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At least 361 records · Page 20Linked to original sources
PubMed1991

Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system.

This paper describes the first application of structural modeling to neuroscience. Structural modeling (also known as path analysis) is a method to assess the relative impact of directional links in a system and how these interrelations may change under different conditions. The objective was to demonstrate how structural modeling can be used to determine the functional interrelationships between brain structures that form the auditory system. Using structural modeling, changes in auditory system 2-DG uptake were examined during long- and short-term habituation of the acoustic startle reflex. Models were based on the anatomical connections between central auditory system structures. Using functional 2-DG data, the correlations between these structures were calculated and numerical weights were computed for each anatomical link. The analysis revealed that the lemniscal path was dominant during short-term habituation, while during long-term habituation this influence was modified through extra-lemniscal pathways. The models are discussed in the context of previous findings to demonstrate how structural modeling can not only complement, but also extract more information from 2-DG mapping experiments.

Acoustic Stimulation↗
PubMed1986

Spectral methods for principal components analysis of event-related brain potentials.

Principal components analysis has been a widely used method for the analysis of event-related, electrical brain potentials (ERPs). Recent emphasis has been placed on measuring the topography of ERPs, as derived from the instantaneous measurements from multiple locations, and on defining diagnostic differences in ERPs among various clinical populations. One goal of the present paper is to discuss inherent difficulties in utilizing PCA as an analytical technique in multiple location and multiple group studies. Another goal is to demonstrate the utility of spectral analysis and its equivalency to PCA when the signal imbedded in stationary noise model is used. Spectral analysis readily permits analysis of multiple lead multiple group studies.

Brain↗
PubMed1992

On AR modelling for MEG spectral estimation, data compression and classification.

The use of the autoregressive (AR) model for magnetoencephalogram (MEG) processing is examined and compared to other methods. Spectral estimation, classification and data compression of MEG signals are studied. In application to spectral estimation the AR model is compared to the classical modified periodogram method. Also, AR modelling appears to perform very successfully when used for the classification of normal and epileptic MEG signals. Finally, the 17:1 to 23:1 data compression achieved by AR modelling, along with the above-mentioned advantages, render it suitable for storage applications. For comparison, the method of feature selection via orthogonal expansion is used as a tool to achieve data reduction. It is seen that while effective, this is less drastic than the compression of data volume achieved by AR modelling.

Brain Mapping↗
PubMed1994

First-passage-time problem for simulated stochastic diffusion processes.

Solving the first-passage-time problem for one-dimensional stochastic diffusion processes is a task with many applications in biomedical research. It has been noted (Musila and Lánský, Int. J. Biomed. Comput. 31, 233-245, 1992) that the first-passage time is overestimated if computed as the time when the simulated trajectory of the process crosses the threshold. It is studied in this paper how the error depends on the simulation step and on the parameters of the process. We propose an adaptive algorithm to make the simulation faster. The presented examples are related to neuronal modelling, but application in other fields is straightforward.

Algorithms↗
PubMed1995

MEG and ECoG localization accuracy test.

We tested the localization accuracy of magnetoencephalography (MEG) and electrocorticography (ECoG) for a current dipole in a saline filled sphere at depths ranging from 1 to 6 cm at 1 cm intervals. We used standard neuromagnetometer placements and subdural electrode grids, previously employed for patient studies, with precise measurements of sensor and electrode locations with a 3-dimensional spatial digitizer. MEG and ECoG had comparable accuracy with mean errors of 1.5 and 1.8 mm, respectively. It appears that use of the spatial digitizer increases accuracy for both MEG and EGoG localizations. The larger errors in the ECoG with increasing depths could be attributed to under-sampling of the spatial pattern of the field which spreads out with deeper sources. It should be noted that in clinical applications a grid of the dimensions used here would most typically be used for superficial sources on the cortex with depth recordings being preferred for investigations of deep epileptogenic activity. Results are encouraging for continued development of non-invasive MEG methods for further definition of epileptogenic zones in the brain.

Cerebral Cortex↗
PubMed1983

"Convulsoid responses" suggesting development of autonomous epileptogenicity.

