WashU weekly Neuroscience publications | Office of Neuroscience Research

“Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns” (2019) Nature Communications

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns
(2019) Nature Communications, 10 (1), art. no. 2548, .

Czamara, D.^a , Eraslan, G.^b ^c , Page, C.M.^d ^e , Lahti, J.^f ^g , Lahti-Pulkkinen, M.^f ^h , Hämäläinen, E.ⁱ , Kajantie, E.^j ^k ^l , Laivuori, H.^m ⁿ ^o ^p , Villa, P.M.^m , Reynolds, R.M.^h , Nystad, W.^q , Håberg, S.E.^e , London, S.J.^r , O’Donnell, K.J.^s ^t , Garg, E.^s , Meaney, M.J.^s ^t ^u , Entringer, S.^v ^w , Wadhwa, P.D.^w ^x , Buss, C.^v ^w , Jones, M.J.^y , Lin, D.T.S.^y , MacIsaac, J.L.^y , Kobor, M.S.^y , Koen, N.^z ^aa , Zar, H.J.^ab , Koenen, K.C.^ac , Dalvie, S.^z , Stein, D.J.^z ^aa , Kondofersky, I.^b ^ad , Müller, N.S.^b , Theis, F.J.^b ^ad , Wray, N.R.^af ^ag , Ripke, S.^ah ^ai ^aj , Mattheisen, M.^ak ^al ^am ^an , Trzaskowski, M.^af , Byrne, E.M.^af , Abdellaoui, A.^ao , Adams, M.J.^ap , Agerbo, E.^an ^aq ^ar , Air, T.M.^as , Andlauer, T.F.M.^a ^at , Bacanu, S.-A.^au , Bækvad-Hansen, M.^an ^av , Beekman, A.T.F.^aw , Bigdeli, T.B.^au ^ax , Blackwood, D.H.R.^ap , Bryois, J.^ay , Buttenschøn, H.N.^am ^an ^az , Bybjerg-Grauholm, J.^an ^av , Cai, N.^ba ^bb , Castelao, E.^bc , Christensen, J.H.^al ^am ^an , Clarke, T.-K.^ap , Coleman, J.R.I.^bd , Colodro-Conde, L.^be , Couvy-Duchesne, B.^bf ^bg , Craddock, N.^bh , Crawford, G.E.^bi ^bj , Davies, G.^bk , Deary, I.J.^bk , Degenhardt, F.^bl ^bm , Derks, E.M.^be , Direk, N.^bn ^bo , Dolan, C.V.^ao , Dunn, E.C.^bp ^bq ^br , Eley, T.C.^bd , Escott-Price, V.^bs , Kiadeh, F.F.H.^bt , Finucane, H.K.^bf ^bu , Forstner, A.J.^bl ^bm ^bv ^bw , Frank, J.^bx , Gaspar, H.A.^bd , Gill, M.^by , Goes, F.S.^bz , Gordon, S.D.^ca , Grove, J.^al ^am ^an ^cb , Hall, L.S.^ap ^cc , Hansen, C.S.^an ^av , Hansen, T.F.^cd ^ce ^cf , Herms, S.^bl ^bm ^bw , Hickie, I.B.^cg , Hoffmann, P.^au ^bl ^bm , Homuth, G.^ch , Horn, C.^ci , Hottenga, J.-J.^ao , Hougaard, D.M.^an ^av , Ising, M.^cj , Jansen, R.^aw , Jorgenson, E.^ck , Knowles, J.A.^cl , Kohane, I.S.^cm ^cn ^co , Kraft, J.^ai , Kretzschmar, W.W.^cp , Krogh, J.^cq , Kutalik, Z.^cr ^cs , Li, Y.^cp , Lind, P.A.^be , MacIntyre, D.J.^ct ^cu , MacKinnon, D.F.^bz , Maier, R.M.^ag , Maier, W.^cv , Marchini, J.^cw , Mbarek, H.^ao , McGrath, P.^cx , McGuffin, P.^bd , Medland, S.E.^be , Mehta, D.^ag ^cy , Middeldorp, C.M.^ao ^cz ^da , Mihailov, E.^db , Milaneschi, Y.^aw , Milani, L.^db , Mondimore, F.M.^bz , Montgomery, G.W.^ag , Mostafavi, S.^dc ^dd , Mullins, N.^bd , Nauck, M.^de ^df , Ng, B.^dd , Nivard, M.G.^ao , Nyholt, D.R.^dg , O’Reilly, P.F.^bd , Oskarsson, H.^dh , Owen, M.J.^di , Painter, J.N.^be , Pedersen, C.B.^an ^aq ^ar , Pedersen, M.G.^an ^aq ^ar , Peterson, R.E.^au ^dj , Pettersson, E.^ay , Peyrot, W.J.^aw , Pistis, G.^bc , Posthuma, D.^dk ^dl , Quiroz, J.A.^dm , Qvist, P.^al ^am ^an , Rice, J.P.^dn , Riley, B.P.^au , Rivera, M.^bd ^do , Mirza, S.S.^bn , Schoevers, R.^dp , Schulte, E.C.^dq ^dr , Shen, L.^ck , Shi, J.^ds , Shyn, S.I.^dt , Sigurdsson, E.^du , Sinnamon, G.C.B.^dv , Smit, J.H.^aw , Smith, D.J.^dw , Stefansson, H.^dx , Steinberg, S.^dx , Streit, F.^bx , Strohmaier, J.^bx , Tansey, K.E.^dy , Teismann, H.^dz , Teumer, A.^ea , Thompson, W.^an ^ce ^eb ^ec , Thomson, P.A.^eb , Thorgeirsson, T.E.^dx , Traylor, M.^ed , Treutlein, J.^bx , Trubetskoy, V.^ai , Uitterlinden, A.G.^ee , Umbricht, D.^ef , Van der Auwera, S.^eg , van Hemert, A.M.^eh , Viktorin, A.^ay , Visscher, P.M.^af ^ag , Wang, Y.^an ^ce ^ec , Webb, B.T.^ei , Weinsheimer, S.M.^an ^ce , Wellmann, J.^dz , Willemsen, G.^ao , Witt, S.H.^bx , Wu, Y.^af , Xi, H.S.^ej , Yang, J.^ag ^ek , Zhang, F.^af , Arolt, V.^el , Baune, B.T.^as , Berger, K.^dz , Boomsma, D.I.^ao , Cichon, S.^bl ^bw ^em ^en , Dannlowski, U.^el , de Geus, E.J.C.^j ^eo , DePaulo, J.R.^bz , Domenici, E.^ep , Domschke, K.^eq , Esko, T.^aj ^db , Grabe, H.J.^eg , Hamilton, S.P.^er , Hayward, C.^es , Heath, A.C.^dn , Kendler, K.S.^au , Kloiber, S.^cj ^et ^eu , Lewis, G.^ev , Li, Q.S.^ew , Lucae, S.^cj , Madden, P.A.F.^dn , Magnusson, P.K.^ay , Martin, N.G.^ca , McIntosh, A.M.^ap ^bk , Metspalu, A.^db ^ex , Mors, O.^an ^ey , Mortensen, P.B.^am ^an ^aq ^ar , Müller-Myhsok, B.^a ^at ^ez , Nordentoft, M.^an ^fa , Nöthen, M.M.^bl ^bm , O’Donovan, M.C.^di , Paciga, S.A.^fb , Pedersen, N.L.^ay , Penninx, B.W.J.H.^aw , Perlis, R.H.^bp ^fc , Porteous, D.J.^fd , Potash, J.B.^fe , Preisig, M.^bc , Rietschel, M.^bx , Schaefer, C.^ck , Schulze, T.G.^bx ^dr ^ff ^fg ^fh , Smoller, J.W.^bp ^bq ^br , Stefansson, K.^ey ^fi , Tiemeier, H.^bn ^fj ^fk , Uher, R.^fl , Völzke, H.^ea , Weissman, M.M.^cx ^fm , Werge, T.^an ^ce ^fn , Lewis, C.M.^bd ^fo , Levinson, D.F.^fp , Breen, G.^bd ^fq , Børglum, A.D.^al ^am ^an , Sullivan, P.F.^ay ^fr ^fs , Räikkönen, K.^f , Binder, E.B.^a ^ae , Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium^af

