The Role of State Versus Trait Anxiety on Cognition in Older Adults With Major Depressive Disorder.

OBJECTIVE: Anxiety superimposed on late life depression (LLD) results in greater changes to prefrontal and medial temporal brain regions compared to depression alone. Yet, the combined impact of anxiety and depression on cognition in LLD has not been thoroughly investigated. The current study investigated whether annual changes in state and trait anxiety were associated with cognitive changes in older adults with major depression. We hypothesized that the presence of anxiety among older depressed adults would be associated with worse cognitive performance in the domains of memory and executive functioning over time. DESIGN: Three-year longitudinal observational study of older adults with LLD who were offered antidepressant treatment. SETTING: Academic Health Center. METHODS: Participants included 124 adults aged 60+ who met criteria for major depression at baseline. The association between anxiety and cognition was examined with separate multilevel linear models that addressed both between-subject and within-person effects of state and trait anxiety on cognitive functioning tests. RESULTS: Individuals who experienced annual increases in anxiety above his/her personal average also experienced cognitive decline. Increases in state anxiety were associated with declines in memory and global cognition. By contrast, increases in trait anxiety were associated with declines in mental flexibility and memory. These findings remained significant even when controlling for changes in depression over time. CONCLUSION: In LLD, individual increases in state and trait anxiety were associated with cognitive declines in different domains.

Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Neuropsychological Assessments of Cognitive Impairment in Major Depressive Disorder: A Systematic Review and Meta-Analysis with Meta-Regression.

INTRODUCTION: Cognitive dysfunction or deficits are common in patients with major depressive disorder (MDD). The current study systematically reviews and meta-analyzes multiple domains of cognitive impairment in patients with MDD. METHODS: PubMed/MEDLINE, PsycINFO, Cochrane Library, Embase, Web of Science, and Google Scholar were searched from inception through May 17, 2023, with no language limits. Studies with the following inclusion criteria were included: (1) patients with a diagnosis of MDD using standardized diagnostic criteria; (2) healthy controls (i.e., those without MDD); (3) neuropsychological assessments of cognitive impairment using Cambridge Neuropsychological Test Automated Battery (CANTAB); and (4) reports of sufficient data to quantify standardized effect sizes. Hedges' g standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were used to quantify effect sizes of cognitive impairments in MDD. SMDs were estimated using a fixed- or random-effects models. RESULTS: Overall, 33 studies consisting of 2,596 subjects (n = 1,337 for patients with MDD and n = 1,259 for healthy controls) were included. Patients with MDD, when compared to healthy controls, had moderate cognitive deficits (SMD, -0.39 [95% CI, -0.47 to -0.31]). In our subgroup analyses, patients with treatment-resistant depression (SMD, -0.56 [95% CI, -0.78 to -0.34]) and older adults with MDD (SMD, -0.51 [95% CI, -0.66 to -0.36]) had greater cognitive deficits than healthy controls. The effect size was small among unmedicated patients with MDD (SMD, -0.19 [95% CI, -0.37 to -0.00]), and we did not find any statistical difference among children. Cognitive deficits were consistently found in all domains, except the reaction time. No publication bias was reported. CONCLUSION: Because cognitive impairment in MDD can persist in remission or increase the risk of major neurodegenerative disorders, remediation of cognitive impairment in addition to alleviation of depressive symptoms should be an important goal when treating patients with MDD.

Life-Course Brain Health as a Determinant of Late-Life Mental Health: American Association for Geriatric Psychiatry Expert Panel Recommendations.

This position statement of the Expert Panel on Brain Health of the American Association for Geriatric Psychiatry (AAGP) emphasizes the critical role of life course brain health in shaping mental well-being during the later stages of life. Evidence posits that maintaining optimal brain health earlier in life is crucial for preventing and managing brain aging-related disorders such as dementia/cognitive decline, depression, stroke, and anxiety. We advocate for a holistic approach that integrates medical, psychological, and social frameworks with culturally tailored interventions across the lifespan to promote brain health and overall mental well-being in aging adults across all communities. Furthermore, our statement underscores the significance of prevention, early detection, and intervention in identifying cognitive decline, mood changes, and related mental illness. Action should also be taken to understand and address the needs of communities that traditionally have unequal access to preventive health information and services. By implementing culturally relevant and tailored evidence-based practices and advancing research in geriatric psychiatry, behavioral neurology, and geroscience, we can enhance the quality of life for older adults facing the unique challenges of aging. This position statement emphasizes the intrinsic link between brain health and mental health in aging, urging healthcare professionals, policymakers, and a broader society to prioritize comprehensive strategies that safeguard and promote brain health from birth through later years across all communities. The AAGP Expert Panel has the goal of launching further activities in the coming months and years.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.

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.

Mid-life leukocyte telomere length and dementia risk: An observational and mendelian randomization study of 435,046 UK Biobank participants.

Telomere attrition is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD). The objective of this study was to assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes. Data from European-ancestry participants in the UK Biobank (n = 435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n = 43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology. Longer TL was associated with a lower risk of incident AD/ADRD (adjusted Hazard Ratio [aHR] per SD = 0.93, 95% CI 0.90-0.96, p = 3.37 × 10-7 ). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume, lower total volume of white matter hyperintensities, and higher fractional anisotropy and lower mean diffusivity in the fornix. In conclusion, longer TL is inversely associated with AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. However, the relationships between genetically determined TL and the outcomes above were not statistically significant based on the results from Mendelian randomization analysis results. Our findings add to the literature of prioritizing risk for AD/ADRD. The causality needs to be ascertained in mechanistic studies.

High Risk of Substance Use Disorder-Related Outcomes in Veterans Released from Correctional Facilities in Mid to Late Life.

BACKGROUND: Veterans Affairs (VA) is likely to encounter a growing number of veterans returning to the community in mid to late life following incarceration (i.e., experiencing reentry). Yet, rates of negative health outcomes due to substance use disorders (SUDs) in this population are unknown. OBJECTIVE: To determine risk of and risk factors for SUD-related emergency department visits and inpatient hospitalizations (ED/IPH) and overdose death among older reentry veterans compared with never-incarcerated veterans. DESIGN: Retrospective cohort study using national VA and Medicare healthcare systems data. PARTICIPANTS: Veterans age ≥50, incarcerated for ≤5 consecutive years, and released between October 1, 2010, and September 30, 2017 (N = 18,803), were propensity score-matched 1:5 with never-incarcerated veterans (N = 94,015) on demographic characteristics, reason for Medicare eligibility, and SUD history. MAIN MEASURES: SUD-related ED/IPH (overall and substance-specific) were obtained from in-/outpatient VA health services and CMS data within the year following release date/index date (through September 30, 2018). Overdose death within 1 year was identified using the National Mortality Data Repository. Fine-Gray proportional hazards regression compared risk of SUD-related ED/IPH and overdose death between the two groups. RESULTS: The number of SUD-related ED/IPHs and overdose deaths was 2470 (13.1%) and 72 (0.38%) in the reentry sample versus 4402 (4.7%) and 198 (0.21%) in the never-incarcerated sample, respectively. Mid-to-late-life reentry was associated with higher risk of any SUD-related ED/IPH (13,136.2 vs. 2252.8 per 100,000/year; adjusted hazard ratio [AHR] = 2.19; 95% confidence interval [CI] = 2.08, 2.30) and overdose death (382.9 vs. 210.6 per 100,000/year; AHR = 2.24, 95% CI = 1.63, 3.08). CONCLUSIONS: Older reentry veterans have more than double the risk of experiencing SUD-related ED/IPH (overall and substance-specific) and overdose death, even after accounting for SUD history and other likely confounders. These findings highlight the vulnerability of this population. Improved knowledge regarding SUD-related negative health outcomes may help to tailor VA reentry programming.

Neuroimaging features of depression-frailty phenotype in older adults: a pilot study.

