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.

Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual-cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.

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.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain-generic student model and two domain-specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi-target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs-fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI-related MDD diagnosis.

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.

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.

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.

Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.

Previous studies have shown that late-life depression (LLD) may be a precursor of neurodegenerative diseases and may increase the risk of dementia. At present, the pathological relationship between LLD and dementia, in particularly Alzheimer's disease (AD) is unclear. Structural MRI (sMRI) can provide objective biomarkers for the computer-aided diagnosis of LLD and AD, providing a promising solution to understand the clinical progression of brain disorders. But few studies have focused on sMRI-based predictive analysis of clinical progression from LLD to AD. In this paper, we develop a deep learning method to predict the clinical progression of LLD to AD up to 5 years after baseline time using T1-weighted structural MRIs. We also analyze several important factors that limit the diagnostic performance of learning-based methods, including data imbalance, small-sample-size, and multi-site data heterogeneity, by leveraging a relatively large-scale database to aid model training. Experimental results on 308 subjects with sMRIs acquired from 2 imaging sites and the publicly available ADNI database demonstrate the potential of deep learning in predicting the clinical progression of LLD to AD. To the best of our knowledge, this is among the first attempts to explore the complex pathophysiological relationship between LLD and AD based on structural MRI using a deep learning method.

The impact of the COVID-19 pandemic on mental health of nurses in British Columbia, Canada using trends analysis across three time points.

PURPOSE: This study examined trends over time in the prevalence of anxiety and depression among Canadian nurses: 6 months before, 1-month after, and 3 months after COVID-19 was declared a pandemic. METHODS: This study adopted a repeated cross-sectional design and surveyed unionized nurses in British Columbia (BC), Canada on three occasions: September 2019 (Time 1, prepandemic), April 2020 (Time 2, early-pandemic) and June 2020 (Time 3). RESULTS: A total of 10,117 responses were collected across three timepoints. This study found a significant increase of 10% to 15% in anxiety and depression between Time 1 and 2, and relative stability between Time 2 and 3, with Time 3 levels still higher than Time 1 levels. Cross-sector analyses showed similar patterns of findings for acute care and community nurses. Long-term care nurses showed a two-fold increase in the prevalence of anxiety early pandemic, followed by a sharper decline mid pandemic. CONCLUSIONS: COVID-19 has had short- and mid-term mental health implications for BC nurses particularly among those in the long-term care sector. Future research should evaluate the impact of COVID-19 on the mental health of health workers in different contexts, such as jurisdictional analyses, and better understand the long-term health and labor market consequences of elevated mental health symptoms over an extended time period.

Cerebral white matter connectivity, cognition, and age-related macular degeneration.

Age-related macular degeneration (AMD) is a common retina disease associated with cognitive impairment in older adults. The mechanism(s) that account for the link between AMD and cognitive decline remain unclear. Here we aim to shed light on this issue by investigating whether relationships between cognition and white matter in the brain differ by AMD status. In a direct group comparison of brain connectometry maps from diffusion weighted images, AMD patients showed significantly weaker quantitative anisotropy (QA) than healthy controls, predominantly in the splenium and left optic radiation. The QA of these tracts, however, did not correlate with the visual acuity measure, indicating that this group effect is not directly driven by visual loss. The AMD and control groups did not differ significantly in cognitive performance.Across all participants, better cognitive performance (e.g. verbal fluency) is associated with stronger connectivity strength in white matter tracts including the splenium and the left inferior fronto-occipital fasciculus/inferior longitudinal fasciculus. However, there were significant interactions between group and cognitive performance (verbal fluency, memory), suggesting that the relation between QA and cognitive performance was weaker in AMD patients than in controls.This may be explained by unmeasured determinants of performance that are more common or impactful in AMD or by a recruitment bias whereby the AMD group had higher cognitive reserve. In general, our findings suggest that neural degeneration in the brain might occur in parallel to AMD in the eyes, although the participants studied here do not (yet) exhibit overt cognitive declines per standard assessments.

The Interaction of Personality and Social Support on Prospective Suicidal Ideation in Men and Women With Late-Life Depression.

