Publications by authors named "Gennady V Roshchupkin"

22 Publications

  • Page 1 of 1

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Commun Biol 2021 09 17;4(1):1094. Epub 2021 Sep 17.

Department of Radiology and Nuclear Medicine, Erasmus MC, Medical Center, Rotterdam, the Netherlands.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.
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http://dx.doi.org/10.1038/s42003-021-02622-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448759PMC
September 2021

Tinnitus and Its Central Correlates: A Neuroimaging Study in a Large Aging Population.

Ear Hear 2021 03 26;42(5):1428-1435. Epub 2021 Mar 26.

Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Objectives: To elucidate the association between tinnitus and brain tissue volumes and white matter microstructural integrity.

Design: Two thousand six hundred sixteen participants (mean age, 65.7 years [SD: 7.5 years]; 53.9% female) of the population-based Rotterdam Study underwent tinnitus assessment (2011 to 2014) and magnetic resonance imaging of the brain (2011 to 2014). Associations between tinnitus (present versus absent) and total, gray, and white matter volume and global white matter microstructure were assessed using multivariable linear regression models adjusting for demographic factors, cardiovascular risk factors, depressive symptoms, Mini-Mental State Examination score, and hearing loss. Finally, potential regional gray matter density and white matter microstructural volume differences were assessed on a voxel-based level again using multivariable linear regression.

Results: Participants with tinnitus (21.8%) had significantly larger brain tissue volumes (difference in SD, 0.09; 95% confidence interval, 0.06 to 0.13), driven by larger white matter volumes (difference, 0.12; 95% confidence interval, 0.04 to 0.21) independent of hearing loss. There was no association between tinnitus and gray matter volumes nor with global white matter microstructure. On a lobar level, tinnitus was associated with larger white matter volumes in each lobe, not with gray matter volume. Voxel-based results did not show regional specificity.

Conclusions: We found that tinnitus in older adults was associated with larger brain tissue volumes, driven by larger white matter volumes, independent of age, and hearing loss. Based on these results, it may be hypothesized that tinnitus potentially has a neurodevelopmental origin in earlier life independent of aging processes.
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http://dx.doi.org/10.1097/AUD.0000000000001042DOI Listing
March 2021

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases.

J Craniofac Surg 2021 Jan 5;Publish Ahead of Print. Epub 2021 Jan 5.

Department of Neurosurgery Department of Radiology and Nuclear Medicine Research Intelligence and Strategy Unit Department of Oral- and Maxillofacial Surgery Department of Plastic, Reconstructive Surgery, and Hand Surgery, Erasmus MC, University Medical Center, Rotterdam Faculty of Applied Sciences, Delft University of Technology, Delft Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.

Abstract: Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.
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http://dx.doi.org/10.1097/SCS.0000000000007379DOI Listing
January 2021

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Nat Commun 2020 09 22;11(1):4796. Epub 2020 Sep 22.

Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
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http://dx.doi.org/10.1038/s41467-020-18367-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508833PMC
September 2020

Prion protein codon 129 polymorphism in mild cognitive impairment and dementia: the Rotterdam Study.

Brain Commun 2020 20;2(1):fcaa030. Epub 2020 Mar 20.

Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, Netherlands.

Creutzfeldt-Jakob disease is a rare, fatal, neurodegenerative disease caused by the accumulation of abnormally folded prion proteins. The common polymorphism at codon 129 (methionine/valine) in the prion protein () gene is the most important determinant of genetic susceptibility. Homozygotes of either allele have a higher risk of sporadic Creutzfeldt-Jakob disease. Various studies suggest that this polymorphism is also involved in other forms of dementia. We studied the association between the codon 129 polymorphism of the gene and mild cognitive impairment in 3605 participants from the Rotterdam Study using logistic regression analyses. Subsequently, we studied the association between this polymorphism and incident dementia, including Alzheimer's disease, in 11 070 participants using Cox proportional hazard models. Analyses were adjusted for age and sex. We found the prevalence of mild cognitive impairment to be higher for carriers of the methionine/methionine genotype (odds ratio, 1.40; 95% confidence interval, 1.11-1.78; =0.005) as well as for carriers of the valine/valine genotype (odds ratio, 1.37; 95% confidence interval, 0.96-1.97; =0.08). The codon 129 polymorphism was not associated with the risk of incident dementia or Alzheimer's disease. In conclusion, we found a statistically significant higher prevalence of mild cognitive impairment in carriers of the methionine/methionine genotype in the codon 129 polymorphism of the gene within this population-based study. No associations were found between the codon 129 polymorphism and dementia or Alzheimer's disease in the general population.
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http://dx.doi.org/10.1093/braincomms/fcaa030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425338PMC
March 2020

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

Stroke 2020 07 10;51(7):2111-2121. Epub 2020 Jun 10.

