Elsevier

NeuroImage

Volume 114, 1 July 2015, Pages 320-327
NeuroImage

Context-specific differences in fronto-parieto-occipital effective connectivity during short-term memory maintenance

https://doi.org/10.1016/j.neuroimage.2015.04.001Get rights and content

Highlights

  • Granger causality-based connectivity was measured between brain regions using EEG data

  • STM maintenance and distraction filtering use different brain networks.

  • Maintenance uses dlPFC to SPL connection via the alpha band.

  • Filtering uses dlPFC to extrastriate and SPL to extrastriate connections via the beta band.

  • Filtering also uses the SPL to dlPFC connection via the alpha and theta bands.

Abstract

Although visual short-term memory (VSTM) performance has been hypothesized to rely on two distinct mechanisms, capacity and filtering, the two have not been dissociated using network-level causality measures. Here, we hypothesized that behavioral tasks challenging capacity or distraction filtering would both engage a common network of areas, namely dorsolateral prefrontal cortex (dlPFC), superior parietal lobule (SPL), and occipital cortex, but would do so according to dissociable patterns of effective connectivity. We tested this by estimating directed connectivity between areas using conditional Granger causality (cGC). Consistent with our prediction, the results indicated that increasing mnemonic load (capacity) increased the top-down drive from dlPFC to SPL, and cGC in the alpha (8–14 Hz) frequency range was a predominant component of this effect. The presence of distraction during encoding (filtering), in contrast, was associated with increased top-down drive from dlPFC to occipital cortices directly and from SPL to occipital cortices directly, in both cases in the beta (15–25 Hz) range. Thus, although a common anatomical network may serve VSTM in different contexts, it does so via specific functions that are carried out within distinct, dynamically configured frequency channels.

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.

References (37)

  • A. Ikkai et al.

    Common neural mechanisms supporting spatial working memory, attention and motor intention

    Neuropsychologia

    (2011)
  • M.G. Machizawa et al.

    Principal component analysis of behavioural individual differences suggests that particular aspects of visual working memory may relate to specific aspects of attention

    Neuropsychologia

    (2011)
  • E.K. Miller et al.

    Cortical circuits for the control of attention

    Curr. Opin. Neurobiol.

    (2013)
  • B.T. Miller et al.

    Searching for “the top” in top-down control

    Neuron

    (2005)
  • A.C. Nobre et al.

    Attention and short-term memory: crossroads

    Neuropsychologia

    (2011)
  • G. Piantoni et al.

    Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

    Neuroimage

    (2013)
  • T.J. Buschman et al.

    Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices

    Science

    (2007)
  • M.V. Chafee et al.

    Inactivation of parietal and prefrontal cortex reveals interdependence of neural activity during memory-guided saccades

    J. Neurophysiol.

    (2000)
  • Cited by (11)

    • Assessing recurrent interactions in cortical networks: Modeling EEG response to transcranial magnetic stimulation

      2019, Journal of Neuroscience Methods
      Citation Excerpt :

      A challenging aspect of applying the modeling methodology described here is identification of the ROIs to include in the cortical network model. Any prior information of brain regions involved in the paradigm being studied should be used to select ROIs as illustrated in the spontaneous EEG studies of Dentico et al. (2014), Kundu et al. (2015). Semi-data-driven ROI selection, such as used in the present study, is closely related to the source localization problem and thus, any source localization method may be used.

    • Dissociated roles of the parietal and frontal cortices in the scope and control of attention during visual working memory

      2017, NeuroImage
      Citation Excerpt :

      First, due to the limited spatial resolution of tDCS, we could not pinpoint further functional dissociations within the PFC and PPC. For example, fMRI studies have suggested that the DLPFC (Feredoes et al., 2011; Kundu et al., 2015) and IFG (Feredoes et al., 2006; Zanto et al., 2011) show different top-down regulations on the parietal or occipital cortices during working memory tasks. Within the PPC, the activation of inferior IPS tracked a fixed number of to-be-remembered items regardless of object complexity, while superior IPS tracked the number of items in visual WM storage as feature complexity changed (Xu and Chun, 2006).

    • Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory

      2017, NeuroImage
      Citation Excerpt :

      In particular, we observed that the precision with which a single item could be remembered was greater in persons that show a higher functional coupling between occipital and parietal regions. This is for example consistent with a recent demonstration of higher top-down connectivity from dorsal parietal regions (i.e., BA7) to extrastriate cortex during visual WM encoding in the presence of distraction (i.e., 2 irrelevant stimuli within a sample of serially presented stimuli) compared to trials with no distraction but equal memory load (Kundu et al., 2015). This is also consistent with previous fMRI research showing functional coherence of pairs of occipital and parietal regions during a sustained attention task, whereby it has been suggested that posterior parietal regions (i.e., IPS1, IPS2) are involved in transmitting attention signals from higher brain regions to occipital cortex (Lauritzen et al., 2009).

    • EEG alpha power during maintenance of information in working memory in adults with ADHD and its plasticity due to working memory training: A randomized controlled trial

      2016, Clinical Neurophysiology
      Citation Excerpt :

      As a neural index, we therefore choose to focus on posterior alpha power because EEG oscillations in alpha frequency bands (9–14 Hz) are considered to play a key role in gating and protecting internal representations from distraction (Bonnefond and Jensen, 2012; Jensen and Mazaheri, 2010; Palva et al., 2011; Sauseng et al., 2009). Alpha power is considered to be particularly important for the sustained maintenance of working memory item information (Kundu et al., 2015; Hsieh et al., 2011). Previous research has shown that increased alpha in parietal–occipital regions during a delayed match-to-sample task was associated with better working memory performance (Jensen et al., 2002).

    View all citing articles on Scopus
    View full text