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1. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Author

García-Marín, Luis M., Campos, Adrian I., Diaz-Torres, Santiago, Rabinowitz, Jill A., Ceja, Zuriel, Mitchell, Brittany L., Grasby, Katrina L., Thorp, Jackson G., Agartz, Ingrid, Alhusaini, Saud, Ames, David, Amouyel, Philippe, Andreassen, Ole A., Arfanakis, Konstantinos, Arias-Vasquez, Alejandro, Armstrong, Nicola J., Athanasiu, Lavinia, Bastin, Mark E., Beiser, Alexa S., Bennett, David A., Bis, Joshua C., Boks, Marco P. M., Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buitelaar, Jan K., Burkhardt, Ralph, Cahn, Wiepke, Calhoun, Vince D., Carmichael, Owen T., Chakravarty, Mallar, Chen, Qiang, Ching, Christopher R. K., Cichon, Sven, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dale, Anders M., Smith, George Davey, de Geus, Eco J. C., De Jager, Philip L., de Zubicaray, Greig I., Debette, Stéphanie, DeCarli, Charles, Depondt, Chantal, Desrivières, Sylvane, Djurovic, Srdjan, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fernández, Guillén, Filippi, Irina, Fisher, Simon E., Fleischman, Debra A., Fletcher, Evan, Fornage, Myriam, Forstner, Andreas J., Francks, Clyde, Franke, Barbara, Ge, Tian, Goldman, Aaron L., Grabe, Hans J., Green, Robert C., Grimm, Oliver, Groenewold, Nynke A., Gruber, Oliver, Gudnason, Vilmundur, Håberg, Asta K., Haukvik, Unn K., Heinz, Andreas, Hibar, Derrek P., Hilal, Saima, Himali, Jayandra J., Ho, Beng-Choon, Hoehn, David F., Hoekstra, Pieter J., Hofer, Edith, Hoffmann, Wolfgang, Holmes, Avram J., Homuth, Georg, Hosten, Norbert, Ikram, M. Kamran, Ipser, Jonathan C., Jack Jr, Clifford R., Jahanshad, Neda, Jönsson, Erik G., Kahn, Rene S., Kanai, Ryota, Klein, Marieke, Knol, Maria J., Launer, Lenore J., Lawrie, Stephen M., Hellard, Stephanie Le, Lee, Phil H., Lemaître, Hervé, Li, Shuo, Liewald, David C. M., Lin, Honghuang, Longstreth, Jr, W. T., Lopez, Oscar L., Luciano, Michelle, Maillard, Pauline, Marquand, Andre F., Martin, Nicholas G., Martinot, Jean-Luc, Mather, Karen A., Mattay, Venkata S., McMahon, Katie L., Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Mirza-Schreiber, Nazanin, Milaneschi, Yuri, Mosley, Thomas H., Mühleisen, Thomas W., Müller-Myhsok, Bertram, Maniega, Susana Muñoz, Nauck, Matthias, Nho, Kwangsik, Niessen, Wiro J., Nöthen, Markus M., Nyquist, Paul A., Oosterlaan, Jaap, Pandolfo, Massimo, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W. J. H., Pike, G. Bruce, Psaty, Bruce M., Pütz, Benno, Reppermund, Simone, Rietschel, Marcella D., Risacher, Shannon L., Romanczuk-Seiferth, Nina, Romero-Garcia, Rafael, Roshchupkin, Gennady V., Rotter, Jerome I., Sachdev, Perminder S., Sämann, Philipp G., Saremi, Arvin, Sargurupremraj, Muralidharan, Saykin, Andrew J., Schmaal, Lianne, Schmidt, Helena, Schmidt, Reinhold, Schofield, Peter R., Scholz, Markus, Schumann, Gunter, Schwarz, Emanuel, Shen, Li, Shin, Jean, Sisodiya, Sanjay M., Smith, Albert V., Smoller, Jordan W., Soininen, Hilkka S., Steen, Vidar M., Stein, Dan J., Stein, Jason L., Thomopoulos, Sophia I., Toga, Arthur W., Tordesillas-Gutiérrez, Diana, Trollor, Julian N., Valdes-Hernandez, Maria C., van ′t Ent, Dennis, van Bokhoven, Hans, van der Meer, Dennis, van der Wee, Nic J. A., Vázquez-Bourgon, Javier, Veltman, Dick J., Vernooij, Meike W., Villringer, Arno, Vinke, Louis N., Völzke, Henry, Walter, Henrik, Wardlaw, Joanna M., Weinberger, Daniel R., Weiner, Michael W., Wen, Wei, Westlye, Lars T., Westman, Eric, White, Tonya, Witte, A. Veronica, Wolf, Christiane, Yang, Jingyun, Zwiers, Marcel P., Ikram, M. Arfan, Seshadri, Sudha, Thompson, Paul M., Satizabal, Claudia L., Medland, Sarah E., and Rentería, Miguel E.

Published
2024
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3. Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients

Author

Biesbroek, J Matthijs, Coenen, Mirthe, DeCarli, Charles, Fletcher, Evan M, Maillard, Pauline M, Initiative, Alzheimer's Disease Neuroimaging, Barkhof, Frederik, Barnes, Josephine, Benke, Thomas, Chen, Christopher PLH, Dal‐Bianco, Peter, Dewenter, Anna, Duering, Marco, Enzinger, Christian, Ewers, Michael, Exalto, Lieza G, Franzmeier, Nicolai, Hilal, Saima, Hofer, Edith, Koek, Huiberdina L, Maier, Andrea B, McCreary, Cheryl R, Papma, Janne M, Paterson, Ross W, Pijnenburg, Yolande AL, Rubinski, Anna, Schmidt, Reinhold, Schott, Jonathan M, Slattery, Catherine F, Smith, Eric E, Sudre, Carole H, Steketee, Rebecca ME, Teunissen, Charlotte E, van den Berg, Esther, van der Flier, Wiesje M, Venketasubramanian, Narayanaswamy, Venkatraghavan, Vikram, Vernooij, Meike W, Wolters, Frank J, Xin, Xu, Kuijf, Hugo J, and Biessels, Geert Jan