^a Max-Planck-Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, 80804, Germany
^b Institute of Computational Biology, Helmholtz-Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
^c School of Life Sciences, Weihenstephan, Technische Universität München, Freising, 85354, Germany
^d Oslo Centre for Biostatistics and Epidemiology, Research Support Unit, Oslo University Hospital, Oslo, 0372, Norway
^e Center for Fertility and Health, Norwegian Institute of Public Health, Oslo, 0213, Norway
^f Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland
^g Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, 00101, Finland
^h British Heart Foundation Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, United Kingdom
ⁱ HUSLAB and Department of Clinical Chemistry, Helsinki University, Helsinki, 00290, Finland
^j Oulu University Hospital and University of Oulu, PEDEGO Research Unit, MRC Oulu, 90014, Finland
^k Hospital for Children and Adolescents, University of Helsinki and Helsinki University Hospital, Helsinki, 00029, Finland
^l National Institute for Health and Welfare, Helsinki, 00271, Finland
^m Medical and Clinical Genetics and Obstetrics and Gynaecology University of Helsinki and Helsinki University Central Hospital, Helsinki, 00014, Finland
ⁿ Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, 00014, Finland
^o Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33100, Finland
^p Department of Obstetrics and Gynecology, Tampere University Hospital, Tampere, 33100, Finland
^q Department of Chronic Diseases and Ageing, Norwegian Institute of Public Health, Oslo, 0213, Norway
^r Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle ParkNC 20814, United States
^s Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, QC H3A 2B4, Canada
^t Sackler Program for Epigenetics and Psychobiology at McGill University, Montreal, QC H3A 0G4, Canada
^u Singapore Institute for Clinical Sciences, Singapore, 117609, Singapore
^v Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Institute of Medical Psychology, Berlin, 10117, Germany
^w University of California, Irvine, Development, Health, and Disease Research Program, Orange, CA 92697, United States
^x Department of Psychiatry and Human Behavior, Obstetrics and Gynecology, and Epidemiology, University of California, Irvine, School of Medicine, Irvine, CA 92697, United States
^y Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia and the BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
^z Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, 7925, South Africa
^aa South African Medical Research Council (SAMRC), Unit on Risk and Resilience in Mental Disorders, Cape Town, 7505, South Africa
^ab Department of Paediatrics & Child Health and SAMRC Unit on Child and Adolescent Health, University of Cape Town, Cape Town, 7505, South Africa
^ac Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States
^ad Department of Mathematics, Technische Universität München, Munich, 85748, Germany
^ae Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, 30329, United States
^af Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
^ag Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
^ah Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, United States
^ai Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, 14129, Germany
^aj Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, United States
^ak Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, SE 17177, Sweden
^al Department of Biomedicine, Aarhus University, Aarhus, 8000, Denmark
^am iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, 8000, Denmark
^an iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, 8000, Denmark
^ao Department of Biological Psychology & EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, 1081 BT, Netherlands
^ap Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, United Kingdom
^aq Centre for Integrated Register-based Research, Aarhus University, Aarhus, 8210, Denmark
^ar National Centre for Register-Based Research, Aarhus University, Aarhus, 8210, Denmark
^as Discipline of Psychiatry, University of Adelaide, Adelaide, SA 5000, Australia
^at Munich Cluster for Systems Neurology (SyNergy), Munich, 81377, Germany
^au Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 22903, United States
^av Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, 2300, Denmark
^aw Department of Psychiatry, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, NL 1081, Netherlands
^ax Virginia Institute for Psychiatric and Behavior Genetics, Richmond, VA 23298, United States
^ay Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE 17177, Sweden
^az Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, 8240, Denmark
^ba Human Genetics, Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, United Kingdom
^bb Statistical genomics and systems genetics, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1 SD, United Kingdom
^bc Department of Psychiatry, University Hospital of Lausanne, Prilly, Vaud, 1004, Switzerland
^bd MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, London, WC2R 2LS, United Kingdom
^be Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia
^bf Centre for Advanced Imaging, The University of Queensland, Saint Lucia, QLD 4072, Australia
^bg Queensland Brain Institute, The University of Queensland, Saint Lucia, QLD 4072, Australia
^bh Psychological Medicine, Cardiff University, Cardiff, CF14 4XN, United Kingdom
^bi Center for Genomic and Computational Biology, Duke University, Durham, NC 27705, United States
^bj Division of Medical Genetics, Department of Pediatrics, Duke University, Durham, NC 27708, United States
^bk Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
^bl Institute of Human Genetics, University of Bonn, Bonn, DE 53127, Germany
^bm Life & Brain Center, Department of Genomics, University of Bonn, Bonn, 53127, Germany
^bn Epidemiology, Erasmus MC, Rotterdam, Zuid-Holland 3015, Netherlands
^bo Psychiatry, Dokuz Eylul University School Of Medicine, Izmir, 35220, Turkey
^bp Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States
^bq Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA 02114, United States
^br Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, United States
^bs Neuroscience and Mental Health, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
^bt Bioinformatics, University of British Columbia, Vancouver, BC V5Z 4S6, Canada
^bu Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02142, United States
^bv Department of Psychiatry (UPK), University of Basel, Basel, 4002, Switzerland
^bw Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, 4031, Switzerland
^bx Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg 68159, Germany
^by Department of Psychiatry, Trinity College Dublin, Dublin 8, Ireland
^bz Psychiatry & Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21287, United States
^ca Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
^cb Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark
^cc Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, NE1 3BZ, United Kingdom
^cd Danish Headache Centre, Department of Neurology, Rigshospitalet, Glostrup, 2600, Denmark
^ce Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Copenhagen, 4000, Denmark
^cf iPSYCH, The Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, 8000, Denmark
^cg Brain and Mind Centre, University of Sydney, Sydney, NSW 2050, Australia
^ch Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Mecklenburg-Vorpommern 17489, Germany
^ci Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, 4070, Switzerland
^cj Max Planck Institute of Psychiatry, Munich, 80804, Germany
^ck Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, United States
^cl Psychiatry & The Behavioral Sciences, University of Southern California, Los Angeles, CA 90033, United States
^cm Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
^cn Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
^co Informatics Program, Boston Children’s Hospital, Boston, MA 02115, United States
^cp Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
^cq Department of Endocrinology at Herlev University Hospital, University of Copenhagen, Copenhagen, 2730, Denmark
^cr Institute of Social and Preventive Medicine (IUMSP), University Hospital of Lausanne, Lausanne, VD 1010, Switzerland
^cs Swiss Institute of Bioinformatics, Lausanne, VD 1015, Switzerland
^ct Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, United Kingdom
^cu Mental Health, NHS 24, Glasgow, G12 0XH, United Kingdom
^cv Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, 53105, Germany
^cw Statistics, University of Oxford, Oxford, OX1 3LB, United Kingdom
^cx Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, United States
^cy School of Psychology and Counseling, Queensland University of Technology, Brisbane, QLD 4059, Australia
^cz Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, South Brisbane, QLD 4000, Australia
^da Child Health Research Centre, University of Queensland, Brisbane, QLD 4101, Australia
^db Estonian Genome Center, University of Tartu, Tartu, 51005, Estonia
^dc Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada
^dd Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
^de DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern 17489, Germany
^df Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern 17489, Germany
^dg Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4059, Australia
^dh Humus, Reykjavik, 101, Iceland
^di MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
^dj Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, United States
^dk Clinical Genetics, Vrije Universiteit Medical Center, Amsterdam, 1081HV, Netherlands
^dl Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, Netherlands
^dm Solid Biosciences, Boston, MA 02139, United States
^dn Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO 63110, United States
^do Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, CP 18100, Spain
^dp Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, Netherlands
^dq Department of Psychiatry and Psychotherapy, Medical Center of the University of Munich, Campus Innenstadt, Munich, 80336, Germany
^dr Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Campus Innenstadt, Munich, 80336, Germany
^ds Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, United States
^dt Behavioral Health Services, Kaiser Permanente Washington, Seattle, WA 98112, United States
^du Faculty of Medicine, Department of Psychiatry, University of Iceland, Reykjavik, 101, Iceland
^dv School of Medicine and Dentistry, James Cook University, Townsville, QLD 4811, Australia
^dw Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, United Kingdom
^dx deCODE Genetics/Amgen, Reykjavik, 101, Iceland
^dy College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF14 4EP, United Kingdom
^dz Institute of Epidemiology and Social Medicine, University of Münster, Münster, Nordrhein-Westfalen 48149, Germany
^ea Institute for Community Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern 17489, Germany
^eb Department of Psychiatry, University of California, San Diego, San Diego, CA 92093, United States
^ec KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, 0407, Norway
^ed Clinical Neurosciences, University of Cambridge, Cambridge, CB2 1QW, United Kingdom
^ee Internal Medicine, Erasmus MC, Rotterdam, Zuid-Holland 3015, Netherlands
^ef Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery & Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, 4070, Switzerland
^eg Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern 17475, Germany
^eh Department of Psychiatry, Leiden University Medical Center, Leiden, 2333 ZA, Netherlands
^ei Virginia Institute of Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, United States
^ej Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, MA 02139, United States
^ek Institute for Molecular Bioscience; Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
^el Department of Psychiatry, University of Münster, Münster, Nordrhein-Westfalen 48149, Germany
^em Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, 4031, Switzerland
^en Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, 52425, Germany
^eo Amsterdam Public Health Institute, Vrije Universiteit Medical Center, Amsterdam, 1081 BT, Netherlands
^ep Centre for Integrative Biology, Università degli Studi di Trento, Trento, Trentino-Alto Adige 38123, Italy
^eq Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 79104, Germany
^er Psychiatry, Kaiser Permanente Northern California, San Francisco, CA 94115, United States
^es Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
^et Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
^eu Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
^ev Division of Psychiatry, University College London, London, W1T 7NF, United Kingdom
^ew Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ 08560, United States
^ex Institute of Molecular and Cell Biology, University of Tartu, Tartu, 51010, Estonia
^ey Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, 8200, Denmark
^ez University of Liverpool, Liverpool, L69 3BX, United Kingdom
^fa Mental Health Center Copenhagen, Copenhagen Universtity Hospital, Copenhagen, 2100, Denmark
^fb Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, CT 06340, United States
^fc Psychiatry, Harvard Medical School, Boston, MA 02215, United States
^fd Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
^fe Psychiatry, University of Iowa, Iowa City, IA 52246, United States
^ff Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21287, United States
^fg Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Niedersachsen 37075, Germany
^fh Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD 20892-9663, United States
^fi Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
^fj Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland 3015, Netherlands
^fk Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland 3015, Netherlands
^fl Psychiatry, Dalhousie University, Halifax, NS B3H 2E2, Canada
^fm Division of Epidemiology, New York State Psychiatric Institute, New York, NY 10032, United States
^fn Department of Clinical Medicine, University of Copenhagen, Copenhagen, 2200, Denmark
^fo Department of Medical & Molecular Genetics, King’s College London, London, WC2R 2LS, United Kingdom
^fp Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305-5717, United States
^fq NIHR BRC for Mental Health, King’s College London, London, SE5 8AF, United Kingdom
^fr Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
^fs Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States

Abstract
Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk. © 2019, The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

“The language of accurate recognition memory” (2019) Cognition

The language of accurate recognition memory
(2019) Cognition, 192, art. no. 103988, .

