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\begin{document}

\title{Joint contributions from brain activity and activity-independent
functional connectivity to working memory aging}



\author[1]{Caishui Yang}%
\author[1]{Jialing Fan}%
\author[2]{Kewei Chen}%
\author[1]{Zhanjun Zhang}%
\affil[1]{Beijing Normal University}%
\affil[2]{Banner Alzheimers Institute}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Working memory (WM) impairment has been well characterized in normal
ageing. Various studies have explored changes in either the regional
activity or the interregional connectivity underlying the WM ageing
process. We proposed that brain activity and connectivity would
independently alter with ageing and affect WM performance. WM was
assessed with a classical N-back task during functional magnetic
resonance imaging in a community-based sample comprising 168 elderly
subjects (aged 55 to 86 years old). Following the rationale of
background functional connectivity, we assessed age-related alterations
in brain activity and seed-based interregional connectivity
independently. Analyses revealed age-related decrease in the activity of
the inferior parietal lobule (IPL) and an increase in the activity of
the ventral anterior cingulate cortex (ACC), and the local functional
dysfunctions were accompanied by alterations in their connectivity to
other cortical regions. Importantly, regional activity impairments in
the IPL and ACC could mediate age-related effects on accuracy rate and
reaction time, respectively, and those effects were further
counterbalanced by enhancement of their background functional
connectivity. We thus claimed that age-induced alterations in regional
activity and interregional connectivity occurred independently and
contributed to WM changes in ageing. Our findings presented the way
brain activity and functional connectivity interact in the late
adulthood, thus providing a new perspective for understanding WM and
cognitive ageing.%
\end{abstract}%



\sloppy


\textbf{Title:}

Joint contributions from brain activity and activity-independent
functional connectivity to working memory aging

\textbf{Running title:}

Contributions from activity and FC to WM aging

\textbf{Authors:}

Caishui Yang\textsuperscript{1,2}, Jialing Fan\textsuperscript{1}, Kewei
Chen\textsuperscript{3}, and Zhanjun Zhang\textsuperscript{1}, for the
Beijing Aging Brain Rejuvenation Initiative

\textsuperscript{1} State Key Laboratory of Cognitive Neuroscience and
Learning, Faculty of Psychology, Beijing Normal University, Beijing
100875, China

\textsuperscript{2} School of Systems Science, Beijing Normal
University, Beijing 100875, China

\textsuperscript{3} Banner Alzheimer's Institute, Phoenix, AZ 85006, USA

\textbf{Corresponding Author:}

Zhanjun Zhang, State Key Laboratory of Cognitive Neuroscience and
Learning, Beijing Normal University, No.19 Xinjiekouwai Street, Beijing
100875, China. E-mail: zhang\_rzs@bnu.edu.cn

\textbf{Acknowledgements:}

This study is supported by State Key Program of National Natural Science
of China (grant number 82130118), Funds for International Cooperation
and Exchange of the National Natural Science Foundation of China (grant
number: 81820108034), China Postdoctoral Science Foundation (grant
number 2021M700485), National Science Fund for Distinguished Young
Scholars (grant number 81625025), and Beijing Municipal Science \&
Technology Commission (grant number Z161100000216135).

All authors have no conflict of interest to declare.



\textbf{Abstract}

Working memory (WM) impairment has been well characterized in normal
ageing. Various studies have explored changes in either the regional
activity or the interregional connectivity underlying the WM ageing
process. We proposed that brain activity and connectivity would
independently alter with ageing and affect WM performance. WM was
assessed with a classical N-back task during functional magnetic
resonance imaging in a community-based sample comprising 168 elderly
subjects (aged 55 to 86 years old). Following the rationale of
background functional connectivity, we assessed age-related alterations
in brain activity and seed-based interregional connectivity
independently. Analyses revealed age-related decrease in the activity of
the inferior parietal lobule (IPL) and an increase in the activity of
the ventral anterior cingulate cortex (ACC), and the local functional
dysfunctions were accompanied by alterations in their connectivity to
other cortical regions. Importantly, regional activity impairments in
the IPL and ACC could mediate age-related effects on accuracy rate and
reaction time, respectively, and those effects were further
counterbalanced by enhancement of their background functional
connectivity. We thus claimed that age-induced alterations in regional
activity and interregional connectivity occurred independently and
contributed to WM changes in ageing. Our findings presented the way
brain activity and functional connectivity interact in the late
adulthood, thus providing a new perspective for understanding WM and
cognitive ageing.

\textbf{Keywords}

Working memory, task activity, background functional connectivity,
cognitive ageing, N-back paradigm

\textbf{Key Points}

1. Both positive and negative brain activity during performing task were
linked to age-related changes in working memory performance.

2. Age-induced changes in brain activity were accompanied by alterations
in regional functional connectivity during performing tasks.

