Review
Characterizing Attention with Predictive Network Models

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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.

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.

Section snippets

What Is Attention and How Do We Measure It?

Perhaps no cognitive capacity is more crucial to navigating daily life than the ability to pay attention. Although we all know what it feels like to pay attention, the concept is notoriously difficult to define. More than a century ago in what has perhaps become one of the most oft-quoted lines in psychology, William James explained attention as ‘the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought’ [1].

Attention: From Brain Areas to Brain Networks

It is hard to imagine meeting the demands of daily life without the ability to focus. Impairments in attention, which are common to clinical populations as diverse as ADHD [4], depression [5], schizophrenia [6], bipolar disorder [7], post-traumatic stress disorder [8], and traumatic brain injury [9], predict a wide range of negative outcomes, from poorer educational achievement to worse employment and job performance, peer acceptance, and family relationships [10].

Although the ability to attend

New Insights from Network Neuroscience and Predictive Modeling

A central question of cognitive neuroscience is how the brain gives rise to the mind and behavior. Some of the earliest evidence in humans came from neuropsychological studies of patients with brain damage, which found that certain lesions were associated with stereotyped deficits such as spatial neglect [53]. Neuroimaging and electrophysiological studies have also identified process-specific brain regions such as the frontal eye fields, which are responsible for covert and overt shifts of

Concluding Remarks

Models that predict attention from patterns of functional brain connectivity, some of the first generalizable neuromarkers of cognitive function, represent a significant contribution to the fast-growing field of individual differences in fMRI 86, 87. In doing so they provide empirical support for a central prediction of network neuroscience: that attention and cognition arise from the dynamic interactions of many distinct regions of the brain 60, 61, 62. Further, successful predictions from

Glossary

Correlational versus predictive studies
fMRI studies of individual differences often claim that a brain-based measure ‘predicts’ a behavioral measure when the two are simply correlated across individuals. Following Gabrieli et al., we reserve the term ‘prediction’ for cross-validated models; that is, models that generalize to novel individuals [86]. Although it is beyond the scope of this article, another sense in which models can be predictive is that they use baseline data from an individual

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