Abstract Attention is central for many aspects of cognitive performance, but there is no singular measure of a person’s overall attentional functioning across tasks. To develop a universal measure that integrates multiple components of attention, we collected data from more than 90 participants performing three different attention-demanding tasks during fMRI. We constructed a suite of whole-brain models that can predict a profile of multiple attentional components – sustained attention, divided attention and tracking, and working memory capacity – from a single fMRI scan type within novel individuals. Multiple brain regions across the frontoparietal, salience, and subcortical networks drive accurate predictions, supporting a universal (general) attention factor across tasks, which can be distinguished from task-specific attention factors and their neural mechanisms. Furthermore, connectome-to-connectome transformation modeling enhanced predictions of an individual’s attention-task connectomes and behavioral performance from their rest connectomes. These models were integrated to produce a new universal attention measure that generalizes best across multiple, independent datasets, and which should have broad utility for both research and clinical applications.

A brain-based universal measure of attention: predicting task-general and task-specific attention performance and their underlying neural mechanisms from task and resting state fMRI