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244 results on '"Vernooij, M."'

5. Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms

Author

Amin, N, Jovanova, O, Adams, H H H, Dehghan, A, Kavousi, M, Vernooij, M W, Peeters, R P, de Vrij, F M S, van der Lee, S J, van Rooij, J G J, van Leeuwen, E M, Chaker, L, Demirkan, A, Hofman, A, Brouwer, R W W, Kraaij, R, Willems van Dijk, K, Hankemeier, T, van Ijcken, W F J, Uitterlinden, A G, Niessen, W J, Franco, O H, Kushner, S A, Ikram, M A, Tiemeier, H, and van Duijn, C M

Published
2017
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7. Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia

Author

Panman, J. L., Venkatraghavan, V., Van Der Ende, E. L., Steketee, R. M. E., Jiskoot, L. C., Poos, J. M., Dopper, E. G. P., Meeter, L. H. H., Kaat, L. D., Rombouts, S. A. R. B., Vernooij, M. W., Kievit, A. J. A., Premi, E., Cosseddu, M., Bonomi, E., Olives, J., Rohrer, J. D., Sanchez-Valle, R., Borroni, B., Bron, E. E., Van Swieten, J. C., Papma, J. M., Klein, S., Afonso, S., Almeida, M. R., Anderl-Straub, S., Andersson, C., Antonell, A., Archetti, S., Arighi, A., Balasa, M., Barandiaran, M., Bargallo, N., Bartha, R., Bender, B., Black, S., Butler, C., Bocchetta, M., Borrego-Ecija, S., Bras, J., Bruffaerts, R., Caroppo, P., Cash, D., Castelo-Branco, M., Convery, R., Cope, T., Danek, A., De Arriba, M., De Mendonca, A., Di Fede, G., Diaz, Z., Ducharme, S., Duro, D., Fenoglio, C., Ferreira, C. B., Finger, E., Flanagan, T., Fox, N., Freedman, M., Fumagalli, G., Gabilondo, A., Galimberti, D., Gasparotti, R., Gauthier, S., Gazzina, S., Gerhard, A., Giaccone, G., Gorostidi, A., Graff, C., Greaves, C., Guerreiro, R., Heller, C., Hoegen, T., Indakoetxea, B., Jelic, V., Karnath, H. -O., Keren, R., Laforce, R., Leitao, M. J., Levin, J., Llado, A., Loosli, S., Maruta, C., Masellis, M., Mead, S., Miltenberger, G., Van Minkelenm Sara Mitchell, R., Moore, K., Moreno, F., Nicholas, J., Oijerstedt, L., Otto, M., Ourselin, S., Padovani, A., Peakman, G., Pijnenburg, Y., Polito, C., Prioni, S., Prix, C., Rademakers, R., Redaelli, V., Rittman, T., Rogaeva, E., Rosa-Neto, P., Rossi, G., Rosser, M., Rowe, J., Santana, I., Santiago, B., Scarpini, E., Schonecker, S., Shafei, E. S. R., Shoesmith, C., Synofzik, M., Tabuas-Pereira, M., Tagliavini, F., Tartaglia, C., Tainta, M., Taipa, R., Tang-Wai, D., Thomas, D. L., Thonberg, H., Timberlake, C., Tiraboschi, P., Todd, E., Vandamme, P., Vandenberghe, R., Vandenbulcke, M., Veldsman, M., Verdelho, A., Villanua, J., Warren, J., Wilkeione, C., Elisabeth, W., Henrik, W., Zulaica, Z. M., Neurology, Physics and medical technology, Radiology & Nuclear Medicine, Clinical Genetics, and Medical Research Council

Subjects
Male, Oncology, Disease, Neuropsychological Tests, GENFI consortium investigators, Primary progressive aphasia, Cognition, Progranulins, 0302 clinical medicine, Neurofilament Proteins, BEHAVIORAL VARIANT, HETEROGENEITY, Gray Matter, 11 Medical and Health Sciences, Language, Psychiatry, 0303 health sciences, Brain, Middle Aged, Magnetic Resonance Imaging, White Matter, 17 Psychology and Cognitive Sciences, ALZHEIMERS-DISEASE, Psychiatry and Mental health, Phenotype, medicine.anatomical_structure, Frontotemporal Dementia, Disease Progression, Biomarker (medicine), Female, Life Sciences & Biomedicine, Frontotemporal dementia, medicine.medical_specialty, Clinical Neurology, EVENT-BASED MODEL, Grey matter, Lateralization of brain function, White matter, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Internal medicine, NEUROFILAMENT LIGHT-CHAIN, medicine, Humans, LOBAR DEGENERATION, PROGRANULIN, Aged, 030304 developmental biology, Science & Technology, Neurology & Neurosurgery, business.industry, DISEASE PROGRESSION, medicine.disease, Mutation, WHITE-MATTER INTEGRITY, Surgery, Neurosciences & Neurology, Neurology (clinical), business, GENFI, Biomarkers, 030217 neurology & neurosurgery, Progressive disease
Abstract

ObjectiveProgranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way.MethodsWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes.ResultsLanguage functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA.ConclusionDegeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage.

Published
2021
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8. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group

Author

Schmaal, L, Veltman, D J, van Erp, T GM, Sämann, P G, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, W J, Vernooij, M W, Ikram, M A, Wittfeld, K, Grabe, H J, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, S N, Hickie, I B, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, M E, Strike, L T, Mills, N T, de Zubicaray, G I, McMahon, K L, Medland, S E, Martin, N G, Gillespie, N A, Wright, M J, Hall, G B, MacQueen, G M, Frey, E M, Carballedo, A, van Velzen, L S, van Tol, M J, van der Wee, N J, Veer, I M, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, A M, Papmeyer, M, Whalley, H C, Sussmann, J E, Godlewska, B R, Cowen, P J, Fischer, F H, Rose, M, Penninx, B WJH, Thompson, P M, and Hibar, D P

Published
2016
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9. Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Author

Schmaal, L, Veltman, D J, van Erp, T GM, Sämann, P G, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, M W, Niessen, W J, Ikram, M A, Wittfeld, K, Grabe, H J, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, S N, Hickie, I B, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, M E, Strike, L T, Wright, M J, de Zubicaray, G I, McMahon, K L, Medland, S E, Gillespie, N A, Hall, G B, van Velzen, L S, van Tol, M-J, van der Wee, N J, Veer, I M, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, A M, Whalley, H C, Sussmann, J E, Godlewska, B R, Fischer, F H, Penninx, B WJH, Thompson, P M, and Hibar, D P

Published
2016
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14. Differences Between MR Brain Region Segmentation Methods:Impact on Single-Subject Analysis

Author

Huizinga, W., Poot, D. H.J., Vinke, E. J., Wenzel, F., Bron, E. E., Toussaint, N., Ledig, C., Vrooman, H., Ikram, M. A., Niessen, W. J., Vernooij, M. W., Klein, S., Huizinga, W., Poot, D. H.J., Vinke, E. J., Wenzel, F., Bron, E. E., Toussaint, N., Ledig, C., Vrooman, H., Ikram, M. A., Niessen, W. J., Vernooij, M. W., and Klein, S.

Abstract

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good ((Formula presented.)), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which sho

Published
2021

16. Erratum: Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms

Author

Amin, N, Jovanova, O, Adams, H H H, Dehghan, A, Kavousi, M, Vernooij, M W, Peeters, R P, de Vrij, F M S, van der Lee, S J, van Rooij, J G J, van Leeuwen, E M, Chaker, L, Demirkan, A, Hofman, A, Brouwer, R W W, Kraaij, R, Willems van Dijk, K, Hankemeier, T, van Ijcken, W F J, Uitterlinden, A G, Niessen, W J, Franco, O H, Kushner, S A, Ikram, M A, Tiemeier, H, and van Duijn, C M

Published
2017
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21. Circulating levels of metabolic biomarkers of site-specific and sex-specific arterial calcification in the multi-cohort BBMRI setting

Author

Bos, M.M, primary, Van Vliet, N.A, additional, Beekman, M, additional, Slagboom, P.E, additional, Vernooij, M, additional, Van Der Grond, J, additional, Van Der Lugt, A, additional, Ahmadizar, F, additional, Ghanbari, M, additional, Ikram, M.A, additional, Van Heemst, D, additional, Bos, D, additional, and Kavousi, M, additional

Published
2020
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27. Genetic architecture of subcortical brain structures in 38,854 individuals worldwide

