Literature DB >> 33170473

Unexpected hubness: a proof-of-concept study of the human connectome using pagerank centrality and implications for intracerebral neurosurgery.

Jacky T Yeung1, Hugh M Taylor2, Isabella M Young3, Peter J Nicholas2, Stéphane Doyen2, Michael E Sughrue4,5.   

Abstract

INTRODUCTION: Understanding 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.
METHODS: We 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.
RESULTS: In 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.
CONCLUSIONS: We 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.

Entities:  

Keywords:  Centrality; Connectome; Connectomics; Hubs; Unexpected hubness

Year:  2020        PMID: 33170473     DOI: 10.1007/s11060-020-03659-6

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  4 in total

1.  Big Data in the Clinical Neurosciences.

Authors:  G Damian Brusko; Gregory Basil; Michael Y Wang
Journal:  Acta Neurochir Suppl       Date:  2022

Review 2.  What's new and what's next in diffusion MRI preprocessing.

Authors:  Chantal M W Tax; Matteo Bastiani; Jelle Veraart; Eleftherios Garyfallidis; M Okan Irfanoglu
Journal:  Neuroimage       Date:  2021-12-26       Impact factor: 7.400

3.  Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery.

Authors:  Onur Tanglay; Isabella M Young; Nicholas B Dadario; Hugh M Taylor; Peter J Nicholas; Stéphane Doyen; Michael E Sughrue
Journal:  J Neurooncol       Date:  2022-02-04       Impact factor: 4.130

Review 4.  The role of artificial intelligence in paediatric neuroradiology.

Authors:  Catherine Pringle; John-Paul Kilday; Ian Kamaly-Asl; Stavros Michael Stivaros
Journal:  Pediatr Radiol       Date:  2022-03-26
  4 in total

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