Dobbins, I.G.^a , Kantner, J.^b

^a Washington University in Saint Louis, United States
^b California State University, Northridge, United States

Abstract
The natural language accompanying recognition judgments is a largely untapped though potentially rich source of information about the kinds of processing that may support recognition memory. The current report illustrates a series of methods using machine learning and receiver operating characteristics (ROCs) to examine whether the language participants use to justify their ‘old’ and ‘new’ recognition decisions (viz., memory justifications) predicts accuracy. The findings demonstrate that the natural language of observers conveys the accuracy of ‘old’ (hits versus false alarms) but not ‘new’ (misses versus correct rejections) decisions. The classifier trained on this language was considerably more predictive of accuracy than the initial speed of the decisions, generalized to the justification language of two independent experiments using different procedures, and appeared sensitive to the presence versus absence of recollective experiences in the observer’s reports. We conclude by considering extensions of the approach to several basic and applied areas, and, more broadly, to identifying the explicit bases (if any) of classification decisions in general. © 2019 Elsevier B.V.

Author Keywords
Language content analysis; Machine learning; Receiver operating characterstics; Recognition memory; Recollection

Document Type: Article
Publication Stage: Final
Source: Scopus

“Mild hypothermia ameliorates anesthesia toxicity in the neonatal macaque brain” (2019) Neurobiology of Disease

Mild hypothermia ameliorates anesthesia toxicity in the neonatal macaque brain
(2019) Neurobiology of Disease, 130, art. no. 104489, .

Ikonomidou, C.^a , Kirvassilis, G.^b , Swiney, B.S.^c , Wang, S.H.^c , Huffman, J.N.^c , Williams, S.L.^c , Masuoka, K.^c , Capuano, S., III^d , Brunner, K.R.^d , Crosno, K.^d , Simmons, H.S.^d , Mejia, A.F.^d , Turski, C.A.^a , Brambrink, A.^e , Noguchi, K.K.^c

^a Department of Neurology, School of Medicine, University of Wisconsin, Madison, WI, United States
^b Department of Anesthesiology, School of Medicine, University of Wisconsin, Madison, WI, United States
^c Department of Psychiatry, School of Medicine, Washington University, St Louis, WA, United States
^d Wisconsin National Primate Research Center, Madison, WI, United States
^e Department of Anesthesiology, Columbia University, New York Presbyterian Hospital, Irving Medical Center, New York, NY, United States

Abstract
Sedatives and anesthetics can injure the developing brain. They cause apoptosis of neurons and oligodendrocytes, impair synaptic plasticity, inhibit neurogenesis and trigger long-term neurocognitive deficits. The projected vulnerable period in humans extends from the third trimester of pregnancy to the third year of life. Despite all concerns, there is no ethically and medically acceptable alternative to the use of sedatives and anesthetics for surgeries and painful interventions. Development of measures that prevent injury while allowing the medications to exert their desired actions has enormous translational value. Here we investigated protective potential of hypothermia against histological toxicity of the anesthetic sevoflurane in the developing nonhuman primate brain. Neonatal rhesus monkeys underwent sevoflurane anesthesia over 5 h. Body temperature was regulated in the normothermic (>36.5 °C), mild hypothermic (35–36.5 °C) and moderately hypothermic (<35 °C) range. Animals were euthanized at 8 h and brains examined immunohistochemically (activated caspase 3) and stereologically to quantify apoptotic neuronal and oligodendroglial death. Sevoflurane anesthesia was well tolerated at all temperatures, with oxygen saturations, end tidal CO2 and blood gases remaining at optimal levels. Compared to controls, sevoflurane exposed brains displayed significant apoptosis in gray and white matter affecting neurons and oligodendrocytes. Mild hypothermia (35–36.5 °C) conferred significant protection from apoptotic brain injury, whereas moderate hypothermia (<35 °C) did not. Hypothermia ameliorates anesthesia-induced apoptosis in the neonatal primate brain within a narrow temperature window (35–36.5 °C). Protection is lost at temperatures below 35 °C. Given the mild degree of cooling needed to achieve significant brain protection, application of our findings to humans should be explored further. © 2019 Elsevier Inc.

Author Keywords
Anesthesia; Apoptosis; Brain injury; Development; Neuroprotection

Document Type: Article
Publication Stage: Final
Source: Scopus

“Multiple neurosteroid and cholesterol binding sites in voltage-dependent anion channel-1 determined by photo-affinity labeling” (2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids

Multiple neurosteroid and cholesterol binding sites in voltage-dependent anion channel-1 determined by photo-affinity labeling
(2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids, 1864 (10), pp. 1269-1279.

Cheng, W.W.L.^a , Budelier, M.M.^a ^b , Sugasawa, Y.^a , Bergdoll, L.^f , Queralt-Martín, M.^h , Rosencrans, W.^h , Rostovtseva, T.K.^h , Chen, Z.-W.^a ^e , Abramson, J.^f , Krishnan, K.^c , Covey, D.F.^a ^c ^d ^e , Whitelegge, J.P.^g , Evers, A.S.^a ^c ^d ^e

^a Department of Anesthesiology, Washington University in St. LouisMO 63110, United States
^b Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisMO 63110, United States
^c Department of Developmental Biology, Washington University in St. LouisMO 63110, United States
^d Department of Psychiatry, Washington University in St. LouisMO 63110, United States
^e Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. LouisMO 63110, United States
^f Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States
^g Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States
^h Section on Molecular Transport, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, United States

Abstract
Voltage-dependent anion channel-1 (VDAC1) is a mitochondrial porin that is implicated in cellular metabolism and apoptosis, and modulated by numerous small molecules including lipids. VDAC1 binds sterols, including cholesterol and neurosteroids such as allopregnanolone. Biochemical and computational studies suggest that VDAC1 binds multiple cholesterol molecules, but photolabeling studies have identified only a single cholesterol and neurosteroid binding site at E73. To identify all the binding sites of neurosteroids in VDAC1, we apply photo-affinity labeling using two sterol-based photolabeling reagents with complementary photochemistry: 5α-6-AziP which contains an aliphatic diazirine, and KK200 which contains a trifluoromethyl-phenyldiazirine (TPD) group. 5α-6-AziP and KK200 photolabel multiple residues within an E73 pocket confirming the presence of this site and mapping sterol orientation within this pocket. In addition, KK200 photolabels four other sites consistent with the finding that VDAC1 co-purifies with five cholesterol molecules. Both allopregnanolone and cholesterol competitively prevent photolabeling at E73 and three other sites indicating that these are common sterol binding sites shared by both neurosteroids and cholesterol. Binding at the functionally important residue E73 suggests a possible role for sterols in regulating VDAC1 signaling and interaction with partner proteins. © 2019

Author Keywords
Cholesterol; Mass spectrometry; Neurosteroid; Photoaffinity labeling; Protein drug interaction; Voltage-dependent anion channel (VDAC)

Document Type: Article
Publication Stage: Final
Source: Scopus

“The etiology of DSM-5 alcohol use disorder: Evidence of shared and non-shared additive genetic effects” (2019) Drug and Alcohol Dependence

The etiology of DSM-5 alcohol use disorder: Evidence of shared and non-shared additive genetic effects
(2019) Drug and Alcohol Dependence, 201, pp. 147-154.

Palmer, R.H.C.^a , Brick, L.A.^b ^c , Chou, Y.-L.^d , Agrawal, A.^d , McGeary, J.E.^b ^c ^e , Heath, A.C.^d , Bierut, L.^d , Keller, M.C.^f , Johnson, E.^g , Hartz, S.M.^d , Schuckit, M.A.^h , Knopik, V.S.ⁱ

^a Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, United States
^b Department of Psychiatry and Human Behavior, Brown University, United States
^c Division of Behavior Genetics, Department of Psychiatry, Rhode Island Hospital, United States
^d Washington University in St. Louis, St. Louis, MO, United States
^e Providence Veterans Affairs Medical Center, United States
^f Department of Psychology and Neuroscience, University of Colorado at Boulder, United States
^g RTI International, United States
^h University of California, San Diego, United States
ⁱ Department of Human Development and Family Studies, Purdue University, United States

Abstract
Background: Alcoholism is a multifactorial disorder influenced by multiple gene loci, each with small effect. Studies suggest shared genetic influences across DSM-IV alcohol dependence symptoms, but shared effects across DSM-5 alcohol use disorder remains unknown. We aimed to test the assumption of genetic homogeneity across the 11 criteria of DSM-5 alcohol use disorder (AUD). Methods: Data from 2596 alcohol using individuals of European ancestry from the Study of Addiction: Genetics and Environment were used to examine the genomewide SNP-heritability (h2SNP) and SNP-covariance (rGSNP) between 11 DSM-5 AUD symptoms. Phenotypic relationships between symptoms were examined to confirm an underlying liability of AUD and the SNP-heritability of the observed latent trait and the co-heritabilityamong AUD symptoms was assessed using Genomic-Relatedness-Matrix-Restricted-Maximum-Likelihood. Genetic covariance among symptoms was examined using factor analysis. Results: Phenotypic relationships confirmed a unidimensional underlying liability to AUD. Factor and parallel analyses of the observed genetic variance/covariance provided evidence of genetic homogeneity. Additive genetic effects on DSM-5 AUD symptoms varied from 0.10 to 0.37 and largely overlapped (rG-SNP across symptoms ranged from 0.49 – 0.92). The additive genetic effect on the DSM-5 AUD factor was 0.36, 0.14 for DSM-5 AUD diagnosis, and was 0.22 for DSM-5 AUD severity. Conclusions: Common genetic variants influence DSM-5 AUD symptoms. Despite evidence for a common AUD factor, the evidence of only partially overlapping genetic effects across AUD symptoms further substantiates the need to simultaneously model common and symptom-specific genetic effects in molecular genetic studies in order to best characterize the genetic liability. © 2019

Author Keywords
Alcohol use disorder; Ancestry; DSM-5; European; Genetics; Heritability

Document Type: Article
Publication Stage: Final
Source: Scopus

“Balance and Gait Alterations Observed More Than 2 Weeks after Concussion: A Systematic Review and Meta-Analysis” (2019) American Journal of Physical Medicine and Rehabilitation

Balance and Gait Alterations Observed More Than 2 Weeks after Concussion: A Systematic Review and Meta-Analysis
(2019) American Journal of Physical Medicine and Rehabilitation, 98 (7), pp. 566-576.

