Biomedical subjects

R S Wijesinghe

Publications and source records attributed to R S Wijesinghe.

14 recordsLinked to original sources

PubMed2001

Spatial filter approach for comparison of the forward and inverse problems of electroencephalography and magnetoencephalography.

We present an analysis of the relative information content of cortical current source reconstructions from electroencephalogram (EEG) and magnetoencephalogram (MEG) forward calculations by examining the spatial filters that relate the internal sources with the externally measured electric potentials and magnetic fields. The forward spatial filters are seen to be low-pass functions of spatial frequency and spatial resolution degrades in external measurements. Inverse spatial filters may be used to reconstruct cortical sources from external data, but since they are high-pass functions of spatial frequency, they must be regularized to avoid instabilities caused by noise at higher spatial frequencies. The regularization process limits the spatial resolution of source reconstructions. EEG forward spatial filters fall off at lower spatial frequencies than MEG filters; hence, there is less information available in higher spatial frequencies resulting in lower spatial resolution in inverse reconstructions. The tangential component of the magnetic field provides even higher spatial resolution than can be obtained using the radial component. An accompanying article examines the surface Laplacian for both the EEG and the MEG.

Electric Conductivity↗

PubMed1999

EEG coherency II: experimental comparisons of multiple measures.

OBJECTIVE: A concentric spheres model was used in an earlier paper to estimate the effects of volume conduction, reference electrode and spatial filtering on different EEG coherence measures. EEG data are used here to verify theoretical predictions. METHODS: Three EEG data sets were: (1) 64 channel, recorded during 7 alternating periods of resting and mental calculation. (2) 128 channel, for comparison of eyes open versus eyes closed coherence. (3) 128 channel, recorded during deep sleep (stages 3 and 4) and REM. RESULTS: The directions of large scale (lobeal) coherency changes between brain states are relatively independent of coherence measure. However, coherence between specific electrode pairs is sensitive to method and frequency. Average reference and digitally linked mastoids provide reasonable semi-quantitative estimates of large-scale neocortical source coherence. Close bipolar, Laplacian, and dura image methods remove most reference electrode and volume conduction distortion, but may underestimate coherence by spatial filtering. CONCLUSION: Each EEG coherence method has its own potential sources of error and provides coherence estimates for different neural population sizes located in different locations. Thus, studies of coherence and brain state should include several different kinds of estimates to take full advantage of information in recorded signals.

Adolescent↗

PubMed1998

Modeling of spreading cortical depression using a realistic head model.

Barkley and colleagues in 1990 reported large amplitude waves (LAWs) in time series magnetoencephalography (MEG) recordings from migraine patients and inferred that these LAWs arose from spreading cortical depression (SCD). SCD propagates slowly across the cortex in all species in which it has been observed. Previously, we reported that LAWs could be simulated and compared with the recorded signals using the four-sphere model (Wijesinghe and Tepley 1997). We showed that LAWs could arise from the propagation of SCD across a sulcus. In this paper, we model LAWs using a realistically shaped head model based on magnetic resonance images (MRI) (Roth et al. 1993). Simulated signals using this model are similar to the recorded signals. In this model, current dipoles represent the excitable neurons in the cortex and magnetic fields created by these individual dipoles are calculated. The magnetic field arising from the excited area of cortex is obtained by summing the fields due to these individual dipoles.

Brain Mapping↗

PubMed1997

A four sphere model for calculating the magnetic field associated with spreading cortical depression.

In our previous model, we ascertained that the large amplitude waves (LAWs), reported by Barkley and coworkers (1990) in time series magnetoencephalography (MEG) recordings from migraine patients, could be simulated and compared with the recorded signals using a simple plane volume conductor model (Tepley and Wijesinghe 1996). In this paper, we model LAWs using the help of more complicated yet reliable four-sphere model. This mathematical model furthermore assumes that the LAWs arise from propagation of Spreading Cortical Depression (SCD) across a sulcus and these simulated signals are more similar to the recorded signals than the ones we obtained from our previous model. SCD propagates slowly across the cortex in all species in which it has been observed. In our model, current dipoles represent the excitable neurons in the cortex and magnetic fields created by these individual dipoles are calculated using a four-sphere model. The magnetic field arising from the excited area of cortex is obtained by summing the fields due to these individual dipoles. Sulci shapes are represented by simple mathematical formulae.

Computer Simulation↗

PubMed1997

EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales.

Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, 'reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.

Action Potentials↗

PubMed1996

A dipole model for spreading cortical depression.

