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1. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, Rachel M, Klein, Marieke, Kremen, William S, Guimaraes, Joao P O F T, Homuth, Georg, Hottenga, Jouke-Jan, Knol, Maria J, Kwok, John B J, Le Hellard, Stephanie, Mather, Karen A, Milaneschi, Yuri, Morris, Derek W, Nöthen, Markus M., Panizzon, Matthew S, Papiol, Sergi, Rietschel, Marcella, Santoro, Marcos L, Steen, Vidar M, Stein, Jason L, Streit, Fabian, Tankard, Rick M, Teumer, Alexander, van 't Ent, Dennis, van der Meer, Dennis, Olde Loohuis, Loes M, van Eijk, Kristel R, Vassos, Evangelos, Vázquez-Bourgon, Javier, Witt, Stephanie H, Consortium, IMAGEN, Adams, Hieab H H, Agartz, Ingrid, Ames, David, Amunts, Katrin, Andreassen, Ole A, Whelan, Christopher D, Arango, Celso, Banaschewski, Tobias, Baune, Bernhard T, Belangero, Sintia I, Bokde, Arun L W, Boomsma, Dorret I, Bressan, Rodrigo A, Brodaty, Henry, Buitelaar, Jan K, Cahn, Wiepke, Aghajani, Moji, Caspers, Svenja, Cichon, Sven, Crespo-Facorro, Benedicto, Cox, Simon R, Dannlowski, Udo, Elvsåshagen, Torbjørn, Espeseth, Thomas, Falkai, Peter, Fisher, Simon E, Flor, Herta, Alloza, Clara, Fullerton, Janice M, Garavan, Hugh, Gowland, Penny A, Grabe, Hans, Hahn, Tim, Heinz, Andreas, Hillegers, Manon, Hoare, Jacqueline, Hoekstra, Pieter J, Ikram, Mohammad A, Alnæs, Dag, Jackowski, Andrea P, Jansen, Andreas, Jönsson, Erik G, Kahn, Rene S, Kircher, Tilo, Korgaonkar, Mayuresh S, Krug, Axel, Lemaitre, Herve, Malt, Ulrik F, Martinot, Jean-Luc, Artiges, Eric, McDonald, Colm, Mitchell, Philip B, Muetzel, Ryan L, Murray, Robin M, Nees, Frauke, Nenadić, Igor, Oosterlaan, Jaap, Ophoff, Roel A, Pan, Pedro M, Penninx, Brenda W J H, Ayesa-Arriola, Rosa, Poustka, Luise, Sachdev, Perminder S, Salum, Giovanni A, Schofield, Peter R, Schumann, Gunter, Shaw, Philip, Sim, Kang, Smolka, Michael N, Stein, Dan J, Trollor, Julian N, Barker, Gareth J, van den Berg, Leonard H, Veldink, Jan H, Walter, Henrik, Westlye, Lars T, Whelan, Robert, White, Tonya, Wright, Margaret J, Medland, Sarah E, Franke, Barbara, Thompson, Paul M, Grasby, Katrina L, Bastin, Mark E, Hulshoff Pol, Hilleke E, Brühl, Rüdiger, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Millenet, Sabina, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth E L, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Schnack, Hugo G, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja M C, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Jahanshad, Neda, Grigis, Antoine, Grotegerd, Dominik, Han, Laura K M, Harris, Mathew, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J., Hohmann, Sarah, Ittermann, Bernd, Teeuw, Jalmar, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn G J C, Lenroot, Rhoshel K, Malchow, Berend, Thomopoulos, Sophia I, Mandl, René C W, Medel, Vicente, Meinert, Susanne, Morgan, Catherine A, Mühleisen, Thomas W, Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J, Paillère Martinot, Marie-Laure, Sprooten, Emma, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Franz, Carol E, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje E M, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Gogtay, Nitin, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, Gonzalez-Peñas, Javier, Child and Adolescent Psychiatry / Psychology, Erasmus MC other, Anesthesiology, Epidemiology, Radiology & Nuclear Medicine, Clinical Genetics, Psychiatry, the IMAGEN Consortium, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Human genetics, Amsterdam Neuroscience - Complex Trait Genetics, Pediatric surgery, APH - Aging & Later Life, APH - Mental Health, APH - Digital Health, European Commission, European Research Council, Comunidad de Madrid, Instituto de Salud Carlos III, Fundación Mutua Madrileña, Fundación Alicia Koplowitz, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry 2, RS: MHeNs - R2 - Mental Health, Psychiatrie & Neuropsychologie, Complex Trait Genetics, Cognitive Psychology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Methodology, Clinical Neuropsychology, IBBA, General Paediatrics, ARD - Amsterdam Reproduction and Development, and Paediatrics
Subjects: Neuroinformatics, EXPRESSION, Aging, SUSCEPTIBILITY LOCI, Longevity, SURFACE-AREA, INDIVIDUAL-DIFFERENCES, All institutes and research themes of the Radboud University Medical Center, SDG 3 - Good Health and Well-being, Aging/genetics, Humans, ddc:610, GENOME-WIDE ASSOCIATION, METAANALYSIS, RISK, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], General Neuroscience, 220 Statistical Imaging Neuroscience, Longevity/genetics, Brain, Magnetic Resonance Imaging, genetics [Aging], VOLUME, RELIABILITY, sense organs, CORTICAL THICKNESS, genetics [Longevity], Genome-Wide Association Study
Abstract: et al., Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging., The ENIGMA-Plasticity Working Group is part of the ENIGMA World Aging Center, funded by NIA grants R56 AG058854 and R01 AG058854. The ENIGMA Consortium core funding was supported by NIH Consortium Grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence. 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported, in part, by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 785907 (Human Brain Project SGA2; Svenja Caspers, Sven Cichon and Katrin Amunts). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A; Sven Cichon) and by the Swiss National Science Foundation (SNSF, grant 156791; Sven Cichon). The authors acknowledge grants supporting their work from the European Union’s Horizon 2020 Research and Innovation Programme (H2020/2014–2020) under grant agreements 667302 (CoCA), 728018 (Eat2beNICE), 785907 (HBP SGA2) and 772376 (EScORIAL) and the Netherlands ALS Foundation.Additional support is received from the European Community’s Seventh Framework Programme (FP7/2007—2013) under grant agreements n° 602805 (Aggressotype), n° 603016 (MATRICS), n° 602450 (IMAGEMEND) and n° 278948 (TACTICS) and from the European Community’s Horizon 2020 Programme (H2020/2014—2020) under grant agreements n° 643051 (MiND) and n° 667302 (CoCA). FP7 Ideas: the European Research Council (ERC-230374 to D.B.). This work was supported by Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012 and PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7- HEALTH-2013-2.2.1-2-603196 (Project PSYSCAN) and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 115916, Project PRISM and grant agreement 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313). The FP7 project MATRICS (603016). The research leading to these results also received support from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 278948 (TACTICS), 602805 (Aggressotype), 603016 (MATRICS) and 602450 (Imagemend) and the Innovation Medicine Initiative grants 115300 (EU-AIMS) and 777394 (AIMS-2-TRIALS).PAFIP: PAFIP was supported by the Instituto de Salud Carlos III (PI14/00639, PI14/00918 and PI17/01056) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT025KG Jebsen Stiftelsen and H2020 CoMorMent (847776).34363). European Union Marie Curie Research Training Network (MRTN-CT-2006-035987).
Published: 2022
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2. PROMISSING: Pruning Missing Values in Neural Networks

