Distractor suppression, the ability to filter and ignore task-irrelevant information, is critical for efficient task performance. While successful distractor suppression relies on a balance of activity in neural networks responsible for attention maintenance (dorsal attention network; DAN), reorientation (ventral attention network; VAN), and internal thought (default mode network, DMN), the degree to which intrinsic connectivity within and between these networks contributes to individual differences in distractor suppression ability is not well-characterized. For the purposes of understanding these interactions, the current study collected resting-state fMRI data from 32 Veterans and, several months later (7±5 months apart), performance on the additional singleton paradigm, a measure of distractor suppression. Using multivariate support vector regression models composed of resting state connectivity between regions of the DAN, VAN, and DMN, and a leave-one-subject-out cross-validation procedure, we were able to predict an individual's task performance, yielding a significant correlation between the actual and predicted distractor suppression (r=0.48, p=0.0053). Network-level analyses revealed that greater within-network DMN connectivity was predictive of better distractor suppression, while greater connectivity between the DMN and attention networks was predictive of poorer distractor suppression. The strongest connection hubs were determined to be the right frontal eye field and temporoparietal junction of the DAN and VAN, respectively, and medial (ventromedial prefrontal and posterior cingulate cortices) and bilateral prefrontal regions of the DMN. These results are amongst a small but growing number of studies demonstrating that resting state connectivity is related to stable individual differences in cognitive ability, and suggest that greater integrity and independence of the DMN is related to better attentional ability.
Keywords: Attention; Distraction; Functional connectivity; Resting-State fMRI; Support vector regression.
Published by Elsevier Ltd.