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Vascular Cognitive Impairment/Dementia, Brain Disorders, Clinical Research, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease Related Dementias (ADRD), Aging, Dementia, Cerebrovascular, Alzheimer's Disease, Neurodegenerative, Neurological, Humans, Female, Middle Aged, Aged, Aged, 80 and over, Male, White Matter, Arteriolosclerosis, Amyloid beta-Peptides, Magnetic Resonance Imaging, amyloid pathology, arteriolosclerosis, dementia, lesion pattern, white matter hyperintensities, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionWhite matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-β1-42 (Aβ42)-positive status.MethodsIndividual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume.ResultsVRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p

Published
2024

5. AI-based association analysis for medical imaging using latent-space geometric confounder correction

Author

Liu, Xianjing, Li, Bo, Vernooij, Meike W., Wolvius, Eppo B., Roshchupkin, Gennady V., and Bron, Esther E.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

AI has greatly enhanced medical image analysis, yet its use in epidemiological population imaging studies remains limited due to visualization challenges in non-linear models and lack of confounder control. Addressing this, we introduce an AI method emphasizing semantic feature interpretation and resilience against multiple confounders. Our approach's merits are tested in three scenarios: extracting confounder-free features from a 2D synthetic dataset; examining the association between prenatal alcohol exposure and children's facial shapes using 3D mesh data; exploring the relationship between global cognition and brain images with a 3D MRI dataset. Results confirm our method effectively reduces confounder influences, establishing less confounded associations. Additionally, it provides a unique visual representation, highlighting specific image alterations due to identified correlations., Comment: 18 pages; 7 figures

Published
2023

6. Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI

Author

Li, Bo, de Bresser, Jeroen, Niessen, Wiro, van Osch, Matthias, van der Flier, Wiesje M., Biessels, Geert Jan, Vernooij, Meike W., and Bron, Esther

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Lacunes of presumed vascular origin, also referred to as lacunar infarcts, are important to assess cerebral small vessel disease and cognitive diseases such as dementia. However, visual rating of lacunes from imaging data is challenging, time-consuming, and rater-dependent, owing to their small size, sparsity, and mimics. Whereas recent developments in automatic algorithms have shown to make the detection of lacunes faster while preserving sensitivity, they also showed a large number of false positives, which makes them impractical for use in clinical practice or large-scale studies. Here, we develop a novel framework that, in addition to lacune detection, outputs a categorical burden score. This score could provide a more practical estimate of lacune presence that simplifies and effectively accelerates the imaging assessment of lacunes. We hypothesize that the combination of detection and the categorical score makes the procedure less sensitive to noisy labels., Comment: Abstract presented at the 2022 annual meeting of the Organization for Human Brain Mapping (OHBM).Second place at the VALDO MICCAI challenge for cerebral lacune detection

Published
2023

7. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Author

Kavousi, Maryam, Bos, Maxime M, Barnes, Hanna J, Lino Cardenas, Christian L, Wong, Doris, Lu, Haojie, Hodonsky, Chani J, Landsmeer, Lennart PL, Turner, Adam W, Kho, Minjung, Hasbani, Natalie R, de Vries, Paul S, Bowden, Donald W, Chopade, Sandesh, Deelen, Joris, Benavente, Ernest Diez, Guo, Xiuqing, Hofer, Edith, Hwang, Shih-Jen, Lutz, Sharon M, Lyytikäinen, Leo-Pekka, Slenders, Lotte, Smith, Albert V, Stanislawski, Maggie A, van Setten, Jessica, Wong, Quenna, Yanek, Lisa R, Becker, Diane M, Beekman, Marian, Budoff, Matthew J, Feitosa, Mary F, Finan, Chris, Hilliard, Austin T, Kardia, Sharon LR, Kovacic, Jason C, Kral, Brian G, Langefeld, Carl D, Launer, Lenore J, Malik, Shaista, Hoesein, Firdaus AA Mohamed, Mokry, Michal, Schmidt, Reinhold, Smith, Jennifer A, Taylor, Kent D, Terry, James G, van der Grond, Jeroen, van Meurs, Joyce, Vliegenthart, Rozemarijn, Xu, Jianzhao, Young, Kendra A, Zilhão, Nuno R, Zweiker, Robert, Assimes, Themistocles L, Becker, Lewis C, Bos, Daniel, Carr, J Jeffrey, Cupples, L Adrienne, de Kleijn, Dominique PV, de Winther, Menno, den Ruijter, Hester M, Fornage, Myriam, Freedman, Barry I, Gudnason, Vilmundur, Hingorani, Aroon D, Hokanson, John E, Ikram, M Arfan, Išgum, Ivana, Jacobs, David R, Kähönen, Mika, Lange, Leslie A, Lehtimäki, Terho, Pasterkamp, Gerard, Raitakari, Olli T, Schmidt, Helena, Slagboom, P Eline, Uitterlinden, André G, Vernooij, Meike W, Bis, Joshua C, Franceschini, Nora, Psaty, Bruce M, Post, Wendy S, Rotter, Jerome I, Björkegren, Johan LM, O’Donnell, Christopher J, Bielak, Lawrence F, Peyser, Patricia A, Malhotra, Rajeev, van der Laan, Sander W, and Miller, Clint L

Subjects
Biological Sciences, Genetics, Heart Disease - Coronary Heart Disease, Human Genome, Cardiovascular, Prevention, Heart Disease, Atherosclerosis, 2.1 Biological and endogenous factors, Aetiology, Humans, Black People, Coronary Artery Disease, Genome-Wide Association Study, Risk Factors, European People, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Published
2023

8. The impact of tau-PET in a selected memory clinic cohort: rationale and design of the TAP-TAU study

Author

Vermeiren, Marie R., Somsen, Joost, Luurtsema, Gert, Reesink, Fransje E., Verwey, Nicolaas A., Hempenius, Liesbeth, Tolboom, Nelleke, Biessels, Geert Jan, Biesbroek, J. Matthijs, Vernooij, Meike W., Veldhuijzen van Zanten, Sophie E. M., Seelaar, Harro, Coomans, Emma M., Teunissen, Charlotte E., Lemstra, Afina W., van Harten, Argonde C., Visser, Leonie N. C., van der Flier, Wiesje M., van de Giessen, Elsmarieke, and Ossenkoppele, Rik

