Abstract
Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological ...traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.
Quantifying the microstructural properties of the human brain's connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging ...(MRI) data in 3,513 generally healthy people aged 44.64-77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain.
Obtaining absolute chromophore concentrations from photoacoustic images obtained at multiple wavelengths is a nontrivial aspect of photoacoustic imaging but is essential for accurate functional and ...molecular imaging. This topic, known as quantitative photoacoustic imaging, is reviewed here. The inverse problems involved are described, their nature (nonlinear and ill-posed) is discussed, proposed solution techniques and their limitations are explained, and the remaining unsolved challenges are introduced.
Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an ...unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44–77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ≤ |β| ≤ 0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ≤ |β| ≤ 0.406) than the consistency-based approach which retained only 30% of connections (0.140 ≤ |β| ≤ 0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC = 0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ≤ |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ≤ |0.219|, p < 0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.
•Many network connections reconstructed by probabilistic tractography are spurious.•Network thresholding provided more age-sensitive measures than unthresholded data.•Threshold level was a stronger driver of age-associations than threshold method.•Stringent thresholding resulted in the most age-sensitive network measures.
The COVID-19 pandemic has affected many aspects of people's lives. Lockdown measures to reduce the spread of COVID-19 have been more stringent for those aged over 70, at highest risk for the disease. ...Here, we examine whether home garden usage is associated with self-reported mental and physical wellbeing in older adults, during COVID-19 lockdown in Scotland. This study analysed data from 171 individuals (mean age 84 ± 0.5 years) from the Lothian Birth Cohort 1936 study who completed an online survey approximately two months after lockdown commenced (May/June, 2020), and reported having access to a home garden. The survey also included items on garden activities (gardening, relaxing), frequency of garden usage during lockdown, and measures of self-rated physical health, emotional and mental health, anxiety about COVID-19, and sleep quality. Ordinal regression models were adjusted for sex, living alone, education, occupational social class, anxiety and depressive symptoms, body mass index, and history of diabetes and cardiovascular disease. Neither gardening nor relaxing in the garden were associated with health outcomes. However, higher frequency of garden usage during lockdown was associated with better self-rated physical health (P = 0.005), emotional and mental health (P = 0.04), sleep quality (P = 0.03), and a composite health score (P = 0.001), after adjusting for covariates. None of the garden measures were associated with perceived change in physical health, mental and emotional health, or sleep quality, from pre-lockdown levels. The results of the current study provide support for positive health benefits of spending time in a garden—though associations may be bidirectional—and suggest that domestic gardens could be a potential health resource during the COVID-19 pandemic.
•Covid-19 restrictions have had adverse impacts on wellbeing in older people.•An online health survey was conducted among an ageing cohort in Scotland.•Spending more time in a home garden associated with greater subjective wellbeing.•Potential benefits include better physical and mental health, and sleep quality.•During lockdowns, home gardens may be a potential health resource in older people.
Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the ...life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.
Discovering why some people's cognitive abilities decline more than others is a key challenge for cognitive ageing research. The most effective strategy may be to address multiple risk factors from ...across the life-course simultaneously in relation to robust longitudinal cognitive data. We conducted a 12-year follow-up of 1091 (at age 70) men and women from the longitudinal Lothian Birth Cohort 1936 study. Comprehensive repeated cognitive measures of visuospatial ability, processing speed, memory, verbal ability, and a general cognitive factor were collected over five assessments (age 70, 73, 76, 79, and 82 years) and analysed using multivariate latent growth curve modelling. Fifteen life-course variables were used to predict variation in cognitive ability levels at age 70 and cognitive slopes from age 70 to 82. Only APOE e4 carrier status was found to be reliably informative of general- and domain-specific cognitive decline, despite there being many life-course correlates of cognitive level at age 70. APOE e4 carriers had significantly steeper slopes across all three fluid cognitive domains compared with non-carriers, especially for memory (β = -0.234, p < 0.001) and general cognitive function (β = -0.246, p < 0.001), denoting a widening gap in cognitive functioning with increasing age. Our findings suggest that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status. We conclude that APOE e4 status is important for identifying those at greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.
