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Predicting response time variability from task and resting-state functional connectivity in the aging brain
Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and atten...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063711/ https://www.ncbi.nlm.nih.gov/pubmed/35007719 http://dx.doi.org/10.1016/j.neuroimage.2022.118890 |
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author | Gbadeyan, Oyetunde Teng, James Prakash, Ruchika Shaurya |
author_facet | Gbadeyan, Oyetunde Teng, James Prakash, Ruchika Shaurya |
author_sort | Gbadeyan, Oyetunde |
collection | PubMed |
description | Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability—the trial-to-trial fluctuations in behavioral responses—as an indirect marker of mind-wandering or an “out-of-the-zone” attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models. |
format | Online Article Text |
id | pubmed-9063711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-90637112022-05-03 Predicting response time variability from task and resting-state functional connectivity in the aging brain Gbadeyan, Oyetunde Teng, James Prakash, Ruchika Shaurya Neuroimage Article Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability—the trial-to-trial fluctuations in behavioral responses—as an indirect marker of mind-wandering or an “out-of-the-zone” attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models. 2022-04-15 2022-01-08 /pmc/articles/PMC9063711/ /pubmed/35007719 http://dx.doi.org/10.1016/j.neuroimage.2022.118890 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) |
spellingShingle | Article Gbadeyan, Oyetunde Teng, James Prakash, Ruchika Shaurya Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title | Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title_full | Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title_fullStr | Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title_full_unstemmed | Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title_short | Predicting response time variability from task and resting-state functional connectivity in the aging brain |
title_sort | predicting response time variability from task and resting-state functional connectivity in the aging brain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063711/ https://www.ncbi.nlm.nih.gov/pubmed/35007719 http://dx.doi.org/10.1016/j.neuroimage.2022.118890 |
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