Spreading Cortical Depression (SCD) is the hyper-excitation, followed by extreme suppression of spontaneous electrical activity in the cortex. This work models SCD propagation using current dipoles to represent excitable pyramidal cells. An area of cortex, either gyrus or sulcus, supporting SCD is represented by surface dipoles oriented perpendicular to the surface. Magnetic fields created by these individual surface dipoles are calculated using the Biot-Savart law. We have assumed a plane volume conductor to represent the sulcus to simplify the mathematical derivation. The sources included in cortical surface area of 10(-4)mm2 is represented by a signal dipole. The magnetic field arising from the entire excited area of the cortex is obtained by summing the fields due to these individual dipoles. The simulated waveforms suggest that the shapes, amplitudes, and durations of the SCD signals depend on the size of the active area of cortex involved in SCD, as well as the location and orientation of the detector. Using this dipole model, we are able to simulate the Large Amplitude Waves (LAWs) similar to those observed by Barkley et al. (1990) while measuring spontaneous activity from migraine headache patients using the assumption that these LAWs arise from propagation of SCD across a sulcus. The shape of the simulated LAW waveform is strongly influenced by the relationships between the detector location and orientation, the propagation direction of the SCD wave, and the orientation of the sulcus.

Cerebral Cortex↗

PubMed1994

A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging.

Two different methods to improve the spatial resolution of EEG are discussed: the surface Laplacian (e.g., current source density) and cortical imaging (e.g., spatial deconvolution). The former methods tend to be independent of head volume conductor model, whereas the latter methods are more model-dependent. Computer simulation of scalp potentials due to either a few isolated sources or 4200 distributed cortical sources and studies of actual EEG data both indicate that the two methods provide similar estimates of cortical potential distribution. Typical correlation coefficients between either spline-Laplacian or cortical image and simulated (calculated) cortical potential are in the 0.8-0.95 range, depending partly on CSF thickness. By contrast, correlation coefficients between simulated scalp and cortical potential are in the 0.4-0.5 range, suggesting that high resolution methods provide much better estimates of cortical potential than is obtained with conventional EEG. The two methods are also applied to steady-state visually evoked potentials and spontaneous EEG. Correlation coefficients obtained from real EEG data are in the same general ranges as correlations obtained from simulations. The new high resolution methods can provide a dramatic increase in the information content of EEG and appear to have widespread application in both clinical and cognitive studies.

Brain↗

PubMed1993

High-resolution EEG using spline generated surface Laplacians on spherical and ellipsoidal surfaces.

Spline generated surface Laplacians are introduced as an effective method for estimating neocortical source activity at moderate scales. The method appears to be robust to the unavoidable perturbations of measured potentials and errors of head geometry and resistivity that are certain to occur in clinical or research settings. In particular, we have derived the surface Laplacian for general ellipsoidal surfaces in terms of the spline function. The spline-Laplacian accurately estimates isolated dipoles or distributed sources, is insensitive to subcortical sources and to sources which originate outside the boundaries of the electrode array, and acts as a bandpass spatial filter whose characteristics appear to provide a good match to the volume conduction of intracranial sources through human heads. As a result, spatial resolution is improved over that obtained with conventional EEG by at least a factor of three. This improvement, whether obtained with spline-Laplacian or model-dependent methods, is likely to have a significant impact on both medical and cognitive studies involving EEG.

Action Potentials↗

PubMed1992

The activating function for magnetic stimulation derived from a three-dimensional volume conductor model.

A three-dimensional volume conductor model of magnetic stimulation is proposed that relates transmembrane potential of an axon to the induced electric field in a uniform volume conductor. This model validates assumptions used to derive a one-dimensional cable model of magnetic stimulation (Roth & Basser, IEEE Trans. Biomed. Eng., vol. 37, pp. 588-597, 1990) of unmyelinated axons. The three-dimensional volume conductor model reduces to this one-dimensional cable equation forced by the activating function, -delta EzA/delta z.

Axons↗

PubMed1991

A model for compound action potentials and currents in a nerve bundle. I: The forward calculation.

We describe a model for the Compound Action Currents (CACs) and Compound Action Potentials (CAPs) produced by a peripheral nerve bundle in vitro. The Single Fiber Action Currents (SFACs) and the extracellular Single Fiber Action Potentials (SFAPs) are calculated using a generalized volume conduction model. Frequency-dependent conductivities, variations in the intracellular action potentials with recording temperature and axon conduction velocity, and the effects of axonal myelination are incorporated into the volume conduction calculation. We demonstrate how the propagation distance and the recording radius affect the simulated Compound Action Signals (CASs) of various nerve bundles. We also demonstrate how the frequency-dependent and -independent conductivities affect the CASs simulated by our model. For this simulation, some of the parameters for the nerve bundles and Conduction Velocity Distributions (CVDs) were obtained from the literature. In accompanying papers, we use the simulated CASs to investigate the effects of variations in the model parameters on the CVDs predicted by our inverse model.

