculated in a forward computation. Another method is to use the measured potential at the scalp to localize the source and to compare the result with the actual position of the dipole. In this paper the measured potential distributions at the scalp due to implanted dipoles were used to evaluate different volume conductor models. Since intracerebral and subdural electrodes were introduced through trephine holes over the fronto-central areas, and the diameter of the holes was rather large, approximately 23 mm, special effort was put into modelling the skull. Two important assumptions could be validated in this study: the electric currents within the head are Ohmic and a dipole can be used to model the induced electric activity of pairs of contacts on subdural electrodes or intra cerebral electrodes.     

The influence of conductivity changes in boundary element compartments on the forward and inverse problem in electroencephalography and magnetoencephalography.

Source localization based on magnetoencephalographic and electroencephalographic data requires knowledge of the conductivity values of the head. The aim of this paper is to examine the influence of compartment conductivity changes on the neuromagnetic field and the electric scalp potential for the widely used three compartment boundary element models. Both the analysis of measurement data and the simulations with dipoles distributed in the brain produced two significant results. First, we found the electric potentials to be approximately one order of magnitude more sensitive to conductivity changes than the magnetic fields. This was valid for the field and potential topology (and hence dipole localization), and for the amplitude (and hence dipole strength). Second, changes in brain compartment conductivity yield the lowest change in the electric potentials topology (and hence dipole localization), but a very strong change in the amplitude (and hence in the dipole strength). We conclude that for the magnetic fields the influence of compartment conductivity changes is not important in terms of dipole localization and strength estimation. For the electric potentials however, both dipole localization and strength estimation are significantly influenced by the compartment conductivity.     

脑源定位技术的精度评估及其在实际中的应用

脑源定位技术旨在通过头皮表面的脑电、脑磁信号来识别大脑内的神经活动源，是研究大脑皮层神经活动、认知过程和病理功能的基础。其毫秒级的时间分辨率可以有效弥补功能核磁共振在低时间分辨率方面的不足。然而，理论分析层面中逆问题的不适定性，以及实践操作层面上不同的记录方式、电极数量和头模型构建等过程带来的误差，给脑源定位的准确性带来极大挑战，也在一定程度上限制了脑源定位方法在神经科学和心理学研究以及临床诊断治疗中的实际应用。因此，理论分析和实践操作层面中的精度评估在脑源定位方法的实际使用中至关重要。针对以上问题，本文在对现有脑源定位方法介绍的基础上，着重分析了脑源定位技术的精度评估方法以及其在基础研究和临床诊断治疗中的实际应用。具体地，本文在理论分析中总结了基于空间分辨率、基于点扩散以及串扰函数的评估方法对于不同脑源定位方法中源的重叠程度和其他源对目标源的影响；在实践操作中介绍了记录方式、电极数量和密度、头部容积传导模型等因素对源定位精度的影响；进一步介绍了脑源定位技术在时频分析、连通性分析中的应用，以及其在临床中的应用，包括癫痫、注意缺陷与多动障碍等脑部疾病。     

Replicability of MEG and EEG measures of the auditory N1/N1m-response

We investigated the replicability of the source location, amplitude and latency measures of the auditory evoked N1 (EEG) and N1m (MEG) responses. Each of the 5 subjects was measured 6 times in two recording sessions. Responses to monaural stimuli were recorded from 122 MEG and 64 EEG channels simultaneously. The EEG data were modeled with a symmetrically-located dipole pair. For the MEG data, one dipole in each hemisphere was located independently using a subset of channels. Standard deviation (SD) was used as a measure for replicability. The average SD of the x, y and z coordinates of the contralateral N1m dipole was about 2 mm, whereas the corresponding figures for the ipsilateral N1m and the contra- and ipsilateral N1 were about twice as large. The SDs of the dipole amplitudes and latencies were almost equal with MEG and EEG. The amplitude and latency measures of the MEG field gradient waveforms were almost as replicable as those of the dipole models. The results suggest that both MEG and EEG can be used for investigating the simultaneous activity of the left and right auditory cortices independently, MEG being superior in certain experimental setups.     

Noninvasive three-dimensional electrocardiographic imaging of ventricular activation sequence

Imaging the myocardial activation sequence is critical for improved diagnosis and treatment of life-threatening cardiac arrhythmias. It is desirable to reveal the underlying cardiac electrical activity throughout the three-dimensional (3-D) myocardium (rather than just the endocardial or epicardial surface) from noninvasive body surface potential measurements. A new 3-D electrocardiographic imaging technique (3-DEIT) based on the boundary element method (BEM) and multiobjective nonlinear optimization has been applied to reconstruct the cardiac activation sequences from body surface potential maps. Ultrafast computerized tomography scanning was performed for subsequent construction of the torso and heart models. Experimental studies were then conducted, during left and right ventricular pacing, in which noninvasive assessment of ventricular activation sequence by means of 3-DEIT was performed simultaneously with 3-D intracardiac mapping (up to 200 intramural sites) using specially designed plunge-needle electrodes in closed-chest rabbits. Estimated activation sequences from 3-DEIT were in good agreement with those constructed from simultaneously recorded intracardiac electrograms in the same animals. Averaged over 100 paced beats (from a total of 10 pacing sites), total activation times were comparable (53.3 +/- 8.1 vs. 49.8 +/- 5.2 ms), the localization error of site of initiation of activation was 5.73 +/- 1.77 mm, and the relative error between the estimated and measured activation sequences was 0.32 +/- 0.06. The present experimental results demonstrate that the 3-D paced ventricular activation sequence can be reconstructed by using noninvasive multisite body surface electrocardiographic measurements and imaging of heart-torso geometry. This new 3-D electrocardiographic imaging modality has the potential to guide catheter-based ablative interventions for the treatment of life-threatening cardiac arrhythmias.     