Author

Satizabal, C., Adams, H., Hibar, D., White, C., Knol, M., Stein, J., Scholz, M., Sargurupremraj, M., Jahanshad, N., Roshchupkin, G., Smith, A., Bis, J., Jian, X., Luciano, M., Hofer, E., Teumer, A., Van der Lee, S., Yang, J., Yanek, L., Lee, T., Li, S., Hu, Y., Koh, J., Eicher, J., Desrivières, S., Arias-Vasquez, A., Chauhan, G., Athanasiu, L., Renteria, M., Kim, S., Höhn, D., Armstrong, N., Chen, Q., Holmes, A., Den Braber, A., Kloszewska, I., Andersson, M., Espeseth, T., Grimm, O., Abramovic, L., Alhusaini, S., Milaneschi, Y., Papmeyer, M., Axelsson, T., Ehrlich, S., Roiz-Santiañez, R., Kraemer, B., Håberg, A., Jones, H., Pike, G., Stein, D., Stevens, A., Bralten, J., Vernooij, M., Harris, T., Filippi, I., Witte, A., Guadalupe, T., Wittfeld, K., Mosley, T., Becker, J., Doan, N., Hagenaars, S., Saba, Y., Cuellar-Partida, G., Amin, N., Hilal, S., Nho, K., Karbalai, N., Arfanakis, K., Becker, D., Ames, D., Goldman, A., Lee, P., Boomsma, D., Lovestone, S., Giddaluru, S., Le Hellard, S., Mattheisen, M., Bohlken, M., Kasperaviciute, D., Schmaal, L., Lawrie, S., Agartz, I., Walton, E., Tordesillas-Gutierrez, D., Davies, G., Shin, J., Ipser, J., Vinke, L., Hoogman, M., Jia, T., Burkhardt, R., Klein, M., Crivello, F., Janowitz, D., Carmichael, O., Haukvik, U., Aribisala, B., Schmidt, H., Strike, L., Cheng, C., Risacher, S., Pütz, B., Fleischman, D., Assareh, A., Mattay, V., Buckner, R., Mecocci, P., Dale, A., Cichon, S., Boks, M., Matarin, M., Penninx, B., Calhoun, V., Chakravarty, M., Marquand, A., Macare, C., Masouleh, S., Oosterlaan, J., Amouyel, P., Hegenscheid, K., Rotter, J., Schork, A., Liewald, D., De Zubicaray, G., Wong, T., Shen, L., Sämann, P., Brodaty, H., Roffman, J., De Geus, E., Tsolaki, M., Erk, S., Van Eijk, K., Cavalleri, G., Van der Wee, N., McIntosh, A., Gollub, R., Bulayeva, K., Bernard, M., Richards, J., Himali, J., Loeffler, M., Rommelse, N., Hoffmann, W., Westlye, L., Valdés Hernández, M., Hansell, N., Van Erp, T., Wolf, C., Kwok, J., Vellas, B., Heinz, A., Olde Loohuis, L., Delanty, N., Ho, B., Ching, C., Shumskaya, E., Singh, B., Hofman, A., Van der Meer, D., Homuth, G., Psaty, B., Bastin, M., Montgomery, G., Foroud, T., Reppermund, S., Hottenga, J., Simmons, A., Meyer-Lindenberg, A., Cahn, W., Whelan, C., Van Donkelaar, M., Yang, Q., Hosten, N., Green, R., Thalamuthu, A., Mohnke, S., Hulshoff Pol, H., Lin, H., Jack Jr., C., Schofield, P., Mühleisen, T., Maillard, P., Potkin, S., Wen, W., Fletcher, E., Toga, A., Gruber, O., Huentelman, M., Smith, G., Launer, L., Nyberg, L., Jönsson, E., Crespo-Facorro, B., Koen, N., Greve, D., Uitterlinden, A., Weinberger, D., Steen, V., Fedko, I., Groenewold, N., Niessen, W., Toro, R., Tzourio, C., Longstreth Jr., W., Ikram, M., Smoller, J., Van Tol, M., Sussmann, J., Paus, T., Lemaître, H., Schroeter, M., Mazoyer, B., Andreassen, O., Holsboer, F., Depondt, C., Veltman, D., Turner, J., Pausova, Z., Schumann, G., Van Rooij, D., Djurovic, S., Deary, I., McMahon, K., Müller-Myhsok, B., Brouwer, R., Soininen, H., Pandolfo, M., Wassink, T., Cheung, J., Wolfers, T., Martinot, J., Zwiers, M., Nauck, M., Melle, I., Martin, N., Kanai, R., Westman, E., Kahn, R., Sisodiya, S., White, T., Saremi, A., Van Bokhoven, H., Brunner, H., Völzke, H., Wright, M., Van 't Ent, D., Nöthen, M., Ophoff, R., Buitelaar, J., Fernández, G., Sachdev, P., Rietschel, M., Van Haren, N., Fisher, S., Beiser, A., Francks, C., Saykin, A., Mather, K., Romanczuk-Seiferth, N., Hartman, C., DeStefano, A., Heslenfeld, D., Weiner, M., Walter, H., Hoekstra, P., Nyquist, P., Franke, B., Bennett, D., Grabe, H., Johnson, A., Chen, C., Van Duijn, C., Lopez, O., Fornage, M., Wardlaw, J., Schmidt, R., DeCarli, C., De Jager, P., Villringer, A., Debette, S., Gudnason, V., Medland, S., Shulman, J., Thompson, P., and Seshadri, S.

