Biomedical subjects

I V Tetko

Publications and source records attributed to I V Tetko.

At least 19 recordsLinked to original sources

PubMed2004

Dopamine modulation of glutamate metabotropic receptors in conditioned reaction of sensory motor cortex neurons of the cat.

The dopamine modulation of microiontophoretic application of antagonists of glutamate metabotropic synaptic transmission was studied in the sensory motor cortex of awake cats during instrumental conditioned reflex. The substances depressed the background and intensity of evoked impulse activity of pyramidal neurons of the sensory-motor cortex and provoked significant increases in the latency of impulse reaction and corresponding conditioned movements of animals. Simultaneous application of the antagonists and dopamine (DA) eliminated their depression of the background and evoked activity of neurons and decreased the latency of the impulse reactions and movements. Similar qualitative effects were observed in experiments with simultaneous application of biccuculine and (RS)-alpha-methyl-4-carboxyphenylglycine. It is supposed that the DA modulation in the brain consists in stabilizing the background and evoked activity of cortical neurons during reduced intensity of metabotropic glutamatergic synaptic transmission. Such modulation can be important when considering some pathological disorders of glutamatergic transmission.

Animals↗

PubMed2004

Parvalbumin deficiency affects network properties resulting in increased susceptibility to epileptic seizures.

Networks of GABAergic interneurons are of utmost importance in generating and promoting synchronous activity and are involved in producing coherent oscillations. These neurons are characterized by their fast-spiking rate and by the expression of the Ca(2+)-binding protein parvalbumin (PV). Alteration of their inhibitory activity has been proposed as a major mechanism leading to epileptic seizures and thus the role of PV in maintaining the stability of neuronal networks was assessed in knockout (PV-/-) mice. Pentylenetetrazole induced generalized tonic-clonic seizures in all genotypes, but the severity of seizures was significantly greater in PV-/- than in PV+/+ animals. Extracellular single-unit activity recorded from over 1000 neurons in vivo in the temporal cortex revealed an increase of units firing regularly and a decrease of cells firing in bursts. In the hippocampus, PV deficiency facilitated the GABA(A)ergic current reversal induced by high-frequency stimulation, a mechanism implied in the generation of epileptic activity. We postulate that PV plays a key role in the regulation of local inhibitory effects exerted by GABAergic interneurons on pyramidal neurons. Through an increase in inhibition, the absence of PV facilitates synchronous activity in the cortex and facilitates hypersynchrony through the depolarizing action of GABA in the hippocampus.

Action Potentials↗

PubMed2003

The effects of activation of glutamate ionotropic connections of neurons in the sensorimotor cortex in a conditioned reflex.

Changes in conditioned reflex spike activity of neurons in the sensorimotor cortex were studied during microiontophoretic application of agonists and antagonists of glutamate and GABAergic transmission. The results of these experiments showed that the glutamate ionotropic receptors (AMPA and NMDA) of neurons in the sensorimotor cortex were intensely activated by the arrival of a conditioned signal in the cortex. This response included not only large pyramidal neurons of the deep cortical layers, but also the surrounding inhibitory interneurons. The existence of constant tonic inhibitory regulation of the activity of large pyramidal neurons by the surrounding inhibitory cells was demonstrated, along with the active involvement of this inhibition in organizing the excitatory responses of neurons in the sensorimotor cortex during a conditioned reflex.

Action Potentials↗

PubMed2002

Dopamine modulation of activity of cat sensorimotor cortex neurons during conditioned reflexes.

The effects of iontophoretic application of dopamine and selective D1 or D2 dopamine receptor agonists and antagonists on impulse activity of neurons of the deep layers of the sensorimotor cortex of cat were investigated during performance of a conditioned paw movement task. The application of dopamine, Quinpirole (selective D2 receptor agonist) or SKF 38393 (selective D1 receptor agonist) increased both background (P<0.001) and evoked impulse activity (P<0.05 for selective agonists). Selective D2 and D1 receptor antagonists (Sulpiride and SKF 83566, respectively) both increased the latency of neural responses and significantly increased the latency of the conditioned paw movements (P<0.01). These data suggest that during natural physiological functions subcortical dopamine neurons provide facilitation of activity pyramidal neurons of sensorimotor cortex.

2,3,4,5-Tetrahydro-7,8-dihydroxy-1-phenyl-1H-3-ben↗

PubMed2001

Volume learning algorithm artificial neural networks for 3D QSAR studies.

The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies.

Algorithms↗

PubMed2001

Different tonic regulation of neuronal activity in the rat dorsal raphe and medial prefrontal cortex via 5-HT(1A) receptors.

It has been established that 5-HT(1A) receptors are expressed both presynaptically as autoreceptors by 5-HT containing neurones, and postsynaptically by a variety of other neurones. Activation of either somatodendritic 5-HT(1A) autoreceptors or postsynaptic 5-HT(1A) receptors induces hyperpolarisation and inhibition of action potential discharge of the neurones, but it is unclear whether 5-HT(1A) receptors are under a general tonic influence by 5-HT. In the present study, using single unit recordings from both anesthetized and non-anesthetized rats, we show that the activity of neurones in the medial prefrontal cortex is not altered by systemic administration of the selective 5-HT(1A) receptor antagonist, WAY 100635. In contrast, WAY 100635 increased the firing rate of 5-HT neurones in the dorsal raphe nucleus. Our findings indicate a tonic activation of presynaptic somatodendritic but not postsynaptic cortical 5-HT(1A) receptors.

Animals↗

PubMed2001

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns.

The existence of precise temporal relations in sequences of spike intervals, referred to as 'spatiotemporal patterns', is suggested by brain theories that emphasize the role of temporal coding. Specific analytical methods able to assess the significance of such patterned activity are extremely important to establish its function for information processing in the brain. This study proposes a new method called 'pattern grouping algorithm' (PGA), designed to identify and evaluate the statistical significance of patterns which differ from each other by a defined and small jitter in spike timing of the order of few ms. The algorithm performs a pre-selection of template patterns with a fast computational approach, optimizes the jitter for each spike in the template and evaluates the statistical significance of the pattern group using three complementary statistical approaches. Simulated data sets characterized by various types of known non stationarities are used for validation of PGA and for comparison of its performance to other methods. Applications of PGA to experimental data sets of simultaneously recorded spike trains are described in a companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings. J Neurosci Method 2000; accompanying article).

Action Potentials↗

PubMed2001

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings.

This study demonstrates the practical application of the pattern grouping algorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 2000; accompanying article), to data sets including up to 30 simultaneously recorded spike trains. The analysis of a large network of simulated neurons shows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cortex was reversibly inactivated suggest that widespread cell assemblies contribute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal cortex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period preceding the cue was associated with changes in reaction time. In conclusion, neuronal network interactions may generate spatiotemporal firing patterns detectable by PGA. We provide evidence of such patterned activity associated with specific animal's behavior, thus suggesting the existence of complex temporal coding schemes in the higher nervous centers of the brain.

Action Potentials↗

PubMed2000

Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet.

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.

Internet↗

PubMed1999

Pharmaceutical fingerprinting in phase space. 1. Construction of phase fingerprints.

The present study proposes a general method for constructing pharmaceutical fingerprints in the analysis of HPLC trace organic impurity patterns. The approach considers signals in phase space and accounts for two different types of noise: additive and perturbative. The first type, additive noise, contributes to distortion of the absolute values of signal peaks. The second type, perturbative noise, contributes to variations of the retention times of signal peaks and distorts the time scale of the trace organic impurity patterns. The ability of the proposed approach to consider both types of noise significantly distinguishes it from existing methods of data analysis that are usually designed to treat only the additive noise. Analysis of the HPLC signals in phase space eliminates the problem of perturbation noise and enables detection and comparison of similar signal segments recorded at different retention times. The current study analyzes the chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan using three HPLC columns. For five manufacturers the variability of data recorded with the same column are in perfect agreement with the proposed model. A significant variance of parameters is detected for one manufacturer, thus indicating a possible change in its product consistency. The analysis in phase space is also used to explain the previously detected variability of HPLC signals across columns. The accompanying paper reports an application of the proposed approach for the pattern recognition of HPLC data.

Algorithms↗

PubMed1999

Pharmaceutical fingerprinting in phase space. 2. Pattern recognition.