3. Brain activity and functional connectivity independently contributed
to age-related changes in working memory.



\textbf{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.

\textbf{2. Materials and Methods}

2.1 Subjects

All procedures and ethical aspects of this study were approved by the
institutional review board of Beijing Normal University Imaging Center
for Brain Research. Written informed consent was obtained from each
subject.

Subjects in this study were from the Beijing Aging Brain Rejuvenation
Initiative (BABRI), which is an ongoing cohort project to investigate
the cognitive ageing and impairment of urban residents in Beijing and to
identify neuroimaging biomarkers for normal ageing and AD (Yang et al.,
2021). In this cross-sectional study, one hundred and sixty-eight (168)
subjects from Wave 1 and 2 of the BABRI cohort were included per the
following criteria: (a) aged 55 years old and above; (b) had at least 6
years of education; (c) scored 24 or higher on the Chinese version
Mini-Mental State Examination (MMSE); (d) had valid N-back task
performance data and N-back task fMRI data; and (e) had no history of
neurological, psychiatric, or systemic illness known to influence
cerebral function.

2.2 fMRI Experimental Paradigm

A blocked periodic design that incorporated alternating 0-, 1- and
2-back conditions was used during the N-back WM task. Each condition
contained three blocks and were pseudo-randomly shown to subjects.
During the 0-back condition, subjects were asked to press a button when
a preassigned digit (e.g., 1) appeared on the screen. In the 1- or
2-back condition, subjects pressed a button when the digit on the screen
matched the digit presented one or two items prior, respectively. Every
block started with a 10-s cue presentation that indicated the 0-, 1- or
2-back, which was followed by 20 consecutive trials of single-digit
stimuli (1000 ms duration and 1000 ms interstimulus interval). The whole
experiment ended with 20-s cue presentation that the task was over, and
he or she could have a rest. The responses and reaction time from each
subject were recorded by an MRI-compatible response button box. The
stimuli were presented using E-Prime (version 1.0, Psychology Software
Tools Inc., Pittsburgh, PA).

To ensure that subjects understood the instructions and performed the
tasks correctly, they were asked to practice a simplified version of the
tasks for 10-15 min before the experiment.

2.3 MRI Data Acquisition and Preprocessing

The MRI data were acquired by scanning on a 3.0 T Siemens scanner at the
Imaging Center for Brain Research at Beijing Normal University. With the
head snugly fixed by straps and foam pads, subjects were asked to
refrain from head movement. The functional images were acquired using an
echo-planar imaging (EPI) sequence as follows: 33 axial slices,
repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, slice thickness
= 3.5 mm, flip angle = 90\selectlanguage{ngerman}°, field of view = 200 mm×200 mm, acquisition
matrix = 64×64, 235 volumes. The T1-weighted structural data were also
collected for spatial normalization using three-dimensional (3D)
magnetization prepared rapid gradient echo (MP-RAGE) sequences as
follows: 176 sagittal slices, repetition time = 1900 ms, echo time =
3.44 ms, slice thickness = 1 mm, flip angle = 9°, field of view = 200
mm×200 mm, acquisition matrix = 256×256.

Functional imaging data were preprocessed using the Statistical
Parametric Mapping toolbox (SPM12, http://www.fil.ion.ucl.ac.uk/spm) run
within MATLAB software (Mathwork, Inc., Natick, MA). The images were
spatially realigned to the iteratively generated (over the realignment
procedure) mean image from the series and then resliced, followed by
slice-time correction for acquisition order (referenced to the first
slice). Based on the transformation parameters from the segmentation of
co-registered structural images, the functional images were spatially
normalized to the Montreal Neurological Institute (MNI) space at 3 mm
isotropic voxel resolution. Together with the six realignment paraments,
the mean signals of the brain ventricle and white matter were extracted
in the naive space and regressed out from the data. The functional
images were then smoothed with a 6-mm full-width half-maximum Gaussian
kernel. After preprocessing, six subjects were excluded due to excessive
head motion (larger than 3 mm or degree), resulting in 162 subjects
included in the final analyses.

For each subject, the preprocessed time series was modelled using GLMs.
Task-evoked neural effects were estimated using separate block
regressors representing 4 task conditions (fixation/cues, 0-back,
1-back, and 2-back) convolved with the canonical haemodynamic response
function (HRF) plus its temporal and dispersion derivatives, and
contrast images of interest (i.e., ``1back - 0back'' and ``2-back -
0-back'') were attained by subtracting the 0-back from the 1-/2-back. To
acquire task-based background brain activity images, residual images
from the GLM were high-pass filtered with a cut-off frequency of 0.01
Hz.

