WashU weekly Neuroscience publications | Office of Neuroscience Research

“High-fidelity mapping of repetition-related changes in the parietal memory network” (2019) NeuroImage

High-fidelity mapping of repetition-related changes in the parietal memory network
(2019) NeuroImage, 199, pp. 427-439.

Gilmore, A.W.^a , Nelson, S.M.^h ⁱ ^j , Laumann, T.O.^b ^c , Gordon, E.M.^h ⁱ , Berg, J.J.^a , Greene, D.J.^c ^d , Gratton, C.^b , Nguyen, A.L.^b , Ortega, M.^b , Hoyt, C.R.^b ^g , Coalson, R.S.^b ^d , Schlaggar, B.L.^b ^k ^l ^m , Petersen, S.E.^a ^b ^d ^e , Dosenbach, N.U.F.^b ^d ^f ^g , McDermott, K.B.^a ^d

^a Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
^b Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
^d Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
^e Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
^f Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
^g Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States
^h VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX 76711, United States
ⁱ Center for Vital Longevity, University of Texas at Dallas, Dallas, TX 75235, United States
^j Department of Psychology and Neuroscience, Baylor University, Waco, TX 76798, United States
^k Kennedy Krieger Institute, Baltimore, MD 21205, United States
^l Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^m Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Abstract
fMRI studies of human memory have identified a “parietal memory network” (PMN) that displays distinct responses to novel and familiar stimuli, typically deactivating during initial encoding but robustly activating during retrieval. The small size of PMN regions, combined with their proximity to the neighboring default mode network, makes a targeted assessment of their responses in highly sampled subjects important for understanding information processing within the network. Here, we describe an experiment in which participants made semantic decisions about repeatedly-presented stimuli, assessing PMN BOLD responses as items transitioned from experimentally novel to repeated. Data are from the highly-sampled subjects in the Midnight Scan Club dataset, enabling a characterization of BOLD responses at both the group and single-subject level. Across all analyses, PMN regions deactivated in response to novel stimuli and displayed changes in BOLD activity across presentations, but did not significantly activate to repeated items. Results support only a portion of initially hypothesized effects, in particular suggesting that novelty-related deactivations may be less susceptible to attentional/task manipulations than are repetition-related activations within the network. This in turn suggests that novelty and familiarity may be processed as separable entities within the PMN. © 2019

Author Keywords
Familiarity; fMRI; Highly-sampled human subjects; Memory; Parietal cortex; Retrieval

Document Type: Article
Publication Stage: Final
Source: Scopus

“The molecular determinants of neurosteroid binding in the GABA(A) receptor” (2019) Journal of Steroid Biochemistry and Molecular Biology

The molecular determinants of neurosteroid binding in the GABA(A) receptor
(2019) Journal of Steroid Biochemistry and Molecular Biology, 192, art. no. 105383, .

Sugasawa, Y.^a , Bracamontes, J.R.^a , Krishnan, K.^b , Covey, D.F.^a ^b ^c ^e , Reichert, D.E.^d ^e , Akk, G.^a ^e , Chen, Q.^f , Tang, P.^f ^g ^h , Evers, A.S.^a ^b ^e , Cheng, W.W.L.^a

^a Departments of Anesthesiology, Washington University in St. Louis, St. Louis, MO 63110, United States
^b Departments of Developmental Biology, Washington University in St. Louis, St. Louis, MO 63110, United States
^c Departments of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
^d Departments of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
^e Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO 63110, United States
^f Departments of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, United States
^g Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, United States
^h Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, United States

Abstract
Neurosteroids positively modulate GABA-A receptor (GABAAR) channel activity by binding to a transmembrane domain intersubunit site. Understanding the interactions in this site that determine neurosteroid binding and its effect is essential for the design of neurosteroid-based therapeutics. Using photo-affinity labeling and an ELIC-α1GABAAR chimera, we investigated the impact of mutations (Q242L, Q242W and W246L) within the intersubunit site on neurosteroid binding. These mutations, which abolish the thermal stabilizing effect of allopregnanolone on the chimera, reduce neither photolabeling within the intersubunit site nor competitive prevention of labeling by allopregnanolone. Instead, these mutations change the orientation of neurosteroid photolabeling. Molecular docking of allopregnanolone in WT and Q242W receptors confirms that the mutation favors re-orientation of allopregnanolone within the binding pocket. Collectively, the data indicate that mutations at Gln242 or Trp246 that eliminate neurosteroid effects do not eliminate neurosteroid binding within the intersubunit site, but significantly alter the preferred orientation of the neurosteroid within the site. The interactions formed by Gln242 and Trp246 within this pocket play a vital role in determining the orientation of the neurosteroid that may be necessary for its functional effect. © 2019 Elsevier Ltd

Author Keywords
ELIC; GABA receptor; Mass spectrometry; Mutation; Neurosteroid; Photoaffinity labeling

Document Type: Article
Publication Stage: Final
Source: Scopus

“Reactive microglia and IL1β/IL-1R1-signaling mediate neuroprotection in excitotoxin-damaged mouse retina” (2019) Journal of Neuroinflammation

Reactive microglia and IL1β/IL-1R1-signaling mediate neuroprotection in excitotoxin-damaged mouse retina
(2019) Journal of Neuroinflammation, 16 (1), art. no. 118, .

Todd, L.^f , Palazzo, I.^a , Suarez, L.^a , Liu, X.^d , Volkov, L.^b , Hoang, T.V.^c , Campbell, W.A.^a , Blackshaw, S.^c , Quan, N.^d ^e , Fischer, A.J.^a

^a Department of Neuroscience, College of Medicine, Ohio State University, 3020 Graves Hall, 333 W. 10th Ave, Columbus, OH 43210-1239, United States
^b Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
^c Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
^d Institute for Behavioral Medicine Research, College of Medicine, Ohio State University, Columbus, OH, United States
^e Division of Biosciences, College of Dentistry, Ohio State University, Columbus, OH, United States
^f Department of Biological Structure, University of Washington, Seattle, WA, United States

Abstract
Background: Microglia and inflammation have context-specific impacts upon neuronal survival in different models of central nervous system (CNS) disease. Herein, we investigate how inflammatory mediators, including microglia, interleukin 1 beta (IL1β), and signaling through interleukin 1 receptor type 1 (IL-1R1), influence the survival of retinal neurons in response to excitotoxic damage. Methods: Excitotoxic retinal damage was induced via intraocular injections of NMDA. Microglial phenotype and neuronal survival were assessed by immunohistochemistry. Single-cell RNA sequencing was performed to obtain transcriptomic profiles. Microglia were ablated by using clodronate liposome or PLX5622. Retinas were treated with IL1β prior to NMDA damage and cell death was assessed in wild type, IL-1R1 null mice, and mice expressing IL-1R1 only in astrocytes. Results: NMDA-induced damage included neuronal cell death, microglial reactivity, upregulation of pro-inflammatory cytokines, and genes associated with IL1β-signaling in different types of retinal neurons and glia. Expression of the IL1β receptor, IL-1R1, was evident in astrocytes, endothelial cells, some Müller glia, and OFF bipolar cells. Ablation of microglia with clodronate liposomes or Csf1r antagonist (PLX5622) resulted in elevated cell death and diminished neuronal survival in excitotoxin-damaged retinas. Exogenous IL1β stimulated the proliferation and reactivity of microglia in the absence of damage, reduced numbers of dying cells in damaged retinas, and increased neuronal survival following an insult. IL1β failed to provide neuroprotection in the IL-1R1-null retina, but IL1β-mediated neuroprotection was rescued when expression of IL-1R1 was restored in astrocytes. Conclusions: We conclude that reactive microglia provide protection to retinal neurons, since the absence of microglia is detrimental to survival. We propose that, at least in part, the survival-influencing effects of microglia may be mediated by IL1β, IL-1R1, and interactions of microglia and other macroglia. © 2019 The Author(s).

Author Keywords
IL-1R1; IL1β; Microglia; Retinal neuroprotection

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

“Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions” (2019) American Journal of Epidemiology

Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions
(2019) American Journal of Epidemiology, 188 (6), pp. 1033-1054.