Author: Kia, Seyed Mostafa, Technology, Eindhoven, Opstal, Daniel van, Schie, Bart van, Marquand, Andre F., Pluim, Josien, Cahn, Wiepke, Schnack, Hugo G., and Department Cognitive Science and Artificial Intelligence
Subjects: stat.ME, cs.LG, cs.AI
Abstract: While data are the primary fuel for machine learning models, they often suffer from missing values, especially when collected in real-world scenarios. However, many off-the-shelf machine learning models, including artificial neural network models, are unable to handle these missing values directly. Therefore, extra data preprocessing and curation steps, such as data imputation, are inevitable before learning and prediction processes. In this study, we propose a simple and intuitive yet effective method for pruning missing values (PROMISSING) during learning and inference steps in neural networks. In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know). Our experiments on simulated data, several classification and regression benchmarks, and a multi-modal clinical dataset show that PROMISSING results in similar prediction performance compared to various imputation techniques. In addition, our experiments show models trained using PROMISSING techniques are becoming less decisive in their predictions when facing incomplete samples with many unknowns. This finding hopefully advances machine learning models from being pure predicting machines to more realistic thinkers that can also say "I do not know" when facing incomplete sources of information.
Published: 2022

3. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, Rachel M, Klein, Marieke, Grasby, Katrina L, Schnack, Hugo G, Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I, Sprooten, Emma, Franz, Carol E, Gogtay, Nitin, Kremen, William S, Panizzon, Matthew S, Olde Loohuis, Loes M, Whelan, Christopher D, Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J, Bastin, Mark E, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth EL, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja MC, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura KM, Harris, Mathew A, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J, Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn GJC, Lenroot, Rhoshel K, Malchow, Berend, Mandl, René CW, Medel, Vicente, Meinert, Susanne, Morgan, Catherine A, Mühleisen, Thomas W, Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J, Paillère Martinot, Marie-Laure, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje EM, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, and Gonzalez-Peñas, Javier
Subjects: Aging, Neurology & Neurosurgery, Prevention, Longevity, Human Genome, Neurosciences, Brain, Magnetic Resonance Imaging, Brain Disorders, Mental Health, Neurological, Genetics, Humans, Biomedical Imaging, 2.1 Biological and endogenous factors, Psychology, Mental health, Cognitive Sciences, Aetiology, IMAGEN Consortium, Genome-Wide Association Study, Biotechnology
Abstract: Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.
Published: 2022

4. PROMISSING: Pruning Missing Values in Neural Networks

Author: Seyed Mostafa Kia, Nastaran Mohammadian Rad, Daniel van Opstal, Bart van Schie, Marquand, Andre F., Josien Pluim, Wiepke Cahn, Schnack, Hugo G., Medical Image Analysis, and EAISI Health
Subjects: Methodology (stat.ME), FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Methodology, Machine Learning (cs.LG)
Abstract: While data are the primary fuel for machine learning models, they often suffer from missing values, especially when collected in real-world scenarios. However, many off-the-shelf machine learning models, including artificial neural network models, are unable to handle these missing values directly. Therefore, extra data preprocessing and curation steps, such as data imputation, are inevitable before learning and prediction processes. In this study, we propose a simple and intuitive yet effective method for pruning missing values (PROMISSING) during learning and inference steps in neural networks. In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know). Our experiments on simulated data, several classification and regression benchmarks, and a multi-modal clinical dataset show that PROMISSING results in similar prediction performance compared to various imputation techniques. In addition, our experiments show models trained using PROMISSING techniques are becoming less decisive in their predictions when facing incomplete samples with many unknowns. This finding hopefully advances machine learning models from being pure predicting machines to more realistic thinkers that can also say "I do not know" when facing incomplete sources of information.
Published: 2022
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5. Active Selection of Classification Features

Author: Kok, Thomas T., Brouwer, Rachel M., Mandl, Rene M., Schnack, Hugo G., and Krempl, Georg
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)
Abstract: Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs. Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model f: x-> y to explain/predict the relationship between an expensive-to-acquire set of variables x and a class label y. Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task's performance most when acquiring their expensive features z and including them to the primary training set. We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data., Accepted for publication at the 19th Intelligent Data Analysis Symposium, 2021. The final authenticated publication will be made available online at springer.com
Published: 2021

6. Implementation of and experimental software for active selection of classification features

Author: Kok, Thomas T., Krempl, Georg, Schnack, Hugo G., Sub Algorithmic Data Analysis, LS Comp.semantiek en kunstm.intelligent., UiL OTS LAPD, LS OZ Lexion en syntaxis, Algorithmic Data Analysis, Sub Algorithmic Data Analysis, LS Comp.semantiek en kunstm.intelligent., ILS LAPD, LS OZ Lexion en syntaxis, Algorithmic Data Analysis, and UiL OTS LAPD
Subjects: Active feature acquisition, Technology and Engineering, Active learning, business.industry, Computer science, Active learning (machine learning), Machine learning experiment evaluation framework, Context (language use), Python (programming language), Machine learning, computer.software_genre, Active selection of classification features, Software, Artificial intelligence, business, computer, Implementation, Selection (genetic algorithm), computer.programming_language
Abstract: In some machine learning applications, obtaining data on the most predictive features is costly, but other features are readily available. Recently, first active learning approaches for this Actively Selecting Classification Features problem (ASCF) have been proposed. In this paper, we introduce a Python package that provides a framework for ASCF, including implementations of a supervised and an unsupervised selection approach, as well as a framework for performing experimental evaluations. This framework has been used in recent publications in the context of neuroimaging research on mental disorders, where its usefulness has been demonstrated in a simulated study design with MRI data.
Published: 2021

7. Referee report. For: Active Acquisition for multimodal neuroimaging [version 1; referees: 1 approved with reservations]

Author: Schnack, Hugo G
Published: 2019
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8. Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: results of the ENIGMA Plasticity Working Group

Author: Brouwer, Rachel M, Panizzon, Matthew S, Glahn, David C, Hibar, Derrek P, Hua, Xue, Jahanshad, Neda, Abramovic, Lucija, De Zubicaray, Greig I, Franz, Carol E, Hansell, Narelle K, Hickie, Ian B, Koenis, Marinka M G, Martin, Nicholas G, Mather, Karen A, McMahon, Katie L, Schnack, Hugo G, Strike, Lachlan T, Swagerman, Suzanne C, Thalamuthu, Anbupalam, Wen, Wei, Gilmore, John H, Gogtay, Nitin, Kahn, René S, Sachdev, Perminder S, Wright, Margaret J, Boomsma, Dorret I, Kremen, William S, Thompson, Paul M, Hulshoff Pol, Hilleke E, Biological Psychology, APH - Mental Health, APH - Methodology, and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep
Subjects: Netherlands Twin Register (NTR), Biological Variation, Individual, Models, Genetic, Models, Neurological, Brain, Organ Size, Magnetic Resonance Imaging, Article, Quantitative Trait, Heritable, Journal Article, Humans, Twin Studies as Topic, Gene-Environment Interaction, sense organs, Longitudinal Studies, skin and connective tissue diseases
Abstract: Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h(2) ) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
Published: 2017
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9. Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis : Applying machine learning techniques to brain imaging data

Author: de Wit, Sanne, Ziermans, Tim B., Nieuwenhuis, M., Schothorst, Patricia F., van Engeland, Herman, Kahn, René S., Durston, Sarah, and Schnack, Hugo G.
Subjects: Ultra-high risk, Radiological and Ultrasound Technology, Neurology, Radiology Nuclear Medicine and imaging, Clinical Neurology, Journal Article, Brain imaging, Anatomy, Prediction, Psychosis, Machine-learning, Outcome
Abstract: An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on to experience remission of their symptoms and function well. The aim of this study was to investigate the possibility of using structural MRI measures collected in UHR adolescents at baseline to quantitatively predict their long-term clinical outcome and level of functioning. We included 64 UHR individuals and 62 typically developing adolescents (12-18 years old at recruitment). At six-year follow-up, we determined resilience for 43 UHR individuals. Support Vector Regression analyses were performed to predict long-term functional and clinical outcome from baseline MRI measures on a continuous scale, instead of the more typical binary classification. This led to predictive correlations of baseline MR measures with level of functioning, and negative and disorganization symptoms. The highest correlation (r=0.42) was found between baseline subcortical volumes and long-term level of functioning. In conclusion, our results show that structural MRI data can be used to quantitatively predict long-term functional and clinical outcome in UHR individuals with medium effect size, suggesting that there may be scope for predicting outcome at the individual level. Moreover, we recommend classifying individual outcome on a continuous scale, enabling the assessment of different functional and clinical scales separately without the need to set a threshold.
Published: 2017