Subjects
nervous system
Abstract

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.

Published
2019

28. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author

Chauhan, G., Adams, H., Satizabal, C., Bis, J., Teumer, A., Sargurupremraj, M., Hofer, E., Trompet, S., Hilal, S., Smith, A., Jian, X., Malik, R., Traylor, M., Pulit, S., Amouyel, P., Mazoyer, B., Zhu, Y., Kaffashian, S., Schilling, S., Beecham, G., Montine, T., Schellenberg, G., Kjartansson, O., Guethnason, V., Knopman, D., Griswold, M., Windham, B., Gottesman, R., Mosley, T., Schmidt, R., Saba, Y., Schmidt, H., Takeuchi, F., Yamaguchi, S., Nabika, T., Kato, N., Rajan, K., Aggarwal, N., De Jager, P., Evans, D., Psaty, B., Rotter, J., Rice, K., Lopez, O., Liao, J., Chen, C., Cheng, C., Wong, T., Ikram, M., van der Lee, S., Amin, N., Chouraki, V., DeStefano, A., Aparicio, H., Romero, J., Maillard, P., DeCarli, C., Wardlaw, J., Hernandez, M., Luciano, M., Liewald, D., Deary, I., Starr, J., Bastin, M., Munoz Maniega, S., Slagboom, P., Beekman, M., Deelen, J., Uh, H., Lemmens, R., Brodaty, H., Wright, M., Ames, D., Boncoraglio, G., Hopewell, J., Beecham, A., Blanton, S., Wright, C., Sacco, R., Wen, W., Thalamuthu, A., Armstrong, N., Chong, E., Schofield, P., Kwok, J., van der Grond, J., Stott, D., Ford, I., Jukema, J., Vernooij, M., Hofman, A., Uitterlinden, A., van der Lugt, A., Wittfeld, K., Grabe, H., Hosten, N., von Sarnowski, B., Volker, U., Levi, C., Jimenez-Conde, J., Sharma, P., Sudlow, C., Rosand, J., Woo, D., Cole, J., Meschia, J., Slowik, A., Thijs, V., Lindgren, A., Melander, O., Grewal, R., Rundek, T., Rexrode, K., Rothwell, P., Arnett, D., Jern, C., Johnson, J., Benavente, O., Wasssertheil-Smoller, S., Lee, J., Wong, Q., Mitchell, B., Rich, S., McArdle, P., Geerlings, M., van der Graaf, Y., de Bakker, P., Asselbergs, F., Srikanth, V., Thomson, R., McWhirter, R., Moran, C., Callisaya, M., Phan, T., Rutten-Jacobs, L., Bevan, S., Tzourio, C., Mather, K., Sachdev, P., van Duijn, C., Worrall, B., Dichgans, M., Kittner, S., Markus, H., Fornage, M., Launer, L., Seshadri, S., Longstreth, W., Debette, S., Stroke Genetics Network, t., the Neurology Working Group of the Cohorts for, H., and Aging Research in Genomic Epidemiology, C.