de Vries, P.S.^a , Brown, M.R.^a , Bentley, A.R.^b , Sung, Y.J.^c , Winkler, T.W.^d , Ntalla, I.^e , Schwander, K.^c , Kraja, A.T.^f , Guo, X.^g , Franceschini, N.^h , Cheng, C.-Y.ⁱ ^j ^k , Sim, X.^l , Vojinovic, D.^m , Huffman, J.E.ⁿ , Musani, S.K.^o , Li, C.^p , Feitosa, M.F.^f , Richard, M.A.^q , Noordam, R.^r , Aschard, H.^s ^t , Bartz, T.M.^u , Bielak, L.F.^v , Deng, X.^w , Dorajoo, R.^x , Lohman, K.K.^y , Manning, A.K.^z ^aa , Rankinen, T.^ab , Smith, A.V.^ac ^ad , Tajuddin, S.M.^ae , Evangelou, E.^af ^ag , Graff, M.^h , Alver, M.^ah , Boissel, M.^ai , Chai, J.F.^l , Chen, X.^aj , Divers, J.^ak , Gandin, I.^al , Gao, C.^am , Goel, A.^an ^ao , Hagemeijer, Y.^ap , Harris, S.E.^aq ^ar , Hartwig, F.P.^as ^at , He, M.^au , Horimoto, A.R.V.R.^av , Hsu, F.-C.^ak , Jackson, A.U.^aw , Kasturiratne, A.^ax , Komulainen, P.^ay , Kühnel, B.^az ^ba , Laguzzi, F.^bb , Lee, J.H.^bc , Luan, J.^bd , Lyytikäinen, L.-P.^be ^bf , Matoba, N.^bg , Nolte, I.M.^bh , Pietzner, M.^bi ^bj , Riaz, M.^bk ^bl , Said, M.A.^ap , Scott, R.A.^bd , Sofer, T.^aa ^bm , Stančáková, A.^bn , Takeuchi, F.^bo , Tayo, B.O.^bp , van der Most, P.J.^bh , Varga, T.V.^bq , Wang, Y.^br , Ware, E.B.^bs , Wen, W.^bt , Yanek, L.R.^bu , Zhang, W.^af ^bv , Zhao, J.H.^bd , Afaq, S.^af , Amin, N.^m , Amini, M.^bh , Arking, D.E.^bw , Aung, T.ⁱ ^k ^bx , Ballantyne, C.^by ^bz , Boerwinkle, E.^ca ^cb , Broeckel, U.^cc , Campbell, A.^cd , Canouil, M.^ai , Charumathi, S.ⁱ ^j , Chen, Y.-D.I.^g , Connell, J.M.^ce , de Faire, U.^bb , de Las Fuentes, L.^c ^cf , de Mutsert, R.^cg , de Silva, H.J.^ch , Ding, J.^ci , Dominiczak, A.F.^cj , Duan, Q.^ck , Eaton, C.B.^cl , Eppinga, R.N.^ap , Faul, J.D.^bs , Fisher, V.^w , Forrester, T.^cm , Franco, O.H.^m ^cn , Friedlander, Y.^co , Ghanbari, M.^m ^cp , Giulianini, F.^go , Grabe, H.J.^cq , Grove, M.L.^a , Gu, C.C.^c , Harris, T.B.^cr , Heikkinen, S.^bn ^cs , Heng, C.-K.^ct ^cu , Hirata, M.^cv , Hixson, J.E.^ca , Howard, B.V.^cw ^cx , Ikram, M.A.^m ^cy ^cz , InterAct Consortium^gp , Jacobs, D.R.^da , Johnson, C.^db , Jonas, J.B.^dc ^dd , Kammerer, C.M.^de , Katsuya, T.^df ^dg , Khor, C.C.^x ^dh , Kilpeläinen, T.O.^di ^dj , Koh, W.-P.^l ^dk , Koistinen, H.A.^dl ^dm ^dn , Kolcic, I.^gq , Kooperberg, C.^do , Krieger, J.E.^av , Kritchevsky, S.B.^dp , Kubo, M.^dq , Kuusisto, J.^bn , Lakka, T.A.^ay ^cs ^dr , Langefeld, C.D.^ak , Langenberg, C.^bd , Launer, L.J.^cr , Lehne, B.^af , Lemaitre, R.N.^ds , Li, Y.^c , Liang, J.^br , Liu, J.^x ^dt , Liu, K.^du , Loh, M.^af ^dv , Louie, T.^dw , Mägi, R.^ah , Manichaikul, A.W.^dx , McKenzie, C.A.^cm , Meitinger, T.^dy ^dz , Metspalu, A.^ah , Milaneschi, Y.^ea , Milani, L.^ah , Mohlke, K.L.^ck , Mosley, T.H.^eb , Mukamal, K.J.^ec , Nalls, M.A.^ed ^ee , Nauck, M.^bi ^bj , Nelson, C.P.^bk ^bl , Sotoodehnia, N.^ef , O’Connell, J.R.^eg ^eh , Palmer, N.D.^ei , Pazoki, R.^af , Pedersen, N.L.^aj , Peters, A.^ba ^bj , Peyser, P.A.^v , Polasek, O.^ej ^ek ^el , Poulter, N.^em , Raffel, L.J.^en , Raitakari, O.T.^eo ^ep , Reiner, A.P.^do , Rice, T.K.^c , Rich, S.S.^eq , Robino, A.^er , Robinson, J.G.^es , Rose, L.M.^et , Rudan, I.^eu , Schmidt, C.O.^ev , Schreiner, P.J.^da , Scott, W.R.^af ^ew , Sever, P.^ew , Shi, Y.ⁱ , Sidney, S.^ex , Sims, M.^o , Smith, B.H.^ey , Smith, J.A.^v ^bs , Snieder, H.^bh , Starr, J.M.^aq ^ez , Strauch, K.^fa ^fb , Tan, N.ⁱ ^k , Taylor, K.D.^g , Teo, Y.Y.^l ^x ^fc ^fd ^fe , Tham, Y.C.ⁱ , Uitterlinden, A.G.^m ^ff , van Heemst, D.^r , Vuckovic, D.^al , Waldenberger, M.^az ^ba , Wang, L.^f , Wang, Y.^h , Wang, Z.^a , Wei, W.B.^fg , Williams, C.^f , Wilson, G.^fh , Wojczynski, M.K.^f , Yao, J.^g , Yu, B.^a , Yu, C.^au , Yuan, J.-M.^fi ^fj , Zhao, W.^v , Zonderman, A.B.^fk , Becker, D.M.^bu , Boehnke, M.^aw , Bowden, D.W.^ei , Chambers, J.C.^af ^bv ^fl ^fm ^fn ^fo , Deary, I.J.^aq ^fp , Esko, T.^ah ^fq , Farrall, M.^an ^ao , Franks, P.W.^bq ^fr ^fs ^ft , Freedman, B.I.^fu , Froguel, P.^ai ^fv , Gasparini, P.^al ^er , Gieger, C.^az ^fw , Horta, B.L.^as , Kamatani, Y.^bg , Kato, N.^bo , Kooner, J.S.^bv ^ew ^fm ^fo , Laakso, M.^bn , Leander, K.^bb , Lehtimäki, T.^be ^bf , Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study)^gr , Magnusson, P.K.E.^aj , Penninx, B.^ea , Pereira, A.C.^av , Rauramaa, R.^ay , Samani, N.J.^bk ^bl , Scott, J.^ew , Shu, X.-O.^bt , van der Harst, P.^ap ^fx , Wagenknecht, L.E.^fy , Wang, Y.X.^fz , Wareham, N.J.^bd , Watkins, H.^an ^ao , Weir, D.R.^bs , Wickremasinghe, A.R.^ax , Zheng, W.^bt , Elliott, P.^af ^fn ^fo ^ga ^gb , North, K.E.^h ^gc , Bouchard, C.^ab , Evans, M.K.^ae , Gudnason, V.^ac ^ad , Liu, C.-T.^w , Liu, Y.^gd , Psaty, B.M.^ge ^gf , Ridker, P.M.^et ^gg , van Dam, R.M.^l ^dt ^fr , Kardia, S.L.R.^v , Zhu, X.^br , Rotimi, C.N.^b , Mook-Kanamori, D.O.^cg ^gh , Fornage, M.^a ^q , Kelly, T.N.^gi , Fox, E.R.^gj , Hayward, C.ⁿ , van Duijn, C.M.^m , Tai, E.S.^l ^dk ^dt , Wong, T.Y.ⁱ ^k ^bx , Liu, J.^gk , Rotter, J.I.^g , Gauderman, W.J.^gl , Province, M.A.^f , Munroe, P.B.^e ^gm , Rice, K.^dw , Chasman, D.I.^et ^gg , Cupples, L.A.^w ^gn , Rao, D.C.^c , Morrison, A.C.^a

