Change Point Detection In Fmri Functional Connectivity Networks

• Activity Number: 518
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Lucy Robinson*+ and Lauren Atlas and Carey Priebe
• Companies: The Johns Hopkins University and Columbia University and The Johns Hopkins University
• Keywords: fMRI ; networks ; change point ; neuroscience

Abstract

We propose a new technique for detecting time-dependent structure in networks of brain regions. Functional connectivity networks are derived from fMRI data, using partial coherence to describe the degree of connection between spatially disjoint locations in the brain. Existing techniques for functional connectivity networks typically assume the network is static over time. Our goal is to determine whether brain network topology remains stationary, or if subsets of the network exhibit changes over unknown time intervals. Changes in network structure may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental stimulus. Our approach can detect localized changes, allowing us to infer which nodes in a network exhibit altered connectivity behavior. Using spectral clustering, we define a time-dependent block structure and a test for temporal homogeneity based on a modified likelihood ratio. Data from an experiment studying mediation of pain response by expectancy of relief are analyzed.