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OBJECTIVES: Assessment of depressive symptoms in older adults is challenging especially in the presence of risks in cognitive impairment. We aimed to examine whether the convergence between two measures of depressive symptoms (self-report and observer ratings) is affected by varying levels of cognitive function in older adults. METHODS: Self-reported scale of depression, informant-based rating of affective symptoms, and global cognitive function were assessed in 2533 older adults with no impairment, mild cognitive impairment, and Alzheimer's disease. The strength of rank-order correlation between the Geriatric Depression Scale (GDS) and behavioral ratings of the Neuropsychiatric Inventory (NPI) was examined as the metric of convergent validity. RESULTS: The results showed that the strength of convergence between the two measurements gradually decreased as a function of lowered cognitive function. Overall tendency showed that diagnoses of cognitive impairment and lower levels of cognitive function were associated with lower correspondence between the two depression measurements. The loss of convergent validity is especially evident in the behavioral symptom of apathy. CONCLUSIONS: Utilizing self-report scales of depression in older adults requires a cautious approach even with minimal or mild levels of cognitive impairment.

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BACKGROUND: Late-life depression (LDD) results from multiple psychosocial and neurobiological changes occurring in later life. The current study investigated how patterns of clinical symptoms and brain structural features are classified into LDD subtypes. METHOD: Self-report scale of depression, behavioral rating of affective symptoms, and brain structural imaging of white matter change and cortical thickness were assessed in 541 older adults with no cognitive impairment or mild cognitive impairment. Latent profile analysis was used to identify distinct subtypes of depression. RESULTS: The latent profile analysis identified four classes with mild to severe depressive symptoms and two classes with minimal symptoms. While the classes primarily differed in the overall severity, the combinatory patterns of clinical symptoms and neuropathological signature distinguished the classes with similar severity. The classes were distinguished in terms of whether or not neurodegenerative risk accompanied the corresponding depressive symptoms. The presence of the negative self-scheme and cortical thinning pattern notably characterized the subtypes of LDD. LIMITATIONS: The underlying etiologies of the biological subtypes are still speculative, and the current study lacks clinical history that differentiates late- and early-onset depression. CONCLUSIONS: Our finding provides insight in identifying heterogeneities of depressive disorder in later life and suggests that self-report and behavioral symptom profile in combination with white matter lesion and cortical thickness effectively characterizes distinct subtypes of LDD.

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BACKGROUND: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. OBJECTIVE: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. METHODS: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer's disease (n = 1,300). Predictive accuracy on functional impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. RESULTS: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. CONCLUSION: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia.

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OBJECTIVE: Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs. METHOD: In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer's type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL. RESULTS: The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive-behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL. CONCLUSION: Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

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Objective: Although quarantine is an effective measure for the prevention of the spread of infectious diseases, it may have negative effects on the mental health of the isolated individual. During the 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in Korea, healthcare workers came in contact with patients with MERS were quarantined either at home or in a hospital ward. In this study, we aimed to compare the psychiatric symptoms of these employees according to the method of quarantine. Methods: All 146 quarantined staff completed self-report questionnaires. Depressive symptoms were measured using the Patient Health Questionnaire-9, anxiety symptoms were assessed using Spielberger's State-Trait Anxiety Inventory, and acute stress disorder (ASD) symptoms were evaluated using the Stanford Acute Stress Reaction Questionnaire. Results: The in-hospital quarantine group had a higher rate of symptoms of depression (p < 0.001) and ASD (p = 0.014) than the group quarantined at home. Logistic regression analysis showed that respondents quarantined in the hospital (OR = 6.342; 95% CI 1.853-21.708) and those quarantined for longer periods (OR = 1.153, 95% CI = 1.036-1.285) had a higher risk of depressive symptoms. Conclusions: In-hospital quarantine and quarantine for longer periods increase the risk of depressive symptoms. When quarantine measures are taken, certain measures are needed to minimize the risk of psychiatric problems. Appropriate interventions should be implemented if psychiatric problems occur.