Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.

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© 2018 Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automated EPVS scoring correlates similarly to age as visual scoring.@en

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a “normal” burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS.

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Next-generation sequencing has contributed to our understanding of the genetics of Alzheimer's disease (AD) and has explained a substantial part of the missing heritability of familial AD. We sequenced 19 exomes from 8 Dutch families with a high AD burden and identified EIF2AK3, encoding for protein kinase RNA-like endoplasmic reticulum kinase (PERK), as a candidate gene. Gene-based burden analysis in a Dutch AD exome cohort containing 547 cases and 1070 controls showed a significant association of EIF2AK3 with AD (OR 1.84 [95% CI 1.07–3.17], p-value 0.03), mainly driven by the variant p.R240H. Genotyping of this variant in an additional cohort from the Rotterdam Study showed a trend toward association with AD (p-value 0.1). Immunohistochemical staining with pPERK and peIF2α of 3 EIF2AK3 AD carriers showed an increase in hippocampal neuronal cells expressing these proteins compared with nondemented controls, but no difference was observed in AD noncarriers. This study suggests that rare variants in EIF2AK3 may be associated with disease risk in AD.

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With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability.

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To study the relation between the microstructure of white matter in the brain and hearing function in older adults we carried out a population-based, cross-sectional study. In 2562 participants of the Rotterdam Study, we conducted diffusion tensor imaging to determine the microstructure of the white-matter tracts. We performed pure-tone audiogram and digit-in-noise tests to quantify hearing acuity. Poorer white-matter microstructure, especially in the association tracts, was related to poorer hearing acuity. After differentiating the separate white-matter tracts in the left and right hemisphere, poorer white-matter microstructure in the right superior longitudinal fasciculus and the right uncinate fasciculus remained significantly associated with worse hearing. These associations did not significantly differ between middle-aged (51–69 years old) and older (70–100 years old) participants. Progressing age was thus not found to be an effect modifier. In a voxel-based analysis no voxels in the white matter were significantly associated with hearing impairment.

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Thyroid hormone (TH) is crucial during neurodevelopment, but high levels of TH have been linked to neurodegenerative disorders. No data on the association of thyroid function with brain imaging in the general population are available. We therefore investigated the association of thyroid-stimulating hormone and free thyroxine (FT4) with magnetic resonance imaging (MRI)-derived total intracranial volume, brain tissue volumes, and diffusion tensor imaging measures of white matter microstructure in 4683 dementia- and stroke-free participants (mean age 60.2, range 45.6–89.9 years). Higher FT4 levels were associated with larger total intracranial volumes (β = 6.73 mL, 95% confidence interval = 2.94–9.80). Higher FT4 levels were also associated with larger total brain and white matter volumes in younger individuals, but with smaller total brain and white matter volume in older individuals (p-interaction 0.02). There was a similar interaction by age for the association of FT4 with mean diffusivity on diffusion tensor imaging (p-interaction 0.026). These results are in line with differential effects of TH during neurodevelopmental and neurodegenerative processes and can improve the understanding of the role of thyroid function in neurodegenerative disorders.

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Increasing evidence shows that thinner retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL), assessed on optical coherence tomography (OCT), are reflecting global brain atrophy. Yet, little is known on the relation of these layers with specific brain regions. Using voxel-based analysis, we aimed to unravel specific brain regions associated with these retinal layers. We included 2,235 persons (mean age: 67.3 years, 55% women) from the Rotterdam Study (2007–2012) who had gradable retinal OCT images and brain magnetic resonance imaging (MRI) scans, including diffusion tensor (DT) imaging. Thicknesses of peripapillary RNFL and perimacular GCL were measured using an automated segmentation algorithm. Voxel-based morphometry protocols were applied to process DT-MRI data. We investigated the association between retinal layer thickness with voxel-wise gray matter density and white matter microstructure by performing linear regression models. We found that thinner RNFL and GCL were associated with lower gray matter density in the visual cortex, and with lower fractional anisotropy and higher mean diffusivity in white matter tracts that are part of the optic radiation. Furthermore, thinner GCL was associated with lower gray matter density of the thalamus. Thinner RNFL and GCL are associated with gray and white matter changes in the visual pathway suggesting that retinal thinning on OCT may be specifically associated with changes in the visual pathway rather than with changes in the global brain. These findings may serve as a basis for understanding visual symptoms in elderly patients, patients with Alzheimer's disease, or patients with posterior cortical atrophy.

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Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.

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Objectives: To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting. Methods: In 42 memory clinic patients, we evaluated whether automated quantification of brain tissue volumes, hippocampal volume and white matter lesion volume improves diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), compared to visual interpretation. Reference data were derived from a dementia-free aging population (n = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard. Results: Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD. Conclusion: Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.

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Objective: To investigate the association of brain volumes, white matter lesion (WML) volumes, and lacunes, with cognitive decline in a population-based cohort of nondemented persons. Methods: Within the Rotterdam Study, 3624 participants underwent brain magnetic resonance imaging. Cognition was evaluated at baseline (2005 to 2009) and at the follow-up visit (2011 to 2013). We used a test battery that tapped into domains of executive function, information processing speed, motor speed, and memory. The volumetric measures assessed were total brain volume, lobar (gray matter and white matter) volumes, and hippocampal volumes. We also studied the association of WML volumes and lacunes with cognitive decline using linear regression models. Results: Total brain volume was associated with decline in global cognition, information processing, and motor speed (P<0.001) in analyses controlled for demographic and vascular factors. Specifically, smaller frontal and parietal lobes were associated with decline in information processing and motor speed, and smaller temporal and parietal lobes were associated with decline in general cognition and motor speed (P<0.001 for all tests). Total WML volume was associated with decline in executive function. Lobar WML volume, hippocampal volume, and lacunes were not associated with cognitive decline. Conclusions: Lower brain volume is associated with subsequent cognitive decline. Although lower total brain volume was significantly associated with decline in global cognition, specific lobar volumes were associated with decline in certain cognitive domains.

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Previous studies have linked global burden of age-related white matter hyperintensities (WMHs) to cognitive impairment. We aimed to determine how WMHs in individual white matter connections relate to measures of cognitive function relative to measures of connectivity which do not take WMHs into account. Brain connectivity and WMH-related disconnectivity were derived from 3714 participants of the population-based Rotterdam Study. Connectivity was represented by the structural connectome, which was defined using diffusion tensor data, whereas the disconnectome represented disconnectivity due to WMH. The relationship between (dis)connectivity and cognitive measures was estimated using linear regression. We found that lower disconnectivity and higher connectivity corresponded to better cognitive function. There were many more significant associations with cognitive function in the disconnectome than in the connectome. Most connectome associations attenuated when disconnection was included in the model. WMH-related disconnectivity was especially related to worse executive functioning. Better cognitive speed corresponded to higher connectivity in specific connections independent of WMH presence. We conclude that WMH-related disconnectivity explains more variation in cognitive function than does connectivity. Efficient wiring in specific connections is important to information processing speed independent of WMH presence.

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Background and Purpose- Inflammation is involved in the pathogenesis of large artery atherosclerosis, ischemic stroke, and Alzheimer dementia. However, the role of inflammation in cerebral small vessel disease and neurodegeneration remains poorly understood. We hypothesize that CRP (C-reactive protein) is associated with brain structural changes and may interact with amyloid to produce vascular and degenerative damage. We examined the association of CRP levels with imaging markers of cerebral small vessel disease and neurodegeneration. Furthermore, we studied the association of CRP with plasma Aβ (amyloid-β) levels and their joint effects with imaging markers. Methods- We included 2814 persons (mean age, 56.9 years; 44.8% women) from the Rotterdam Study with complete data on CRP and 1.5 T brain magnetic resonance imaging scans. Aβ levels were measured in a subsample (n=736). Markers of cerebral small vessel disease included lacunes, white matter hyperintensities, microbleeds, and enlarged perivascular spaces. Neurodegeneration was assessed by smaller volumes of gray matter, white matter, and hippocampus. Plasma levels of Aβ1-38, Aβ1-40, and Aβ1-42 were assessed using ELISA. Results- Higher CRP levels were associated with larger white matter hyperintensities volume (β=0.07; 95% CI, 0.00-0.13), increasing lacunar (rate ratios, 1.61; 95% CI, 1.19-2.19), enlarged perivascular spaces (rate ratios, 1.01; 95% CI, 1.00-1.03), and deep/infratentorial microbleeds (rate ratios, 1.30; 95% CI, 1.00-1.69) counts. People with high CRP levels had small gray matter volume. We also found significant interaction between CRP and Aβ such that among persons in higher tertiles of Aβ1-42, a strong association was observed between CRP and lacunar ( P interaction, 0.004), enlarged perivascular spaces ( P interaction, 0.002), and microbleed counts ( P interaction, <0.001). Similarly, among persons in higher tertile of Aβ1-38, a strong association was observed between CRP and microbleed counts ( P interaction, 0.004). Conclusions- Higher CRP levels were associated with subclinical markers of cerebral small vessel disease and neurodegeneration. This effect was augmented by an interaction between CRP and Aβ levels. Future longitudinal studies focusing on joint effects of CRP and Aβ on progression of magnetic resonance imaging markers and cognitive decline are warranted.

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We present the largest population-based heritability study of the human brain structural connectome, including a pathology-sensitive extension, the disconnectome. The disconnectome maps the effect of white matter lesions throughout the brain. The connectome and disconnectome were generated from diffusion-weighted images of 3255 unrelated subjects from the Rotterdam Study aged between 45 and 99 years. Graph theory measures were derived for both the connectome and disconnectome. Genotypes were used to derive genetic relationship matrices between individuals for heritability analyses. High measures of heritability, from 33% to 51%, were found across all connectivity measures. The disconnectome showed more significantly heritable connectivity measures than the connectome, suggesting that the new proposed measure may reveal additional or complementary information about the genetic architecture of the human brain.

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The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

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Background: The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm). Methods: Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals. Results: Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10⁻¹³). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels. Conclusion: Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408–2423, 2017.

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Purpose: To investigate the association between N-terminal pro-Btype natriuretic peptide (NT-proBNP), which is a marker of heart disease, and markers of subclinical brain damage on magnetic resonance (MR) images in community-dwelling middle-aged and elderly subjects without dementia and without a clinical diagnosis of heart disease. Materials and Methods: This prospective population-based cohort study was approved by a medical ethics committee overseen by the national government, and all participants gave written informed consent. Serum levels of NT-proBNP were measured in 2397 participants without dementia or stroke (mean age, 56.6 years; age range, 45.7-87.3 years) and without clinical diagnosis of heart disease who were drawn from the population-based Rotterdam Study. All participants were examined with a 1.5-T MR imager. Multivariable linear and logistic regression analyses were used to investigate the association between NT-proBNP level and MR imaging markers of subclinical brain damage, including volumetric, focal, and microstructural markers. Results: A higher NT-proBNP level was associated with smaller total brain volume (mean difference in z score per standard deviation increase in NT-proBNP level, -0.021; 95% confidence interval [CI]: -0.034, -0.007; P = .003) and was predominantly driven by gray matter volume (mean difference in z score per standard deviation increase in NT-proBNP level, -0.037; 95% CI: -0.057, -0.017; P < .001). Higher NT-proBNP level was associated with larger white matter lesion volume (mean difference in z score per standard deviation increase in NT-proBNP level, 0.090; 95% CI: 0.051, 0.129; P < .001), with lower fractional anisotropy (mean difference in z score per standard deviation increase in NT-proBNP level, -0.048; 95% CI: -0.088, -0.008; P = .019) and higher mean diffusivity (mean difference in z score per standard deviation increase in NT-proBNP level, 0.054; 95% CI: 0.018, 0.091; P = .004) of normal-appearing white matter. Conclusion: In community-dwelling persons, higher serum NT-proBNP levels are associated with volumetric and microstructural MR imaging markers of subclinical brain damage.

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Background: Brain MRI-markers are risk factors of dementia and decline in cognition and daily functioning. It is unknown to what extent the associations of brain MRI-markers with cognition and daily functioning are part of the pathway leading to dementia. We aimed to investigate associations of brain MRI-markers with change in cognition and daily functioning during 15 years of follow-up, including their relation to dementia. Design, Setting, and Participants: Four hundred and sixty three stroke-free and non-demented participants from the population-based Rotterdam Study that underwent brain-MRI, yielding brain volumetrics, between 1995 and 1996. Measurements: We assessed cognition using the Mini-Mental State Examination (MMSE) and daily functioning using instrumental and basic activities of daily living (IADL and BADL) up to seven times between 1990 and 2011. Analyses were performed both including and excluding incident demented participants. Results: Smaller brain volume associated with larger decline in MMSE, IADL, and BADL. Larger white matter lesion volume associated with larger decline in MMSE. Frontal lobe volume associated strongest with decline in IADL and BADL, and temporal lobe volume with decline in MMSE. After excluding incident demented participants (n = 63), associations with IADL and BADL remained, while associations with MMSE disappeared. Conclusions: Smaller brain volumes and larger white matter volume associate with larger decline in cognition and daily functioning, during 15 years of follow-up. Importantly, the relation of brain volume with cognition, but not daily functioning, was driven by those individuals that ultimately developed dementia.

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We investigated the association of specific retinal sublayer thicknesses on optical coherence tomography (OCT) with brain magnetic resonance imaging (MRI) markers. We included 2124 persons (mean age 67.0 years; 56% women) from the Rotterdam Study who had gradable retinal OCT images and brain MRI scans. Thickness of retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer were measured on OCT images. Volumetric, microstructural, and focal markers of brain tissue were assessed on MRI. We found that thinner RNFL, GCL, and inner plexiform layer were associated with smaller gray-matter and white-matter volume. Furthermore, we found that thinner RNFL and GCL were associated with worse white-matter microstructure. No association was found between retinal sublayer thickness and white-matter lesion volumes, cerebral microbleeds, or lacunar infarcts. Markers of retinal neurodegeneration are associated with markers of cerebral atrophy, suggesting that retinal OCT may provide information on neurodegeneration in the brain.

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We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.

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