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Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise
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Ge, Tian, Luo, Qiang, Grabenhorst, Fabian, Feng, Jianfeng and Rolls, Edmund T. (2013) Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise. PLoS Computational Biology, 9 (10). . e1003265. doi:10.1371/journal.pcbi.1003265
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WRAP_Luo_journal.pcbi.1003265.pdf - Published Version Available under License Creative Commons Attribution. Download (1819Kb) | Preview |
Official URL: http://dx.doi.org/10.1371/journal.pcbi.1003265
Abstract
We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention.
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