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      Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity

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          Abstract

          We used Magnetoencephalography (MEG) in combination with Representational Similarity Analysis to probe neural activity associated with distinct, item-specific lexico-semantic predictions during language comprehension. MEG activity was measured as participants read highly constraining sentences in which the final words could be predicted. Before the onset of the predicted words, both the spatial and temporal patterns of brain activity were more similar when the same words were predicted than when different words were predicted. The temporal patterns localized to the left inferior and medial temporal lobe. These findings provide evidence that unique spatial and temporal patterns of neural activity are associated with item-specific lexico-semantic predictions. We suggest that the unique spatial patterns reflected the prediction of spatially distributed semantic features associated with the predicted word, and that the left inferior/medial temporal lobe played a role in temporally ‘binding’ these features, giving rise to unique lexico-semantic predictions.

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          Most cited references56

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          Distributed and overlapping representations of faces and objects in ventral temporal cortex.

          The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
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            The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors.

            The equation for the magnetic lead field for a given magnetoencephalography (MEG) channel is well known for arbitrary frequencies omega but is not directly applicable to MEG in the quasi-static approximation. In this paper we derive an equation for omega = 0 starting from the very definition of the lead field instead of using Helmholtz's reciprocity theorems. The results are (a) the transpose of the conductivity times the lead field is divergence-free, and (b) the lead field differs from the one in any other volume conductor by a gradient of a scalar function. Consequently, for a piecewise homogeneous and isotropic volume conductor, the lead field is always tangential at the outermost surface. Based on this theoretical result, we formulated a simple and fast method for the MEG forward calculation for one shell of arbitrary shape: we correct the corresponding lead field for a spherical volume conductor by a superposition of basis functions, gradients of harmonic functions constructed here from spherical harmonics, with coefficients fitted to the boundary conditions. The algorithm was tested for a prolate spheroid of realistic shape for which the analytical solution is known. For high order in the expansion, we found the solutions to be essentially exact and for reasonable accuracies much fewer multiplications are needed than in typical implementations of the boundary element methods. The generalization to more shells is straightforward.
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              A Toolbox for Representational Similarity Analysis

              Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                21 December 2018
                2018
                : 7
                : e39061
                Affiliations
                [1 ]deptDepartment of Psychiatry Harvard Medical School BostonUnited States
                [2 ]deptAthinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownUnited States
                [3 ]deptDepartment of Psychology Tufts University MedfordUnited States
                [4 ]deptSchool of Psychology Centre for Human Brain Health, University of Birmingham BirminghamUnited Kingdom
                University of Cambridge United Kingdom
                University of Pennsylvania United States
                University of Cambridge United Kingdom
                Author information
                http://orcid.org/0000-0001-6911-0660
                http://orcid.org/0000-0001-6093-7872
                http://orcid.org/0000-0001-8193-8348
                Article
                39061
                10.7554/eLife.39061
                6322859
                30575521
                c1bd9881-825d-4839-b509-0ee548537a37
                © 2018, Wang et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 14 June 2018
                : 20 December 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31540079
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China;
                Award ID: 2012CB825500
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China;
                Award ID: 2015CB351701
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: R01 HD08252
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000913, James S. McDonnell Foundation;
                Award ID: Understanding Human Cognition Collaborative Award: 220020448
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: Investigator Award in Science: 207550
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000288, Royal Society;
                Award ID: Wolfson Research Merit
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                The prediction of specific words is associated with distinct spatial and temporal patterns of neural activity within the left inferior and medial temporal regions before the predicted word is presented.

                Life sciences
                language,prediction,meg,inferior temporal,spatial pattern,temporal pattern,human
                Life sciences
                language, prediction, meg, inferior temporal, spatial pattern, temporal pattern, human

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