title: A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.
abstract: We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated 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. The methods are applied to an auditory ECoG dataset.
authors.affiliation: Department of Statistics, University of Virginia, Charlottesville, VA, USA.
authors.forename: Tingting
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Statistics, University of Virginia, Charlottesville, VA, USA.
authors.forename: Jingwei
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Statistical Science, Duke University, Durham, NC, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Caffo
authors.affiliation: Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Dana
authors.initials: D
authors.lastname: Boatman-Reich
keywords: Potts model; brain mapping; dynamic system; effective connectivity; ordinary differential equation (ODE)