OBJECTIVE: Evidence suggests a cross-sectional association between personality traits and suicidal ideation in LLD. Yet, it is unclear how personality may influence suicidal ideation over time in LLD, or whether such an association would be moderated by psychosocial and biological individual differences. The present study had three aims: 1) to examine whether personality traits increase suicidal ideation in LLD over time, 2) to understand whether this relationship is influenced by subjective social support, and 3) to determine whether the potential relationship between social support, personality, and suicidal ideation is different for men and women. DESIGN: Participants were enrolled in the Duke University Neurocognitive Outcomes of Depression in the Elderly (NCODE), a longitudinal investigation of the predictors of poor illness course in LLD. Patients were initially enrolled in the NCODE study between December 1994 and June 2000 and were followed for an average of six years. SETTING: NCODE operates in a naturalistic treatment milieu. PARTICIPANTS: One hundred twelve participants aged 60 and older with a current diagnosis of major depressive disorder. MEASUREMENTS: Annual assessments of depression, suicidal ideation, and social support (measured with the Duke Social Support Index). Participants also completed the NEO Personality Inventory-Revised (NEO-PI-R) providing measures of the five major personality dimensions (neuroticism, extraversion, openness, conscientiousness, and agreeableness). RESULTS: Univariate logistic generalized linear mixed modeling (GLMM) analyses revealed that higher levels of depression at baseline, less subjective social support, higher neuroticism, and lower extraversion were significantly associated with an increased likelihood of suicidal ideation over time. While the relationship between these dimensions and suicidal ideation were no longer significant in multivariate analyses, there was a significant moderating effect of social support on the association between suicidal ideation and certain neuroticism and extraversion personality facets. Decreased subjective social support was associated with an increased likelihood of suicidal ideation in LLD patients with high (but not low) impulsiveness and low (but not high) gregariousness and positive emotions. Across all models, social support was beneficial to women, but not men, in decreasing the likelihood of future suicidal ideation. CONCLUSION: Changes in social support may contribute to suicidal ideation in older depressed adults with certain personality traits. Irrespective of personality traits, changes in social support had a significant effect on the suicidal ideation of women but not men. These relationships were apparent even when controlling for depression severity, age, and history of suicide attempt.

Lagged versus concurrent changes between burnout and depression symptoms and unique contributions from job demands and job resources.

Burnout and depression pose significant threats to emotional and occupational functioning; however, questions exist over how these 2 conditions are associated with each other over time, and how these are related to underlying job stressors. The job demands-resources model provides a useful framework for understanding how job demands and job resources may lead to burnout, but questions remain about their distinct association with depression symptoms. The current study examined these questions in a sample of 402 nursing workers. The Exhaustion subscale of the Oldenburg Burnout Inventory, the Patient Health Questionnaire-9 depression assessment, and items reflecting job demands and job resources from the Copenhagen Psychosocial Questionnaire were assessed at baseline; additionally, Oldenburg Burnout Inventory Exhaustion and Patient Health Questionnaire-9 depression were assessed over 12 monthly follow-ups. Linear mixed models assessed longitudinal bidirectional associations between burnout and depression in both concurrent and lagged models. Longitudinal models found bidirectional relationships between burnout and depression symptoms over time, with relatively stronger associations for concurrent models relative to lagged models. Job demands and job resources each predicted unique variance in burnout and depression symptoms over time. Results provide evidence that burnout and depression symptoms change in the same direction, in tandem, rather than one condition having a distinctly stronger temporal association over the other. Results also indicate that both job demands and job resources are associated with depression symptoms independent from their association with burnout symptoms. Our results highlight the importance of considering burnout symptoms, depression symptoms, and job stressors concurrently in evaluating worker mental health. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Brain network functional connectivity and cognitive performance in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD. METHODS: 51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups. RESULTS: Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group. CONCLUSIONS: These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD.

Dual-Task Gait and Alzheimer's Disease Genetic Risk in Cognitively Normal Adults: A Pilot Study.

BACKGROUND: Dual-task paradigms, in which an individual performs tasks separately and then concurrently, often demonstrate that people with neurodegenerative disorders experience more dual-task interference, defined as worse performance in the dual-task condition compared to the single-task condition. OBJECTIVE: To examine how gait-cognition dual-task performance differs between cognitively normal older adults with and without an APOE ɛ4 allele. METHODS: Twenty-nine individuals ages 60 to 72 with normal cognition completed a dual-task protocol in which walking and cognitive tasks (executive function, memory) were performed separately and concurrently. Fourteen participants carried APOE ɛ4 alleles (ɛ3/ɛ4 or ɛ2/ɛ4); fifteen had APOE genotypes (ɛ2/ɛ2, ɛ2/ɛ3, or ɛ3/ɛ3) associated with lower risk of Alzheimer's disease (AD). RESULTS: The two risk groups did not differ by age, sex, race, education, or gait or cognitive measures under single-task conditions. Compared to low risk participants, APOE ɛ4 carriers tended to exhibit greater dual-task interference. Both the memory and executive function tasks resulted in dual-task interference on gait, but effect sizes for a group difference were larger when the cognitive task was executive function. In the dual-task protocol that combined walking and the executive function task, effect sizes for group difference in gait interference were larger (0.62- 0.70) than for cognitive interference (0.45- 0.47). DISCUSSION: Dual-task paradigms may reveal subtle changes in brain function in asymptomatic individuals at heightened risk of AD.

