Biology subjects

Wardlaw, J. M.

Publications and source records attributed to Wardlaw, J. M..

17 recordsLinked to original sources

bioRxiv2026

APOE4 genotype, blood-brain barrier leakage and ischaemic stroke subtype and location

BackgroundApolipoprotein E (APOE) has been implicated in blood-brain barrier (BBB) dysfunction and may influence ischaemic cerebrovascular disease and cerebral small-vessel disease (cSVD). This study examined associations between APOE genotype, BBB permeability, and infarct distribution in patients with mild ischemic stroke. MethodsWe recruited patients with mild ischemic stroke who underwent structural and dynamic contrast-enhanced MRI (DCE-MRI) and APOE genotyping. Infarct type and location, white matter hyperintensities (WMH), and perivascular spaces (PVS) were assessed. BBB-related metrics were quantified using fractional plasma volume (vP) and permeability-surface area product (PS) across five brain regions of interest: deep grey matter (DGM), hippocampus, thalamus, normal-appearing white matter (NAWM), and WMH. Associations between genotype, BBB metrics, vascular risk factors, and age were evaluated using linear mixed-effects models. Binary logistic regression was also applied to assess the association between APOE status and infarct location by vascular territory (anterior vs posterior circulation). ResultsAmong 147 patients with APOE genotype and BBB measures, APOE4 carriers (n=44) demonstrated a greater proportion of posterior circulation infarcts than E3/E3 individuals (n=80; 56.4% versus 32.9%), including higher frequencies of posterior cerebral artery cortical, posterior borderzone, and thalamic infarcts. Mean PS and vP did not differ significantly by genotype. Increasing age was associated with lower PS across multiple regions and lower vP in WMH, while higher vascular risk burden was associated with lower vP in NAWM and WMH. Inclusion of regional BBB metrics did not substantially alter APOE4 effect estimates in infarct-location models. ConclusionsAPOE4 carriers showed a posterior-predominant infarct distribution despite similar BBB permeability and vascularity measures. Age and vascular risk burden were more strongly associated with BBB-related imaging metrics than APOE genotype. These findings add to evidence suggesting that APOE genotype may influence regional cerebrovascular vulnerability and that this effect is unlikely to be fully explained by DCE-MRI-derived measures of BBB permeability and vascularity alone.

neuroscience↗

bioRxiv2026

Abrupt Pericyte Loss Precedes Endothelial Activation in Cerebral Small Vessel Disease

Cerebral small vessel disease (cSVD) is a leading cause of stroke and vascular cognitive impairment, yet the cellular mechanisms underlying microvascular dysfunction in human disease remain incompletely understood. In particular, the relationship between pericyte alterations and endothelial activation, two key features of blood-brain barrier dysfunction, remains unresolved. Here, we performed a quantitative single-vessel analysis of the human cortical microvasculature across increasing cSVD severity and ageing. Using multiplex immunohistochemistry combined with spectral unmixing and automated image analysis, we analysed 11,409 microvascular fragments from post-mortem brain tissue derived from 20 cases. Endothelial cells, pericytes, and endothelial activation were assessed using PECAM-1, PDGFR{beta}, and VCAM-1, respectively. Microvascular density and diameter differed between cortical grey matter and the underlying white matter, with white matter vessels being less dense and wider in controls. While vessel diameter remained stable across disease stages, microvascular density increased with cSVD severity and age in the white matter. At the molecular level, PDGFR{beta} signal decreased markedly with increasing cSVD severity, consistent with progressive pericyte loss. This reduction was observed in both grey and white matter and correlated with disease severity and age. Notably, intermediate disease groups displayed marked heterogeneity, with vessels exhibiting either preserved or near-complete pericyte coverage, suggesting a potentially bimodal transition. In parallel, endothelial markers PECAM-1 and VCAM-1 increased significantly with disease severity, reflecting endothelial activation. Unsupervised Gaussian mixture clustering of marker expression identified three vascular states characterised by (i) preserved pericytes with low endothelial activation, (ii) marked pericyte loss without endothelial activation, and (iii) combined pericyte depletion and endothelial activation. These clusters broadly aligned with clinical severity but revealed intermediate states not captured by post-mortem diagnosis alone. Together, these findings suggest that pericyte loss and endothelial activation are partially dissociated processes that occur in a sequential progression in human cSVD, supporting pericyte dysfunction as an early event and highlighting it as a potential therapeutic target in microvascular disease.

pathology↗

bioRxiv2020

Three major dimensions of human brain cortical ageing in relation to cognitive decline across the 8th decade of life.

Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflates information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the 8th decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1,376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions, that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.

neuroscience↗

bioRxiv2019

Enhanced prediction of cognitive function using aging-sensitive networks within the human structural connectome

Using raw structural and diffusion brain MRI data from the UK Biobank (UKB; N = 3,155, ages 45-75 years) and the Lothian Birth Cohort 1936 (LBC1936; N = 534, all age 73 years), we examine aging of regional grey matter volumes (nodes) and white matter structural connectivity (edges) within networks-of-interest in the human brain connectome. In UKB, the magnitude of age-differences in individual node volumes and edge weights corresponds closely with their loadings on their respective principal components of connectome-wide integrity (|rnodes| = 0.459; |redges| = 0.595). In LBC1936, connectome-wide and subnetwork-specific composite indices of node integrity were predictive of processing speed, visuospatial ability, and memory, whereas composite indices of edge integrity were associated specifically with processing speed. Childhood IQ was associated with greater node integrity at age 73. However, node and edge integrity remained associated with age 73 cognitive function after controlling for childhood IQ. Adult connectome integrity is therefore both a marker of early-life cognitive function and a substrate of late-life cognitive aging.

neuroscience↗

bioRxiv2019

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the models architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

neuroscience↗

bioRxiv2019

An epigenetic predictor of death captures multi-modal measures of brain health

Individuals of the same chronological age exhibit disparate rates of biological ageing. Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm) - a commonly studied epigenetic mechanism. A novel epigenetic clock, termed DNAm GrimAge has outperformed its predecessors in predicting the risk of mortality as well as a number of age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n=709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over twelve years of follow-up (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 x 10-16). Higher DNAm GrimAge was associated with lower age 11 IQ ({beta}=-0.11), lower age 73 general cognitive ability ({beta}=-0.18), decreased brain volume ({beta}=-0.25) and increased brain white matter hyperintensities ({beta}=0.17). Sixty-eight of 137 health- and brain-related phenotypes tested were significantly associated with DNAm GrimAge. Adjusting all models for childhood cognitive ability attenuated to non-significance a small number of associations (12/68 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge cross-sectionally associates with lower cognitive ability and brain vascular lesions in older age, independently of early life cognitive ability. Thus, this epigenetic predictor of mortality is also associated with multiple different measures of brain health and may aid in the prediction of age-related cognitive decline.

neuroscience↗

bioRxiv2019

Neurology-related protein biomarkers are associated with general fluid cognitive ability and brain volume in older age

Identifying the biological correlates of late life cognitive function is important if we are to ascertain biomarkers for, and develop treatments to help reduce, age-related cognitive decline. This study investigated the associations between plasma levels of 91 neurology-related proteins (Olink(R) Proteomics) and general fluid cognitive ability in the Lothian Birth Cohort 1936 (LBC1936, N=798), the Lothian Birth Cohort 1921 (LBC1921, N=165), and the INTERVAL BioResource, (N=4,451). In LBC1936, we also examined mediation of protein-cognitive ability associations by MRI-derived indices of brain structure. In the LBC1936, 22 of the proteins and the first principal component (PC) created from a PC analysis of the 91 proteins, were associated with general fluid cognitive ability ({beta} between -0.11 and -0.17, p<0.0029). Total brain volume partially mediated the association between 10 of these proteins and general fluid cognitive ability. Effect sizes for the 22 proteins, although smaller, were all in the same direction as in LBC1936 in an age-matched subsample of INTERVAL. Similar effect sizes were found for the majority of these 22 proteins in the older LBC1921. The associations were not replicated in a younger subset of INTERVAL. In conclusion, we identified plasma levels of a number of neurology-related proteins that were associated with general fluid cognitive ability in later life, some of which were mediated by brain volume.

neuroscience↗

bioRxiv2019

Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map

We propose a Generative Adversarial Network (GAN) model named Disease Evolution Predictor GAN (DEP-GAN) to predict the evolution (i.e., progression and regression) of White Matter Hyperintensities (WMH) in small vessel disease. In this study, the evolution of WMH is represented by the \"Disease Evolution Map\" (DEM) produced by subtracting irregularity map (IM) images from two time points: baseline and follow-up. DEP-GAN uses two discriminators (critics) to enforce anatomically realistic follow-up image and DEM. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we propose modulating an array of random noises to the DEP-GANs generator which forces the model to imitate a wider spectrum of alternatives in the results. Our study shows that the use of two critics and random noises modulation in the proposed DEP-GAN improves its performance predicting the evolution of WMH in small vessel disease. DEP-GAN is able to estimate WMH volume in the follow-up year with mean (std) estimation error of -1.91 (12.12) ml and predict WMH evolution with mean rate of 72.01% accuracy (i.e., 88.69% and 23.92% better than Wasserstein GAN).

neuroscience↗

bioRxiv2019

Polygenic architecture of human neuroanatomical diversity

We analysed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26,000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r[~]0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes ([~]51%) captured [~]1.5 times more genetic variance than the rest; and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF<5% captured <1/4th of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG[~]0.45. Genetic correlations were similar to phenotypic and environmental correlations, however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available at https://github.com/neuroanatomy/genomic-architecture.

neuroscience↗

bioRxiv2019

Intracranial functional haemodynamic relationships in patients with cerebral small vessel disease

BackgroundCerebral small vessel disease (SVD) is a major cause of stroke and dementia. The underlying cerebrovascular dysfunction is poorly understood. We investigated cerebrovascular reactivity, blood flow, vascular and cerebrospinal fluid (CSF) pulsatility, and their independent relationship to SVD features, in patients with minor ischaemic stroke and MRI evidence of SVD.\n\nMethodsWe recruited patients with minor ischaemic stroke and assessed CVR using Blood Oxygen Level Dependent (BOLD) MRI during a hypercapnic challenge, cerebral blood flow, vascular and CSF pulsatility using phase contrast MRI, and structural MR brain imaging to quantify white matter hyperintensities (WMH) and perivascular spaces (PVS). We quantified CVR in seven white matter and six subcortical grey matter regions, measured blood flow in carotid and vertebral arteries, intracranial venous sinuses, internal jugular veins and CSF flow at the aqueduct and foramen magnum. We used multiple regression to identify SVD features, blood flow and pulsatility parameters associated with CVR, controlling for patient characteristics.\n\nResultsIn 53 of 60 patients with complete data (age 68.0{+/-}8.8, 74% male, 75% hypertensive), CVR in grey and white matter decreased with increasing blood pressure (BP, respectively -0.001%/mmHg, p=0.01 and -0.006%/mmHg, p=0.01, per mmHg increase in BP). After controlling for age, gender and systolic BP, white matter CVR decreased with increasing WMF volume (-0.01%/mmHg per log10 increase in WMH volume, p=0.02) and basal ganglia PVS (-0.01%/mmHg per point increase in PVS score, p=0.02). White matter CVR decreased with increasing venous pulsatility (superior sagittal sinus -0.03%/mmHg, p=0.02, per unit increase in pulsatility index) but not with cerebral blood flow (p=0.58). Lower foramen magnum CSF stroke volume was associated with worse white matter CVR (0.04%/mmHg per ml increase in stroke volume, p=0.04) and increased basal ganglia PVS.\n\nConclusionsContemporaneous assessment of CVR, intracranial vascular and CSF pulsatility demonstrates important interrelationships of these vascular functions in humans. Decreased CVR, increased venous pulsatility and reduced foramen magnum CSF stroke volume suggests that dynamic vascular dysfunctions underpin PVS dysfunction and WMH development. Improved understanding of microvascular dysfunction and CSF dynamics offers new intervention targets to reduce SVD lesion development and their impact on cognitive dysfunction and stroke.

