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Characterizing Attention with Predictive Network Models.

M D Rosenberg1, E S Finn2, D Scheinost3

  • 1Department of Psychology, Yale University, New Haven, CT 06520, USA.

Trends in Cognitive Sciences
|February 28, 2017
PubMed
Summary

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This summary is machine-generated.

Brain network functional connectivity models predict attentional abilities, revealing attention as a network property measurable even at rest. This approach may enhance cognitive dysfunction assessment and treatment.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Systems Neuroscience

Background:

  • Functional connectivity in large-scale brain networks offers potential neuromarkers for cognitive function.
  • Previous research suggests brain network properties relate to individual differences in cognition.

Purpose of the Study:

  • To investigate if functional connectivity models can predict attentional abilities.
  • To provide empirical evidence on the network properties of attention.

Main Methods:

  • Utilized models based on functional connectivity within large-scale brain networks.
  • Measured functional architecture during resting state (no explicit task).

Main Results:

  • Functional connectivity models successfully predicted individuals' attentional abilities.
Keywords:
attentionconnectomefMRIfunctional connectivitypredictive modelssustained attention

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  • Attention was demonstrated to be a network property of brain computation.
  • The measured functional architecture supports a general attentional ability.
  • This general attentional ability is impaired in attention deficit hyperactivity disorder (ADHD).

    • Conclusions:

    • Functional connectivity provides generalizable neuromarkers for cognitive function, specifically attention.
    • Brain network architecture at rest underlies general attentional capacity.
    • Connectivity-based models hold promise for improving the assessment, diagnosis, and treatment of clinical dysfunctions like ADHD.