Wood, T.A.^a , Hsieh, K.L.^a , An, R.^b , Ballard, R.A.^c , Sosnoff, J.J.^a

^a Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 906 S Goodwin Ave, Urbana, IL 61801, United States
^b Brown School, Washington University, St. Louis, MO, United States
^c Division of Intercollegiate Athletics, University of Illinois at Urbana-Champaign, Champaign, IL, United States

Abstract
Objective The aim of the study was to systematically review and quantitatively synthesize the existing evidence of balance and gait alterations lasting more than 2 wks after concussion in adults. Design A systematic review was conducted through PubMed, CINAHL, SPORTDiscus, and Web of Science. Investigations must include adult participants with at least one concussion, were measured for 14 days after injury, and reported balance or gait measures. Balance error scoring system scores, center of pressure sway area and displacement, and gait velocity were extracted for the meta-Analysis. Results Twenty-Two studies were included. Balance alterations were observed for 2 wks after concussion when participants were tested with eyes closed, for longer durations of time, and with nonlinear regulatory statistics. The meta-Analysis of center of pressure sway area with no visual feedback indicated that concussed individuals had greater sway area (P < 0.001). Various gait alterations were also observed, which may indicate that concussed individuals adopt a conservative gait strategy. The meta-Analysis revealed that concussed participants walked 0.12 m/sec (P < 0.001) and 0.06 m/sec (P = 0.023) slower in single and dual-Task conditions, respectively. Conclusions Subtle balance and gait alterations were observed after 2 wks after a concussion. Understanding these alterations may allow clinicians to improve concussion diagnosis and prevent subsequent injury. © Wolters Kluwer Health, Inc. All rights reserved.

Author Keywords
Brain Concussion; Gait Analysis; Meta-Analysis; Postural Control; Traumatic Brain Injury

Document Type: Review
Publication Stage: Final
Source: Scopus

“Editorial: Causal, Predispositional, or Correlate? Group Differences in Cognitive Control-Related Brain Function in Cannabis-Using Youth Raise New Questions” (2019) Journal of the American Academy of Child and Adolescent Psychiatry

Editorial: Causal, Predispositional, or Correlate? Group Differences in Cognitive Control-Related Brain Function in Cannabis-Using Youth Raise New Questions
(2019) Journal of the American Academy of Child and Adolescent Psychiatry, 58 (7), pp. 665-667.

Baranger, D.A.A.^a , Bogdan, R.^b

^a University of PittsburghPA, United States
^b Division of Biology and Biomedical Sciences, Neurosciences, Human and Statistical Genetics, Molecular Genetics and Genomics, Washington University in St. LouisMO, United States

Abstract
Increasingly permissive attitudes and laws surrounding cannabis have been accompanied by more prevalent use and increased perceptions of its safety.1 However, in stark contrast to this sea-change, remarkably little is known about the potential consequences and etiology of cannabis involvement. In particular, it is unclear what biological mechanisms may undergird associations with negative outcomes (eg, reduced cognition, increased psychosis, depression)2 and whether these substrates arise from cannabis use and/or represent predispositional risk factors. As cannabis remains at the forefront of public discussion and policy, it is increasingly important to identify potential biological mechanisms contributing to associations with negative outcomes and evaluate the plausibility that these represent a consequence of exposure and/or predispositional risk factors or nonetiologic correlates. The knowledge generated from this Herculean task may dam the present sea-change of increasing cannabis permissiveness and/or remove the few remaining boulders impeding it. © 2019

Document Type: Editorial
Publication Stage: Final
Source: Scopus

“Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence” (2019) The Lancet Psychiatry

Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence
(2019) The Lancet Psychiatry, 6 (7), pp. 590-600. Cited 1 time.

Kim, J.Y.^a , Son, M.J.^a , Son, C.Y.^b , Radua, J.^c ^d ^e ^f , Eisenhut, M.^g , Gressier, F.^h , Koyanagi, A.ⁱ ^j ^k , Carvalho, A.F.^k ^l , Stubbs, B.^m ⁿ , Solmi, M.^c ^o , Rais, T.B.^p , Lee, K.H.^q ^r , Kronbichler, A.^s , Dragioti, E.^t , Shin, J.I.^q ^r , Fusar-Poli, P.^c ^u

^a Yonsei University College of Medicine, Seoul, South Korea
^b Department of Psychological & Brain Sciences, Washington University in St. LouisMO, United States
^c Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
^d FIDMAG Germanes Hospitalaries, CIBERSAM, Barcelona, Spain
^e Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
^f Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
^g Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, United Kingdom
^h CESP, Inserm UMR1178, Department of Psychiatry, Assistance Publique-Hôpitaux de Paris, Bicêtre University Hospital, Le Kremlin Bicêtre, France
ⁱ Research and Development Unit, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Fundació Sant Joan de Déu, Barcelona, Spain
^j Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
^k Centre for Addiction & Mental Health, Toronto, ON, Canada
^l Department of Psychiatry, University of Toronto, Toronto, ON, Canada
^m Physiotherapy Department, South London and Maudsley NHS Foundation Trust, London, United Kingdom
ⁿ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^o Department of Neurosciences and Neurosciences Center, University of Padua, Padua, Italy
^p Department of Psychiatry, University of Toledo Medical Center, Toledo, Ohio, United States
^q Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
^r Department of Pediatrics, Severance Children’s Hospital, Seoul, South Korea
^s Department of Internal Medicine IV, Medical University Innsbruck, Anichstraße 35, Innsbruck, 6020, Austria
^t Pain and Rehabilitation center and Department of Medicine and Health Sciences (IMH), Faculty of Health Sciences University of Linköping, Linköping, Sweden
^u OASIS Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom

Abstract
Background: Numerous studies have identified potential risk factors and biomarkers for autism spectrum disorder. We aimed to study the strength and validity of the suggested environmental risk factors or biomarkers of autism spectrum disorder. Methods: We did an umbrella review and systematically appraised the relevant meta-analyses of observational studies. We searched PubMed, Embase, and the Cochrane Database of Systematic Reviews for papers published between database inception and Oct 17, 2018, and screened the reference list of relevant articles. We obtained the summary effect, 95% CI, heterogeneity, and 95% prediction intervals. We examined small study effects and excess significance. We did analyses under credibility ceilings. This review is registered with PROSPERO, number CRD42018091704. Findings: 46 eligible articles yielded data on 67 environmental risk factors (544 212 cases, 81 708 787 individuals) and 52 biomarkers (15 614 cases, 15 433 controls). Evidence of association was convincing for maternal age of 35 years or over (relative risk [RR] 1·31, 95% CI 1·18–1·45), maternal chronic hypertension (odds ratio [OR] 1·48, 1·29–1·70), maternal gestational hypertension (OR 1·37, 1·21–1·54), maternal overweight before or during pregnancy (RR 1·28, 1·19–1·36), pre-eclampsia (RR 1·32, 1·20–1·45), prepregnancy maternal antidepressant use (RR 1·48, 1·29–1·71), and maternal selective serotonin reuptake inhibitor (SSRI) use during pregnancy (OR 1·84, 1·60–2·11). Only two associations, maternal overweight before or during pregnancy and SSRI use during pregnancy, retained their high level of evidence under subset sensitivity analyses. Evidence from biomarkers was scarce, being supported by p values close to the significance threshold and too few cases. Interpretation: Convincing evidence suggests that maternal factors, such as age and features of metabolic syndrome, are associated with risk of autism spectrum disorder. Although SSRI use during pregnancy was also associated with such risk when exposed and non-exposed groups were compared, this association could be affected by other confounding factors, considering that prepregnancy maternal antidepressant use was also convincingly associated with higher risk of autism spectrum disorder. Findings from previous studies suggest that one possible confounding factor is underlying maternal psychiatric disorders. Funding: None. © 2019 Elsevier Ltd

Document Type: Article
Publication Stage: Final
Source: Scopus

“Central Amygdala Prepronociceptin-Expressing Neurons Mediate Palatable Food Consumption and Reward” (2019) Neuron

Central Amygdala Prepronociceptin-Expressing Neurons Mediate Palatable Food Consumption and Reward
(2019) Neuron, 102 (5), pp. 1037-1052.

