Clustering of white matter asymmetries in aging and aging-related diseases
Poster No:
1783
Submission Type:
Abstract Submission
Authors:
Duxiao Guo1, Yuhao Duan1, Xiangzhen Kong1
Institutions:
1Zhejiang University, Hangzhou, China
First Author:
Co-Author(s):
Introduction:
Asymmetry is a fundamental organizational feature of the human brain. While much attention has been devoted to hemispheric differences in gray matter (Kong et al., 2022; Pu et al., 2024), relatively less is known about white matter asymmetry, particularly in the context of aging and aging-related disease. Addressing these gaps is essential for advancing our understanding of the mechanisms underlying normal aging and its deviations.
In this project, we aimed to chart the trajectories of white matter asymmetry across various microstructural metrics (e.g., FA, and ICVF) throughout the aging process. By identifying clusters of asymmetries based on their aging patterns, we sought to uncover common and divergent aging processes in different white matter tracts. Additionally, we examined the relationships between these asymmetries and cognitive performance, shedding light on their functional significance. Finally, we extended our investigation to aging-related diseases, exploring how atypical white matter asymmetry patterns manifest in patients Alzheimer's disease (AD) and Parkinson's disease (PD).
In this project, we aimed to chart the trajectories of white matter asymmetry across various microstructural metrics (e.g., FA, and ICVF) throughout the aging process. By identifying clusters of asymmetries based on their aging patterns, we sought to uncover common and divergent aging processes in different white matter tracts. Additionally, we examined the relationships between these asymmetries and cognitive performance, shedding light on their functional significance. Finally, we extended our investigation to aging-related diseases, exploring how atypical white matter asymmetry patterns manifest in patients Alzheimer's disease (AD) and Parkinson's disease (PD).
Methods:
The UK Biobank data from 40,518 healthy elderly individuals aged 45 to 82 years were analyzed. Nine microstructural metrics – FA, MD, L1, L2, L3, MO, ICVF, ISOVF, and OD – across 42 white matter tracts were examined. In addition, behavioral data of ten cognitive functions were used to derive a measure for general cognitive ability, and data from aging-related patients ('Dementia', 'Alzheimer's', 'Parkinsonism', and 'Parkinson's') were also included.
To chart the aging trajectories of white matter asymmetries, we first calculated Cohen's d for the left-right differences in each of the nine white matter metrics across age groups. Based on the aging trajectories of various white matter tracts, we employed Partitioning Around Medoids (PAM) clustering, using Silhouette analysis to determine the optimal number of clusters, to identify the underlying clusters for each white matter metric. Subsequently, we calculated the asymmetry index at the cluster level and examined its correlations with the general cognitive ability in each age group (50-59, 60-69, and 70-79). Additionally, we charted the aging trajectories of patients with AD or PD and compared them with the typical patterns.
To chart the aging trajectories of white matter asymmetries, we first calculated Cohen's d for the left-right differences in each of the nine white matter metrics across age groups. Based on the aging trajectories of various white matter tracts, we employed Partitioning Around Medoids (PAM) clustering, using Silhouette analysis to determine the optimal number of clusters, to identify the underlying clusters for each white matter metric. Subsequently, we calculated the asymmetry index at the cluster level and examined its correlations with the general cognitive ability in each age group (50-59, 60-69, and 70-79). Additionally, we charted the aging trajectories of patients with AD or PD and compared them with the typical patterns.
Results:
PAM clustering with Silhouette analysis revealed two clusters for each white matter metrics (Fig.1). The resulting white matter clusters showed consistent asymmetry patterns during aging. For example, Cluster 1 of ICVF demonstrated a pronounced leftward asymmetry that declined noticeably with age. Moreover, we observed substantial consistency in the clustering results across different microstructural metrics, indicating shared aging processes underlying white matter microstructures.
Regarding the functional relevance of the derived clusters, we observed age-related differences in the correlations between cluster-level asymmetry index and the general cognitive ability. Notably, both ICVF and ISOVF Cluster 0 showed significant differences in the brain-behavioral correlations between age group 50-59 and 70-79 (ps < 0.01), a novel finding not previously reported in the literature.
The analyses with patients revealed significant deviations from the typical aging trajectories in several white matter metrics, including ICVF Cluster 0 and ISOVF Cluster 0. These differences align with age-related variations in the correlations between white matter asymmetry and general cognitive ability, collectively suggesting that white matter asymmetries in ICVF and ISOVF play a potential role in cognitive decline during aging.
Regarding the functional relevance of the derived clusters, we observed age-related differences in the correlations between cluster-level asymmetry index and the general cognitive ability. Notably, both ICVF and ISOVF Cluster 0 showed significant differences in the brain-behavioral correlations between age group 50-59 and 70-79 (ps < 0.01), a novel finding not previously reported in the literature.
The analyses with patients revealed significant deviations from the typical aging trajectories in several white matter metrics, including ICVF Cluster 0 and ISOVF Cluster 0. These differences align with age-related variations in the correlations between white matter asymmetry and general cognitive ability, collectively suggesting that white matter asymmetries in ICVF and ISOVF play a potential role in cognitive decline during aging.
Conclusions:
Through this work, we provide a comprehensive framework for understanding white matter asymmetry in aging, offering insights into its contributions to cognitive aging and its potential as a biomarker for age-related neurodegenerative diseases.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Aging
Data analysis
Open Data
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Asymmetry
1|2Indicates the priority used for review
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Provide references using APA citation style.
2.Pu, Y. (2024). Global brain asymmetry. Trends in Cognitive Sciences. Advance online publication. https://doi.org/10.1016/j.tics.2024.10.008
3.Roe, J. M. (2020). Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s Disease. bioRxiv. https://doi.org/10.1101/2020.06.18.158980
4.Korbmacher, M. (2024). Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer’s Disease in the UK Biobank. Biological Psychiatry Global Open Science, 4(4), https://doi.org/10.1016/j.bpsgos.20