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Phenotype Network and Brain Structural Covariance Network of Major Depression

Authors
Yun, Je-YeonKim, Yong-Ku
Issue Date
2021
Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
Keywords
Brain structural covariance network; Graph theory; Magnetic resonance imaging; Major depressive disorder; Phenotype network
Citation
MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES, v.1305, pp.3 - 18
Indexed
SCIE
SCOPUS
Journal Title
MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES
Volume
1305
Start Page
3
End Page
18
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/130288
DOI
10.1007/978-981-33-6044-0_1
ISSN
0065-2598
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.
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