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Written by Zhao Haichao - Wang Sizhen, Fang Yiyi
Editor—Summer Leaf
Aging has significant negative effects
on human brain structure and cognitive function.
In recent years, life-cycle studies have found that white matter structure undergoes a complex degenerative process in old age: it remains relatively stable in mid-adulthood and subsequently declines at an accelerated rate [1].
However, most previous studies have focused on the gray matter structure and functional degeneration patterns of young and elderly people under 80 years old, while a few studies focusing on the successful aging of the elderly have proposed that the key to successful cognitive aging in the elderly lies in the slow degradation of cortical structure and function [2-4
。 However, the pattern of "successful aging" of white matter structure in advanced older adults is currently unclear, which is useful for understanding how to successfully age and exploring brain diseases associated with aging.
It has important reference value
.
Recently, the team of Professor Liu Tao, School of Biological and Medical Engineering of Beijing University of Aeronautics and Astronautics and Beijing Biomedical Engineering Advanced Innovation Center, delivered a speech at Cerebral Cortex Research paper "An accelerated degeneration of white matter microstructure and networks in the nondemented old–old".
Based on the dispersion magnetic resonance image data of 419 elderly people from the Sydney Memory and Ageing Study, the researchers used the directional field analysis of the innovative data framework [5-7].
The directional morphology of white matter fibers and the topological properties of the structural network were quantified, which verified that the elderly had an accelerated aging mode of white matter structure different from that of young and elderly people, and revealed that accelerated degeneration of white matter structure is one of the main factors of cognitive decline, which is of great significance
for promoting the understanding of the underlying neural mechanism of cognitive aging in old age.
One.
Accelerated degradation of the microstructure of white matter in the elderly and in the direction of morphology and integrity
.
Finally, the authors calculated the average eigenvalues of each fiber bundle through the fiber bundle analysis of interest, and compared the differences between
the young-old group and the old-old group 。 In addition, the authors performed a simple linear regression analysis of the age effect of the microstructural indicators of the whole sample and each age group, calculated and normalized the degradation rate of each index with age, and finally used a permutation test to compare the differences
in the degradation rate of white matter microstructure attributes between the young group and the older group.
The results showed that the older group showed obvious age-enhancing degeneration
in terms of the integrity and directional morphological attributes of white matter fiber bundles such as large forceps, small forceps and corpus callosum.
Among them, the elderly group showed a trend of accelerated aging in terms of microstructural integrity and directional morphology of the small clamp fiber bundle compared with the younger group (Fig.
1).
Fig.
1 Accelerated degradation of white matter microstructure integrity and directional morphology in the elderly
(Source: Zhao H, et al.
, Cereb Cortex, 2022).
II Accelerated degradation of topological properties of white matter structure network in the elderly
The researchers constructed a white matter fiber connection matrix through deterministic probabilistic fiber tracking and combined with the brain connectome map.The topological properties
of white matter structure networks at the whole brain, subnetwork and node levels are systematically explored.
In addition, in order to quantify the module connections between subnetworks, the authors defined and extracted five brain modules based on existing prior templates and previous research results: sensorimotor network (SMN) and visual network （ VN), Prominent Network (SAN), Executive Control Network (ECN).
and the default network (DMN).
Based on the network module structure, the author defines the intra-module connection as the total number of fiber edges existing in a single module, and the intra-module connection strength is the average connection strength
of all fiber connections in a single module.
The inter-module connection is the number of fiber edges that exist between any two modules, and the inter-module connection strength is the average strength
of the fiber edges that exist between any two modules.
Finally, the authors compare and analyze the differences
between the younger and older groups in these topological attributes.
In addition, the authors performed a simple linear regression analysis of the age effect of topological indicators across the entire sample and each age group, calculated and normalized the annual change rate of these indicators, and then used a permutation test to compare the differences
in the rate of degradation of network topological attributes between the younger and older groups.
The results showed that the connection strength of the white matter structure network was significantly reduced
in the elderly people.
In terms of whole-brain topological attributes, the ability of whole-brain information communication in non-dementia elderly people decreased significantly, which was manifested by a significant increase
in the length of standardized feature paths.