Published
2024
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11. The implications of the COVID-19 pandemic on eating disorder features and comorbid psychopathology among adolescents with anorexia nervosa and matched controls: a comparative cohort design study

Author

Bracké, Katrien, Steegers, Cathelijne, van der Harst, Tess, Pons, Rozemarijn, Legerstee, Jeroen, Dierckx, Bram, de Nijs, Pieter, Bax-van Berkel, Marieke, van Elburg, Annemarie, Hekkelaan, Marion, Hokke, Joke, de Jong-Zuidema, Hetty, Korthals Altes, Lucas, Lengton-van der Spil, Farida, Luijkx, Judith, Schuurmans, Femke, Smeets, Carien, van Wijk, Lia, Woltering, Claire, Vernooij, Meike, Hillegers, Manon, White, Tonya, and Dieleman, Gwen

Published
2024
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13. Gender, Networks and Academic Leadership: A Systematic Review

Author

van Helden, Daphne L., den Dulk, Laura, Steijn, Bram, and Vernooij, Meike W.

Abstract

This article systematically reviews 35 empirical articles on the topic of networks and career advancement into academic leadership positions. Our objectives in this systematic review are to clarify: (1) the functions that networks fulfil; (2) the outcomes of these functions in terms of career advancement; and (3) whether networks are likely to result in more or less career advancement for women compared to men. Four databases were searched using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) approach to select eligible studies published between 1990 and 2019. We distinguish three different functions that networks may fulfil: operational, developmental and strategic. Mixed results are found for the operational and developmental network functions, while the results suggest that the strategic function contributes to career advancement, particularly for men. The reason for this can be found in the masculine academic context. Our findings implicate that a solution involving equal network opportunities needs to be taken up by the academic community as a whole. To further develop the research field, we call for: (1) more consistent conceptualisation; (2) more attention to the multiple functions of networks and networking behaviour; and (3) more advanced study designs.

Published
2023
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14. The Rotterdam Study. Design update and major findings between 2020 and 2024

Author

Ikram, M. Arfan, Kieboom, Brenda C.T., Brouwer, Willem Pieter, Brusselle, Guy, Chaker, Layal, Ghanbari, Mohsen, Goedegebure, André, Ikram, M. Kamran, Kavousi, Maryam, de Knegt, Rob J., Luik, Annemarie I., van Meurs, Joyce, Pardo, Luba M., Rivadeneira, Fernando, van Rooij, Frank J.A., Vernooij, Meike W., Voortman, Trudy, and Terzikhan, Natalie

Published
2024
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17. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Author

Sudre, Carole H., Van Wijnen, Kimberlin, Dubost, Florian, Adams, Hieab, Atkinson, David, Barkhof, Frederik, Birhanu, Mahlet A., Bron, Esther E., Camarasa, Robin, Chaturvedi, Nish, Chen, Yuan, Chen, Zihao, Chen, Shuai, Dou, Qi, Evans, Tavia, Ezhov, Ivan, Gao, Haojun, Sanguesa, Marta Girones, Gispert, Juan Domingo, Anson, Beatriz Gomez, Hughes, Alun D., Ikram, M. Arfan, Ingala, Silvia, Jaeger, H. Rolf, Kofler, Florian, Kuijf, Hugo J., Kutnar, Denis, Lee, Minho, Li, Bo, Lorenzini, Luigi, Menze, Bjoern, Molinuevo, Jose Luis, Pan, Yiwei, Puybareau, Elodie, Rehwald, Rafael, Su, Ruisheng, Shi, Pengcheng, Smith, Lorna, Tillin, Therese, Tochon, Guillaume, Urien, Helene, van der Velden, Bas H. M., van der Velpen, Isabelle F., Wiestler, Benedikt, Wolters, Frank J., Yilmaz, Pinar, de Groot, Marius, Vernooij, Meike W., and de Bruijne, Marleen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Published
2022

18. Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Author

Yang, Yunju, Knol, Maria J, Wang, Ruiqi, Mishra, Aniket, Liu, Dan, Luciano, Michelle, Teumer, Alexander, Armstrong, Nicola, Bis, Joshua C, Jhun, Min A, Li, Shuo, Adams, Hieab HH, Aziz, Nasir Ahmad, Bastin, Mark E, Bourgey, Mathieu, Brody, Jennifer A, Frenzel, Stefan, Gottesman, Rebecca F, Hosten, Norbert, Hou, Lifang, Kardia, Sharon LR, Lohner, Valerie, Marquis, Pascale, Maniega, Susana Muñoz, Satizabal, Claudia L, Sorond, Farzaneh A, Valdés Hernández, Maria C, van Duijn, Cornelia M, Vernooij, Meike W, Wittfeld, Katharina, Yang, Qiong, Zhao, Wei, Boerwinkle, Eric, Levy, Daniel, Deary, Ian J, Jiang, Jiyang, Mather, Karen A, Mosley, Thomas H, Psaty, Bruce M, Sachdev, Perminder S, Smith, Jennifer A, Sotoodehnia, Nona, DeCarli, Charles S, Breteler, Monique MB, Ikram, M Arfan, Grabe, Hans J, Wardlaw, Joanna, Longstreth, WT, Launer, Lenore J, Seshadri, Sudha, Debette, Stephanie, and Fornage, Myriam

Subjects
Health Sciences, Genetics, Aging, Brain Disorders, Neurosciences, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Middle Aged, Humans, Aged, White Matter, Genome-Wide Association Study, Brain, DNA Methylation, Magnetic Resonance Imaging, Epigenesis, Genetic, Protein-Arginine N-Methyltransferases, Repressor Proteins, epigenome-wide association study, white matter hyperintensities, cerebral small vessel disease, integrative cross-omics analysis, blood-brain barrier dysfunction, blood–brain barrier dysfunction, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

