Joanna Su Xian Chong1, Yng Miin Loke1, Saima Hilal2,3, Mohammad Kamran Ikram3,4, Xin Xu2,3, Boon Yeow Tan5, Narayanaswamy Venketasubramanian6, Christopher Li-Hsian Chen2,3, and Juan Zhou1,7
1Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore, 2Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore, Singapore, 3Memory Ageing & Cognition Centre, National University Health System, Singapore, Singapore, 4Duke-National University of Singapore Graduate Medical School, Singapore, Singapore, 5St. Luke's Hospital, Singapore, Singapore, 6Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore, 7Clinical Imaging Research Centre, The Agency for Science, Technology and Research and National University of Singapore, Singapore, Singapore
Synopsis
Cerebrovascular
disease (CVD) frequently co-occurs with Alzheimer’s disease (AD), however its
effects on the organization of brain networks in AD patients remain unknown.
This study aimed to examine the influence of CVD on grey matter (GM) structural
covariance (SC) networks in prodromal and early AD patients. Divergent changes
in GM volumes and SC of higher-order networks were found between CVD and
non-CVD subtypes. Specifically, the default mode network showed changes in
non-CVD subtypes but was spared in CVD subtypes. These findings highlight the
different pathophysiology underlying AD patients with CVD and those without CVD.Purpose
Alzheimer’s disease (AD) is a
neurodegenerative disease characterized by episodic memory dysfunction and large-scale
brain network dysconnectivity. Specifically, several networks, notably the
default mode network (DMN), show reduced connectivity in AD patients compared
to healthy controls
1,2, and connectivity changes are evident even at
prodromal stages of AD
3. However, AD also frequently co-occurs with
cerebrovascular disease (CVD), and the influence of CVD on network changes in
AD remains unknown. This study therefore aimed to examine the influence of CVD
on grey matter (GM) atrophy and structural covariance (SC) network changes
4
(particularly of higher-order networks) in patients with prodromal and early stages
of AD.
Methods
The dataset consisted of 235
participants, with 47 participants in each of the five groups: no cognitive
impairment (NCI; controls), cognitive impairment no dementia (CIND), CIND with
CVD (CIND+CVD), AD, and AD with CVD (AD+CVD). T1-weighted structural MR images collected
from these participants were first preprocessed using voxel-based morphometry to
obtain individual normalized GM probability maps. Whole-brain voxelwise
analyses were then conducted to compare GM volumes across groups.
To examine SC networks across groups,
mean GM volumes of three 4-mm spherical regions of interest (ROIs) were
extracted from each individual’s GM images. The three ROIs corresponded to core
seed regions within the DMN, salience network (SN) and executive control
network (ECN) respectively. Separate regression analyses for each ROI were then
performed by entering mean ROI GM volumes as the covariate of interest and
group as the grouping variable. Resultant group maps were thresholded at a
height threshold of p < 0.001 and a cluster-extent threshold of p < 0.05
(familywise-error corrected). Finally, group differences in the extent of
networks were quantified by calculating the total number of voxels whose GM volumes significantly covaried
with each ROI.
Results
Group comparisons of GM volumes revealed
extensive atrophy in the bilateral frontal, temporal and parietal regions for
the AD and AD+CVD groups, and atrophy in the right frontal and temporal regions
for the CIND+CVD group compared to controls. No significant atrophy was
observed for the CIND group. Further analyses showed that both AD+CVD and
CIND+CVD groups displayed significant frontal atrophy compared to non-CVD AD
and CIND groups respectively.
SC networks showed divergent changes
between CVD and non-CVD subtypes. In non-CVD subtypes, the SC of the DMN and
ECN exhibited an inverted U-shaped trend, with increased extent in CIND mainly
in the frontal lobe, while SC of the SN showed a U-shaped trend, with increased
extent in AD mainly in the frontal and temporal lobes. In contrast, CVD
subtypes showed a progressive increase in SC with AD severity for all networks
except the DMN, which remained unchanged across groups. Sharp increases in SC
extent were observed for the AD+CVD group in bilateral frontal and temporal
regions, which overlapped largely with regions showing GM atrophy in AD+CVD
compared to controls.
Discussion
Consistent with past findings showing
deficits in frontal lobe functions for older adults with CVD 5,
greater atrophy in the frontal lobe regions was observed for CVD subtypes
compared to non-CVD subtypes. Examination of SC networks across groups further
revealed divergent changes in higher-order SC networks between CVD and non-CVD
subtypes. Non-CVD subtypes showed changes in SC that were largely consistent
with past findings on functional connectivity, including findings of reduced DMN connectivity and increased SN
connectivity in AD 6, and increased frontal connectivity in the DMN
at prodromal stages of AD 7.
In comparison, CVD subtypes showed a
progressive increase in the SC of ECN and SN with AD severity, with marked
increases in extent for the AD+CVD group. Given that these networks overlap
largely with one another as well as with regions of atrophy in the AD+CVD
group, the large increase in SC network extent could imply correlated grey
matter atrophy and a re-organization of networks caused by widespread CVD-related
white matter damage. The DMN, on the other hand, did not show any changes in SC
with AD severity. Taken together, the pathology of CVD subtype might differ
from that of non-CVD subtypes.
Conclusion
Our study found disparate changes in GM
volumes and SC of higher-order networks between prodromal and early AD
patients with and without CVD. Future research is required to examine
functional connectivity changes in these groups and elucidate the mechanisms
underlying these connectivity changes.
Acknowledgements
This work was supported by an NMRC Centre Grant (NMRC/CG/013/2013
and NMRC/CG/NUHS/2010 to CC), the Biomedical Research Council, Singapore (BMRC 04/1/36/372 to JZ), the National Medical Research
Council, Singapore (NMRC/CIRG/1390/2014 to JZ), and Duke-NUS Graduate Medical School Signature Research Program funded by Ministry of Health, Singapore. References
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