•We train a neural network to solve the heterogeneous Helmholtz equation.•The algorithm requires considerably less iterations than GMRES.•Using a physics loss, no ground-truth data are needed.•The ...average maximum error to a reference solution for the test set is 0.36%.•The network is capable of generalizing on problems outside the training set.
Transcranial ultrasound therapy is increasingly used for the non-invasive treatment of brain disorders. However, conventional numerical wave solvers are currently too computationally expensive to be used online during treatments to predict the acoustic field passing through the skull (e.g., to account for subject-specific dose and targeting variations). As a step towards real-time predictions, in the current work, a fast iterative solver for the heterogeneous Helmholtz equation in 2D is developed using a fully-learned optimizer. The lightweight network architecture is based on a modified UNet that includes a learned hidden state. The network is trained using a physics-based loss function and a set of idealized sound speed distributions with fully unsupervised training (no knowledge of the true solution is required). The learned optimizer shows excellent performance on the test set, and is capable of generalization well outside the training examples, including to much larger computational domains, and more complex source and sound speed distributions, for example, those derived from x-ray computed tomography images of the skull.
•60 studies of association between DNAm and human brain MRI were identified.•Differential DNAm is associated with brain structure and function throughout life.•There is modest consistency between ...DNAm and image endophenotypes.•Approaches for reducing heterogeneity in DNAm-MRI analyses are proposed.
MRI has enhanced our capacity to understand variations in brain structure and function conferred by the genome. We identified 60 studies that report associations between DNA methylation (DNAm) and human brain structure/function. Forty-three studies measured candidate loci DNAm; seventeen measured epigenome-wide DNAm. MRI features included region-of-interest and whole-brain structural, diffusion and functional imaging features. The studies report DNAm-MRI associations for: neurodevelopment and neurodevelopmental disorders; major depression and suicidality; alcohol use disorder; schizophrenia and psychosis; ageing, stroke, ataxia and neurodegeneration; post-traumatic stress disorder; and socio-emotional processing. Consistency between MRI features and differential DNAm is modest. Sources of bias: variable inclusion of comparator groups; different surrogate tissues used; variation in DNAm measurement methods; lack of control for genotype and cell-type composition; and variations in image processing. Knowledge of MRI features associated with differential DNAm may improve understanding of the role of DNAm in brain health and disease, but caution is required because conventions for linking DNAm and MRI data are not established, and clinical and methodological heterogeneity in existing literature is substantial.
The DNA methylation-based 'epigenetic clock' correlates strongly with chronological age, but it is currently unclear what drives individual differences. We examine cross-sectional and longitudinal ...associations between the epigenetic clock and four mortality-linked markers of physical and mental fitness: lung function, walking speed, grip strength and cognitive ability.
DNA methylation-based age acceleration (residuals of the epigenetic clock estimate regressed on chronological age) were estimated in the Lothian Birth Cohort 1936 at ages 70 (n = 920), 73 (n = 299) and 76 (n = 273) years. General cognitive ability, walking speed, lung function and grip strength were measured concurrently. Cross-sectional correlations between age acceleration and the fitness variables were calculated. Longitudinal change in the epigenetic clock estimates and the fitness variables were assessed via linear mixed models and latent growth curves. Epigenetic age acceleration at age 70 was used as a predictor of longitudinal change in fitness. Epigenome-wide association studies (EWASs) were conducted on the four fitness measures.
Cross-sectional correlations were significant between greater age acceleration and poorer performance on the lung function, cognition and grip strength measures (r range: -0.07 to -0.05, P range: 9.7 x 10(-3) to 0.024). All of the fitness variables declined over time but age acceleration did not correlate with subsequent change over 6 years. There were no EWAS hits for the fitness traits.
Markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration). However, age acceleration does not associate with decline in these measures, at least over a relatively short follow-up.
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