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      Interpreting temporal fluctuations in resting-state functional connectivity MRI

      , , , ,
      NeuroImage
      Elsevier BV

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          Generalized autoregressive conditional heteroskedasticity

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            Situating the default-mode network along a principal gradient of macroscale cortical organization.

            Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.
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              Testing for nonlinearity in time series: the method of surrogate data

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                Author and article information

                Journal
                NeuroImage
                NeuroImage
                Elsevier BV
                10538119
                September 2017
                September 2017
                :
                :
                Article
                10.1016/j.neuroimage.2017.09.012
                28916180
                32c34c2c-8c34-45c5-b852-c16f3a59cb34
                © 2017

                http://www.elsevier.com/tdm/userlicense/1.0/

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