OBJECTIVE: Frailty and late-life depression (LLD) often coexist and share several structural brain changes. We aimed to study the joint effect LLD and frailty have on brain structure. DESIGN: Cross-sectional study. SETTING: Academic Health Center. PARTICIPANTS: Thirty-one participants (14 LLD+Frail and 17 Never-depressed+Robust). MEASUREMENT: LLD was diagnosed by a geriatric psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition for single episode or recurrent major depressive disorder without psychotic features. Frailty was assessed using the FRAIL scale (0-5), classifying subjects as robust (0), prefrail (1-2), and frail (3-5). Participants underwent T1-weighted magnetic resonance imaging in which covariance analysis of subcortical volumes and vertex-wise analysis of cortical thickness values were performed to access changes in grey matter. Participants also underwent diffusion tensor imaging in which tract-based spatial statistics was used with voxel-wise statistical analysis on fractional anisotropy and mean diffusion values to assess changes in white matter (WM). RESULTS: We found a significant difference in mean diffusion values (48,225 voxels; peak voxel: pFWER=0.005, MINI coord. (X,Y,Z) = -26,-11,27) between the LLD-Frail group and comparison group. The corresponding effect size (f=0.808) was large. CONCLUSION: We showed the LLD+Frailty group is associated with significant microstructural changes within WM tracts compared to Never-depressed+Robust individuals. Our findings indicate the possibility of a heightened neuroinflammatory burden as a potential mechanism underlying the co-occurrence of both conditions and the possibility of a depression-frailty phenotype in older adults.

The Risk for Loneliness and Major Depression among Solo Agers.

Solo agers may be vulnerable to social isolation and mental health sequelae, particularly if they lack close family or friendship ties. This study examined associations among indicators of solo aging, frequency of loneliness, and Major Depressive Disorder among adults aged 60+. Depressed participants were diagnosed by a geriatric psychiatrist and control participants were not depressed. We hypothesized that older adults with more indicators of solo aging (i.e., living alone, being unmarried, not having family or friends nearby) would be more often lonely and more likely to be depressed. In multivariate analyses controlling for health comorbidities and financial difficulty, each additional solo aging indicator significantly increased the likelihood of frequent loneliness, 95% CI OR [1.50, 2.80], and having a depression diagnosis 95% CI OR [1.04, 2.07]. Solo agers may be vulnerable to loneliness and depression, reinforcing the need for assessment and intervention for social isolation among older adults.

Reliable Cognitive Decline in Late-Life Major Depression.

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.

Effects of Longitudinal Changes in Neuroticism and Stress on Cognitive Decline.

OBJECTIVE: The relationships among depression, personality factors, stress, and cognitive decline in the elderly are complex. Depressed elders score higher in neuroticism than nondepressed older individuals. Independently, the presence of neuroticism and the number of stressful life events are each associated with worsening cognitive decline in depressed older adults. Yet little is known about combined effects of changes in neuroticism and changes in stress on cognitive decline among older depressed adults. DESIGN: Longitudinal observational study. SETTING: Academic Health Center. PARTICIPANTS: The authors examined 62 participants in the Neurobiology of Late-life depression (NBOLD) study to test the hypothesis that, compared with older depressed subjects who experience improved neuroticism and lower psychosocial stressors over time, those with worsening neuroticism and greater psychosocial stressors will demonstrate more cognitive decline. MEASUREMENTS: The authors measured neuroticism using the NEO-Personality Inventory-Revised at baseline and 1 year. Study psychiatrists measured depression using the Montgomery-Ǻsberg Depression Rating Scale. At annual assessments, subjects reported the number of psychosocial stressors in the prior year and completed a neuropsychological evaluation. Participants completed a detailed neuropsychological battery at baseline and annually over 3 years. The battery included a test of delayed story memory (Logical Memory-2 or LMII). The outcome 3-year change in cognitive scores was regressed against 3-year change scores of neuroticism and number of psychosocial stressors, plus their interaction, while adjusting for sex, age, race, education, baseline cognitive score, and 3-year change in MADRS score as covariates. RESULTS: In multivariable linear regression analysis with the above covariates, the interaction effect of 3-year change in Total Neuroticism score and 3-year change in Total Stressors on change in LMII performance was statistically significant (B = -0.080[95%CL: -0.145 to -0.015], T = -2.48, df = 52, p = 0.017). Further exploration of this finding showed that 1) when total stressors increased by 2 or more over 3 years, LMII change was inversely associated with neuroticism change; and 2) when neuroticism improved less, LMII change score was inversely associated with total stressor change. There were no other significant interactions between stress and neuroticism on cognition. CONCLUSION: Our findings document the importance of tracking change in neuroticism and monitoring psychosocial stress over the long-term course of treatment in geriatric depression. Both factors exert important combined effects on memory over time. Future studies in larger samples are needed to confirm our results and to extend them to examine both cognitive change and development of dementia.

