ArticleProceedings of SPIE--the International Society for Optical Engineering
Longitudinal changes of connectomes and graph theory measures in aging.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Topological turning points across the human lifespan.Nature communications · 2025Article
- Electrophysiological resting-state signatures link polygenic scores to general intelligence.Scientific reports · 2025Observational
- Harmonizing 10,000 connectomes: site-invariant representation learning for multi-site analysis of network connectivity and cognitive impairment.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Learning disentangled representations to harmonize connectome network measures.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Learning site-invariant features of connectomes to harmonize complex network measures.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.ArXiv · 2024Article
- Arterial spin labeling MRI applied to migraine: current insights and future perspectives.The journal of headache and pain · 2023Review
- Physical fitness, cognition, and structural network efficiency of brain connections across the lifespan.Neuropsychologia · 2023Article
- Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that are correlated with neurobiological variations and can help guide quantitative study of brain function. However, the understanding of how connectomes change longitudinally is limited. In this work, we evaluate modern pipelines to obtain the connectomics data from diffusion weighted MRI scans across different sessions from control subjects (55-99 years old) in the Baltimore Longitudinal Study of Aging and Cambridge Centre for Ageing and Neuroscience. From the connectivity matrices, we compute graph theory measures to understand their brain networks and apply a linear mixed-effects model to study their longitudinal changes with respect to age. With this approach, we computed 14 graph theory measures of 1469 healthy subjects (2476 scans) and found statistically significant correlations between all 14 measures and age. In this analysis: 1) the brain becomes more segregated but less integrated in aging; 2) the overall network cost increases for older subjects; 3) the small-world organizations remain stable; and 4) due to high intra-subject variance, there is not clear trend for longitudinal changes of graph theory measures of individual subjects. Therefore, while useful to investigate brain evolution in aging at the population level, improvements in the connectome reconstruction are needed to decrease single subject variability for individual inference.
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