^a Human Genetics Center, Department of Epidemiology, Human Genetics, Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States
^b Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
^c Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
^e Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, Barts and The London School of Medicine and Dentistry, London, United Kingdom
^f Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^g Pediatrics, Institute for Translational Genomics and Population Sciences, LA BioMed at Harbor-UCLA Medical Center, Torrance, CA, United States
^h Epidemiology, University of North Carolina Gilling School of Global Public Health, Chapel Hill, NC, United States
ⁱ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
^j Centre for Quantitative Medicine, Academic Medicine Research Institute, Ophthalmology & Visual Sciences Academic Clinical Program
^k Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
^l Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
^m Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
ⁿ Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^o Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
^p Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, Georgia, France
^q Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, United States
^r Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
^s Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
^t Centre de Bioinformatique, Biostatistique et Biologie Intégrative
^u Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, WA, United States
^v Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
^w Biostatistics, Boston University School of Public Health, Boston, MA, United States
^x Genome Institute of Singapore, Agency for Science Technology and Research, Singapore
^y Public Health Sciences, Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, United States
^z Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, United States
^aa Department of Medicine, Harvard Medical School, Boston, MA, United States
^ab Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, United States
^ac Icelandic Heart Association, Kopavogur, Iceland
^ad Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^ae Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
^af Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
^ag Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
^ah Estonian Genome Center, University of TartuTartu, Estonia
^ai CNRS UMR 8199, European Genomic Institute for Diabetes
^aj Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^ak Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
^al Department of Medical Sciences, University of Trieste, Trieste, Italy
^am Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, United States
^an Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
^ao Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
^ap Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^aq Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
^ar Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^as Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
^at Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
^au Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
^av Laboratory of Genetics and Molecular Cardiology, Heart Institute, Spain
^aw Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
^ax Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
^ay Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
^az Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
^ba Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
^bb Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
^bc Sergievsky Center and Taub Institute, Columbia University Medical CenterNY, United States
^bd MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
^be Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
^bf Department of Clinical Chemistry, Finnish Cardiovascular Research Center – Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
^bg Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
^bh Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^bi Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
^bj DZHK
^bk Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
^bl NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
^bm Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, United States
^bn Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
^bo Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and MedicineTokyo, Japan
^bp Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, United States
^bq Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
^br Department of Population and Quantitative Health and Sciences, Case Western Reserve University, Cleveland, OH, United States
^bs Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
^bt Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, United States
^bu Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^bv Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom
^bw McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^bx Ophthalmology & Visual Sciences Academic Clinical Program
^by Section of Cardiovascular Research, Baylor College of Medicine, Houston, TX, United States
^bz Houston Methodist Debakey Heart and Vascular Center, Houston, TX, United States
^ca Department of Epidemiology, Human Genetics, Environmental Sciences, University of Texas School of Public Health, Houston, TX, United States
^cb Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States
^cc Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, WI, United States
^cd Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^ce Ninewells Hospital & Medical School, University of Dundee, Dundee, United Kingdom
^cf Cardiovascular Division, Department of Medicine, Washington University, St. Louis, MO, United States
^cg Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
^ch Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
^ci Center on Diabetes, Obesity, Metabolism, Gerontology and Geriatric Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, United States
^cj Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
^ck Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
^cl Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, RI, United States
^cm Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona, Jamaica
^cn Institute of Social and Preventive Medicine, Greece
^co Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
^cp Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
^cq Department Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
^cr Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^cs Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
^ct Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
^cu Khoo Teck Puat – National University Children’s Medical Institute, National University Health System, Singapore
^cv Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Japan
^cw MedStar Health Research Institute, Hyattsville, MD, United States
^cx Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, District of Columbia
^cy Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
^cz Department of Neurology, Erasmus University Medical Center, Rotterdam, Netherlands
^da Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States
^db Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, United States
^dc Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
^dd Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China
^de Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
^df Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
^dg Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita, Japan
^dh Department of Biochemistry, National University of Singapore, Singapore
^di Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
^dj Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount SinaiNY, United States
^dk Duke-NUS Medical School, Singapore
^dl Department of Health, National Institute for Health and Welfare, Helsinki, Finland
^dm Endocrinology, University of Helsinki and Helsinki University Central Hospital, Department of Medicine and Abdominal Center, Helsinki, Finland
^dn Minerva Foundation Institute for Medical Research, Helsinki, Finland
^do Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA, United States
^dp Sticht Center for Health Aging and Alzheimer’s Prevention, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
^dq Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
^dr Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
^ds Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA, United States
^dt Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
^du Epidemiology, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
^dv Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore
^dw Department of Biostatistics, University of Washington, Seattle, WA, United States
^dx Biostatistics Section, Center for Public Health Genomics, University of Virginia, School of Medicine, West Complex, Charlottesville, VA, United States
^dy Institute of Human Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
^dz Institute of Human Genetics, Technische Universität München, Munich, Germany
^ea Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, Netherlands
^eb Geriatrics, Medicine, University of Mississippi Medical Center, Jackson, MS, United States
^ec General Medicine & Primary Care, Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
^ed Data Tecnica International, Glen Echo, MD, United States
^ee Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, United States
^ef Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, WA, United States
^eg Division of Endocrinology, Diabetes, Nutrition, University of Maryland School of Medicine, Baltimore, MD, United States
^eh Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
^ei Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, United States
^ej Department of Public Health, Department of Medicine, University of Split, Split, Croatia
^ek Psychiatric Hospital “Sveti Ivan”, Zagreb, Croatia
^el Gen-info Ltd, Zagreb, Croatia
^em School of Public Health, Imperial College London, London, United Kingdom
^en Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, CA, United States
^eo Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
^ep Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
^eq Center for Public Health Genomics, University of Virginia, School of Medicine, West Complex, Charlottesville, VA, United States
^er Institute for Maternal and Child Health – IRCCS “Burlo Garofolo”, Trieste, Italy
^es Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA, United States
^et Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
^eu Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
^ev Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
^ew National Heart and Lung Institute, Imperial College London, London, United Kingdom
^ex Division of Research, Kaiser Permanente of Northern California, Oakland, CA, United States
^ey Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
^ez Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
^fa Institute of Genetic Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
^fb Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
^fc Life Sciences Institute, National University of Singapore, Singapore
^fd NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore
^fe Department of Statistics and Applied Probability, National University of Singapore, Singapore
^ff Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
^fg Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
^fh Jackson Heart Study, School of Public Health, Jackson State University, Jackson, MS, United States
^fi Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
^fj Division of Cancer Control and Population Sciences, University of Pittsburgh, Pittsburgh, PA, United States
^fk Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
^fl Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
^fm Imperial College Healthcare NHS Trust, London, United Kingdom
^fn MRC-PHE Centre for Environment and Health, Imperial College London, London, United Kingdom
^fo NIHR Imperial College Biomedical Research Centre, Imperial College London, London, United Kingdom
^fp Psychology, University of Edinburgh, Edinburgh, United Kingdom
^fq Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, MA, United States
^fr Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
^fs Department of Public Health & Clinical Medicine, Umeå University, Umeå, Västerbotten, Sweden
^ft OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
^fu Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
^fv Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
^fw German Center for Diabetes Research
^fx Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^fy Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
^fz Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China
^ga Health Data Research UK
^gb UK Dementia Research Institute
^gc Carolina Center of Genome Sciences, University of North Carolina, Chapel Hill, NC, United States
^gd Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, NC, United States
^ge Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, United States
^gf Health Research Institute, Seattle, WA, United States
^gg Harvard Medical School, Boston, MA, United States
^gh Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
^gi Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
^gj Cardiology, Medicine, University of Mississippi Medical Center, Jackson, MS, United States
^gk Fred Hutchinson Cancer Research Center, Seattle, WA, United States
^gl Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, CA, United States
^gm NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, United Kingdom
^gn NHLBI Framingham Heart Study, Framingham, MA, United States

Abstract
A person’s lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019.

Author Keywords
alcohol consumption; cholesterol; gene-environment interactions; gene-lifestyle interactions; genome-wide association studies; lipids; triglycerides

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

“Apolipoprotein E associated with reconstituted high-density lipoprotein-like particles is protected from aggregation” (2019) FEBS Letters

Apolipoprotein E associated with reconstituted high-density lipoprotein-like particles is protected from aggregation
(2019) FEBS Letters, 593 (11), pp. 1144-1153.

Hubin, E.^a ^b ^c , Verghese, P.B.^d , van Nuland, N.^b ^c , Broersen, K.^a ^e ^f

^a Nanobiophysics Group, Technical Medical Centre, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
^b Structural Biology Brussels, Department of Biotechnology (DBIT), Vrije Universiteit Brussel (VUB), Belgium
^c Structural Biology Research Center, VIB, Brussels, Belgium
^d Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^e Applied Stem Cell Technologies, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
^f C2N Diagnostics, Center for Emerging Technologies, 4041 Forest Park Ave, St. Louis, MO 63108, United States

Abstract
Apolipoprotein E (APOE) genotype determines Alzheimer’s disease (AD) susceptibility, with the APOE ε4 allele being an established risk factor for late-onset AD. The ApoE lipidation status has been reported to impact amyloid-beta (Aβ) peptide metabolism. The details of how lipidation affects ApoE behavior remain to be elucidated. In this study, we prepared lipid-free and lipid-bound ApoE particles, mimicking the high-density lipoprotein particles found in vivo, for all three isoforms (ApoE2, ApoE3, and ApoE4) and biophysically characterized them. We find that lipid-free ApoE in solution has the tendency to aggregate in vitro in an isoform-dependent manner under near-physiological conditions and that aggregation is impeded by lipidation of ApoE. © 2019 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Author Keywords
aggregation; Alzheimer’s disease; apolipoprotein E; high-density lipoprotein; isoform; lipidation

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

“Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising” (2019) Cerebral Cortex (New York, N.Y. : 1991)

Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising
(2019) Cerebral Cortex (New York, N.Y. : 1991), 29 (6), pp. 2455-2469.

Nielsen, A.N.^a , Greene, D.J.^b ^c , Gratton, C.^a , Dosenbach, N.U.F.^a ^d , Petersen, S.E.^a ^c ^d ^e , Schlaggar, B.L.^a ^b ^c ^d ^f

^a Department of Neurology, 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 Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
^e Department of Psychology, Washington University in St. Louis, St. Louis, MO, United States
^f Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States

Abstract
The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Author Keywords
development; fMRI; functional connectivity; machine learning

Document Type: Article
Publication Stage: Final
Source: Scopus

“Acute Posterior Multifocal Placoid Pigment Epitheliopathy Complicated by Fatal Cerebral Vasculitis” (2019) Journal of Neuro-Ophthalmology : the Official Journal of the North American Neuro-Ophthalmology Society

Acute Posterior Multifocal Placoid Pigment Epitheliopathy Complicated by Fatal Cerebral Vasculitis
(2019) Journal of Neuro-Ophthalmology : the Official Journal of the North American Neuro-Ophthalmology Society, 39 (2), pp. 260-267.

Maamari, R.N., Stunkel, L., Kung, N.H., Ferguson, C.J., Tanabe, J., Schmidt, R.E., Dahiya, S., Dhand, A., Van Stavern, G.P., Rajagopal, R., Harocopos, G.J.

Departments of Ophthalmology and Visual Sciences (RNM, GPVS, RR, GJH) and Neurology (LS), Washington University School of Medicine in St. Louis, St. Louis, Missouri; Blue Sky Neurology (NHK), Denver, Colorado; Department of Pathology and Immunology (CJF, RES, SD, GJH), Washington University School of Medicine, St. Louis, Missouri; Department of Radiology (JT), University of Colorado Anschutz Medical Campus, Aurora, Colorado; and Department of Neurology (AD), Brigham and Women’s Hospital, Boston, Massachusetts

Abstract
A 21-year-old man experienced unilateral vision loss associated with multiple atrophic chorioretinal lesions. He was treated for a presumptive diagnosis of acute retinal necrosis, but his vision did not improve with antiviral therapy. Over the course of several weeks, his symptoms progressed to involve both eyes. The fellow eye showed characteristic yellow-white placoid lesions, prompting treatment with oral corticosteroids for acute posterior multifocal placoid pigment epitheliopathy (APMPPE). Despite high-dose therapy with prednisone 80 mg daily, the patient developed the acute onset of mental status changes within the next several days. Neuroimaging revealed multifocal large-vessel strokes associated with cerebral edema; these infarcts led to herniation and death. Postmortem histopathologic examination confirmed granulomatous inflammation in both ocular and cerebral vasculatures. Together with findings from multimodal imaging obtained throughout this patient’s clinical course, our findings support the notion that granulomatous choroiditis is the mechanism of the ocular lesions seen in APMPPE. This granulomatous inflammation can also affect cerebral vessels, leading to strokes.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Opioid Sensitivity in Children with and without Obstructive Sleep Apnea” (2019) Anesthesiology

Opioid Sensitivity in Children with and without Obstructive Sleep Apnea
(2019) Anesthesiology, 130 (6), pp. 936-945.

Montana, M.C., Juriga, L., Sharma, A., Kharasch, E.D.

