A brain-based general measure of attention

Area of Science:

• Cognitive neuroscience
• Neuroimaging
• Brain networks

Background:

• Attention is crucial for cognition, yet no single neural measure captures overall attentional function across diverse tasks.
• Existing methods lack a unified approach to assess broad attentional capabilities.
• Understanding the neural basis of general attention is vital for cognitive science and clinical applications.

Purpose of the Study:

• To develop and validate a whole-brain modeling approach for predicting individual attentional profiles.
• To identify neural correlates of a general attention factor across different cognitive tasks.
• To create a standardized, generalizable measure of attentional functioning.

Main Methods:

• Functional magnetic resonance imaging (fMRI) data from 92 participants across three attention-demanding tasks.
• Construction of whole-brain models to predict sustained attention, divided attention/tracking, and working memory capacity.
• Application of connectome-to-connectome transformation modeling using resting-state fMRI.
• Validation across four independent datasets (N=495) incorporating diverse attentional measures.

Main Results:

• Whole-brain models accurately predicted individual attentional profiles.
• Salience, subcortical, and frontoparietal networks were key predictors, supporting a general attention factor.
• Connectome transformation from resting-state fMRI significantly enhanced predictive accuracy.
• The combined model demonstrated superior generalization across independent datasets.

Conclusions:

• A novel, standardized measure of general attention was developed, integrating brain connectivity and network activity.
• The findings support a common neural basis for attention that transcends specific tasks.
• This approach holds significant potential for advancing cognitive research and clinical assessment of attentional deficits.