1. Introduction
Working memory (WM) is the ability to efficiently maintain and manipulate temporarily available information, and provides a crucial functional backbone for complex behaviours, such as memory, learning, and problem solving (Baddeley, 2012; Diamond, 2013; D’Esposito and Postle, 2015). Motivated by the centrality of WM throughout human cognition, as well as the remarkable vulnerability of WM to ageing, most theories or hypotheses on cognitive ageing have implicated WM as one fundamental cause of more general age-related declines in cognitive performance (Park et al., 2002; Reuter-Lorenz and Sylvester, 2005; Craik and Salthouse, 2011; Anderson and Craik, 2017). Therefore, illustrating the neural mechanism of WM ageing would help untangle the cognitive ageing process and develop strategies to protect and promote cognition in old age.
Over the past decades, studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have shown that WM relies heavily on the prefrontal cortex (especially its dorsolateral part) and regions of the parietal lobes (e.g., superior parietal lobule and inferior parietal lobule) (Curtis and D’Esposito, 2003; Wager and Smith, 2003; Bledowski et al., 2009; Rottschy et al., 2012), and numerous studies have examined their alterations in ageing, linking extended patterns of neural activity and functional connectivity changes to WM impairment in old age (Spreng et al., 2010; Grady, 2012). When compared to their younger counterparts, older individuals have shown both decreased and increased WM-related activity (Rypma and D’Esposito, 2000; Grady et al., 2008; Carp et al., 2010; Piefke et al., 2012; Toepper et al., 2014), and the interpretation of these age-related changes has been a long-standing issue in the field. While decreased activity is usually considered to be a reflection of neurocognitive decline, those increases (i.e., hyperactivity or over-recruitment) have been viewed as signs of either compensation or dedifferentiation (Cabeza, 2002; Reuter-Lorenz, 2002; Reuter-Lorenz and Cappell, 2008), based on the correlations between brain activities and task performance. In addition, recent evidence from functional connectivity investigations on WM ageing, including those using task or resting-state fMRI, has revealed diverse results in older individuals as well, ranging from weakened long-range functional correlations (mainly between frontal and posterior regions) (Podell et al., 2012; Heinzel et al., 2017; Tsvetanov et al., 2018), to increased functional covariations between adjacent cortical regions during tasks (Cook et al., 2007; Sambataro et al., 2012).
Taken together, these findings highlight the important roles for both neural activity and functional connectivity in the WM ageing process, consistent with the idea that the two functional measurements provide complementary information on the brain physiology underlying cognition (Friston, 2011; Turk-Browne, 2013; Misic and Sporns, 2016). However, to the best of our knowledge, limited studies have applied the two methods in concert to better understand cognitive functions or their ageing (Murphy et al., 2016; Tsvetanov et al., 2018). This lack of integration might be partly because most connectivity studies have been conducted based on resting-state fMRI data, which lacks cognitive measurements during data acquisition, and for methods to examine connectivity in task fMRI data, such as psychophysiological interactions (PPI) (Friston et al., 1997) and dynamic causal models (DCM) (Friston et al., 2003), task-evoked activations have been incorporated into the modelling of time series (Friston, 2011), thus without clear distinctions between brain activity and connectivity. Hence, we proposed that the distinction and joint evaluation of brain activity and connectivity would improve the understanding of how the brain was functionally shaped for higher cognitive function and how cognitive ageing occurred in the brain.
We therefore applied a recently introduced approach, background connectivity, to process task-induced blood oxygen level-dependent (BOLD) signals. The background connectivity could be thought of as an extension of resting connectivity, where one does not need to assume a stable default state, and the logic of background connectivity emphasizes that the BOLD signals during performing cognitive tasks carry two components of task-related information, one is the task stimuli reaction (task-evoked activity) and the other one is the current cognitive status maintenance (state-related activity) (Al-Aidroos et al., 2012; Norman-Haignere et al., 2012; Turk-Browne, 2013; Duncan et al., 2014); therefore, the connectivity among regions could be assessed independently by regressing out the task-evoked activity. In the present cross-sectional research, we aimed to characterize those two functional components with a classical visual N-back WM task and to then examine their contributions to age-related changes in WM.