Published
2023

19. Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts

Author

Coenen, Mirthe, Biessels, Geert Jan, DeCarli, Charles, Fletcher, Evan F, Maillard, Pauline M, Initiative, Alzheimer's Disease Neuroimaging, Barkhof, Frederik, Barnes, Josephine, Benke, Thomas, Boomsma, Jooske MF, Chen, Christopher PLH, Dal-Bianco, Peter, Dewenter, Anna, Duering, Marco, Enzinger, Christian, Ewers, Michael, Exalto, Lieza G, Franzmeier, Nicolai, Groeneveld, Onno, Hilal, Saima, Hofer, Edith, Koek, Huiberdina L, Maier, Andrea B, McCreary, Cheryl R, Papma, Janne M, Paterson, Ross W, Pijnenburg, Yolande AL, Rubinski, Anna, Schmidt, Reinhold, Schott, Jonathan M, Slattery, Catherine F, Smith, Eric E, Sudre, Carole H, Steketee, Rebecca ME, van den Berg, Esther, van der Flier, Wiesje M, Venketasubramanian, Narayanaswamy, Vernooij, Meike W, Wolters, Frank J, Xin, Xu, Biesbroek, J Matthijs, and Kuijf, Hugo J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Alzheimer's Disease Related Dementias (ADRD), Cerebrovascular, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Machine Learning and Artificial Intelligence, Clinical Research, Acquired Cognitive Impairment, Vascular Cognitive Impairment/Dementia, Aging, Neurodegenerative, Networking and Information Technology R&D (NITRD), Neurosciences, Brain Disorders, Dementia, Neurological, Humans, White Matter, Brain, Magnetic Resonance Imaging, Neuroimaging, Cognitive Dysfunction, Multicenter Studies as Topic, White matter hyperintensities, Brain MRI, Distribution frequencies, Lesion location, Alzheimer's Disease Neuroimaging Initiative, Biological psychology, Clinical and health psychology
Abstract

IntroductionThe spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns.MethodsIndividual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented.ResultsWMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected.DiscussionAggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

Published
2023

23. Gene-mapping study of extremes of cerebral small vessel disease reveals TRIM47 as a strong candidate

Author

Mishra, Aniket, Duplaà, Cécile, Vojinovic, Dina, Suzuki, Hideaki, Sargurupremraj, Muralidharan, Zilhão, Nuno R, Li, Shuo, Bartz, Traci M, Jian, Xueqiu, Zhao, Wei, Hofer, Edith, Wittfeld, Katharina, Harris, Sarah E, van der Auwera-Palitschka, Sandra, Luciano, Michelle, Bis, Joshua C, Adams, Hieab HH, Satizabal, Claudia L, Gottesman, Rebecca F, Gampawar, Piyush G, Bülow, Robin, Weiss, Stefan, Yu, Miao, Bastin, Mark E, Lopez, Oscar L, Vernooij, Meike W, Beiser, Alexa S, Völker, Uwe, Kacprowski, Tim, Soumare, Aicha, Smith, Jennifer A, Knopman, David S, Morris, Zoe, Zhu, Yicheng, Rotter, Jerome I, Dufouil, Carole, Hernández, Maria Valdés, Maniega, Susana Muñoz, Lathrop, Mark, Boerwinkle, Erik, Schmidt, Reinhold, Ihara, Masafumi, Mazoyer, Bernard, Yang, Qiong, Joutel, Anne, Tournier-Lasserve, Elizabeth, Launer, Lenore J, Deary, Ian J, Mosley, Thomas H, Amouyel, Philippe, DeCarli, Charles S, Psaty, Bruce M, Tzourio, Christophe, Kardia, Sharon LR, Grabe, Hans J, Teumer, Alexander, van Duijn, Cornelia M, Schmidt, Helena, Wardlaw, Joanna M, Ikram, M Arfan, Fornage, Myriam, Gudnason, Vilmundur, Seshadri, Sudha, Matthews, Paul M, Longstreth, William T, Couffinhal, Thierry, and Debette, Stephanie

Subjects
Epidemiology, Health Sciences, Acquired Cognitive Impairment, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Stroke, Neurosciences, Human Genome, Biotechnology, Clinical Research, Dementia, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Cardiovascular, Animals, Brain Ischemia, Cerebral Small Vessel Diseases, Endothelial Cells, Genome-Wide Association Study, Mice, cerebral small vessel disease, endothelial cells, GWAS, TRIM47, whole-exome association study, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Cerebral small vessel disease is a leading cause of stroke and a major contributor to cognitive decline and dementia, but our understanding of specific genes underlying the cause of sporadic cerebral small vessel disease is limited. We report a genome-wide association study and a whole-exome association study on a composite extreme phenotype of cerebral small vessel disease derived from its most common MRI features: white matter hyperintensities and lacunes. Seventeen population-based cohorts of older persons with MRI measurements and genome-wide genotyping (n = 41 326), whole-exome sequencing (n = 15 965), or exome chip (n = 5249) data contributed 13 776 and 7079 extreme small vessel disease samples for the genome-wide association study and whole-exome association study, respectively. The genome-wide association study identified significant association of common variants in 11 loci with extreme small vessel disease, of which the chr12q24.11 locus was not previously reported to be associated with any MRI marker of cerebral small vessel disease. The whole-exome association study identified significant associations of extreme small vessel disease with common variants in the 5' UTR region of EFEMP1 (chr2p16.1) and one probably damaging common missense variant in TRIM47 (chr17q25.1). Mendelian randomization supports the causal association of extensive small vessel disease severity with increased risk of stroke and Alzheimer's disease. Combined evidence from summary-based Mendelian randomization studies and profiling of human loss-of-function allele carriers showed an inverse relation between TRIM47 expression in the brain and blood vessels and extensive small vessel disease severity. We observed significant enrichment of Trim47 in isolated brain vessel preparations compared to total brain fraction in mice, in line with the literature showing Trim47 enrichment in brain endothelial cells at single cell level. Functional evaluation of TRIM47 by small interfering RNAs-mediated knockdown in human brain endothelial cells showed increased endothelial permeability, an important hallmark of cerebral small vessel disease pathology. Overall, our comprehensive gene-mapping study and preliminary functional evaluation suggests a putative role of TRIM47 in the pathophysiology of cerebral small vessel disease, making it an important candidate for extensive in vivo explorations and future translational work.

