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Psychiatric and Mental Health Commons™
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Articles 1 - 2 of 2
Full-Text Articles in Psychiatric and Mental Health
Ambient Air Pollution Exposure And Increasing Depressive Symptoms In Older Women: The Mediating Role Of The Prefrontal Cortex And Insula, Andrew J. Petkus, Susan M. Resnick, Xinhui Wang, Daniel P. Beavers, Mark A. Espeland, Margaret Gatz, Tara Gruenewald, Joshua Millstein, Helena C. Chui, Joel D. Kaufman, Joann E. Manson, Gregory A. Wellenius, Eric A. Whitsel, Keith Widaman, Diana Younan, Jiu-Chiuan Chen
Ambient Air Pollution Exposure And Increasing Depressive Symptoms In Older Women: The Mediating Role Of The Prefrontal Cortex And Insula, Andrew J. Petkus, Susan M. Resnick, Xinhui Wang, Daniel P. Beavers, Mark A. Espeland, Margaret Gatz, Tara Gruenewald, Joshua Millstein, Helena C. Chui, Joel D. Kaufman, Joann E. Manson, Gregory A. Wellenius, Eric A. Whitsel, Keith Widaman, Diana Younan, Jiu-Chiuan Chen
Psychology Faculty Articles and Research
Exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) have been associated with the emergence of depressive symptoms in older adulthood, although most studies used cross-sectional outcome measures. Elucidating the brain structures mediating the adverse effects can strengthen the causal role between air pollution and increasing depressive symptoms. We evaluated whether smaller volumes of brain structures implicated in late-life depression mediate associations between ambient air pollution exposure and changes in depressive symptoms. This prospective study included 764 community-dwelling older women (aged 81.6 ± 3.6 in 2008–2010) from the Women's Health Initiative Memory Study (WHIMS) Magnetic …
Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae
Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae
Psychology Faculty Articles and Research
Background
As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.
Methods
Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.
Results
A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …