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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
Summary
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.
- 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.