Abstract

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 x 10(-8); and LINC00539/ZDHHC20, p = 5.82 x 10(-9). Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 x 10(-25); p [SSBI] = 5.23 x 10(-14) for hypertension), smoking (p [BI] = 4.4 x 10(-10); p [SSBI] = 1.2 x 10(-4)), diabetes (p [BI] = 1.7 x 10(-8); p [SSBI] = 2.8 x 10(-3)), previous cardiovascular disease (p [BI] = 1.0 x 10(-18); p [SSBI] = 2.3 x 10(-7)), stroke (p [BI] = 3.9 x 10(-69); p [SSBI] = 3.2 x 10(-24)), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 x 10(-157); p [SSBI] = 3.16 x 10(-106)), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p

Published
2019

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

Author

SATIZABAL, C. L., ADAMS, H. H. H., HIBAR, D. P., WHITE, C. C., KNOL, M. J., STEIN, J. L., Scholz, M., Sargurupremraj, Muralidharan, JAHANSHAD, N., ROSHCHUPKIN, G. V., SMITH, A. V., BIS, J. C., JIAN, X., LUCIANO, M., Hofer, E., TEUMER, A., VAN DER LEE, S. J., Yang, J., YANEK, L. R., LEE, T. V., Li, S., Hu, Y., KOH, J. Y., EICHER, J. D., DESRIVIERES, S., ARIAS-VASQUEZ, A., Chauhan, G., ATHANASIU, L., RENTERIA, M. E., Kim, S., HOEHN, D., ARMSTRONG, N. J., Chen, Q., HOLMES, A. J., DEN BRABER, A., KLOSZEWSKA, I., Andersson, M., ESPESETH, T., Grimm, O., ABRAMOVIC, L., ALHUSAINI, S., MILANESCHI, Y., PAPMEYER, M., AXELSSON, T., Ehrlich, S., ROIZ-SANTIANEZ, R., KRAEMER, B., HABERG, A. K., JONES, H. J., Pike, G. B., STEIN, D. J., Stevens, A., BRALTEN, J., VERNOOIJ, M. W., HARRIS, T. B., FILIPPI, I., WITTE, A. V., Guadalupe, T., WITTFELD, K., MOSLEY, T. H., BECKER, J. T., DOAN, N. T., HAGENAARS, S. P., SABA, Y., CUELLAR-PARTIDA, G., Amin, N., HILAL, S., NHO, K., MIRZA-SCHREIBER, N., ARFANAKIS, K., BECKER, D. M., Ames, D., GOLDMAN, A. L., LEE, P. H., Boomsma, D. I., LOVESTONE, S., GIDDALURU, S., LE HELLARD, S., Mattheisen, M., BOHLKEN, M. M., KASPERAVICIUTE, D., SCHMAAL, L., LAWRIE, S. M., Agartz, I., Walton, E., TORDESILLAS-GUTIERREZ, D., DAVIES, G. E., Shin, J., IPSER, J. C., VINKE, L. N., HOOGMAN, M., Jia, T., BURKHARDT, R., Klein, M., Crivello, Fabrice, JANOWITZ, D., CARMICHAEL, O., HAUKVIK, U. K., ARIBISALA, B. S., Schmidt, H., STRIKE, L. T., CHENG, C. Y., RISACHER, S. L., PUTZ, B., FLEISCHMAN, D. A., ASSAREH, A. A., MATTAY, V. S., BUCKNER, R. L., MECOCCI, P., DALE, A. M., Cichon, S., BOKS, M. P., MATARIN, M., PENNINX, Bwjh, CALHOUN, V. D., CHAKRAVARTY, M. M., MARQUAND, A. F., MACARE, C., KHARABIAN MASOULEH, S., OOSTERLAAN, J., Amouyel, P., HEGENSCHEID, K., ROTTER, J. I., SCHORK, A. J., LIEWALD, D. C. M., DE ZUBICARAY, G. I., WONG, T. Y., Shen, L., SAMANN, P. G., BRODATY, H., ROFFMAN, J. L., DE GEUS, E. J. C., TSOLAKI, M., ERK, S., VAN EIJK, K. R., CAVALLERI, G. L., VAN DER WEE, N. J. A., MCINTOSH, A. M., GOLLUB, R. L., BULAYEVA, K. B., Bernard, M., RICHARDS, J. S., HIMALI, J. J., Loeffler, M., ROMMELSE, N., Hoffmann, W., WESTLYE, L. T., VALDES HERNANDEZ, M. C., HANSELL, N. K., VAN ERP, T. G. M., Wolf, C., KWOK, J. B. J., Vellas, B., Heinz, A., OLDE LOOHUIS, L. M., DELANTY, N., HO, B. C., CHING, C. R. K., SHUMSKAYA, E., Singh, B., Hofman, A., VAN DER MEER, D., Homuth, G., PSATY, B. M., BASTIN, M. E., MONTGOMERY, G. W., FOROUD, T. M., REPPERMUND, S., HOTTENGA, J. J., Simmons, A., Meyer-Lindenberg, A., Cahn, W., WHELAN, C. D., VAN DONKELAAR, M. M. J., Yang, Q., HOSTEN, N., GREEN, R. C., THALAMUTHU, A., MOHNKE, S., HULSHOFF POL, H. E., Lin, H., JACK, C. R., Jr., SCHOFIELD, P. R., MUHLEISEN, T. W., MAILLARD, P., POTKIN, S. G., Wen, W., FLETCHER, E., TOGA, A. W., Gruber, O., HUENTELMAN, M., DAVEY SMITH, G., LAUNER, L. J., Nyberg, L., JONSSON, E. G., CRESPO-FACORRO, B., KOEN, N., GREVE, D. N., UITTERLINDEN, A. G., WEINBERGER, D. R., STEEN, V. M., FEDKO, I. O., GROENEWOLD, N. A., Niessen, W. J., TORO, R., Tzourio, Christophe, LONGSTRETH, W. T., Jr., SMOLLER, J. W., VAN TOL, M. J., SUSSMANN, J. E., PAUS, T., Lemaitre, H., SCHROETER, M. L., Mazoyer, B., ANDREASSEN, O. A., Holsboer, F., DEPONDT, C., VELTMAN, D. J., Turner, J. A., PAUSOVA, Z., Schumann, G., Van Rooij, D., Djurovic, S., DEARY, I. J., MCMAHON, K. L., MULLER-MYHSOK, B., BROUWER, R. M., Soininen, H., Pandolfo, M., WASSINK, T. H., CHEUNG, J. W., WOLFERS, T., MARTINOT, J. L., ZWIERS, M. P., Nauck, M., Melle, I., Martin, N. G., Kanai, R., WESTMAN, E., KAHN, R. S., Sisodiya, S. M., White, T., SAREMI, A., van Bokhoven, H., Brunner, H. G., VOLZKE, H., WRIGHT, M. J., VAN 'T ENT, D., NOTHEN, M. M., OPHOFF, R. A., BUITELAAR, J. K., Fernandez, G., SACHDEV, P. S., Rietschel, M., VAN HAREN, N. E. M., Fisher, S. E., BEISER, A. S., Francks, C., SAYKIN, A. J., MATHER, K. A., ROMANCZUK-SEIFERTH, N., HARTMAN, C. A., DeStefano, A. L., HESLENFELD, D. J., WEINER, M. W., Walter, H., HOEKSTRA, P. J., NYQUIST, P. A., Franke, B., BENNETT, D. A., Grabe, H. J., JOHNSON, A. D., Chen, C., VAN DUIJN, C. M., LOPEZ, O. L., FORNAGE, M., WARDLAW, J. M., Schmidt, R., DeCarli, C., DE JAGER, P. L., VILLRINGER, A., Debette, Stephanie, GUDNASON, V., Medland, S. E., SHULMAN, J. M., THOMPSON, P. M., SESHADRI, S., IKRAM, M. K., Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS)

