Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network.
Cereb Cortex
; 33(6): 2415-2425, 2023 03 10.
Article
em En
| MEDLINE
| ID: mdl-35641181
ABSTRACT
Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtorno Depressivo Maior
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Cereb Cortex
Assunto da revista:
CEREBRO
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China