The current study introduces an approach for pattern recognition of drug manufacturers according to their HPLC trace impurity data. This method considers signals in phase space and accounts for two different types of noise: additive and perturbative. The pharmaceutical fingerprints are estimated as mean trajectories of HPLC trace impurity data and are used as reference models for recognition of new data by the minimal length classifier. The chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan are analyzed as an example. The prediction ability of the new method tested using three different cross-validation procedures remains about 95% even if the number of available data in the training sets decreases by 5 times. The accuracy of prediction in phase space is superior compared to results calculated using a Window Preprocessing method and artificial neural networks. The difference in performance between new and previous methods becomes more significant under particular conditions that are more adequate for practical application of the method. In addition, the current approach enables simple and comprehensive interpretation of the calculated results.

Artificial Intelligence↗

PubMed1999

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task.

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.

Acoustic Stimulation↗

PubMed1999

Corticofugal modulation of functional connectivity within the auditory thalamus of rat, guinea pig and cat revealed by cooling deactivation.

Microelectrode recordings were simultaneously performed at multiple sites in the medial geniculate body (MGB) of anesthetized cats, rats and guinea pigs. We studied the effect of cortical deactivation on the association of neural activity within the thalamus during spontaneous activity. The corticofugal influence was suppressed by temporary cooling of the auditory cortex. Pairs of spike trains recorded from the same electrode were distinguished from cases where units were in MGB but recorded with different electrodes. Time domain analyses included crosscorrelations and search for precise repetition of complex spatiotemporal firing patterns of reverberating thalamic circuits. As a complementary approach we performed bispectral analyses of simultaneously recorded local field potentials in order to uncover the frequency components of their power spectra which are non linearly coupled. All results suggest that new functional neuronal circuits might appear at the thalamic level in the absence of input from the cortex. The newly active intrathalamic connections would provide the necessary input to sustain the reverberating activity of thalamic cell assemblies and generate low frequency non-linear interactions. The dynamic control exerted by the cortex over the functional segregation of information processing carried out in the thalamus conforms with theoretical neural network studies and with the functional selectivity-adaptive filtering theory of thalamic neuronal assemblies. Although this general conclusion remains valid across species, specific differences are discussed in the frame of known differences of the microcircuitry elements.

Animals↗

PubMed1999

Prediction of partition coefficient based on atom-type electrotopological state indices.

The aim of this study was to determine the efficacy of atom-type electrotopological state indices for estimation of the octanol-water partition coefficient (log P) values in a set of 345 drug compounds or related complex chemical structures. Multilinear regression analysis and artificial neural networks were used to construct models based on molecular weights and atom-type electrotopological state indices. Both multilinear regression and artificial neural networks provide reliable log P estimations. For the same set of parameters, application of neural networks provided better prediction ability for training and test sets. The present study indicates that atom-type electrotopological state indices offer valuable parameters for fast evaluation of octanol-water partition coefficients that can be applied to screen large databases of chemical compounds, such as combinatorial libraries.

Chemical Phenomena↗

PubMed1998

Computer assisted neurophysiology by a distributed Java program.

We have developed a distributed software package which allows neurophysiologists to have at hand a set of network and graphic tools in order to perform the analysis of data collected from the electrical activity of neurons, studied in the experimental laboratory. This system is built around three major components. First, the experimental data are processed in order to select specific data files from the networked computer and display the spike trains as raster dots. The second part selects one type of analysis (e.g., cross-correlation, out of a set of possible choices) after configuration of some specific parameters. Finally, the third component deals with the results of data analyses, which are selected and displayed on a multi-parameter graphic "ring binder." We have used the Java programming language to implement this application. This recent object-oriented language offers platform-independent software tools including graphic package and standard network protocols.

Algorithms↗

PubMed1997

Data modelling with neural networks: advantages and limitations.

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.

Data Interpretation, Statistical↗

PubMed1995

[Evolutionary programming for finding "structure-activity" relationships in a series of 3-phenoxychromone and 3-phenoxy-4-hydroxycoumarin derivatives].

Based on a set of compounds possessing hypolipidemic activity, it was demonstrated that evolutionary algorithms can be successfully used to compile an informative set of molecular parameters. The parameter sets selected using the method of potential functions allowed correct prediction of the activity of test molecules.

4-Hydroxycoumarins↗

PubMed1994

HIV-1 reverse transcriptase inhibitor design using artificial neural networks.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

Algorithms↗