Dynamic Directional Model for Effective Brain Connectivity Using Electrocorticographic (ECoG) Time Series

• Activity Number: 347
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Jingwei Wu*+ and Tingting Zhang and Fan Li and Dana Boatman and Brian Scott Caffo
• Companies: University of Virginia and University of Virginia and Duke University and Johns Hopkins University and Johns Hopkins University
• Keywords: brain imaging ; ordinary differential equation (ODE) ; dynamic system ; effective connectivity

Abstract

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on Electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components, and observation equations linking the underlying neuronal states to observed data. The highly localized property and high temporal resolution of the ECoG data result in a much simpler DDM, allowing us to investigate connections within a large brain system with many regions while many existing methods cannot. To identify functionally-segregated sub-networks we propose a modified Potts model within the general DDM. We represent the neuronal states of brain components by b-spline bases and estimate the parameters by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. An L1 penalty is also considered for comparison. We apply the proposed methods to analyze an ECoG data set measured from patients with intractable epilepsy.