No Cover Image

Journal article 387 views 63 downloads

Brain morphometric similarity and flexibility

Vesna Vuksanovic Orcid Logo

Cerebral Cortex Communications, Volume: 3, Issue: 3

Swansea University Author: Vesna Vuksanovic Orcid Logo

  • CerCortComm_VV22_tgac024.pdf

    PDF | Version of Record

    © The Author(s) 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

    Download (1.26MB)

Check full text

DOI (Published version): 10.1093/texcom/tgac024

Abstract

This study aimed to investigate the modular organization of the cortical surface morphometric similarity networks (MSNs) through the representation of the cortex as a multilayer network. Studying the cortex through the MSNs multilayers have revealed some network properties that could not be evaluate...

Full description

Published in: Cerebral Cortex Communications
ISSN: 2632-7376
Published: Oxford University Press (OUP) 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60224
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: This study aimed to investigate the modular organization of the cortical surface morphometric similarity networks (MSNs) through the representation of the cortex as a multilayer network. Studying the cortex through the MSNs multilayers have revealed some network properties that could not be evaluated using conventional network metrics. For the first time, this study has mapped flexible and inflexible morphometric similarity hubs, and evidence has been provided about variations of the modular network topology across the multilayers with age and IQ. The results contribute to understanding of intra-regional characteristics in cortical interactions, which potentially can be used to map heterogeneous neurodegeneration patterns in diseased brains.
Keywords: morphometric similarity network, multilayer networks, flexibility, general intelligence, ageing
College: Faculty of Medicine, Health and Life Sciences
Funders: The author received financial support for publication by a UK Research & Innovation (UKRI) Medical Research Council (MRC) grant Health Data Research (HDR9006).
Issue: 3