Cargando…
Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model
OBJECTIVES: Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). PARTICIPANTS: Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. DESIGN: Cross-sectional. MEASUREMENTS:...
Autores principales: | Cao, Bing, Yang, Erkun, Wang, Lihong, Mo, Zhanhao, Steffens, David C., Zhang, Han, Liu, Mingxia, Potter, Guy G. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394384/ https://www.ncbi.nlm.nih.gov/pubmed/37539384 http://dx.doi.org/10.3389/fnins.2023.1209906 |
Ejemplares similares
-
Altered Cerebellar-Cerebral Functional Connectivity in Geriatric Depression
por: Alalade, Emmanuel, et al.
Publicado: (2011) -
Altered Synchronizations among Neural Networks in Geriatric Depression
por: Wang, Lihong, et al.
Publicado: (2015) -
Recent advances in the use of imaging in psychiatry: functional magnetic resonance imaging of large-scale brain networks in late-life depression
por: Manning, Kevin, et al.
Publicado: (2019) -
Depressive Symptom Network Associated With Comorbid Anxiety in Late-Life Depression
por: An, Min Ho, et al.
Publicado: (2019) -
Hippocampus Shape Analysis and Late-Life Depression
por: Zhao, Zheen, et al.
Publicado: (2008)