Interior_Banner_Events

Graph Theoretical Analysis of the Default Mode Network in Adults with Mild Cognitive Impairment and Alzheimer's Disease: An fMRI Project

Carter Jenkins and Dr. Amy Overman, Department of Psychology, Elon University, 100 Campus Drive, Elon NC 27244

The default mode network (DMN) has been linked to memory decline in individuals with Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD), as brain regions that show reduced functional activity in the DMN are also responsible for memory decline in dementias such as AD. Additionally, AD patients generally show lower activity in the DMN compared to age-matched controls. However, it is still unknown exactly how AD preferentially targets regions of the DMN. Univariate analyses have dominated the field in the past, resulting in a focus on individual brain regions, rather than relationships between brain regions. More robust multivariate analyses and larger sample sizes are needed to understand patterns of brain activity. This study addresses this gap by conducting a large-scale multivariate analysis based on graph theory, which allows activation in brain regions to be parcellated into nodes that contribute to a larger network. This study uses structural and functional neuroimaging data from the OASIS3 dataset and includes scans from 104 healthy controls and 104 MCI and AD patients. Eighty-seven grey matter regions of interest were defined for a large network analysis; additionally, two sub-networks were established based on previous work. Graph theoretical analysis was conducted using the Brain Connectivity Toolbox (BCT) in MATLAB. No differences were seen for the large network analysis; however, some significant differences were seen at the sub-network scale. Specifically, Cluster 2 showed changes that suggest a decrease in small worldness. This suggests that changes between groups at the large network level may be undetectable. Additionally, AD may target Cluster 2 first, resulting in compensation by regions in Cluster 1. Further analysis may reveal statistical differences in structural connectivity between healthy and impaired networks that could be a potential biomarker for AD diagnosis.




Additional Abstract Information

Presenter: Carter Jenkins

Institution: Elon University

Type: Poster

Subject: Psychology

Status: Approved


Time and Location

Session: Poster 10
Date/Time: Wed 1:30pm-2:30pm
Session Number: 6646