| Literature DB >> 32114149 |
Bidhan Lamichhane1, Andrew Westbrook2, Michael W Cole3, Todd S Braver4.
Abstract
Working memory (WM) function has traditionally been investigated in terms of two dimensions: within-individual effects of WM load, and between-individual differences in task performance. In human neuroimaging studies, the N-back task has frequently been used to study both. A reliable finding is that activation in frontoparietal regions exhibits an inverted-U pattern, such that activity tends to decrease at high load levels. Yet it is not known whether such U-shaped patterns are a key individual differences factor that can predict load-related changes in task performance. The current study investigated this question by manipulating load levels across a much wider range than explored previously (N = 1-6), and providing a more comprehensive examination of brain-behavior relationships. In a sample of healthy young adults (n = 57), the analysis focused on a distinct region of left lateral prefrontal cortex (LPFC) identified in prior work to show a unique relationship with task performance and WM function. In this region it was the linear slope of load-related activity, rather than the U-shaped pattern, that was positively associated with individual differences in target accuracy. Comprehensive supplemental analyses revealed the brain-wide selectivity of this pattern. Target accuracy was also independently predicted by the global resting-state connectivity of this LPFC region. These effects were robust, as demonstrated by cross-validation analyses and out-of-sample prediction, and also critically, were primarily driven by the high-load conditions. Together, the results highlight the utility of high-load conditions for investigating individual differences in WM function.Entities:
Keywords: Default mode network; Dorsolateral prefrontal cortex; Frontal-parietal network; N-back; Salience network; Working memory
Mesh:
Year: 2020 PMID: 32114149 PMCID: PMC7781187 DOI: 10.1016/j.neuroimage.2020.116683
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556