Functional Data Analysis for Rodent Sleep Data

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.
Leslie Wilson  
Neurobehavioral Core Laboratory/NIEHS
Wei Fan  
Metabolism, Genes, and Environment Group/NIEHS
Jesse Cushman  
Neurobehavioral Core Laboratory/NIEHS
Xiaoling Li  
Metabolism, Genes, and Environment Group/NIEHS
Leping Li  
Biostatistics Branch/NIEHS
Helen Cunny  
Division of Translational Toxicology/NIEHS
Keith Shockley  
Biostatistics Branch/NIEHS

First Author:

Kathryn Konrad  
Social and Scientific Systems, Inc., a DLH Holdings Corp Company, Durham, North Carolina.

Presenting Author:

Kathryn Konrad  
DLH

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

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand