© 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

Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks: estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.

@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.

@en

Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study. We assessed the impact of any differences on individual assessment of brain structure volumes. Percentile curves were fitted on the three healthy cohorts. Next, percentile values for these subcortical structures for individual patients from these three cohorts, 91 mild cognitive impairment and 95 Alzheimer's disease cases and patients from the Alzheimer Center, were calculated, based on the distributions of each of the three cohorts. Overall, we found that the subcortical volume normative data from these cohorts are highly interchangeable, suggesting more flexibility in clinical implementation.

@en

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

@en

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.

@en

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.

@en

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.

@en

Despite a substantial genetic component, efforts to identify common genetic variation underlying depression have largely been unsuccessful. In the current study we aimed to identify rare genetic variants that might have large effects on depression in the general population. Using high-coverage exome-sequencing, we studied the exonic variants in 1265 individuals from the Rotterdam study (RS), who were assessed for depressive symptoms. We identified a missense Asn396Ser mutation (rs77960347) in the endothelial lipase (LIPG) gene, occurring with an allele frequency of 1% in the general population, which was significantly associated with depressive symptoms (P-value=5.2 × 10 -08, β=7.2). Replication in three independent data sets (N=3612) confirmed the association of Asn396Ser (P-value=7.1 × 10 -03, β=2.55) with depressive symptoms. LIPG is predicted to have enzymatic function in steroid biosynthesis, cholesterol biosynthesis and thyroid hormone metabolic processes. The Asn396Ser variant is predicted to have a damaging effect on the function of LIPG. Within the discovery population, carriers also showed an increased burden of white matter lesions (P-value=3.3 × 1 -02) and a higher risk of Alzheimer's disease (odds ration=2.01; P-value=2.8 × 10 -02) compared with the non-carriers. Together, these findings implicate the Asn396Ser variant of LIPG in the pathogenesis of depressive symptoms in the general population.

@en

Background: Multiple sclerosis (MS) affects brain structure and cognitive function and has a heritable component. Over a 100 common genetic risk variants have been identified, but most carriers do not develop MS. For other neurodegenerative diseases, risk variants have effects outside patient populations, but this remains uninvestigated for MS. Objectives: To study the effect of MS-associated genetic variants on brain structure and cognitive function in the general population. Methods: We studied middle-aged and elderly individuals (mean age = 65.7 years) from the population-based Rotterdam Study. We determined 107 MS variants and additionally created a risk score combining all variants. Magnetic resonance imaging (N = 4710) was performed to obtain measures of brain macrostructure, white matter microstructure, and gray matter voxel-based morphometry. A cognitive test battery (N = 7556) was used to test a variety of cognitive domains. Results: The MS risk score was associated with smaller gray matter volume over the whole brain (β_standardized = −0.016; p = 0.044), but region-specific analyses did not survive multiple testing correction. Similarly, no significant associations with brain structure were observed for individual variants. For cognition, rs2283792 was significantly associated with poorer memory (β = −0.064; p = 3.4 × 10⁻⁵). Conclusion: Increased genetic susceptibility to MS may affect brain structure and cognition in persons without disease, pointing to a “hidden burden” of MS.

@en

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.

@en

Large-scale distributed analyses of over 30,000 magnetic resonance imaging scans recently detected common genetic variants associated with the volumes of subcortical brain structures. Scaling up these efforts, still greater computational challenges arise in screening the genome for statistical associations at each voxel in the brain, localizing effects using "image-wide genome-wide" testing (voxelwise genome-wide association studies, vGWASs). Here we benefit from distributed computations at multiple sites to metaanalyze genome-wide image-wide data, allowing private genomic data to stay at the site where it was collected. Site-specific tensor-based morphometry is performed with a custom template for each site, using a multichannel registration. A single vGWAS testing 10⁷ variants against 2million voxels can yield hundreds of terabytes (TB) of summary statistics, which would need to be transferred and pooled for metaanalysis. We propose a two-step method, which reduces data transfer for each site to a subset of single-nucleotide polymorphisms and voxels guaranteed to contain all significant hits.

@en

The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.

@en

The neural substrate of genetic risk variants for Alzheimer's disease (AD) remains unknown. We studied their effect on healthy brain morphology to provide insight into disease etiology in the preclinical phase. We included 4071 nondemented, elderly participants of the population-based Rotterdam Study who underwent brain magnetic resonance imaging and genotyping. We performed voxel-based morphometry (VBM) on all gray-matter voxels for 19 previously identified, common AD risk variants. Whole-brain expression data from the Allen Human Brain Atlas was used to examine spatial overlap between VBM association results and expression of genes in AD risk loci regions. Brain regions most significantly associated with AD risk variants were the left postcentral gyrus with ABCA7 (rs4147929, p = 4.45 × 10⁻⁶), right superior frontal gyrus by ZCWPW1 (rs1476679, p = 5.12 × 10⁻⁶), and right postcentral gyrus by APOE (p = 6.91 × 10⁻⁶). Although no individual voxel passed multiple-testing correction, we found significant spatial overlap between the effects of AD risk loci on VBM and the expression of genes (MEF2C, CLU, and SLC24A4) in the Allen Brain Atlas. Results are available online on www.imagene.nl/ADSNPs/. In this single largest imaging genetics data set worldwide, we found that AD risk loci affect cortical gray matter in several brain regions known to be involved in AD, as well as regions that have not been implicated before.

@en

High-throughput technology can now provide rich information on a person's biological makeup and environmental surroundings. Important discoveries have been made by relating these data to various health outcomes in fields such as genomics, proteomics, and medical imaging. However, cross-investigations between several high-throughput technologies remain impractical due to demanding computational requirements (hundreds of years of computing resources) and unsuitability for collaborative settings (terabytes of data to share). Here we introduce the HASE framework that overcomes both of these issues. Our approach dramatically reduces computational time from years to only hours and also requires several gigabytes to be exchanged between collaborators. We implemented a novel meta-analytical method that yields identical power as pooled analyses without the need of sharing individual participant data. The efficiency of the framework is illustrated by associating 9 million genetic variants with 1.5 million brain imaging voxels in three cohorts (total N = 4,034) followed by meta-analysis, on a standard computational infrastructure. These experiments indicate that HASE facilitates high-dimensional association studies enabling large multicenter association studies for future discoveries.

@en

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

@en

Whether novel risk variants of Alzheimer's disease (AD) identified through genome-wide association studies also influence magnetic resonance imaging-based intermediate phenotypes of AD in the general population is unclear. We studied association of 24 AD risk loci with intracranial volume, total brain volume, hippocampal volume (HV), white matter hyperintensity burden, and brain infarcts in a meta-analysis of genetic association studies from large population-based samples (N= 8175-11,550). In single-SNP based tests, AD risk allele of APOE (rs2075650) was associated with smaller HV (. p= 0.0054) and CD33 (rs3865444) with smaller intracranial volume (. p= 0.0058). In gene-based tests, there was associations of HLA-DRB1 with total brain volume (. p= 0.0006) and BIN1 with HV (. p= 0.00089). Aweighted AD genetic risk score was associated with smaller HV (beta ± SE=-0.047 ± 0.013, p= 0.00041), even after excluding the APOE locus (. p= 0.029). However, only association of AD genetic risk score with HV, including APOE, was significant after multiple testing correction (including number of independent phenotypes tested). These results suggest that novel AD genetic risk variants may contribute to structural brain aging in nondemented older community persons.

@en