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Inter‐ and intra‐individual variation in brain structural‐cognition relationships in aging
AbstractBackgroundThe sources of inter‐ and intra‐individual variability in age‐related cognitive decline remain poorly understood. We examined the association between 20‐year trajectories of cognitive decline and multimodal brain structure and morphology in older age.MethodWe used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid‐life (mean age = 53.2 ±4.9 years) and late‐life (mean age = 67.7 ± 4.9). Using non‐negative matrix factorisation, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities (Figure 1).ResultWe observed two latent variables describing distinct brain‐cognition associations. The first describes variations in 5 structural components associated with low mid‐life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid‐life performance in fluency and memory, but retention of multiple abilities (Figure 2). Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9) (Figure 3).ConclusionLongitudinal cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with distinct markers of cortical structure. Latent brain‐behaviour relationships predict future cognitive performance.
Automated quality control of structural brain MRI scans from aging and dementia datasets
AbstractBackgroundDementias platform UK (DPUK) currently hosts over 40 dementia cohorts and provides a remote analysis environment for multimodal cross‐cohort data analysis. Nineteen cohorts include brain MRI data, hence automated quality control (QC) becomes crucial for the reliability of image analyses. This work aims to provide a framework for quality prediction of T1‐weighted (T1w) scans from aging and dementia datasets: we compared manual quality ratings with the output of two existing automated QC pipelines and propose a new QC classifier.MethodWe used 2438 T1w scans (including patients and controls, 1.5T and 3T scanners, 3 manufacturers, 11 sites) from 4 different aging and dementia datasets (ADNI, Whitehall II, Oxford Parkinson’s Disease Centre, Oxford Brain Health Clinic). Manual quality ratings for all images were performed by the dataset owners. Each image was processed in MRIQC (Esteban et al. 2017, PLoS ONE) and CAT12 (Computational Anatomy Toolbox, Gaser et al. 2022, bioRxiv). We compared the quality agreement for MRIQC predictions and CAT12’s weighted image quality ratings (IQR) with manual QC using inter‐rater reliability. Further, we explored the effect of changing the accept‐reject threshold from automated QC tools on the reliability measure. Finally, we designed a custom QC classifier (manual QC as target) by combining MRIQC and CAT12’s quality metrics as features and training linear support vector machines in a nested cross‐validation framework.ResultWe found overall agreement between automated QC methods and manual QC when applied to dementia datasets (MRIQC: Kappa = 0.30, p = 0.001; CAT12: Kappa = 0.28, p = 0.001). Adjusting the acceptance thresholds of the automated tools within dataset improved the agreement. On the combined data (training N = 1951; test N = 487), the proposed classifier showed 94.2% accuracy (sensitivity 94.3%, specificity 85.7%) on the test data. Leaving one site out (training with 10 sites, N = 2055), we found 86.4% accuracy (sensitivity 86.2%, specificity 100%) on the test site (N = 383).ConclusionThe performance of the proposed classifier appears promising on these heterogeneous datasets from different scanners. In the future, we aim to utilise this framework for improving generalisability of prediction and release the classifier on the DPUK data portal to robustly QC T1w scans for other aging and dementia cohorts.
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 (R2 ), 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 R2 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.
Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
Associations Between Depression and Brain Structure Across the Lifespan: Preliminary Results From the European Lifebrain Consortium
Background Depression diagnosis is associated with decreased prefrontal thickness and hippocampal volume. Less is known about these associations in the general population and across the lifespan. We investigate associations between (subclinical) depression and brain structure, and moderating effects of age and sex. Methods We included 1870 participants between 18 and 85 years old from 3 sites (UK, Norway, Netherlands) of the European Lifebrain consortium. Meta-analyses were performed, calculating per-site and pooled effect sizes for associations between mild/moderate depression and FreeSurfer-derived rACC, mOFC thickness or hippocampal volume. In ongoing analyses, we include additional cohorts (Spain, Germany, Sweden, UK, Denmark; final N∼3500) and explore the effects of age and sex. Results 374 participants met criteria for mild-to-moderate depression, with 181 meeting criteria for moderate depression. Mild-to-moderate depression (vs. no depression) was not significantly associated with rACC (Cohen’s d=-0.090) or mOFC (d=-0.114) thickness, or hippocampal volume (d=0.018). Similarly, moderate depression was not significantly associated with rACC (d=-0.164) or mOFC thickness (d=-0.088), or hippocampal volume (d=-0.012). Conclusions Mild or moderate depression was not associated with medial prefrontal thickness, or hippocampal volume, although effect-sizes for medial prefrontal areas were similar to previous observations in the ENIGMA consortium. However, associations with hippocampal volume were not in line with earlier ENIGMA findings, potentially due to few participants with moderate depression. Of relevance, earlier studies often had a more limited age range, and associations might change across the lifespan. Therefore, in ongoing analyses we investigate potential moderating effects of age which will be presented at the conference.
Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life
We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96 SD=5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Assessment, MoCA). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β=3.36, 95% CI [0.42- 6.30]), and faster 25-year cognitive decline in letter fluency (β=-0.07, 95% CI [-0.13–-0.01]), and verbal reasoning (β=-0.05, 95% CI [-0.11–-0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected p<0.05). The latter association was most pronounced in individuals with cognitive impairments on MoCA (F3,608=2.14, p=0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.
The effects of APOE on the functional architecture of the resting brain.
There is a well-established association between APOE genotype and the risk of developing Alzheimer's disease (AD). Relative to individuals with the common ε3/ε3 genotype, carriers of the ε4 allele are at increased risk of developing AD, while carriers of the ε2 allele appear to be protected against the disease. However, we recently reported that in a sample of cognitively healthy adults, both ε4 and ε2 carriers showed nearly identical changes in the pattern of fMRI activity during memory and non-memory tasks, relative to ε3 homozygotes. These findings suggest that the effects of APOE on brain function are not tightly linked to the effects of this gene on AD risk. Here we test the hypothesis that APOE has an intrinsic effect on the brain's functional networks. Resting-state fMRI was used to compare the pattern of functional connectivity of a variety of resting-state networks between 77 cognitively healthy participants aged 32 to 55 with different APOE genotypes (23 ε2/ε3, 20 ε3/ε3, 26 ε3/ε4, and 8 ε4/ε4). Differences between genotype groups were found in two hippocampal networks, the auditory network, the left frontal-parietal network, and the lateral visual network. While there was considerable variety in the brain regions affected and the direction of change across networks, the main finding was that changes in functional connectivity were similar in ε4 and ε2 carriers, relative to ε3 homozygotes. APOE appears to have an intrinsic effect on the differentiation of functional networks in the brain. This effect is apparent in cognitively healthy adults and does not manifest in a manner reflective of the link between APOE and AD risk. Rather, the effects of APOE on brain function may relate to the role of this gene in neurodevelopment.
Magnetic resonance imaging in late-life depression: multimodal examination of network disruption.
CONTEXT: Disruption of frontal-subcortical and limbic networks is hypothesized to have a key role in late-life depression (LLD) and can be examined using magnetic resonance imaging (MRI) techniques. Gray matter can be examined using T1-weighted MRI, white matter using T2-weighted MRI and diffusion tensor imaging, and functional connectivity in resting-state networks using functional MRI. Although independent MRI studies have supported gray and white matter abnormalities in frontosubcortical and limbic networks and increased functional connectivity in the default-mode network in depression, no study has concurrently examined gray matter, white matter, and functional connectivity. OBJECTIVE: To examine whether results of different MRI techniques are complementary, multimodal MRI was used to compare gray matter, white matter, and resting-state networks between LLD and control groups. DESIGN: Cross-sectional, case-control, multimodal MRI analysis. SETTING: University research department. PARTICIPANTS: Thirty-six recovered participants with LLD (mean age, 71.8 years) and 25 control participants (mean age, 71.8 years). MAIN OUTCOME MEASURES: Gray matter was examined across the whole brain using voxel-based morphometry. Subcortical gray matter structures were also automatically segmented, and volumetric and shape analyses were performed. For white matter analysis, fractional anisotropy, axial diffusivity, and radial diffusivity values were examined using tract-based spatial statistics. For resting-state network analysis, correlation coefficients were compared using independent components analysis followed by dual regression. RESULTS: White matter integrity was widely reduced in LLD, without significant group differences in gray matter volumes or functional connectivity. CONCLUSIONS: The present work strongly supports the hypothesis that white matter abnormalities in frontal-subcortical and limbic networks play a key role in LLD even in the absence of changes in resting functional connectivity and gray matter. Factors that could contribute to the lack of significant differences in gray matter and functional connectivity measures, including current symptom severity, medication status, and age of participants with LLD, are discussed.