Hardaway, J.A.^a , Halladay, L.R.^b , Mazzone, C.M.^c , Pati, D.^d , Bloodgood, D.W.^c , Kim, M.^d , Jensen, J.^d , DiBerto, J.F.^d , Boyt, K.M.^d , Shiddapur, A.^d , Erfani, A.^d , Hon, O.J.^c , Neira, S.^c , Stanhope, C.M.^d , Sugam, J.A.^d , Saddoris, M.P.^e , Tipton, G.^d , McElligott, Z.^a , Jhou, T.C.^f , Stuber, G.D.^g , Bruchas, M.R.^h , Bulik, C.M.ⁱ , Holmes, A.^j , Kash, T.L.^d

^a Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
^b Laboratory of Behavioral and Genomic Neuroscience, National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, MD, USA; Department of Psychology, Santa Clara University, Santa Clara, CA 95053, USA
^c Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
^d Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
^e Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, United States
^f Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, United States
^g Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
^h Division of Basic Research, Department of Anesthesiology, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, USA; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA 98195, USA; Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA; Department of Pharmacology, University of Washington, Seattle, WA 98195, USA
ⁱ Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^j Laboratory of Behavioral and Genomic Neuroscience, National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, MD, United States

Abstract
Food palatability is one of many factors that drives food consumption, and the hedonic drive to feed is a key contributor to obesity and binge eating. In this study, we identified a population of prepronociceptin-expressing cells in the central amygdala (PnocCeA) that are activated by palatable food consumption. Ablation or chemogenetic inhibition of these cells reduces palatable food consumption. Additionally, ablation of PnocCeA cells reduces high-fat-diet-driven increases in bodyweight and adiposity. PnocCeA neurons project to the ventral bed nucleus of the stria terminalis (vBNST), parabrachial nucleus (PBN), and nucleus of the solitary tract (NTS), and activation of cell bodies in the central amygdala (CeA) or axons in the vBNST, PBN, and NTS produces reward behavior but did not promote feeding of palatable food. These data suggest that the PnocCeA network is necessary for promoting the reinforcing and rewarding properties of palatable food, but activation of this network itself is not sufficient to promote feeding. Copyright © 2019 Elsevier Inc. All rights reserved.

Author Keywords
binge eating; central amygdala; nociceptin; nucleus of the solitary tract; obesity; parabrachial nucleus; reward

Document Type: Article
Publication Stage: Final
Source: Scopus

“Vesicular Glutamatergic Transmission in Noise-Induced Loss and Repair of Cochlear Ribbon Synapses” (2019) The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

Vesicular Glutamatergic Transmission in Noise-Induced Loss and Repair of Cochlear Ribbon Synapses
(2019) The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 39 (23), pp. 4434-4447.

Kim, K.X.^a , Payne, S.^a , Yang-Hood, A.^a , Li, S.-Z.^a , Davis, B.^a ^b , Carlquist, J.^a , V-Ghaffari, B.^a , Gantz, J.A.^a , Kallogjeri, D.^a , Fitzpatrick, J.A.J.^c , Ohlemiller, K.K.^a , Hirose, K.^a , Rutherford, M.A.^d

^a Department of Otolaryngology, Washington University School of Medicine, United States
^b Program in Audiology and Communication Sciences, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Washington University Center for Cellular Imaging, Department of Neuroscience, Department of Cell Biology and Physiology, Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States
^d Department of Otolaryngology, Washington University School of Medicine, United States

Abstract
Noise-induced excitotoxicity is thought to depend on glutamate. However, the excitotoxic mechanisms are unknown, and the necessity of glutamate for synapse loss or regeneration is unclear. Despite absence of glutamatergic transmission from cochlear inner hair cells in mice lacking the vesicular glutamate transporter-3 (Vglut3KO ), at 9-11 weeks, approximately half the number of synapses found in Vglut3WT were maintained as postsynaptic AMPA receptors juxtaposed with presynaptic ribbons and voltage-gated calcium channels (CaV1.3). Synapses were larger in Vglut3KO than Vglut3WT In Vglut3WT and Vglut3+/- mice, 8-16 kHz octave-band noise exposure at 100 dB sound pressure level caused a threshold shift (∼40 dB) and a loss of synapses (>50%) at 24 h after exposure. Hearing threshold and synapse number partially recovered by 2 weeks after exposure as ribbons became larger, whereas recovery was significantly better in Vglut3WT Noise exposure at 94 dB sound pressure level caused auditory threshold shifts that fully recovered in 2 weeks, whereas suprathreshold hearing recovered faster in Vglut3WT than Vglut3+/- These results, from mice of both sexes, suggest that spontaneous repair of synapses after noise depends on the level of Vglut3 protein or the level of glutamate release during the recovery period. Noise-induced loss of presynaptic ribbons or postsynaptic AMPA receptors was not observed in Vglut3KO , demonstrating its dependence on vesicular glutamate release. In Vglut3WT and Vglut3+/-, noise exposure caused unpairing of presynaptic ribbons and presynaptic CaV1.3, but not in Vglut3KO where CaV1.3 remained clustered with ribbons at presynaptic active zones. These results suggest that, without glutamate release, noise-induced presynaptic Ca2+ influx was insufficient to disassemble the active zone. However, synapse volume increased by 2 weeks after exposure in Vglut3KO , suggesting glutamate-independent mechanisms.SIGNIFICANCE STATEMENT Hearing depends on glutamatergic transmission mediated by Vglut3, but the role of glutamate in synapse loss and repair is unclear. Here, using mice of both sexes, we show that one copy of the Vglut3 gene is sufficient for noise-induced threshold shift and loss of ribbon synapses, but both copies are required for normal recovery of hearing function and ribbon synapse number. Impairment of the recovery process in mice having only one functional copy suggests that glutamate release may promote synapse regeneration. At least one copy of the Vglut3 gene is necessary for noise-induced synapse loss. Although the excitotoxic mechanism remains unknown, these findings are consistent with the presumption that glutamate is the key mediator of noise-induced synaptopathy. Copyright © 2019 the authors.

Author Keywords
cochlea; excitotoxicity; glutamate; noise exposure; regeneration; synaptopathy

Document Type: Article
Publication Stage: Final
Source: Scopus

“A Randomized Placebo-Controlled Trial of Omega-3 and Sertraline in Depressed Patients With or at Risk for Coronary Heart Disease” (2019) The Journal of Clinical Psychiatry

A Randomized Placebo-Controlled Trial of Omega-3 and Sertraline in Depressed Patients With or at Risk for Coronary Heart Disease
(2019) The Journal of Clinical Psychiatry, 80 (4), .

Carney, R.M.^a ^b , Freedland, K.E.^b , Rubin, E.H.^b , Rich, M.W.^c , Steinmeyer, B.C.^b , Harris, W.S.^d

^a Behavioral Medicine Center, Washington University School of Medicine, 4320 Forest Park Ave, Ste 301, St. Louis, MO 63108
^b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Internal Medicine, LLC, University of South Dakota and OmegaQuant, Sioux Falls, SD, United States

Abstract
OBJECTIVE: Studies of depressed psychiatric patients have suggested that antidepressant efficacy can be increased by adding eicosapentaenoic acid (EPA), one of the omega-3 fatty acids found in fish oils. The purpose of this study was to determine whether the addition of EPA improves the response to sertraline in depressed patients with or at high risk for coronary heart disease (CHD). METHODS: Between May 2014 and June 2018, 144 patients with DSM-5 major depressive disorder seen at the Washington University School of Medicine with or at high risk for CHD were randomized to receive either 50 mg/d of sertraline and 2 g/d of EPA or 50 mg/d of sertraline and corn oil placebo capsules for 10 weeks. The Beck Depression Inventory II (BDI-II) was the primary outcome measure. RESULTS: After 10 weeks of treatment, there were no differences between the arms on the mean baseline-adjusted BDI-II (placebo, 10.3; EPA, 12.1; P = .22), the 17-item Hamilton Depression Rating Scale (placebo, 7.2; EPA, 8.0; P = .40), or the 10-week remission rate (BDI-II score ≤ 8: placebo, 50.6%; EPA, 46.7%; odds ratio = 0.85; 95% CI, 0.43 to 1.68; P = .63). CONCLUSIONS: Augmentation of sertraline with 2 g/d of EPA for 10 weeks did not result in greater improvement in depressive symptoms compared to sertraline and corn oil placebo in patients with major depressive disorder and CHD or CHD risk factors. Identifying the characteristics of cardiac patients whose depression may benefit from omega-3 and clarifying the pathways linking omega-3 to improvement in depression symptoms are important directions for future research. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02021669; FDA IND registration number: 121107. © Copyright 2019 Physicians Postgraduate Press, Inc.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Overlap in the Genetic Architecture of Stroke Risk, Early Neurological Changes, and Cardiovascular Risk Factors” (2019) Stroke

Overlap in the Genetic Architecture of Stroke Risk, Early Neurological Changes, and Cardiovascular Risk Factors
(2019) Stroke, 50 (6), pp. 1339-1345.