From the Department of Anesthesiology, Washington University in St. Louis, School of Medicine, St. Louis, Missouri (M.C.M., L.J., A.S., E.D.K.) the Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina (E.D.K.)

Abstract
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: Children with obstructive sleep apnea are at greater risk for postoperative hypoxia and other respiratory events as compared with children without this disorderThere is some reason to believe that children with obstructive sleep apnea may be at greater risk for opioid-induced respiratory depression due to increased sensitivity to the drugs WHAT THIS ARTICLE TELLS US THAT IS NEW: The authors hypothesized that children with obstructive sleep apnea would be more sensitive to the effects of an opioid (remifentanil) on pupil size-a very good indicator of opioid effectsWhile remifentanil did reduce pupil size in the expected dose-related fashion, there were no differences between children with obstructive sleep apnea and those withoutWhile the authors did not observe any differences in the effect of remifentanil on respiration, the study was not designed to examine this factor in detail BACKGROUND:: Opioids are a mainstay of perioperative analgesia. Opioid use in children with obstructive sleep apnea is challenging because of assumptions for increased opioid sensitivity and assumed risk for opioid-induced respiratory depression compared to children without obstructive sleep apnea. These assumptions have not been rigorously tested. This investigation tested the hypothesis that children with obstructive sleep apnea have an increased pharmacodynamic sensitivity to the miotic and respiratory depressant effects of the prototypic μ-opioid agonist remifentanil. METHODS: Children (8 to 14 yr) with or without obstructive sleep apnea were administered a 15-min, fixed-rate remifentanil infusion (0.05, 0.1, or 0.15 μg · kg · min). Each dose group had five patients with and five without obstructive sleep apnea. Plasma remifentanil concentrations were measured by tandem liquid chromatography mass spectrometry. Remifentanil effects were measured via miosis, respiratory rate, and end-expired carbon dioxide. Remifentanil pharmacodynamics (miosis vs. plasma concentration) were compared in children with or without obstructive sleep apnea. RESULTS: Remifentanil administration resulted in miosis in both non-obstructive sleep apnea and obstructive sleep apnea patients. No differences in the relationship between remifentanil concentration and miosis were seen between the two groups at any of the doses administered. The administered dose of remifentanil did not affect respiratory rate or end-expired carbon dioxide in either group. CONCLUSIONS: No differences in the remifentanil concentration-miosis relation were seen in children with or without obstructive sleep apnea. The dose and duration of remifentanil administered did not alter ventilatory parameters in either group.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Emergent Functional Network Effects in Parkinson Disease” (2019) Cerebral Cortex (New York, N.Y. : 1991)

Emergent Functional Network Effects in Parkinson Disease
(2019) Cerebral Cortex (New York, N.Y. : 1991), 29 (6), pp. 2509-2523. Cited 1 time.

Gratton, C.^a , Koller, J.M.^b , Shannon, W.^c , Greene, D.J.^b ^d , Maiti, B.^a , Snyder, A.Z.^a ^d , Petersen, S.E.^a ^d ^e ^f ^g ^h , Perlmutter, J.S.^a ^d ^f ⁱ ^j , Campbell, M.C.^a ^d

^a Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
^b Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
^c BioRankings, St. Louis, MO, United States
^d Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
^e Department of Psychology, Washington University in St. Louis, St. Louis, MO, United States
^f Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
^g Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
^h Department of Neurological Surgery, Washington University in St. Louis, St. Louis, MO, United States
ⁱ Department of Occupational Therapy, Washington University in St. Louis, St. Louis, MO, United States
^j Department of Physical Therapy, Washington University in St. Louis, St. Louis, MO, United States

Abstract
The hallmark pathology underlying Parkinson disease (PD) is progressive synucleinopathy, beginning in caudal brainstem that later spreads rostrally. However, the primarily subcortical pathology fails to account for the wide spectrum of clinical manifestations in PD. To reconcile these observations, resting-state functional connectivity (FC) can be used to examine dysfunction across distributed brain networks. We measured FC in a large, single-site study of nondemented PD (N = 107; OFF medications) and healthy controls (N = 46) incorporating rigorous quality control measures and comprehensive sampling of cortical, subcortical and cerebellar regions. We employed novel statistical approaches to determine group differences across the entire connectome, at the network-level, and for select brain regions. Group differences respected well-characterized network delineations producing a striking “block-wise” pattern of network-to-network effects. Surprisingly, these results demonstrate that the greatest FC differences involve sensorimotor, thalamic, and cerebellar networks, with notably smaller striatal effects. Split-half replication demonstrates the robustness of these results. Finally, block-wise FC correlations with behavior suggest that FC disruptions may contribute to clinical manifestations in PD. Overall, these results indicate a concerted breakdown of functional network interactions, remote from primary pathophysiology, and suggest that FC deficits in PD are related to emergent network-level phenomena rather than focal pathology. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Author Keywords
fMRI; functional connectivity; networks; Parkinson disease

Document Type: Article
Publication Stage: Final
Source: Scopus

“Identifying Incidence of and Risk Factors for Fluoroscopy-Guided Lumbar Puncture and Subsequent Persistent Low-Pressure Syndrome in Patients With Idiopathic Intracranial Hypertension” (2019) Journal of Neuro-Ophthalmology : the Official Journal of the North American Neuro-Ophthalmology Society

Identifying Incidence of and Risk Factors for Fluoroscopy-Guided Lumbar Puncture and Subsequent Persistent Low-Pressure Syndrome in Patients With Idiopathic Intracranial Hypertension
(2019) Journal of Neuro-Ophthalmology : the Official Journal of the North American Neuro-Ophthalmology Society, 39 (2), pp. 161-164.

Lu, P., Goyal, M., Huecker, J.B., Gordon, M.O., Van Stavern, G.P.

Department of Ophthalmology and Visual Sciences (PL, JBH, MOG, GPVS), Washington University School of Medicine, St. Louis, Missouri; Mallinckrodt Institute of Radiology (MG), Washington University School of Medicine, St. Louis, Missouri; and Department of Neurology (MG, GPVS), Washington University School of Medicine, St. Louis, Missouri

Abstract
BACKGROUND: To explore the incidence of and potential risk factors for developing persistent low-pressure syndrome after lumbar puncture (LP) in patients with idiopathic intracranial hypertension (IIH), as measured by use of blood patches. METHODS: A retrospective chart review was conducted of patients with definitively diagnosed IIH by clinical examination and LP, comparing them to patients with multiple sclerosis (MS) as controls who also received diagnostic LPs. Demographic, clinical, and radiological data were collected for each patient. The main outcome measure was the rate of post-LP blood patches in IIH patients compared with MS patients. Secondary outcome measures were the likelihood of undergoing an epidural blood patch related to age, body mass index, volume removed, opening pressure, the difference between opening and closing pressure, and the level of puncture within the IIH cohort. RESULTS: One hundred four IIH patients and 149 MS patients were included in the study. Among IIH patients, 12/104 (11.5%) underwent an epidural blood patch after LP as compared to 8/149 (5.4%) of the MS control patients (P = 0.086). Within the IIH population, none of the clinical or LP parameters were significantly correlated with increased risk of needing a blood patch. CONCLUSIONS: The incidence of low-pressure syndrome, as measured by blood patches, is similar in IIH patients and MS controls. This suggests that having elevated intracranial pressure before an LP is not protective against developing postpuncture low-pressure syndrome, contrary to common assumptions.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Attenuation of Unevoked Mechanical and Cold Pain Hypersensitivities Associated With Experimental Neuropathy in Mice by Angiotensin II Type-2 Receptor Antagonism” (2019) Anesthesia and Analgesia

Attenuation of Unevoked Mechanical and Cold Pain Hypersensitivities Associated With Experimental Neuropathy in Mice by Angiotensin II Type-2 Receptor Antagonism
(2019) Anesthesia and Analgesia, 128 (6), pp. e84-e87.

Shepherd, A.J., Mohapatra, D.P.

From the Department of Anesthesiology and Washington University Pain Center, Washington University School of Medicine in St Louis, St Louis, MO, United States

Abstract
Recent findings from a phase II clinical trial showed analgesic effects of an angiotensin II type-2 receptor (AT2R) antagonist in postherpetic neuralgia patients. This study aimed to investigate whether AT2R antagonism could provide effective analgesia in voluntary measures of unevoked/ongoing pain-like behaviors in mice with experimental neuropathy. Mice were subjected to spared nerve injury to induce neuropathy and tested in 2 operant behavioral tests to measure ongoing mechanical and cold pain hypersensitivities. Systemic administration of an AT2R antagonist provided effective analgesia in these behavioral measures of mechanical and cold pain in spared nerve injury mice, suggesting its effectiveness in neuropathic pain.

Document Type: Article
Publication Stage: Final
Source: Scopus

“Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the International League Against Epilepsy Neuroimaging Task Force” (2019) Epilepsia

Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the International League Against Epilepsy Neuroimaging Task Force
(2019) Epilepsia, 60 (6), pp. 1054-1068.