The modular connection analysis shows that the connection strength of the elderly in the modules of SMN, VN and ECN is significantly reduced, and the connection strength is significantly reduced in SMN-ECN SMN-DMN, VN-ECN, VN-DMN, SAN-ECN, SAN-DMN and The inter-module connection strength of ECN-DMN is significantly reduced
.
In terms of edge connection strength, the connection strength between the right auxiliary motor area and the left olfactory cortex in the SMN network decreased significantly (Fig.
2).
At the same time, the young and elderly only showed a relatively stable aging decline trend in all topological indicators of the white matter structure network, while the elderly showed a relatively stable aging downward trend, while the elderly showed SMN, VN and DMN In terms of intra-module connection strength, as well as in SN-SAN , VN-ECN , VNN-DMN, SAN-ECN, The inter-module connection strength of SAN-DMN and ECN-DMN shows a significant accelerated decrease in aging (Figure 3).
Fig.
2 Degradation
of the topological attributes of the fabric network in the elderly group.
(Source: Zhao H, et al.
, Cereb Cortex, 2022).
Fig.
3 Accelerated degradation of module connectivity in the elderly group
(Source: Zhao H, et al.
, Cereb Cortex, 2022).
Three.
Potential links between white matter connectivity, age, and overall cognitive level
between microstructural connectivity and structural network connectivity and overall cognitive levels in all non-dementia older adults and two age groups.
Then, mediated analyses were used to assess whether and how microstructural connectivity and structural network connectivity modulate the effect of age on overall cognitive levels, assessing the direct and indirect effects
between these variables.
In addition, the authors weighted the connection strength of each white matter bundle or structural network to obtain an index
that characterizes the overall connectivity of the white matter microstructure and the overall connectivity of the structural network.
The specific publicity is as follows: (1) Microstructure connectivity: SFi = 1/N * ∑j ∈ G (Fij * βj)； (2) Fabric network connectivity: SSCi = 1/N * ∑ j∈G (Sij * βj )
。 Among them, SF i and SSC i refer to the microscopic connectivity of the whole brain and the connectivity of the whole brain network, respectively.
F ij, S ij refer to the average of subjects i in the white matter fiber bundle j, respectively FA value, the average connection strength of all connections to the participant i in/between structural network module j; β j refers to the standardized regression coefficients of the white matter fiber bundle j or the j-degree of the structural network module obtained by the linear regression analysis above (Figure 4 ）
。
Finally, the authors use epsilon-SVR of the linear kernel function LIBSVM to construct a support vector machine model for predicting cognitive longitudinal decline by white matter structure connectivity, and use the 10-fold cross-validation framework to evaluate the effectiveness of the
。 The results showed that the overall cognitive level was significantly strongly correlated
with the microstructure connectivity and structural network connectivity of white matter in the non-dementia elderly, young group and elderly group.
However, white matter connectivity significantly modulates the effect of age on overall cognition in the older group, an effect that is not significant
in the younger group.
At the same time, machine learning models based on white matter structural connectivity have shown good predictive effect on individual cognitive development (Figure 4).
Fig.
4 Mediating model of overall cognitive level, white matter structural connectivity and age
(Source: Zhao H, et al.
, Cereb Cortex, 2022).
of white matter damage in old age.
White matter structural connectivity modulates the effect of age on overall cognition and has a relatively significant predictive effect
on longitudinal cognition in non-dementia elderly.
These findings suggest that the deterioration of white matter connectivity is the neural basis of cognitive aging in humans, and elucidate novel imaging markers for predicting cognitive aging based on white matter connectivity
.
It is worth pointing out that the current research still has some shortcomings, for example, the study does not consider the effects of white matter high signal on the cognitive state of the elderly and the microstructure and brain network of white matter; Moreover, the structure network is based on the prior module to extract the module structure, which cannot truly reflect the organization of the elderly white matter structure network, which requires future research to use the module extraction algorithm to investigate the tissue degradation mode of
the old white matter structure.
Original link: https://doi.
org/10.
1093/cercor/bhac372
Dr.
Zhao Haichao from the School of Biological and Medical Engineering of Beihang University is the first author of the paper, and Professor Liu Tao from the School of Biological and Medical Engineering of Beihang University and researcher Cheng Jian from the School of Computer Science are the co-corresponding authors
.
This research was supported by the National Natural Science Foundation of China (61971017, 81871434) and the Beijing Municipal Natural Science Foundation of China (Z200016).
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End of this article