Published
2022

27. Circulating Metabolome and White Matter Hyperintensities in Women and Men

Author

Sliz, Eeva, Shin, Jean, Ahmad, Shahzad, Williams, Dylan M, Frenzel, Stefan, Gauß, Friederike, Harris, Sarah E, Henning, Ann-Kristin, Hernandez, Maria Valdes, Hu, Yi-Han, Jiménez, Beatriz, Sargurupremraj, Muralidharan, Sudre, Carole, Wang, Ruiqi, Wittfeld, Katharina, Yang, Qiong, Wardlaw, Joanna M, Völzke, Henry, Vernooij, Meike W, Schott, Jonathan M, Richards, Marcus, Proitsi, Petroula, Nauck, Matthias, Lewis, Matthew R, Launer, Lenore, Hosten, Norbert, Grabe, Hans J, Ghanbari, Mohsen, Deary, Ian J, Cox, Simon R, Chaturvedi, Nishi, Barnes, Josephine, Rotter, Jerome I, Debette, Stephanie, Ikram, M Arfan, Fornage, Myriam, Paus, Tomas, Seshadri, Sudha, Pausova, Zdenka, and Group, for the NeuroCHARGE Working

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Prevention, Clinical Research, Aging, Aged, Brain, Diabetes Mellitus, Type 2, Female, Humans, Magnetic Resonance Imaging, Male, Metabolome, Middle Aged, White Matter, NeuroCHARGE Working Group, brain, glucuronic acid, hydroxyphenylpyruvate, lipid ratios, lipidomics, lipids, lysophosphatidylcholines, metabolomics, sphingomyelins, white matter, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundWhite matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.MethodsWe studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)

Published
2022

28. Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT

Author

Bortsova, Gerda, Bos, Daniel, Dubost, Florian, Vernooij, Meike W., Ikram, M. Kamran, van Tulder, Gijs, and de Bruijne, Marleen

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Purpose: To evaluate a fully-automated deep-learning-based method for assessment of intracranial carotid artery calcification (ICAC). Methods: Two observers manually delineated ICAC in non-contrast CT scans of 2,319 participants (mean age 69 (SD 7) years; 1154 women) of the Rotterdam Study, prospectively collected between 2003 and 2006. These data were used to retrospectively develop and validate a deep-learning-based method for automated ICAC delineation and volume measurement. To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with respect to 1) the agreement with an independent observer's assessment (available in a random subset of 47 scans); 2) the accuracy in delineating ICAC as judged via blinded visual comparison by an expert; 3) the association with first stroke incidence from the scan date until 2012. All method performance metrics were computed using 10-fold cross-validation. Results: The automated delineation of ICAC reached sensitivity of 83.8% and positive predictive value (PPV) of 88%. The intraclass correlation between automatic and manual ICAC volume measures was 0.98 (95% CI: 0.97, 0.98; computed in the entire dataset). Measured between the assessments of independent observers, sensitivity was 73.9%, PPV was 89.5%, and intraclass correlation was 0.91 (95% CI: 0.84, 0.95; computed in the 47-scan subset). In the blinded visual comparisons, automatic delineations were more accurate than manual ones (p-value = 0.01). The association of ICAC volume with incident stroke was similarly strong for both automated (hazard ratio, 1.38 (95% CI: 1.12, 1.75) and manually measured volumes (hazard ratio, 1.48 (95% CI: 1.20, 1.87)). Conclusions: The developed model was capable of automated segmentation and volume quantification of ICAC with accuracy comparable to human experts., Comment: Accepted for publication in Radiology: Artificial Intelligence (https://pubs.rsna.org/journal/ai), which is published by the Radiological Society of North America (RSNA)

Published
2021
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30. Associations of Pulmonary Function with MRI Brain Volumes: A Coordinated Multi-Study Analysis

Author

Frenzel, Stefan, Bis, Joshua C, Gudmundsson, Elias F, O’Donnell, Adrienne, Simino, Jeannette, Yaqub, Amber, Bartz, Traci M, Brusselle, Guy GO, Bülow, Robin, DeCarli, Charles S, Ewert, Ralf, Gharib, Sina A, Ghosh, Saptaparni, Gireud-Goss, Monica, Gottesman, Rebecca F, Ikram, M Arfan, Knopman, David S, Launer, Lenore J, London, Stephanie J, Longstreth, WT, Lopez, Oscar L, van Lent, Debora Melo, O’Connor, George, Satizabal, Claudia L, Shrestha, Srishti, Sigurdsson, Sigurdur, Stubbe, Beate, Talluri, Rajesh, Vasan, Ramachandran S, Vernooij, Meike W, Völzke, Henry, Wiggins, Kerri L, Yu, Bing, Beiser, Alexa S, Gudnason, Vilmundur, Mosley, Thomas, Psaty, Bruce M, Wolters, Frank J, Grabe, Hans J, and Seshadri, Sudha

Subjects
Brain Disorders, Lung, Neurosciences, Clinical Research, Aging, Humans, Aged, Forced Expiratory Volume, Cross-Sectional Studies, Magnetic Resonance Imaging, Brain, Dementia, epidemiology, magnetic resonance imaging, respiratory function tests, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

BackgroundPrevious studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent.ObjectiveTo study the cross-sectional associations of pulmonary function with structural brain variables.MethodsData from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses.ResultsFEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume.ConclusionIn cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.

Published
2022

31. Loneliness, Not Social Support, Is Associated with Cognitive Decline and Dementia Across Two Longitudinal Population-Based Cohorts

Author

Freak-Poli, Rosanne, Wagemaker, Nina, Wang, Rui, Lysen, Thom S, Ikram, M Arfan, Vernooij, Meike W, Dintica, Christina S, Vernooij-Dassen, Myrra, Melis, Rene JF, Laukka, Erika J, Fratiglioni, Laura, Xu, Weili, and Tiemeier, Henning

Subjects
Acquired Cognitive Impairment, Aging, Alzheimer's Disease, Brain Disorders, Prevention, Behavioral and Social Science, Neurodegenerative, Mental Health, Depression, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Clinical Research, Neurosciences, Mental health, Neurological, Good Health and Well Being, Aged, Aged, 80 and over, Cognitive Dysfunction, Female, Humans, Loneliness, Longitudinal Studies, Male, Mental Status and Dementia Tests, Middle Aged, Netherlands, Prevalence, Proportional Hazards Models, Risk Factors, Social Isolation, Sweden, cognition, dementia, depressive symptoms, interpersonal relations, loneliness, social support, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