To test a convenient and reliable neuronal index that suggests the development of autonomous epileptogenicity, we conducted in acute experiments on rabbits a comparative study of the changes in direct cortical responses (DCRs) in primary and mirror sites during electrically induced primary (PRID) and projected seizure discharges (PROD). It has been assumed that the primary site shows autonomous epileptogenicity during the PRID, whereas the mirror site during the PROD does not. We observed that in the primary site, the dendritic potentials (field EPSPs) were remarkably suppressed in amplitude or disappeared during the PRID and that there was a loss of the after-positivity (field IPSPs); these DCRs recovered gradually after termination of the PRID. Instead of DCRs, "convulsoid responses" (a general term for responses similar to the individual spontaneous waves which occur in all types of seizures discharges) were usually elicited in the primary site during PRID. In the mirror site during PROD, the DCRs showed three different behaviors: they were either unaffected, increased, or suppressed. Convulsoid responses were never elicited. In a few cases, independent seizure discharges were induced in the mirror site. During the independent seizure discharges, the DCRs in the mirror site disappeared and convulsoid responses similar to the individual independent waves were usually elicited, as in the primary site during PRID. We conclude that the convulsoid response was the most reliable indicator of the development of autonomous epileptogenicity, and that suppression or disappearance of DCRs was the supplementary sign of that development. These neuronal indexes may be useful for identification of secondary epileptogenesis.

Animals↗
PubMed1988

Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin.

Neural networks provide a basis for semiempirical studies of pattern matching between the primary and secondary structures of proteins. Networks of the perceptron class have been trained to classify the amino-acid residues into two categories for each of three types of secondary feature: alpha-helix or not, beta-sheet or not, and random coil or not. The explicit prediction for the helices in rhodopsin is compared with both electron microscopy results and those of the Chou-Fasman method. A new measure of homology between proteins is provided by the network approach, which thereby leads to quantification of the differences between the primary structures of proteins.

Amino Acid Sequence↗
PubMed1987

A study on the best order for autoregressive EEG modelling.

The autoregressive (AR) model is a widely used tool in electroencephalogram (EEG) analysis. The dependence of the AR model on both the segment length and several characteristic EEG patterns is addressed. The best AR model order is computed with three different criteria. The results show that the Rissanen criteria provides the more consistent order estimate for the EEG patterns considered. This study shows that for our data set, a 5th order AR model represents adequately 1- or 2-s EEG segments with the exception of featureless background, where higher order models are necessary.

Electroencephalography↗
PubMed1987

Adrenoceptors and the pharmacology of affective illness: a unifying theory.

Based on recent clinical and preclinical research, it is theorized that antimanic and antidepressant effects of clinically available drugs can be produced through their actions on alpha-1 adrenoreceptor-mediated neurotransmission in the central nervous system. The theory suggests that final effects on alpha-1 mediated neurotransmission may be produced not only by drugs which have direct effects on the alpha-1 receptor or its second messenger, but also by drugs having effects on neurotransmitter systems such as acetylcholine, GABA, and serotonin, among others, which modulate the activity of central norepinephrine neurons or, via feedback mechanisms, by drugs having effects on adrenergic receptors other than the alpha-1 receptor itself.

Adrenergic alpha-Agonists↗
PubMed1989

The effect of a random initial value in neural first-passage-time models.

The effect of a random initial value is examined in several stochastic integrate-and-fire neural models with a constant threshold and a constant input. The three models considered are approximations of Stein's model, namely: (1) a leaky integrator with deterministic trajectories, (2) a Wiener process with drift, and (3) an Ornstein-Uhlenbeck process. For model 1, different distributions for the initial value lead to commonly observed interspike interval distributions. For model 2, a discrete and a uniform distribution for the initial value are examined along with some parameter estimation procedures. For model 3, with a truncated normal distribution for the initial value, the coefficient of variation is shown to be greater than 1, and as the threshold becomes large the first-passage-time distribution approaches an exponential distribution. The relationships among the models and between them and previous models are also discussed, along with the robustness of the model assumptions and methods of their verification. The effects of a random initial value are found to be most pronounced at high firing rates.

Action Potentials↗