ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022
Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-bioRxiv : the preprint server for biology · 2026Article
- Divergence unveils further distinct phenotypic traits of human brain connectomics fingerprint.iScience · 2026Article
- Reconstructing brain causal dynamics for subject and task fingerprints using fMRI time-series data.Health information science and systems · 2025Article
- Fair CCA for Fair Representation Learning: An ADNI Study.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling.Medical image analysis · 2025Article
- Characterizing Amyloid Pathogenic Spread in Alzheimer's Disease Through A Network Diffusion Model.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Causality-based Subject and Task Fingerprints using fMRI Time-series Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024Article
- Sex-specific topological structure associated with dementia via latent space estimation.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- Article
- PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Homological landscape of human brain functional sub-circuits.Mathematics (Basel, Switzerland) · 2024Article
- Exploring Alzheimer's disease: a comprehensive brain connectome-based survey.Psychoradiology · 2024Review
- Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
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7 authors.
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Abstract
Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD's relationship with disrupted connectivity.
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