10. Multi-center MRI prediction models : Predicting sex and illness course in first episode psychosis patients

Author: Nieuwenhuis, Mireille, Schnack, Hugo G., van Haren, Neeltje E., Kahn, René S., Lappin, Julia, Dazzan, Paola, Morgan, Craig, Reinders, Antje A., Gutierrez-Tordesillas, Diana, Roiz-Santiañez, Roberto, Crespo-Facorro, Benedicto, Schaufelberger, Maristela S., Rosa, Pedro G., Zanetti, Marcus V., Busatto, Geraldo F., McGorry, Patrick D., Velakoulis, Dennis, Pantelis, Christos, Wood, Stephen J., and Mourao-Miranda, Janaina
Subjects: Neurology, Cognitive Neuroscience, Journal Article
Abstract: Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n = 67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n = 97); University of São Paulo (Brazil) (n = 64); University of Cantabria, Santander (Spain) (n = 107); and University of Melbourne (Australia) (n = 54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7 years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either “continuous” (n = 94) or “remitting” (n = 118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p
Published: 2017

11. Multi-center MRI prediction models:Predicting sex and illness course in first episode psychosis patients

Author: Nieuwenhuis, Mireille, Schnack, Hugo G., van Haren, Neeltje E., Lappin, Julia, Morgan, Craig, Reinders, Antje A., Gutierrez-Tordesillas, Diana, Roiz-Santiañez, Roberto, Schaufelberger, Maristela S., Rosa, Pedro G., Zanetti, Marcus V., Busatto, Geraldo F., Crespo-Facorro, Benedicto, McGorry, Patrick D., Velakoulis, Dennis, Pantelis, Christos, Wood, Stephen J., Kahn, René S., Mourao-Miranda, Janaina, Dazzan, Paola, and Universidad de Cantabria
Subjects: Neurology, Cognitive Neuroscience
Abstract: Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either "continuous" (n=94) or "remitting" (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p
Published: 2017
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12. Individual prediction of long‐term outcome in adolescents at ultra‐high risk for psychosis: Applying machine learning techniques to brain imaging data

Author: de Wit, Sanne, Ziermans, Tim B., Nieuwenhuis, M., Schothorst, Patricia F., van Engeland, Herman, Kahn, René S., Durston, Sarah, and Schnack, Hugo G.
Subjects: Male, Psychiatric Status Rating Scales, Adolescent, Brain, Neuropsychological Tests, Magnetic Resonance Imaging, Machine Learning, Psychotic Disorders, ROC Curve, Predictive Value of Tests, Risk Factors, Image Processing, Computer-Assisted, Humans, Child, Research Articles
Abstract: An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on to experience remission of their symptoms and function well. The aim of this study was to investigate the possibility of using structural MRI measures collected in UHR adolescents at baseline to quantitatively predict their long-term clinical outcome and level of functioning. We included 64 UHR individuals and 62 typically developing adolescents (12-18 years old at recruitment). At six-year follow-up, we determined resilience for 43 UHR individuals. Support Vector Regression analyses were performed to predict long-term functional and clinical outcome from baseline MRI measures on a continuous scale, instead of the more typical binary classification. This led to predictive correlations of baseline MR measures with level of functioning, and negative and disorganization symptoms. The highest correlation (r = 0.42) was found between baseline subcortical volumes and long-term level of functioning. In conclusion, our results show that structural MRI data can be used to quantitatively predict long-term functional and clinical outcome in UHR individuals with medium effect size, suggesting that there may be scope for predicting outcome at the individual level. Moreover, we recommend classifying individual outcome on a continuous scale, enabling the assessment of different functional and clinical scales separately without the need to set a threshold. Hum Brain Mapp 38:704-714, 2017. © 2016 Wiley Periodicals, Inc.
Published: 2016

13. Identification of common variants associated with human hippocampal and intracranial volumes

Author: Stein, Jason L, Medland, Sarah E, Bernard, Manon, Nauck, Matthias, Nöthen, Markus M., Olvera, Rene L, Pandolfo, Massimo, Pike, G Bruce, Puls, Ralf, Reinvang, Ivar, Rentería, Miguel E, Rietschel, Marcella, Roffman, Joshua L, Brown, Andrew A, Royle, Natalie A, Rujescu, Dan, Savitz, Jonathan, Schnack, Hugo G, Schnell, Knut, Seiferth, Nina, Smith, Colin, Steen, Vidar M, Valdés Hernández, Maria C, Van den Heuvel, Martijn, Cannon, Dara M, van der Wee, Nic J, Van Haren, Neeltje E M, Veltman, Joris A, Völzke, Henry, Walker, Robert, Westlye, Lars T, Whelan, Christopher D, Agartz, Ingrid, Boomsma, Dorret I, Cavalleri, Gianpiero L, Chakravarty, M Mallar, Dale, Anders M, Djurovic, Srdjan, Drevets, Wayne C, Hagoort, Peter, Hall, Jeremy, Heinz, Andreas, Jack, Clifford R, Foroud, Tatiana M, Le Hellard, Stephanie, Macciardi, Fabio, Christoforou, Andrea, Montgomery, Grant W, Poline, Jean Baptiste, Porteous, David J, Sisodiya, Sanjay M, Starr, John M, Sussmann, Jessika, Toga, Arthur W, Veltman, Dick J, Walter, Henrik, Weiner, Michael W, Domin, Martin, Initiative, Alzheimer's Disease Neuroimaging, Consortium, EPIGEN, Consortium, IMAGEN, Group, Saguenay Youth Study, Bis, Joshua C, Ikram, M Arfan, Smith, Albert V, Gudnason, Vilmundur, Tzourio, Christophe, Vernooij, Meike W, Grimm, Oliver, Launer, Lenore J, DeCarli, Charles, Seshadri, Sudha, Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Andreassen, Ole A, Apostolova, Liana G, Bastin, Mark E, Blangero, John, Brunner, Han G, Buckner, Randy L, Hollinshead, Marisa, Cichon, Sven, Coppola, Giovanni, de Zubicaray, Greig I, Deary, Ian J, Donohoe, Gary, de Geus, Eco J C, Espeseth, Thomas, Fernández, Guillén, Glahn, David C, Grabe, Hans J, Holmes, Avram J, Hardy, John, Hulshoff Pol, Hilleke E, Jenkinson, Mark, Kahn, René S, McDonald, Colm, McIntosh, Andrew M, McMahon, Francis J, McMahon, Katie L, Meyer-Lindenberg, Andreas, Morris, Derek W, Homuth, Georg, Müller-Myhsok, Bertram, Nichols, Thomas E, Ophoff, Roel A, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W, Potkin, Steven G, Sämann, Philipp G, Saykin, Andrew J, Schumann, Gunter, Vasquez, Alejandro Arias, Hottenga, Jouke-Jan, Smoller, Jordan W, Wardlaw, Joanna M, Weale, Michael E, Martin, Nicholas G, Franke, Barbara, Wright, Margaret J, Thompson, Paul M, Consortium, Enhancing Neuro Imaging Genetics through Meta-Analysis, Weiner, Michael, Aisen, Paul, Langan, Camilla, Petersen, Ronald, Jagust, William, Trojanowki, John Q, Beckett, Laurel, Green, Robert C, Morris, John, Liu, Enchi, Lopez, Lorna M, Montine, Tom, Gamst, Anthony, Thomas, Ronald G, Donohue, Michael, Walter, Sarah, Gessert, Devon, Sather, Tamie, Hansell, Narelle K, Harvey, Danielle, Kornak, John, Dale, Anders, Bernstein, Matthew, Felmlee, Joel, Fox, Nick, Hwang, Kristy S, Thompson, Paul, Schuff, Norbert, Alexander, Gene, Bandy, Dan, Koeppe, Robert A, Foster, Norm, Reiman, Eric M, Chen, Kewei, Mathis, Chet, Kim, Sungeun, Cairns, Nigel J, Taylor-Reinwald, Lisa, Trojanowki, J. Q., Shaw, Les, Lee, Virginia M Y, Korecka, Magdalena, Crawford, Karen, Neu, Scott, Laje, Gonzalo, Potkin, Steven, Shen, Li, Kachaturian, Zaven, Frank, Richard, Snyder, Peter J, Molchan, Susan, Kaye, Jeffrey, Quinn, Joseph, Lee, Phil H, Lind, Betty, Dolen, Sara, Schneider, Lon S, Pawluczyk, Sonia, Spann, Bryan M, Brewer, James, Vanderswag, Helen, Heidebrink, Judith L, Lord, Joanne L, Liu, Xinmin, Johnson, Kris, Doody, Rachelle S, Villanueva-Meyer, Javier, Chowdhury, Munir, Stern, Yaakov, Honig, Lawrence S, Bell, Karen L, Morris, John C, Ances, Beau, Carroll, Maria, Loth, Eva, Leon, Sue, Mintun, Mark A, Schneider, Stacy, Marson, Daniel, Griffith, Randall, Clark, David, Grossman, Hillel, Mitsis, Effie, Romirowsky, Aliza, deToledo-Morrell, Leyla, Hibar, Derrek P, Lourdusamy, Anbarasu, Shah, Raj C, Duara, Ranjan, Varon, Daniel, Roberts, Peggy, Albert, Marilyn, Onyike, Chiadi, Kielb, Stephanie, Rusinek, Henry, de Leon, Mony J, Glodzik, Lidia, Mattingsdal, Morten, De Santi, Susan, Doraiswamy, P Murali, Petrella, Jeffrey R, Coleman, R Edward, Arnold, Steven E, Karlawish, Jason H, Wolk, David, Smith, Charles D, Jicha, Greg, Hardy, Peter, Mohnke, Sebastian, Lopez, Oscar L, Oakley, MaryAnn, Simpson, Donna M, Porsteinsson, Anton P, Goldstein, Bonnie S, Martin, Kim, Makino, Kelly M, Ismail, M Saleem, Mulnard, Ruth A, Thai, Gaby, Maniega, Susana Muñoz, Mc-Adams-Ortiz, Catherine, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Diaz-Arrastia, Ramon, King, Richard, Weiner, Myron, Martin-Cook, Kristen, DeVous, Michael, Levey, Allan I, Nho, Kwangsik, Lah, James J, Cellar, Janet S, Burns, Jeffrey M, Anderson, Heather S, Swerdlow, Russell H, Apostolova, Liana, Lu, Po H, Bartzokis, George, Silverman, Daniel H S, Graff-Radford, Neill R, Nugent, Allison C, Parfitt, Francine, Johnson, Heather, Farlow, Martin R, Hake, Ann Marie, Matthews, Brandy R, Herring, Scott, van Dyck, Christopher H, Carson, Richard E, MacAvoy, Martha G, Chertkow, Howard, O'Brien, Carol, Bergman, Howard, Hosei, Chris, Black, Sandra, Stefanovic, Bojana, Caldwell, Curtis, Hsiung, Ging-Yuek Robin, Feldman, Howard, Mudge, Benita, Assaly, Michele, Kertesz, Andrew, Papmeyer, Martina, Rogers, John, Trost, Dick, Bernick, Charles, Munic, Donna, Kerwin, Diana, Mesulam, Marek-Marsel, Lipowski, Kristina, Wu, Chuang-Kuo, Johnson, Nancy, Sadowsky, Carl, Pütz, Benno, Martinez, Walter, Villena, Teresa, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, Sperling, Reisa A, Johnson, Keith A, Marshall, Gad, Frey, Meghan, Yesavage, Jerome, Ramasamy, Adaikalavan, Taylor, Joy L, Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Sabbagh, Marwan, Belden, Christine, Jacobson, Sandra, Kowall, Neil, Killiany, Ronald, Budson, Andrew E, Senstad, Rudy E, Rasmussen, Jerod, Norbash, Alexander, Johnson, Patricia Lynn, Obisesan, Thomas O, Wolday, Saba, Bwayo, Salome K, Lerner, Alan, Hudson, Leon, Ogrocki, Paula, Fletcher, Evan, Carmichael, Owen, Rijpkema, Mark, Olichney, John, Kittur, Smita, Borrie, Michael, Lee, T-Y, Bartha, Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M, Risacher, Shannon L, Preda, Adrian, Nguyen, Dana, Tariot, Pierre, Fleisher, Adam, Reeder, Stephanie, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Scharre, Douglas W, Kataki, Maria, Roddey, J Cooper, Zimmerman, Earl A, Celmins, Dzintra, Brown, Alice D, Pearlson, Godfrey D, Blank, Karen, Anderson, Karen, Santulli, Robert B, Schwartz, Eben S, Sink, Kaycee M, Rose, Emma J, Williamson, Jeff D, Garg, Pradeep, Watkins, Franklin, Ott, Brian R, Querfurth, Henry, Tremont, Geoffrey, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Rosen, Howard J, Ryten, Mina, Miller, Bruce L, Mintzer, Jacobo, Longmire, Crystal Flynn, Spicer, Kenneth, Finger, Elizabeth, Rachinsky, Irina, Drost, Dick, Cavalleri, Gianpiero, Alhusaini, Saud, Delanty, Norman, Whelan, Christopher, Sisodiya, Sanjay, Kasperaviciute, Dalia, Matarin, Mar, Depondt, Chantal, Goldstein, David B, Heinzen, Erin L, Shianna, Kevin, Sprooten, Emma, Radtke, Rodney, Ottmann, Ruth, Sergievsky, G. H., Schumann, G., Conrod, P., Reed, L., Barker, G., Williams, S., Loth, E., Struve, M., Strengman, Eric, Lourdusamy, A., Cattrell, A., Nymberg, C., Topper, L., Smith, L., Havatzias, S., Stueber, K., Mallik, C., Stacey, D., Wong, C Peng, Teumer, Alexander, Werts, H., Andrew, C., Desrivieres, S., Heinz, A., Gallinat, J., Häke, I., Ivanov, N., Klär, A., Reuter, J., Winkler, Anderson M, Trabzuni, Daniah, Palafox, C., Hohmann, C., Schilling, C., Lüdemann, K., Romanowski, A., Ströhle, A., Wolff, E., Rapp, M., Ittermann, B., Brühl, R., Turner, Jessica, Ihlenfeld, A., Walaszek, B., Schubert, F., Garavan, H., Connolly, C., Jones, J., Lalor, E., McCabe, E., Ní Shiothcháin, A., Whelan, R., van Eijk, Kristel, Spanagel, R., Leonardi-Essmann, F., Sommer, W., Flor, H., Vollstaedt-Klein, S., Nees, F., Banaschewski, T., Poustka, L., Steiner, S., Mann, K., van Erp, Theo G M, Buehler, M., Rietschel, M., Stolzenburg, E., Schmal, C., Schirmbeck, F., Paus, T., Gowland, P., Heym, N., Lawrence, C., Newman, C., van Tol, Marie-Jose, Pausova, Z., Smolka, M., Huebner, T., Ripke, S., Mennigen, E., Muller, K., Ziesch, V., Büchel, C., Bromberg, U., Fadai, T., Wittfeld, Katharina, Lueken, L., Yacubian, J., Finsterbusch, J., Martinot, J. L., Artiges, E., Bordas, N., de Bournonville, S., Bricaud, Z., Gollier Briand, F., Lemaitre, H., Wolf, Christiane, Massicotte, J., Miranda, R., Paillère Martinot, M. L., Penttilä, J., Poline, J. B., Barbot, A., Schwartz, Y., Lalanne, C., Frouin, V., Thyreau, B., Woudstra, Saskia, Dalley, J., Mar, A., Robbins, T., Subramaniam, N., Theobald, D., Richmond, N., de