Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?
- PMID: 31940476
- DOI: 10.1016/j.neuroimage.2020.116535
Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?
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.
Keywords: Attentional blink; Connection-based predictive modeling; Diffuse attention; Functional architecture; Selective attention; Sustained attention.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
Declaration of competing interest None.
Similar articles
-
Connectome-based models predict attentional control in aging adults.Neuroimage. 2019 Feb 1;186:1-13. doi: 10.1016/j.neuroimage.2018.10.074. Epub 2018 Oct 28. Neuroimage. 2019. PMID: 30394324
-
Large-scale reconfiguration of connectivity patterns among attentional networks during context-dependent adjustment of cognitive control.Hum Brain Mapp. 2021 Aug 15;42(12):3821-3832. doi: 10.1002/hbm.25467. Epub 2021 May 14. Hum Brain Mapp. 2021. PMID: 33987911 Free PMC article.
-
Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.Neuroimage. 2018 Feb 15;167:11-22. doi: 10.1016/j.neuroimage.2017.11.010. Epub 2017 Nov 6. Neuroimage. 2018. PMID: 29122720 Free PMC article.
-
Characterizing Attention with Predictive Network Models.Trends Cogn Sci. 2017 Apr;21(4):290-302. doi: 10.1016/j.tics.2017.01.011. Epub 2017 Feb 23. Trends Cogn Sci. 2017. PMID: 28238605 Free PMC article. Review.
-
Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention.Psychol Bull. 2013 Jul;139(4):870-900. doi: 10.1037/a0030694. Epub 2012 Nov 19. Psychol Bull. 2013. PMID: 23163491 Free PMC article. Review.
Cited by
-
Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.J Neurosci. 2024 Apr 3;44(14):e1543232024. doi: 10.1523/JNEUROSCI.1543-23.2024. J Neurosci. 2024. PMID: 38316565
-
Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability.J Neurosci. 2024 Mar 27;44(13):e0856232024. doi: 10.1523/JNEUROSCI.0856-23.2024. J Neurosci. 2024. PMID: 38290847
-
Functional brain connectivity predicts sleep duration in youth and adults.Hum Brain Mapp. 2023 Dec 15;44(18):6293-6307. doi: 10.1002/hbm.26488. Epub 2023 Nov 2. Hum Brain Mapp. 2023. PMID: 37916784 Free PMC article.
-
Poorer Inhibitory Control Uniquely Contributes to Greater Functional Disability in Post-9/11 Veterans.Arch Clin Neuropsychol. 2023 Aug 24;38(6):944-961. doi: 10.1093/arclin/acad012. Arch Clin Neuropsychol. 2023. PMID: 36781401 Free PMC article.
-
Differences in the functional brain architecture of sustained attention and working memory in youth and adults.PLoS Biol. 2022 Dec 21;20(12):e3001938. doi: 10.1371/journal.pbio.3001938. eCollection 2022 Dec. PLoS Biol. 2022. PMID: 36542658 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources