Vascular risk status as a predictor of later-life depressive symptoms: A cohort study

Background: Common etiology of vascular diseases and later-life depression may provide important synergies for prevention. We examined whether standard clinical risk profiles developed for vascular diseases also predict depressive symptoms in older adults. Methods: Data were drawn from the Whitehall II study with baseline examination in 1991; follow-up screenings in 1997, 2003, and 2008; and additional disease ascertainment from hospital data and registry linkage on 5318 participants (mean age 54.8 years, 31% women) without depressive symptoms at baseline. Vascular risk was assessed with the Framingham Cardiovascular, Coronary Heart Disease, and Stroke Risk Scores. New depressive symptoms at each follow-up screening were identified by General Health Questionnaire caseness, a Center for Epidemiologic Studies Depression Scale score <16, and use of antidepressant medication. Results: Diagnosed vascular disease (that is, coronary heart disease or stroke) was associated with an increased risk for depressive symptoms, age- and sex-adjusted odds ratios from 1.5 (95% confidence interval 1.0-2.2) to 2.0 (1.4-3.0), depending on the indicator of depressive symptoms. Among participants without manifest vascular disease, the Stroke Risk Score was associated with Center for Epidemiologic Studies Depression Scale depressive symptoms before age 65 (age- and sex-adjusted odds ratio per 10% absolute change in the score = 3.1 [1.5-6.5]), but none of the risk scores predicted new-onset depressive symptoms in those aged <65 (odds ratios from.8 to 1.2). Conclusions: These data suggest that public health measures to improve vascular risk status will influence the incidence of later-life depressive symptoms via reduced rates of manifest vascular disease

Uptake of 99mTc-exametazime shown by single photon emission computed tomography before and after lithium withdrawal in bipolar patients: associations with mania

BACKGROUND: Early manic relapse following lithium discontinuation offers an important opportunity to investigate the relationship between symptoms, effects of treatment and regional brain activation in bipolar affective disorder.&lt;p&gt;&lt;/p&gt; METHOD: Fourteen stable bipolar patients on lithium were examined with neuropsychological measures, clinical ratings and single photon emission computed tomography (SPECT) before and after acute double-blind withdrawal of lithium. Brain perfusion maps were spatially transformed into standard stereotactic space and compared pixel-by-pixel. A parametric analysis was used to examine the change in brain perfusion on lithium withdrawal, and the relationship between symptom severity and brain perfusion separately both between and within subjects.&lt;p&gt;&lt;/p&gt; RESULTS: Lithium withdrawal was associated with an important redistribution of brain perfusion, with increases in inferior posterior regions and decreases in limbic areas, particularly anterior cingulate cortex. Seven of the 14 patients developed manic symptoms during the placebo phase, correlating with relative increases in perfusion of superior anterior cingulate and possibly left orbito-frontal cortex.&lt;p&gt;&lt;/p&gt; CONCLUSIONS: The important effect of lithium withdrawal on brain perfusion implies that after withdrawal of lithium, the brain develops an abnormal state of activity in limbic cortex. The structures involved did not co-localise with those apparently modulated by manic symptoms

Age-related adaptations of brain function during a memory task are also present at rest

Several studies have demonstrated age-related regional differences in the magnitude of the BOLD signal using task-based fMRI. It has been suggested that functional changes reflect either compensatory or de-differentiation mechanisms, both of which assume response to a specific stimulus. Here, we have tested whether ageing affects both task-based and resting brain function, and the extent to which functional changes are mediated by reductions in grey matter (GM) volume.\ud \ud Two groups, of 22 healthy younger and 22 older volunteers, underwent an imaging protocol involving structural and functional MRI, both during a memory task and at rest. The two groups had similar socio-demographical characteristics and cognitive performance. Image analysis revealed both structural and functional differences. Increased BOLD signal in older relative to younger volunteers was mainly observed in the frontal lobes, both during the task and at rest. Functional changes in the frontal lobes were largely located in brain regions spared from GM loss, and adding GM covariates to the fMRI analysis did not significantly alter the group differences. Our results are consistent with the suggestion that, during normal ageing, the brain responds to neuronal loss by fine-tuning connections between spared neurons. Longitudinal studies will be necessary to fully test this hypothesis\u

Mind the gap: Performance metric evaluation in brain-age prediction.

