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32 results on '"Chixiang Chen"'

1. Surgical Decompression of Traumatic Cervical Spinal Cord Injury: A Pilot Study Comparing Real-Time Intraoperative Ultrasound After Laminectomy With Postoperative MRI and CT Myelography

Author

Timothy Chryssikos, Jesse A. Stokum, Abdul-Kareem Ahmed, Chixiang Chen, Aaron Wessell, Gregory Cannarsa, Nicholas Caffes, Jeffrey Oliver, Joshua Olexa, Phelan Shea, Mohamed Labib, Graeme Woodworth, Alexander Ksendzovsky, Uttam Bodanapally, Kenneth Crandall, Charles Sansur, Gary Schwartzbauer, and Bizhan Aarabi

Subjects
Surgery, Neurology (clinical)
Published
2022
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3. Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros

Author

Biyi Shen, Chixiang Chen, Vernon M. Chinchilli, Nasrollah Ghahramani, Lijun Zhang, and Ming Wang

Subjects
Statistics and Probability, Models, Statistical, Research Design, Cluster Analysis, Humans, Computer Simulation, General Medicine, Statistics, Probability and Uncertainty
Abstract

Clustered or longitudinal data are commonly encountered in clinical trials and observational studies. This type of data could be collected through a real-time monitoring scheme associated with some specific event, such as disease recurrence, hospitalization, or emergency room visit. In these contexts, the cluster size could be informative because of its potential correlation with disease status, since more frequency of observations may indicate a worsening health condition. However, for some clusters/subjects, there are no measures or relevant medical records. Under such circumstances, these clusters/subjects may have a considerably lower risk of an event occurrence or may not be susceptible to such events at all, indicating a nonignorable zero cluster size. There is a substantial body of literature using observations from those clusters with a nonzero informative cluster size only, but few works discuss informative nonignorable zero-sized clusters. To utilize the information from both event-free and event-occurring participants, we propose a weighted within-cluster-resampling (WWCR) method and its asymptotically equivalent method, dual-weighted generalized estimating equations (WWGEE) by adopting the inverse probability weighting technique. The asymptotic properties are rigorously presented theoretically. Extensive simulations and an illustrative example of the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study are performed to analyze the finite-sample behavior of our methods and to show their advantageous performance compared to the existing approaches.

Published
2022
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4. An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis

Author

Chixiang Chen, Ming Wang, and Shuo Chen

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Medicine, General Agricultural and Biological Sciences, Statistics - Methodology, General Biochemistry, Genetics and Molecular Biology
Abstract

Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called Multiple Information Borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension., Comment: Contact Email: chixiang.chen@som.umaryland.edu

Published
2023
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5. Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank

Author

Li Feng, Zhenyao Ye, Chen Mo, Jingtao Wang, Song Liu, Si Gao, Hongjie Ke, Travis A Canida, Yezhi Pan, Kathryn S Hatch, Yizhou Ma, Chixiang Chen, Braxton D. Mitchell, L.Elliot Hong, Peter Kochunov, Shuo Chen, and Tianzhou Ma

Abstract

BackgroundElevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter (WM) brain aging remains unclear.MethodsIn this study, we focused on N=219,968 non-pregnant, family-unrelated individuals of European ancestry who had genotype data and two non-null clinical BP measurements available (99,532 male and 120,436 female, mean age=56.55, including 16,901 participants with neuroimaging data available) collected from UK Biobank (UKB). We adopted a chronological age-adjusted brain age metric, Brain Age Gap (BAG), as the outcome variable to measure the brain aging status. As a first step, we established a machine learning model to compute BAG based on white matter microstructure integrity measured by fractional anisotropy (FA) derived from diffusion tensor imaging data in a training set of subjects without hypertension (N=7,728). We then performed a two-sample Mendelian Randomization (MR) analysis to estimate the causal effect of BP on WM BAG in the whole population and subgroups stratified by gender and age brackets using two non-overlapping data sets (N=20,3067 for the set with genotype and BP data but no FA data; and N=8,822 for the set with genotype, BP and FA data). The main MR method used was generalized inverse variance weighted (gen-IVW) with other MR methods also included as sensitivity analysis.ResultsThe hypertension group is on average 0.3098 years (95%CI=0.1313,0.4884; p ConclusionHypertension and genetic predisposition to higher BP can accelerate WM brain aging specifically targeting at late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Published
2023
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6. Improving main analysis by borrowing information from auxiliary data

Author

Peisong Han, Fan He, and Chixiang Chen

Subjects
Statistics and Probability, Estimation, Epidemiology, Computer science, Carry (arithmetic), computer.software_genre, Weighting, Atherosclerosis Risk in Communities, Empirical likelihood, Research Design, Information index, Humans, Computer Simulation, Observational study, Data mining, computer
Abstract

In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

Published
2021
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7. Chronic neuronal activation leads to elevated lactate dehydrogenase A through the AMP-activated protein kinase/hypoxia-inducible factor-1α hypoxia pathway

Author

Alexander Ksendzovsky, Muznabanu Bachani, Marcelle Altshuler, Stuart Walbridge, Armin Mortazavi, Mitchell Moyer, Chixiang Chen, Islam Fayed, Joseph Steiner, Nancy Edwards, Sara K Inati, Jahandar Jahanipour, Dragan Maric, John D Heiss, Jaideep Kapur, and Kareem A Zaghloul

Subjects
Cellular and Molecular Neuroscience, Psychiatry and Mental health, Neurology, Original Article, Biological Psychiatry
Abstract

Recent studies suggest that changes in neuronal metabolism are associated with epilepsy. High rates of ATP depletion, lactate dehydrogenase A and lactate production have all been found in epilepsy patients, animal and tissue culture models. As such, it can be hypothesized that chronic seizures lead to continuing elevations in neuronal energy demand which may lead to an adapted metabolic response and elevations of lactate dehydrogenase A. In this study, we examine elevations in the lactate dehydrogenase A protein as a long-term cellular adaptation to elevated metabolic demand from chronic neuronal activation. We investigate this cellular adaptation in human tissue samples and explore the mechanisms of lactate dehydrogenase A upregulation using cultured neurones treated with low Mg2+, a manipulation that leads to NMDA-mediated neuronal activation. We demonstrate that human epileptic tissue preferentially upregulates neuronal lactate dehydrogenase A, and that in neuronal cultures chronic and repeated elevations in neural activity lead to upregulation of neuronal lactate dehydrogenase A. Similar to states of hypoxia, this metabolic change occurs through the AMP-activated protein kinase/hypoxia-inducible factor-1α pathway. Our data therefore reveal a novel long-term bioenergetic adaptation that occurs in chronically activated neurones and provide a basis for understanding the interplay between metabolism and neural activity during epilepsy.

Published
2022
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8. Fn14-Directed DART Nanoparticles Selectively Target Neoplastic Cells in Preclinical Models of Triple-Negative Breast Cancer Brain Metastasis

Author

Christine P. Carney, Anshika Kapur, Pavlos Anastasiadis, Rodney M. Ritzel, Chixiang Chen, Graeme F. Woodworth, Jeffrey A. Winkles, and Anthony J. Kim

Subjects
Drug Discovery, Pharmaceutical Science, Molecular Medicine
Abstract

Triple-negative breast cancer (TNBC) patients with brain metastasis (BM) face dismal prognosis due to the limited therapeutic efficacy of the currently available treatment options. We previously demonstrated that paclitaxel-loaded PLGA-PEG nanoparticles (NPs) directed to the Fn14 receptor, termed "DARTs", are more efficacious than Abraxane─an FDA-approved paclitaxel nanoformulation─following intravenous delivery in a mouse model of TNBC BM. However, the precise basis for this difference was not investigated. Here, we further examine the utility of the DART drug delivery platform in complementary xenograft and syngeneic TNBC BM models. First, we demonstrated that, in comparison to nontargeted NPs, DART NPs exhibit preferential association with Fn14-positive human and murine TNBC cell lines cultured

Published
2022

11. Synthesizing secondary data into survival analysis to improve estimation efficiency

Author

Chixiang Chen, Biyi Shen, Tonghui Yu, and Ming Wang

Subjects
Statistics and Probability, General Medicine, Statistics, Probability and Uncertainty
Abstract

The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.

Published
2022

12. Modeling multivariate age-related imaging variables with dependencies

Author

Hwiyoung Lee, Chixiang Chen, Peter Kochunov, Liyi Elliot Hong, and Shuo Chen

Subjects
Statistics and Probability, Aging, Diffusion Tensor Imaging, Epidemiology, Brain, Humans, White Matter, Algorithms
Abstract

Neuroimaging techniques have been increasingly used to understand the neural biology of aging brains. The neuroimaging variables from distinct brain locations and modalities can exhibit age-related patterns that reflect localized neural decline. However, it is a challenge to identify the impacts of risk factors (eg, mental disorders) on multivariate imaging variables while simultaneously accounting for the dependence structure and nonlinear age trajectories using existing tools. We propose a mixed-effects model to address this challenge by building random effects based on the latent brain aging status. We develop computationally efficient algorithms to estimate the parameters of new random effects. The simulations show that our approach provides accurate parameter estimates, improves the inference efficiency, and reduces the root mean square error compared to existing methods. We further apply this method to the UK Biobank data to investigate the effects of tobacco smoking on the white matter integrity of the entire brain during aging and identify the adverse effects on white matter integrity with multiple fiber tracts.

Published
2022

13. Brain charts for the human lifespan

Author

Armin Raznahan, Eric Courchesne, Andrea Parolin Jackowski, Kamen A. Tsvetanov, Cameron T. Ellis, R.C. Gur, Bin Bae J, Park Mtm, Pedro A. Valdes-Sosa, Simon N. Vandekar, Jacob W. Vogel, Juan Zhou, Machteld Marcelis, Kiho Im, Patricia Ellen Grant, Minhui Ouyang, Blesa Cabez M, Michael V. Lombardo, Sarah E. Morgan, James P. Boardman, Adamson C, Calhoun Vd, Delarue M, James H. Cole, Pichet Binette A, Roberto Toro, David H. Rowitch, Nynke A. Groenewold, Kevin M. Anderson, David T.W. Jones, Michael Schöll, Wang Ys, Aiden Corvin, R.E. Gur, Damien A. Fair, Gareth Ball, Herma Lina Schaare, Andrew Zalesky, Evdokia Anagnostou, Michael J. Meaney, Taki Y, Gareth J. Sullivan, Warrier, Petra E. Vértes, Chixiang Chen, Lisa T. Eyler, Wei Liao, Tomáš Paus, Jeremy A. Elman, Phillip McGuire, Hisham Ziauddeen, William S. Kremen, Etienne Vachon-Presseau, E.T. Bullmore, Christophe Tzourio, White, Hammill Cf, Mothersill D, Richard N. Henson, Jiang Qiu, Duncan E. Astle, Fabrice Crivello, Paul C. Fletcher, Chertavian C, Kim K, Jennifer Crosbie, Russell Schachar, Gabriel A. Devenyi, Manfred G. Kitzbichler, Tianye Jia, Trey Hedden, Sang Jae Lee, Ross D. Markello, Silke Kern, Ian M. Goodyer, Keith A. Johnson, Frauke Beyer, Bernard Mazoyer, A. Heinz, Sylvane Desrivières, Rosenberg, Gary Donohoe, Ong Mq, Alexander D. Edwards, Dan J. Stein, Nenad Medic, Zuo Xn, Travis T. Mallard, Peter Fonagy, Lindsay W. Victoria, Ingmar Skoog, Avram J. Holmes, Jason P. Lerch, Jed T. Elison, Jianfu Li, John H. Gilmore, Rosemary Holt, Caitlin K. Rollins, Carol E. Franz, Pedro Mario Pan, Saashi A Bedford, Yang N, Jonathan C Ipser, Richard A. I. Bethlehem, Tuulari Jj, Stolicyn A, Hua Huang, Bratislav Misic, Conor Liston, Ayub M, Lisa Ronan, Yeo Bt, Sophie Adler, Charles J. Lynch, Faith M. Gunning, Konrad Wagstyl, M. Mallar Chakravarty, John Suckling, Theodore D. Satterthwaite, Bharath Holla, Yap Seng Chong, Jinglei Lv, Jakob Seidlitz, Niall J Bourke, Xinlei Qian, Simon Baron-Cohen, Cynthia M. Ortinau, Deirel Paz Linares, Thyreau B, René S. Kahn, Aaron P. Schultz, Vanessa Cropley, Eric Westman, Mitchell Valdés-Sosa, Rik Ossenkoppele, André Zugman, Hasse Karlsson, Sylvia Villeneuve, Katja Heuer, Di Biase Ma, Margaret L. Westwater, Sofie L. Valk, David J. Sharp, Brigitte Landeau, Matthew Borzage, Kirsten A. Donald, Timothy Rittman, Richard Beare, Giovanni Abrahão Salum, Gunter Schumann, Ryuta Kawashima, Romero-Garcia R, John Blangero, Yun Hj, Russel T. Shinohara, Nicolas Crossley, Simon K. Warfield, Karen Pierce, George S. Alexopoulos, Katharine Dunlop, David C. Glahn, Francois Lalonde, Anqi Qiu, Lana Vasung, Gaël Chételat, Lídice Galán-García, Clifford R. Jack, Reisa A. Sperling, Anna Zettergren, Elizabeth Kelley, Arno Villringer, Andrea Mechelli, Benegal, Aaron Alexander-Bloch, Nicholas B. Turk-Browne, van Amelsvoort T, John D. Lewis, Heather C. Whalley, A. V. Witte, Zdenka Pausova, Joel T. Nigg, Heather J. Zar, Raymond J. Dolan, Christopher D. Smyser, Jay N. Giedd, Lena Palaniyappan, Ali Gholipour, Areces-Gonzalez A, Peter B. Jones, Jacqueline Hoare, Oskar Hansson, Linnea Karlsson, C Pantelis, Paly L, Bonnie Auyeung, Jorge Bosch-Bayard, Bethlehem, Richard [0000-0002-0714-0685], White, Simon [0000-0001-8642-7037], Astle, Duncan [0000-0002-7042-5392], Baron-Cohen, Simon [0000-0001-9217-2544], Henson, Rik [0000-0002-0712-2639], Jones, Peter [0000-0002-0387-880X], Kitzbichler, Manfred [0000-0002-4494-0753], Rittman, Timothy [0000-0003-1063-6937], Rowitch, David [0000-0002-0079-0060], Tsvetanov, Kamen A. [0000-0002-3178-6363], Westwater-Wozniak, Margaret [0000-0002-2918-0979], Ziauddeen, Hisham [0000-0003-4044-1719], Apollo - University of Cambridge Repository, British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, University of Cambridge [UK] (CAM), University of Pennsylvania, Yale University [New Haven], Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Child and Adolescent Psychiatry Department [AP- HP Hôpital Robert Debré], AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neuroscience - Department of Neuroscience, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Neurology, Amsterdam Neuroscience - Neurodegeneration, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Auyeung, B.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Baron-Cohen, S.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bedford, S. A.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Holt, R.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Lombardo, M. V.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Seidlitz, J.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Vogel, J. W.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. E.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. C.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Jackowski, A. P.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Chertavian, C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. E.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [White, S. R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Goodyer, I. M.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Henson, R. N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Jones, P. B.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Medic, N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Morgan, S. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Romero-Garcia, R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ronan, L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Suckling, J.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Vertes, P. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Warrier, V.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Westwater, M. L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ziauddeen, H.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Bullmore, E. T.] Univ Cambridge, Dept Psychiat, Cambridge, England, [White, S. R.] Univ Cambridge, MRC Biostat Unit, Cambridge, England, [Vogel, J. W.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Anderson, K. M.] Yale Univ, Dept Psychol, New Haven, CT USA, [Ellis, C. T.] Yale Univ, Dept Psychol, New Haven, CT USA, [Turk-Browne, N. B.] Yale Univ, Dept Psychol, New Haven, CT USA, [Adamson, C.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Ball, G.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Beare, R.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Jackowski, A. P.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Adamson, C.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Beare, R.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Adler, S.] UCL Great Ormond St Inst Child Hlth, London, England, [Alexopoulos, G. S.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Anagnostou, E.] Univ Toronto, Dept Pediat, Toronto, ON, Canada, [Anagnostou, E.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Pierce, K.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Areces-Gonzalez, A.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Paz-Linares, D.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Areces-Gonzalez, A.] Univ Pinar del Rio Hermanos Saiz Montes de Oca, Pinar Del Rio, Cuba, [Astle, D. E.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Henson, R. N.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Whalley, H. C.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Auyeung, B.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Pausova, Z.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Ayub, M.] Queens Univ, Dept Psychiat, Ctr Neurosci Studies, Kingston, ON, Canada, [Ayub, M.] UCL, Mental Hlth Neurosci Res Dept, Div Psychiat, London, England, [Bae, J.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Ball, G.] Univ Melbourne, Dept Paediat, Melbourne, Vic, Australia, [Baron-Cohen, S.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge Lifetime Asperger Syndrome Serv CLASS, Cambridge, England, [Benegal, V.] Natl Inst Mental Hlth & Neurosci NIMHANS, Ctr Addict Med, Bengaluru, India, [Beyer, F.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Villringer, A.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Witte, A. V.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Blangero, J.] Univ Texas Rio Grande Valley, South Texas Diabet & Obes Inst, Dept Human Genet, Edinburg, TX USA, [Blesa Cabez, M.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Boardman, J. P.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Sullivan, G.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Borzage, M.] Univ Southern Calif, Childrens Hosp Los Angeles, Keck Sch Med, Fetal & Neonatal Inst,Div Neonatol,Dept Pediat, Los Angeles, CA 90007 USA, [Bosch-Bayard, J. F.] Montreal Neurol Inst, Ludmer Ctr Neuroinformat & Mental Hlth, McGill Ctr Integrat Neurosci, Montreal, PQ, Canada, [Bosch-Bayard, J. F.] McGill Univ, Montreal, PQ, Canada, [Chakravarty, M. M.] McGill Univ, Montreal, PQ, Canada, [Bourke, N.] Imperial Coll London, Dept Brain Sci, London, England, [Sharp, D.] Imperial Coll London, Dept Brain Sci, London, England, [Alexander-Bloch, A. F.] Imperial Coll London, Dept Brain Sci, London, England, [Bourke, N.] Dementia Res Inst, Care Res & Technol Ctr, London, England, [Calhoun, V. D.] Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA, [Calhoun, V. D.] Emory Univ, Atlanta, GA 30322 USA, [Chakravarty, M. M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Comp Brain Anat CoBrA Lab, Montreal, PQ, Canada, [Chen, C.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Shinohara, R. T.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Chetelat, G.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Delarue, M.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Landeau, B.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Paly, L.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Chong, Y. S.] Agcy Sci Technol & Res, Singapore Inst Clin Sci, Singapore, Singapore, [Chong, Y. S.] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Obstet & Gynaecol, Singapore, Singapore, [Cole, J. H.] UCL, Ctr Med Image Comp CMIC, London, England, [Cole, J. H.] UCL, Dementia Res Ctr DRC, London, England, [Corvin, A.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Costantino, M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Verdun, PQ, Canada, [Costantino, M.] McGill Univ, Undergrad Program Neurosci, Montreal, PQ, Canada, [Courchesne, E.] Univ Calif San Diego, Dept Neurosci, San Diego, CA 92103 USA, [Courchesne, E.] Univ Calif San Diego, Autism Ctr Excellence, San Diego, CA 92103 USA, [Crivello, F.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Mazoyer, B.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Cropley, V. L.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Di Biase, M. A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Lv, J.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Zalesky, A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Hammill, C. F.] Hosp Sick Children, Toronto, ON, Canada, [Schachar, R. J.] Hosp Sick Children, Toronto, ON, Canada, [Crossley, N.] Pontificia Univ Catolica Chile, Sch Med, Dept Psychiat, Santiago, Chile, [Crossley, N.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [McGuire, P.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [Crossley, N.] Inst Milenio Intelligent Healthcare Engn, Santiago, Chile, [Delorme, R.] Robert Debre Univ Hosp, AP HP, Child & Adolescent Psychiat Dept, Paris, France, [Delorme, R.] Inst Pasteur, Human Genet & Cognit Funct, Paris, France, [Desrivieres, S.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Social Genet & Dev Psychiat Ctr, London, England, [Devenyi, G. A.] Douglas Mental Hlth Univ Inst, McGill Dept Psychiat, Cerebral Imaging Ctr, Montreal, PQ, Canada, [Devenyi, G. A.] McGill Univ, Dept Psychiat, Montreal, PQ, Canada, [Di Biase, M. A.] Harvard Med Sch, Brigham & Womens Hosp, Dept Psychiat, Boston, MA 02115 USA, [Dolan, R.] UCL, Max Planck UCL Ctr Computat Psychiat & Ageing Res, London, England, [Dolan, R.] Wellcome Ctr Human Neuroimaging, London, England, [Wagstyl, K.] Wellcome Ctr Human Neuroimaging, London, England, [Donald, K. A.] Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Div Dev Paediat, Cape Town, South Africa, [Donald, K. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Groenewold, N. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Donohoe, G.] Natl Univ Ireland Galway, Sch Psychol, Ctr Neuroimaging Cognit & Genom NICOG, Galway, Ireland, [Dunlop, K.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Lynch, C.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Edwards, A. D.] Kings Coll London, Ctr Dev Brain, London, England, [Edwards, A. D.] Evelina London Childrens Hosp, London, England, [Edwards, A. D.] MRC Ctr Neurodev Disorders, London, England, [Elison, J. T.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Fair, D. A.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Feczko, E.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Ellis, C. T.] Haskins Labs Inc, New Haven, CT USA, [Elman, J. A.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Franz, C. E.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Kremen, W. S.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Eyler, L.] VA San Diego Healthcare, Desert Pacific Mental Illness Res Educ & Clin Ctr, San Diego, CA USA, [Eyler, L.] Univ Calif San Diego, Dept Psychiat, Los Angeles, CA USA, [Fletcher, P. C.] Univ Cambridge, Dept Psychiat, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Wellcome Trust MRC Inst Metab Sci, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Jones, P. B.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Suckling, J.