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


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          We describe an overarching organization of large-scale connectivity that situates the default-mode network at the opposite end of a spectrum from primary sensory and motor regions. This topography, based on the differentiation of connectivity patterns, is also embedded in the spatial distance along the cortical surface between these respective systems. In addition, this connectivity gradient accounts for the respective positions of canonical networks and captures a functional spectrum from perception and action to more abstract cognitive functions. These results suggest that the default-mode network consists of regions at the top of a representational hierarchy that describe the current cognitive landscape in the most abstract terms.


          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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          From sensation to cognition.

          M. Mesulam (1998)
          Sensory information undergoes extensive associative elaboration and attentional modulation as it becomes incorporated into the texture of cognition. This process occurs along a core synaptic hierarchy which includes the primary sensory, upstream unimodal, downstream unimodal, heteromodal, paralimbic and limbic zones of the cerebral cortex. Connections from one zone to another are reciprocal and allow higher synaptic levels to exert a feedback (top-down) influence upon earlier levels of processing. Each cortical area provides a nexus for the convergence of afferents and divergence of efferents. The resultant synaptic organization supports parallel as well as serial processing, and allows each sensory event to initiate multiple cognitive and behavioural outcomes. Upstream sectors of unimodal association areas encode basic features of sensation such as colour, motion, form and pitch. More complex contents of sensory experience such as objects, faces, word-forms, spatial locations and sound sequences become encoded within downstream sectors of unimodal areas by groups of coarsely tuned neurons. The highest synaptic levels of sensory-fugal processing are occupied by heteromodal, paralimbic and limbic cortices, collectively known as transmodal areas. The unique role of these areas is to bind multiple unimodal and other transmodal areas into distributed but integrated multimodal representations. Transmodal areas in the midtemporal cortex, Wernicke's area, the hippocampal-entorhinal complex and the posterior parietal cortex provide critical gateways for transforming perception into recognition, word-forms into meaning, scenes and events into experiences, and spatial locations into targets for exploration. All cognitive processes arise from analogous associative transformations of similar sets of sensory inputs. The differences in the resultant cognitive operation are determined by the anatomical and physiological properties of the transmodal node that acts as the critical gateway for the dominant transformation. Interconnected sets of transmodal nodes provide anatomical and computational epicentres for large-scale neurocognitive networks. In keeping with the principles of selectively distributed processing, each epicentre of a large-scale network displays a relative specialization for a specific behavioural component of its principal neurospychological domain. The destruction of transmodal epicentres causes global impairments such as multimodal anomia, neglect and amnesia, whereas their selective disconnection from relevant unimodal areas elicits modality-specific impairments such as prosopagnosia, pure word blindness and category-specific anomias. The human brain contains at least five anatomically distinct networks. The network for spatial awareness is based on transmodal epicentres in the posterior parietal cortex and the frontal eye fields; the language network on epicentres in Wernicke's and Broca's areas; the explicit memory/emotion network on epicentres in the hippocampal-entorhinal complex and the amygdala; the face-object recognition network on epicentres in the midtemporal and temporopolar cortices; and the working memory-executive function network on epicentres in the lateral prefrontal cortex and perhaps the posterior parietal cortex. Individual sensory modalities give rise to streams of processing directed to transmodal nodes belonging to each of these networks. The fidelity of sensory channels is actively protected through approximately four synaptic levels of sensory-fugal processing. The modality-specific cortices at these four synaptic levels encode the most veridical representations of experience. Attentional, motivational and emotional modulations, including those related to working memory, novelty-seeking and mental imagery, become increasingly more pronounced within downstream components of unimodal areas, where they help to create a highly edited subjective version of the world. (ABSTRACT TRUNCATED)
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            Is Open Access

            A Tutorial on Spectral Clustering

            In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.
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              Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition.

              Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed 'frontoparietal control network' might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by analyzing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Task-related functional connectivity analyses demonstrate that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes. Copyright 2010 Elsevier Inc. All rights reserved.

                Author and article information

                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                1 November 2016
                18 October 2016
                18 October 2016
                : 113
                : 44
                : 12574-12579
                [1] aMax Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig 04103, Germany;
                [2] bMcGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, MA 02139;
                [3] cDepartment of Otolaryngology, Harvard Medical School , Cambridge, MA 02115;
                [4] dDepartment of Computational Neuroscience, University Medical Center Hamburg-Eppendorf , Hamburg 20246, Germany;
                [5] eNeurocomputation and Neuroimaging Unit, Department of Education and Psychology, Free University of Berlin , Berlin 14195, Germany;
                [6] fDepartment of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna , Vienna A-1090, Austria;
                [7] gComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology , Cambridge, MA 02139;
                [8] hMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University , Montreal, QC, Canada H3A 2B4;
                [9] iInstitute for Neuroscience and Medicine, Research Center Jülich , Juelich 52428, Germany;
                [10] jInstitute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University , Duesseldorf 40225, Germany;
                [11] kChild Study Center, Department of Child and Adolescent Psychiatry, New York University Langone Medical Center , New York, NY 10016;
                [12] l Nathan Kline Institute for Psychiatric Research , Orangeburg, NY 10962;
                [13] mCognitive Neuroscience Unit, Montreal Neurological Institute, McGill University , Montreal, QC, Canada H3A 2B4;
                [14] nDepartment of Psychology, University of York , York YO10 5DD, United Kingdom;
                [15] oYork Neuroimaging Centre, University of York , York YO10 5DD, United Kingdom
                Author notes
                1To whom correspondence should be addressed. Email: margulies@ 123456cbs.mpg.de .

                Edited by Peter L. Strick, University of Pittsburgh, Pittsburgh, PA, and approved September 9, 2016 (received for review May 27, 2016)

                Author contributions: D.S.M., M.P., E.J., and J.S. designed research; D.S.M. performed research; D.S.M., S.S.G., M.F., J.M.H., G.L., G.B., and S.B.E. contributed new reagents/analytic tools; D.S.M. analyzed data; and D.S.M., S.S.G., A.G., M.F., J.M.H., G.L., G.B., S.B.E., F.X.C., M.P., E.J., and J.S. wrote the paper.

                Author information
                PMC5098630 PMC5098630 5098630 201608282

                Freely available online through the PNAS open access option.

                Page count
                Pages: 6
                Funded by: EC | European Research Council (ERC) 501100000781
                Award ID: 283530-SEMBIND
                Funded by: EC | European Research Council (ERC) 501100000781
                Award ID: WANDERINGMINDS - 646927
                Funded by: John Templeton Foundation 100000925
                Award ID: Prospective Psychology Stage 2: A Research Competition
                Funded by: Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de recherche en santé du Canada) 501100000024
                Award ID: FDN-143212
                Funded by: NIH
                Award ID: 1R01EB020740-01A1
                Funded by: NIH
                Award ID: 1P41EB019936-01A1
                Funded by: NIH
                Award ID: 3R01MH092380-04S2
                Funded by: NIH
                Award ID: 1U01MH108168-01
                Biological Sciences
                From the Cover

                gradients,topography,connectivity,cortical organization,default-mode network


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