Action Potentials↗

PubMed1991

A model for compound action potentials and currents in a nerve bundle. II: A sensitivity analysis of model parameters for the forward and inverse calculations.

We present a detailed analysis of the sensitivity of simulated Compound Action Current (CAC) and Compound Action Potential (CAP) recordings to specific model parameters, including the Single Fiber Action Currents (SFACs) and Single Fiber Action Potentials (SFAPs) that represent the contributions of each axon in the nerve bundle. In the preceding paper, we described a general method for simulating CACs and CAPs. This method uses a volume conduction model that incorporates the effects of the nerve bundle and other anisotropic properties of the region of the bundle that surrounds an individual nerve axon. In this paper, we present a complete analysis of the effects of incorrectly assigned model parameters on the simulated CAC and CAP. We also investigate the effects of incorrectly assigned parameters, recording noise, and data smoothing on the Conduction Velocity Distributions (CVDs) predicted from the CAC and CAP. We find that the simulated CAC is less sensitive to most of the parameters than is the CAP.

Action Potentials↗

PubMed1991

A model for compound action potentials and currents in a nerve bundle. III: A comparison of the conduction velocity distributions calculated from compound action currents and potentials.

In this paper, we present the experimentally measured Compound Action Current (CACs) and Compound Action Potentials (CAPs) from frog sciatic nerves and earthworm nerve cords. We used histologically prepared cross sections of these nerve bundles to determine the distribution of fiber diameters. A modified volume conduction model that includes frequency-dependent conductivities was used to compute the Single Fiber Action Signals (SFASs). The recorded CACs and CAPs are used to predict the Conduction Velocity Distributions (CVDs) from the nerve bundles. The predicted CVDs are then compared with the histological CVDs. Analysis of Compound Action Signals from the three giant axons in the earthworm nerve cord and microelectrode data for the transmembrane action potential demonstrate the validity of our mathematical model. We found that the CVDs predicted from the recorded CACs and CAPs differ from the histological CVD for a variety of reasons. The validity of the assumption of a linear relationship between axon diameter and conduction velocity of a propagating action signal was investigated using CVDs from both the CAC and CAP. Variations of the CVDs with the propagation distance of the CASs and the recording temperature were investigated.

Action Potentials↗

PubMed1991

A mathematical model for calculating the vector magnetic field of a single muscle fiber.

A mathematical model is described for calculating the volume-conducted magnetic field from active muscle fibers in an anisotropic bundle. With earlier models, the azimuthal magnetic field of a nerve bundle was calculated and the results were compared with the fields measured by toroidal pickup coils. The present model is capable of evaluating all three of the magnetic field components and is thus applicable for analyzing SQUID magnetometer recordings of fields from a muscle bundle. The component of the magnetic field parallel to the fiber axis is more than an order of magnitude smaller than either of the other two components. The amplitude of the magnetic signal is strongly dependent upon the anisotropy of the muscle bundle, the intracellular conductivity, the radius of the muscle fiber, the radius of the muscle bundle, and the location of the fiber in the muscle bundle. The peak-to-peak amplitude of the single-muscle-fiber action field increases linearly with increasing intracellular conductivity, as the square of the radius of the muscle fiber, and exponentially with the distance between the location of the fiber and the center of the bundle.

Action Potentials↗

PubMedDate not supplied

Magnetic stimulation of axons in a nerve bundle: effects of current redistribution in the bundle.

Recently, we developed a model of magnetic stimulation of a concentric axon in an anisotropic nerve bundle. In that earlier paper, we considered a single axon surrounded by a nerve bundle represented as a homogeneous anisotropic monodomain medium. In this paper we extend our previous calculations to examine excitation of axons within a nerve bundle without neglecting the presence of other axons in the nerve bundle. A three-dimensional axial symmetry volume conductor model is used to determine the transmembrane potential response along an axon due to induced electric fields produced by a toroidal coil. Our principal objective is to examine the effect of current redistribution to other axons in the bundle on excitation characteristics. We derive the transmembrane potential along an axon for two currently available models of current redistribution: the biodomain model and the spatial--frequency monodomain model. Results indicate that a reduction in the transmembrane potential along an axon due to the presence of other nerve fibers in the bundle is observed. Axons located at the periphery of a nerve bundle have lower thresholds and different excitation sites compared with axons located near the center of a nerve bundle.

Axons↗