Predictors of recurrence in remitted late-life depression.

BACKGROUND: Late-life depression (LLD) is associated with a fragile antidepressant response and high recurrence risk. This study examined what measures predict recurrence in remitted LLD. METHODS: Individuals of age 60 years or older with a Diagnostic and Statistical Manual - IV (DSM-IV) diagnosis of major depressive disorder were enrolled in the neurocognitive outcomes of depression in the elderly study. Participants received manualized antidepressant treatment and were followed longitudinally for an average of 5 years. Study analyses included participants who remitted. Measures included demographic and clinical measures, medical comorbidity, disability, life stress, social support, and neuropsychological testing. A subset underwent structural magnetic resonance imaging (MRI). RESULTS: Of 241 remitted elders, approximately over 4 years, 137 (56.8%) experienced recurrence and 104 (43.2%) maintained remission. In the final model, greater recurrence risk was associated with female sex (hazard ratio [HR] = 1.536; confidence interval [CI] = 1.027-2.297), younger age of onset (HR = 0.990; CI = 0.981-0.999), higher perceived stress (HR = 1.121; CI = 1.022-1.229), disability (HR = 1.060; CI = 1.005-1.119), and less support with activities (HR = 0.885; CI = 0.812-0.963). Recurrence risk was also associated with higher Montgomery-Asberg Depression Rating Scale (MADRS) scores prior to censoring (HR = 1.081; CI = 1.033-1.131) and baseline symptoms of suicidal thoughts by MADRS (HR = 1.175; CI = 1.002-1.377) and sadness by Center for Epidemiologic Studies-Depression (HR = 1.302; CI, 1.080-1.569). Sex, age of onset, and suicidal thoughts were no longer associated with recurrence in a model incorporating report of multiple prior episodes (HR = 2.107; CI = 1.252-3.548). Neither neuropsychological test performance nor MRI measures of aging pathology were associated with recurrence. CONCLUSIONS: Over half of the depressed elders who remitted experienced recurrence, mostly within 2 years. Multiple clinical and environmental measures predict recurrence risk. Work is needed to develop instruments that stratify risk.

Cognitive performance in antidepressant-free recurrent major depressive disorder.

BACKGROUND: Cognitive complaints are common in depression, and cognition may be an important treatment target as cognitive problems often remain during remission and may contribute to recurrence risk. Previous studies of cognitive performance in depression have mainly examined late-life depression, with a focus on older adults, or assessed performance in specific cognitive tasks rather than cognitive domains. METHODS: This study examined cognitive performance across multiple cognitive domains in antidepressant-free depressed adults with early onset recurrent depression compared to never-depressed controls. Domain scores were calculated for episodic memory, executive function, processing speed, and working memory, and the effect of depression diagnosis, depression severity, and depression duration on each domain score was examined, including interactions with age, sex, and education. RESULTS: Currently depressed adults (n = 91) exhibited poorer performance in the processing speed domain compared with never-depressed adults (n = 105). Additionally, there was an interactive effect of depression duration and age on processing speed and executive function domain performance, such that performance was worse with older age and longer duration of depression. There were no effects of depression severity on performance across the cognitive domains. CONCLUSIONS: These findings support that processing speed deficits appear in young adults with early onset depression that may not be related to current mood. Additionally, the effects of cumulative depressive episodes may interact with aging such that cognitive performance deficits worsen with recurrence over the lifespan.

Evaluation of inner retinal layers as biomarkers in mild cognitive impairment to moderate Alzheimer's disease.

Inner retina in Alzheimer's Disease (AD) may experience neuroinflammation resulting in atrophy. The objective of our study was to determine whether retinal GCIPL (ganglion cell-inner plexiform layer) or nerve fiber layer (NFL) thickness may serve as noninvasive biomarkers to diagnose AD. This cross-sectional case-control study enrolled 15 mild cognitive impairment (MCI) patients, 15 mild-moderate AD patients, and 18 cognitively normal adults. NFL and GCIPL thicknesses on optical coherence tomography (OCT) were measured using Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP) and Spectralis software. We demonstrated that regional thicknesses of NFL or GCIPL on macular or nerve OCTs did not differ between groups. However, a multi-variate regression analysis identified macular areas with a significant thickening or thinning in NFL and GCIPL in MCI and AD patients. Our primary findings controvert previous reports of thinner NFL in moderate-to-severe AD. The areas of thickening of GCIPL and NFL in the macula adjacent to areas of thinning, as revealed by a more complex statistical model, suggest that NFL and GCIPL may undergo dynamic changes during AD progression.

