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      Positive affect, surprise, and fatigue are correlates of network flexibility

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          Abstract

          Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. Recent work has shown that flexible reconfiguration of human brain networks over short timescales supports cognitive flexibility and learning. However, modulating network flexibility to enhance learning requires an understanding of an as-yet unknown relationship between flexibility and brain state. Here, we investigate the relationship between network flexibility and affect, leveraging an unprecedented longitudinal data set. We demonstrate that indices associated with positive mood and surprise are both associated with network flexibility – positive mood portends a more flexible brain while increased levels of surprise portend a less flexible brain. In both cases, these relationships are driven predominantly by a subset of brain regions comprising the somatomotor system. Our results simultaneously suggest a network-level mechanism underlying learning deficits in mood disorders as well as a potential target – altering an individual’s mood or task novelty – to improve learning.

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          Most cited references 59

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          Complex brain networks: graph theoretical analysis of structural and functional systems.

          Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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            Development and validation of brief measures of positive and negative affect: the PANAS scales.

            In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.
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              Community detection in graphs

              The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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                Author and article information

                Affiliations
                [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Bioengineering, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Neuropsychiatry Section, Department of Psychiatry, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Neuroscience, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [4 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Electrical and Systems Engineering, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                Contributors
                dsb@seas.upenn.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 March 2017
                31 March 2017
                2017
                : 7
                28364117 5428446 425 10.1038/s41598-017-00425-z
                © The Author(s) 2017

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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