دانلود مقاله ISI انگلیسی شماره 27855
عنوان فارسی مقاله

بیوسنسور برای شناسایی تغییرات در پردازش شناختی بر اساس تجزیه و تحلیل سیستم های غیر خطی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
27855 2001 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A biosensor for detecting changes in cognitive processing based on nonlinear systems analysis
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Biosensors and Bioelectronics, Volume 16, Issues 7–8, September 2001, Pages 491–501

کلمات کلیدی
بیوسنسور - تکه هیپوکامپ - تجزیه و تحلیل سیستم های غیر خطی
پیش نمایش مقاله
پیش نمایش مقاله بیوسنسور برای شناسایی تغییرات در پردازش شناختی بر اساس تجزیه و تحلیل سیستم های غیر خطی

چکیده انگلیسی

A new type of biosensor, based on hippocampal slices cultured on multielectrode arrays, and using nonlinear systems analysis for the detection and classification of agents interfering with cognitive function is described. A new method for calculating first and second order kernel was applied for impulse input–spike output datasets and results are presented to show the reliability of the estimations of this parameter. We further decomposed second order kernels as a sum of nine exponentially decaying Laguerre base functions. The data indicate that the method also reliably estimates these nine parameters. Thus, the state of the system can now be described with a set of ten parameters (first order kernel plus nine coefficients of Laguerre base functions) that can be used for detection and classification purposes.

مقدمه انگلیسی

From antiquity to modern times, there has been a need for detecting threats in the environment. This need is even more acute nowadays as increased environmental pollution as well as activities of groups of terrorists or of various types of fanatics (as illustrated by the attack of the Tokyo subway; Falkenrath et al., 1998 and Tucker, 2000) cause serious threats to the general population. Analytical systems have become extremely sensitive and discriminative and are effective sensors as long as the nature of the detected agent is known. This is, in particular, the case of the MM1 (Rostker, 1997), the M22 ACADA, for automatic Chemical Agent Detector (http://www.gulflink.osd.mil/campmont/index.html) or a hybrid technology system, the M90-D1-C (Environics, Milliken, Finland). Even then, such systems do not provide much information on the biological effects of the detected agent. Biological systems, in principle at least, provide ideal detectors and reporters for environmental threats, as they are complex systems, which have evolved to maintain homeostasis despite continuously changing environments. Moreover, they do provide information regarding the biological effects of the perturbing agents. The problem is made more difficult when the agents to be detected affect cognitive processes. In this case, the detection requires either sophisticated behavioral tests not easily implemented in a biosensor, or indirect assays that are strongly indicative of potential cognitive dysfunction. We have developed a biosensor consisting of a multi-electrode array monitoring the functioning of complex neuronal networks contained in a cortical structure involved in cognitive processing. The underlying assumption is that agents that affect hippocampal function will also affect cognitive function in humans. The evolution of multi-electrode array (MEA) recording from an experimental project to a routine physiological tool makes it possible to study spatially extended populations of interconnected neurons (i.e. networks) in brain slices (Gross, 1979, Pine, 1980, Gross et al., 1982, Gross et al., 1985, Gross et al., 1993, Novak and Wheeler, 1988, Boppart et al., 1992, Meister et al., 1994, Stoppini et al., 1997, Egert et al., 1998, Maher et al., 1999 and Oka et al., 1999). With a large number of appropriately spaced electrodes, and appropriate support hardware, it is possible to design input patterns with the spatio-temporal richness needed to activate complex network activity. The present data indicate that random train stimulation consisting of as few as 400 impulses delivered over 200 s is sufficient to accurately determine high order kernels, which are a mathematical expression of the nonlinearities of the network. Both theoretical and experimental work indicates that any agent affecting neuronal function will produce a distinct modification of higher order kernels (Scalabassi et al., 1988). Having 64 electrodes in the arrays ensures that there is always a sufficient number of pairs of stimulating/recording electrodes to perform random train stimulation and kernel analysis within each slice (our current experience indicates that as many as four kernels can be obtained from one stimulation site). Furthermore, pairs of stimulating/recording electrodes can be located in different hippocampal subfields, thus providing for an additional spatial parameter that can be used to further characterize the effect of an agent on the state of the system. Thus, not only does random train stimulation offers a rapid way of detecting the presence of a potentially hazardous agent, but it also provides unique information about this agent. By comparison with a known library of molecules, it will be possible to identify the agent itself, or, if not, its site of action. We also present an outline of how our analysis will provide for an efficient classification system for any molecule tested with our system.

نتیجه گیری انگلیسی

Several of the requirements for a tissue-based biosensor have been implemented. First, we and others have established that the properties of electrophysiological responses from acute as well as cultured hippocampal slices are similar using several multi-electrode arrays and traditional electrophysiological techniques. In particular, the nonlinearities of the system, which form the basis for the sensitivity and discriminability of the biosensor, are similar in acute and cultured hippocampal slices. Second, we have established a new mathematical and computational tool to rapidly calculate the kernels of the Laguerre–Volterra expansion series. The new method generates a set of ten parameters to fully and reproducibly characterize the nonlinearities of the Schaffer collateral inputs to CA1 pyramidal neurons. Using this tool, we have demonstrated that picrotoxin produces specific alterations in the second order kernels, thus illustrating the validity of our assumption that second order kernels represent a signature of the state of the system under various conditions. In preliminary experiments, we have also found that different drugs produced different alterations in second order kernels, further illustrating the power of the method. In particular, trimethylopropane (TMPP), a jet fuel residue that has been shown to act as a GABAA receptor antagonist (Kao et al., 1999), produced an effect that can be distinguished from that of picrotoxin. Third, we posit that the biosensor is sensitive, i.e. more sensitive than existing biosensors based on cultured neuronal cells, and discriminative, i.e. that the presence of different pharmacological agents results in different alterations of the state of the network that can be classified by the analytical tools we have developed. The advantage of the hippocampal slice culture over dissociated neuronal cultures is that the slice culture maintains the same neuronal circuit organization that is found in intact organisms, including humans. Thus, the culture preparation should be able to detect concentrations of drugs that are biologically significant, i.e. concentrations that are expected to affect biological functions carried on by the hippocampus. What remains to be established is to further expand on this approach and to show for instance that cross-kernel analysis obtained from random train stimulation using two inputs—one output will provide for superior sensitivity and specificity. Finally, improved culture conditions are required for long-term recording and/or long-term storage of cultured slices leading to a real field-deployable biosensor.

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