Department of Psychiatry (C.F.-N.), University of California, San Diego, La Jolla, CA.

Background And Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.

Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.

Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (), 10q23.1 (), and 10q24.33 ( In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 () and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: (2q32.1), (3q27.1), (5q27.1), and (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.

Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
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http://dx.doi.org/10.1161/STROKEAHA.119.027544DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365038PMC
July 2020

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes.

Cereb Cortex 2020 06;30(7):4121-4139

Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04109 Leipzig, Germany.

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.
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http://dx.doi.org/10.1093/cercor/bhaa035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947185PMC
June 2020

The genetic architecture of the human cerebral cortex.

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

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.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

Gray Matter Age Prediction as a Biomarker for Risk of Dementia.

Proc Natl Acad Sci U S A 2019 10 1;116(42):21213-21218. Epub 2019 Oct 1.

Department of Medical Informatics, Erasmus MC University Medical Center, 3015 CE, Rotterdam, The Netherlands;

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and ε4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.
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http://dx.doi.org/10.1073/pnas.1902376116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800321PMC
October 2019

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Commun Biol 2019 2;2:285. Epub 2019 Aug 2.

17Department of Biomedical Data Sciences, Statistical Genetics, Leiden University Medical Center, Leiden, 2333ZA the Netherlands.

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications ( and ), or close to genes causing Mendelian brain-related diseases ( and ). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
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http://dx.doi.org/10.1038/s42003-019-0537-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677735PMC
April 2020

High-Dimensional Mapping of Cognition to the Brain Using Voxel-Based Morphometry and Subcortical Shape Analysis.

J Alzheimers Dis 2019 ;71(1):141-152

Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.

Background: It is increasingly recognized that the complex functions of human cognition are not accurately represented by arbitrarily-defined anatomical brain regions. Given the considerable functional specialization within such regions, more fine-grained studies of brain structure could capture such localized associations. However, such analyses/studies in a large community-dwelling population are lacking.

Objective: To perform a fine-mapping of cognitive ability to cortical and subcortical grey matter on magnetic resonance imaging (MRI).

Methods: In 3,813 stroke-free and non-demented persons from the Rotterdam Study (mean age 69.1 (±8.8) years; 55.8% women) with cognitive assessments and brain MRI, we performed voxel-based morphometry and subcortical shape analysis on global cognition and separate tests that tapped into memory, information processing speed, fine motor speed, and executive function domains.

Results: We found that the different cognitive tests significantly associated with grey matter density in differential but also overlapping brain regions, primarily in the left hemisphere. Clusters of significantly associated voxels with global cognition were located within multiple anatomic regions: left amygdala, hippocampus, parietal lobule, superior temporal gyrus, insula and posterior temporal lobe. Subcortical shape analysis revealed associations primarily within the head and tail of the caudate nucleus, putamen, ventral part of the thalamus, and nucleus accumbens, more equally distributed among the left and right hemisphere. Within the caudate nucleus both positive (head) as well as negative (tail) associations were observed with global cognition.

Conclusions: In a large population-based sample, we mapped cognitive performance to cortical and subcortical grey matter density using a hypothesis-free approach with high-dimensional neuroimaging. Leveraging the power of our large sample size, we confirmed well-known associations as well as identified novel brain regions related to cognition.
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http://dx.doi.org/10.3233/JAD-181297DOI Listing
October 2020

Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings.

Alzheimers Dement (Amst) 2019 Dec 30;11:286-290. Epub 2019 Mar 30.

Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.

Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets. In our previous EU Joint Programme-Neurodegenerative Disease Research (JPND) Working Group, called HD-READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high-dimensional multimodality data. For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full-HD Working Group, we focused on such harmonization of high-dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra-high-dimensional data easily accessible in neurodegeneration research.
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http://dx.doi.org/10.1016/j.dadm.2019.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441785PMC
December 2019

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics.

Neuroinformatics 2019 10;17(4):583-592

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.

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.
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http://dx.doi.org/10.1007/s12021-019-09416-zDOI Listing
October 2019

Thinner retinal layers are associated with changes in the visual pathway: A population-based study.

