Kaicheng li1, Xiao Luo1, Qingze Zeng1, Peiyu Huang1, Yong Zhang2, and Min-Ming Zhang1
1The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China, 2GE Healthcare Shanghai, Shanghai, China
Synopsis
Alzheimer’s disease (AD) is a clinical-pathologic
entity with a long pathological phase before the dementia onset. The latest ATN
classification system is a effective tool in AD research and can provide a more
accurate AD stages. Here, we aim to explore the evolution patterns of gray
matter structural covariance networks (SCNs) along AD continuum by using the ATN
classification system.
Introduction
Defined
as a clinical-pathologic entity, the Alzheimer’s disease (AD) has a long pathological
phase before the dementia onset. Previous studies regarded AD as a
disconnection disease and suggested that the cognitive decline was a
consequence of structural and functional connectivity disruptions. However, the
trajectory of structural network connectivity changes along AD continuum is
still unclear. Therefore, our study aimed to explore the evolution patterns of
gray matter structural covariance networks (SCNs) along AD continuum.Methods
We
used the latest ATN classification system to divide subjects into four stages
based on cerebrospinal fluid (CSF) amyloid-beta 1–42 (A) and phosphorylated tau
protein 181 (T). Combined with cognitive status, we included 101 pre-dementia
AD individuals with normal CSF (Stage 0, A-T-), 40 pre-dementia AD individuals with
positive amyloid pathology (Stage 1, A+T−), 101 pre-dementia AD individuals
with both abnormal CSF (Stage 2, A+T+) and 91 AD patients with both abnormal
CSF (Stage 3, A+T+). We used four ROIs (left posterior
cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral
prefrontal cortex) to anchor default mode network (DMN), salience network (SN)
and executive control network (ECN). Finally, we assessed the SCN alternations
using a multi-regression model-based linear-interaction analysis. Results
Along
with disease progresses, DMN (medial temporal subsystem) showed initially
increased structural association between the entorhinal cortex (EC) and middle
temporal gyrus (MTG) and subsequently decreased association with MTG and
superior frontal gyrus. Moreover, SN firstly showed an increased structural
association between frontoinsular and precuneus and subsequently decreased
association in the inferior temporal gyrus along the disease continuum. Regarding
ECN, we found an increased structural association between the dorsolateral
prefrontal cortex and prefrontal regions in subjects with abnormal CSF. Discussion
AD is a network degeneration
disease, with pathologies expand along functional networks, consequently
leading to failure of these networks. Thereinto, DMN, SN and ECN cooperate with
each other to maintain cognitive abilities including memory, and executive
function [1,
2].
Here, we found initially increased then decreased structural association in both
DMN and SN. This result is in line with previous functional studies [3,
4].
Combining with pathological classification, the increased association may be the
initial network response to amyloid pathology which works as a compensatory
mechanism [5,
6],
and further decreased connectivity will follow in the presence of neurodegeneration
[7].
On the other hand, ECN guides decision-making by integrating external
information and storing internal representations [2,
8].
It is always recruited as a compensatory network associated with cognitive
reservation in AD patients [9].
Our data support the idea by finding a continuously increased association in
ECN. Conclusively, DMN, SN and ECN showed interactive and dynamic changes with
the pathology progress. Regarding the increased structural association, it may
be a harmful compensation at the expense of the neuron damage. Stronger
covariance strength between the seed and peak clusters indicated more intra-network
connections to maintain cognition [10].
We assumed this compensation would finally be unsustainable as AD progress. As
for the decreased structural association, it reflects the inconsistency GM loss
between two regions. Such an asynchronous structural change matched the
functional changes to some extent and provided supplementary information.Conclusion
Our
results proved the network disconnection hypothesis in AD and showed a dynamic trajectory
of SCNs changes along the AD continuum. Specifically, during the disease
progression, DMN and SN showed the initial phase of hyperconnectivity and then hypoconnectivity. However, ECN showed the potential compensatory role
in AD patients. Besides, our study suggested SCN may serve as an effective
biomarker for AD progression tracking.Acknowledgements
No acknowledgement found.References
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