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Objective: To determine whether the presence of diffusion-weighted imaging-positive (DWI+) lesions is associated with recurrent stroke after intracerebral haemorrhage (ICH)., Methods: The REstart or STop Antithrombotics Randomised Trial (RESTART) assessed the effect of restarting versus avoiding antiplatelet therapy after ICH on major vascular events for up to 5 years. We rated DWI sequences of MRI done before randomisation for DWI+ lesion presence, masked to outcome and antiplatelet use. Cox proportional hazards regression models were used to quantify associations., Results: Of 537 participants in RESTART, 247 (median (IQR) age 75.7 (69.6-81.1) years; 170 men (68.8%); 120 started vs 127 avoided antiplatelet therapy) had DWI sequences on brain MRI at a median of 57 days (IQR 19-103) after ICH, of whom 73 (30%) had one or more DWI+ lesion. During a median follow-up of 2 years (1-3), 18 participants had recurrent ICH and 21 had ischaemic stroke. DWI+ lesion presence was associated with all stroke, (adjusted HR 2.2 (95% CI 1.1 to 4.2)) and recurrent ICH (4.8 (95% CI 1.8 to 13.2)), but not ischaemic stroke (0.9 (95% CI 0.3 to 2.5)). DWI+ lesion presence (0.5 (95% CI 0.2 to 1.3)) vs absence (0.6 (95% CI 0.3 to 1.5), p interaction =0.66) did not modify the effect of antiplatelet therapy on a composite outcome of recurrent stroke., Conclusions: DWI+ lesion presence in ICH survivors is associated with recurrent ICH, but not with ischaemic stroke. We found no evidence of modification of effects of antiplatelet therapy on recurrent stroke after ICH by DWI+ lesion presence. These findings provide a new perspective on the significance of DWI+ lesions, which may be markers of microvascular mechanisms associated with recurrent ICH., Trial Registration Number: ISRCTN71907627., Competing Interests: Competing interests: MdCVH and JW are supported by the Row Fogo Charitable Trust who funds the Row Fogo Charitable Trust Centre for Research into the Ageing and the Brain Re No: AD.ROW4.35.BRO-D.FID 3668413. DJW reports personal fees from Bayer and JFB Consulting, outside the submitted work (no award/grant number). F-EdL was supported by a clinical established investigator grant of the Dutch Heart Foundation (grant 2014T060), and by a VIDI innovational grant from The Netherlands Organisation for Health Research and Development, ZonMw (grant 016126351). CJMK is supported by a clinical established investigator grant of the Dutch Heart Foundation (grant no. 2012 T077) and an Aspasia grant from The Netherlands Organization for Health Research and Development (ZonMw grant no. 015.008.048). RA-SS reports a grant from the British Heart Foundation (SP/12/2/29422) paid to the University of Edinburgh for the conduct of RESTART, and grants from the Stroke Association, Chest Heart and Stroke Scotland and GE Healthcare Limited, outside the submitted work (no award/grant number). PW reports personal fees from Stryker Global Advisory Board on Hemorrhagic Stroke and MicroVention-Terumo, and a grant from MicroVention-Terumo outside the submitted work (no award/grant number). JW is supported by European Union Horizon 2020, PHC-03-15, project No 666881, ‘SVDs@Target’, Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease, ref no. 16 CVD 05, the UK Dementia Research Institute which receives its funding from DRI, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK, and by grants from the EU Framework 7, Medical Research Council and the British Heart Foundation, outside the submitted work (no award/grant number)., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Multi-scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T 1 -weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community-dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6-11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between-scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612-.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504-.763). Between-scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475-.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole-brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275-.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533-.647). Regression-based k-fold cross-validation showed that, particularly for global volumes, between-scanner differences could be largely eliminated (R 2 range .615-.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background and Purpose- Perivascular spaces (PVS) around venules may help drain interstitial fluid from the brain. We examined relationships between suspected venules and PVS visible on brain magnetic resonance imaging. Methods- We developed a visual venular quantification method to examine the spatial relationship between venules and PVS. We recruited patients with lacunar stroke or minor nondisabling ischemic stroke and performed brain magnetic resonance imaging and retinal imaging. We quantified venules on gradient echo or susceptibility-weighted imaging and PVS on T2-weighted magnetic resonance imaging in the centrum semiovale and then determined overlap between venules and PVS. We assessed associations between venular count and patient demographic characteristics, vascular risk factors, small vessel disease features, retinal vessels, and venous sinus pulsatility. Results- Among 67 patients (69% men, 69.0±9.8 years), only 4.6% (range, 0%-18%) of venules overlapped with PVS. Total venular count increased with total centrum semiovale PVS count in 55 patients after accounting for venule-PVS overlap (β=0.468 [95% CI, 0.187-0.750]) and transverse sinus pulsatility (β=0.547 [95% CI, 0.309-0.786]) and adjusting for age, sex, and systolic blood pressure. Conclusions- Despite increases in both visible PVS and suspected venules, we found minimal spatial overlap between them in patients with sporadic small vessel disease, suggesting that most magnetic resonance imaging-visible centrum semiovale PVS are periarteriolar rather than perivenular.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

