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Ample evidence indicates that loneliness in old age is associated with poor bodily and mental health. However, little is known about structural cerebral correlates of loneliness in healthy older adults. We examined such correlates in a magnetic resonance imaging (MRI) subsample of 319 older adults aged 61 to 82 years drawn from the Berlin Aging Study II. Using voxel-based morphometry (VBM) and structural equation modeling (SEM), latent hierarchical regression analyses were performed to examine associations of (i) loneliness, (ii) a range of covariates, and (iii) loneliness by covariate interactions with latent brain volume estimates of brain structures known to be involved in processing, expressing, and regulating emotions. Results from whole-brain VBM analyses showed that individuals with higher loneliness scores tended to have smaller gray matter volumes in three clusters comprising (i) the left amygdala/anterior hippocampus, (ii) the left posterior parahippocampus and (iii) the left cerebellum. Significant associations and interactions between loneliness and latent factors for the amygdala and the hippocampus were confirmed with a region-of-interest (ROI)-based approach. These findings suggest that individual differences in loneliness among older adults are correlated with individual differences in the volumes of brain regions that are central to cognitive processing and emotional regulation, also after correcting for confounders such as social network size. We discuss possible mechanisms underlying these associations and their implications.
Pubmed ID: 31537846
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Database of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.
View all literature mentionsStatistical modeling program that provides a wide choice of models, estimators, and algorithms in a program that has graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program. The Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
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