Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2020.116535
Title: Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?
Authors: Wu, E.X.W. 
Liaw, G.J. 
Goh, R.Z.
Chia, T.T.Y.
Chee, A.M.J.
Obana, T. 
Rosenberg, M.D.
Yeo, B.T.T. 
Asplund, C.L. 
Keywords: Attentional blink
Connection-based predictive modeling
Diffuse attention
Functional architecture
Selective attention
Sustained attention
Issue Date: 2020
Publisher: Academic Press Inc.
Citation: Wu, E.X.W., Liaw, G.J., Goh, R.Z., Chia, T.T.Y., Chee, A.M.J., Obana, T., Rosenberg, M.D., Yeo, B.T.T., Asplund, C.L. (2020). Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?. NeuroImage 209 : 116535. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2020.116535
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brain's attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectome-based Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (n = 73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the network's negative edges predicting better VAB performance. The vabCPM's positive edges partially yet significantly overlapped with the saCPM's negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salience/ventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individual's propensity to deploy attention in a more diffuse or a more focused manner. © 2020 The Authors
Source Title: NeuroImage
URI: https://scholarbank.nus.edu.sg/handle/10635/198613
ISSN: 10538119
DOI: 10.1016/j.neuroimage.2020.116535
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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