Characterizing Attention with Predictive Network Models. Issue 4 (April 2017)

• Record Type: Journal Article
• Authors: Rosenberg, M.D.; Finn, E.S.; Scheinost, D.; Constable, R.T.; Chun, M.M.
• Is Part Of: Trends in cognitive sciences. Volume 21:Issue 4(2017)
• Page Start: 290
• Page End: 302
• Publication Date: 2017-04
• Subjects: attention -- sustained attention -- fMRI -- functional connectivity -- connectome -- predictive models; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2017.01.011
• Languages: English
• ISSNs: 1364-6613

Abstract

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. Trends: Valuable research has described the attention system of the human brain using mostly group-level analyses of neuroimaging data. fMRI research is moving towards single-subject-level analyses, which afford significant scientific and practical benefits such as personalized assessment, diagnosis, or prediction. Recent work shows that models based on functional brain networks can predict how well individual people pay attention. Predictive models provide empirical evidence that attention is a network property of the brain and that the functional architecture that underlies attention can be measured while people are not engaged in any explicit task. Looking ahead, connectivity-based predictive models of attention and other cognitive abilities may improve the assessment, diagnosis, and treatment of clinical dysfunction.