Functional independence in resting-state connectivity facilitates higher-order cognition

Abstract

Growing evidence suggests that intrinsic functional connectivity (i.e. highly structured patterns of communication between brain regions during wakeful rest) may encode cognitive ability. However, the generalizability of these findings is limited by between-study differences in statistical methodology and cognitive domains evaluated. To address this barrier, we evaluated resting-state neural representations of multiple cognitive domains within a relatively large normative adult sample. Forty-four participants (mean(sd) age = 31(10) years; 18 male and 26 female) completed a resting-state functional MRI scan and neuropsychological assessments spanning motor, visuospatial, language, learning, memory, attention, working memory, and executive function performance. Robust linear regression related cognitive performance to resting-state connectivity among 200 a priori determined functional regions of interest (ROIs). Only higher-order cognitions (such as learning and executive function) demonstrated significant relationships between brain function and behavior. Additionally, all significant relationships were negative – characterized by moderately positive correlations among low performers and weak to moderately negative correlations among high performers. These findings suggest that functional independence among brain regions at rest facilitates cognitive performance. Our interpretation is consistent with graph theoretic analyses which represent the brain as independent functional nodes that undergo dynamic reorganization with task demand. Future work will build upon these findings by evaluating domain-specific variance in resting-state neural representations of cognitive impairment among patient populations.

Frontoparietal and default mode network connectivity varies with age and intelligence

“…Moreover, regions comprising these networks show anticorrelated task-induced activations that increase with increasingly demanding cognitive load ( Hampson et al, 2010 ; Persson et al, 2007 ; Douw et al, 2016 ; Leech et al, 2011 ; Grady et al, 2010 ; Hugdahl et al, 2019 ; Amer et al, 2016 ; Avelar-Pereira et al, 2017 ). Similar results are observed at rest, such that greater anticorrelation between task positive and negative networks is associated with improved cognitive control capacity measured outside of the scanner ( Keller et al, 2015 ; Kelly et al, 2008 ; Reineberg et al, 2018 ; Kim and Kang, 2018 ; James et al, 2016 ; Medaglia et al, 2018 ). Such anticorrelation suggests that when task positive networks are more active, e.g., during a cognitively demanding task, the task negative network is less active, and vice-versa.…”

Neural signatures of Trail Making Test performance: Evidence from lesion-mapping and neuroimaging studies

“…In the study, it was reported that faster TMT-B performance (indexed by the completion time cost of TMT-B relative to the TMT-A) was associated with increased FC between bilateral intraparietal sulci and the remainder of the executive control network. More recently, a rs-FC study in a cohort of young, healthy adults extended these findings by highlighting the role of the FC between the left superior parietal lobule and right ventrolateral prefrontal cortex in mediating TMT switching performance ( James et al, 2016 ). In this study, the TMT variant of the D-KEFS ( Delis et al, 2001 ) was used to estimate the completion time increase for the letter-number switching condition (analogue of the TMT-B) relative to the number sequencing baseline condition (analogue of the TMT-A).…”

“…Resting-state fMRI studies of the TMT highlighted the importance of FC between the bilateral superior parietal cortex and prefrontal regions ( Seeley et al, 2007 , James et al, 2016 ) for adequate performance on the TMT-B ( Fig. 1 c).…”

“… Anterior default mode network comprising clusters including superior frontal gyrus (x = −6, y = 66, z = 14); posterior cingulate (x = −8, 46, 34); bilateral angular gyrus (x = −44, y = −60, z = 30; x = 43, y = −62, z = 28). James et al (2016) 44 (TMT subset = 42) 31 ± 10 TMT from the Denis-Kaplan Executive System Battery (D-KEFS). Primary outcome measure: Age- and education-corrected difference completion time scores.…”

Theoretical Modeling of Cognitive Dysfunction in Schizophrenia by Means of Errors and Corresponding Brain Networks

“…The number of studies comparing resting state fMRI (rsfMRI) activity and neuropsychological tests performance is sparse. A recent study in healthy volunteers (James et al, 2016 ) showed a significant effect of the TMT B performance (time to complete) to resting-state connectivity of two regions of interest (ROIs), the right VLPFC and left superior parietal lobule.…”