Subjects
nervous system, VINTAGE, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, HEALTHY
Abstract

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.

Published
2019
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31. 3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI

Author

Dubost, F. (Florian), Adams, H.H.H. (Hieab), Bortsova, G. (Gerda), Ikram, M.A. (Arfan), Niessen, W.J. (Wiro), Vernooij, M. (Meike), Bruijne, M. (Marleen) de, Dubost, F. (Florian), Adams, H.H.H. (Hieab), Bortsova, G. (Gerda), Ikram, M.A. (Arfan), Niessen, W.J. (Wiro), Vernooij, M. (Meike), and Bruijne, M. (Marleen) de

Abstract

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.

Published
2019
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32. Cavum septum pellucidum in the general pediatric population and its relation to surrounding brain structure volumes, cognitive function, and emotional or behavioral problems

Author

Dremmen, M. H.G., Bouhuis, R. H., Blanken, L. M.E., Muetzel, R. L., Vernooij, M. W., Marroun, H. E., Jaddoe, V. W.V., Verhulst, F. C., Tiemeier, H., White, T., Dremmen, M. H.G., Bouhuis, R. H., Blanken, L. M.E., Muetzel, R. L., Vernooij, M. W., Marroun, H. E., Jaddoe, V. W.V., Verhulst, F. C., Tiemeier, H., and White, T.

