Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients.

Sci Rep

National Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.

Published: December 2017

AI Article Synopsis

  • Recent research has identified key topological properties of healthy brain connectomes, but the impact of brain lesions on network topology remains unclear.
  • A study using multivariate support vector regression on data from 96 brain-damaged patients revealed that specific lesion patterns predict changes in functional network topology, leading to the identification of "lesion hubs."
  • These lesions can cause network topology to shift in two distinct ways, either towards increased integration (global) or increased segregation (local), suggesting complex dynamics in functional brain networks post-injury.

Article Abstract

Various important topological properties of healthy brain connectome have recently been identified. However, the manner in which brain lesion changes the functional network topology is unknown. We examined how critical specific brain areas are in the maintenance of network topology using multivariate support vector regression analysis on brain structural and resting-state functional imaging data in 96 patients with brain damages. Patients' cortical lesion distribution patterns could significantly predict the functional network topology and a set of regions with significant weights in the prediction models were identified as "lesion hubs". Intriguingly, we found two different types of lesion hubs, whose lesions associated with changes of network topology towards relatively different directions, being either more integrated (global) or more segregated (local), and correspond to hubs identified in healthy functional network in complex manners. Our results pose further important questions about the potential dynamics of the functional brain network after brain damage.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738424PMC
http://dx.doi.org/10.1038/s41598-017-17886-xDOI Listing

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