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Ziauddeen, H.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Fonagy, P.] UCL, Dept Clin Educ & Hlth Psychol, London, England, [Fonagy, P.] Anna Freud Natl Ctr Children & Families, London, England, [Galan-Garcia, L.] Cuban Ctr Neurosci, Havana, Cuba, [Valdes-Sosa, M. J.] Cuban Ctr Neurosci, Havana, Cuba, [Gholipour, A.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Warfield, S. K.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Giedd, J.] Univ Calif San Diego, Dept Child & Adolescent Psychiat, San Diego, CA 92103 USA, [Giedd, J.] Univ Calif San Diego, Dept Psychiat, San Diego, CA 92103 USA, [Gilmore, J. H.] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA, [Glahn, D. C.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Im, K.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Mathias, S. R.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Rodrigue, A.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Glahn, D. C.] Harvard Med Sch, Boston, MA 02115 USA, [Im, K.] Harvard Med Sch, Boston, MA 02115 USA, [Johnson, K. A.] Harvard Med Sch, Boston, MA 02115 USA, [Mathias, S. R.] Harvard Med Sch, Boston, MA 02115 USA, [Rodrigue, A.] Harvard Med Sch, Boston, MA 02115 USA, [Schultz, A. P.] Harvard Med Sch, Boston, MA 02115 USA, [Sperling, R. A.] Harvard Med Sch, Boston, MA 02115 USA, [Grant, P. E.] Harvard Med Sch, Fetal Neonatal Neuroimaging & Dev Sci Ctr, Boston Childrens Hosp, Div Newborn Med & Neuroradiol, Boston, MA 02115 USA, [Groenewold, N. A.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Zar, H. J.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Gunning, F. M.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Victoria, L. W.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Hammill, C. F.] Mouse Imaging Ctr, Toronto, ON, Canada, [Hansson, O.] Lund Univ, Dept Clin Sci Malmo, Clin Memory Res Unit, Malmo, Sweden, [Hansson, O.] Skane Univ Hosp, Memory Clin, Malmo, Sweden, [Hedden, T.] Icahn Sch Med Mt Sinai, Dept Neurol, New York, NY 10029 USA, [Hedden, T.] Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Boston, MA 02115 USA, [Heinz, A.] Charite Univ Med Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Free Univ Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Humboldt Univ, Dept Psychiat & Psychotherapy, Charite Campus Mitte, Berlin, Germany, [Heuer, K.] Max Planck Inst Human Cognit & Brain Sci, Dept Neuropsychol, Leipzig, Germany, [Heuer, K.] Univ Paris, Paris, France, [Toro, R.] Univ Paris, Paris, France, [Hoare, J.] Univ Cape Town, Dept Psychiat, Cape Town, South Africa, [Holla, B.] NIMHANS, Dept Integrat Med, Bengaluru, India, [Holla, B.] NIMHANS, Dept Psychiat, Accelerator Program Discovery Brain Disorders Usi, Bengaluru, India, [Holmes, A. J.] Yale Univ, Dept Psychol, New Haven, CT USA, [Villeneuve, S.] Yale Univ, Dept Psychol, New Haven, CT USA, [Holmes, A. J.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Villeneuve, S.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Huang, H.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Ouyang, M.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Huang, H.] Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA, [Ipser, J.] Univ Cape Town, Dept Psychiat & Mental Hlth, Clin Neurosci Inst, Cape Town, South Africa, [Jack, C. R., Jr.] Mayo Clin, Dept Radiol, Rochester, MN USA, [Jones, D. T.] Mayo Clin, Dept Radiol, Rochester, MN USA, Univ Fed Sao Paulo, Dept Psychiat, Sao Paulo, Brazil, [Jackowski, A. P.] Natl Inst Dev Psychiat, Beijing, Peoples R China, [Jia, T.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China, [Jia, T.] Fudan Univ, Minist Educ, Key Lab Computat Neurosci & Brain Inspired Intell, Shanghai, Peoples R China, [Jia, T.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Ctr Populat Neurosci & Precis Med PONS, SGDP Ctr, London, England, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Sperling, R. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Johnson, K. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Sperling, R. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Radiol, Boston, MA USA, [Jones, D. T.] Mayo Clin, Dept Neurol, Rochester, MN USA, [3R-BRAIN] Mayo Clin, Dept Neurol, Rochester, MN USA, [Kahn, R. S.] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY 10029 USA, [Karlsson, H.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, L.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, H.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, L.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Tuulari, J. J.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Turku, Finland, [Tuulari, J. J.] Turku Univ Hosp, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, H.] Univ Turku, Turku, Finland, [Karlsson, L.] Univ Turku, Turku, Finland, [Kawashima, R.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Taki, Y.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Thyreau, B.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychol, Kingston, ON, Canada, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychiat, Kingston, ON, Canada, [Kern, S.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Skoog, I.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Zettergren, A.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Kern, S.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Skoog, I.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Kim, K. W.] Seoul Natl Univ, Dept Brain & Cognit Sci, Coll Nat Sci, Seoul, South Korea, [Kim, K. W.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Kim, K. W.] Seoul Natl Univ, Dept Psychiat, Coll Med, Seoul, South Korea, [Kim, K. W.] SNU MRC, Inst Human Behav Med, Seoul, South Korea, [Lalonde, F.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Raznahan, A.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Lee, S.] Seoul Natl Univ, Coll Nat Sci, Dept Brain & Cognit Sci, Seoul, South Korea, [Lerch, J.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada, [Lerch, J.] Univ Oxford, Nuffield Dept Clin Neurosci, FMRIB, Wellcome Ctr Integrat Neuroimaging, Oxford, England, [Lewis, J. D.] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada, [Li, J.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liao, W.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Valdes-Sosa, P. A.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liston, C.] Weill Cornell Med, Dept Psychiat, New York, NY USA, [Liston, C.] Weill Cornell Med, Brain & Mind Res Inst, New York, NY USA, [Lombardo, M. V.] Ist Italiano Tecnol, Ctr Neurosci & Cognit Syst UniTn, Lab Autism & Neurodev Disorders, Rovereto, Italy, [Lv, J.] Univ Sydney, Sch Biomed Engn, Sydney, NSW, Australia, [Lv, J.] Univ Sydney, Brain & Mind Ctr, Sydney, NSW, Australia, [Mallard, T. T.] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA, [Marcelis, M.] Maastricht Univ, Sch Mental Hlth & Neurosci, Dept Psychiat & Neuropsychol, EURON,Med Ctr, Maastricht, Netherlands, [Marcelis, M.] Inst Mental Hlth Care Eindhoven GGzE, Eindhoven, Netherlands, [Markello, R. D.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Misic, B.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Vasung, L.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Mazoyer, B.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Singapore Inst Clin Sci, Singapore, Singapore, [Mechelli, A.] Bordeaux Univ Hosp, Bordeaux, France, [Morgan, S. E.] Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England, [Morgan, S. E.] Alan Turing Inst, London, England, [Vertes, P. E.] Alan Turing Inst, London, England, [Mothersill, D.] Natl Coll Ireland, Sch Business, Dept Psychol, Dublin, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Sch Psychol, Galway, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Ctr Neuroimaging & Cognit Genom, Galway, Ireland, [Mothersill, D.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Nigg, J.] Oregon Hlth & Sci Univ, Dept Psychiat, Sch Med, Portland, OR 97201 USA, [Ong, M. Q. W.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Qian, X.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Ortinau, C.] Washington Univ, Dept Pediat, St Louis, MO 63130 USA, [Ossenkoppele, R.] Vrije Univ Amsterdam, Alzheimer Ctr Amsterdam, Amsterdam UMC, Dept Neurol,Amsterdam Neurosci, Amsterdam, Netherlands, [Ossenkoppele, R.] Lund Univ, Clin Memory Res Unit, Lund, Sweden, [Palaniyappan, L.] Univ Western Ontario, Robarts Res Inst, London, ON, Canada, [Palaniyappan, L.] Univ Western Ontario, Brain & Mind Inst, London, ON, Canada, [Pan, P. M.] Fed Univ Sao Poalo UNIFESP, Dept Psychiat, Sao Poalo, Brazil, [Pan, P. M.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Zugman, A.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Pantelis, C.] Univ Melbourne, Dept Psychiat, Melbourne Neuropsychiat Ctr, Carlton, Vic, Australia, [Pantelis, C.] Melbourne Hlth, Carlton, Vic, Australia, [Pantelis, C.] Univ Melbourne, Melbourne Sch Engn, Parkville, Vic, Australia, [Pantelis, C.] Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia, [Park, M. M.] Western Univ, Schulich Sch Med & Dent, Dept Psychiat, London, ON, Canada, [Rollins, C. K.] Univ Montreal, Dept Psychiat, Fac Med, Montreal, PQ, Canada, [Rollins, C. K.] Univ Montreal, CHU St Justine, Montreal, PQ, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychiat, Toronto, ON, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Physiol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Nutr Sci, Toronto, ON, Canada, [Paz-Linares, D.] Cuban Neurosci Ctr, Havana, Cuba, [Pichet Binette, A.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Villeneuve, S.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Pichet Binette, A.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Villeneuve, S.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Qiu, J.] Southwest Univ, Sch Psychol, Chongqing, Peoples R China, [Qiu, A.] Natl Univ Singapore, N1 Inst Hlth, Dept Biomed Engn, Singapore, Singapore, [Rittman, T.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Tsvetanov, K. A.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Rollins, C. K.] Harvard Med Sch, Dept Neurol, Boston, MA 02115 USA, [Rollins, C. K.] Boston Childrens Hosp, Dept Neurol, Boston, MA USA, [Romero-Garcia, R.] Univ Seville, Dpto Fisiol Med & Biofis, Inst Biomed Sevilla IBiS HUVR CSIC, Seville, Spain, [Rosenberg, M. D.] Univ Chicago, Dept Psychol, 5848 S Univ Ave, Chicago, IL 60637 USA, [Rosenberg, M. D.] Univ Chicago, Inst Neurosci, Chicago, IL USA, [Rowitch, D. H.] Univ Cambridge, Dept Paediat, Cambridge, England, [Rowitch, D. H.] Univ Cambridge, Wellcome MRC Cambridge Stem Cell Inst, Cambridge, England, [Salum, G. A.] Univ Fed Rio Grande Sul UFRGS, Hosp Clin Porto Alegre, Dept Psychiat, Porto Alegre, RS, Brazil, [Salum, G. A.] Natl Inst Dev Psychiat INPD, Sao Paulo, Brazil, [Schaare, H. L.] Max Planck Inst Human Cognit & Brain Sci, Otto Hahn Grp Cognit Neurogenet, Leipzig, Germany, [Schaare, H. L.] Res Ctr Juelich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Charlestown, MA USA, [Schumann, G.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Ctr Populat Neurosci & Stratified Med PONS, Shanghai, Peoples R China, [Schumann, G.] Charite Campus Mitte, Dept Psychiat & Psychotherapy, Charite Mental Hlth, PONS Ctr, Berlin, Germany, [Scholl, M.] Univ Gothenburg, Wallenberg Ctr Mol & Translat Med, Gothenburg, Sweden, [Scholl, M.] Univ Gothenburg, Dept Psychiat & Neurochem, Gothenburg, Sweden, [Scholl, M.] UCL, Queens Sq Inst Neurol, Dementia Res Ctr, London, England, [Sharp, D.] UK Dementia Res Inst, Care Res & Technol Ctr, London, England, [Shinohara, R. T.] Univ Penn, Perelman Sch Med, Dept Radiol, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Neurol, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Pediat, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA, [Stein, D. J.] Univ Cape Town, Dept Psychiat, SA MRC Unit Risk & Resilience Mental Disorders, Cape Town, South Africa, [Stein, D. J.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Stolicyn, A.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Whalley, H. C.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Toro, R.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Univ Paris 05, Ctr Res & Interdisciplinar CRI, Paris, France, [Tsvetanov, K. A.] Univ Cambridge, Dept Psychol, Cambridge, England, [Turk-Browne, N. B.] Yale Univ, Wu Tsai Inst, New Haven, CT USA, [Tuulari, J. J.] Univ Turku, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Turku Coll Sci Med & Technol, Turku, Finland, [Tzourio, C.] Univ Bordeaux, Bordeaux Populat Hlth Res Ctr, CHU Bordeaux, U1219,INSERM, Bordeaux, France, [Vachon-Presseau, E.] McGill Univ, Fac Dent Med & Oral Hlth Sci, Montreal, PQ, Canada, [Valdes-Sosa, P. A.] McGill Univ, Alan Edwards Ctr Res Pain AECRP, Montreal, PQ, Canada, [Valk, S. L.] Forschungszentrum Julich, Inst Neurosci & Med 7, Julich, Germany, [Valk, S. L.] Max Planck Inst Human Cognit & Brain Sci, Leipzig, Germany, [van Amelsvoort, T.] Maastricht Univ, Dept Psychiat & Neurosychol, Maastricht, Netherlands, [Vandekar, S. N.] Vanderbilt Univ, Dept Biostat, 221 Kirkland Hall, Nashville, TN 37235 USA, [Villeneuve, S.] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA, [Villringer, A.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Witte, A. V.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Zuo, X. N.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Wang, Y. S.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yeo, B.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Wang, Y. S.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Wang, Y. S.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Yang, N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Zuo, X. N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Wang, Y. S.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Yang, N.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Westman, E.] Karolinska Inst, Ctr Alzheimer Res, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden, [Witte, A. V.] Univ Leipzig, CRC 1052 Obes Mech, Fac Med, Leipzig, Germany, [Zhou, J. H.] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat MR Res, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, N1 Inst Hlth, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Inst Digital Med, Singapore, Singapore, [Yun, H.] Natl Univ Singapore, Integrat Sci & Engn Programme ISEP, Singapore, Singapore, [Zar, H. J.] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic, Australia, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat Magnet Resonance Res, Singapore, Singapore, [Ziauddeen, H.] Univ Cambridge, Wellcome Trust MRC Inst Metab Sci, Cambridge, England, [Zugman, A.] NIMH, NIH, Bethesda, MD 20892 USA, [Zugman, A.] Escola Paulista Med, Dept Psychiat, Sao Paulo, Brazil, [Zuo, X. N.] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning, Peoples R China, British Academy Postdoctoral fellowship, NIMH, UKRI Medical Research Council, NIHR Cambridge Biomedical Research Centre, NIHR Senior Investigator award, MRC research infrastructure award, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Ontario Brain Institute

