title: Characterizing Attention with Predictive Network Models.
abstract: Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.
authors.affiliation: Department of Psychology, Yale University, New Haven, CT 06520, USA.
authors.forename: M D
authors.initials: MD
authors.lastname: Rosenberg
authors.affiliation: Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
authors.forename: E S
authors.initials: ES
authors.lastname: Finn
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
authors.forename: D
authors.initials: D
authors.lastname: Scheinost
authors.affiliation: Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
authors.forename: R T
authors.initials: RT
authors.lastname: Constable
authors.affiliation: Department of Psychology, Yale University, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. Electronic address: marvin.chun@yale.edu.
authors.forename: M M
authors.initials: MM
authors.lastname: Chun
mesh_terms: D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology; D063132:Connectome; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology
keywords: attention; connectome; fMRI; functional connectivity; predictive models; sustained attention