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The objective of the study was to examine variability across multiple prospective cohort studies in level and rate of cognitive decline by race/ethnicity and years of education. We compare data across studies, we harmonized estimates of common latent factors representing overall or general cognitive performance, memory, and executive function derived from the: (a) Washington Heights, Hamilton Heights, Inwood Columbia Aging Project (N = 4,115), (b) Spanish and English Neuropsychological Assessment Scales (N = 525), (c) Duke Memory, Health, and Aging study (N = 578), and (d) Neurocognitive Outcomes of Depression in the Elderly (N = 585). We modeled cognitive change over age for cognitive outcomes by race, education, and study. We adjusted models for sex, dementia status, and study-specific characteristics. The results found that for baseline levels of overall cognitive performance, memory, and executive function, differences in race and education tended to be larger than between-study differences and consistent across studies. This pattern did not hold for rate of cognitive decline: effects of education and race/ethnicity on cognitive change were not consistently observed across studies, and when present were small, with racial/ethnic minorities and those with lower education declining at faster rates. In this diverse set of datasets, non-Hispanic Whites and those with higher education had substantially higher baseline cognitive test scores. However, differences in the rate of cognitive decline by race/ethnicity and education did not follow this pattern. This study suggests that baseline test scores and longitudinal change have different determinants, and future studies to examine similarities and differences of causes of cognitive decline in racially/ethnically and educationally diverse older groups is needed.

ObjectiveThe objective of this study is to determine whether differential item functioning (DIF) due to cognitive status impacted three depressive symptoms measures commonly used with older adults.MethodsDifferential item functioning in depressive symptoms was assessed among participants (N = 3558) taking part in four longitudinal studies of cognitive aging, using the Geriatric Depression Scale, the Montgomery-Åsberg Depression Rating Scale, and the Center for Epidemiologic Studies Depression Scale. Participants were grouped by cognitive status using a general cognitive performance score derived from each study's neuropsychological battery and linked to a national average using a population-based survey representative of the US population. The Clinical Dementia Rating score was used as an alternate grouping variable in three of the studies.ResultsAlthough statistically significant DIF based on cognitive status was found for some depressive symptom items (e.g., items related to memory complaints, appetite loss, lack of energy, and mood), the effect of item bias on the total score for each scale was negligible.ConclusionsThe depressive symptoms scales in these four studies measured depression in the same way, regardless of cognitive status. This may reduce concerns about using these depression measures in cognitive aging research, as relationships between depression and cognitive decline are unlikely to have been due to item bias, at least in the ways that were measured in the datasets we considered.

ObjectivePrevious studies have identified differential item function (DIF) in depressive symptoms measures, but the impact of DIF has been rarely reported. Given the critical importance of depressive symptoms assessment among older adults, we examined whether DIF due to demographic characteristics resulted in salient score changes in commonly used measures.MethodsFour longitudinal studies of cognitive aging provided a sample size of 3754 older adults and included individuals both with and without a clinical diagnosis of major depression. Each study administered at least one of the following measures: the Center for Epidemiologic Studies Depression scale (20-item ordinal response or 10-item dichotomous response versions), the Geriatric Depression Scale, and the Montgomery-Åsberg Depression Rating Scale. Hybrid logistic regression-item response theory methods were used to examine the presence and impact of DIF due to age, sex, race/ethnicity, and years of education on the depressive symptoms items.ResultsAlthough statistically significant DIF due to demographic factors was present on several items, its cumulative impact on depressive symptoms scores was practically negligible.ConclusionsThe findings support substantive meaningfulness of previously reported demographic differences in depressive symptoms among older adults, showing that these individual differences were unlikely to have resulted from item bias attributable to demographic characteristics we examined.

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: Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results: Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions: We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information. [ABSTRACT FROM AUTHOR]

Objective Major depression in older adults increases the statistical likelihood of dementia. It is challenging to translate statistical evidence of cognitive decline at the group level into knowledge of individual cognitive outcomes. The objective of the current study is to investigate 2-year reliable cognitive change in late-life depression (LLD), which will enhance understanding of cognitive changes in LLD and provide a means to assess individual change. Methods In a sample of non-depressed cognitively normal older adults or NDCN (n  = 113), we used linear regression to predict tests of global cognition, processing speed-executive functioning, and memory administered 1 and 2 years later. Stepwise regression was used to select covariates among demographics and raw test scores (either baseline or year 1) and we cross-validated the final models using the predicted residual error sum of squares (PRESS). We then derived a z -change score from the difference between actual and predicted follow-up scores and investigated the proportion of LLD patients (n  = 199) and NDCN adults who experienced reliable "decline" (a z -score < −1.645), "stability" (z -scores between + − 1.645), and "improvement" (z scores > +1.645). Results A greater proportion LLD compared with NDCN experienced cognitive decline in processing speed/executive functioning and global cognition over 2 years. When compared to NDCN, a greater proportion of LLD also significantly improved on one test of processing speed over 2 years. Conclusions Older adults with LLD are at risk of meaningful cognitive decline over a relatively short period, particularly in the domain of executive functioning and processing speed. This study provides a series of reliable change equations for common neuropsychological tests that can be applied clinically. [ABSTRACT FROM AUTHOR]