Subjects
631/378/2649, OpenPain Project, KNE96, Growth, Psychiatric-disorders, DISEASE, 3R-BRAIN, Brain charts, MRI Brain, OASIS-3, Disease, CCNP, 631/378/2571, UMN BCP, Multidisciplinary, medicine.diagnostic_test, PSYCHIATRIC-DISORDERS, article, Brain, Human brain, ASSOCIATION, Magnetic Resonance Imaging, Harvard Aging Brain Study, The Mayo Clinic Study of Aging, NSPN, medicine.anatomical_structure, GROWTH, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ddc:500, BURDEN, WHITE-MATTER, FinnBrain, Harvard Aging Brain Study, Organization, Mri, MRI, medicine.medical_specialty, Concurrent validity, MODELS, Cam-CAN, Longevity, CALM Team, POND, Neuroimaging, Burden, ORGANIZATION, AIBL, The PREVENT-AD Research Group, VETSA, Cortical thickness, Association, Physical medicine and rehabilitation, FinnBrain, IMAGEN, KNE96, White-matter, medicine, Humans, ASRB, 631/378/1689, COBRE, business.industry, 631/378/2611, Brain morphometry, Neurosciences, Alzheimer’s Disease Repository Without Borders Investigators, Magnetic resonance imaging, Alzheimer’s Disease Neuroimaging Initiative, Anthropometry, Body Height, Brain growth, Birth, 59/57, Normative, IMAGEN, ENIGMA Developmental Brain Age working group, NSPN, business, CCNP, 3R-BRAIN, CORTICAL THICKNESS, Developing Human Connectome Project, ENIGMA Developmental Brain Age working group, The PREVENT-AD Research Group, VETSA, Bullmore, E.T
Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Published
2022
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14. Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