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R &lt;sup&gt;2&lt;/sup&gt; ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R &lt;sup&gt;2&lt;/sup&gt; values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females

Blunted response to feedback information in depressive illness

Depressive illness is associated with sustained widespread cognitive deficits, in addition to repeated experience of distressing emotions. An accepted theory, which broadly accounts for features of the syndrome, and its delayed response to antidepressant medication, is lacking. One possibility, which has received considerable attention, is that depressive illness is associated with a specific underlying deficit: a blunted or impaired ability to respond to feedback information. Unlike healthy controls, if patients with a depressive illness commit an error, they can be at increased risk of committing a subsequent error, possibly due to a failure to adjust performance in order to reduce the risk of error. In some speeded tasks, performance adjustment in humans is reliably associated with trial-to-trial change in reaction times (RTs), such as 'post-error slowing'. Previous studies of abnormal response to feedback have not investigated RT change in any detail. We used a combination of quantitative modelling of RTs and fMRI in 15 patients and 14 matched controls to test the hypothesis that depressive illness was associated with a blunted behavioural and neural response to feedback information during a gambling task. The results supported the hypothesis. Controls responded to negative ('lose') feedback by an increase in RT and activation of the anterior cingulate, the extent of which correlated with RT change. Patients did not significantly increase their RTs, nor activate the anterior cingulate. Controls responded to positive ('win') feedback by a reduction in RT and activation of the ventral striatum, the extent of which correlated with RT change. Patients neither reduced their RT nor activated the ventral striatum. RT adjustment correlated with self-reported anhedonia for both patients and controls. This behavioural deficit, together with its associated pattern of abnormal neural activity, implies that the anterior midline cortical substrate for error correction, which includes projections from the monoamine systems, is dysfunctional in depressive illness. Many studies have reported abnormalities of the medial frontal cortex in depressive illness; however, the mechanism by which antidepressant medication acts via the monoamine systems remains elusive. Our results suggest a direct link between the core subjective symptom of anhedonia, replicated neuropsychological deficits, electrophysiological and imaging abnormalities, and hypothesized dysfunction of the error correction system

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort.

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

Item does not contain fulltextThe identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses

Stakeholder engagement in European brain research: Experiences of the Lifebrain consortium.

INTRODUCTION: Stakeholder engagement remains scarce in basic brain research. However, it can greatly improve the relevance of investigations and accelerate the translation of study findings to policy. The Lifebrain consortium investigated risk and protective factors influencing brain health using cognition, lifestyle and imaging data from European cohorts. Stakeholder activities of Lifebrain-organized in a separate work package-included organizing stakeholder events, investigating public perceptions of brain health and dissemination. Here, we describe the experiences of researchers and stakeholders regarding stakeholder engagement in the Lifebrain project. METHODS: Stakeholder engagement in Lifebrain was evaluated through surveys among researchers and stakeholders and stakeholders' feedback at stakeholder events through evaluation forms. Survey data were analysed using a simple content analysis approach, and results from evaluation forms were summarized after reviewing the frequency of responses. RESULTS: Consortium researchers and stakeholders experienced the engagement activities as meaningful and relevant. Researchers highlighted that it made the research and research processes more visible and contributed to new networks, optimized data collection on brain health perceptions and the production of papers and provided insights into stakeholder views. Stakeholders found research activities conducted in the stakeholder engagement work package to be within their field of interest and research results relevant to their work. Researchers identified barriers to stakeholder engagement, including lack of time, difficulties in identifying relevant stakeholders, and challenges in communicating complex scientific issues in lay language and maintaining relationships with stakeholders over time. Stakeholders identified barriers such as lack of budget, limited resources in their organization, time constraints and insufficient communication between researchers and stakeholders. CONCLUSION: Stakeholder engagement in basic brain research can greatly benefit researchers and stakeholders alike. Its success is conditional on dedicated human and financial resources, clear communication, transparent mutual expectations and clear roles and responsibilities. PUBLIC CONTRIBUTION: Patient organizations, research networks, policymakers and members of the general public were involved in engagement and research activities throughout the project duration