Cerebral grey and white matter MRI parameters are related to general intelligence and some specific cognitive abilities. Less is known about how structural brain measures relate specifically to verbal processing abilities. We used multi-modal structural MRI to investigate the grey matter (GM) and white matter (WM) correlates of verbal ability in 556 healthy older adults (mean age = 72.68 years, s.d. = .72 years). Structural equation modelling was used to decompose verbal performance into two latent factors: a storage factor that indexed participants' ability to store representations of verbal knowledge and an executive factor that measured their ability to regulate their access to this information in a flexible and task-appropriate manner. GM volumes and WM fractional anisotropy (FA) for components of the language/semantic network were used as predictors of these verbal ability factors. Volume of the ventral temporal cortices predicted participants' storage scores (β = .12, FDR-adjusted p = .04), consistent with the theory that this region acts as a key substrate of semantic knowledge. This effect was mediated by childhood IQ, suggesting a lifelong association between ventral temporal volume and verbal knowledge, rather than an effect of cognitive decline in later life. Executive ability was predicted by FA fractional anisotropy of the arcuate fasciculus (β = .19, FDR-adjusted p = .001), a major language-related tract implicated in speech production. This result suggests that this tract plays a role in the controlled retrieval of word knowledge during speech. At a more general level, these data highlight a basic distinction between information representation, which relies on the accumulation of tissue in specialised GM regions, and executive control, which depends on long-range WM pathways for efficient communication across distributed cortical networks., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To assess the association between Mediterranean-type diet (MeDi) and change in brain MRI volumetric measures and mean cortical thickness across a 3-year period in older age (73-76 years)., Methods: We focused on 2 longitudinal brain volumes (total and gray matter; n = 401 and 398, respectively) plus a longitudinal measurement of cortical thickness (n = 323), for which the previous cross-sectional evidence of an association with the MeDi was strongest. Adherence to the MeDi was calculated from data gathered from a food frequency questionnaire at age 70, 3 years prior to the baseline imaging data collection., Results: In regression models adjusting for relevant demographic and physical health indicators, we found that lower adherence to the MeDi was associated with greater 3-year reduction in total brain volume (explaining 0.5% of variance, p < 0.05). This effect was half the size of the largest covariate effect (i.e., age). Cross-sectional associations between MeDi and baseline MRI measures in 562 participants were not significant. Targeted analyses of meat and fish consumption did not replicate previous associations with total brain volume or total gray matter volume., Conclusions: Lower adherence to the MeDi in an older Scottish cohort is predictive of total brain atrophy over a 3-year interval. Fish and meat consumption does not drive this change, suggesting that other components of the MeDi or, possibly, all of its components in combination are responsible for the association., (© 2017 American Academy of Neurology.)