Author

Chen Mo, Jingtao Wang, Zhenyao Ye, Hongjie Ke, Song Liu, Kathryn Hatch, Si Gao, Jessica Magidson, Chixiang Chen, Braxton D. Mitchell, Peter Kochunov, L. Elliot Hong, Tianzhou Ma, and Shuo Chen

Subjects
Psychiatry and Mental health, Medicine (miscellaneous)
Abstract

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.United Kingdom.The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Published
2022

15. Omnibus and robust deconvolution scheme for bulk RNA sequencing data integrating multiple single-cell reference sets and prior biological knowledge

Author

Chixiang Chen, Yuk Yee Leung, Matei Ionita, Li-San Wang, and Mingyao Li

Subjects
Statistics and Probability, Computational Mathematics, Computational Theory and Mathematics, Sequence Analysis, RNA, Gene Expression Profiling, RNA, Single-Cell Analysis, Molecular Biology, Biochemistry, Software, Computer Science Applications
Abstract

Motivation Cell-type deconvolution of bulk tissue RNA sequencing (RNA-seq) data is an important step toward understanding the variations in cell-type composition among disease conditions. Owing to recent advances in single-cell RNA sequencing (scRNA-seq) and the availability of large amounts of bulk RNA-seq data in disease-relevant tissues, various deconvolution methods have been developed. However, the performance of existing methods heavily relies on the quality of information provided by external data sources, such as the selection of scRNA-seq data as a reference and prior biological information. Results We present the Integrated and Robust Deconvolution (InteRD) algorithm to infer cell-type proportions from target bulk RNA-seq data. Owing to the innovative use of penalized regression with a new evaluation criterion for deconvolution, InteRD has three primary advantages. First, it is able to effectively integrate deconvolution results from multiple scRNA-seq datasets. Second, InteRD calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Third, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system. Extensive numerical evaluations and real-data applications demonstrate that InteRD yields more accurate and robust cell-type proportion estimates that agree well with known biology. Availability and implementation The proposed InteRD framework is implemented in R and the package is available at https://cran.r-project.org/web/packages/InteRD/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2022

16. Single Nucleotide Polymorphisms (SNP) and SNP-SNP Interactions of the Surfactant Protein Genes Are Associated With Idiopathic Pulmonary Fibrosis in a Mexican Study Group; Comparison With Hypersensitivity Pneumonitis

Author

Ata Abbasi, Chixiang Chen, Chintan K. Gandhi, Rongling Wu, Annie Pardo, Moises Selman, and Joanna Floros

Subjects
Surface-Active Agents, Immunology, Immunology and Allergy, Humans, Pulmonary Surfactants, Fibrosis, Polymorphism, Single Nucleotide, Idiopathic Pulmonary Fibrosis, Alveolitis, Extrinsic Allergic
Abstract

