ArticleFrontiers in aging neuroscience2019
Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease.
Article in Frontiers in aging neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- Review
- 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
- 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
- Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- The trend of disruption in the functional brain network topology of Alzheimer's disease.Scientific reports · 2022Article
- A framework For brain atlases: Lessons from seizure dynamics.NeuroImage · 2022Article
- Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- A hands-on tutorial on network and topological neuroscience.Brain structure & function · 2022Review
- A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.NeuroImage · 2022Article
- Altered functional connectivity and topology structures in default mode network induced by inflammatory exposure in aged rat: A resting-state functional magnetic resonance imaging study.Frontiers in aging neuroscience · 2022Article
- Structural and Functional Asymmetry in Precentral and Postcentral Gyrus in Patients With Unilateral Chronic Shoulder Pain.Frontiers in neurology · 2022Article
- Identification of Alzheimer's Disease Progression Stages Using Topological Measures of Resting-State Functional Connectivity Networks: A Comparative Study.Behavioural neurology · 2022Article
- A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults.Human brain mapping · 2021Article
- Standardizing human brain parcellations.Scientific data · 2021Article
- Distinct network topology in Alzheimer's disease and behavioral variant frontotemporal dementia.Alzheimer's research & therapy · 2021Article
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Abstract
Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at
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