Abstract

BACKGROUND AND PURPOSE: The cavum septum pellucidum, a cavity filled with CSF, is localized between the 2 lateral ventricles of the brain. The cavum is present in all neonates, but it typically closes within 5 months after birth. In some cases, this closure does not occur and a persistent or enlarged cavum septum pellucidum has been linked, in some studies, to psychiatric disorders. However, the clinical relevance in the general population is unknown. In this study, we examined the relationship between the cavum septum pellucidum and volumes of brain structures, cognitive function, and emotional and behavioral problems in children. MATERIALS AND METHODS: This study was embedded in the Generation R Study, a prospective cohort in Rotterdam, the Netherlands. MR imaging studies of 1070 children, 6-10 years of age, were systematically evaluated for the presence and length of a persistent cavum septum pellucidum. An enlarged cavum septum pellucidum was defined as a cavum length of ≥6 mm. Groups without, with persistent, and with enlarged cavum septi pellucidi were compared for brain structure volumes, nonverbal intelligence, and emotional and behavioral problems. RESULTS: The prevalence of cavum septi pellucidi in our sample was 4.6%. Children with an enlarged cavum septum pellucidum had a larger corpus callosum, greater thalamic and total white matter-to-total brain volume ratio, and smaller lateral ventricle volumes. We did not find a relationship between cavum septi pellucidi and cognitive function or emotional and behavioral problems. CONCLUSIONS: The cavum septum pellucidum is a normal structural brain variation without clinical implications in this population-based sample of school-aged children.

Published
2019

33. Risico’s van vitamine K-antagonisten bij cerebrale microbloedingen

Author

Verhaar, B J H, Vernooij, M W, Biessels, G J, and Muller, M

Abstract

Cerebral microbleeds are associated with a higher risk of intracerebral hemorrhage.- When microbleeds are detected, the possible underlying pathology should be considered; this includes cerebral amyloid angiopathy and other factors that increase the risk of haemorrhage, particularly hypertension. - No randomised trials have yet been conducted into haemorrhagic complications and cerebral infarctions in patients with microbleeds who take vitamin K antagonists. This means that it is not clear whether the intended prevention of cerebral infarctions outweighs the increased risk of haemorrhage associated with use of vitamin K antagonists by these patients.- When deciding whether or not an older patient should be given anticoagulants the following should be taken into consideration as well: comorbidities, polypharmacy, the risk of falls and the probability that the patient can be optimally titrated to vitamin K antagonists. - If there is an increased risk of intracerebral haemorrhage but anticoagulants are indicated, direct oral anticoagulants (DOACs) might be preferable to vitamin K antagonists in patients with a history of cerebral microbleeds.

Published
2018

39. Enlarged perivascular spaces in brain MRI: Automated quantification in four regions

Author

Dubost, F. (Florian), Yilmaz, P. (Pinar), Adams, H.H.H. (Hieab), Bortsova, G. (Gerda), Ikram, M.A. (Arfan), Niessen, W.J. (Wiro), Vernooij, M. (Meike), Bruijne, M. (Marleen) de, Dubost, F. (Florian), Yilmaz, P. (Pinar), Adams, H.H.H. (Hieab), Bortsova, G. (Gerda), Ikram, M.A. (Arfan), Niessen, W.J. (Wiro), Vernooij, M. (Meike), and Bruijne, M. (Marleen) de

Abstract

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

Published
2018
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42. Cortical abnormalities in adults and adolescents with majordepression based on brain scans from 20 cohorts worldwide inthe ENIGMA Major Depressive Disorder Working Group

Author

Schmaal, L., Hibar, D. P., Vernooij, M. W., Niessen, W. J., Tiemeier, H., Hofman, A., Wittfeld, K., Bokhan, N. A., Sämann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., Cheung, J. W., Van Erp, T. G. M., Bos, D., and Ikram, M. A.

Subjects
депрессивные расстройства, взрослые, магнитно-резонансная томография головного мозга, mental disorders, behavioral disciplines and activities, подростки, корковые аномалии
Abstract

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.

