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roebroeck11.htm
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roebroeck11.htm
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<div class=Section1>
<p class=MsoNormal style='line-height:normal'><b><span lang=EN-US
style='font-size:18.0pt;font-family:"Times New Roman","serif"'>Causal Time
Series Analysis of Functional Magnetic Resonance Imaging Data.</span></b></p>
<p class=MsoNormal style='line-height:normal'><b><i><span lang=EN-US
style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Alard Roebroeck,
Anil K. Seth, Pedro Valdes-Sosa</span></i></b><span lang=EN-US
style='font-size:12.0pt;font-family:"Times New Roman","serif"'>; JMLR W&CP
12:65-94, 2011.</span></p>
<p class=MsoNormal style='line-height:normal'><b><span lang=EN-US
style='font-size:13.5pt;font-family:"Times New Roman","serif"'>Abstract</span></b></p>
<p class=MsoNormal><span lang=EN-US style='font-size:12.0pt;line-height:115%;
font-family:"Times New Roman","serif"'>This review focuses on dynamic causal
analysis of functional magnetic resonance (fMRI) data to infer brain
connectivity from a time series analysis and dynamical systems perspective.
Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS)
tradition and dynamical systems are treated in a state space modeling
framework. The nature of the fMRI signal is reviewed with emphasis on the
involved neuronal, physiological and physical processes and their modeling as
dynamical systems. In this context, two streams of development in modeling
causal brain connectivity using fMRI are discussed: time series approaches to
causality in a discrete time tradition and dynamic systems and control theory
approaches in a continuous time tradition. This review closes with discussion
of ongoing work and future perspectives on the integration of the two
approaches.</span></p>
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