Posters
Presentation Type
Poster
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
Background: Most psychopathology focuses on the current mental picture of an individual with some including relations to the past experience. One problem with the biomedical studies of psychopathologies is the difficulty in using animals to describe and model these mental states in humans. This difficult task has been confounded by the inability to classify animals in a way that will provide general models that will allow better translation to hypotheses in humans. Therefore, the present investigation explores statistical/research strategies to organize variables using lab animals to facilitate the translation of this information to humans.
Methods: Factor analysis/PCA was used to reduce the number of variables, from a laboratory animal behavior dataset, into fewer constructs/factors. SPSS statistical software was used for all analyses. This data reduction technique helps to organize variables into more manageable groupings (factors), and the guiding hypothesis is that each factor will more accurately represent a behavioral or affective state that is present in humans.
Results: Using the factor analysis/PCA method, eight components/factors were extracted from a total of 44 variables. However, after inspecting the scree plot, it was apparent that three additional variables approached and were just under the 1.0 Eigenvalue threshold. The top component/factor that accounted for 29% of the variance was associated with anxiety-like and submissive variables. This approach identifies latent constructs that are more relatable to the human condition.
Conclusion: The major goal of this research is to systemically observe different variables and draw parallels to theories of human personalities. The idea is to begin to classify animal samples by a currently rudimentary version of personality, with the goal to model addictions in a more holistic way that encompasses more of who the individual is rather than just the effect of the drug. This research will establish the groundwork for modeling different psychopathologies in similar ways to not only look back from past to current but to help model and predict future behaviors based on the distinction between animal traits and psychopathologies. Ultimately the goal is to describe a better model within animals that can translate to humans in the context of psychopathology.
Academic/Professional Position
Medical Student
Mentor/PI Department
Neuroscience
Recommended Citation
Reckley, William and Gil, Mario, "From Bench to Bedside: Bridging the Gap Between Animal Behavior Research and the Study of Human Personality and Psychopathology" (2023). Research Symposium. 111.
https://scholarworks.utrgv.edu/somrs/2022/posters/111
Included in
From Bench to Bedside: Bridging the Gap Between Animal Behavior Research and the Study of Human Personality and Psychopathology
Background: Most psychopathology focuses on the current mental picture of an individual with some including relations to the past experience. One problem with the biomedical studies of psychopathologies is the difficulty in using animals to describe and model these mental states in humans. This difficult task has been confounded by the inability to classify animals in a way that will provide general models that will allow better translation to hypotheses in humans. Therefore, the present investigation explores statistical/research strategies to organize variables using lab animals to facilitate the translation of this information to humans.
Methods: Factor analysis/PCA was used to reduce the number of variables, from a laboratory animal behavior dataset, into fewer constructs/factors. SPSS statistical software was used for all analyses. This data reduction technique helps to organize variables into more manageable groupings (factors), and the guiding hypothesis is that each factor will more accurately represent a behavioral or affective state that is present in humans.
Results: Using the factor analysis/PCA method, eight components/factors were extracted from a total of 44 variables. However, after inspecting the scree plot, it was apparent that three additional variables approached and were just under the 1.0 Eigenvalue threshold. The top component/factor that accounted for 29% of the variance was associated with anxiety-like and submissive variables. This approach identifies latent constructs that are more relatable to the human condition.
Conclusion: The major goal of this research is to systemically observe different variables and draw parallels to theories of human personalities. The idea is to begin to classify animal samples by a currently rudimentary version of personality, with the goal to model addictions in a more holistic way that encompasses more of who the individual is rather than just the effect of the drug. This research will establish the groundwork for modeling different psychopathologies in similar ways to not only look back from past to current but to help model and predict future behaviors based on the distinction between animal traits and psychopathologies. Ultimately the goal is to describe a better model within animals that can translate to humans in the context of psychopathology.