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Background: intracranial volume (ICV) is commonly used as a marker of premorbid brain size in neuroimaging studies as it is thought to remain fixed throughout adulthood. However, inner skull table thickening would encroach on ICV and could mask actual brain atrophy., Objective: we investigated the effect that thickening might have on the associations between brain atrophy and cognition., Methods: the sample comprised 57 non-demented older adults who underwent structural brain MRI at mean age 72.7 ± 0.7 years and were assessed on cognitive ability at mean age 11 and 73 years. Principal component analysis was used to derive factors of general cognitive ability (g), information processing speed and memory from the recorded cognitive ability data. The total brain tissue volume and ICV with (estimated original ICV) and without (current ICV) adjusting for the effects of inner table skull thickening were measured. General linear modelling was used to test for associations., Results: all cognitive ability variables were significantly (P < 0.01) associated with percentage total brain volume in ICV measured without adjusting for skull thickening (g: η(2) = 0.177, speed: η(2) = 0.264 and memory: η(2) = 0.132). After accounting for skull thickening, only speed was significantly associated with percentage total brain volume in ICV (η(2) = 0.085, P = 0.034), not g or memory., Conclusions: not accounting for skull thickening when computing ICV can distort the association between brain atrophy and cognitive ability in old age. Larger samples are required to determine the true effect., (© The Author 2014. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

White matter hyperintensities (WMH) are associated with hypertension. We examined interactions among blood pressure (BP), internal carotid artery (ICA) flow velocity parameters, and WMH. We obtained BP measurements from 694 community-dwelling subjects at mean ages 69.6 (±0.8) years and again at 72.6 (±0.7) years, plus brain MRI and ICA ultrasound at age 73±1 years. Diastolic and mean BP decreased and pulse pressure increased, but systolic BP did not change between 70 and 73 years. Multiple linear regression, corrected for vascular disease and risk factors, showed that WMH at the age of 73 years were associated with history of hypertension (β=0.13; P<0.001) and with BP at the age of 70 years (systolic β=0.08, mean β=0.09, diastolic β=0.08; all P<0.05); similar but attenuated associations were seen for BP at the age of 73 years. Lower diastolic BP and higher pulse pressure were associated with higher ICA pulsatility index at the age 73 years (diastolic BP age 70 years: standardized β=-0.24, P<0.001; pulse pressure age 70 years: β=0.19, P<0.001). WMH were associated with higher ICA pulsatility index (β=0.13; P=0.002) after adjusting for BP and correction for multiple testing. Therefore, falling diastolic BP and increased pulse pressure are associated with increased ICA pulsatility index, which in turn is associated with WMH. This suggests that hypertension and WMH may either associate indirectly because hypertension increases arterial stiffness that leads to WMH over time, or coassociate through advancing age and stiffer vessels, or both. Reducing vascular stiffness may reduce WMH progression and should be tested in randomized trials, in addition to testing antihypertensive therapy.

Background and Purpose: White matter hyperintensities (WMH) and perivascular spaces (PVS) are features of small vessel disease, found jointly on MRI of older people. Inflammation is a prominent pathological feature of small vessel disease. We examined the association between inflammation, PVS, and WMH in the Lothian Birth Cohort 1936 (N=634)., Methods: We measured plasma fibrinogen, C-reactive protein, and interleukin-6 and rated PVS in 3 brain regions. We measured WMH volumetrically and visually using the Fazekas scale. We derived latent variables for PVS, WMH, and Inflammation from measured PVS, WMH, and inflammation markers and modelled associations using structural equation modelling., Results: After accounting for age, sex, stroke, and vascular risk factors, PVS were significantly associated with WMH (β=0.47; P<0.0001); Inflammation was weakly but significantly associated with PVS (β=0.12; P=0.048), but not with WMH (β=0.02; P=NS)., Conclusions: Circulating inflammatory markers are weakly associated with MR-visible PVS, but not directly with WMH. Longitudinal studies should examine whether visible PVS predate WMH progression and whether inflammation modulators can prevent small vessel disease.

Brain tissue deterioration is a significant contributor to lower cognitive ability in later life; however, few studies have appropriate data to establish how much influence prior brain volume and prior cognitive performance have on this association. We investigated the associations between structural brain imaging biomarkers, including an estimate of maximal brain volume, and detailed measures of cognitive ability at age 73 years in a large (N = 620), generally healthy, community-dwelling population. Cognitive ability data were available from age 11 years. We found positive associations (r) between general cognitive ability and estimated brain volume in youth (male, 0.28; females, 0.12), and in measured brain volume in later life (males, 0.27; females, 0.26). Our findings show that cognitive ability in youth is a strong predictor of estimated prior and measured current brain volume in old age but that these effects were the same for both white and gray matter. As 1 of the largest studies of associations between brain volume and cognitive ability with normal aging, this work contributes to the wider understanding of how some early-life factors influence cognitive aging., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: Cerebral atrophy and white matter lesions (WMLs) are common in older people with common risk factors, but it is unclear if they are related. We investigated whether and to what degree they are related in deep and superficial structures using both volumetric and visual ratings., Methods: The intracranial, total brain tissue (TBV), cerebrospinal fluid (CSF), ventricular superficial subarachnoid space (SSS), grey matter, normal-appearing white matter, WMLs, and combined CSF, venous sinuses and dural volumes were measured. WMLs were also rated using the Fazekas scale., Results: Amongst 672 adults (mean age 73 ± 1 years), WMLs were associated with global brain atrophy (TBV, β = -0.43 mm(3), P < 0.01) and specifically with deep (ventricular enlargement, β = 0.10 mm(3), P = 0.03) rather than superficial (SSS, β = 0.09 mm(3), P = 0.55) atrophy. A 1 mm(3) increase in WML volume was associated with a 0.43 mm(3) decrease in TBV and 0.10 mm(3) increase in ventricular volume. WMLs were associated with combined CSF + Venous Sinuses + Meninges volumes, but not CSF volume alone. Some of the associations were attenuated after correcting for vascular risk factors. The associations were similar for visually scored WMLs., Conclusion: WMLs are associated with brain atrophy, primarily with deep brain structures. Measures of brain atrophy should include all intracranial structures when assessing brain shrinkage.

Objectives: Increased participation in leisure and physical activities may be cognitively protective. Whether activity might protect the integrity of the brain's white matter, or reduce atrophy and white matter lesion (WML) load, was examined in the Lothian Birth Cohort 1936 (n = 691), a longitudinal study of aging., Methods: Associations are presented between self-reported leisure and physical activity at age 70 years and structural brain biomarkers at 73 years. For white matter integrity, principal components analysis of 12 major tracts produced general factors for fractional anisotropy (FA) and mean diffusivity. Atrophy, gray and normal-appearing white matter (NAWM) volumes, and WML load were assessed using computational image processing methods; atrophy and WML were also assessed visually., Results: A higher level of physical activity was associated with higher FA, larger gray and NAWM volumes, less atrophy, and lower WML load. The physical activity associations with atrophy, gray matter, and WML remained significant after adjustment for covariates, including age, social class, and health status. For example, physical activity (standardized β = -0.09, nonstandardized β = -0.09, p = 0.029) and stroke (standardized β = 0.18, nonstandardized β = 0.69, p = 0.003) each had an independent effect on rated WML load. Leisure activity was associated with NAWM volume, but was nonsignificant after including covariates., Conclusions: In this large, narrow-age sample of adults in their 70s, physical activity was associated with less atrophy and WML. Its role as a potential neuroprotective factor is supported; however, the direction of causation is unclear from this observational study.

General intelligence is a robust predictor of important life outcomes, including educational and occupational attainment, successfully managing everyday life situations, good health and longevity. Some neuronal correlates of intelligence have been discovered, mainly indicating that larger cortices in widespread parieto-frontal brain networks and efficient neuronal information processing support higher intelligence. However, there is a lack of established associations between general intelligence and any basic structural brain parameters that have a clear functional meaning. Here, we provide evidence that lower brain-wide white matter tract integrity exerts a substantial negative effect on general intelligence through reduced information-processing speed. Structural brain magnetic resonance imaging scans were acquired from 420 older adults in their early 70s. Using quantitative tractography, we measured fractional anisotropy and two white matter integrity biomarkers that are novel to the study of intelligence: longitudinal relaxation time (T1) and magnetisation transfer ratio. Substantial correlations among 12 major white matter tracts studied allowed the extraction of three general factors of biomarker-specific brain-wide white matter tract integrity. Each was independently associated with general intelligence, together explaining 10% of the variance, and their effect was completely mediated by information-processing speed. Unlike most previously established neurostructural correlates of intelligence, these findings suggest a functionally plausible model of intelligence, where structurally intact axonal fibres across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting general intelligence.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).

Rationale: As the population of the world ages, age-related cognitive decline is becoming an ever-increasing problem. However, the changes in brain structure that accompany normal aging, and the role they play in cognitive decline, remain to be fully elucidated., Aims: This study aims to characterize changes in brain structure in old age, and to investigate relationships between brain aging and cognitive decline using the Lothian Birth Cohort 1936. Here, we report the rationale, design and methodology of the brain and neurovascular imaging protocol developed to study this cohort., Design: An observational, longitudinal study of the Lothian Birth Cohort 1936, which comprises 1091 relatively healthy individuals now in their 70s and living in the Edinburgh area. They are surviving participants of the Scottish Mental Survey 1947, which involved a test of general intelligence taken at age 11 years. At age 70 years, the Lothian Birth Cohort 1936 undertook detailed cognitive, medical and genetic testing, and provided social, family, nutritional, quality of life and physical activity information. At mean age 73 years they underwent detailed brain MRI and neurovascular ultrasound imaging, repeat cognitive and other testing. The MRI protocol is designed to provide qualitative and quantitative measures of gray and white matter atrophy, severity and location of white matter lesions, enlarged perivascular spaces, brain mineral deposits, microbleeds and integrity of major white matter tracts. The neurovascular ultrasound imaging provides velocity, stenosis and intima-media thickness measurements of the carotid and vertebral arteries., Study: This valuable imaging dataset will be used to determine which changes in brain structural parameters have the largest effects on cognitive aging. Analysis will include multimodal image analysis and multivariate techniques, such as factor analysis and structural equation modelling. Especially valuable is the ability within this sample to examine the influence that early life intelligence has on brain structural parameters in old age, and the role of genetic, vascular, educational and lifestyle factors., Outcomes: Final outcomes include associations between early and late life cognition and integrity of key white matter tracts, volume of gray and white matter, myelination, brain water content, and visible abnormalities such as white matter lesions and mineral deposits; and influences of vascular risk factors, diet, environment, social metrics, education and genetics on healthy brain aging. It is intended that this information will help to inform and develop strategies for successful cognitive aging., (© 2011 The Authors. International Journal of Stroke © 2011 World Stroke Organization.)

Purpose: To test the reliability of two computational methods for segmenting cerebral iron deposits (IDs) in the aging brain, given that its measurement in magnetic resonance imaging (MRI) is challenging due to the similar effect produced by other minerals, especially calcium, on T2*-weighted sequences., Materials and Methods: T1-, T2*-weighted, and fluid-attenuated inversion recovery (FLAIR) MR brain images obtained at 1.5T from 70 subjects in their early 70s who displayed a wide range of brain IDs were analyzed. The first segmentation method used a multispectral approach based on the fusion of two or more structural sequences registered and mapped in the red/green color space followed by Minimum Variance Quantization. The second method employed a combined thresholding, size and shape analysis using T2*-weighted images augmented with visual information from T1-weighted data., Results: Both segmentation techniques had high intra- and interobserver agreement (95% confidence interval [CI] = ± 57 voxels in a range from 0 to 1800), which decreased in subjects with significant microbleeds and/or IDs. However, the thresholding method was more observer dependent in identifying microbleeds and IDs boundaries than the multispectral approach., Conclusion: Both techniques proved to be in agreement and have good intra- and interobserver reliability. However, they have limitations, specifically with regard to automation and observer independence, so further work is required to develop fully user-independent methods of identifying cerebral IDs., (Copyright © 2010 Wiley-Liss, Inc.)