Hum Brain Mapp 2018 11 23;39(11):4290-4301. Epub 2018 Jun 23.

Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.

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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http://dx.doi.org/10.1002/hbm.24246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866563PMC
November 2018

White-matter microstructure and hearing acuity in older adults: a population-based cross-sectional DTI study.

Neurobiol Aging 2018 01 27;61:124-131. Epub 2017 Sep 27.

Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.

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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http://dx.doi.org/10.1016/j.neurobiolaging.2017.09.018DOI Listing
January 2018

Corrigendum: Hearing Impairment Is Associated with Smaller Brain Volume in Aging.

Front Aging Neurosci 2017 8;9:131. Epub 2017 May 8.

Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical CenterRotterdam, Netherlands.

[This corrects the article on p. 2 in vol. 9, PMID: 28163683.].
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http://dx.doi.org/10.3389/fnagi.2017.00131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420703PMC
May 2017

Genetic susceptibility to multiple sclerosis: Brain structure and cognitive function in the general population.

Mult Scler 2017 Nov 21;23(13):1697-1706. Epub 2016 Dec 21.

Department of Epidemiology, Erasmus University Medical Center (Erasmus MC), Rotterdam, The Netherlands/Department of Radiology, Erasmus University Medical Center (Erasmus MC), Rotterdam, The Netherlands.

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 = -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).

Conclusion: Increased genetic susceptibility to MS may affect brain structure and cognition in persons without disease, pointing to a "hidden burden" of MS.
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http://dx.doi.org/10.1177/1352458516682104DOI Listing
November 2017

Hearing Impairment Is Associated with Smaller Brain Volume in Aging.

Front Aging Neurosci 2017 20;9. Epub 2017 Jan 20.

Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center Rotterdam, Netherlands.

Although recent studies show that age-related hearing impairment is associated with cerebral changes, data from a population perspective are still lacking. Therefore, we studied the relation between hearing impairment and brain volume in a large elderly cohort. From the population-based Rotterdam Study, 2,908 participants (mean age 65 years, 56% female) underwent a pure-tone audiogram to quantify hearing impairment. By performing MR imaging of the brain we quantified global and regional brain tissue volumes (total brain volume, gray matter volume, white matter (WM) volume, and lobe-specific volumes). We used multiple linear regression models, adjusting for age, sex, head size, time between hearing test and MR imaging, and relevant cognitive and cardiovascular covariates. Furthermore, we performed voxel-based morphometry to explore sub-regional differences. We found that a higher pure-tone threshold was associated with a smaller total brain volume [difference in standardized brain volume per decibel increase in hearing threshold in the age-sex adjusted model: -0.003 (95% confidence interval -0.004; -0.001)]. Specifically, WM volume was associated. Both associations were more pronounced in the lower frequencies. All associations were consistently present in all brain lobes in the lower frequencies and in most lobes in the higher frequencies, and were independent of cognitive function and cardiovascular risk factors. In voxel-based analyses we found associations of hearing impairment with smaller white volumes and some smaller and larger gray volumes, yet these were statistically non-significant. Our findings demonstrate that hearing impairment in elderly is related to smaller total brain volume, independent of cognition and cardiovascular risk factors. This mainly seems to be driven by smaller WM volume, throughout the brain.
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http://dx.doi.org/10.3389/fnagi.2017.00002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247429PMC
January 2017

Gray matter heritability in family-based and population-based studies using voxel-based morphometry.

Hum Brain Mapp 2017 05 1;38(5):2408-2423. Epub 2017 Feb 1.

Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.

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 ). 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. © 2017 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867052PMC
May 2017

Heritability of the shape of subcortical brain structures in the general population.

Nat Commun 2016 12 15;7:13738. Epub 2016 Dec 15.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam 3015 CE, The Netherlands.

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.
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http://dx.doi.org/10.1038/ncomms13738DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5172387PMC
December 2016

Fine-mapping the effects of Alzheimer's disease risk loci on brain morphology.

Neurobiol Aging 2016 12 4;48:204-211. Epub 2016 Sep 4.

Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Neurology, Erasmus MC, Rotterdam, the Netherlands. Electronic address:

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), right superior frontal gyrus by ZCWPW1 (rs1476679, p = 5.12 × 10), and right postcentral gyrus by APOE (p = 6.91 × 10). 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.
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http://dx.doi.org/10.1016/j.neurobiolaging.2016.08.024DOI Listing
December 2016
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