Quantifying information flow in fMRI using the Kullbakc-Leibler divergence
Date
2011
Authors
Seghouane, Abd-Krim
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Publisher
IEEE Signal Processing Society
Abstract
Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using a measure derived from the Kullback-Leibler divergence. A parametric approach based on the autoregressive (AR) and autoregressive exogenous (ARX) modelling is proposed for estimating this measure. The links between the proposed measure and other existing information measures for quantifying the directional interaction between neuronal sites is discussed. The significance and effectiveness of the proposed measure is illustrated on both simulated and real fMRI data sets.
Description
Keywords
Keywords: Auto-regressive; Brain areas; Brain functions; Directional interactions; effective connectivity; fMRI data; Functional magnetic resonance imaging; Functional MRI; Information flows; Information measures; Kullback Leibler divergence; Parametric approach; B effective connectivity; Functional MRI; information flow; Kullback-Leibler divergence
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Source
From Nano to Macro
Type
Conference paper
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2037-12-31