Abstract
Background Multivariate autoregressive (MAR) models can be used in the identification of causal relations from functional MRI time series. Connectivity information is extracted from large neural networks combining graphical modeling methods and Granger causality. The aim of this paper is to demonstrate the feasibility of working with the MAR models to identify functional circuits in the human motor system, and demonstrates their application to data of motor performance in patients with Parkinson's disease (PD).
Highlights
Sixteenth Annual Computational Neuroscience Meeting: CNS*2007 William R Holmes Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here http://www.biomedcentral.com/content/pdf/1471-2202-8-S2-info.pdf
Multivariate autoregressive (MAR) models can be used in the identification of causal relations from functional MRI time series
The aim of this paper is to demonstrate the feasibility of working with the MAR models to identify functional circuits in the human motor system, and demonstrates their application to data of motor performance in patients with Parkinson's disease (PD)
Summary
Exploring sparse connectivity in the motor system using multivariate autoregression analysis. Rafael Rodriguez-Rojas*1, Mayrim Vega-Hernandez, Agustín Lage, Jose Sanchez, Maylen Carballo, Jorge Bosh and Pedro Valdes-Sosa. Address: 1Brain Images Processing Group, International Center for Neurological Restoration, Havana, Cuba and 2Neurophysics Department, Cuban Neuroscience Center, Havana, Cuba. Sixteenth Annual Computational Neuroscience Meeting: CNS*2007 William R Holmes Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here http://www.biomedcentral.com/content/pdf/1471-2202-8-S2-info.pdf
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