Association of Race, Ethnicity, Education, and Neighborhood Context With Dementia Prevalence and Cognitive Impairment Severity Among Older Adults Receiving Medicaid-Funded Home and Community-Based Services.

OBJECTIVE: While racial, ethnic, and socioeconomic group disparities in cognitive impairment and dementia prevalence are well-documented among community-dwelling older adults, little is known about these disparity trends among older adults receiving Medicaid-funded home- and community-based services (HCBS) in lieu of nursing home admission. The authors determined how dementia prevalence and cognitive impairment severity compare by race, ethnicity, educational attainment, and neighborhood context in a Medicaid HCBS population. DESIGN/SETTING: A cross-sectional study in Connecticut. PARTICIPANTS: Adults age ≥65 in the HCBS program, January-March 2019 (N = 3,520). MEASUREMENTS: The data source was Connecticut's HCBS program Universal Assessment tool. The authors employed two outcomes: Cognitive Performance Scale (CPS2), a 9-point measure ranging from cognitively intact-very severe impairment; and presence or not of either diagnosed dementia or CPS2 score ≥4 (major impairment). Neighborhood context was measured using the Social Vulnerability Index (SVI). RESULTS: Cohort characteristics: 75.7% female; mean(SD) age = 79.1(8.2); Non-Hispanic White = 47.8%; Hispanic = 33.6%; Non-Hispanic Black = 15.9%. Covariate-adjusted multivariate analyses revealed no dementia/major impairment prevalence differences among White, Black, and Hispanic individuals, but impairment severity was greater among Hispanic participants (b = 0.22; p = 0.02). People with more than HS education had less severe impairment (b = -0.12; p <0.001) and lower likelihood of dementia/major impairment (AOR = 0.61; p <0.001). Dementia/major impairment likelihood and impairment severity were greater in less socially vulnerable neighborhoods. CONCLUSION: Racial and ethnic group differences in cognitive impairment are less pronounced in Medicaid-funded HCBS cohorts than in other community-dwelling older adult cohorts. SVI results suggest that, among other possible explanations, older adults with dementia may move to lower social vulnerability neighborhoods where supportive family members reside.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.

The neurobiology of apathy in depression and neurocognitive impairment in older adults: a review of epidemiological, clinical, neuropsychological and biological research.

Apathy is a common condition that involves diminished initiative, diminished interest and diminished emotional expression or responsiveness. It is highly prevalent in the context of a variety of neuropsychiatric disorders and is related to poor health outcomes. Presence of apathy is associated with cognitive and functional decline in dementia. Despite its negative impact on health, there is no definitive treatment for apathy, a clinical reality that may be due in part to lack of knowledge about assessment, neuropsychological features and neurobiological underpinnings. Here, we review and synthesize evidence from clinical, epidemiological, neuropsychological, peripheral biomarker and neuroimaging research. Apathy is a common feature of depression and cognitive disorders and is associated with impairment in executive function. Neuropsychological and neuroimaging studies point to dysfunction of brain circuitry involving the prefrontal cortex, especially the dorsolateral prefrontal cortex circuit, the dorsomedial prefrontal cortex circuit, and the ventromedial prefrontal cortex circuit. However, inconsistent findings, particularly in neuroimaging may be due to heterogeneity of apathy symptoms (with a need to better elucidate subtypes), neuropsychiatric comorbidities, the severity of cognitive impairment and other factors. These factors need to be accounted for in future studies so that biomarker research can make progress. On the whole, the literature on apathy has identified likely neurocognitive, peripheral biomarker and neuroimaging targets for understanding apathy, but also points to the need to address methodological issues that will better inform future studies. In turn, as we learn more about the underpinning of apathy and its subtypes, subsequent research can focus on new neurally based interventions that will strengthen the clinical management of apathy in the context of its comorbidities.