Degree Granting Department
Biology (Cell Biology, Microbiology, Molecular Biology)
Gary Arendash, Ph.D.
Professor: Huntington Potter, Ph.D.
Gordon Fox, Ph.D.
Patrick Bradshaw, Ph.D.
Chuanhai Cao, Ph.D.
neuropathology, neural networks, caffeine, GRK5, GMCSF
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 con
ventional 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.
Scholar Commons Citation
Leighty, Ralph E., "Statistical and Data Mining Methodologies for Behavioral Analysis in Transgenic Mouse Models of Alzheimer’s Disease: Parallels with Human AD Evaluation" (2009). Graduate Theses and Dissertations.