neuroscience↗

bioRxiv2019

Associations between vascular risk factors and brain MRI indices in UK Biobank

AimsSeveral factors are known to increase risk for cerebrovascular disease and dementia, but there is limited evidence on associations between multiple vascular risk factors (VRFs) and detailed aspects of brain macro- and microstructure in large community-dwelling populations across middle- and older age.\n\nMethods and ResultsAssociations between VRFs (smoking, hypertension, pulse pressure, diabetes, hypercholersterolaemia, BMI, and waist-hip ratio) and both global and regional brain structural and diffusion MRI markers were examined in UK Biobank (N = 9722, age range 44-77 years). A larger number of VRFs was associated with greater brain atrophy, lower grey matter volume, and poorer white matter health. Effect sizes were small (brain structural R2 [≤] 1.8%). Higher aggregate vascular risk was related to multiple regional MRI hallmarks associated with dementia risk: lower frontal and temporal cortical volumes, lower subcortical volumes, higher white matter hyperintensity volumes, and poorer white matter microstructure in association and thalamic pathways. Smoking pack years, hypertension and diabetes showed the most consistent associations across all brain measures. Hypercholesterolaemia was not uniquely associated with any MRI marker.\n\nConclusionHigher levels of VRFs were associated with poorer brain health across grey and white matter macro- and microstructure. Effects are mainly additive, converging upon frontal and temporal cortex, subcortical structures, and specific classes of white matter fibres. Though effect sizes were small, these results emphasise the vulnerability of brain health to vascular factors even in relatively healthy middle and older age, and the potential to partly ameliorate cognitive decline by addressing these malleable risk factors.

neuroscience↗

bioRxiv2018

Retinal microvasculature and cerebral small vessel disease in the Lothian Birth Cohort 1936 and Mild Stroke Study

Research has suggested that the retinal vasculature may act as a surrogate marker for diseased cerebral vessels. Retinal vascular parameters were measured using Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE) software in two cohorts: (i) community-dwelling older subjects of the Lothian Birth Cohort 1936; and (ii) patients with recent minor ischaemic stroke of the Mild Stroke Study. Imaging markers of small vessel disease (SVD) (white matter hyperintensities [WMH] on structural MRI, visual scores and volume; perivascular spaces; lacunes and microbleeds), and vascular risk measures were assessed in both cohorts. We assessed associations between retinal and brain measurements using structural equation modelling and regression analysis. In the Lothian Birth Cohort 1936 arteriolar fractal dimension accounted for 4% of the variance in WMH load. In the Mild Stroke Study lower arteriolar fractal dimension was associated with deep WMH scores (odds ratio [OR] 0.53; 95% CI, 0.32-0.87). No other retinal measure was associated with SVD. Reduced fractal dimension, a measure of vascular complexity, is related to SVD imaging features in older people. The results provide some support for the use of the retinal vasculature in the study of brain microvascular disease.

neuroscience↗

bioRxiv2018

Spatial Gradient of Microstructural Changes in Normal-Appearing White Matter in Tracts Affected by White Matter Hyperintensities in Older Age

Brain white matter hyperintensities (WMH), common in older adults, may contribute to cortical disconnection and cognitive dysfunction. The presence of WMH within white matter (WM) tracts indicates underlying microstructural WM changes that may also affect the normal-appearing WM (NAWM) of a tract. We performed an exploratory study using diffusion magnetic resonance imaging of 52 healthy participants from the Lothian Birth Cohort 1936 (age 72.2 {+/-} 0.7 years) selected to include a range of WMH burden, to quantify microstructural changes of tracts intersecting WMH. We reconstructed tracts using automated tractography and identified intersections with WMH. Tissue volumes and water diffusion tensor parameters (mean diffusivity (MD) and fractional anisotropy (FA)) were established for tract-WMH and tract-NAWM. MD and FA were also measured for tract-NAWM at 2 mm incremental distances from the tract-WMH edge, and from the edge of nearby, non-intersecting, WMH. We observed microstructural changes in tract-WMH suggestive of tissue damage. Tract-NAWM also showed a spatial gradient of FA and MD abnormalities, which diminished with distance from the tract-WMH. Nearby WMH lesions, not directly crossed by the tract, also affected tract microstructure with a similar pattern. Additionally, both FA and MD changes in tract-NAWM were predicted by FA and MD changes respectively in tract-WMH. FA was also predicted by tract-WMH overlap volume, whereas MD was better predicted by whole-brain WMH load. These results suggest that tract-NAWM microstructure is affected by the pathological process underlying WMH, when WMH are either within or near to the tract. The changes in NAWM tract tissue may indicate future lesion progression and may play an important role in cognitive ageing.

