Novel Approach for Analyzing Neurochemical Data: Generalized Linear Models

Authors: Ada Hepner, Deborah Kreiss

Field of Study: Science, Technology, Engineering, and/or Math

Program Affiliation: First-year Research Immersion (FRI)

Faculty Mentors: Deborah Kreiss

Easel: 46

Timeslot: Morning

Abstract: Published data concerning measurement of neurochemical levels is typically analyzed using analysis of variance (ANOVA). However, most analyses are performed without verifying assumptions of linearity, independence, normality, and homoscedasticity. Application of ANOVAs to data that violates the required assumptions may lead to misinterpretation of results. The aim of this study was to employ generalized linear models to determine the effects of sudden removal of high social enrichment on neurochemical levels in rats. Monoamine concentrations in post-mortem tissue from the medial and lateral thalamic nuclei were measured using High Performance Liquid Chromatography (HPLC). To assess this data set using generalized linear regression models, data were clustered by neurochemical and evaluated for the proper statistical assumptions. Then, data were transformed using the Box-Cox method of power transformations and analyzed using the appropriate linear model. Application of generalized linear models enhances the ability to correctly interpret neurochemical findings and to derive appropriate conclusions.