Context-specific differences in fronto-parieto-occipital effective connectivity during short-term memory maintenance
Introduction
A growing body of evidence suggests that visual short-term memory (VSTM), and the related construct of working memory, may share common neural bases with selective attention (e.g. Nobre and Stokes, 2011, D'Esposito and Postle, 2015). In the domain of spatial cognition, for example, both engage a highly overlapping network of frontoparietal regions (e.g., Ikkai and Curtis, 2011), from which information can be “read out”, depending on context, to accomplish oculomotor, attentional, or mnemonic goals (Jerde et al., 2012). Additionally, training on a visual working memory task has comparable effects on event-related potential (ERP) components associated with VSTM (the contralateral delay activity; CDA) and with visual selective attention (the contralateral search activity (CSA)) (Kundu et al., 2013), suggesting that there is a relationship between the underlying mechanisms supporting VSTM and visual selective attention. The CDA is an ERP component derived during a VSTM task, for which the amplitude scales monotonically with the number of items being held in VSTM, and plateaus at an individual's VSTM capacity (Vogel and Machizawa, 2004). The CSA is an ERP component derived during visual search for which the amplitude correlates with individual differences in VSTM capacity (Emrich et al., 2010).
One influential model of attentional control, operationalized through the Attentional Network Task, is organized into three dissociable components: alerting, orienting, and executive control (Fan et al., 2002). Machizawa and Driver (2011) related this framework to working memory by applying a principal components analysis to a behavioral dataset, and found that not only did putative measures of alerting, orienting, and executive control load independently onto the first three principle components, but so too did measures relating to three constructs from VSTM: capacity, precision, and filtering, respectively. In this paper we focus on the constructs of capacity and filtering. The former, in particular, has been of interest due to its ability to predict individual variation in cognitive measures such as search efficiency (Emrich et al., 2010) and filtering efficiency (Vogel et al., 2005), as well as higher-order measures such as educational achievement (Cowan et al., 2005; c.f. Cusack et al., 2009). Although capacity and filtering have both been related to the CDA (Vogel and Machizawa, 2004, Vogel et al., 2005), the two have not, to our knowledge, been dissociated at the network level. The goal of this study, therefore, was to interrogate the dorsal frontoparietal network with a method capable of detecting context-dependent differences in its EEG dynamics.
Specifically, we tested whether there is evidence of systematic differences in effective connectivity within a network comprising dorsolateral prefrontal cortex (dlPFC), superior parietal cortex (SPL), and extrastriate cortex during the delay-period of a VSTM task that emphasized either capacity or filtering. The rationale for choosing these areas lies in the findings from Kundu et al. (2013) which showed that working-memory training increases transcranial magnetic stimulation (TMS)-based measures of effective connectivity between dlPFC and SPL, as well as between SPL and extrastriate visual areas. It also showed that connectivity between dlPFC and SPL increases with VSTM load. Importantly, single pulse TMS provides a measure of effective connectivity such that we know exactly where and when stimulation occurred and thus we can measure its downstream effects in time through a data-driven manner (Casali et al., 2010). However, this method is limited in that it can only address the relative differences in connection strengths between the area stimulated and other distal areas. It cannot probe a predetermined connection between any two regions. Thus, the present study builds on the network model implicated by Kundu et al. (2013), but tests the hypothesis that different task contexts will be associated with systematic variation in the strength and direction of connectivity within the network.
This was accomplished using high-density (EEG) data and a recently developed method (Cheung et al., 2010) to estimate the conditional Granger causality (cGC) metric (Bressler and Seth, 2011) between dlPFC, SPL, and occipital cortex. Thus this method measures effective connectivity in its simplest sense, which is the change in electrical activity at one location as a weighted sum of changes elsewhere (Friston, 1994; and as explicated by us previously in Dentico et al. (2014) and Piantoni et al. (2013)). We do note, however, that the term ‘effective connectivity’ has also been used to refer more specifically to causal interactions measured in neurobiologically based models, such as dynamic causal modeling (see Friston, 2011 for a review).
The cGC metric of effective connectivity can address the precise chronometry between networks that act as candidate sources of top-down control (Miller and D'Esposito, 2005). We hypothesized that increases in memory load and increases in filtering demands would produce differences in the strength and/or direction of effective connectivity between dlPFC and SPL, as well as between these areas and extrastriate occipital cortex, depending on context.
Section snippets
Participants
Data reported in the present study were taken from the pre-training session of a working memory training study (Kundu et al., 2013). 30 participants (16 female, mean age = 20.9 years, SD = 2.75 years) were recruited for the study from the University of Wisconsin-Madison community. The inclusion criteria selected healthy participants between the ages of 18–35 years, with normal or corrected-to-normal visual acuity and normal color vision, and who were not currently taking medication for psychiatric
Behavioral analysis
A complete set of behavioral data was acquired from 26 participants. Performance on Load 2 trials was better than on Load 4 trials (Table 1; p < 0.001) for the location VSTM task. In the TD task, there was a main effect of Load (Table 1; p < 0.001) such that performance on Load 2d trials was better than on Load 4 trials (t(18) = − 5.05, p < 0.001), but performance on Load 2d trials was worse than the Load 2 (t(18) = 4.53, p < 0.001).
Connectivity analysis
Electrophysiological data was acquired from 19 subjects for the Location
Discussion
Behavioral evidence suggests that there exist dissociable processes underlying VSTM that relate to processes underlying visual selective attention (Machizawa and Driver, 2011). Here, we tested the hypothesis that two of these behaviorally dissociable constructs of VSTM — capacity and filtering — are supported by different network dynamics within the frontoparietal system, and between these regions and extrastriate occipital cortex. To address this question, we measured the casual connectivity
Author contributions
B.K. and B.R.P. designed the study; B.K. and B.D.V. developed the analysis plan; B.K. performed the experiments; B.K. and J.Y. analyzed data; and all authors contributed to the writing of the manuscript.
Acknowledgments
This research was conducted using the High-Throughput Computing Center at the University of Wisconsin-Madison. All work was conducted at University of Wisconsin-Madison and was supported by MH095428 (B.K.), MH095984 (B.R.P.), and MH064498 (B.R.P.) from the National Institutes of Health, and EB009749 (B.D.V.) and EB015542 (B.D.V.) from the National Institute of Biomedical Imaging and Bioengineering.
Competing financial interests
The authors declare no competing financial interests.
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