Published
2017

44. METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research

Author

Dichgans, M., Wardlaw, J., Smith, E., Zietemann, V., Seshadri, S., Sachdev, P., Biessels, G.J., Fazekas, F., Benavente, O., Pantoni, L., Leeuw, F. de, Norrving, B., Matthews, P., Chen, C., Mok, V., During, M., Whiteley, W., Shuler, K., Alonso, A., Black, S.E., Brayne, C., Chabriat, H., Cordonnier, C., Doubal, F., Duzel, E., Ewers, M., Frayne, R., Hachinski, V., Ikram, M.A., Jessen, F., Jouvent, E., Linn, J., O'Brien, J., Oostenbrugge, R. van, Malik, R., Mazoyer, B., Schmidt, R., Sposato, L.A., Stephan, B., Swartz, R.H., Vernooij, M., Viswanathan, A., Werring, D., Abe, K., Allan, L., Arba, F., Bae, H.J., Bath, P.M.W., Bordet, R., Breteler, M., Choi, S., Deary, I., DeCarli, C., Ebmeier, K., Feng, L., Greenberg, S.M., Ihara, M., Kalaria, R., Kim, S., Lim, J.S., Lindley, R.I., Mead, G., Murray, A., Quinn, T., Ritchie, C., Sacco, R., Salman, R.A., Sprigg, N., Sudlow, C., Thomas, A., Boxtel, M. van, Grond, J. van der, Lugt, A. van der, Yang, Y.H., VISTA Collaboration, and METACOHORTS Consortiums

Subjects
Dementia, Neurodegeneration, Cerebrovascular disease, Survey, Cohorts, Small vessel disease
Published
2016

45. Size matters; but so does what you do with it! Response

Author

Schmaal, L., Veltman, D. J., van Erp, T. G. M., Saemann, P. G., Frodl, T., Jahanshad, N., Loehrer, E., Vernooij, M. W., Niessen, W. J., Ikram, M. A., Wittfeld, K., Grabe, H. J., Block, A., Hegenscheid, K., Hoehn, D., Czisch, M., Lagopoulos, J., Hatton, S. N., Hickie, I. B., Goya-Maldonado, R., Kraemer, B., Gruber, O., Couvy-Duchesne, B., Renteria, M. E., Strike, L. T., Wright, M. J., de Zubicaray, G. I., McMahon, K. L., Medland, S. E., Gillespie, N. A., Hall, G. B., van Velzen, L. S., van Tol, M-J, van der Wee, N. J., Veer, I. M., Walter, H., Schramm, E., Normann, C., Schoepf, D., Konrad, C., Zurowski, B., McIntosh, A. M., Whalley, H. C., Sussmann, J. E., Godlewska, B. R., Fischer, F. H., Penninx, B. W. J. H., Thompson, P. M., Hibar, D. P., Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Anatomy and neurosciences

Published
2016
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46. Response to: Size matters; but so does what you do with it!

Author

Schmaal, L., Veltman, D.J., van Erp, T. G. M., Saemann, P. G., Frodl, T., Jahanshad, N., Loehrer, E., Vernooij, M. W., Niessen, W. J., Ikram, M. A., Wittfeld, K., Grabe, H. J., Block, A., Hegenscheid, K., Hoehn, D., Czisch, M., Lagopoulos, J., Hatton, S. N., Hickie, I. B., Goya-Maldonado, R., Kraemer, B., Gruber, O., Couvy-Duchesne, B., Renteria, M. E., Strike, L. T., Wright, M. J., de Zubicaray, G. I., McMahon, K. L., Medland, S. E., Gillespie, N. A., Hall, G B, van Velzen, L. S., van Tol, M-J., van der Wee, N. J., Veer, I. M., Walter, H., Schramm, E., Normann, C., Schoepf, D., Konrad, C., Zurowski, B., McIntosh, A. M., Whalley, H. C., Sussmann, J. E., Godlewska, B. R., Fischer, F. H., Penninx, B. W. J. H., Thompson, P. M., Hibar, D. P., Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), and Clinical Cognitive Neuropsychiatry Research Program (CCNP)

Subjects
MAJOR DEPRESSIVE DISORDER, HISTORY, EARLY-ONSET, ANXIETY
Published
2016

47. Modeling the brain morphology distribution in the general aging population

Author

Huizinga, W., Poot, D.H.J., Roshchupkin, G., Bron, E. E., Ikram, M. A., Vernooij, M. W., Rueckert, D., Niessen, W.J., Klein, S., Gimi, Barjor, and Krol, Andrzej

Subjects
education.field_of_study, Population ageing, business.industry, Computer science, Population, Brain morphometry, Imaging study, Statistical model, Spatiotemporal atlas, Disease cause, 030218 nuclear medicine & medical imaging, Entire brain, Partial least squares regression, Statistical modeling, 03 medical and health sciences, 0302 clinical medicine, Non-rigid groupwise registration, LMS method, Artificial intelligence, sense organs, education, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

Published
2016

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