Surfactant proteins (SPs) are important for normal lung function and innate immunity of the lungs and their genes have been identified with significant genetic variability. Changes in quantity or quality of SPs due to genetic mutations or natural genetic variability may alter their functions and contribute to the host susceptibility for particular diseases. Alternatively, SP single nucleotide polymorphisms (SNPs) can serve as markers to identify disease risk or response to therapies, as shown for other genes in a number of other studies. In the current study, we evaluated associations of SFTP SNPs with idiopathic pulmonary fibrosis (IPF) by studying novel computational models where the epistatic effects (dominant, additive, recessive) of SNP-SNP interactions could be evaluated, and then compared the results with a previously published hypersensitivity pneumonitis (HP) study where the same novel models were used. Mexican Hispanic patients (IPF=84 & HP=75) and 194 healthy control individuals were evaluated. The goal was to identify SP SNPs and SNP-SNP interactions that associate with IPF as well as SNPs and interactions that may be unique to each of these interstitial diseases or common between them. We observed: 1) in terms of IPF, i) three single SFTPA1 SNPs to associate with decreased IPF risk, ii) three SFTPA1 haplotypes to associate with increased IPF risk, and iii) a number of three-SNP interactions to associate with IPF susceptibility. 2) Comparison of IPF and HP, i) three SFTPA1 and one SFTPB SNP associated with decreased risk in IPF but increased risk in HP, and one SFTPA1 SNP associated with decreased risk in both IPF and HP, ii) a number of three-SNP interactions with the same or different effect pattern associated with IPF and/or HP susceptibility, iii) one of the three-SNP interactions that involved SNPs of SFTPA1, SFTPA2, and SFTPD, with the same effect pattern, was associated with a disease-specific outcome, a decreased and increased risk in HP and IPF, respectively. This is the first study that compares the SP gene variants in these two phenotypically similar diseases. Our findings indicate that SNPs of all SFTPs may play an important role in the genetic susceptibility to IPF and HP. Importantly, IPF and HP share some SP genetic variants, suggesting common pathophysiological mechanisms and pathways regarding surfactant biogenesis, but also some differences, highlighting the diverse underlying pathogenic mechanisms between an inflammatory-driven fibrosis (HP) and an epithelial-driven fibrosis (IPF). Alternatively, the significant SNPs identified here, along with SNPs of other genes, could serve as markers to distinguish these two devastating diseases.

Published
2021

17. A Computational Atlas of Tissue-specific Regulatory Networks

Author

Rongling Wu, Libo Jiang, Chixiang Chen, Biyi Shen, Ming Wang, Vernon M. Chinchilli, and Christopher H. Griffin

Subjects
Set (abstract data type), Human health, Computer science, Atlas (topology), Expression data, Genomic data, Gene regulatory network, Tissue specific, Computational biology, Gene
Abstract

The pattern of how gene co-regulation varies across tissues determines human health. However, inferring tissue-specific regulatory networks and associating them with human phenotypes represent a substantial challenge because multi-tissue projects, including the GTEx, typically contain expression data measured only at one time point from highly heterogeneous donors. Here, we implement an interdisciplinary framework for assembling and programming genomic data from multiple tissues into fully informative gene networks, encapsulated by a complete set of bi-directional, signed, and weighted interactions, from static expression data. This framework can monitor how gene networks change simultaneously across tissues and individuals, infer gene-driven inter-tissue wiring networks, compare and test topological alterations of gene/tissue networks between health states, and predict how regulatory networks evolve across spatiotemporal gradients. Our framework provides a tool to catalogue a comprehensive encyclopedia of mechanistic gene networks that walk medical researchers through tissues in each individual and through individuals for each tissue, facilitating the translation of multi-tissue data into clinical practices.

Published
2021
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18. DHODH inhibition impedes glioma stem cell proliferation, induces DNA damage, and prolongs survival in orthotopic glioblastoma xenografts

Author

Raffaella, Spina, Ian, Mills, Fahim, Ahmad, Chixiang, Chen, Heather M, Ames, Jeffrey A, Winkles, Graeme F, Woodworth, and Eli E, Bar

Subjects
Mice, Brain Neoplasms, Cell Line, Tumor, Dihydroorotate Dehydrogenase, Neoplastic Stem Cells, Humans, Animals, Glioma, Glioblastoma, DNA Damage, Cell Proliferation
Abstract

Glioma stem cells (GSCs) promote tumor progression and therapeutic resistance and exhibit remarkable bioenergetic and metabolic plasticity, a phenomenon that has been linked to their ability to escape standard and targeted therapies. However, specific mechanisms that promote therapeutic resistance have been somewhat elusive. We hypothesized that because GSCs proliferate continuously, they may require the salvage and de novo nucleotide synthesis pathways to satisfy their bioenergetic needs. Here, we demonstrate that GSCs lacking EGFR (or EGFRvIII) amplification are exquisitely sensitive to de novo pyrimidine synthesis perturbations, while GSCs that amplify EGFR are utterly resistant. Furthermore, we show that EGFRvIII promotes BAY2402234 resistance in otherwise BAY2402234 responsive GSCs. Remarkably, a novel, orally bioavailable, blood-brain-barrier penetrating, dihydroorotate dehydrogenase (DHODH) inhibitor BAY2402234 was found to abrogate GSC proliferation, block cell-cycle progression, and induce DNA damage and apoptosis. When dosed daily by oral gavage, BAY2402234 significantly impaired the growth of two different intracranial human glioblastoma xenograft models in mice. Given this observed efficacy and the previously established safety profiles in preclinical animal models and human clinical trials, the clinical testing of BAY2402234 in patients with primary glioblastoma that lacks EGFR amplification is warranted.

Published
2021

19. Simultaneous confidence bands for growth incidence curves in weighted sup-norm metrics

Author

Fabian Dunker, Biyi Shen, and Chixiang Chen

Subjects
Statistics and Probability, 010104 statistics & probability, Uniform norm, 0502 economics and business, 05 social sciences, Statistics, 0101 mathematics, Statistics, Probability and Uncertainty, 01 natural sciences, 050205 econometrics, Incidence (geometry), Mathematics
Abstract

The so-called growth incidence curve (GIC) is a popular way to evaluate the distributional pattern of economic growth and pro-poorness of growth in development economics. The log-transforma...

Published
2019
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20. np2<scp>QTL</scp>: networking phenotypic plasticity quantitative trait loci across heterogeneous environments

Author

Ming Wang, Libo Jiang, Rongling Wu, Chixiang Chen, Xuli Zhu, and Meixia Ye

Subjects
0106 biological sciences, 0301 basic medicine, Phenotypic plasticity, fungi, food and beverages, Cell Biology, Plant Science, Biology, Quantitative trait locus, 01 natural sciences, Genetic architecture, 03 medical and health sciences, 030104 developmental biology, Gene mapping, Evolutionary biology, Genetics, Epistasis, Weighted network, Allometry, Adaptation, 010606 plant biology & botany
Abstract

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

Published
2019
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21. Vascular Inflammation, Calf Muscle Oxygen Saturation, and Blood Glucose are Associated With Exercise Pressor Response in Symptomatic Peripheral Artery Disease

Author

Marcos Kuroki, Andrew W. Gardner, Chixiang Chen, Danielle Jin Kwang Kim, Ming Wang, and Polly S. Montgomery

Subjects
Adult, Blood Glucose, Male, medicine.medical_specialty, medicine.medical_treatment, Inflammation, 030204 cardiovascular system & hematology, Carbohydrate metabolism, Microcirculation, Peripheral Arterial Disease, 03 medical and health sciences, Oxygen Consumption, 0302 clinical medicine, Internal medicine, Diabetes mellitus, medicine, Humans, Treadmill, Muscle, Skeletal, Exercise, Aged, Exercise Tolerance, business.industry, Insulin, Intermittent Claudication, Middle Aged, medicine.disease, Oxygen, Oxidative Stress, Blood pressure, Cardiology, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, Claudication, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

We determined whether calf muscle oxygen saturation (StO2) and vascular biomarkers of inflammation and oxidative stress were associated with an exercise pressor response during treadmill walking in 179 patients with symptomatic peripheral artery disease (PAD). The exercise pressor response was measured as the change in blood pressure from rest to the end of the first 2-minute treadmill stage (2 mph, 0% grade). There was a wide range in the change in systolic blood pressure (−46 to 50 mm Hg) and in diastolic blood pressure (−23 to 38 mm Hg), with mean increases of 4.3 and 1.4 mm Hg, respectively. In multiple regression analyses, significant predictors of systolic pressure included glucose ( P < .001) and insulin ( P = .039). Significant predictors of diastolic pressure included cultured endothelial cell apoptosis ( P = .019), the percentage drop in exercise calf muscle (StO2; P = .023), high-sensitivity C-reactive protein ( P = .032), and glucose ( P = .033). Higher levels in pro-inflammatory vascular biomarkers, impaired calf muscle StO2 during exercise, and elevated blood glucose were independently associated with greater exercise pressor response in patients with symptomatic PAD. The clinical implication is that exercise and nutritional interventions designed to improve inflammation, microcirculation, and glucose metabolism may also lower blood pressure during exercise in patients with symptomatic PAD.

