title: A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.
abstract: In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. mutluali@msu.edu
authors.forename: Ali Yener
authors.initials: AY
authors.lastname: Mutlu
authors.forename: Edward
authors.initials: E
authors.lastname: Bernat
authors.forename: Selin
authors.initials: S
authors.lastname: Aviyente
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D015225:Magnetoencephalography / Q000379:methods; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors