title: Detecting effective connectivity in networks of coupled neuronal oscillators.
abstract: The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.
authors: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. erinrt@gmail.com
authors: Erin R
authors: ER
authors: Boykin
authors: Pramod P
authors: PP
authors: Khargonekar
authors: Paul R
authors: PR
authors: Carney
authors: William O
authors: WO
authors: Ogle
authors: Sachin S
authors: SS
authors: Talathi
mesh_terms: D000200:Action Potentials; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D003663:Decision Trees; D016002:Discriminant Analysis; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D012680:Sensitivity and Specificity; D013569:Synapses / Q000502:physiology; D013997:Time Factors