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.
Type
Journal articleSubject
Potts modelbrain mapping
dynamic system
effective connectivity
ordinary differential equation (ODE)
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https://hdl.handle.net/10161/10304Published Version (Please cite this version)
10.1080/01621459.2014.988213Publication Info
Zhang, Tingting; Wu, Jingwei; Li, Fan; Caffo, Brian; & Boatman-Reich, Dana (2015). A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic
(ECoG) Time Series. J Am Stat Assoc, 110(509). pp. 93-106. 10.1080/01621459.2014.988213. Retrieved from https://hdl.handle.net/10161/10304.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Fan Li
Professor of Statistical Science

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