Rover, M., Molander, A., Jordan, E., Robinson, E., Aleman, Andre, Hipolata, L., Moreno, M., Arroyo, M., Stephens, D., Ripley, T., Crombag, H., Pena, Y., Lathrop, M., Zelenika, D., Heath, S., Lanzerath, D., Heinrichs, B., Spranger, T., Fuchs, B., Speiser, C., Resch, F., Haffner, J., Parzer, P., Brunner, R., Klaassen, A., Toro, Roberto, Almasy, Laura, Klaassen, I., Constant, P., Mignon, X., Thomsen, T., Zysset, S., Vestboe, A., Ireland, J., Rogers, J., Binder, Elisabeth B, Chakravarty, Mallar, Smith, Albert Vernon, van der Lijn, Fedde, Crivello, Fabrice, Fornage, Myriam, Shulman, Joshua M, Brohawn, David G, Schmidt, Helena, Srikanth, Velandai, Schuur, Maaike, Yu, Lei, Choi, Seung-Hoan, Sigurdsson, Sigurdur, Verhaaren, Benjamin F J, DeStefano, Anita L, Lambert, Jean-Charles, Cantor, Rita M, Struchalin, Maksim, Stankovich, Jim, Ibrahim-Verbaas, Carla A, Fleischman, Debra, Zijdenbos, Alex, den Heijer, Tom, Mazoyer, Bernard, Coker, Laura H, Enzinger, Christian, Danoy, Patrick, Carless, Melanie A, Amin, Najaf, Arfanakis, Konstantinos, van Buchem, Mark A, de Bruijn, Renée F A G, Beiser, Alexa, Dufouil, Carole, Huang, Juebin, Cavalieri, Margherita, Thomson, Russell, Niessen, Wiro J, Corvin, Aiden, Chibnik, Lori B, Gislason, Gauti K, Hofman, Albert, Pikula, Aleksandra, Amouyel, Philippe, Freeman, Kevin B, Phan, Thanh G, Oostra, Ben A, Nalls, Michael A, Uitterlinden, Andre G, Czisch, Michael, Au, Rhoda, Elbaz, Alexis, Beare, Richard J, van Swieten, John C, Lopez, Oscar, Harris, Tamara B, Chouraki, Vincent, Breteler, Monique M B, De Jager, Philip L, Becker, James T, Curran, Joanne E, Knopman, David, Fazekas, Franz, Wolf, Philip A, van der Lugt, Aad, Longstreth, W. T., Brown, Mathew A, Bennett, David A, van Duijn, Cornelia M, Davies, Gail, Mosley, Thomas H, Schmidt, Reinhold, de Almeida, Marcio A A, Appel, Katja, Duggirala, Ravi, Dyer, Thomas D, Erk, Susanne, Fagerness, Jesen, Fox, Peter T, Freimer, Nelson B, Gill, Michael, Göring, Harald H H, Bartecek, Richard, Hagler, Donald J, Hoehn, David, Holsboer, Florian, Hoogman, Martine, Hosten, Norbert, Jahanshad, Neda, Johnson, Matthew P, Kent, Jack W, Kochunov, Peter, Bergmann, Ørjan, Lancaster, Jack L, Lawrie, Stephen M, Liewald, David C, Mandl, René, Mattheisen, Manuel, Meisenzahl, Eva, Melle, Ingrid, Moses, Eric K, Mühleisen, Thomas W, David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California-University of California, Queensland Institute of Medical Research, Radboud University Medical Center [Nijmegen], Yale University School of Medicine, Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Universität Greifswald - University of Greifswald, Universität Heidelberg [Heidelberg], University Medical Center [Utrecht], University of Oslo (UiO), University of Toronto, National University of Ireland [Galway] (NUI Galway), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], University of Bergen (UiB), Harvard University [Cambridge], VU University Amsterdam, University of Edinburgh, Structure et Réactivité des Systèmes Moléculaires Complexes (SRSMC), Institut de Chimie du CNRS (INC)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), Department of Forensic and Neurodevelopmental Sciences, King‘s College London, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Georgia State University, University System of Georgia (USG), Department of Psychiatry and Human Behavior [Irvine], University of California [Irvine] (UCI), Leiden University Medical Center (LUMC), Dundee Technopole, CXR Biosciences Ltd, University of Groningen [Groningen], Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland (RCSI), Department of Genetics, Southwest Foundation for Biomedical Research, Bijvoet Center of Biomolecular Research [Utrecht], Utrecht University [Utrecht], Neurology Division, Beaumont Hospital, Dublin 9, Ireland, Beaumont Hospital, The University of Texas Health Science Center at Houston (UTHealth), Center for Neurobehavioral Genetics, Max Planck Institute of Psychiatry, Max-Planck-Gesellschaft, Department of Computer Science, Durham University, Laboratoire des symbioses tropicales et méditerranéennes (UMR LSTM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], University of California, Institute of Neurology [London], University College of London [London] (UCL), University of California [San Francisco] (UCSF), Department of Medicine, University of Washington [Seattle], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Centre Émile Durkheim (CED), Sciences Po Bordeaux - Institut d'études politiques de Bordeaux (IEP Bordeaux)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Salermo, Università degli Studi di Salerno (UNISA), School of Psychology, University of Queensland, University of Queensland [Brisbane], Hartford Hospital, Lancaster University, Centre for Advanced Imaging, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Stanley Center for Psychiatric Research, Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Faculteit Medische Wetenschappen/UMCG, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Biological Psychology, Neuroscience Campus Amsterdam - Brain Imaging, EMGO+ - Mental Health, EPIGEN Consortium, IMAGENConsortium, Saguenay Youth Study Group, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium, Psychiatry, NCA - Brain Imaging, EMGO - Mental health, Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Virology, Epidemiology, Clinical Chemistry, Erasmus MC other, Radiology & Nuclear Medicine, University of California (UC)-University of California (UC), Yale School of Medicine [New Haven, Connecticut] (YSM), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Universität Heidelberg [Heidelberg] = Heidelberg University, Harvard University, Vrije Universiteit Amsterdam [Amsterdam] (VU), University of California [Irvine] (UC Irvine), Universiteit Leiden, University of California (UC), University of California [San Francisco] (UC San Francisco), Università degli Studi di Salerno = University of Salerno (UNISA), University of Iceland [Reykjavik], McGill University, University of Bergen (UIB), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Bijvoet Center of Biomolecular Research, Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, UMR5116, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), and McGill University-McGill University
Subjects: Netherlands Twin Register (NTR), Pathology, 110 012 Social cognition of verbal communication, [SDV]Life Sciences [q-bio], Hippocampus, Genome-wide association study, DCN PAC - Perception action and control, Hippocampal formation, physiopathology [Brain], Bioinformatics, 0302 clinical medicine, 130 000 Cognitive Neurology & Memory, TEMPORAL-LOBE EPILEPSY, 110 014 Public activities, Renal disorder [IGMD 9], 0303 health sciences, medicine.diagnostic_test, Translational research Immune Regulation [ONCOL 3], Brain, Human brain, Genomic disorders and inherited multi-system disorders [DCN PAC - Perception action and control IGMD 3], ALZHEIMERS-DISEASE, medicine.anatomical_structure, Brain size, genetics [Chromosomes, Human, Pair 12], genetics [Polymorphism, Single Nucleotide], Biomarker (medicine), NA+/H+ EXCHANGER, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Genetic Markers, medicine.medical_specialty, 110 000 Neurocognition of Language, DCN MP - Plasticity and memory, A neurocomputational model for the Processing of Linguistic Utterances based on the Unification-Space architecture [110 007 PLUS], BRAIN VOLUME, UNIFIED APPROACH, 110 013 Binding and the MUC-model, Neuroimaging, Biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, Article, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], 03 medical and health sciences, AUTOMATED SEGMENTATION, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, ddc:570, FUNCTIONAL IMPLICATIONS, Genetics, medicine, Humans, GENOME-WIDE ASSOCIATION, 030304 developmental biology, Chromosomes, Human, Pair 12, Magnetic resonance imaging, Genetic Loci, physiopathology [Hippocampus], 110 009 The human brain and Chinese prosody, Genetics and epigenetic pathways of disease Genomic disorders and inherited multi-system disorders [NCMLS 6], HUMAN HEIGHT, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: Contains fulltext : 108202.pdf (Publisher’s version ) (Closed access) Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 x 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 x 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 x 10(-7)). 01 mei 2012
Published: 2012
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14. Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study

