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      A neuromarker of sustained attention from whole-brain functional connectivity

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          Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person’s overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

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          Most cited references 35

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            Reasoning ability is (little more than) working-memory capacity?!

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              Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

              In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. Copyright © 2013 Elsevier Inc. All rights reserved.

                Author and article information

                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                31 October 2015
                23 November 2015
                January 2016
                01 July 2016
                : 19
                : 1
                : 165-171
                [1 ]Department of Psychology, Yale University, New Haven, CT 06520
                [2 ]Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520 USA
                [3 ]Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520 USA
                [4 ]Department of Biomedical Engineering, Yale University, New Haven, CT 06520 USA
                [5 ]Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520 USA
                [6 ]Department of Neurobiology, Yale University, New Haven, CT 06520 USA
                Author notes
                Correspondence should be addressed to monica.rosenberg@ 123456yale.edu

                These authors contributed equally to this work.


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