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      Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise

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          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.

          Author Summary

          We show that in cortical areas such as the insular, orbitofrontal, and lateral prefrontal cortex, the variation of the blood-oxygen level dependent (BOLD) time series across trials measured with functional magnetic resonance imaging (fMRI) increases with the magnitude of the signal. We describe a new method of measuring causal effects with Granger causality that takes into account this signal-dependent noise. We show in a functional neuroimaging investigation with the new method that there is a causal influence from the anterior lateral prefrontal cortex that during attention to the pleasantness of taste stimuli increases the response of the orbitofrontal cortex to the taste; and there is a causal influence from the posterior lateral prefrontal cortex to the insular taste cortex during attention to the intensity of taste stimuli. This shows how part of the circuitry involved in the effects of selective attention on the pleasantness and intensity of stimuli operates in the brain.

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          Most cited references55

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          Dynamic causal modelling.

          In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
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            Partial directed coherence: a new concept in neural structure determination.

            This paper introduces a new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to reflect a frequency-domain representation of the concept of Granger causality.
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              Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space.

              In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2013
                October 2013
                24 October 2013
                : 9
                : 10
                : e1003265
                Affiliations
                [1 ]Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, PR China
                [2 ]College of Information Systems and Management, National University of Defense Technology, Hunan, PR China
                [3 ]Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, PR China
                [4 ]Department of Computer Science, University of Warwick, Coventry, United Kingdom
                [5 ]Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
                [6 ]Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: QL TG FG JF ETR. Performed the experiments: QL TG FG ETR. Analyzed the data: QL TG FG ETR. Contributed reagents/materials/analysis tools: QL TG FG JF ETR. Wrote the paper: QL TG FG JF ETR.

                Article
                PCOMPBIOL-D-13-00120
                10.1371/journal.pcbi.1003265
                3808464
                24204221
                1f946259-84d2-4f41-b9da-aa30327c4eb0
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 January 2013
                : 20 August 2013
                Page count
                Pages: 15
                Funding
                QL is partly supported by grants from the National Natural Sciences Foundation of China (No. 11101429, No. 11271121, No. 71171195), Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20114307120019), and National Basic Research Program of China (No. 2011CB707802). FG was supported by the Gottlieb-Daimler- and Karl Benz-Foundation. JF is a Royal Society Wolfson Research Merit Award holder, partially supported by the National Centre for Mathematics and Interdisciplinary Sciences (NCMIS) of the Chinese Academy of Sciences and Key Program of National Natural Science Foundation of China (No. 91230201). The fMRI investigation was supported by the McDonnell Centre for Cognitive Neuroscience at the University of Oxford, and was performed at the Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) at the University of Oxford. Support was also received from the Oxford Centre for Computational Neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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