The influence of local tissue conductivity changes on the magnetoencephalogram and the electroencephalogram.

We examined the influence of local tissue conductivity changes in the vicinity of a dipolar source on the neuromagnetic field and the electric scalp potential using a high resolution finite element method model of the human head. We found that the topology of both the electric scalp potential and the neuromagnetic field (and consequently dipole localization) is influenced significantly by conductivity changes only in voxels adjacent to the source. Conductivity changes in these voxels yield a greater change in the amplitude of the magnetic field (and consequently in the dipole strength) than in the amplitude of the electric potential.     

Real-Time Localization of Moving Dipole Sources for Tracking Multiple Free-Swimming Weakly Electric Fish

In order to survive, animals must quickly and accurately locate prey, predators, and conspecifics using the signals they generate. The signal source location can be estimated using multiple detectors and the inverse relationship between the received signal intensity (RSI) and the distance, but difficulty of the source localization increases if there is an additional dependence on the orientation of a signal source. In such cases, the signal source could be approximated as an ideal dipole for simplification. Based on a theoretical model, the RSI can be directly predicted from a known dipole location; but estimating a dipole location from RSIs has no direct analytical solution. Here, we propose an efficient solution to the dipole localization problem by using a lookup table (LUT) to store RSIs predicted by our theoretically derived dipole model at many possible dipole positions and orientations. For a given set of RSIs measured at multiple detectors, our algorithm found a dipole location having the closest matching normalized RSIs from the LUT, and further refined the location at higher resolution. Studying the natural behavior of weakly electric fish (WEF) requires efficiently computing their location and the temporal pattern of their electric signals over extended periods. Our dipole localization method was successfully applied to track single or multiple freely swimming WEF in shallow water in real-time, as each fish could be closely approximated by an ideal current dipole in two dimensions. Our optimized search algorithm found the animal’s positions, orientations, and tail-bending angles quickly and accurately under various conditions, without the need for calibrating individual-specific parameters. Our dipole localization method is directly applicable to studying the role of active sensing during spatial navigation, or social interactions between multiple WEF. Furthermore, our method could be extended to other application areas involving dipole source localization.     

Setup and dosimetry for exposing anaesthetised pigs in vivo to 900 MHz GSM mobile phone fields

The aim of this study was a dosimetrical analysis of the setup used in the exposure of the heads of domestic pigs to GSM-modulated radio frequency electromagnetic fields (RF-EMF) at 900 MHz. The heads of pigs were irradiated with a half wave dipole using three different exposure routines; short bursts of 1-3 s at two different exposure levels and a continuous 10-min exposure. The electroencephalogram (EEG) was registered continuously during the exposures to search for RF-EMF originated changes. The dosimetry was based on simulations with the anatomical heterogeneous numerical model of the pig head. The simulation results were validated by experimental measurements with the exposure dipole and a homogeneous liquid phantom resembling the pig head. The specific absorption rate (SAR), defined as a maximum average over 10 g tissue mass (SAR(10g)), was 7.3 W/kg for the first set of short bursts and 31 W/kg for the second set of short bursts. The SAR(10g) in the continuous 10-min exposure was 31 W/kg. The estimated uncertainty for the dosimetry was +/-25% (K = 2).     

The multifactor method of EEG separation into the cortical and deep components

A new method of separation of multichannel brain electrical activity into cortical and subcortical components with the help of multifactor analysis is proposed. The method provides a means for isolation and, consequently, more reliable localization of sources of electrical activity not only in deep brain structures (on the basis of the dipole model) but also on the cortical surface. The proposed method does not depend on the rotation and interpretation of factors, and no data losses occur. The mufasel algorithm is based on integration of all selected factors (within a particular EEG or EP time segment) in two groups using a statistical criterion, which defines general and specific factors. It is assumed that general factors loaded with highly correlated derivations predominantly describe the electrical activity of deep brain structures, whereas specific factors loaded by the dynamics of electrical activity in individual derivations, reflect the integrated activity of cortical brain structures.     

Estimation of localization and dipole moment of alpha- and theta-rhythm sources by cluster analysis in healthy subjects and schizophrenics

In 12 healthy subjects and 9 schizophrenic patients in the background conditions (with eyes closed) EEG was recorded from 16 standard derivations (10-20 system) during 3 min. The record underwent the spectral analysis detecting alpha- and theta-frequency bands. After the preliminary narrow band filtration for the main frequencies the sources of the spontaneous rhythms were localized. The data on localization for all healthy subjects and patients were summarized. The K-means clustering was used for identification of the sources clusters which were revealed in occipital and parietal lobes and limbic cortex for alpha-rhythm and also in frontal, temporal and parietal regions, limbic cortex and hippocampus for theta-rhythm. In schizophrenic patients in comparison with healthy subjects there was revealed significant increase of the numbers of dipole sources of alpha-rhythm in the clusters localized in limbic cortex and hippocampus. For theta-rhythm there was significant increase of the dipole moment of the sources in the clusters localized in the temporal and frontal cortices and hippocampus in patients in comparison with the norm.     

A new current dipole tracing method from EEG which is very useful in research of human brain activity in the space station

The space station is available from 2004 for scientific research including human physiology and medicine. In that instance, non-invasive research of human brain in the microgravity condition was highly required. The present newly developed dipole tracing method fits this research purpose, by determining current source in the brain from EEG activity. EEG is also very helpful to monitor the conditions of subjects in various hazard cases. We strongly recommend to use this apparatus in the space station.