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°, 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 < 0.001 and cluster level p < 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 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 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 < 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 (Muthén and Muthén, 2012), and bootstrapped
95% confidence intervals (CIs) were reported alongside parameter
estimates (Preacher et al., 2007; MacKinnon, 2008).