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Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity
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Gorrostieta, Cristina, Fiecas, Mark, Ombao, Hernando, Burke, Erin and Cramer, Steven C. (2013) Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity. Frontiers in Computational Neuroscience, Volume 7 . Article number 159. doi:10.3389/fncom.2013.00159 ISSN 1662-5188.
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Official URL: http://dx.doi.org/10.3389/fncom.2013.00159
Abstract
Vector auto-regressive (VAR) models typically form the basis for
constructing directed graphical models for investigating connectivity in
a brain network with brain regions of interest (ROIs) as nodes. There
are limitations in the standard VAR models. The number of parameters in
the VAR model increases quadratically with the number of ROIs and
linearly with the order of the model and thus due to the large number of
parameters, the model could pose serious estimation problems. Moreover,
when applied to imaging data, the standard VAR model does not account
for variability in the connectivity structure across all subjects. In
this paper, we develop a novel generalization of the VAR model that over
comes these limitations. To deal with the high dimensionality of the
parameter space, we propose a Bayesian hierarchical framework for the
VAR model that will account for both temporal correlation within a
subject and between subject variation. Our approach uses prior
distributions that give rise to estimates that correspond to penalized
least squares criterion with the elastic net penalty. We apply the
proposed model to investigate differences in effective connectivity
during a hand grasp experiment between healthy controls and patients
with residual motor deficit following a stroke.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Autoregression (Statistics), Magnetic resonance imaging, Multivariate analysis, Time-series analysis | ||||
Journal or Publication Title: | Frontiers in Computational Neuroscience | ||||
Publisher: | Frontiers Research Foundation | ||||
ISSN: | 1662-5188 | ||||
Official Date: | 12 November 2013 | ||||
Dates: |
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Volume: | Volume 7 | ||||
Number of Pages: | 11 | ||||
Page Range: | Article number 159 | ||||
DOI: | 10.3389/fncom.2013.00159 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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