2.4 Voxel-wise Task-evoked Activity Analyses

Group-level one-sample t tests were first conducted to identify regions
with task-evoked positive and negative activity in the contrasts of
1back - 0back and 2-back - 0-back, and binary masks of positive and
negative regions during both contrasts were generated, under which
age-related changes in task-evoked activity were then separately
calculated for positive and negative regions in each contrast via
multiple regression models in SPM12 toolbox, with gender and years of
education as covariates.

For the above analyses, the Gaussian random field (GRF) correction was
used for multiple comparison corrections on the results with voxel-level
p \textless{}\selectlanguage{english} 0.001 and cluster level p \textless{}\selectlanguage{english} 0.05. Brain regions
showing significant age-related effects were identified as seed regions
for further functional connectivity analyses.

2.5 Task-based Background Functional Connectivity Analyses

The background functional connectivity examined the functional
correlations among brain regions that occurred in the background of
stimulus-locked changes, that is, independent of the evoked responses to
individual stimuli during the tasks. To assess such correlations, for
each subject, based on the acquired background brain activity images,
the strength of connectivity was evaluated through Pearson's
correlations between the averaged time series of voxels in each seed
region (significant regions in \emph{2.4 Voxel-wise Task-evoked Activity
Analyses} ) and the time series of each voxel in the rest of the brain.
Then, Fisher's r to z transformation was applied to normalize the
original correlation maps, followed by whole-brain z score
standardization to rescale the functional connectivity using Data
Processing \& Analysis for Brain Imaging (DPABI) (Yan et al., 2016).

Next, multiple regression models were performed on the standardized FC
maps using the SPM12 toolbox to determine regions where FC showed
significant age-related changes, with gender, years of education, and
mean task-evoked activity of the seed region included as covariates, and
all the results were corrected for multiple comparisons using the same
method in \emph{2.4 Voxel-wise Task-evoked Activity Analyses} . The mean
functional connectivity of the resulting areas was then extracted for
subsequent analyses.

2.6 Statistical Analyses

Relationships between N-back task performance and task-evoked activity,
as well as task-based background FCs, were separately estimated via
linear regression models using SPSS (version 22.0, IBM Inc., New York,
NY), with gender, years of education, age, and task-evoked activity as
covariates, and a statistical significance level of p \textless{} 0.05
was applied.

To test how task-evoked activity and task-based background FC mediated
age effects on N-back task performance, mediation analyses were further
conducted using Mplus 7.11 (Muthen and Muthen, 2012), and bootstrapped
95\% confidence intervals (CIs) were reported alongside parameter
estimates (Preacher et al., 2007; MacKinnon, 2008).

\textbf{3. Results}

3.1. Demographic, neuropsychological and N-back task measurements

Table 1 presents the demographics, general cognitive status, and WM task
performances of current study cohort. As shown in the table, the cohort
showed relatively intact general cognitive function, with no significant
age-related decline in MMSE scores (\textbar{}r\textbar{} \textless{}
0.1, p \textgreater{} 0.1). The WM task performances were remarkably
affected by ageing, as significant age-related decreases in accuracy
rate (AR) were found in all three task conditions (r = -0.325, 0.343,
and 0.310 for 0-, 1-, and 2-back condition, respectively, all ps
\textless{} 0.001), along with significantly longer reaction time (RT)
in 0- and 1-back conditions (r = 0.222 and 0.208, and p = 0.003 and
0.008, respectively) in advanced age (Table 1).

3.2. Age effects on N-back task-evoked brain activity

Brain activities typically associated with WM tasks were found in the
contrasts of ``2-back - 0-back'', with positive regions mainly clustered
in the lateral prefrontal and parietal lobes and negative areas located
around the cingulate cortex and temporal lobe (Figure 1). Further
regression models identified regions showing significant age-related
alterations within the positive and negative areas, respectively. As the
right inferior parietal lobule (rIPL, peak voxel coordinates: 48, -51,
45) presented an age-related decline in activity, the right anterior
cingulate cortex (rACC, peak voxel coordinates: 3, 36, -3) showed an
age-related increase (Figure 1, Table 2).

3.3. Age effects on the N-back task-based background functional
connectivity

With the task-based background brain activity, task-based background FC
maps were separately computed using the rIPL and rACC as seed regions
(Figure S1). Significantly enhanced connectivity was then observed
between the rIPL and those clustered in the right posterior cingulate
cortex (rPCC, peak voxel coordinates: -3, -57, 30) with increasing age
(Figure 2, Table 2). On the other hand, background connectivity between
the rACC and the right precentral gyrus (rPreCG, peak voxel coordinates:
48, 9, 30), the left IPL (lIPL, peak voxel coordinates: -39, -48, 42)
and the right angular gyrus (rAG, peak voxel coordinates: 42, -57, 54)
were also found to be strengthened with ageing (Figure 2, Table 2).
There were no other regions showing significant age-related alterations
in functional connectivity with either ROI.

