Population clustering of structural brain aging and its association with brain development
Curation statements for this article:-
Curated by eLife
eLife assessment
This study divided structural brain aging into two groups, revealing that one group is more vulnerable to aging and brain-related diseases compared to the other group. This study is valuable as such subtyping could be utilized in predicting and diagnosing cognitive decline and neurodegenerative brain disorders in the future. However, the authors' claims remain incomplete, as there appears to be a lack of connection between this and the authors' claims.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- Medicine (eLife)
Abstract
Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the “last in, first out” mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.
Article activity feed
-
-
-
-
eLife assessment
This study divided structural brain aging into two groups, revealing that one group is more vulnerable to aging and brain-related diseases compared to the other group. This study is valuable as such subtyping could be utilized in predicting and diagnosing cognitive decline and neurodegenerative brain disorders in the future. However, the authors' claims remain incomplete, as there appears to be a lack of connection between this and the authors' claims.
-
Reviewer #1 (Public Review):
Summary:
Duan et al analyzed brain imaging data in UKBK and found a pattern in brain structure changes by aging. They identified two patterns and found links that can be differentiated by the categorization.Strengths:
This discovery harbors a substantial impact on aging and brain structure and function.Weaknesses:
Therefore, the study requires more validation efforts. Most importantly, data underlying the stratification of the two groups are not obvious and lack further details. Can they also stratified by different methods? i.e. PCA?Are there any external data that can be used for validation?
Other previous discoveries or claims supporting the results of the study should be explored to support the conclusion.
Sex was merely used as a covariate. Were there sex differences during brain aging? What was the …
Reviewer #1 (Public Review):
Summary:
Duan et al analyzed brain imaging data in UKBK and found a pattern in brain structure changes by aging. They identified two patterns and found links that can be differentiated by the categorization.Strengths:
This discovery harbors a substantial impact on aging and brain structure and function.Weaknesses:
Therefore, the study requires more validation efforts. Most importantly, data underlying the stratification of the two groups are not obvious and lack further details. Can they also stratified by different methods? i.e. PCA?Are there any external data that can be used for validation?
Other previous discoveries or claims supporting the results of the study should be explored to support the conclusion.
Sex was merely used as a covariate. Were there sex differences during brain aging? What was the sex ratio difference in groups 1 and 2?
Although statistically significant, Figure 3 shows minimal differences. LTL and phenoAge are displayed in adjusted values but what are the actual values that differ between patterns 1 and 2?
It is not intuitive to link gene expression results shown in Figure 8 and brain structure and functional differences between patterns 1 and 2. Any overlap of genes identified from analyses shown in Figure 6 (GWAS) and 8 (gene expression)?
-
Reviewer #2 (Public Review):
Summary:
The authors aimed to understand the heterogeneity of brain aging by analyzing brain imaging data. Based on the concept of structural brain aging, they divided participants into two groups based on the volume and rate of decrease of gray matter volume (GMV). The group with rapid brain aging showed accelerated biological aging and cognitive decline and was found to be vulnerable to certain neuropsychiatric disorders. Furthermore, the authors claimed the existence of a "last in, first out" mirroring pattern between brain aging and brain development, which they argued is more pronounced in the group with rapid brain aging. Lastly, the authors identified genetic differences between the two groups and speculated that the cause of rapid brain aging may lie in genetic differences.Strengths:
The authors …Reviewer #2 (Public Review):
Summary:
The authors aimed to understand the heterogeneity of brain aging by analyzing brain imaging data. Based on the concept of structural brain aging, they divided participants into two groups based on the volume and rate of decrease of gray matter volume (GMV). The group with rapid brain aging showed accelerated biological aging and cognitive decline and was found to be vulnerable to certain neuropsychiatric disorders. Furthermore, the authors claimed the existence of a "last in, first out" mirroring pattern between brain aging and brain development, which they argued is more pronounced in the group with rapid brain aging. Lastly, the authors identified genetic differences between the two groups and speculated that the cause of rapid brain aging may lie in genetic differences.Strengths:
The authors supported their claims by analyzing a large amount of data using various statistical techniques. There seems to be no doubt about the quality and quantity of the data. Additionally, they demonstrated their strength in integrating diverse data through various analysis techniques to conclude.Weaknesses:
There appears to be a lack of connection between the analysis results and their claims. Readers lacking sufficient background knowledge of the brain may find it difficult to understand the paper. It would be beneficial to modify the figures and writing to make the authors' claims clearer to readers. Furthermore, the paper gives an overall impression of being less polished in terms of abbreviations, figure numbering, etc. These aspects should be revised to make the paper easier for readers to understand. -