Bernasconi, A.^a , Cendes, F.^b , Theodore, W.H.^c , Gill, R.S.^a , Koepp, M.J.^d , Hogan, R.E.^e , Jackson, G.D.^f , Federico, P.^g , Labate, A.^h , Vaudano, A.E.ⁱ , Blümcke, I.^j , Ryvlin, P.^k , Bernasconi, N.^a

^a Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
^b Department of Neurology, University of Campinas, Campinas, Brazil
^c Clinical Epilepsy Section, National Institutes of Health, Bethesda, MD, United States
^d Institute for Neurology, University College London, London, United Kingdom
^e Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
^f Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia
^g Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
^h Institute of Neurology, University of Catanzaro, Catanzaro, Italy
ⁱ Neurology Unit, Azienda Ospedaliero Universitaria, University of Modena and Reggio Emilia, Modena, Italy
^j Department of Neuropathology, University Hospital Erlangen, Erlangen, Germany
^k Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland

Abstract
Structural magnetic resonance imaging (MRI) is of fundamental importance to the diagnosis and treatment of epilepsy, particularly when surgery is being considered. Despite previous recommendations and guidelines, practices for the use of MRI are variable worldwide and may not harness the full potential of recent technological advances for the benefit of people with epilepsy. The International League Against Epilepsy Diagnostic Methods Commission has thus charged the 2013-2017 Neuroimaging Task Force to develop a set of recommendations addressing the following questions: (1) Who should have an MRI? (2) What are the minimum requirements for an MRI epilepsy protocol? (3) How should magnetic resonance (MR) images be evaluated? (4) How to optimize lesion detection? These recommendations target clinicians in established epilepsy centers and neurologists in general/district hospitals. They endorse routine structural imaging in new onset generalized and focal epilepsy alike and describe the range of situations when detailed assessment is indicated. The Neuroimaging Task Force identified a set of sequences, with three-dimensional acquisitions at its core, the harmonized neuroimaging of epilepsy structural sequences—HARNESS-MRI protocol. As these sequences are available on most MR scanners, the HARNESS-MRI protocol is generalizable, regardless of the clinical setting and country. The Neuroimaging Task Force also endorses the use of computer-aided image postprocessing methods to provide an objective account of an individual’s brain anatomy and pathology. By discussing the breadth and depth of scope of MRI, this report emphasizes the unique role of this noninvasive investigation in the care of people with epilepsy. Wiley Periodicals, Inc. © 2019 International League Against Epilepsy

Author Keywords
adults; epilepsy; pediatrics; structural magnetic resonance imaging

Document Type: Article
Publication Stage: Final
Source: Scopus

“The subjective value of cognitive effort is encoded by a domain-general valuation network” (2019) Journal of Neuroscience

The subjective value of cognitive effort is encoded by a domain-general valuation network
(2019) Journal of Neuroscience, 39 (20), pp. 3934-3947.

Westbrook, A.^a ^b ^c , Lamichhane, B.^d , Braver, T.^d ^e ^f

^a Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525 EN, Netherlands
^b Department of Psychiatry, Radboud University Medical Centre, Nijmegen, Netherlands
^c Department of Cognitive, Linguistics and Psychological Sciences, Brown University, Providence, RI 02912, United States
^d Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
^e Departments of Radiology, School of Medicine, St. Louis, MO 63110, United States
^f Neuroscience Washington University in St. Louis, School of Medicine, St. Louis, MO 63110, United States

Abstract
Cognitive control is necessary for goal-directed behavior, yet people treat cognitive control demand as a cost, which discounts the value of rewards in a similar manner as other costs, such as delay or risk. It is unclear, however, whether the subjective value (SV) of cognitive effort is encoded in the same putatively domain-general brain valuation network implicated in other cost domains, or instead engages a distinct frontoparietal network, as implied by recent studies. Here, we provide rigorous evidence that the valuation network, with core foci in the ventromedial prefrontal cortex and ventral striatum, also encodes SV during cognitive effort-based decision-making in healthy, male and female adult humans. We doubly dissociate this network from frontoparietal regions that are instead recruited as a function of decision difficulty. We show that the domain-general valuation network jointly and independently encodes both reward benefits and cognitive effort costs. We also demonstrate that cognitive effort SV signals predict choice and are influenced by state and trait motivation, including sensitivity to reward and anticipated task performance. These findings unify cognitive effort with other cost domains, and suggest candidate neural mechanisms underlying state and trait variation in willingness to expend cognitive effort. © 2019 the authors.

Author Keywords
Cognitive control; Cognitive effort; Decision-making; Motivation; Subjective value; VmPFC

Document Type: Article
Publication Stage: Final
Source: Scopus

“Increasing Suicide Rates in Early Adolescent Girls in the United States and the Equalization of Sex Disparity in Suicide: The Need to Investigate the Role of Social Media” (2019) JAMA Network Open

Increasing Suicide Rates in Early Adolescent Girls in the United States and the Equalization of Sex Disparity in Suicide: The Need to Investigate the Role of Social Media
(2019) JAMA Network Open, 2 (5), p. e193916.

Luby, J.^a , Kertz, S.^a ^b

^a Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO, United States
^b Department of Psychology, Southern Illinois University, Carbondale, United States

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

“Comparison of Opioid Prescribing by Dentists in the United States and England” (2019) JAMA Network Open

Comparison of Opioid Prescribing by Dentists in the United States and England
(2019) JAMA Network Open, 2 (5), p. e194303.

Suda, K.J.^a ^b , Durkin, M.J.^c , Calip, G.S.^b , Gellad, W.F.^d ^e , Kim, H.^f , Lockhart, P.B.^g , Rowan, S.A.^h , Thornhill, M.H.^g ⁱ

^a Center of Innovation for Complex Chronic Healthcare, Edward Hines Jr Veterans Administration Hospital, Chicago, IL, United States
^b College of Pharmacy, University of Illinois at Chicago, Uganda
^c School of Medicine, Washington University in St Louis, St Louis, MO, United States
^d Center for Health Equity Research and Promotion, Pittsburgh Veterans Administration Healthcare System, Pittsburgh, PA, United States
^e School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
^f Center for Clinical and Translational Science, University of Illinois at Chicago, Uganda
^g Department of Oral Medicine, Carolinas Medical Center, Charlotte, NC, United States
^h College of Dentistry, University of Illinois at Chicago, Uganda
ⁱ School of Clinical Dentistry, University of Sheffield, Sheffield, United Kingdom

Abstract
Importance: The United States consumes most of the opioids worldwide despite representing a small portion of the world’s population. Dentists are one of the most frequent US prescribers of opioids despite data suggesting that nonopioid analgesics are similarly effective for oral pain. While oral health and dentist use are generally similar between the United States and England, it is unclear how opioid prescribing by dentists varies between the 2 countries. Objective: To compare opioid prescribing by dentists in the United States and England. Design, Setting, and Participants: Cross-sectional study of prescriptions for opioids dispensed from outpatient pharmacies and health care settings between January 1 and December 31, 2016, by dentists in the United States and England. Data were analyzed from October 2018 to January 2019. Exposures: Opioids prescribed by dentists. Main Outcomes and Measures: Proportion and prescribing rates of opioid prescriptions. Results: In 2016, the proportion of prescriptions written by US dentists that were for opioids was 37 times greater than the proportion written by English dentists. In all, 22.3% of US dental prescriptions were opioids (11.4 million prescriptions) compared with 0.6% of English dental prescriptions (28 082 prescriptions) (difference, 21.7%; 95% CI, 13.8%-32.1%; P < .001). Dentists in the United States also had a higher number of opioid prescriptions per 1000 population (35.4 per 1000 US population [95% CI, 25.2-48.7 per 1000 population] vs 0.5 per 1000 England population [95% CI, 0.03-3.7 per 1000 population]) and number of opioid prescriptions per dentist (58.2 prescriptions per dentist [95% CI, 44.9-75.0 prescriptions per dentist] vs 1.2 prescriptions per dentist [95% CI, 0.2-5.6 prescriptions per dentist]). While the codeine derivative dihydrocodeine was the sole opioid prescribed by English dentists, US dentists prescribed a range of opioids containing hydrocodone (62.3%), codeine (23.2%), oxycodone (9.1%), and tramadol (4.8%). Dentists in the United States also prescribed long-acting opioids (0.06% of opioids prescribed by US dentists [6425 prescriptions]). Long-acting opioids were not prescribed by English dentists. Conclusions and Relevance: This study found that in 2016, dentists in the United States prescribed opioids with significantly greater frequency than their English counterparts. Opioids with a high potential for abuse, such as oxycodone, were frequently prescribed by US dentists but not prescribed in England. These results illustrate how 1 source of opioids differs substantially in the United States vs England. To reduce dental opioid prescribing in the United States, dentists could adopt measures similar to those used in England, including national guidelines for treating dental pain that emphasize prescribing opioids conservatively.

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

“How to implant a phrenic nerve stimulator for treatment of central sleep apnea?” (2019) Journal of Cardiovascular Electrophysiology

How to implant a phrenic nerve stimulator for treatment of central sleep apnea?
(2019) Journal of Cardiovascular Electrophysiology, 30 (5), pp. 792-799.

Augostini, R.S.^a , Afzal, M.R.^a , Costanzo, M.R.^b , Westlund, R.^c , Stellbrink, C.^d , Gutleben, K.^e , Gupta, S.^f , Saleem, M.^b , Smith, T.W.^g , Peterson, M.^h , Drucker, M.ⁱ , Merliss, A.^j , Hayes, J.^k , Butter, C.^l , Hutchinson, M.^m , Jagielski, D.ⁿ

^a The Ohio State University Wexner Medical Center, Columbus, OH, United States
^b Advocate Heart Institute, Chicago, IL, United States
^c Respicardia Inc, Minnetonka, MN, United States
^d Klinikum Bielefeld, Bielefeld, DE, United States
^e Ruhruniversität Bochum, Bad Oeynhausen, DE, United States
^f Department of Cardiology, University of Missouri-Kansas City School of Medicine, Saint Luke’s Mid-America Heart Institute, Kansas City, MO, United States
^g Washington University School of Medicine, St Louis, MO, United States
^h United Heart and Vascular Clinic, St Paul, MN, United States
ⁱ Novant Health Cardiology, Winston-Salem, NC, United States
^j Bryan Medical Center, Lincoln, NE, United States
^k Marshfield Clinic, Marshfield, WI, United States
^l Heart Center Brandenburg in Bernau/Berlin & Brandenburg Medical School, Bernau, DE, United States
^m University of Arizona College of Medicine, Tucson, AZ, United States
ⁿ 4th Military Hospital, Wroclaw, Poland

Abstract
Background: Central sleep apnea (CSA) is a breathing disorder caused by the intermittent absence of central respiratory drive. Transvenous phrenic nerve stimulation is a new therapeutic option, recently approved by the FDA, for the treatment of CSA. Objective: To describe the technique used to implant the transvenous phrenic nerve stimulation system (the remedē System, Respicardia, Inc). Methods: The remedē System is placed in the pectoral region, typically on the right side. A single stimulation lead is placed in either the left pericardiophrenic vein (PPV) or the right brachiocephalic vein (RBC). A sensing lead is placed into the azygous vein to detect respiration. Results: In the remedē System Pivotal trial, 147 of 151 (97%) patients were successfully implanted with the system. Sixty-two percent of stimulation leads were placed in the PPV and 35% in the RBC. Mean procedure time was 2.7 ± 0.8 hours and 94% of patients were free from implant-related serious adverse events through 6 months. Conclusion: In patients with CSA, transvenous phrenic nerve stimulation is an effective and safe therapy with an implant procedure similar to that of cardiac implantable electronic devices. © 2019 Wiley Periodicals, Inc.

