4. Discussion
Reduced WM performance in old age is associated with changes in both
local neural dysfunction and functional connectivity. In the current
study, alterations in task-evoked activity and task-based functional
connectivity were independently examined by applying the background
connectivity approach, and the major and novel findings are that: (1) a
decline in the magnitudes of task-evoked activity was accompanied by
increased seed-based background functional connectivity with posterior
parietal regions, and both were tightly correlated with WM performance;
and (2) while task-evoked activity mediated age-related WM impairment,
task-based connectivity, on the contrary, has contributed to better WM
performance in old age.
Standard univariate analyses on task-evoked activity revealed an
age-related decrease in activity of the right IPL and an increase in
activity of the right ACC. Although alterations in the IPL activities of
older individuals have been repeatedly identified in previous studies
(Cabeza et al., 2004; Carp et al., 2010), the involvement of the ACC has
been relatively rarely reported in WM ageing. Some studies have
identified special changes in the ACC related to WM performance (Bush et
al., 2000; Lenartowicz and McIntosh, 2005). There was evidence from
developmental studies that neural responses (negative activity during
task) in the ventral ACC could predict between-subject differences in WM
(Huang et al., 2016; Vogan et al., 2016), and several training programs
on WM also highlighted the importance of modulations in ACC activities
(Olesen et al., 2004; Brehmer et al., 2011). All these reports linked a
more deactivated ACC (especially ventral) to better WM performance,
possibly due to the successful inhibition of irrelevant information
during tasks (Jonides et al., 1998; Vogt, 2009). It should also be noted
that as previous investigations into WM ageing mainly focused on regions
with task-evoked positive activity (as detailed in recent meta-analyses
(Wager and Smith, 2003; Rottschy et al., 2012)), our findings of ACC
with negative activity during task, along with a few recent
investigations into task-negative areas (Sambataro et al., 2010;
Anticevic et al., 2012a), thus implied that those negatively activated
regions were as important as the positively activated regions in
supporting human cognitions, especially for externally oriented
cognitive functions such as working memory and executive function.
Moreover, our studies failed to find any pattern of increased activity
in frontal and parietal regions, and it was possible that the
age-related regression analysis method based on a single group of older
individuals, rather than a typical comparison between young and older
individuals, was the reason that no increases were identified in
activated regions.
Along with the alterations in local cortical processing, subsequent
seed-based functional connectivity analyses on the background activity
derived from the WM task also showed that, regions with increased and
decreased task-evoked activity were more functionally connected to other
regions, as the ACC was connected to the bilateral lateral parietal area
and the IPL was coupled with the posterior cingulate gyrus, and these
patterns of a more connected brain were linked to better performance on
the N-back task. The associations between functional connectivity and WM
performance have been well-characterized in previous studies. Consistent
evidence from resting state neuroimaging data has identified that
regions activated in the WM task, such as DLPFC, PreCG, and IPL, were
highly connected (Fox et al., 2005; Power et al., 2011) and that
connections among task-deactivated regions (such as the medial PFC and
PCC) also made considerable contributions to WM (Hampson et al., 2006;
Hampson et al., 2010; Sambataro et al., 2010). Furthermore, task fMRI
studies, which modelled task-induced BOLD signals using methods such as
PPI and DCM, provided further evidence on how information flow was
modulated during WM tasks, depicting more details on frontoparietal
interactions in different conditions (Ma et al., 2012; Heinzel et al.,
2017; Jung et al., 2018).
In addition to the well-documented relationships between local
task-evoked brain activity and WM, we found seed-based connectivity
closely associated with task performance after regressing out the local
activity of seed regions. These results fairly agreed with the basic
logic of the background connectivity approach, that the task-induced
BOLD signals contained two sources of variance (i.e., stimuli-related
and state-related), with distinct and complementary information on
cognitive processes (Otten et al., 2002; Rissman et al., 2004). Classic
FC estimated by resting-state fMRI data has been best suited to studying
the latent functional architecture of the brain at rest, however, it
still lacks the ability to well depict the relationship of brain
interactions to specific cognitive tasks (Friston, 2011; Turk-Browne,
2013; Misic and Sporns, 2016). In contrast, there have been several
studies showing that during cognitive tasks such as visual attention and
memory, background connectivity could selectively respond to specific
stimulus types or cognitive processes (Summerfield et al., 2006;
Al-Aidroos et al., 2012; Norman-Haignere et al., 2012; Wimmer and
Shohamy, 2012; Duncan et al., 2014), and changes in connectivity could
be detected even when cognitive demands were modulated by diseases (Lou
et al., 2015) or drugs (Anticevic et al., 2012b). Thus, our results
provided more evidence that brain activity and connectivity played
independent but complementary roles in the process of WM ageing, and
since age-related changes in WM made critical contributions to the
ageing of other higher cognition such as memory and decision-making,
this evidence could also be one possible neural mechanism underlying the
ageing of broader cognitive functions.
Meanwhile, based on results from mediation modelling, that is, while a
decline in activity was linked to the age-related decrease in AR and
increase in the RT, the background connectivity was enhanced to support
higher AR and shorter RT, we proposed that along with local processing
impairment in advanced age, more regions were involved in the current WM
task as a result of functional compensation (Reuter-Lorenz and Cappell,
2008; Park and Reuter-Lorenz, 2009). Specifically, although we could not
clarify which alteration happened early, the compensational mechanisms
might be the situation in which the age-related decline in activity led
to the decline in behavioural performance, followed by the enhancement
of background functional connectivity, to reduce the impact and to help
older individuals maintain behavioural performance. In addition, our
findings also revealed patterns that positively activated regions (IPL)
during tasks were more connected to regions that were negatively
activated (PCC), and connections were enhanced between regions that were
negatively activated (ACC) and those that were positively activated (IPL
and PreCG). Since these regions were the core regions in the
frontoparietal network (FPN) and default mode network (DMN), the pattern
might also catch a glimpse of network reconfiguration among brain
networks (i.e., FPN and DMN) in ageing to support higher cognitive
functions (Reuter-Lorenz and Lustig, 2005).
There are several points that could be explored in future studies.
First, the block design of the current experiment could not fully
consider correct and failed trials during the WM task, which made the
impacts of regional activity and connectivity on successful WM difficult
to assess. However, based on the decent accuracy rate of WM tasks (90.91
± 6.04%) in all subjects, it could be inferred that they were focused
on the tasks, which agreed with the hypothesis of maintaining cognitive
status in background connectivity analyses. Second, the computation of
functional connectivity was based on seed regions derived from regional
activity analyses, which could have been computed at the whole brain
level to examine the network reconfiguration or reorganization. Due to
the primary proposal of the present study to jointly explore and compare
the roles of brain activation and connectivity in WM ageing, the
seed-based analyses could provide more direct evidence on their
relationships and contributions to WM ageing. Finally, we assessed
functional alterations within a cross-sectional sample. Although a group
of elderly subjects avoided the possible biases of strategies or
patterns in WM tasks, a longitudinal design would give an improved and
more detailed depiction of the regional activity and connectivity
alterations as well as their contributions to the ageing of WM.