Detection and classification of neurotoxins using a novel short-term plasticity quantification method

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• 作者：Gholmieh ; Ghassan ; Courellis ; Spiros ; Fakheri ; Saman ; Cheung ; Eric ; Marmarelis ; Vasilis ; Baudry ; Michel ; et. al.
• 关键词：CA1 ; Kernels ; Nonlinear analysis ; Paired pulse ; Random train ; Short-term plasticity
• 刊名：Biosensors and Bioelectronics
• 出版年：2003
• 出版时间：October 15, 2003
• 年：2003
• 卷：18
• 期：12
• 页码：1467-1478
• 全文大小：606 K

文摘

A tissue-based biosensor is described for screening chemical compounds that rapidly affect the nervous system. The proposed sensor is an extension of a previous work on cultured hippocampal slices [Biosens. Bioelectron. 16 (2001) 491]. The detection of the chemical compounds is based on a novel quantification method of short-term plasticity (STP) of the CA1 system in acute hippocampal slices, using random electrical impulse sequences as inputs and population spike (PS) amplitudes as outputs. STP is quantified by the first and the second order kernels using a variant of the Volterra modeling approach. This approach is more specific and time-efficient than the conventional paired pulse and fixed frequency train methods [J. Neurosci. Methods 2 (2002) 111]. Describing the functional state of the biosensor, the kernels changed accordingly as chemical compounds were added. The second order kernel was decomposed into nine Laguerre functions. The corresponding Laguerre coefficients along with the first order kernel were used as features for classification purposes. The biosensor was tested using picrotoxin (100 μM), trimethylopropane phosphate (10 μM), tetraethylammonium (4 mM), valproate (5 mM), carbachol (5 mM), DAP5 (25 μM), CNQX (3 μM), and DNQX (0.15, 1.5, 3, 5 and 10 μM). Each chemical compound gave a different feature profile corresponding to its pharmacological class. The first order kernel and the Laguerre coefficients formed the input to an artificial neural network (ANN) comprised of a single layer of perceptrons. The ANN was able to classify each tested compound into its respective class.