3.4. Brain activity, functional connectivity, and WM task performance

Relationships between brain activity, FC, and WM task performance were
explored by linear regression models, controlling for the effects of
age, gender, and years of education. As both AR and RT of 0-back were
significantly associated with rIPL and rACC activities (for AR,
\textbar{} r \textbar{} \textgreater{} 0.266, both p \textless{} 0.001;
for RT, \textbar{} r \textbar{} \textgreater{} 0.174, p = 0.002, 0.028,
respectively), only AR of 2-back was correlated with these activities
(\textbar{} r \textbar{} \textgreater{} 0.241, p = 0.002, \textless{}
0.001, respectively). Then, after regressing out the effects of the
activity in seed regions, we identified specific relationships between
the rACC-lIPL connectivity and RT of 0-back (r = -0.170, p = 0.032), and
between the rIPL-rPCC connectivity and AR of 2-back (r = 0.166, p =
0.037).

Since the AR of the 2-back and the RT of the 0-back were affected by
both activity and connectivity, we subsequently conducted mediation
analyses to inspect their roles in mediating ageing effects on task
performance. The results showed that age-related impacts on the AR of
2-back were partly mediated by rIPL activity (indirect effect {[}IE{]} =
-0.105, 95\% CI {[}-0.173, -0.037{]}), as well as a trend of mediation
of the AR of 2-back by the rIPL-rPCC connectivity (IE = 0.047, 95\% CI
{[}-0.002, 0.096{]}). Similarly, alterations in RT of 0-back during
ageing were also fully mediated through the activity of the rACC (IE =
0.045, 95\% CI {[}0.005, 0.085{]}) and partially mediated by the
rACC-lIPL connectivity (IE = -0.057, 95\% CI {[}-0.111, -0.002{]}). More
importantly, the total of indirect effects of ageing on AR and RT,
mediated by the two different functional measurements, were not
significant (for AR of 2-back, sum of IE = -0.059, 95\% CI {[}-0.143,
0.026{]}; for RT of 0-back, sum of IE = -0.012, 95\% CI {[}-0.080,
0.056{]}; Figure 3).

\textbf{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.

\textbf{5. Conclusion}

By applying the background connectivity approach, this study considered
the alterations of brain activities and connectivity and their roles in
WM ageing. The findings revealed independent age-related changes between
two functional features and identified that they exhibited
counterbalanced impacts on age-related changes in WM performance, which
provided a richer repertoire of brain dynamics in ageing, a possible
explanation from connectivity on the brain compensation in ageing, with
implications for a comprehensive understanding of higher cognitive
functions and their alterations during ageing or disease-related
processes.

\textbf{6. Data Availability Statement}

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

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\textbf{Figure Legends}

\textbf{Figure 1.} WM-related activation and deactivation and their
age-related changes.

A: lateral and medial maps of task-evoked activity in the ``2back
-0back'' condition, red/yellow indicates positive activity, blue/cyan
indicates negative activity; B: age-related changes of task-evoked
activity in the ``2back -0back'' condition, red/yellow indicates changes
in positive regions, blue/cyan indicates changes in negative regions; C:
scatter plots of brain activities of regions showing significant
age-related alterations. The results were corrected for multiple
comparisons at p \textless{} 0.05. Brain maps were generated using the
BrainNet viewer toolbox (Xia et al., 2013).

\textbf{Figure 2.} Regions showing age-related enhanced background
connectivity with dysfunctional task-evoked seed regions.

Upper: seed region in the inferior parietal lobule (IPL) and brain
regions where significant age impacts on background connectivity derived
from the IPL were found; Lower: seed region in the anterior cingulate
cortex (ACC) and brain regions where significant age impacts on
background connectivity derived from the ACC were found. The results
were thresholded at corrected p \textless{} 0.05.

\textbf{Figure 3.} Mediation models on age, regional activity,
background connectivity and WM performance.

A: Activation and background connectivity in the inferior parietal
lobule (IPL) mediated age-related effects on the accuracy rate (AR) of
the 2-back; B: Deactivation and background connectivity in the anterior
cingulate cortex (ACC) mediated age-related effects on the reaction time
(RT) of the 0-back. All coefficients are standardized and presented as
Estimate (Standard Error). Covariates included gender and years of
education and were not shown in the models. *** \textless{} 0.001, **
\textless{} 0.01, * \textless{} 0.05. CI, confidence interval.

Figure S1. Maps of background connectivity derived from seed regions.

A: Group-level connectivity map derived from the anterior cingulate
cortex (ACC); B: group-level connectivity map derived from the inferior
parietal lobule (IPL). Images were thresholded at uncorrected p
\textless{} 0.001.

\textbf{Hosted file}

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\textbf{Hosted file}

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