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Characterizing Attention with Predictive Network Models
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DOI：10.1016/j.tics.2017.01.011.png)
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Abstract
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Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. As 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.
Keywords:
SHORT-TERM-MEMORY
DEFAULT NETWORK
INDIVIDUAL-DIFFERENCES
SUSTAINED ATTENTION
FUNCTIONAL CONNECTIVITY
BRAIN NETWORKS
DEFICIT/HYPERACTIVITY DISORDER
SELECTIVE ATTENTION
VISUAL-ATTENTION
WANDERING MINDS
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