title: The Advantages of Structural Equation Modeling to Address the Complexity of Spatial Reference Learning.
abstract: BACKGROUND
  Cognitive performance is a complex process influenced by multiple factors. Cognitive assessment in experimental animals is often based on longitudinal datasets analyzed using uni- and multi-variate analyses, that do not account for the temporal dimension of cognitive performance and also do not adequately quantify the relative contribution of individual factors onto the overall behavioral outcome. To circumvent these limitations, we applied an Autoregressive Latent Trajectory (ALT) to analyze the Morris water maze (MWM) test in a complex experimental design involving four factors: stress, age, sex, and genotype. Outcomes were compared with a traditional Mixed-Design Factorial ANOVA (MDF ANOVA).
  
  
  RESULTS
  In both the MDF ANOVA and ALT models, sex, and stress had a significant effect on learning throughout the 9 days. However, on the ALT approach, the effects of sex were restricted to the learning growth. Unlike the MDF ANOVA, the ALT model revealed the influence of single factors at each specific learning stage and quantified the cross interactions among them. In addition, ALT allows us to consider the influence of baseline performance, a critical and unsolved problem that frequently yields inaccurate interpretations in the classical ANOVA model.
  
  
  CONCLUSIONS
  Our findings suggest the beneficial use of ALT models in the analysis of complex longitudinal datasets offering a better biological interpretation of the interrelationship of the factors that may influence cognitive performance.
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Pedro S
authors.initials: PS
authors.lastname: Moreira
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Sotiropoulos
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Joana
authors.initials: J
authors.lastname: Silva
authors.affiliation: RIKEN Brain Science Institute, Laboratory for Alzheimers DiseaseSaitama, Japan; Department of Aging Neurobiology, National Center for Geriatrics and GerontologyOhbu, Japan.
authors.forename: Akihiko
authors.initials: A
authors.lastname: Takashima
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Nuno
authors.initials: N
authors.lastname: Sousa
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Hugo
authors.initials: H
authors.lastname: Leite-Almeida
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of MinhoBraga, Portugal; ICVS/3B's - PT Government Associate LaboratoryBraga/Guimarães, Portugal.
authors.forename: Patrício S
authors.initials: PS
authors.lastname: Costa
keywords: auto-regressive latent trajectories; longitudinal assessments; reference learning