BackgroundPoor social health is likely associated with cognitive decline and risk of dementia; however, studies show inconsistent results. Additionally, few studies separate social health components or control for mental health.ObjectiveTo investigate whether loneliness and social support are independently associated with cognitive decline and risk of dementia, and whether depressive symptoms confound the association.MethodsWe included 4,514 participants from the population-based Rotterdam Study (RS; aged 71±7SD years) followed up to 14 years (median 10.8, interquartile range 7.4-11.6), and 2,112 participants from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; aged 72±10SD years) followed up to 10 years (mean 5.9±1.6SD). At baseline, participants were free of major depression and scored on the Mini-Mental State Examination (MMSE) ≥26 for RS and ≥25 for SNAC-K. We investigated loneliness, perceived social support, and structural social support (specifically marital status and number of children). In both cohorts, dementia was diagnosed and cognitive function was repeatedly assessed with MMSE and a global cognitive factor (g-factor).ResultsLoneliness was prospectively associated with a decline in the MMSE in both cohorts. Consistently, persons who were lonely had an increased risk of developing dementia (RS: HR 1.34, 95%CI 1.08-1.67; SNAC-K: HR 2.16, 95%CI 1.12-4.17). Adjustment for depressive symptoms and exclusion of the first 5 years of follow-up did not alter results. Neither perceived or structural social support was associated with cognitive decline or dementia risk.ConclusionLoneliness, not social support, predicted cognitive decline and incident dementia independently of depressive symptoms.

Published
2022

32. Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration

Author

Li, Bo, Niessen, Wiro J., Klein, Stefan, de Groot, Marius, Ikram, M. Arfan, Vernooij, Meike W., and Bron, Esther E.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility comparing with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time., Comment: To appear in NeuroImage

Published
2020
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33. Cortical superficial siderosis in the general population: The Framingham Heart and Rotterdam studies

Author

Shoamanesh, Ashkan, Akoudad, Saloua, Himali, Jayandra J, Beiser, Alexa S, DeCarli, Charles, Seshadri, Sudha, Ikram, M Arfan, Romero, Jose R, and Vernooij, Meike W

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Neurosciences, Dementia, Clinical Research, Acquired Cognitive Impairment, Aging, Stroke, Brain Disorders, Neurological, Aged, Cerebral Amyloid Angiopathy, Cerebral Hemorrhage, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Siderosis, Brain microbleeds, cerebral amyloid angiopathy, cerebral hemorrhage, community, cortical superficial siderosis, stroke facilities, Neurology & Neurosurgery, Clinical sciences, Allied health and rehabilitation science
Abstract

ObjectiveWe aimed to characterize cortical superficial siderosis, its determinants and sequel, in community-dwelling older adults.MethodsThe sample consisted of Framingham (n = 1724; 2000-2009) and Rotterdam (n = 4325; 2005-2013) study participants who underwent brain MRI. In pooled individual-level analysis, we compared baseline characteristics in patients with cortical superficial siderosis to two reference groups: (i) persons without hemorrhagic MRI markers of cerebral amyloid angiopathy (no cortical superficial siderosis and no microbleeds) and (ii) those with presumed cerebral amyloid angiopathy based on the presence of strictly lobar microbleeds but without cortical superficial siderosis.ResultsAmong a total of 6049 participants, 4846 did not have any microbleeds or cortical superficial siderosis (80%), 401 had deep/mixed microbleeds (6.6%), 776 had strictly lobar microbleeds without cortical superficial siderosis (12.8%) and 26 had cortical superficial siderosis with/without microbleeds (0.43%). In comparison to participants without microbleeds or cortical superficial siderosis and to those with strictly lobar microbleeds but without cortical superficial siderosis, participants with cortical superficial siderosis were older (OR 1.09 per year, 95% CI 1.05, 1.14; p 999.99; p = 0.006). During a mean follow-up of 5.6 years, 42.4% participants with cortical superficial siderosis had a stroke (five intracerebral hemorrhage, two ischemic strokes and four undetermined strokes), 19.2% had transient neurological deficits and 3.8% developed incident dementia.ConclusionOur study adds supporting evidence to the association between cortical superficial siderosis and cerebral amyloid angiopathy within the general population. Community-dwelling persons with cortical superficial siderosis may be at high risk for intracerebral hemorrhage and future neurological events.

Published
2021

34. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Author

Sudre, Carole H., Van Wijnen, Kimberlin, Dubost, Florian, Adams, Hieab, Atkinson, David, Barkhof, Frederik, Birhanu, Mahlet A., Bron, Esther E., Camarasa, Robin, Chaturvedi, Nish, Chen, Yuan, Chen, Zihao, Chen, Shuai, Dou, Qi, Evans, Tavia, Ezhov, Ivan, Gao, Haojun, Girones Sanguesa, Marta, Gispert, Juan Domingo, Gomez Anson, Beatriz, Hughes, Alun D., Ikram, M. Arfan, Ingala, Silvia, Jaeger, H. Rolf, Kofler, Florian, Kuijf, Hugo J., Kutnar, Denis, Lee, Minho, Li, Bo, Lorenzini, Luigi, Menze, Bjoern, Molinuevo, Jose Luis, Pan, Yiwei, Puybareau, Elodie, Rehwald, Rafael, Su, Ruisheng, Shi, Pengcheng, Smith, Lorna, Tillin, Therese, Tochon, Guillaume, Urien, Hélène, van der Velden, Bas H.M., van der Velpen, Isabelle F., Wiestler, Benedikt, Wolters, Frank J., Yilmaz, Pinar, de Groot, Marius, Vernooij, Meike W., and de Bruijne, Marleen

Published
2024
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35. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Author