Author Keywords
central sleep apnea; phrenic nerve; phrenic nerve stimulation; transvenous stimulation

Document Type: Article
Publication Stage: Final
Source: Scopus

“General anesthesia does not have persistent effects on attention in rodents” (2019) Frontiers in Behavioral Neuroscience

General anesthesia does not have persistent effects on attention in rodents
(2019) Frontiers in Behavioral Neuroscience, 13, art. no. 76, .

Hambrecht-Wiedbusch, V.S.^a ^b , Latendresse, K.A.^a , Avidan, M.S.^c , Nelson, A.G.^a , Phyle, M.^a , Ajluni, R.E.^a , Mashour, G.A.^a ^b

^a Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
^b Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
^c Department of Anesthesiology, Washington University, St. Louis, MO, United States

Abstract
Background: Studies in animals have shown that general anesthesia can cause persistent spatial memory impairment, but the influence of anesthetics on other cognitive functions is unclear. This study tested whether exposure to general anesthesia without surgery caused a persistent deficit in attention in rodents. Methods: To evaluate whether anesthesia has persistent effects on attention, rats were randomized to three groups. Group A was exposed for 2 h to isoflurane anesthesia, and tested the following seven days for attentional deficits. Group B was used as a control and received room air before attentional testing. Since there is some evidence that a subanesthetic dose of ketamine can improve cognition and reduce disorders of attention after surgery, rats in group C were exposed to isoflurane anesthesia in combination with a ketamine injection before cognitive assessment. Attention was measured in rats using the 5-Choice Serial Reaction Time Task, for which animals were trained to respond with a nose poke on a touchscreen to a brief, unpredictable visual stimulus in one of five possible grid locations to receive a food reward. Attention was analyzed as % accuracy, % omission, and premature responses. Results: Evaluating acute attention by comparing baseline values with data from the day after intervention did not reveal any differences in attentional measurements. No significant differences were seen in % accuracy, % omission, and premature responses for the three groups tested for 7 consecutive days. Conclusion: These data in healthy rodents suggest that general anesthesia without surgery has no persistent effect on attention and the addition of ketamine does not alter the outcome. © 2019 Hambrecht-Wiedbusch, LaTendresse, Avidan, Nelson, Phyle, Ajluni and Mashour.

Author Keywords
5-Choice serial reaction time task; Accuracy; Cognitive dysfunction; Delirium; Isoflurane; Ketamine; Omission; Premature response

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

“Franz joseph gall on the cerebellum as the organ for the reproductive drive” (2019) Frontiers in Neuroanatomy

Franz joseph gall on the cerebellum as the organ for the reproductive drive
(2019) Frontiers in Neuroanatomy, 13, art. no. 40, .

Eling, P.^a , Finger, S.^b

^a Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
^b Department of Psychological and Brain Sciences, Washington University, Saint Louis, MO, United States

Abstract
Franz Joseph Gall (1758–1828) is best remembered for his belief that bumps on the skull reflect the growth of small, underlying brain areas, though among some historians, more positively for introducing the concept of cortical localization of function. All but one of Gall’s 27 settled-upon cortical faculties involved the cerebral cortex, the exception being his most primitive faculty, reproductive instinct, which he associated with the cerebellar cortex. This article examines Gall’s earlier subcortical organs, with an emphasis on why he associated the cerebellum with this drive. It draws from accounts by several physicians, who attended his Vienna lectures or heard him speak in Germany and the Netherlands in 1805–1806 [i.e., before he published his finalized list in his Anatomie et Physiologie (1810–1819)]. These early accounts show that early on he localized at least four faculties in brainstem structures, including a reproductive drive in the cerebellar cortex. He based his structure–function association primarily on cranial differences between men and women, and what he found in males and females of other species, although cranioscopy was not his sole method. It is also shown that, in opposition to his cerebellar–reproductive drive association, Marie Jean Pierre Flourens linked coordinated skeletal movements to the cerebellum after conducting lesion experiments, mainly on birds. Flourens did not design his experiments to challenge Gall’s ideas on localization of function, but they did just that. Gall responded that ablation methods lack precision and lead to misguided conclusions. How Gall continued to associate the reproductive instinct with the cerebellar cortex, even after deleting his other brainstem-based associations from his faculties of mind, tells us much about him and the faith he had in his methods and doctrine. © 2019 Eling and Finger.

Author Keywords
Cerebellum; Cranioscopy; Flourens (Marie Jean Pierre); Gall (Franz Joseph); Localization of function; Mental faculties; Movement disorders; Reproduction

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

“Translating Neurobiology into Practice in Tobacco, Alcohol, Drug, and Behavioral Addictions” (2019) Handbook of Behavioral Neuroscience

Translating Neurobiology into Practice in Tobacco, Alcohol, Drug, and Behavioral Addictions
(2019) Handbook of Behavioral Neuroscience, 29, pp. 389-401.

Srivastava, A.B.^a ^b , Gold, M.S.^a

^a Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, United States
^b Department of Psychiatry, Columbia University Medical Center/New York State Psychiatric Institute, New York, NY, United States

Abstract
Addiction is a major public health problem in the United States, accounting for significant morbidity and mortality. Drugs of abuse produce neuroplasticity changes in the brain that profoundly alter how the brain experiences and responds to rewarding and stressful situations and stimuli. These changes in brain plasticity and the resultant endophenotypes are relatively well characterized, and current pharmacotherapy for addiction is largely directed towards blocking craving and mitigating binge use, but does not address changes in reward threshold (e.g., anhedonia) or negative affective states that are characteristic of addictive disorders and often drive recidivism. Translational research towards drug development should thus focus on directly targeting anhedonic and negative affect states. © 2019 Elsevier B.V.

Author Keywords
Acamprosate; Acamprosate; Disulfiram; Morphine; mu-Opioid receptor; Naltrexone

Document Type: Book Chapter
Publication Stage: Final
Source: Scopus

“Hypothesizing Major Depression as a Subset of Reward Deficiency Syndrome (RDS) Linked to Polymorphic Reward Genes: Considerations for Translational Medicine Approaches for Future Drug Development” (2019) Handbook of Behavioral Neuroscience

Hypothesizing Major Depression as a Subset of Reward Deficiency Syndrome (RDS) Linked to Polymorphic Reward Genes: Considerations for Translational Medicine Approaches for Future Drug Development
(2019) Handbook of Behavioral Neuroscience, 29, pp. 419-426.

Blum, K.^a ^b ^c ^d ^e ^f ^g ^h ⁱ ^j , Gold, M.S.^c ^k , Modestino, E.J.^l , Elman, I.^j , Baron, D.^a ^c , Badgaiyan, R.D.^m ⁿ , Bowirrat, A.^o

^a Western University Health Sciences, Graduate School of Biomedical Sciences, Pomona, CA, United States
^b Division of Addiction Services, Dominion Diagnostics LLC, North Kingstown, RI, United States
^c Division of Neuroscience and Addiction Research, Pathway Healthcare, Birmingham, AL, United States
^d Division of Neuroscience Research and Addiction Therapy, Shores Treatment and Recovery Center, Port Saint Lucie, FL, United States
^e Human Integrated Services Unit, University of Vermont Centre for Clinical & Translational Science, College of Medicine, Burlington, VT, United States
^f Eötvös Loránd University, Institute of Psychology, Budapest, Hungary
^g Division of Clinical Neurology, Path Foundation NY, New York, NY, United States
^h Division of Addiction Research & Therapy, Nupathways, Innsbrook, MO, United States
ⁱ Division of Precision Medicine, Geneus Health LLC, San Antonio, TX, United States
^j Department of Psychiatry, Wright State University, Boonshoft School of Medicine, Dayton, OH, United States
^k Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^l Department of Psychology, Curry College, Milton, MA, United States
^m Department of Psychiatry, South Texas Veteran Health Care System, Audie L. Murphy Memorial VA Hospital, San Antonio, TX, United States
ⁿ Long School of Medicine, University of Texas Medical Center, San Antonio, TX, United States
^o Division of Anatomy, Biochemistry and Genetics, Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine

Abstract
We hypothesize that major depressive disorder (MDD), especially anhedonia, (excluding bipolar) should be included as a subtype of Reward Deficiency Syndrome (RDS) following more extensive genetic and molecular neurobiological research. RDS, first coined by Blum in 1995, is a failure of the reward system that usually confers satisfaction, resulting in behaviors such as overeating, heavy cigarette smoking, drug and alcohol abuse (substance use disorder [SUD]), hoarding, internet addiction, gambling, and hyperactivity. RDS is caused by hypodopaminergia. We suggest that the inclusion of MDD within RDS will assist in more appropriate treatment in the addiction recovery community, whereby the goal of achieving dopamine stabilization or homeostasis will result in better clinical outcomes. One therapeutic technique to achieve this laudable goal is via epigenetic induction involving the administration of gene expression modulators that may have a positive impact on reversing hypodopaminergia, SUD, and anhedonia. We hereby encourage further research into dopaminergic homeostasis in SUD with MDD that will guide future drug development programs. © 2019 Elsevier B.V.

