Journal article 474 views 86 downloads
Brain morphometric similarity and flexibility
Cerebral Cortex Communications, Volume: 3, Issue: 3
Swansea University Author:
Vesna Vuksanovic
-
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)
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...
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 |