Abstract
General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA.
Highlights
In addition to particular abilities associated with individual cognitive tasks, there is substantial evidence for an overarching general ability involved in performance across a diverse range of tasks (Carroll, 2003; Horn & Noll, 1997; Mackintosh & Mackintosh, 2011; Neisser et al, 1996; Spearman, 1904)
Task-based imaging provides a promising route for constructing brainbased predictive models of general cognitive ability (GCA) because tasks can potentially selectively activate brain regions responsible for effective cognitive
The importance of frontoparietal network (FPN), as well as related executive regions, for GCA has been highlighted in previous work, especially in Jung and Haier's influential frontoparietal integration theory (Jung & Haier, 2007)
Summary
In addition to particular abilities associated with individual cognitive tasks, there is substantial evidence for an overarching general ability involved in performance across a diverse range of tasks (Carroll, 2003; Horn & Noll, 1997; Mackintosh & Mackintosh, 2011; Neisser et al, 1996; Spearman, 1904). A notable feature of many of these previous task-based studies is that they are mainly concerned with localization and correlation: they mainly seek to identify specific brain regions whose activation correlates with GCA Another important goal has emerged in cognitive neuroscience: prediction (Rosenberg, Casey, & Holmes, 2018; Varoquaux & Poldrack, 2019; Yarkoni & Westfall, 2017). An alternative approach for building predictive models of GCA, which appears to be relatively less utilized (cf Greene, Gao, Scheinost, & Constable, 2018; Stern, Gazes, Razlighi, Steffener, & Habeck, 2018), employs a rationale similar to that for cardiac treadmill testing This approach attempts to first place the brain in an activated state that engages the cognitive abilities associated with GCA. Tasks that produce greater frontoparietal activation and default mode network (DMN) deactivation, which is associated with higher cognitive demand, are more effective at GCA prediction
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