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      Activity flow over resting-state networks shapes cognitive task activations

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          Abstract

          Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

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          Most cited references37

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          Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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            Neurons with graded response have collective computational properties like those of two-state neurons.

            J Hopfield (1984)
            A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons. The content- addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.
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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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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                10 September 2016
                10 October 2016
                December 2016
                10 April 2017
                : 19
                : 12
                : 1718-1726
                Affiliations
                [1 ]Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA, 07102
                [2 ]Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA, 19104
                [3 ]Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA, 19104
                Author notes
                Corresponding author: Michael W. Cole, Center for Molecular and Behavioral Neuroscience, 197 University Ave, Suite 212, Newark, NJ 07102, 973-353-3249, mwcole@ 123456mwcole.net
                Article
                NIHMS815430
                10.1038/nn.4406
                5127712
                27723746
                8612cee6-89b8-4d30-9c56-a68d00218ea0

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                Neurosciences
                connectivity,networks,resting state,functional connectivity,task-evoked activations,neuroimaging

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