Dataset Information

Brain connectivity in late-life depression and aging revealed by network analysis.

ABSTRACT: To use novel methods to examine age associations across an integrated brain network in healthy older adults (HOA) and individuals with late-life depression (LLD). Graph theory metrics describe the organizational configuration of both the global network and specified brain regions.Cross-sectional data were acquired. Graph theory was used to explore diffusion tensor imaging-derived white matter networks. Forty-eight HOA and 28 adults with LLD were recruited from the community. Global and local metrics in prefrontal, cingulate, and temporal regions were calculated. Group differences and associations with age were explored.Group differences were noted in local metrics of the right prefrontal and temporal regions, but no significant differences were observed on global metrics. Local (not global) metrics were associated with age differently across groups. For HOA, local metrics across all regions correlated with age, whereas in adults with LLD, correlations were only observed within temporal regions. In keeping with hypothesized regions impacted by LLD, stronger hubs in right temporal regions were observed among HOA, whereas LLD individuals were characterized by robust hubs in frontal regions.We demonstrate widespread age-related changes in local network properties among HOA with different and more restricted local changes in LLD. Although a preliminary analysis, different patterns of correlations in local networks coupled with equivalent global metrics may reflect altered local structural brain networks in patients with LLD.

SUBMITTER: Charlton RA

PROVIDER: S-EPMC4327993 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Brain connectivity in late-life depression and aging revealed by network analysis.

Charlton Rebecca A RA Leow Alex A GadElkarim Johnson J Zhang Aifeng A Ajilore Olusola O Yang Shaolin S Lamar Melissa M Kumar Anand A

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 20140801 6

<h4>Objective</h4>To use novel methods to examine age associations across an integrated brain network in healthy older adults (HOA) and individuals with late-life depression (LLD). Graph theory metrics describe the organizational configuration of both the global network and specified brain regions.<h4>Methods</h4>Cross-sectional data were acquired. Graph theory was used to explore diffusion tensor imaging-derived white matter networks. Forty-eight HOA and 28 adults with LLD were recruited from t ...[more]

PMID: 25154538

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Project description:BackgroundThere is limited research examining the impact of risk and protective factors on late-life depression using large population-based datasets, particularly those examining differences among older migrants and non-migrants in Europe countries. Thus, the first aim was to analyze differences between migrants and non-migrants regarding socioeconomic status, depression, multimorbidity, healthy aging, and lifestyle behaviors. The second aim was to examine the impact of healthy aging on late-life depression in older migrants compared to their counterparts without a history of international migration in extensive and harmonized data from different population-based cohort studies.Materials and methodsWe analyzed cross-sectional, predominantly nationally representative, community-based data from European participants in the Aging Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) cohort. The descriptive analyses included sociodemographic variables, somatic comorbidities, multimorbidity, healthy aging, and lifestyle behaviors according to migration status. The effects of these variables on late-life depression were examined in a multivariate logistic regression model, including migration status and years since migration as predictors.ResultsData of 122,571 individuals aged ≥ 50 years were analyzed, of which 11,799 (9.60%) were migrants. The descriptive analyses indicated that compared to non-migrants, migrants showed a higher prevalence of diabetes (25.6%), hypertension (38.0%), coronary artery disease (49.4%), stroke (4.9%), and depression (31.1%). Healthy aging was also better in non-migrants (51.7; SD = 9.7) than in migrants (39.6; SD = 18.2). The results of the logistic regression showed that migration status [OR = 1.231 (CIs: 0.914-1.547)] and increased number of years since migration in the host country [OR = 0.003 (CIs: 0.001-0.005)] were associated with greater levels of depressive symptoms. Concerning health variables, multimorbidity was associated with higher levels of depressive symptoms [OR = 0.244 (CIs: 0.211-0.278)], whereas better healthy aging was associated with fewer depressive symptoms [OR = -0.100 (CIs: -0.102 to -0.098)]. The interaction between migration and healthy aging status was also significant [OR. = -0.019 (CIs: -0.025 to -0.014)].ConclusionMigrants reported higher risks for worse health outcomes compared to non-migrants. Significantly, worse healthy aging was associated with a greater risk of depressive symptoms in migrants than in non-migrants. Shedding light on migration and aging processes is essential for promoting a cross-cultural understanding of late-life depression in Europe.

Project description:BackgroundMajor depressive disorders rank in the top ten causes of ill health in all but four countries worldwide and are the leading cause of years lived with disability in Europe (WHO). Recent research suggests that neurodegenerative pathology may contribute to the development of late-life depression (LLD) in a sub-group of patients and represent a target for prevention and early diagnosis. In parallel, electroconvulsive therapy (ECT), which is the most effective treatment for severe LLD, has been associated with significant brain structural changes. In both LLD and ECT hippocampal volume change plays a central role; however, the neurobiological mechanism underlying it and its relevance for clinical outcomes remain unresolved.MethodsThis is a monocentric, clinical cohort study with a cross-sectional arm evaluating PET-MR imaging and behavioural measures in 64 patients with LLD compared to 64 healthy controls, and a longitudinal arm evaluating the same imaging and behavioural measures after 10 ECT sessions in 20 patients receiving ECT as part of their normal clinical management. Triple tracer PET-MRI data will be used to measure: hippocampal volume (high resolution MRI), synaptic density using [11C]UCB-J, which targets the Synaptic Vesicle Glycoprotein 2A receptor, tau pathology using [18F]MK-6240, and cerebral amyloid using [18F]-Flutemetamol, which targets beta-amyloid neuritic plaques in the brain. Additional MRI measures and ultrasound will assess cerebral vascular structure and brain connectivity. Formal clinical and neuropsychological assessments will be conducted alongside experience sampling and physiological monitoring to assess mood, stress, cognition and psychomotor function.DiscussionThe main aim of the study is to identify the origin and consequences of hippocampal volume differences in LLD by investigating how biomarkers of pathological ageing contribute to medial temporal lobe pathology. Studying how synaptic density, tau, amyloid and vascular pathology relate to neuropsychological, psychomotor function, stress and ECT, will increase our pathophysiological understanding of the in vivo molecular, structural and functional alterations occurring in depression and what effect this has on clinical outcome. It may also lead to improvements in the differential diagnosis of depression and dementia yielding earlier, more optimal, cost-effective clinical management. Finally, it will improve our understanding of the neurobiological mechanism of ECT.Trial registrationClinicalTrials.gov Identifier: NCT03849417 , 21/2/2019.

Dataset Information

Brain connectivity in late-life depression and aging revealed by network analysis.

Publications

Brain connectivity in late-life depression and aging revealed by network analysis.

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