Ibanez, L.^a , Heitsch, L.^b ^c , Dube, U.^a , Farias, F.H.G.^a , Budde, J.^a , Bergmann, K.^a , Davenport, R.^a , Bradley, J.^a , Carrera, C.^d , Kinnunen, J.^e , Sallinen, H.^e , Strbian, D.^e , Slowik, A.^f , Fernandez-Cadenas, I.^d , Montaner, J.^d ^g ^h , Lee, J.-M.^c , Cruchaga, C.^a

^a From the Department of Psychiatry (L.I., U.D., R.D., Washington University School of Medicine, St. Louis, MO, United States
^b Division of Emergency Medicine (L.H.), Washington University School of Medicine, St. Louis, MO, United States
^c Department of Neurology (L.H., Washington University School of Medicine, St. Louis, MO, United States
^d Neurovascular Research Laboratory, Vall d’Hebron Institute of Research, I.F.-C., Barcelona, Spain
^e Department of Neurology, Helsinki University Hospital, Finland (J.K., H.S.
^f Department of Neurology, Jagiellonian University Medical College, Poland (A.S.), Kraków
^g Institute of Biomedicine of Seville (IBiS), Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Cientificas (CSIC), University of Seville, Spain
^h Department of Neurology, Hospital Universitario Virgen Macarena, Seville, United States

Abstract
Background and Purpose- The genetic relationships between stroke risk, stroke severity, and early neurological changes are complex and not completely understood. Genetic studies have identified 32 all stroke risk loci. Polygenic risk scores can be used to compare the genetic architecture of related traits. In this study, we compare the genetic architecture of stroke risk, stroke severity, and early neurological changes with that of 2 stroke risk factors: type 2 diabetes mellitus (T2DM) and hypertension. Methods- We assessed the degree of overlap in the genetic architecture of stroke risk, T2DM, hypertension, and 2 acute stroke phenotypes based on the National Institutes of Health Stroke Scale (NIHSS), which ranges from 0 for no stroke symptoms to 21 to 42 for a severe stroke: baseline (within 6 hours after onset) and change in NIHSS (ΔNIHSS=NIHSS at baseline-NIHSS at 24 hours). This was done by (1) single-nucleotide polymorphism by single-nucleotide polymorphism comparison, (2) weighted polygenic risk scores with sentinel variants, and (3) whole-genome polygenic risk scores using multiple P thresholds. Results- We found evidence of genetic architecture overlap between stroke risk and T2DM ( P=2.53×10-169), hypertension ( P=3.93×10-04), and baseline NIHSS ( P=0.03). However, there was no evidence of overlap between ΔNIHSS and stroke risk, T2DM, or hypertension. Conclusions- The genetic architecture of stroke risk is correlated with that of T2DM, hypertension, and initial stroke severity (NIHSS within 6 hours of stroke onset). However, the genetic architecture of early neurological change after stroke (ΔNIHSS) is not correlated with that of ischemic stroke risk, T2DM, or hypertension. Thus, stroke risk and early neurological change after stroke have distinct genetic architectures.

Author Keywords
diabetes mellitus; genetics; hypertension; risk factors; stroke

Document Type: Article
Publication Stage: Final
Source: Scopus

“Hydroxyurea reduces cerebral metabolic stress in patients with sickle cell anemia” (2019) Blood

Hydroxyurea reduces cerebral metabolic stress in patients with sickle cell anemia
(2019) Blood, 133 (22), pp. 2436-2444. Cited 2 times.

Fields, M.E.^a , Guilliams, K.P.^b ^c , Ragan, D.^d , Binkley, M.M.^e , Mirro, A.^f , Fellah, S.^f , Hulbert, M.L.^a , Blinder, M.^g , Eldeniz, C.^h , Vo, K.^h , Shimony, J.S.^h , Chen, Y.^f , McKinstry, R.C.^h , An, H.^h , Lee, J.-M.^e ^h ^f ^h , Ford, A.L.^f

^a Division of Pediatric Hematology/Oncology, Washington University School of Medicine, St. Louis, MO, United States
^b Division of Pediatric Neurology, Washington University School of Medicine, St. Louis, MO, United States
^c Division of Pediatric Critical Care Medicine, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
^e Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, United States
^f Department of Neurology, Washington University School of Medicine, 660 South Euclid, Campus Box 8111, St. Louis, MO 63110, United States
^g Division of Hematology, Washington University School of Medicine, St. Louis, MO, United States
^h Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Chronic transfusion therapy (CTT) prevents stroke in selected patients with sickle cell anemia (SCA). We have shown that CTT mitigates signatures of cerebral metabolic stress, reflected by elevated oxygen extraction fraction (OEF), which likely drives stroke risk reduction. The region of highest OEF falls within the border zone, where cerebral blood flow (CBF) nadirs; OEF in this region was reduced after CTT. The neuroprotective efficacy of hydroxyurea (HU) remains unclear. To test our hypothesis that patients receiving HU therapy have lower cerebral metabolic stress compared with patients not receiving disease-modifying therapy, we prospectively obtained brain magnetic resonance imaging scans with voxel-wise measurements of CBF and OEF in 84 participants with SCA who were grouped by therapy: no disease-modifying therapy, HU, or CTT. There was no difference in whole-brain CBF among the 3 cohorts (P 5. 148). However, whole-brain OEF was significantly different (P <. 001): participants with out disease-modifying therapy had the highest OEF (median 42.9% [interquartile range (IQR) 39.1%-49.1%]), followed by HU treatment (median 40.7% [IQR 34.9%-43.6%]), whereas CTT treatment had the lowest values (median 35.3% [IQR 32.2%-38.9%]). Moreover, the percentage of white matter at highest risk for ischemia, defined by OEF greater than 40% and 42.5%, was lower in the HU cohort compared with the untreated cohort (P 5. 025 and P 5. 034 respectively), but higher compared with the CTT cohort (P 5. 018 and P 5. 029 respectively). We conclude that HU may offer neuroprotection by mitigating cerebral metabolic stress in patients with SCA, but not to the same degree as CTT. © 2019 by The American Society of Hematology.

Document Type: Conference Paper
Publication Stage: Final
Source: Scopus

“When all else fails, listen to the patient: A viewpoint on the use of ecological momentary assessment in clinical trials” (2019) Journal of Medical Internet Research

When all else fails, listen to the patient: A viewpoint on the use of ecological momentary assessment in clinical trials
(2019) Journal of Medical Internet Research, 21 (5), art. no. e11845, .

Mofsen, A.M.^a , Rodebaugh, T.L.^b , Nicol, G.E.^a , Depp, C.A.^c , Miller, J.^d , Lenze, E.J.^a

^a Department of Psychiatry, School of Medicine, Washington University in St Louis, St Louis, MO, United States
^b Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, United States
^c Department of Psychiatry, University of California – San Diego, 9500 Gilman Drive, San Diego, CA 92093, United States
^d Division of Biostatistics, School of Medicine, Washington University in St Louis, St Louis, MO, United States

Abstract
A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)-frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone-can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response. © 2019 Journal of Medical Internet Research. All rights reserved.

Author Keywords
Controlled clinical trial; Ecological momentary assessment; Health technology; Mental health; Psychiatry

Document Type: Review
Publication Stage: Final
Source: Scopus
Access Type: Open Access

“CROI 2019: neurologic complications of HIV disease” (2019) Topics in Antiviral Medicine

CROI 2019: neurologic complications of HIV disease
(2019) Topics in Antiviral Medicine, 27 (1), pp. 26-33.

Ances, B.M.^a , Letendre, S.L.^b

^a Washington University School of Medicine in St Louis, St Louis, MO, United States
^b University of California San Diego, San Diego, CA, United States

Abstract
Investigators reported many new neuroHIV research findings at the 2019 Conference on Retroviruses and Opportunistic Infections (CROI). These findings included confirmation that HIV-associated neurocognitive disorder (HAND) remains common with an increasingly recognized role for comorbidities (eg, obesity) and neurodegenerative conditions (eg, Alzheimer’s disease), especially as persons living with HIV (PLWH) advance into their seventh decade of life and beyond. HAND is increasingly recognized as a heterogeneous disorder that differs between individuals (eg, by sex) in the trajectory of specific neurocognitive abilities (eg, executive functioning). A more recent focus at this year’s conference was toxicity of combination antiretroviral therapy: neurocognitive performance and neuroimaging data from several studies were presented but did not consistently support that integrase strand transfer inhibitors are associated with worse neurologic outcomes. Neuroimaging studies found that white matter changes reflect a combination of the effects of HIV and comorbidities (including cerebrovascular small vessel disease) and best correlate with blood markers of inflammation. The pathogenesis of HIV in the central nervous system (CNS) was the focus of a plenary lecture and numerous presentations on HIV compartmentalization in the CNS and cerebrospinal fluid viral escape. Novel findings were also presented on associations between HIV-associated neurologic complications and glycomics, neuron-derived exosomes, and DNA methylation in monocytes. This summary will review findings from CROI and identify new research and clinical opportunities.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Technological interventions for medication adherence in adult mental health and substance use disorders: A systematic review” (2019) Journal of Medical Internet Research

Technological interventions for medication adherence in adult mental health and substance use disorders: A systematic review
(2019) Journal of Medical Internet Research, 21 (3), art. no. e12493, .

Steinkamp, J.M.^a , Goldblatt, N.^b , Borodovsky, J.T.^c , LaVertu, A.^d , Kronish, I.M.^e , Marsch, L.A.^f , Schuman-Olivier, Z.^b ^f ^g

^a Boston University School of Medicine, Boston, MA, United States
^b Outpatient Addiction Services, Department of Psychiatry, Cambridge Health Alliance, 26 Central Street, Somerville, MA 02143, United States
^c Washington University School of Medicine, St Louis, MO, United States
^d Tufts University School of Medicine, Boston, MA, United States
^e Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, New York City, NY, United States
^f Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
^g Department of Psychiatry, Harvard Medical School, Boston, MA, United States

