Use of artificial neural networks to determine cognitive impairment and therapeutic effectiveness in Alzheimer's transgenic mice.

Behavioral testing of transgenic mouse models of Alzheimer's disease (AD) is the functional endpoint for determining the effectiveness of therapeutic interventions and elucidating AD pathogenesis. Utilizing these mouse models, there have been remarkably few attempts to analyze multiple behavioral measures/tasks with higher-level computation techniques, either to distinguish performance between transgenic groups or to reveal any "overall" cognitive benefit of a given therapeutic. The present study compared the classificatory accuracy of artificial neural networks (ANNs) versus more traditional discriminant function analysis (DFA) using multiple behavioral measures/tasks from two AD transgenic mouse investigations. These investigations were to determine if AD transgenic mice could be cognitively-protected by either long-term caffeine administration (CA) or by a cognitively-stimulating environment (SE). Both the entire set of behavioral measures and a subset of 8 cognitive-based measures were analyzed. Both classifiers revealed a beneficial "overall" effect of CA and SE to protect AD transgenic mice across multiple cognitive measures/tasks. However, for both CA and SE datasets, the ANN was superior to DFA for discerning transgenicity (non-transgenic vs. transgenic-controls) across multiple behavioral measures. These results indicate that ANNs have an excellent capacity to discriminate cognitive impairment in AD transgenic mice and thus designate ANNs as a novel, sensitive method for cognitive assessment in Alzheimer's research.