Duperron, Marie-Gabrielle, Knol, Maria J., Le Grand, Quentin, Evans, Tavia E., Mishra, Aniket, Tsuchida, Ami, Roshchupkin, Gennady, Konuma, Takahiro, Trégouët, David-Alexandre, Romero, Jose Rafael, Frenzel, Stefan, Luciano, Michelle, Hofer, Edith, Bourgey, Mathieu, Dueker, Nicole D., Delgado, Pilar, Hilal, Saima, Tankard, Rick M., Dubost, Florian, Shin, Jean, Saba, Yasaman, Armstrong, Nicola J., Bordes, Constance, Bastin, Mark E., Beiser, Alexa, Brodaty, Henry, Bülow, Robin, Carrera, Caty, Chen, Christopher, Cheng, Ching-Yu, Deary, Ian J., Gampawar, Piyush G., Himali, Jayandra J., Jiang, Jiyang, Kawaguchi, Takahisa, Li, Shuo, Macalli, Melissa, Marquis, Pascale, Morris, Zoe, Muñoz Maniega, Susana, Miyamoto, Susumu, Okawa, Masakazu, Paradise, Matthew, Parva, Pedram, Rundek, Tatjana, Sargurupremraj, Muralidharan, Schilling, Sabrina, Setoh, Kazuya, Soukarieh, Omar, Tabara, Yasuharu, Teumer, Alexander, Thalamuthu, Anbupalam, Trollor, Julian N., Valdés Hernández, Maria C., Vernooij, Meike W., Völker, Uwe, Wittfeld, Katharina, Wong, Tien Yin, Wright, Margaret J., Zhang, Junyi, Zhao, Wanting, Zhu, Yi-Cheng, Schmidt, Helena, Sachdev, Perminder S., Wen, Wei, Yoshida, Kazumichi, Joutel, Anne, Satizabal, Claudia L., Sacco, Ralph L., Bourque, Guillaume, Lathrop, Mark, Paus, Tomas, Fernandez-Cadenas, Israel, Yang, Qiong, Mazoyer, Bernard, Boutinaud, Philippe, Okada, Yukinori, Grabe, Hans J., Mather, Karen A., Schmidt, Reinhold, Joliot, Marc, Ikram, M. Arfan, Matsuda, Fumihiko, Tzourio, Christophe, Wardlaw, Joanna M., Seshadri, Sudha, Adams, Hieab H. H., and Debette, Stéphanie

Published
2023
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36. Diagnostic imaging strategies of acute intracerebral hemorrhage in European academic hospitals—a decision-making analysis

Author

Putora, Paul Martin, Almeida, Gonçalo G., Wildermuth, Simon, Weber, Johannes, Dietrich, Tobias, Vernooij, Meike W., van Doormaal, Pieter Jan, Smagge, Lucas, Zeleňák, Kamil, Krainik, Alexandre, Bonneville, Fabrice, van Den Hauwe, Luc, Möhlenbruch, Markus, Bruno, Federico, Ramgren, Birgitta, Ramos-González, Ana, Schellhorn, Till, Waelti, Stephan, and Fischer, Tim

Published
2023
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37. Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI

Author

Li, Bo, Niessen, Wiro J., Klein, Stefan, Ikram, M. Arfan, Vernooij, Meike W., and Bron, Esther E.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers., Comment: SPIE Medical Imaging 2021 (oral)

Published
2020

38. Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Author

Li, Bo, de Groot, Marius, Steketee, Rebecca M. E., Meijboom, Rozanna, Smits, Marion, Vernooij, Meike W., Ikram, M. Arfan, Liu, Jiren, Niessen, Wiro J., and Bron, Esther E.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice., Comment: Preprint to be published in NeuroImage

Published
2020

39. When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans

Author

Werner, Oliver, van Wijnen, Kimberlin M. H., Niessen, Wiro J., de Groot, Marius, Vernooij, Meike W., Dubost, Florian, and de Bruijne, Marleen

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

To measure the volume of specific image structures, a typical approach is to first segment those structures using a neural network trained on voxel-wise (strong) labels and subsequently compute the volume from the segmentation. A more straightforward approach would be to predict the volume directly using a neural network based regression approach, trained on image-level (weak) labels indicating volume. In this article, we compared networks optimized with weak and strong labels, and study their ability to generalize to other datasets. We experimented with white matter hyperintensity (WMH) volume prediction in brain MRI scans. Neural networks were trained on a large local dataset and their performance was evaluated on four independent public datasets. We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH. The attention maps of networks trained with weak labels did not seem to delineate WMHs, but highlighted instead areas with smooth contours around or near WMHs. By correcting for possible confounders we showed that networks trained on weak labels may have learnt other meaningful features that are more suited to generalization to unseen data. Our results suggest that for imaging biomarkers that can be derived from segmentations, training networks to predict the biomarker directly may provide more robust results than solving an intermediate segmentation step., Comment: 11 pages, 3 figures

Published
2020

41. Association of common genetic variants with brain microbleeds: A Genome-wide Association Study

Author

Knol, Maria J, Lu, Dongwei, Traylor, Matthew, Adams, Hieab HH, Romero, José Rafael J, Smith, Albert V, Fornage, Myriam, Hofer, Edith, Liu, Junfeng, Hostettler, Isabel C, Luciano, Michelle, Trompet, Stella, Giese, Anne-Katrin, Hilal, Saima, van den Akker, Erik B, Vojinovic, Dina, Li, Shuo, Sigurdsson, Sigurdur, van der Lee, Sven J, Jack, Clifford R, Wilson, Duncan, Yilmaz, Pinar, Satizabal, Claudia L, Liewald, David CM, van der Grond, Jeroen, Chen, Christopher, Saba, Yasaman, van der Lugt, Aad, Bastin, Mark E, Windham, B Gwen, Cheng, Ching Yu, Pirpamer, Lukas, Kantarci, Kejal, Himali, Jayandra J, Yang, Qiong, Morris, Zoe, Beiser, Alexa S, Tozer, Daniel J, Vernooij, Meike W, Amin, Najaf, Beekman, Marian, Koh, Jia Yu, Stott, David J, Houlden, Henry, Schmidt, Reinhold, Gottesman, Rebecca F, MacKinnon, Andrew D, DeCarli, Charles, Gudnason, Vilmundur, Deary, Ian J, van Duijn, Cornelia M, Slagboom, P Eline, Wong, Tien Yin, Rost, Natalia S, Jukema, J Wouter, Mosley, Thomas H, Werring, David J, Schmidt, Helena, Wardlaw, Joanna M, Ikram, M Arfan, Seshadri, Sudha, Launer, Lenore J, Markus, Hugh S, and Initiative, for the Alzheimer’s Disease Neuroimaging

Subjects
Stroke, Human Genome, Neurosciences, Brain Disorders, Genetics, Prevention, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Alleles, Apolipoprotein E2, Apolipoprotein E4, Apolipoproteins E, Case-Control Studies, Cerebral Hemorrhage, Cerebral Small Vessel Diseases, Cohort Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Polymorphism, Single Nucleotide, Risk, White Matter, Alzheimer's Disease Neuroimaging Initiative, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 × 10-10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 × 10-6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.

