Complementary topology of maintenance and manipulation brain networks in working memory

Abstract

Working memory (WM) is assumed to consist of a process that sustains memory representations in an active state (maintenance) and a process that operates on these activated representations (manipulation). We examined evidence for two distinct, concurrent cognitive functions supporting maintenance and manipulation abilities by testing brain activity as participants performed a WM alphabetization task. Maintenance was investigated by varying the number of letters held in WM and manipulation by varying the number of moves required to sort the list alphabetically. We found that both maintenance and manipulation demand had significant effects on behavior that were associated with different cortical regions: maintenance was associated with bilateral prefrontal and left parietal cortex, and manipulation with right parietal activity, a link that is consistent with the role of parietal cortex in symbolic computations. Both structural and functional architecture of these systems suggested that these cognitive functions are supported by two dissociable brain networks. Critically, maintenance and manipulation functional networks became increasingly segregated with increasing demand, an effect that was positively associated with individual WM ability. These results provide evidence that network segregation may act as a protective mechanism to enable successful performance under increasing WM demand.

Site-specific effects of online rTMS during a working memory task in healthy older adults

“…In our studies, online rTMS was applied with a goal of enhancing the manipulation of information in WM, as assessed by performance on the DRAT. As reported in Davis et al (2018), group analysis of fMRI acquired during the second visit of these studies revealed that when participants are mentally maintaining and alphabetizing letters during the delay period of the DRAT, the left lateral parietal cortex produced strong activation. This finding is consistent with a role of parietal cortex in symbolic computations (Piazza et al, 2007;Dehaene et al, 2003;Park et al, 2013) and led to targeting of the parietal cortex, rather than DLPFC, in the current study.…”

“…The stimulation target was individually defined as the peak activation within the left lateral parietal cortex associated with a parametric increase in task difficulty during the delay period of the DRAT. According to the results obtained in our previous study (Davis et al, 2018), both Set Size (the number of letters in an array) and Sorting Steps (the minimum number of operations required to alphabetize the array) contribute to the difficulty of an individual trial. Therefore, to obtain a more accurate representation of increases in DRAT difficulty a parametric delay-period regressor, defined by the interaction between Set Size and Sorting Steps was used estimate task difficulty.…”

“…As mentioned in the introduction, the goal of the current study was to optimize the faciliatory effects of rTMS observed in Beynel et al 2019, where 5Hz rTMS was applied over the left dorsolateral prefrontal cortex. Here, we wished to maximize the difference between active and sham rTMS by stimulating the parietal cortex, which has been shown to be more activated during the DRAT (Davis et al, 2018), and by modifying some stimulation parameters. To compare the effect sizes for these two studies, Cohen's d was calculated from the individual conditions that yielded the largest effects in each study (hardest difficulty level in the Invalid trials in Beynel et al (2019) and 100% Eref strength for the current study).…”

“…The goal of the present study was therefore to attempt to increase the potency of online rTMS for enhancing WM manipulation by stimulating another cortical area involved in this task. Because results from our previous fMRI study indicated that manipulation of information in the DRAT produced the greatest activation in the superior parietal lobule (Davis et al, 2018), fMRI-BOLD activity in the left lateral parietal cortex was used to derive the target for rTMS in the current study. This target was defined on an individual basis to determine TMS coil location (projected to scalp surface) and orientation (related to nearest sulci wall) by information derived from fMRI activation and brain anatomy (MRI).…”

Age-related compensatory reconfiguration of PFC connections during episodic memory retrieval

“…That is to say, we focused on whether and to what extent the global connectivity patterns of PFC nodes in the high memory state display dissimilarity with the global connectivity patterns in the low memory state. Converging lines of evidence suggest that transient, dynamic changes in patterns of functional connectivity are crucial for supporting cognitive functions across task domains and demands (Cole et al 2013;Cole et al 2014;Spielberg et al 2015;Simony et al 2016;Gallen et al 2016;Geib, Stanley, Dennis, et al 2017;Geib, Stanley, Wing, et al 2017;Davis et al 2018;Stanley et al 2019). The ability for PFC regions to flexibly reconfigure their functional connections is thought to subserve control-related functions (Cole et al 2013), including those that aid in memory retrieval (Monge et al 2018).…”