Detailed Information

Cited 7 time in webofscience Cited 8 time in scopus
Metadata Downloads

Phenotype Network and Brain Structural Covariance Network of Major Depression

Authors
Yun, Je-YeonKim, Yong Ku
Issue Date
Apr-2021
Publisher
Springer
Keywords
Brain structural covariance network; Graph theory; Magnetic resonance imaging; Major depressive disorder; Phenotype network
Citation
Advances in Experimental Medicine and Biology, v.1305, pp 3 - 18
Pages
16
Indexed
SCIE
SCOPUS
Journal Title
Advances in Experimental Medicine and Biology
Volume
1305
Start Page
3
End Page
18
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/53024
DOI
10.1007/978-981-33-6044-0_1
ISSN
0065-2598
2214-8019
Abstract
Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles, neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered. Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs. © 2021, Springer Nature Singapore Pte Ltd.
Files in This Item
There are no files associated with this item.
Appears in
Collections
2. Clinical Science > Department of Psychiatry > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Yong Ku photo

Kim, Yong Ku
Ansan Hospital (Department of Psychiatry, Ansan Hospital)
Read more

Altmetrics

Total Views & Downloads

BROWSE