Association of trajectories of depressive symptoms with vascular risk, cognitive function and adverse brain outcomes: The Whitehall II MRI sub-study.
BACKGROUND: Trajectories of depressive symptoms over the lifespan vary between people, but it is unclear whether these differences exhibit distinct characteristics in brain structure and function. METHODS: In order to compare indices of white matter microstructure and cognitive characteristics of groups with different trajectories of depressive symptoms, we examined 774 participants of the Whitehall II Imaging Sub-study, who had completed the depressive subscale of the General Health Questionnaire up to nine times over 25 years. Twenty-seven years after the first examination, participants underwent magnetic resonance imaging to characterize white matter hyperintensities (WMH) and microstructure and completed neuropsychological tests to assess cognition. Twenty-nine years after the first examination, participants completed a further cognitive screening test. OUTCOMES: Using K-means cluster modelling, we identified five trajectory groups of depressive symptoms: consistently low scorers ("low"; n = 505, 62·5%), a subgroup with an early peak in depression scores ("early"; n = 123, 15·9%), intermediate scorers ("middle"; n = 89, 11·5%), a late symptom subgroup with an increase in symptoms towards the end of the follow-up period ("late"; n = 29, 3·7%), and consistently high scorers ("high"; n = 28, 3·6%). The late, but not the consistently high scorers, showed higher mean diffusivity, larger volumes of WMH and impaired executive function. In addition, the late subgroup had higher Framingham Stroke Risk scores throughout the follow-up period, indicating a higher load of vascular risk factors. INTERPRETATION: Our findings suggest that tracking depressive symptoms in the community over time may be a useful tool to identify phenotypes that show different etiologies and cognitive and brain outcomes.
Education and Income Show Heterogeneous Relationships to Lifespan Brain and Cognitive Differences Across European and US Cohorts.
Higher socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. We ask whether relationships between SES, brain volumes and cognitive ability differ across cohorts, by age and national origin. European and US cohorts covering the lifespan were studied (4-97 years, N = 500 000; 54 000 w/brain imaging). There was substantial heterogeneity across cohorts for all associations. Education was positively related to intracranial (ICV) and total gray matter (GM) volume. Income was related to ICV, but not GM. We did not observe reliable differences in associations as a function of age. SES was more strongly related to brain and cognition in US than European cohorts. Sample representativity varies, and this study cannot identify mechanisms underlying differences in associations across cohorts. Differences in neuroanatomical volumes partially explained SES-cognition relationships. SES was more strongly related to ICV than to GM, implying that SES-cognition relations in adulthood are less likely grounded in neuroprotective effects on GM volume in aging. The relatively stronger SES-ICV associations rather are compatible with SES-brain volume relationships being established early in life, as ICV stabilizes in childhood. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.
Vascular risk factors and depression in later life: a systematic review and meta-analysis.
Reports of the association between cardiovascular risk factors and depression in later life are inconsistent; to establish the nature of their association seems important for prevention and treatment of late-life depression. We searched MEDLINE, EMBASE, and PsycINFO for relevant cohort or case control studies over the last 22 years; 1097 were retrieved; 26 met inclusion criteria. Separate meta-analyses were performed for Risk Factor Composite Scores (RFCS) combining different subsets of risk factors, Framingham Stroke Risk Score, and single factors. We found a positive association (odds ratio [OR]: 1.49; 95% confidence interval [CI]: 1.27-1.75) between RFCS and late-life depression. There was no association between Framingham Stroke Risk Score (OR: 1.25; 95% CI: .99-1.57), hypertension (OR: 1.14; 95% CI: .94-1.40), or dyslipidemia (OR: 1.08; 95% CI: .91-1.28) and late-life depression. The association with smoking was weak (OR: 1.35; 95% CI: 1.00-1.81), whereas positive associations were found with diabetes (OR: 1.51; 95% CI: 1.30-1.76), cardiovascular disease (OR: 1.76; 95% CI: 1.52-2.04), and stroke (OR: 2.11; 95% CI: 1.61-2.77). Moderate to high heterogeneity was found in the results for RFCS, smoking, hypertension, dyslipidemia, and stroke, whereas publication bias was detected for RFCS and diabetes. We therefore found convincing evidence of a strong relationship between key diseases and depression (cardiovascular disease, diabetes, and stroke) and between composite vascular risk and depression but not between some vascular risk factors (hypertension, smoking, dyslipidemia) and depression. More evidence is needed to be accumulated from large longitudinal epidemiological studies, particularly if complemented by neuroimaging.
Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis.
OBJECTIVES: We performed a systematic review to assess alterations in measures of diffusion tensor imaging (DTI) in parkinsonian syndromes, exploring the potential role of DTI in diagnosis and as a candidate biomarker. METHODS: We searched EMBASE and Medline databases for DTI studies comparing parkinsonian syndromes or related dementias with controls or another defined parkinsonian syndrome. Key details for each study regarding participants, imaging methods, and results were extracted. Estimates were pooled, where appropriate, by random-effects meta-analysis. RESULTS: Of 333 results, we identified 43 studies suitable for inclusion (958 patients, 764 controls). DTI measures detected alterations in all parkinsonian syndromes, with distribution varying differentially with disease type. Nine studies were included in a meta-analysis of the substantia nigra in Parkinson disease. A notable effect size was found for lowered fractional anisotropy in the substantia nigra for patients with Parkinson disease vs controls (-0.639, 95% confidence interval -0.860 to -0.417, p < 0.0001). CONCLUSION: DTI may be a promising biomarker in parkinsonian syndromes and have a future role in differential diagnosis. Larger cohort studies are required to investigate some encouraging preliminary findings. Given the complexity of the parkinsonian syndromes, it is likely that any potential DTI biomarker would be used in combination with other relevant biomarkers.
A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression.
Gray matter abnormalities within frontal-subcortical and limbic networks are hypothesized to play a key role in the pathophysiology of late-life depression. In this work, gray matter abnormalities in late-life depression are examined in a systematic review and meta-analysis of magnetic resonance imaging studies. In the systematic review, 27 articles were identified that compared participants with late-life depression with comparison group participants, and 17 studies were suitable for inclusion in meta-analyses of volumes of the whole brain, orbitofrontal cortex, caudate, hippocampus, putamen, and thalamus. Volume reductions were detected in 7 of 15 comparisons of the hippocampus and a meta-analysis revealed a significant, but small, effect size. Although examined by fewer studies, meta-analyses also revealed significant volume reductions in the orbitofrontal cortex, putamen, and thalamus. A more systematic and comprehensive analysis of the global distribution of gray matter abnormalities, and an examination of subcortical abnormalities were identified as key areas for future research.
Early diagnosis beneficial in Alzheimer's disease.
GPs should consider a diagnosis of dementia when a patient presents with functional impairment in addition to at least two changes in cognitive function e.g. short-term memory, language, reasoning, spatial orientation, or personality change. The patient, friends, family or professional carers should have noticed these changes for at least six months. Patients should be referred to a memory clinic to make a formal diagnosis of probable or possible Alzheimer's disease and to exclude other types of dementia. Key to assessment is a careful history of cognitive and functional changes, medical conditions and past psychiatric history. An objective cognitive assessment is important, and in primary care screening tools such as the General Practitioner Assessment of Cognition provide a useful adjunct to justify referral to specialist services. Patients should have a physical examination and a dementia screen to exclude treatable causes of cognitive impairment. Acetylcholinesterase inhibitors and memantine both slow the progression of cognitive decline and extend independence in activities of daily living. NICE recommends donepezil, rivastigmine or galantamine for mild to moderate Alzheimer's disease, and memantine for severe disease. Primary care is optimally placed to screen for cognitive impairments, to provide essential longitudinal information that will make a diagnosis of dementia safer. Primary care also has a crucial role in primary and, particularly secondary, prevention programmes to tackle excessive weight, lack of activity, smoking, and other lifestyle risk factors for dementia, including Alzheimer's disease, as well as the treatment of medical conditions which increase dementia risk.