Author Keywords
Hypodopaminergia; Major depressive disorder; Reward Deficiency Syndrome; Substance use disorder

Document Type: Book Chapter
Publication Stage: Final
Source: Scopus

“Risk factors for strabismus following glaucoma drainage device implantation for refractory childhood glaucoma” (2019) Journal of AAPOS

Risk factors for strabismus following glaucoma drainage device implantation for refractory childhood glaucoma
(2019) Journal of AAPOS, .

Talsania, S.D.^a , Nallasamy, N.^b , Lee, A.R.^c , Freedman, S.F.^d

^a Eye Surgery Associates, Hollywood, FL, United States
^b Kellogg Eye Center, University of Michigan, Ann Arbor, MI, United States
^c Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, Saint Louis, MO, United States
^d Department of Ophthalmology, Duke University Medical Center, Durham, NC, United States

Abstract
Background: Strabismus is common in children after glaucoma drainage device (GDD) implantation, but the risk factors for postoperative strabismus remain speculative. The purpose of this study was to investigate possible risk factors for strabismus following GDD implantation for refractory childhood glaucoma. Methods: The medical records of consecutive patients who underwent GDD implantation for refractory childhood glaucoma at Duke Eye Center from 2005 to 2016 were reviewed retrospectively. Pre- and postoperative motility and alignment, best-corrected visual acuity, and demographic and surgical data were extracted from the record for analysis. Results: A total of 81 patients (mean age, 7.9 ± 4.8 years) met inclusion criteria. The most common glaucoma type was glaucoma following cataract surgery (GFCS), and the most common GDD was a Baerveldt 250 mm2 device. Before GDD surgery, 38 patients (47%) had documented strabismus. After GDD implantation, 25 (31%) had new or worsened strabismus, with vertical (16% of new/worsened), horizontal strabismus (exotropia, 48% of new/worsened; esotropia, 12% of new/worsened) and vertical and horizontal (24% of new/worsened) noted. New motility limitation occurred in 32 of 81 (40%) patients. Risk factors including age, type/location/number of GDD, revision, motility limitation, glaucoma type, asymmetric visual acuity, and visual impairment were not significantly associated with new or worsened post-GDD strabismus. Conclusions: Children with refractory childhood glaucoma are at high risk for strabismus, which increases after GDD implantation; this study identified no clear risk factors for new or worsened post-GDD strabismus. © 2019 American Association for Pediatric Ophthalmology and Strabismus

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

“Stable Isotope Labeling Kinetics in CNS Translational Medicine: Introduction to SILK Technology” (2019) Handbook of Behavioral Neuroscience

Stable Isotope Labeling Kinetics in CNS Translational Medicine: Introduction to SILK Technology
(2019) Handbook of Behavioral Neuroscience, 29, pp. 173-190.

Bateman, R.J.^a , West, T.^b , Yarasheski, K.^b , Patterson, B.W.^c , Lucey, B.^a , Cirrito, J.R.^a , Lehmann, S.^d , Hirtz, C.^d , Gabelle, A.^e , Miller, T.^a , Barthelemy, N.^a , Sato, C.^a , Bollinger, J.G.^a , Kotzbauer, P.^a , Paumier, K.^a

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b C2N Diagnostics, LLC, St. Louis, MO, United States
^c Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, United States
^d Laboratoire de Biochimie Protéomique Clinique, CHU de Montpellier, Université de Montpellier, INSERM U1183, Montpellier, France
^e Memory Research and Resources Center, Gui de Chauliac Hospital, Université de Montpellier, Montpellier, France

Abstract
Alzheimer’s disease (AD) is the most common cause of dementia, and it is currently estimated to afflict 5 million people in the United States. Several therapeutic targets have been identified as contributors to Alzheimer’s disease pathophysiology such as Aβ, tau, inflammation, and other disease-causing mechanisms, but highly effective therapies and accurate diagnostic tests are still currently unavailable. Most current therapeutic approaches target Aβ, and the treatment of Alzheimer’s disease is often initiated during the mild-to-moderate stage of dementia, which may be too late as 50% of hippocampal neurons are typically dead at this point. Initially, in order to understand Aβ kinetics in the pathophysiology of Alzheimer’s disease, we developed a novel method to metabolically label central nervous system (CNS) proteins during protein translation and sample labeled proteins from cerebrospinal fluid (CSF) during and after labeling to measure the kinetics of proteins in the CNS. We have utilized this technique of stable isotope labeling kinetics (SILK) to successfully measure the production and clearance of Aβ in the human CNS and have since translated this approach to other model systems including in vitro cell culture and animal models and to other proteins and diseases such as apolipoprotein E, soluble amyloid precursor proteins, tau, superoxide dismutase-1 (SOD1) in amyotrophic lateral sclerosis, and alpha-synuclein in Parkinson’s disease. This chapter will review recent advancements in the development and application of SILK for measuring the pathophysiology and drug development for CNS diseases. SILK has provided important insights into Alzheimer’s disease pathophysiology with altered synthesis and clearance of amyloid-beta, and drug effects on amyloid-beta. Further, the risk factors of AD, including aging, the largest AD risk factor, reveal profound slowing of amyloid-beta clearance, and the most prevalent genetic risk factor, apolipoprotein E, is being assessed. The initial work in AD has been expanded to specific proteins involved in the pathogenesis of other CNS disorders including amyotrophic lateral sclerosis (i.e., SOD) and Parkinson’s disease (i.e., alpha-synuclein). The use of SILK technology with specific disease-causing proteins has been generalized using SILAV to expand its application to addressing questions in proteomics and the peripheral compartments outside of the CNS. © 2019 Elsevier B.V.

Author Keywords
Alzheimer’s disease; Amyloid-beta; Central nervous system; Kinetics; Protein; Proteomics

Document Type: Book Chapter
Publication Stage: Final
Source: Scopus

“APOE Effect on Amyloid-β PET Spatial Distribution, Deposition Rate, and Cut-Points” (2019) Journal of Alzheimer’s Disease

APOE Effect on Amyloid-β PET Spatial Distribution, Deposition Rate, and Cut-Points
(2019) Journal of Alzheimer’s Disease, 69 (3), pp. 783-793.

Toledo, J.B.^a ^c , Habes, M.^d , Sotiras, A.^d ^e , Bjerke, M.^a ^b , Fan, Y.^d , Weiner, M.W.^f , Shaw, L.M.^a , Davatzikos, C.^d , Trojanowski, J.Q.^a

^a Department of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman, School of Medicine, Medical Center, Philadelphia, PA, United States
^b Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
^c Department of Neurology, Houston Methodist Hospital, Houston, TX, United States
^d Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
^e Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
^f Department of Radiology, Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, University of California San Francisco, San Francisco, CA, United States

Abstract
There are conflicting results regarding how APOE genotype, the strongest genetic risk factor for Alzheimer’s disease (AD), influences spatial and longitudinal amyloid-β (Aβ) deposition and its impact on the selection of biomarker cut-points. In our study, we sought to determine the impact of APOE genotype on cross-sectional and longitudinal florbetapir positron emission tomography (PET) amyloid measures and its impact in classification of patients and interpretation of clinical cohort results. We included 1,019 and 1,072 Alzheimer’s Disease Neuroimaging Initiative participants with cerebrospinal fluid Aβ 1 – 42 and florbetapir PET values, respectively. 623 of these subjects had a second florbetapir PET scans two years after the baseline visit. We evaluated the effect of APOE genotype on Aβ distribution pattern, pathological biomarker cut-points, cross-sectional clinical associations with Aβ load, and longitudinal Aβ deposition rate measured using florbetapir PET scans. 1) APOE ϵ4 genotype influences brain amyloid deposition pattern; 2) APOE ϵ4 genotype does not modify Aβ biomarker cut-points estimated using unsupervised mixture modeling methods if white matter and brainstem references are used (but not when cerebellum is used as a reference); 3) findings of large differences in Aβ biomarker value differences based on APOE genotype are due to increased probability of having AD neuropathology and are most significant in mild cognitive impairment subjects; and 4) APOE genotype and age (but not gender) were associated with increased Aβ deposition rate. APOE ϵ4 carrier status affects rate and location of brain Aβ deposition but does not affect choice of biomarker cut-points if adequate references are selected for florbetapir PET processing. © 2019 – IOS Press and the authors. All rights reserved.

Author Keywords
Alzheimer’s disease; amyloid-β; cerebrospinal fluid; diagnosis; mild cognitive impairment; positron emission tomography

Document Type: Article
Publication Stage: Final
Source: Scopus

“Duration of EEG suppression does not predict recovery time or degree of cognitive impairment after general anaesthesia in human volunteers” (2019) British Journal of Anaesthesia

Duration of EEG suppression does not predict recovery time or degree of cognitive impairment after general anaesthesia in human volunteers
(2019) British Journal of Anaesthesia, .

