TY - JOUR
T1 - A Dynamic Directional Model for Effective Brain Connectivity Using Electrocorticographic (ECoG) Time Series
AU - Zhang, Tingting
AU - Wu, Jingwei
AU - Li, Fan
AU - Caffo, Brian
AU - Boatman-Reich, Dana
N1 - Publisher Copyright:
© 2015, American Statistical Association.
PY - 2015/1/2
Y1 - 2015/1/2
N2 - 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 subnetworks, 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.
AB - 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 subnetworks, 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.
KW - Brain mapping
KW - Dynamic system
KW - Effective connectivity
KW - Ordinary differential equation (ODE)
KW - Potts model
UR - http://www.scopus.com/inward/record.url?scp=84928264308&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928264308&partnerID=8YFLogxK
U2 - 10.1080/01621459.2014.988213
DO - 10.1080/01621459.2014.988213
M3 - Article
C2 - 25983358
AN - SCOPUS:84928264308
SN - 0162-1459
VL - 110
SP - 93
EP - 106
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 509
ER -