neuroscience↗

bioRxiv2018

Planar cell polarity pathway and development of the human visual cortex

The radial unit hypothesis provides a framework for global (proliferation) and regional (distribution) expansion of the primate cerebral cortex. Using principal component analysis (PCA), we have identified cortical regions with shared variance in their surface area and cortical thickness, respectively, segmented from magnetic resonance images obtained in 23,800 participants. We then carried out meta-analyses of genome-wide association studies of the first two principal components for each phenotype. For surface area (but not cortical thickness), we have detected strong associations between each of the components and single nucleotide polymorphisms in a number of gene loci. The first (global) component was associated mainly with loci on chromosome 17 (9.5e-32 [≤] p [≤] 2.8e-10), including those detected previously as linked with intracranial volume and/or general cognitive function. The second (regional) component captured shared variation in the surface area of the primary and adjacent secondary visual cortices and showed a robust association with polymorphisms in a locus on chromosome 14 containing Disheveled Associated Activator of Morphogenesis 1 (DAAM1; p=2.4e-34). DAAM1 is a key component in the planar-cell-polarity signaling pathway. In follow-up studies, we have focused on the latter finding and established that: (1) DAAM1 is highly expressed between 12th and 22nd post-conception weeks in the human cerebral cortex; (2) genes co-expressed with DAAM1 in the primary visual cortex are enriched in mitochondria-related pathways; and (3) volume of the lateral geniculate nucleus, which projects to regions of the visual cortex staining for cytochrome oxidase (a mitochondrial enzyme), correlates with the surface area of the visual cortex in major-allele homozygotes but not in carriers of the minor allele. Altogether, we speculate that, in concert with thalamocortical input to cortical subplate, DAAM1 enables migration of neurons to cytochrome-oxidase rich regions of the visual cortex, and, in turn, facilitates regional expansion of this set of cortical regions during development.

neuroscience↗

bioRxiv2018

Brain peak width of skeletonised mean diffusivity (PSMD), processing speed, and other cognitive domains

It is suggested that the brain's peak width of skeletonised water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, a crucial contributor to cognitive ageing. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age 73 years (SD=0.7); analytical sample was 656-680. Cognitive domains tested were: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters and hyperintensity volumes, grey and white matter volumes, and perivascular spaces. PSMD was significantly associated with all processing speed tests; absolute standardised beta values were 0.11 to 0.23 (mean = 0.17). Other structural brain-imaging variables correlated as or more strongly. PSMD was significantly associated with processing speed (-0.27), visuospatial (-0.23), memory (-0.17), and general cognitive ability (-0.25). PSMD correlated with processing speed: but not more strongly than with other cognitive domains; and not more strongly than other brain-imaging measures.

neuroscience↗

bioRxiv2017

Brain Structural Differences Between 73- And 92-Year Olds Matched For Childhood Intelligence, Social Background, And Intracranial Volume

Fully characterizing age differences in the brain is a key task for combatting ageing-related cognitive decline. Using propensity score matching on two independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age 92 years (n = 42) to very similar participants at mean age 73 (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in grey and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural ageing, in which brains from the eighth and tenth decades of life differ widely despite the same cognitive, socio-economic, and brain-volumetric starting points.

neuroscience↗

bioRxiv2017

Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining normality); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

neuroscience↗