Author: Schnack, Hugo G, van Haren, Neeltje E M, Nieuwenhuis, Mireille, Hulshoff Pol, Hilleke E, Cahn, Wiepke, Kahn, René S, SGPL Stadsgeografie, Social Urban Transitions, SGPL Stadsgeografie, and Social Urban Transitions
Subjects: Longitudinal sample, Adult, Male, medicine.medical_specialty, Aging, Support Vector Machine, Adolescent, Audiology, Models, Biological, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Neuroimaging, mental disorders, medicine, Journal Article, Humans, Longitudinal Studies, Young adult, Gray Matter, Brain aging, Aged, medicine.diagnostic_test, Case-control study, Healthy subjects, Brain, Magnetic resonance imaging, Chronological age, Middle Aged, Magnetic Resonance Imaging, 030227 psychiatry, Psychiatry and Mental health, Case-Control Studies, Disease Progression, Schizophrenia, Female, Psychology, 030217 neurology & neurosurgery, Algorithms, Clinical psychology
Abstract: OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question. METHOD: In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample. RESULTS: In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant. CONCLUSIONS: The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment.
Published: 2016

15. Common variants at 12q14 and 12q24 are associated with hippocampal volume

Author: Bis, Joshua C, DeCarli, Charles, Schuur, Maaike, Grimm, Oliver, Hollinshead, Marisa, Holmes, Avram J, Homuth, Georg, Hottenga, Jouke-Jan, Langan, Camilla, Lopez, Lorna M, Hansell, Narelle K, Hwang, Kristy S, Kim, Sungeun, Yu, Lei, Laje, Gonzalo, Lee, Phil H, Liu, Xinmin, Loth, Eva, Lourdusamy, Anbarasu, Maniega, Susana Muñoz, Mattingsdal, Morten, Mohnke, Sebastian, Nho, Kwangsik, Nugent, Allison C, Choi, Seung-Hoan, O'Brien, Carol, Papmeyer, Martina, Pütz, Benno, Ramasamy, Adaikalavan, Rasmussen, Jerod, Rijpkema, Mark, Risacher, Shannon L, Roddey, J Cooper, Rose, Emma J, Ryten, Mina, Sigurdsson, Sigurdur, Shen, Li, Sprooten, Emma, Strengman, Eric, Teumer, Alexander, Trabzuni, Daniah, Turner, Jessica, van Eijk, Kristel, van Erp, Theo G M, van Tol, Marie-Jose, Wittfeld, Katharina, Verhaaren, Benjamin F J, Wolf, Christiane, Woudstra, Saskia, Aleman, Andre, Alhusaini, Saud, Almasy, Laura, Binder, Elisabeth B, Brohawn, David G, Cantor, Rita M, Carless, Melanie A, Corvin, Aiden, DeStefano, Anita L, Czisch, Michael, Curran, Joanne E, Davies, Gail, de Almeida, Marcio A A, Delanty, Norman, Depondt, Chantal, Duggirala, Ravi, Dyer, Thomas D, Erk, Susanne, Fagerness, Jesen, Lambert, Jean-Charles, Fox, Peter T, Freimer, Nelson B, Gill, Michael, Göring, Harald H H, Hagler, Donald J, Hoehn, David, Holsboer, Florian, Hoogman, Martine, Hosten, Norbert, Jahanshad, Neda, Jack, Clifford R, Johnson, Matthew P, Kasperaviciute, Dalia, Kent, Jack W, Kochunov, Peter, Lancaster, Jack L, Lawrie, Stephen M, Liewald, David C, Mandl, René, Matarin, Mar, Mattheisen, Manuel, Struchalin, Maksim, Meisenzahl, Eva, Melle, Ingrid, Moses, Eric K, Mühleisen, Thomas W, Nauck, Matthias, Nöthen, Markus M, Olvera, Rene L, Pandolfo, Massimo, Pike, G Bruce, Puls, Ralf, Stankovich, Jim, Reinvang, Ivar, Rentería, Miguel E, Rietschel, Marcella, Roffman, Joshua L, Royle, Natalie A, Rujescu, Dan, Savitz, Jonathan, Schnack, Hugo G, Schnell, Knut, Seiferth, Nina, Smith, Albert Vernon, Ibrahim-Verbaas, Carla A, Smith, Colin, Steen, Vidar M, Valdés Hernández, Maria C, Van den Heuvel, Martijn, van der Wee, Nic J, Van Haren, Neeltje E M, Veltman, Joris A, Völzke, Henry, Walker, Robert, Westlye, Lars T, Fleischman, Debra, Whelan, Christopher D, Agartz, Ingrid, Boomsma, Dorret I, Cavalleri, Gianpiero L, Dale, Anders M, Djurovic, Srdjan, Drevets, Wayne C, Hagoort, Peter, Hall, Jeremy, Heinz, Andreas, Zijdenbos, Alex, Foroud, Tatiana M, Le Hellard, Stephanie, Macciardi, Fabio, Montgomery, Grant W, Poline, Jean Baptiste, Porteous, David J, Sisodiya, Sanjay M, Starr, John M, Sussmann, Jessika, den Heijer, Tom, Toga, Arthur W, Veltman, Dick J, Walter, Henrik, Weiner, Michael W, Andreassen, Ole A, Apostolova, Liana G, Bastin, Mark E, Blangero, John, Brunner, Han G, Buckner, Randy L, Mazoyer, Bernard, Cichon, Sven, Coppola, Giovanni, de Zubicaray, Greig I, Deary, Ian J, Donohoe, Gary, de Geus, Eco J C, Espeseth, Thomas, Fernández, Guillén, Glahn, David C, Grabe, Hans J, Coker, Laura H, Hardy, John, Hulshoff Pol, Hilleke E, Jenkinson, Mark, Kahn, René S, McDonald, Colm, McIntosh, Andrew M, McMahon, Francis J, McMahon, Katie L, Meyer-Lindenberg, Andreas, Morris, Derek W, Enzinger, Christian, Müller-Myhsok, Bertram, Nichols, Thomas E, Ophoff, Roel A, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W, Potkin, Steven G, Sämann, Philipp G, Saykin, Andrew J, Schumann, Gunter, Danoy, Patrick, Smoller, Jordan W, Wardlaw, Joanna M, Weale, Michael E, Martin, Nicholas G, Franke, Barbara, Wright, Margaret J, Thompson, Paul M, Amin, Najaf, Arfanakis, Konstantinos, van der Lijn, Fedde, van Buchem, Mark A, de Bruijn, Renée F A G, Beiser, Alexa, Dufouil, Carole, Huang, Juebin, Cavalieri, Margherita, Thomson, Russell, Niessen, Wiro J, Chibnik, Lori B, Gislason, Gauti K, Crivello, Fabrice, Hofman, Albert, Pikula, Aleksandra, Amouyel, Philippe, Freeman, Kevin B, Phan, Thanh G, Oostra, Ben A, Stein, Jason L, Medland, Sarah E, Vasquez, Alejandro Arias, Hibar, Derrek P, Fornage, Myriam, Consortium, Enhancing Neuro Imaging Genetics through Meta-Analysis, Nalls, Michael A, Uitterlinden, Andre G, Au, Rhoda, Elbaz, Alexis, Beare, Richard J, Debette, Stephanie, van Swieten, John C, Lopez, Oscar L, Harris, Tamara B, Chouraki, Vincent, Breteler, Monique M B, De Jager, Philip L, Becker, James T, Vernooij, Meike W, Knopman, David, Fazekas, Franz, Shulman, Joshua M, Wolf, Philip A, van der Lugt, Aad, Gudnason, Vilmundur, Longstreth, W. T., Brown, Matthew A, Bennett, David A, van Duijn, Cornelia M, Mosley, Thomas H, Schmidt, Reinhold, Tzourio, Christophe, Schmidt, Helena, Launer, Lenore J, Ikram, M Arfan, Seshadri, Sudha, Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Arias Vasquez, Alejandro, Senstad, Rudy E, Winkler, Anderson M, Srikanth, Velandai, Toro, Roberto, Appel, Katja, Bartecek, Richard, Bergmann, Ørjan, Bernard, Manon, Brown, Andrew A, Cannon, Dara M, Chakravarty, Mallar, Christoforou, Andrea, Domin, Martin, University of Washington [Seattle], University of California [Davis] (UC Davis), University of California, Center for Neuroscience, University of California-University of California, Icelandic Heart Association, Kopavogur, Iceland., Faculty of Medicine, University of Iceland [Reykjavik], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Groupe d'Imagerie Neurofonctionnelle (GIN - UMR 5296), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, The University of Texas Health Science Center at Houston (UTHealth), Human Genetics Center, Institut National de la Santé et de la Recherche Médicale (INSERM), U708, Neuroepidemiology, Paris, France, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital [Boston], Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Institute of Molecular Biology and Biochemistry, Medical University Graz, Stroke and Ageing Research Centre, Southern Clinical School, Department of Medicine, Monash University, Monash University [Melbourne], Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia, University of Tasmania [Hobart, Australia] (UTAS), Rush University Medical Center [Chicago], Framingham Heart Study, Boston University [Boston] (BU)-National Heart, Lung, and Blood Institute [Bethesda] (NHLBI), Université Lille Nord de France (COMUE), Epidémiologie des maladies chroniques : impact des interactions gène environnement sur la santé des populations, Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé, Department of Radiology, Mayo Clinic, Biospective [Montréal], Department of Neurology, Sint Franciscus Gasthuis, Rotterdam, The Netherlands, Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, Department of Neurology, Medical University Graz, Graz, Austri, University of Queensland, Diamantina Institute, Princess Alexandra Hospital, Brisbane, Queensland, Australia, Department of Biomedical Engineering, Illinois Institute of Technology (IIT), Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands, University of Mississippi Medical Center (UMMC), Delft University of Technology (TU Delft), Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), Queensland Institute of Medical Research, Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Department of Human Genetics [Nijmegen], Radboud University Medical Center [Nijmegen], Donders Institute for Brain, Cognition and Behaviour, Radboud university [Nijmegen], National Institutes of Health [Bethesda] (NIH), Unité de recherche Neuroépidémiologie [CHU Pitié-Salpêtrière], Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Murdoch Children's Research Institute (MCRI), University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Harvard University [Cambridge], Harvard Medical School [Boston] (HMS), Mayo Clinic [Rochester], Human Genetics Group, University of Queensland Diamantina Institute, Princess Alexandra Hospital, Woolloongabba, Brisbane, Australia, Center for Medical Systems Biology, Netherlands Genomics Initiative, Leiden University Medical Center, Leiden, The Netherlands, University of Bordeaux, U708, Bordeaux, France, INSERM, Neuroepidemiology U708, Bordeaux, France, University of California (UC), University of California (UC)-University of California (UC), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], Radboud University [Nijmegen], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Harvard University, Other departments, Human genetics, EMGO - Mental health, NCA - Attention & Cognition, Radiology & Nuclear Medicine, Epidemiology, Neurology, Cardiothoracic Surgery, Clinical Genetics, Internal Medicine, Biological Psychology, Neuroscience Campus Amsterdam - Attention & Cognition, and EMGO+ - Mental Health
Subjects: Netherlands Twin Register (NTR), genetics [Alzheimer Disease], Genome-wide association study, DCN PAC - Perception action and control, genetics [Cognition Disorders], Hippocampal formation, Hippocampus, 0302 clinical medicine, Polymorphism (computer science), Cognitive decline, Genetics, 0303 health sciences, Translational research Immune Regulation [ONCOL 3], Genomic disorders and inherited multi-system disorders [DCN PAC - Perception action and control IGMD 3], genetics [Chromosomes, Human, Pair 12], genetics [Polymorphism, Single Nucleotide], Alzheimer's disease, Genetic Markers, medicine.medical_specialty, DCN MP - Plasticity and memory, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], 03 medical and health sciences, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, genetics [Dementia], Alzheimer Disease, ddc:570, Internal medicine, medicine, Humans, SNP, Chromosome 12, 030304 developmental biology, Chromosomes, Human, Pair 12, [SCCO.NEUR]Cognitive science/Neuroscience, medicine.disease, Endocrinology, Genetic Loci, physiopathology [Hippocampus], Dementia, Cognition Disorders, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: Contains fulltext : 110608.pdf (Publisher’s version ) (Closed access) Aging is associated with reductions in hippocampal volume that are accelerated by Alzheimer's disease and vascular risk factors. Our genome-wide association study (GWAS) of dementia-free persons (n = 9,232) identified 46 SNPs at four loci with P values of
Published: 2012
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16. Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

