ArticleJournal of neuro-oncology2021
Unexpected hubness: a proof-of-concept study of the human connectome using pagerank centrality and implications for intracerebral neurosurgery.
Article in Journal of neuro-oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Brain state theory as a comprehensive model for addressing the "non-eloquent" brain in glioma surgery.Journal of neuro-oncology · 2025Article
- Discernible interindividual patterns of global efficiency decline during theoretical brain surgery.Scientific reports · 2024Article
- Intrinsic and Microenvironmental Drivers of Glioblastoma Invasion.International journal of molecular sciences · 2024Review
- A comparison of neuroelectrophysiology databases.Scientific data · 2023Review
- Development of an educational method to rethink and learn oncological brain surgery in an "a la carte" connectome-based perspective.Acta neurochirurgica · 2023Article
- Article
- Review
- Advances in computational and translational approaches for malignant glioma.Frontiers in physiology · 2023Review
- Connectomic insight into unique stroke patient recovery after rTMS treatment.Frontiers in neurology · 2023Article
- Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities.Frontiers in human neuroscience · 2023Review
- The role of artificial intelligence in paediatric neuroradiology.Pediatric radiology · 2022Review
- What's new and what's next in diffusion MRI preprocessing.NeuroImage · 2022Review
- Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery.Journal of neuro-oncology · 2022Article
- Big Data in the Clinical Neurosciences.Acta neurochirurgica. Supplement · 2022Article
- Reducing the Cognitive Footprint of Brain Tumor Surgery.Frontiers in neurology · 2021Review
- Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionUnderstanding the human connectome by parcellations allows neurosurgeons to foretell the potential effects of lesioning parts of the brain during intracerebral surgery. However, it is unclear whether there exist variations among individuals such that brain regions that are thought to be dispensable may serve as important networking hubs.
methodsWe obtained diffusion neuroimaging data from two healthy cohorts (OpenNeuro and SchizConnect) and applied a parcellation scheme to them. We ranked the parcellations on average using PageRank centrality in each cohort. Using the OpenNeuro cohort, we focused on parcellations in the lower 50% ranking that displayed top quartile ranking at the individual level. We then queried whether these select parcellations with over 3% prevalence would be reproducible in the same manner in the SchizConnect cohort.
resultsIn the OpenNeuro (n = 68) and SchizConnect cohort (n = 195), there were 27.9% and 43.1% of parcellations, respectively, in the lower half of all ranks that displayed top quartile ranks. We noted three outstanding parcellations (L_V6, L_a10p, and L_7PL) in the OpenNeuro cohort that also appeared in the SchizConnect cohort. In the larger Schizconnect cohort, L_V6, L_a10p, and L_7PL had unexpected hubness in 3.08%, 5.13%, and 8.21% of subjects, respectively.
conclusionsWe demonstrated that lowly-ranked parcellations may serve as important hubs in a subset of individuals, highlighting the importance of studying parcellation ranks at the personalized level in planning supratentorial neurosurgery.
Indexed as
Identifiers
33170473What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.