Predicting response time variability from task and resting-state functional connectivity in the aging brain

Autor: Oyetunde Gbadeyan, James Teng, Ruchika Shaurya Prakash
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: NeuroImage, Vol 250, Iss , Pp 118890- (2022)
Druh dokumentu: article
ISSN: 1095-9572
DOI: 10.1016/j.neuroimage.2022.118890
Popis: 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.
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