Abstract Number:
2671
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Kathryn Konrad (1), Katherine Allen-Moyer (1), Leslie Wilson (2), Wei Fan (3), Jesse Cushman (2), Xiaoling Li (3), Leping Li (4), Helen Cunny (5), Keith Shockley (4)
Institutions:
(1) Social and Scientific Systems, Inc., a DLH Holdings Corp Company, Durham, North Carolina., United States, (2) Neurobehavioral Core Laboratory/NIEHS, United States, (3) Metabolism, Genes, and Environment Group/NIEHS, United States, (4) Biostatistics Branch/NIEHS, United States, (5) Division of Translational Toxicology/NIEHS, United States
Co-Author(s):
Katherine Allen-Moyer
Social and Scientific Systems, Inc., a DLH Holdings Corp Company, Durham, North Carolina.
Wei Fan
Metabolism, Genes, and Environment Group/NIEHS
Xiaoling Li
Metabolism, Genes, and Environment Group/NIEHS
First Author:
Kathryn Konrad
Social and Scientific Systems, Inc., a DLH Holdings Corp Company, Durham, North Carolina.
Presenting Author:
Abstract Text:
As statistical methods for continuous data progress, there remains a need for applying sophisticated statistical techniques to complex behavioral neuroscience datasets. In an experiment studying the impact of Vitamin K deficiency on sleep following changes in dietary Vitamin K, rodent electroencephalography (EEG) and electromyography (EMG) data were collected using implanted wireless physiological telemetry devices and rodent sleep state scoring was performed. While the data collected are continuous, current analysis approaches typically model averages of responses over time using an analysis of variance (ANOVA) or repeated measures ANOVA model. One approach that leverages the original complexity of the data is functional data analysis (FDA). In this talk, we discuss functional data analysis and its fitness for analyzing a longitudinal dataset, as well as its limitations or when traditional models may remain the preferred approach. We will fit a functional model to our neurological dataset and demonstrate the process for selecting appropriate functional mean and variance structures.
Keywords:
Functional Data Analysis|Neuroscience|Longitudinal Data|Rodent Studies| |
Sponsors:
Section on Statistical Learning and Data Science
Tracks:
Functional Data
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