Dynamic Network Analysis in Resting-State fMRI for Alzheimer's Disease

• Activity Number: 249
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Heather Shappell* and Yorghos Tripodis and Ronald J. Killiany and Eric Kolaczyk
• Companies: and Boston University and Boston University and Boston University
• Keywords: Stochastic Actor-Oriented Models ; functional connectivity ; functional networks ; neuroscience ; resting-state fMRI ; Alzheimer's Disease

Abstract

Stochastic actor-oriented models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time markov chain framework. Developed in the social network setting, these models allow for the testing of hypotheses through the estimation of parameters expressing possible influences on social network changes. We extend the application of these models to the resting-state fMRI functional network setting and demonstrate their use with data from the ongoing, longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) Study. We draw from the neuroscience literature to construct hypotheses, matching these hypotheses to specific effects that we specify in the models, and we conduct a comparison between a subset of healthy and diseased subjects drawn from the repository of ADNI participants. Our findings demonstrate the usefulness of these models in the neuroscience setting as a way of testing differential effects across brain regions of interest (ROIs) while controlling for ROI specific covariates, such as lobe and hemisphere, as well as endogenous effects, such as triangle formation.