title: Habituation and sensitization in rat auditory evoked potentials: a single-trial analysis with wavelet denoising.
abstract: In this work, systematic changes of single-trial auditory evoked potentials elicited in rats were studied. Single-trial evoked potentials were obtained with the help of wavelet denoising, a very recently proposed method that has already been shown to be useful in the analysis of scalp human evoked potentials. For the evoked components in the 13-24-ms range (i.e. P13, N18, P20 and N24), it was possible to identify slow exponential decreases in the peak amplitudes, most likely related to a slow habituation process, while for N18, an initial increase in amplitude was also found. On the contrary, the slower components (N38 and N52) habituated within a few trials, and we therefore propose that they are related to a different functional process. The outcomes of the present study show that wavelet denoising is a useful technique for analyzing evoked potentials in rats at the single-trial level. In fact, in the present study it was possible to obtain more information than the one described in previous related works. This allows the study of other forms of learning processes in rats with the aid of evoked potentials. Finally, the outcomes of this study may have some relevance for the comparison of human and rat evoked potentials.
authors.affiliation: John von Neumann Institute for Computing, Forschungszentrum Jülich, D-52425, Jülich, Germany. r.quianquiroga@fz-juelich.de
authors.forename: R
authors.initials: R
authors.lastname: Quian Quiroga
authors.forename: E L J M
authors.initials: EL
authors.lastname: van Luijtelaar
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004567:Electrodes, Implanted; D004569:Electroencephalography; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D005583:Fourier Analysis; D006185:Habituation, Psychophysiologic / Q000502:physiology*; D008297:Male; D051381:Rats; D017208:Rats, Wistar; D012044:Regression Analysis