Introduction
Introduction
The brain reorganizes its network architecture to meet cognitive demands [1], and the balance between segregated and integrated systems is thought to shift with cognitive load [2]. How system segregation changes as load increases, and whether those changes support performance, remains unresolved. This is the case particularly for working memory, where prior findings conflict on whether segregation or integration underpins successful function, and have mainly focused on differences between rest and task rather than incremental increases in task load [3].
Methods
Methods
Using functional MRI in healthy adults, we measured system segregation across five canonical resting-state networks from rest (N=69) to an N-back working memory task (N=27). We also measured system segregation across four levels of task load (0- to 3-back). We correlated system segregation against working memory accuracy and used repeated measures analyses of variance (RM-ANOVA) to compare segregation across task loads. Reproducibility testing included measuring system segregation across structural and functional (Yeo, [4]) parcellations, sparsity thresholds, and approximating segregation with modularity, a parcellation-independent measure. For a granular understanding, segregation was measured on a subnetwork level as well.
Results
Results
At rest, greater system segregation predicted higher working memory accuracy after controlling for age, sex, and motion. Within the task, segregation increased with load, and both 3-back segregation and its change across load predicted 3-back accuracy. These load effects reproduced when using the parcellation-independent modularity measure, but not under the functional parcellations. This may be related to the divided representation of executive-cognitive regions in these functional networks, as subnetwork analyses revealed that load effects in our primary parcellation was driven by increased segregation of the unified attention/executive network.
Discussion
Discussion
These findings indicate that the relationship between segregation and cognition is state- and scale-dependent. Load-dependent strengthening of cognitive-network connectivity, rather than global integration, appears to support working memory performance, refining the common assumption that higher cognitive demand requires greater network integration.
References
[1] Park, H. J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science, 342(6158), 1238411.
[2] Cohen, J. R., & D\'Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience, 36(48), 12083-12094.
[3] Finc, K., Bonna, K., He, X., et al, & Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. Nature communications, 11(1), 2435.
[4] Yeo, BT Thomas, Fenna M. Krienen, .. Roffman et al. "The organization of the human cerebral cortex estimated by intrinsic functional connectivity." Journal of neurophysiology (2011).
Acknowledgement
\nCanadian Institute of Health Research (CIHR) Project Grant [PJT-168878]* \nNSHA Fibromyalgia Research Fibromyalgia Grant*
