Predicting attention across time and contexts with functional brain connectivity. (August 2021)

• Record Type: Journal Article
• Authors: Song, Hayoung; Rosenberg, Monica D
• Is Part Of: Current opinion in behavioral sciences. Volume 40(2021)
• Page Start: 33
• Page End: 44
• Publication Date: 2021-08
• Subjects: Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.cobeha.2020.12.007
• Languages: English
• ISSNs: 2352-1546

Abstract

Highlights: Functional connectivity-based models predict individual differences in attentional abilities. Time-varying functional connectivity predicts changes in attentional states during controlled and naturalistic tasks. Predictive modeling with multi-session neuroimaging can disentangle state-like from trait-like variance in functional connectivity. Abstract : The ability to sustain attention differs across people and varies over time within a person. Models based on patterns of static functional brain connectivity observed during task performance and rest show promise for predicting individual differences in sustained attention as well as other forms of attention. The sensitivity of connectome-based models to attentional state changes, however, is less well characterized. Here, we review recent evidence that time-varying functional brain connectivity predicts fluctuations in attention in controlled and naturalistic task contexts. We propose that building connectome-based models to predict changes in attention across multiple timescales and experimental contexts can help further disentangle state versus trait influences on functional connectivity patterns, elucidate the behavioral relevance of functional connectivity dynamics, and contribute to the development of a comprehensive suite of generalizable neuromarkers of attention. To achieve this goal, we suggest collecting multi-task, multi-session neuroimaging samples with concurrent behavioral and physiological measures of attentional state.