Published
2020

42. Cerebral small vessel disease genomics and its implications across the lifespan.

Author

Sargurupremraj, Muralidharan, Suzuki, Hideaki, Jian, Xueqiu, Sarnowski, Chloé, Evans, Tavia E, Bis, Joshua C, Eiriksdottir, Gudny, Sakaue, Saori, Terzikhan, Natalie, Habes, Mohamad, Zhao, Wei, Armstrong, Nicola J, Hofer, Edith, Yanek, Lisa R, Hagenaars, Saskia P, Kumar, Rajan B, van den Akker, Erik B, McWhirter, Rebekah E, Trompet, Stella, Mishra, Aniket, Saba, Yasaman, Satizabal, Claudia L, Beaudet, Gregory, Petit, Laurent, Tsuchida, Ami, Zago, Laure, Schilling, Sabrina, Sigurdsson, Sigurdur, Gottesman, Rebecca F, Lewis, Cora E, Aggarwal, Neelum T, Lopez, Oscar L, Smith, Jennifer A, Valdés Hernández, Maria C, van der Grond, Jeroen, Wright, Margaret J, Knol, Maria J, Dörr, Marcus, Thomson, Russell J, Bordes, Constance, Le Grand, Quentin, Duperron, Marie-Gabrielle, Smith, Albert V, Knopman, David S, Schreiner, Pamela J, Evans, Denis A, Rotter, Jerome I, Beiser, Alexa S, Maniega, Susana Muñoz, Beekman, Marian, Trollor, Julian, Stott, David J, Vernooij, Meike W, Wittfeld, Katharina, Niessen, Wiro J, Soumaré, Aicha, Boerwinkle, Eric, Sidney, Stephen, Turner, Stephen T, Davies, Gail, Thalamuthu, Anbupalam, Völker, Uwe, van Buchem, Mark A, Bryan, R Nick, Dupuis, Josée, Bastin, Mark E, Ames, David, Teumer, Alexander, Amouyel, Philippe, Kwok, John B, Bülow, Robin, Deary, Ian J, Schofield, Peter R, Brodaty, Henry, Jiang, Jiyang, Tabara, Yasuharu, Setoh, Kazuya, Miyamoto, Susumu, Yoshida, Kazumichi, Nagata, Manabu, Kamatani, Yoichiro, Matsuda, Fumihiko, Psaty, Bruce M, Bennett, David A, De Jager, Philip L, Mosley, Thomas H, Sachdev, Perminder S, Schmidt, Reinhold, Warren, Helen R, Evangelou, Evangelos, Trégouët, David-Alexandre, International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium (IHGC), Ikram, Mohammad A, Wen, Wei, DeCarli, Charles, Srikanth, Velandai K, Jukema, J Wouter, Slagboom, Eline P, and Kardia, Sharon LR

Subjects
International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium, Humans, Alzheimer Disease, Hypertension, Medical History Taking, Risk Assessment, Risk Factors, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Stroke, Genome-Wide Association Study, Young Adult, Diffusion Tensor Imaging, Genetic Loci, Mendelian Randomization Analysis, Cerebral Small Vessel Diseases, White Matter, and over
Abstract

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Published
2020

45. Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset

Author

Li, Bo, de Groot, Marius, Vernooij, Meike, Ikram, Arfan, Niessen, Wiro, and Bron, Esther

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure., Comment: Machine Learning in Medical Imaging (MLMI), 2018

Published
2019

46. A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes

Author

Li, Bo, Niessen, Wiro, Klein, Stefan, de Groot, Marius, Ikram, Arfan, Vernooij, Meike, and Bron, Esther

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time., Comment: MICCAI 2019 (oral presentation)

Published
2019

47. Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network

Author

van Wijnen, Kimberlin M. H., Dubost, Florian, Yilmaz, Pinar, Ikram, M. Arfan, Niessen, Wiro J., Adams, Hieab, Vernooij, Meike W., and de Bruijne, Marleen

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance., Comment: MICCAI 2019

Published
2019

48. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation

Author

Dubost, Florian, de Bruijne, Marleen, Nardin, Marco, Dalca, Adrian V., Donahue, Kathleen L., Giese, Anne-Katrin, Etherton, Mark R., Wu, Ona, de Groot, Marius, Niessen, Wiro, Vernooij, Meike, Rost, Natalia S., and Schirmer, Markus D.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas' ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations and then selecting the registration that yielded the highest ventricle overlap. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.

Published
2019

49. Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks

Author

Dubost, Florian, Adams, Hieab, Yilmaz, Pinar, Bortsova, Gerda, van Tulder, Gijs, Ikram, M. Arfan, Niessen, Wiro, Vernooij, Meike, and de Bruijne, Marleen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions. These attention maps are computed using the last feature maps of a segmentation network optimized only with global image-level labels. The proposed method can generate attention maps at full input resolution without need for interpolation during preprocessing, which allows small lesions to appear in attention maps. For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective. This regression objective optimizes the number of occurrences of the target object in an image, e.g. the number of brain lesions in a scan, or the number of digits in an image. We study the behavior of the proposed method in MNIST-based detection datasets, and evaluate it for the challenging detection of enlarged perivascular spaces - a type of brain lesion - in a dataset of 2202 3D scans with point-wise annotations in the center of all lesions in four brain regions. In the brain dataset, the weakly supervised detection methods come close to the human intrarater agreement in each region. The proposed method reaches the best area under the curve in two out of four regions, and has the lowest number of false positive detections in all regions, while its average sensitivity over all regions is similar to that of the other best methods. The proposed method can facilitate epidemiological and clinical studies of enlarged perivascular spaces., Comment: New formatting. A few changes in introduction, discussion and conclusion

Published
2019

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