Dynamics of the Human Structural Connectome Underlying Working Memory Training

Abstract

Brain region-specific changes have been demonstrated with a variety of cognitive training interventions. The effect of cognitive training on brain subnetworks in humans, however, remains largely unknown, with studies limited to functional networks. Here, we used a well-established working memory training program and state-of-the art neuroimaging methods in 40 healthy adults (21 females, mean age 26.5 years). Near and far-transfer training effects were assessed using computerized working memory and executive function tasks. Adaptive working memory training led to improvement on (non)trained working memory tasks and generalization to tasks of reasoning and inhibition. Graph theoretical analysis of the structural (white matter) network connectivity ("connectome") revealed increased global integration within a frontoparietal attention network following adaptive working memory training compared with the nonadaptive group. Furthermore, the impact on the outcome of graph theoretical analyses of different white matter metrics to infer "connection strength" was evaluated. Increased efficiency of the frontoparietal network was best captured when using connection strengths derived from MR metrics that are thought to be more sensitive to differences in myelination (putatively indexed by the [quantitative] longitudinal relaxation rate, R 1 ) than previously used diffusion MRI metrics (fractional anisotropy or fiber-tracking recovered streamlines). Our findings emphasize the critical role of specific microstructural markers in providing important hints toward the mechanisms underpinning training-induced plasticity that may drive working memory improvement in clinical populations.

Are Measures of Transfer Effects Missing the Target?

“…That is, whether effects seen after training lead to improvements in more broadly defined cognitive functioning and/or in everyday tasks that are highly dependent on WM. While some studies observe transfer effects to for instance inhibition (Klingberg et al 2005;Caeyenberghs et al 2016;Foy & Mann, 2014;Bigorra et al 2015, also Weicker et al 2016 for a meta-analysis), others do not (Stevens et al 2016;Dunning et al 2013;Westerberg et al 2007), and for the field to advance it is important to review the theoretical framework behind such potential transfer and design studies with this framework in mind. Many studies lack a discussion on the expected specificity of the training gains and do not include measures to reflect convergent and discriminant validity (Noack et al 2014).…”

“…While test performance can increase for a number of reasons such as strategy use, motivation, placebo/expectancy effects, regression to the mean to name a few, studies exploring training-related effects in the brain offer convincing support for the malleability of WM. Improvements in WM following training have been shown to be associated with changes in the brain, both with regards to WM-related activity (Olesen et al 2004;Brehmer et al 2011;Stevens et al 2016), changes in the dopaminergic receptor density in cortical and striatal regions (McNab et al 2009;Bäckman et al 2011), and through changes in global integration of the pathways associated with WM (Caeyenberghs et al 2016;Barnes et al 2016). Change in activity has also been observed in the resting brain (Astle et al 2015;Takeuchi et al 2013), effectively demonstrating that the training effect is not (merely) related to differences between individuals or groups in motivation, use of strategy, or even task specific, but demonstrates a change in the underlying function.…”

“…Change in activity has also been observed in the resting brain (Astle et al 2015;Takeuchi et al 2013), effectively demonstrating that the training effect is not (merely) related to differences between individuals or groups in motivation, use of strategy, or even task specific, but demonstrates a change in the underlying function. These observations may be explained by the observed increase in white matter following training (Takeuchi et al 2010;Metzler-Baddeley et al 2017;Caeyenberghs et al 2016). One of these randomized controlled trials (Caeyenberghs et al 2016) using an active control condition and latent constructs of near and far transfer in an adult sample, observed a significant overall effect from training on both these constructs and on neuroimaging measures, indicating an increased global efficiency and global integration in the frontoparietal network.…”

“…These observations may be explained by the observed increase in white matter following training (Takeuchi et al 2010;Metzler-Baddeley et al 2017;Caeyenberghs et al 2016). One of these randomized controlled trials (Caeyenberghs et al 2016) using an active control condition and latent constructs of near and far transfer in an adult sample, observed a significant overall effect from training on both these constructs and on neuroimaging measures, indicating an increased global efficiency and global integration in the frontoparietal network. These changes were associated with the improvements seen on the trained tasks.…”

Disruption of Neurosynaptic Physiology and Neuron Network Dysfunction in Brain Disorders: An Environmental and Occupational Health Perspective

“…The fluctuations may be due to rapid formation and dissolution of functional connections. This dynamic nature of synchronization, even during resting state is called fragile binding [83]. Inter-regional synchronization is also clearly influenced by behavioural state and cognition.…”