Abstract
Background: Medication adherence is critical to the effectiveness of psychopharmacologic therapy. Psychiatric disorders present special adherence considerations, notably an altered capacity for decision making and the increased street value of controlled substances. A wide range of interventions designed to improve adherence in mental health and substance use disorders have been studied; recently, many have incorporated information technology (eg, mobile phone apps, electronic pill dispensers, and telehealth). Many intervention components have been studied across different disorders. Furthermore, many interventions incorporate multiple components, making it difficult to evaluate the effect of individual components in isolation. Objective: The aim of this study was to conduct a systematic scoping review to develop a literature-driven, transdiagnostic taxonomic framework of technology-based medication adherence intervention and measurement components used in mental health and substance use disorders. Methods: This review was conducted based on a published protocol (PROSPERO: CRD42018067902) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review guidelines. We searched 7 electronic databases: MEDLINE, EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials, Web of Science, Engineering Village, and ClinicalTrials.gov from January 2000 to September 2018. Overall, 2 reviewers independently conducted title and abstract screens, full-text screens, and data extraction. We included all studies that evaluate populations or individuals with a mental health or substance use disorder and contain at least 1 technology-delivered component (eg, website, mobile phone app, biosensor, or algorithm) designed to improve medication adherence or the measurement thereof. Given the wide variety of studied interventions, populations, and outcomes, we did not conduct a risk of bias assessment or quantitative meta-analysis. We developed a taxonomic framework for intervention classification and applied it to multicomponent interventions across mental health disorders. Results: The initial search identified 21,749 results; after screening, 127 included studies remained (Cohen kappa: 0.8, 95% CI 0.72-0.87). Major intervention component categories include reminders, support messages, social support engagement, care team contact capabilities, data feedback, psychoeducation, adherence-based psychotherapy, remote care delivery, secure medication storage, and contingency management. Adherence measurement components include self-reports, remote direct visualization, fully automated computer vision algorithms, biosensors, smart pill bottles, ingestible sensors, pill counts, and utilization measures. Intervention modalities include short messaging service, mobile phone apps, websites, and interactive voice response. We provide graphical representations of intervention component categories and an element-wise breakdown of multicomponent interventions. Conclusions: Many technology-based medication adherence and monitoring interventions have been studied across psychiatric disease contexts. Interventions that are useful in one psychiatric disorder may be useful in other disorders, and further research is necessary to elucidate the specific effects of individual intervention components. Our framework is directly developed from the substance use disorder and mental health treatment literature and allows for transdiagnostic comparisons and an organized conceptual mapping of interventions. © 2019 Journal of Medical Internet Research. All rights reserved.

Author Keywords
Medication adherence; Medication compliance; Mental health; Mhealth; Psychiatry; Substance-related disorders; Systematic review

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

“Beliefs about the automaticity of positive mood regulation: examination of the BAMR-Positive Emotion Downregulation Scale in relation to emotion regulation strategies and mood symptoms” (2019) Cognition and Emotion

Beliefs about the automaticity of positive mood regulation: examination of the BAMR-Positive Emotion Downregulation Scale in relation to emotion regulation strategies and mood symptoms
(2019) Cognition and Emotion, .

Dodd, A.L.^a , Gilbert, K.^b , Gruber, J.^c

^a Department of Psychology, Northumbria University, Newcastle Upon Tyne, United Kingdom
^b Department of Psychiatry, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
^c Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States

Abstract
Emotion regulation is a topic of great interest due to its relevance to navigating everyday life, as well as its relevance to psychopathology. Recent research indicates that beliefs about the automaticity of mood regulation are critical to psychological health. In the present study we assessed beliefs about the automaticity of positive mood regulation in relationship to self-reported mood symptoms and explicit emotion regulation strategies. Participants (n= 200) completed an online survey including a scale assessing beliefs about automatic downregulation of positive emotions (i.e. BAMR-PED), beliefs about automatic mood regulation for negative emotions, mood symptoms, and emotion regulation strategies. Results suggested that beliefs about automatic positive emotion regulation were associated with unhelpful emotion regulation strategies and reduced negative affect as well as fewer depressive, manic, and anxiety symptoms. Test-retest of the novel BAMR-PED measure was tested with a further sample (n = 46) and found to be acceptable. Future research should explore how these automatic beliefs have relevance to clinical disorders characterised by positive emotion disturbance, such as bipolar disorder. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
affect; bipolar disorder; Emotion regulation; hypomanic personality

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Restoration of Sensation and Thumb Opposition Using Nerve Transfers Following Resection of a Synovial Sarcoma of the Median Nerve” (2019) Journal of Hand Surgery Global Online

Restoration of Sensation and Thumb Opposition Using Nerve Transfers Following Resection of a Synovial Sarcoma of the Median Nerve
(2019) Journal of Hand Surgery Global Online, .

Power, H.A.^a ^b , Fox, I.K.^b , Kahn, L.C.^c , Mackinnon, S.E.^b

^a Division of Plastic Surgery, University of Alberta, Edmonton, Alberta, Canada
^b Division of Plastic and Reconstructive Surgery, Department of Surgery, Washington University School of Medicine, St Louis, MO, United States
^c Department of Occupational Therapy, Milliken Hand Rehabilitation Center, Washington University School of Medicine, St Louis, MO, United States

Abstract
Synovial sarcomas of peripheral nerves are rare and often leave major functional deficits after wide excision. We present a case of median nerve reconstruction using nerve transfers after resection of a synovial sarcoma of the median nerve. Sensation was restored by transferring the fourth common digital nerve to the first and second common digital nerves. Thumb opposition was restored by transferring the abductor digiti minimi branch to the recurrent motor branch. The soft tissue defect was reconstructed with a free gracilis muscle flap. Fifteen months after surgery, there was Medical Research Council grade 4+ opposition strength with co-contraction of the abductor digiti minimi. Sensation recovered slowly over time. The Disabilities of the Arm, Shoulder, and Hand score decreased from 47.4 before surgery to 13.3 afterward. The patient was able to use the right hand for writing and crafting pottery. Distal nerve transfers are a reliable option for reconstruction of complete median nerve defects. © 2019 The Authors

Author Keywords
median nerve; nerve transfer; opponensplasty; sensory nerve transfer; synovial sarcoma

Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Access Type: Open Access

“Erroneous inference based on a lack of preference within one group: Autism, mice, and the social approach task” (2019) Autism Research

Erroneous inference based on a lack of preference within one group: Autism, mice, and the social approach task
(2019) Autism Research, .

Nygaard, K.R.^a ^b , Maloney, S.E.^b ^c , Dougherty, J.D.^a ^b ^c

^a Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^c Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, MO, United States

Abstract
The Social Approach Task is commonly used to identify sociability deficits when modeling liability factors for autism spectrum disorder (ASD) in mice. It was developed to expand upon existing assays to examine distinct aspects of social behavior in rodents and has become a standard component of mouse ASD-relevant phenotyping pipelines. However, there is variability in the statistical analysis and interpretation of results from this task. A common analytical approach is to conduct within-group comparisons only, and then interpret a difference in significance levels as if it were a group difference, without any direct comparison. As an efficient shorthand, we named this approach EWOCs: Erroneous Within-group Only Comparisons. Here, we examined the prevalence of EWOCs and used simulations to test whether this approach could produce misleading inferences. Our review of Social Approach studies of high-confidence ASD genes revealed 45% of papers sampled used only this analytical approach. Through simulations, we then demonstrate how a lack of significant difference within one group often does not correspond to a significant difference between groups, and show this erroneous interpretation increases the rate of false positives up to 25%. Finally, we define a simple solution: use an index, like a social preference score, with direct statistical comparisons between groups to identify significant differences. We also provide power calculations to guide sample size in future studies. Overall, elimination of EWOCs and adoption of direct comparisons should result in more accurate, reliable, and reproducible data interpretations from the Social Approach Task across ASD liability models. Autism Res 2019. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: The Social Approach Task is widely used to assess social behavior in mice and is frequently used in studies modeling autism. However, reviewing published studies showed nearly half do not use correct comparisons to interpret these data. Using simulated and original data, we argue the correct statistical approach is a direct comparison of scores between groups. This simple solution should reduce false positives and improve consistency of results across studies. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.

Author Keywords
autism spectrum disorder; mice; social behavior; social preference index; statistics; three-chambered social approach task

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans” (2019) Genes, Brain and Behavior

Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans
(2019) Genes, Brain and Behavior, art. no. e12580, .

Wetherill, L.^a , Lai, D.^a , Johnson, E.C.^b , Anokhin, A.^b , Bauer, L.^c , Bucholz, K.K.^b , Dick, D.M.^d , Hariri, A.R.^e , Hesselbrock, V.^c , Kamarajan, C.^f , Kramer, J.^g , Kuperman, S.^g , Meyers, J.L.^f , Nurnberger, J.I., Jr.^h , Schuckit, M.ⁱ , Scott, D.M.^j , Taylor, R.E.^k , Tischfield, J.^l , Porjesz, B.^f , Goate, A.M.^m , Edenberg, H.J.^a ⁿ , Foroud, T.^a , Bogdan, R.^o , Agrawal, A.^b

^a Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indiana University, Indianapolis, IN, United States
^b Department of Psychiatry, Washington University School of Medicine, Washington University, Saint Louis, MO, United States
^c Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut, Farmington, CT, United States
^d Department of Psychology & College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, United States
^e Department of Psychology, Duke Institute for Brain Sciences, Duke University, Durham, NC, United States
^f SUNY. Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States
^g Department of Psychiatry, University of Iowa Roy J and Lucille A Carver College of Medicine, University of Iowa, Iowa City, IA, United States
^h Department of Psychiatry, Indiana University School of Medicine, Indiana University, Indianapolis, IN, United States
ⁱ Department of Psychiatry, University of California San Diego, San Diego, CA, United States
^j Departments of Pediatrics and Human Genetics, Howard University, Washington, DC, United States
^k Department of Pharmacology, Howard University, Washington, DC, United States
^l Department of Genetics, Rutgers University, Piscataway, NJ, United States
^m Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
ⁿ Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University, Indianapolis, IN, United States
^o Department of Psychological and Brain Sciences, Washington University in Saint Louis, Saint Louis, MO, United States

