| Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of human senile dementia. Alzheimer's represents a significant public health concern, having widespread social and economic implications. Consequently, protocols for early detection and therapeutic intervention (both behavioral and pharmacologic) constitute important targets for medical investigation. Furthermore, contemporary research depends upon comprehensive neurobehavioral assessment and advanced statistical and computational analytic methodologies for characterizing AD-associated sensorimotor and cognitive impairment, as well as evaluating therapeutic efficacy. This dissertation introduces data mining-based techniques (decision trees, neural networks, support vector machines) for behavioral analysis in both nontransgenic and Alzheimer's transgenic mice, to evaluate the cognitive benefits of long-term caffeine treatment. Both treatment and transgenic effects are identified through advanced statistical (discriminant analysis) and data mining approaches. In addition, a novel mouse-based cognitive assessment paradigm, adapted from a human interference learning AD-diagnostic protocol, is implemented to evaluate both genetic (GRK5) and therapeutic (GM-CSF) effects in mice, against an Alzheimer's transgenic background. Data mining techniques are shown to be comparable to conventional statistical analyses, often providing complementary diagnostic information. Indeed, comparisons between data mining-based and multivariate statistical analyses, with respect to groupwise discriminability, support the use of both methodologies in neurobehavioral research. Future work involving both data mining-based and multivariate statistical analyses of cognitive-behavioral data is discussed, emphasizing the need for longitudinal studies, repeated-measure designs, and spatiotemporal modeling for evaluating the time-course of both human AD and AD-like pathology in transgenic mouse models. |