Author: Brouwer, Rachel M., Klein, Marieke, Grasby, Katrina L., Schnack, Hugo G., Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I., Sprooten, Emma, Franz, Carol E., Gogtay, Nitin, Kremen, William S., Panizzon, Matthew S., Loohuis, Loes M. Olde, Whelan, Christopher D., Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J., Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A., Bright, Joanna K., Buimer, Elizabeth E. L., Bülow, Robin, Cannon, Dara M., Ciufolini, Simone, Crossley, Nicolas A., Damatac, Christienne G., Dazzan, Paola, Mol, Casper L. de, Zwarte, Sonja M. C. de, Desrivières, Sylvane, Díaz-Caneja, Covadonga M., Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H., Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura K. M., Hartman, Catharina A., Heany, Sarah J., Heindel, Walter, Heslenfeld, Dirk J., Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R., Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn G. J. C., Lenroot, Rhoshel K., Malchow, Berend, Mandl, René C. W., Medel, Vicente, Meinert, Susanne, Morgan, Catherine A., Mühleisen, Thomas W., Nabulsi, Leila, Opel, Nils, Ortiz-García de la Foz, Víctor, Overs, Bronwyn J., Paillère Martinot, Marie-Laure, Quinlan, Erin B., Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V., Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje E. M., Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zugman, Andre, Armstrong, Nicola J., Bralten, Janita, Dalvie, Shareefa, Forti, Marta Di, Ding, Linda, Donohoe, Gary, Forstner, Andreas J., Gonzalez-Peñas, Javier, Guimaraes, Joao P. O. F. T., Homuth, Georg, Hottenga, Jouke-Jan, Knol, Maria J., Kwok, John B. J., Hellard, Stephanie Le, Mather, Karen A., Milaneschi, Yuri, Morris, Derek W., Nöthen, Markus M., Papiol, Sergi, Rietschel, Marcella, Santoro, Marcos L., Steen, Vidar M., Stein, Jason L., Streit, Fabian, Tankard, Rick M., Teumer, Alexander, van ’t Ent, Dennis, van der Meer, Dennis, van Eijk, Kristel R., Vassos, Evangelos, Vázquez-Bourgon, Javier, Witt, Stephanie H., Adams, Hieab H. H., Agartz, Ingrid, Ames, David, Amunts, Katrin, Andreassen, Ole A., Arango, Celso, Banaschewski, Tobias, Baune, Bernhard T., Belangero, Sintia I., Bokde, Arun L. W., Boomsma, Dorret I., Bressan, Rodrigo A., Brodaty, Henry, Buitelaar, Jan K., Cahn, Wiepke, Caspers, Svenja, Cichon, Sven, Facorro, Benedicto Crespo, Dannlowski, Udo, Elvsåshagen, Torbjørn, Espeseth, Thomas, Falkai, Peter G., Fisher, Simon E., Flor, Herta, Fullerton, Janice M., Garavan, Hugh, Gowland, Penny A., Grabe, Hans J., Hahn, Tim, Heinz, Andreas, Hillegers, Manon, Hoare, Jacqueline, Hoekstra, Pieter J., Ikram, Mohammad A., Jackowski, Andrea P., Jansen, Andreas, Jönsson, Erik G., Kahn, Rene S., Kircher, Tilo, Korgaonkar, Mayuresh S., Krug, Axel, Lemaitre, Herve, Malt, Ulrik F., Martinot, Jean-Luc, McDonald, Colm, Mitchell, Philip B., Muetzel, Ryan L., Murray, Robin M., Nees, Frauke, Nenadic, Igor, Oosterlaan, Jaap, Ophoff, Roel A., Pan, Pedro M., Penninx, Brenda W. J. H., Poustka, Luise, Sachdev, Perminder S., Salum, Giovanni A., Schofield, Peter R., Schumann, Gunter, Shaw, Philip, Sim, Kang, Smolka, Michael N., Stein, Dan J., Trollor, Julian, van den Berg, Leonard H., Veldink, Jan H., Walter, Henrik, Westlye, Lars T., Whelan, Robert, White, Tonya, Wright, Margaret J., Medland, Sarah E., Franke, Barbara, Thompson, Paul M., and Pol, Hilleke E. Hulshoff
Subjects: ddc:570
Abstract: Human brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world’s largest longitudinal imaging genetics dataset we identified genetic variants that alter age-dependent brain growth and atrophy throughout our lives. One-sentence summary We identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide.
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17. Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

