A comparison of univariate and multivariate statistical and data mining approaches to the behavioural and biochemical effects of vestibular loss related to the hippocampus : a thesis submitted in partial fulfilment of the requirements of the MApplStat in Applied Statistics, Massey University, Manawatu
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Date
2013
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Massey University
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Abstract
Vestibular dysfunction is associated with a complex syndrome of cognitive and anxiety disorders. However, most studies have used simple univariate analyses of the effects of vestibular loss on behaviour and brain function. In this thesis, univariate statistical, and multivariate statistical and data mining approaches, to the behavioural and neurochemical effects of bilateral vestibular deafferentation (BVD), were compared. Using linear mixed model analyses, including repeated measures analyses of variance and analyses with the covariance structure of the repeated measures specified, rats with BVD were found to exhibit increased locomotor activity, reduced rearing and reduced thigmotaxis. By contrast, there were no significant differences between BVD and sham control animals in the elevated plus maze and the BVD animals exhibited a longer escape latency in the elevated T maze, with no change in avoidance latency. In the spatial T maze, the BVD animals demonstrated a significant decrease in accuracy compared to the sham control animals. Using linear discriminant analysis, cluster analysis, random forest classification and support vector machines, BVD animals could be distinguished from sham controls by their behavioural syndrome. Using multiple linear regression and random forest regression, the best predictors of performance in the spatial T maze were whether the animals had received a BVD or sham lesion, and the duration of rearing. In the neurochemical data set, the expression of 5-7 glutamate receptor subunits was measured in 3 different subregions of the rat hippocampus, at various times following BVD, using western blotting. In the 6 month group, half of the animals underwent training in a T-maze. Using multivariate analyses of variance, there was no significant effect of surgery for any hippocampal subregion. Linear discriminant analysis could not determine a linear discriminant function that could separate BVD from sham control animals. A random forest classification analysis was also unsuccessful in this respect. However, for the 6 month data set, T maze training had a significant effect independently of surgery. The results of these experiments suggest that BVD results in profound spatial memory deficits that are not associated with large changes in the expression of glutamate receptors in the hippocampus. The results of the multivariate statistical and data mining analyses, applied to both the
behavioural and neurochemical data sets, suggested that research in this field of neuroscience would benefit from analysing multiple variables in relation to one another, rather than simply conducting univariate analyses. Since the different behavioural and neurochemical variables do interact with one another, it is important to determine the nature of these interactions in the analyses conducted. However, this will require researchers to design experiments in which multiple variables can be measured under the one set of conditions.
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Multivariate analysis, Vestibular apparatus, Hippocampus (Brain), Pathophysiology, Statistical methods, Data mining