Biomedical subjects

S L Gonzalez Andino

Publications and source records attributed to S L Gonzalez Andino.

6 recordsLinked to original sources

PubMed2001

Non-stationary distributed source approximation: an alternative to improve localization procedures.

Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.

Adolescent↗

PubMed2000

Imaging the electrical activity of the brain: ELECTRA.

The construction of a tomography of neuronal sources is limited by a lack of information. A possible way around this problem is to change the biophysical model that underlies the statement of the inverse problem, i.e., searching for magnitudes that can be better determined from the available data. In this report, we describe a mathematical characterization of the type of currents that are actually able to produce the scalp-recorded EEG. Considering this characterization, we reformulate the bioelectric inverse problem. This approach, called ELECTRA, yields some advantages over the classical formulation in terms of the current density vector: (1) the number of unknowns can be reduced, which is equivalent to increasing the number of independent measurements, (2) the constraints used to reformulate the problem are undeniable since they do not imply any hypothesis about brain function but are instead based on the character of the measurements, and (3) existing experimental evidence suggests that the proposed source model characterizes the type of currents that arise in excitable tissues. We conclude that if the latter fact proves to be true for brain tissues, then no additional information is added to the inverse problem by using a more general source model than the one proposed here. Images obtained using this method for synthetic data, as well as early and middle components of human visual evoked responses to checkerboard stimuli, are presented to illustrate the characteristics of the reconstructed maps and their interpretation.

Brain↗

PubMed2000

Measuring the complexity of time series: an application to neurophysiological signals.

Measures of signal complexity can be used to distinguish neurophysiological activation from noise in those neuroimaging techniques where we record variations of brain activity with time, e.g., fMRI, EEG, ERP. In this paper we explore a recently developed approach to calculate a quantitative measure of deterministic signal complexity and information content: The Renyi number. The Renyi number is by definition an entropy, i.e., a classically used measure of disorder in physical systems, and is calculated in this paper over the basis of the time frequency representation (TFRs) of the measured signals. When calculated in this form, the Renyi entropy (RE) indirectly characterizes the complexity of a signal by providing an approximate counting of the number of separated elementary atoms that compose the time series in the time frequency plane. In this sense, this measure conforms closely to our visual notion of complexity since low complexity values are obtained for signals formed by a small number of "components". The most remarkable properties of this measure are twofold: 1) It does not rely on assumptions about the time series such as stationarity or gaussianity and 2) No model of the neural process under study is required, e.g., no hemodynamic response model for fMRI. The method is illustrated in this paper using fMRI, intracranial ERPs and intracranial potentials estimated from scalp recorded ERPs through an inverse solution (ELECTRA). The main theoretical and practical drawbacks of this measure, especially its dependence of the selected TFR, are discussed. Also the capability of this approach to produce, with less restrictive hypothesis, results comparable to those obtained with more standard methods but is emphasized.

Algorithms↗

PubMed1999

Backus and Gilbert method for vector fields.

This report describes the theory of Backus and Gilbert with special emphasis for the case of vector fields as required for the solution of the electromagnetic inverse problem. A description of the method is presented with the detailed mathematical derivation of the coefficients that determine the solution for the retrieval of vector fields. Such derivation, to our knowledge, has never been reported in the literature. We also identify some crucial points that can (and had) lead to misuse of this solution and describe some disadvantages of this theory for the case of vector fields suggesting some alternatives to deal with them.

Brain Mapping↗

PubMed1994

Single dipole localization: some numerical aspects and a practical rejection criterion for the fitted parameters.

There have been a number of attempts in the last years to localize the generators of brain electromagnetic activity, considering one current dipole as the source model. Single Dipole Localization (SDL) requires the selection of an optimization algorithm (OA). General aspects related with the selection, implementation and evaluation of some of the OA employed for SDL are discussed in this paper. Specifically the performance of two algorithms, those of Hooke-Jeeves and Levenberg-Marquardt, are tested by simulations. Suggestions for including restrictions to the dipole position and comments about some commonly used measures of the goodness of fit are given. Examples of erroneous implementations of these algorithms are also illustrated. A simple graphic rejection criterion, which can be easily used by inexperienced researchers, is introduced and tested in noisy and noise free simulations.

Algorithms↗

PubMed1990

Brain electrical field measurements unaffected by linked earlobes reference.

Recent theoretical analysis supports the possibility that using a linked earlobe reference in EEG studies might appreciatively distort the measured electrical field due to current flow over a low resistance path across the wire joining both ears. Such an effect would invalidate published quantitative EEG norms. Evidence for the balancing effect of this distortion was sought for in the EEG of 4 patients with well localized unilateral lesions, a situation in which this distortion would be most apparent. Statistical tests failed to reveal significant differences between EEGs recorded when ears were linked or unlinked. An analysis of the equivalent circuit reveals that a high skin/electrode impedance effectively makes the linked ear reference behave as an ordinary reference.

Brain↗