Putamen Volume Differences Among Older Adults: Depression Status, Melancholia, and Age.

BACKGROUND: Individuals with major depressive disorder (MDD) may exhibit smaller striatal volumes reflecting deficits in the reward circuit. Deficits may change with age and be more pronounced among the melancholic subtype. Limited research has investigated striatal volume differences in older adults and by depression subtypes. METHOD: We used baseline data from the Neurocognitive Outcomes of Depression in the Elderly study. We examined volumetric differences in the putamen and caudate nucleus among older adults (60 years and older), comparing healthy control participants (n = 134) to depressed participants (n = 226), and comparing nonmelancholic depressed participants (n = 93) to melancholic depressed participants (n = 133). Group-by-age interactions were examined. RESULTS: There were no significant group differences for the caudate nucleus. For the left putamen, investigation of the significant group-by-age interaction revealed that volume size was greater for the healthy controls compared to the depressed participants but only at younger ages (60-65 years); group differences diminished with increasing age. Examining volume by depression subtype revealed that the melancholic depressed participants had a smaller left putamen compared to the nonmelancholic depressed participants. Anhedonia symptoms were related to both smaller left and right putamen. CONCLUSION: Structural abnormalities in reward regions may underlie the anhedonic phenotype. Volume loss associated with MDD may attenuate in older age.

Language processing in age-related macular degeneration associated with unique functional connectivity signatures in the right hemisphere.

Age-related macular degeneration (AMD) is a retinal disease associated with significant vision loss among older adults. Previous large-scale behavioral studies indicate that people with AMD are at increased risk of cognitive deficits in language processing, particularly in verbal fluency tasks. The neural underpinnings of any relationship between AMD and higher cognitive functions, such as language processing, remain unclear. This study aims to address this issue using independent component analysis of spontaneous brain activity at rest. In 2 components associated with visual processing, we observed weaker functional connectivity in the primary visual cortex and lateral occipital cortex in AMD patients compared with healthy controls, indicating that AMD might lead to differences in the neural representation of vision. In a component related to language processing, we found that increasing connectivity within the right inferior frontal gyrus was associated with better verbal fluency performance across all older adults, and the verbal fluency effect was greater in AMD patients than controls in both right inferior frontal gyrus and right posterior temporal regions. As the behavioral performance of our patients is as good as that of controls, these findings suggest that preservation of verbal fluency performance in AMD patients might be achieved through higher contribution from right hemisphere regions in bilateral language networks. If that is the case, there may be an opportunity to promote cognitive resilience among seniors with AMD or other forms of late-life vision loss.

Relating Sensory, Cognitive, and Neural Factors to Older Persons' Perceptions about Happiness: An Exploratory Study.

Despite increased rates of disease, disability, and social losses with aging, seniors consistently report higher levels of subjective well-being (SWB), a construct closely related to happiness, than younger adults. In this exploratory study, we utilized an available dataset to investigate how aspects of health commonly deteriorating with age, including sensory (i.e., vision and hearing) and cognitive status, relate to variability in self-described contributors to happiness. Community-dwelling seniors (n = 114) responded to a single-item prompt: "name things that make people happy." 1731 responses were categorized into 13 domains of SWB via structured content analysis. Sensory health and cognition were assessed by Snellen visual acuity, pure-tone audiometry, and in-person administration of the Brief Test of Adult Cognition by Telephone (BTACT) battery. A subset of eligible participants (n = 57) underwent functional magnetic resonance imaging (fMRI) to assess resting state functional connectivity (FC) within a previously described dopaminergic network associated with reward processing. SWB response patterns were relatively stable across gender, sensory status, and cognitive performance with few exceptions. For example, hearing-impaired participants listed fewer determinants of SWB (13.59 vs. 17.16; p < 0.001) and were less likely to name things in the "special events" category. Participants with a higher proportion of responses in the "accomplishments" domain (e.g., winning, getting good grades) demonstrated increased FC between the ventral tegmental area and nucleus accumbens, regions implicated in reward and motivated behavior. While the framework for determinants of happiness among seniors was largely stable across the factors assessed here, our findings suggest that subtle changes in this construct may be linked to sensory loss. The possibility that perceptions about determinants of happiness might relate to differences in intrinsic connectivity within reward-related brain networks also warrants further investigation.