Abstract
Genetic influences on alcohol and drug dependence partially overlap, however, specific loci underlying this overlap remain unclear. We conducted a genome-wide association study (GWAS) of a phenotype representing alcohol or illicit drug dependence (ANYDEP) among 7291 European-Americans (EA; 2927 cases) and 3132 African-Americans (AA: 1315 cases) participating in the family-based Collaborative Study on the Genetics of Alcoholism. ANYDEP was heritable (h2 in EA = 0.60, AA = 0.37). The AA GWAS identified three regions with genome-wide significant (GWS; P < 5E-08) single nucleotide polymorphisms (SNPs) on chromosomes 3 (rs34066662, rs58801820) and 13 (rs75168521, rs78886294), and an insertion-deletion on chromosome 5 (chr5:141988181). No polymorphisms reached GWS in the EA. One GWS region (chromosome 1: rs1890881) emerged from a trans-ancestral meta-analysis (EA + AA) of ANYDEP, and was attributable to alcohol dependence in both samples. Four genes (AA: CRKL, DZIP3, SBK3; EA: P2RX6) and four sets of genes were significantly enriched within biological pathways for hemostasis and signal transduction. GWS signals did not replicate in two independent samples but there was weak evidence for association between rs1890881 and alcohol intake in the UK Biobank. Among 118 AA and 481 EA individuals from the Duke Neurogenetics Study, rs75168521 and rs1890881 genotypes were associated with variability in reward-related ventral striatum activation. This study identified novel loci for substance dependence and provides preliminary evidence that these variants are also associated with individual differences in neural reward reactivity. Gene discovery efforts in non-European samples with distinct patterns of substance use may lead to the identification of novel ancestry-specific genetic markers of risk. © 2019 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society

Author Keywords
African-American; alcohol dependence; drug dependence; European-American; fMRI; genetics; GWAS; heritability; neural reward; ventral striatum

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Changes in associations of prescription opioid use disorder and illegal behaviors among adults in the United States from 2002 to 20” (2019) Addiction

Changes in associations of prescription opioid use disorder and illegal behaviors among adults in the United States from 2002 to 20
(2019) Addiction, .

Mintz, C.M., Hartz, S.M., Borodovsky, J.T., Bierut, L.J., Grucza, R.A.

Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, United States

Abstract
Background and Aims: In the United States, the availability of prescription opioids has decreased in recent years. Whether there have been corresponding changes in the likelihood of people with prescription opioid use disorder (POUD) to engage in illegal behaviors related to drug use remains unknown. We examined changes in prevalence of illegal behaviors between people with and without POUD over time, and how transactions for obtaining opioids have changed among people with POUD over time. Design: Temporal trend analysis of repeated cross-sectional data. Setting: United States household dwelling population from all 50 states and District of Columbia. Participants: Adult subsamples from the 2002–14 National Survey of Drug Use and Health (n = 5393 people with POUD; n = 486 768 people without POUD). Measurements: Outcome variables were selected illegal behaviors and sources of opioids used non-medically. POUD was defined using the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. Time was treated as a continuous variable. The variable of interest for each illegal behavior analysis was the interaction between POUD diagnosis and time. Covariates included age, sex and race/ethnicity. Findings: During the 13-year period examined, the adjusted interaction odds ratio (AIOR) describing the change in association between POUD and selling illicit drugs increased by a factor of 2.41 [95% confidence interval (CI) = 1.56–3.71, P < 0.001]. Similar trends were noted for stealing (AIOR = 2.12, 95% CI = 1.31–3.44, P = 0.002) and for life-time history of arrest (AIOR = 1.53, 95% CI = 1.06–2.19, P = 0.021). People with POUD became less likely to receive opioids for free from friends and family [adjusted odds ratio (AOR) = 0.42, 95% CI = 0.25–0.71, P = 0.001] and more likely to buy them from friends and family (AOR = 3.29, 95% CI = 1.76–6.13, P < 0.001) from 2005 to 2014. Conclusions: In the United States, against a backdrop of a decreasing prescription opioid supply, rates of some crimes potentially related to drug use increased among people with prescription opioid use disorder compared with those without prescription opioid use disorder from 2002 to 2014. © 2019 Society for the Study of Addiction

Author Keywords
Crime; drug policy; illegal behavior; non-medical opioid source; opioid use disorder; prescription opioid use disorder

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“End-of-life measures in Huntington disease: HDQLIFE Meaning and Purpose, Concern with Death and Dying, and End of Life Planning” (2019) Journal of Neurology

End-of-life measures in Huntington disease: HDQLIFE Meaning and Purpose, Concern with Death and Dying, and End of Life Planning
(2019) Journal of Neurology, .

Carlozzi, N.E.^a , Boileau, N.R.^a , Paulsen, J.S.^b ^c ^d , Perlmutter, J.S.^e , Lai, J.-S.^f , Hahn, E.A.^f , McCormack, M.K.^g ^h , Nance, M.A.ⁱ ^j , Cella, D.^f ^k ^l , Barton, S.K.^e , Downing, N.R.^m

^a Department of Physical Medicine and Rehabilitation, University of Michigan, North Campus Research Complex, 2800 Plymouth Road, Building NCRC B14, Room G216, Ann Arbor, MI 8109-2800, United States
^b Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, IA, United States
^c Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, United States
^d Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA, United States
^e Neurology, Radiology, Neuroscience, Physical Therapy, Occupational Therapy, Washington University in St. Louis, St. Louis, MO, United States
^f Department of Medical Social Sciences, Northwestern University, Chicago, IL, United States
^g Department of Pathology, Rowan University—SOM, Stratford, NJ, United States
^h Department of Psychiatry, Rutgers University, RWJMS, Piscataway, NJ, United States
ⁱ Struthers Parkinson’s Center, Golden Valley, MN, United States
^j Hennepin County Medical Center, Minneapolis, MN, United States
^k Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
^l Northwestern University, Evanston, IL, United States
^m College of Nursing, Texas A&M University, Bryan, TX, United States

Abstract
Background and purpose: Huntington disease (HD) is a progressive neurodegenerative disorder. There are no HD-specific measures to assess for end-of-life (EOL) preferences that have been validated for clinical use. The purpose of this study is to demonstrate reliability and validity of three HD-specific EOL measures for use in and clinical research settings. Methods: We examined internal reliability, test–retest reliability, floor and ceiling effects, convergent and discriminant validity, known groups’ validity, measurement error, and change over time to systematically examine reliability and validity of the HDQLIFE EOL measures. Results: Internal consistency and test–retest reliability were > 0.70. The measures were generally free of floor and ceiling effects and measurement error was minimal. Convergent and discriminant validity were consistent with well-known constructs in the field. Hypotheses for known groups validity were partially supported (there were generally group differences for the EOL planning measures, but not for meaning and purpose or concern with death and dying). Measurement error was acceptable and there were minimal changes over time across the EOL measures. Conclusions: Results support the clinical utility of the HDQLIFE EOL measures in persons with HD. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Author Keywords
End of life; HDQLIFE; Huntingon disease; Reliability; Validity

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Youth and Adult Arrests for Cannabis Possession after Decriminalization and Legalization of Cannabis” (2019) JAMA Pediatrics

Youth and Adult Arrests for Cannabis Possession after Decriminalization and Legalization of Cannabis
(2019) JAMA Pediatrics, . Cited 1 time.

Plunk, A.D.^a , Peglow, S.L.^b , Harrell, P.T.^a , Grucza, R.A.^c

^a Department of Pediatrics, Eastern Virginia Medical School, Norfolk, United States
^b Department of Psychiatry, Eastern Virginia Medical School, Norfolk, United States
^c Department of Psychiatry, Washington University, School of Medicine in St Louis, St Louis, MO, United States

Abstract
Importance: Civil liberty advocates typically support legalization of cannabis, which targets adult use, rather than decriminalization, which can affect both adults and youths. However, it is unknown how arrests of youths for cannabis possession change when adult use of cannabis is legalized. Objective: To model changes in arrest rates of adults and youths after decriminalization and legalization of cannabis. Design, Setting, and Participants: This quasi-experimental study used the publicly available Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race administrative data set to examine arrest rates in 38 states from January 1, 2000, to December 31, 2016. Adult (age, ≥18 years) and youth (age, <18 years) arrests for possession of cannabis were examined. States were excluded if they did not report complete arrest data or if a policy was implemented that reduced penalties for possession of cannabis but fell short of decriminalization. Fixed-effects regression was used in an extended difference-in-differences framework. The analyses in their final form were conducted between January 17 and February 28, 2019. Exposure: Living in a state with a cannabis decriminalization policy (ie, making the penalty for cannabis possession similar to the small fine for a traffic violation) or legalization policy (ie, creating a legal supply of cannabis along with the removal of penalties for possession of a small amount of cannabis for recreational use). Main Outcome and Measures: State cannabis possession arrest rate per 100000 population. Results: Data from 38 states were examined, including 4 states with cannabis legalization policies and 7 states with cannabis decriminalization policies. The adult arrest rate decreased by 131.28 (95% CI, 106.23-154.21) per 100000 population after the implementation of decriminalization and 168.50 (95% CI, 158.64-229.65) per 100000 population after the implementation of legalization. The arrest rate for youths decreased by 60 (95% CI, 42-75) per 100000 population after decriminalization but did not significantly change after legalization in a state (7 per 100000 population; 95% CI, -15 to 30). Conclusions and Relevance: Legalization, as implemented through 2016, did not appear to reduce arrests for cannabis possession among youths, despite having benefited adults. The study’s findings suggest that decriminalization reduces youth arrests in most cases, but these findings also suggest that any benefit for youths could be lost when adult use has also been legalized. To address this problem, it appears that state decriminalization policies should take the additional step to explicitly describe when youths can be arrested for possession of small amounts of cannabis.. © 2019 American Medical Association. All rights reserved.

Document Type: Article
Publication Stage: Article in Press
Source: Scopus