Published
2019
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22. Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor

Author

Suresh V. Kuchipudi, Dishary Banerjee, Nina R. Boyle, Ratul Chowdhury, Chixiang Chen, Ruth H. Nissly, Vandergrift K, Victoria S. Cavener, Abhinay Gontu, Costas D. Maranas, Veda Sheersh Boorla, and Meera Surendran Nair

Subjects
chemistry.chemical_classification, Molecular dynamics, symbols.namesake, chemistry, Viral entry, Binding energy, Pi, Solvation, symbols, Computational biology, van der Waals force, Receptor, Amino acid
Abstract

The association of the receptor binding domain (RBD) of SARS-CoV-2 viral spike with human angiotensin converting enzyme (hACE2) represents the first required step for viral entry. Amino acid changes in the RBD have been implicated with increased infectivity and potential for immune evasion. Reliably predicting the effect of amino acid changes in the ability of the RBD to interact more strongly with the hACE2 receptor can help assess the public health implications and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born electrostatic solvation, hydrogen-bonding, π-π packing and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity using the decomposed energy terms as descriptors. The computational base achieves an accuracy of 82.2% in terms of correctly classifying single amino-acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient r of 0.69 between predicted and experimentally calculated binding affinities. Both metrics are calculated using a 5-fold cross validation test. Our method thus sets up a framework for effectively screening binding affinity change with unknown single and multiple amino-acid changes. This can be a very valuable tool to predict host adaptation and zoonotic spillover of current and future SARS-CoV-2 variants.

Published
2021
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23. Apoptotic Extracellular Vesicles (ApoEVs) Safeguard Liver Homeostasis and Regeneration via Assembling an ApoEV-Golgi Organelle

Author

Ma L, Sui B, Yanbin V. Wang, Yueqin Liu, Wang R, Jun Tang, Yongshui Fu, Jacobson O, Kwok Rtk, Kou X, Yan X, Haixiang Liu, Chixiang Chen, Xiang L, Xuyao Zhang, Songtao Shi, Deyan Wu, Wang M, Ho-Chou Tu, Yan Jin, Ben Zhong Tang, Yan H, L. Lu, and Xuejun Chen

Subjects
Liver injury, media_common.quotation_subject, Golgi apparatus, Biology, medicine.disease, Liver regeneration, Cell biology, symbols.namesake, Microtubule, Organelle, medicine, symbols, Internalization, Cytokinesis, Homeostasis, media_common
Abstract

SummaryApoptosis is an integral physiological cell death process that occurs frequently and generates a huge number of apoptotic extracellular vesicles (apoEVs). However, whether apoEVs are necessary for maintaining organ homeostasis remains unclear. Here, we show that circulatory apoEVs engraft in liver and undergo specialized internalization by hepatocytes (HCs) based on surface signature of galactose and N-acetylgalactosamine. Furthermore, apoEVs rescue liver injury in apoptotic-deficient Fas mutant and Caspase-3 knockout mice, which is exerted by restoring the featured hepatic ploidy homeostasis. Surprisingly, apoEVs form a chimeric organelle complex with recipient Golgi apparatus via SNARE-mediated membrane interaction, which consequently facilitates microtubule organization and HC cytokinesis. Notably, through Golgi recovery and ploidy transition, apoEVs contribute to liver regeneration and protect against acute hepatic failure. Collectively, these results identify a previously unrecognized role for apoEVs and the specific mechanisms by which they safeguard liver homeostasis, and suggest the potential of apoEV-based therapy for liver disorders.

Published
2021
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24. A multiple robust propensity score method for longitudinal analysis with intermittent missing data

Author

Rongling Wu, Biyi Shen, Ming Wang, Aiyi Liu, and Chixiang Chen

Subjects
Statistics and Probability, Computer science, Inference, Feature selection, Machine learning, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Article, Covariate, Propensity Score, Likelihood Functions, Models, Statistical, General Immunology and Microbiology, business.industry, Applied Mathematics, Relaxation (iterative method), General Medicine, Missing data, Empirical likelihood, Research Design, Data Interpretation, Statistical, Propensity score matching, Observational study, Artificial intelligence, General Agricultural and Biological Sciences, business, computer
Abstract

Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

Published
2020

25. Inhibition of UDP-glucose dehydrogenase by 6-thiopurine and its oxidative metabolites: Possible mechanism for its interaction within the bilirubin excretion pathway and 6TP associated liver toxicity

Author

Chixiang Chen, Ryan J. Rafferty, Chamitha Weeramange, and Cassie M. Binns

Subjects
0301 basic medicine, Antimetabolites, Bilirubin, Metabolite, Clinical Biochemistry, Glucuronidation, Pharmaceutical Science, Oxidative phosphorylation, Pharmacology, Uridine Diphosphate Glucose Dehydrogenase, Analytical Chemistry, Excretion, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Drug Discovery, medicine, Spectroscopy, Mercaptopurine, Jaundice, 030104 developmental biology, Liver, chemistry, 030220 oncology & carcinogenesis, Toxicity, Microsomes, Liver, Chemical and Drug Induced Liver Injury, medicine.symptom, Drug metabolism
Abstract

6-Thiopurine (6TP) is an actively prescribed drug in the treatment of various diseases ranging from Crohn’s disease and other inflammatory diseases to acute lymphocytic leukemia and non-Hodgkin’s leukemia. While 6TP has beneficial therapeutic uses, severe toxicities are also reported with its use, such as jaundice and liver toxicity. While numerous investigations into the mode in which toxicity originates has been undertaken. None have investigated the effects of inhibition towards UDP-Glucose Dehydrogenase (UDPGDH), an oxidative enzyme responsible for UDP-glucuronic acid (UDPGA) formation or UDP-Glucuronosyl transferase (UGT1A1), which is responsible for the conjugation of bilirubin with UDPGA for excretion. Failure to excrete bilirubin leads to jaundice and liver toxicity. We proposed that either 6TP or its primary oxidative excretion metabolites inhibit one or both of these enzymes, resulting in the observed toxicity from 6TP administration. Inhibition analysis of these purines revealed that 6-thiopurine has weak to no inhibition towards UDPGDH with a Ki of 288 μM with regard to varying UDP-glucose, but 6-thiouric (primary end metabolite, fully oxidized at carbon 2 and 8, and highly retained by the body) has a near six-fold increased inhibition towards UDPGDH with a Ki of 7 μM. Inhibition was also observed by 6-thioxanthine (oxidized at carbon 2) and 8-OH-6TP with Ki values of 54 and 14 μM, respectively. Neither 6-thiopurine or its excretion metabolites were shown to inhibit UGT1A1. Our results show that the C2 and C8 positions of 6TP are pivotal in said inhibition towards UDPGDH and have no effect upon UGT1A1, and that blocking C8 could lead to new analogs with reduced, if not eliminated jaundice and liver toxicities.

Published
2018
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26. Effect of adipose tissue thickness, muscle site, and sex on near-infrared spectroscopy derived total-[hemoglobin + myoglobin]

Author

Samuel L. Wilcox, Jesse C. Craig, Thomas J. Barstow, Ryan M. Broxterman, and Chixiang Chen

Subjects
Adult, Male, medicine.medical_specialty, Physiology, Adipose tissue, 030204 cardiovascular system & hematology, Hemoglobins, Young Adult, 03 medical and health sciences, chemistry.chemical_compound, Sex Factors, 0302 clinical medicine, Nuclear magnetic resonance, Physiology (medical), Internal medicine, medicine, Humans, Muscle, Skeletal, Spectroscopy, Spectroscopy, Near-Infrared, Myoglobin, Chemistry, Attenuation, Near-infrared spectroscopy, 030229 sport sciences, Total hemoglobin, Endocrinology, Adipose Tissue, Female
Abstract