Author: Brouwer, Rachel M., Klein, Marieke, Grasby, Katrina L., Schnack, Hugo G., Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I., Sprooten, Emma, Franz, Carol E., Gogtay, Nitin, Kremen, William S., Panizzon, Matthew S, Olde Loohuis, Loes M., Whelan, Christopher D., Aghajani, Moji, Alloza, Clara, Alnaes, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J., Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A., Bright, Joanna K., Buimer, Elizabeth E.L., Bülow, Robin, Cannon, Dara M., Ciufolini, Simone, Crossley, Nicolas A., Damatac, Christienne G., Dazzan, Paola, de Mol, Casper L., de Zwarte, Sonja M.C., Desrivières, Sylvane, Díaz-Caneja, Covadonga M., Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H., Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura K.M., Hartman, Catharina A., Heany, Sarah J., Heindel, Walter, Heslenfeld, Dirk J., Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R., Janssen, Joost, Jia, Tianya, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn G. J. C., Lenroot, Rhoshel K., Malchow, Berend, Mandl, Rene C.W., Medel, Vicente, Meinert, Susanne, Morgan, Catherine A., Mühleisen, Thomas W., Nabulsi, Leila, Opel, Nils, Ortiz-García de la Foz, Victor, Overs, Bronwyn J., Paillère Martinot, Marie Laure, Quinlan, Erin B., Redlich, Ronny, Reis Marques, Tiago, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V., Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje E.M., Wen, Wei, Westeneng, Henk-Jan, Wittfield, Katharina, Zugman, Andre, Armstrong, Nicola J., Bralten, Janita, Dalvie, Shareefa, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J., Gonzalez-Peñas, Javier, Guimaraes, Joao P. O. F. T., Homuth, Georg, Hottenga, Jouke-Jan, Knol, Maria J., Kwok, John B.J., Le Hellard, Stephanie, Mather, Karen A., Milaneschi, Yuri, Morris, Derek W., Nöthen, Markus M., Papiol, Sergi, Rietsche, Marcella, Santoro, Marcus L., Steen, Vidar M., Stein, Jason L., Streit, Fabian, Tankard, Rick M., Teumer, Alexander, van't Ent, Dennis, van der Meer, Dennis, van Eijk, Kristel R., Vassos, Evangelos, Vázquez-Bourgon, Javier, Witt, Stephanie H., Alzheimer's Disease Neuroimaging Initiative, Adams, Hieab H.H., Agartz, Ingrid, Ames, David, Amunts, Katrin, Andreassen, Ole A., Arango, Celso, Banaschewski, Tobias, Baune, Bernhard T., Belangero, Sintia I., Bokde, Arun L.W., Boomsma, Dorret I., Bressan, Rodrigo A., Brodaty, Henry, Buitelaar, Jan K., Cahn, Wiepke, Caspers, Svenja, Cichon, Sven, Crespo Facorro, Benedicto, Dannlowski, Udo, Elvsåshagen, Torbjørn, Espeseth, Thomas, Falkai, Peter G., Fisher, Simon E., Flor, Herta, Fullerton, Janice M., Garavan, Hugh, Gowland, Penny A., Grabe, Hans J., Hahn, Tim, Heinz, Andreas, Hillegers, Manon, Hoare, Jacqueline, Hoekstra, Pieter J., Ikram, Mohammad A., Jackowski, Andrea P., Jansen, Andreas, Jönsson, Erik G., Kahn, Rene S., Kircher, Tilo, Korgaonkar, Mayuresh S., Krug, Axel, Lemaitre, Herve, Malt, Ulrik F., Martinot, Jean-Luc, McDonald, Colm, Mitchell, Philip B., Muetzel, Ryan L., Murray, Robin M., Nees, Frauke, Nenadic, Igor, Oosterlaan, Jaap, Ophoff, Roel A., Pan, Pedro M., Penninx, Brenda W.J.H., Poustka, Luise, Sachdev, Perminder S., Salum, Giovanni A., Schofield, Peter R., Schumann, Gunter, Shaw, Philip, Sim, Kang, Smolka, Michael N., Stein, Dan J., Trollor, Julian, van den Berg, Leonard H., Veldink, Jan H., Walter, Henrik, Westlye, Lars T., Whelan, Robert, White, Tonya, Wright, Margaret J., Medland, Sarah E., Franke, Barbara, Thompson, Paul M., and Hulshoff Pol, Hilleke E
Subjects: 3. Good health
Abstract: The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Any views expressed are those of the author(s) and not necessarily those of the funders.

18. Quantified speech as a marker: computational linguistics in schizophrenia-spectrum disorders

Author: Voppel, Alban, Sommer, Iris, and Schnack, Hugo G.
Abstract: Speech contains a wealth of information about the speaker's state of mind, not only in the words used, but also in the way these words are pronounced. Recent developments in Natural Language Processing (NLP) have paved the way for rapid, systematic recording and analysis of quantifiable properties of spoken language. Schizophrenia spectrum disorders, a collection of serious psychiatric disorders, involve different aspects of language. Abnormalities in language are used by practitioners to make a diagnosis or to estimate the severity of complaints. By using NLP techniques it has become possible to make these deviations objective and quantifiable, so that subtle changes can also be detected. This thesis contains a number of studies on the application of NLP techniques to the spoken language of people with a schizophrenia spectrum disorder. The focus of the research presented here is mainly on semantics (what is told) and phonetics of speech (how is told). These aspects are used to distinguish individuals with schizophrenia spectrum disorders from healthy controls, to investigate the relationship between speech features and specific symptoms, the relationship with clinical subtypes, as well as the relationship with antipsychotic medication.
Published: 2022

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18 results on '"Schnack, Hugo G."'

1. Genetic variants associated with longitudinal changes in brain structure across the lifespan

2. PROMISSING: Pruning Missing Values in Neural Networks

3. Genetic variants associated with longitudinal changes in brain structure across the lifespan

4. PROMISSING: Pruning Missing Values in Neural Networks

5. Active Selection of Classification Features

6. Implementation of and experimental software for active selection of classification features

7. Referee report. For: Active Acquisition for multimodal neuroimaging [version 1; referees: 1 approved with reservations]

8. Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: results of the ENIGMA Plasticity Working Group

9. Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis : Applying machine learning techniques to brain imaging data

10. Multi-center MRI prediction models : Predicting sex and illness course in first episode psychosis patients

11. Multi-center MRI prediction models:Predicting sex and illness course in first episode psychosis patients

12. Individual prediction of long‐term outcome in adolescents at ultra‐high risk for psychosis: Applying machine learning techniques to brain imaging data

13. Identification of common variants associated with human hippocampal and intracranial volumes

14. Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study

15. Common variants at 12q14 and 12q24 are associated with hippocampal volume

16. Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

17. Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

18. Quantified speech as a marker: computational linguistics in schizophrenia-spectrum disorders

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18 results on '"Schnack, Hugo G."'

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