Influences of dopaminergic system dysfunction on late-life depression.

Deficits in cognition, reward processing, and motor function are clinical features relevant to both aging and depression. Individuals with late-life depression often show impairment across these domains, all of which are moderated by the functioning of dopaminergic circuits. As dopaminergic function declines with normal aging and increased inflammatory burden, the role of dopamine may be particularly salient for late-life depression. We review the literature examining the role of dopamine in the pathogenesis of depression, as well as how dopamine function changes with aging and is influenced by inflammation. Applying a Research Domain Criteria (RDoC) Initiative perspective, we then review work examining how dopaminergic signaling affects these domains, specifically focusing on Cognitive, Positive Valence, and Sensorimotor Systems. We propose a unified model incorporating the effects of aging and low-grade inflammation on dopaminergic functioning, with a resulting negative effect on cognition, reward processing, and motor function. Interplay between these systems may influence development of a depressive phenotype, with an initial deficit in one domain reinforcing decline in others. This model extends RDoC concepts into late-life depression while also providing opportunities for novel and personalized interventions.

Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression.

OBJECTIVE: Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. DESIGN AND INTERVENTIONS: Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. PARTICIPANTS: Ninety-five individuals with LLD. MEASUREMENTS: A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. RESULTS: In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time. CONCLUSION: sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression.

Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain-age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.