Craig JC, Broxterman RM, Wilcox SL, Chen C, Barstow TJ. Effect of adipose tissue thickness, muscle site, and sex on near-infrared spectroscopy derived total-[hemoglobin + myoglobin]. J Appl Physiol 123: 1571–1578, 2017. First published September 21, 2017; doi: 10.1152/japplphysiol.00207.2017 .—Adipose tissue thickness (ATT) attenuates signals from near-infrared spectroscopy (NIRS) and diminishes the absolute quantification of underlying tissues by contemporary NIRS devices. Based on the relationship between NIRS-derived total-[hemoglobin + myoglobin] (total-[Hb + Mb]) and ATT, we tested the hypotheses that the correction factor for ATT 1) is muscle site specific; 2) does not differ between men and women; and that 3) exclusion of the shortest source-detector distance from data analysis increases total-[Hb + Mb]. Fourteen healthy subjects (7 men) rested in a neutral body position (supine or prone) while measurements of total-[Hb + Mb] and ATT were taken at four muscles common to resting and exercise studies: vastus lateralis (VL), rectus femoris (RF), gastrocnemius (GS), and flexor digitorum superficialis (FDS). ATT averaged 6.0 ± 0.4 mm across all muscles. Every muscle showed a negative slope ( r2: 0.6–0.94; P < 0.01) for total-[Hb + Mb] as a function of ATT: VL (−34 μM/mm), RF (−26 μM/mm), GS (−54 μM/mm), and FDS (−33 μM/mm). The projected total-[Hb + Mb] at 0 mm ATT ( y-intercept) was 452, 372, 620, and 456 μM for VL, RF, GS, and FDS, respectively. No differences were found between the sexes within VL, RF, or FDS, but men had a greater projected total-[Hb + Mb] at 0 mm for GS (688 ± 44 vs. 552 ± 40 μM; P < 0.05). Exclusion of the shortest source-detector distance increased total-[Hb + Mb] by 12 ± 1 μM ( P < 0.05). The present findings demonstrate that total-[Hb + Mb] should be corrected for ATT using muscle site-specific factors which are not sex specific, except in the case of GS. NEW & NOTEWORTHY Near-infrared spectroscopy (NIRS) is an important tool for physiologists and clinicians. However, adipose tissue greatly attenuates the signals from these devices. Correcting for this attenuation has been suggested based on the strength of the relationship between NIRS-derived measurements and the adipose tissue thickness. We show that this relationship is unique to the muscle site of interest but may not be sex specific. Accurate quantification of underlying tissue mandates researchers correct for adipose tissue thickness.

Published
2017
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27. A Robust Consistent Information Criterion for Model Selection based on Empirical Likelihood

Author

Ming Wang, Rongling Wu, Chixiang Chen, and Runze Li

Subjects
Statistics and Probability, FOS: Computer and information sciences, Mathematical optimization, Computer science, Model selection, Estimator, Feature selection, Information Criteria, Estimating equations, Marginal likelihood, Methodology (stat.ME), Empirical likelihood, Statistics, Probability and Uncertainty, Generalized estimating equation, Statistics - Methodology
Abstract

Conventional likelihood-based information criteria for model selection rely on the distribution assumption of data. However, for complex data that are increasingly available in many scientific fields, the specification of their underlying distribution turns out to be challenging, and the existing criteria may be limited and are not general enough to handle a variety of model selection problems. Here, we propose a robust and consistent model selection criterion based upon the empirical likelihood function which is data-driven. In particular, this framework adopts plug-in estimators that can be achieved by solving external estimating equations, not limited to the empirical likelihood, which avoids potential computational convergence issues and allows versatile applications, such as generalized linear models, generalized estimating equations, penalized regressions and so on. The formulation of our proposed criterion is initially derived from the asymptotic expansion of the marginal likelihood under variable selection framework, but more importantly, the consistent model selection property is established under a general context. Extensive simulation studies confirm the out-performance of the proposal compared to traditional model selection criteria. Finally, an application to the Atherosclerosis Risk in Communities Study illustrates the practical value of this proposed framework., Comment: JSM Student Paper Award, ASA Nonparametric Section

Published
2020
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28. Assessing predictive accuracy of survival regressions subject to nonindependent censoring

Author

Qi Long, Ming Wang, Lijun Zhang, and Chixiang Chen

Subjects
Statistics and Probability, Epidemiology, Proportional hazards model, Computer science, Estimator, Informative censoring, Accelerated failure time model, Censoring (statistics), Survival Analysis, Regression, Weighting, Inverse probability, Statistics, Humans, Computer Simulation, Probability, Proportional Hazards Models
Abstract

Survival regression is commonly applied in biomedical studies or clinical trials, and evaluating their predictive performance plays an essential role for model diagnosis and selection. The presence of censored data, particularly if informative, may pose more challenges for the assessment of predictive accuracy. Existing literature mainly focuses on prediction for survival probabilities with limitation work for survival time. In this work, we focus on accuracy measures of predicted survival times adjusted for a potentially informative censoring mechanism (ie, coarsening at random (CAR); non-CAR) by adopting the technique of inverse probability of censoring weighting. Our proposed predictive metric can be adaptive to various survival regression frameworks including but not limited to accelerated failure time models and proportional hazards models. Moreover, we provide the asymptotic properties of the inverse probability of censoring weighting estimators under CAR. We consider the settings of high-dimensional data under CAR or non-CAR for extensions. The performance of the proposed method is evaluated through extensive simulation studies and analysis of real data from the Critical Assessment of Microarray Data Analysis.

Published
2019

29. A unified DNA sequence and non-DNA sequence mapping model of complex traits

Author

Yanru Zeng, Weiwu Yu, Rongling Wu, Lidan Sun, Kalins Banerjee, Xuli Zhu, Chixiang Chen, and Bingsong Zheng

Subjects
0106 biological sciences, 0301 basic medicine, DNA, Plant, Linkage Disequilibrium Mapping, Inheritance (genetic algorithm), Cell Biology, Plant Science, Computational biology, Quantitative trait locus, Biology, 01 natural sciences, DNA sequencing, Genetic architecture, Linkage Disequilibrium, 03 medical and health sciences, 030104 developmental biology, Quantitative Trait, Heritable, Gene mapping, Genetics, Trait, Algorithms, 010606 plant biology & botany, Sequence (medicine)
Abstract

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non-DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed of randomly sampling maternal plants from a natural population and their half-sib seeds. This design forms a two-level hierarchical platform for a joint linkage-linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence-based effects and non-DNA sequence-based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non-DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA-based effects and non-DNA sequence-based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.

Published
2018

30. Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

Author

Ming Wang, Lijun Zhang, Chixiang Chen, Yuan Xue, and Biyi Shen

Subjects
FOS: Computer and information sciences, Statistics and Probability, Patient Dropouts, Computer science, Information Criteria, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Article, Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, Bayesian information criterion, Econometrics, Humans, Computer Simulation, Longitudinal Studies, 0101 mathematics, Generalized estimating equation, Statistics - Methodology, 030304 developmental biology, 0303 health sciences, Likelihood Functions, Models, Statistical, General Immunology and Microbiology, Applied Mathematics, Model selection, General Medicine, Missing data, Regression, Empirical likelihood, Data Interpretation, Statistical, Akaike information criterion, General Agricultural and Biological Sciences
Abstract

Longitudinal data are common in clinical trials and observational studies, where missing outcomes due to dropouts are always encountered. Under such context with the assumption of missing at random, the weighted generalized estimating equations (WGEE) approach is widely adopted for marginal analysis. Model selection on marginal mean regression is a crucial aspect of data analysis, and identifying an appropriate correlation structure for model fitting may also be of interest and importance. However, the existing information criteria for model selection in WGEE have limitations, such as separate criteria for the selection of marginal mean and correlation structures, unsatisfactory selection performance in small-sample set-ups and so on. In particular, there are few studies to develop joint information criteria for selection of both marginal mean and correlation structures. In this work, by embedding empirical likelihood into the WGEE framework, we propose two innovative information criteria named a joint empirical Akaike information criterion (JEAIC) and a joint empirical Bayesian information criterion (JEBIC), which can simultaneously select the variables for marginal mean regression and also correlation structure. Through extensive simulation studies, these empirical-likelihood-based criteria exhibit robustness, flexibility, and outperformance compared to the other criteria including the weighted quasi-likelihood under the independence model criterion, the missing longitudinal information criterion and the joint longitudinal information criterion. In addition, we provide a theoretical justification of our proposed criteria, and present two real data examples in practice for further illustration., Earlier version won the Student Paper Award at the 2018 International Chinese Statistical Association (ICSA) Applied Statistics Symposium

Published
2018

31. np

Author

Meixia, Ye, Libo, Jiang, Chixiang, Chen, Xuli, Zhu, Ming, Wang, and Rongling, Wu

Subjects
Phenotype, Genotype, Quantitative Trait Loci, Zea mays
Abstract

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np

Published
2018

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