Shortal, B.P.^a , Hickman, L.B.^b , Mak-McCully, R.A.^c , Wang, W.^h , Brennan, C.^a , Ung, H.^d , Litt, B.^d ^e , Tarnal, V.^f , Janke, E.^f , Picton, P.^f , Blain-Moraes, S.^g , Maybrier, H.R.^b , Muench, M.R.^b , Lin, N.^h , Avidan, M.S.^b , Mashour, G.A.^f , McKinstry-Wu, A.R.ⁱ , Kelz, M.B.ⁱ , Palanca, B.J.^b , Proekt, A.^g ⁱ , the ReCCognition Study Group^j ^k ^l

^a Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, United States
^b Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO, United States
^c Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^d Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
^e Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
^f Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
^g School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
^h Department of Mathematics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
ⁱ Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^j Department of Anesthesiology and Critical Care, University of Pennsylvania, United States
^k Department of Anesthesiology, Washington University, St. Louis, MO, United States
^l Center for Consciousness Science, Department of Anesthesiology, Ann Arbor, MI, United States

Abstract
Background: Burst suppression occurs in the EEG during coma and under general anaesthesia. It has been assumed that burst suppression represents a deeper state of anaesthesia from which it is more difficult to recover. This has not been directly demonstrated, however. Here, we test this hypothesis directly by assessing relationships between EEG suppression in human volunteers and recovery of consciousness. Methods: We recorded the EEG of 27 healthy humans (nine women/18 men) anaesthetised with isoflurane 1.3 minimum alveolar concentration (MAC) for 3 h. Periods of EEG suppression and non-suppression were separated using principal component analysis of the spectrogram. After emergence, participants completed the digit symbol substitution test and the psychomotor vigilance test. Results: Volunteers demonstrated marked variability in multiple features of the suppressed EEG. In order to test the hypothesis that, for an individual subject, inclusion of features of suppression would improve accuracy of a model built to predict time of emergence, two types of models were constructed: one with a suppression-related feature included and one without. Contrary to our hypothesis, Akaike information criterion demonstrated that the addition of a suppression-related feature did not improve the ability of the model to predict time to emergence. Furthermore, the amounts of EEG suppression and decrements in cognitive task performance relative to pre-anaesthesia baseline were not significantly correlated. Conclusions: These findings suggest that, in contrast to current assumptions, EEG suppression in and of itself is not an important determinant of recovery time or the degree of cognitive impairment upon emergence from anaesthesia in healthy adults. © 2019

Author Keywords
anaesthetic, inhaled; burst suppression; cognitive dysfunction; electroencephalography; isoflurane; principal component analysis

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

“Higher Body Mass Index Is Associated with Lower Cortical Amyloid-β Burden in Cognitively Normal Individuals in Late-Life” (2019) Journal of Alzheimer’s Disease

Higher Body Mass Index Is Associated with Lower Cortical Amyloid-β Burden in Cognitively Normal Individuals in Late-Life
(2019) Journal of Alzheimer’s Disease, 69 (3), pp. 817-827.

Thirunavu, V.^a , McCullough, A.^b , Su, Y.^c , Flores, S.^b , Dincer, A.^b , Morris, J.C.^d ^e , Cruchaga, C.^d ^f , Benzinger, T.L.S.^b ^d , Gordon, B.A.^b ^d

^a Department of Biology, Washington University, St. Louis, MO, United States
^b Department of Radiology, Washington University, School of Medicine, 660 South Euclid, St. Louis, MO 63110, United States
^c Banner Alzheimer’s Institute, Phoenix, AZ, United States
^d Knight Alzheimer Disease Research Center, Washington University, St. Louis, MO, United States
^e Department of Neurology, Washington University, St. Louis, MO, United States
^f Department of Psychiatry, Washington University, St. Louis, MO, United States

Abstract
Background: Both low and high body mass index (BMI) have been associated with an increased risk of dementia, including that caused by Alzheimer’s disease (AD). Specifically, high middle-age BMI or a low late-age BMI has been considered a predictor for the development of AD dementia. Less studied is the relationship between BMI and AD pathology. Objective: We explored the association between BMI and cortical amyloid-β (Aβ) burden in cognitively normal participants that were either in mid-life (45-60 years) or late-life (>60). Methods: We analyzed cross-sectional baseline data from the Knight Alzheimer Disease Research Center (ADRC) at Washington University. Aβ pathology was measured in 373 individuals with Aβ PET imaging and was quantified using Centiloid units. We split the cohort into mid- and late-life groups for analyses (n=96 and n=277, respectively). We ran general linear regression models to predict Aβ levels from BMI while controlling for age, sex, years of education, and APOE4 status. Analyses were also conducted to test the interaction between BMI and APOE4 genotype and between BMI and sex. Results: Higher BMI was associated with lower cortical Aβ burden in late-life (β=-0.81, p=0.0066), but no relationship was found in mid-life (β=0.04, p>0.5). The BMI×APOE4+ and BMI×male interaction terms were not significant in the mid-life (β=0.28, p=0.41; β=0.64, p=0.13) or the late-life (β=0.17, p>0.5; β=0.50, p=0.43) groups. Conclusion: Higher late-life BMI is associated with lower cortical Aβ burden in cognitively normal individuals. © 2019 – IOS Press and the authors. All rights reserved.

Author Keywords
Alzheimer disease; amyloid-β; apolipoproteins E; body mass index; obesity; positron emission tomography

Document Type: Article
Publication Stage: Final
Source: Scopus

“Acute Rodent Tolerability, Toxicity, and Radiation Dosimetry Estimates of the S1P1-Specific Radioligand (11C)CS1P1” (2019) Molecular Imaging and Biology

Acute Rodent Tolerability, Toxicity, and Radiation Dosimetry Estimates of the S1P1-Specific Radioligand [11C]CS1P1
(2019) Molecular Imaging and Biology, .

Liu, H., Laforest, R., Gu, J., Luo, Z., Jones, L.A., Gropler, R.J., Benzinger, T.L.S., Tu, Z.

Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Purpose: In preclinical studies with rodent models of inflammatory diseases, [11C]CS1P1 has been identified as a promising imaging agent targeting sphingosine-1-phosphate receptor 1 (S1P1) in the central nervous system and other tissues. In preparation for USA Food and Drug Administration (FDA) approval of [11C]CS1P1 for human use, an acute biodistribution study in mice and an acute tolerability and toxicity evaluation in rats were conducted. Procedures: Acute organ biodistribution and excretion data was obtained using male and female Swiss Webster mice intravenously (IV) injected with 4.8–10 MBq of [11C]CS1P1. The organ residence times for each harvested organ were calculated using the animal biodistribution data, and were entered in the program OLINDA/EXM for C-11 to obtain human radiation dosimetry estimates. Acute tolerability and toxicity studies were conducted in male and female Sprague Dawley rats. Rats were administered an IV bolus of either the vehicle control or 0.3 mg/kg CS1P1. Blood samples were collected and a gross post-mortem examination was conducted at day 2 or day 15 post-injection. Results: The extrapolated human radiation dose estimates revealed that the highest organ dose was received by the liver with 24.05 μGy/MBq in males and 32.70 μGy/MBq in females. The effective dose (ED) estimates of [11C]CS1P1 were calculated at 3.5 μSv/MBq in males and 5.9 μSv/MBq in females. The acute tolerability and toxicity study identified 0.3 mg/kg as a no observable adverse effect level (NOAEL) dose, which is a ~ 300-fold dose multiple of the human equivalent dose of the mass to be injected for positron emission tomography (PET) imaging studies in humans as a no-observable-effect limit. Conclusions: The toxicity study in rats suggested that injection dose of radiotracer [11C]CS1P1 with mass amount < 10 μg is safe for performing a human PET study. The dosimetry data supported an injection of 0.74 GBq (20 mCi) dose for human studies would be acceptable. © 2019, World Molecular Imaging Society.

Author Keywords
Dosimetry; Microdose; Rodent; Sphingosine-1-phosphate receptor 1; [11C]CS1P1

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

“Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria” (2019) Genes, Brain and Behavior

Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria
(2019) Genes, Brain and Behavior, .

Lai, D.^a , Wetherill, L.^a , Bertelsen, S.^b , Carey, C.E.^c , Kamarajan, C.^d , Kapoor, M.^b , Meyers, J.L.^d , Anokhin, A.P.^e , Bennett, D.A.^f , Bucholz, K.K.^e , Chang, K.K.^c , De Jager, P.L.^g ^h , Dick, D.M.ⁱ , Hesselbrock, V.^j , Kramer, J.^k , Kuperman, S.^k , Nurnberger, J.I., Jr.^a ^l , Raj, T.^b , Schuckit, M.^m , Scott, D.M.ⁿ ^o , Taylor, R.E.^p , Tischfield, J.^q , Hariri, A.R.^r , Edenberg, H.J.^a ^s , Agrawal, A.^e , Bogdan, R.^c , Porjesz, B.^d , Goate, A.M.^b , Foroud, T.^a

^a Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
^b Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
^c BRAIN Lab, Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, United States
^d Henri Begleiter Neurodynamics Lab, Department of Psychiatry, Downstate Medical Center, State University of New York, Brooklyn, NY, United States
^e Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^f Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
^g Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
^h Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, United States
ⁱ Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States
^j Department of Psychiatry, University of Connecticut, Farmington, CT, United States
^k Department of Psychiatry, Roy Carver College of Medicine, University of Iowa, Iowa City, IA, United States
^l Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
^m Department of Psychiatry, San Diego Medical School, University of California, San Diego, CA, United States
ⁿ Department of Pediatrics, Howard University, Washington, DC, United States
^o Department of Human Genetics, Howard University, Washington, DC, United States
^p Department of Pharmacology, Howard University, Washington, DC, United States
^q Department of Genetics, Rutgers University, Newark, NJ, United States
^r Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
^s Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States

Abstract
Genome-wide association studies (GWAS) of alcohol dependence (AD) have reliably identified variation within alcohol metabolizing genes (eg, ADH1B) but have inconsistently located other signals, which may be partially attributable to symptom heterogeneity underlying the disorder. We conducted GWAS of DSM-IV AD (primary analysis), DSM-IV AD criterion count (secondary analysis), and individual dependence criteria (tertiary analysis) among 7418 (1121 families) European American (EA) individuals from the Collaborative Study on the Genetics of Alcoholism (COGA). Trans-ancestral meta-analyses combined these results with data from 3175 (585 families) African-American (AA) individuals from COGA. In the EA GWAS, three loci were genome-wide significant: rs1229984 in ADH1B for AD criterion count (P = 4.16E−11) and Desire to cut drinking (P = 1.21E−11); rs188227250 (chromosome 8, Drinking more than intended, P = 6.72E−09); rs1912461 (chromosome 15, Time spent drinking, P = 1.77E−08). In the trans-ancestral meta-analysis, rs1229984 was associated with multiple phenotypes and two additional loci were genome-wide significant: rs61826952 (chromosome 1, DSM-IV AD, P = 8.42E−11); rs7597960 (chromosome 2, Time spent drinking, P = 1.22E−08). Associations with rs1229984 and rs18822750 were replicated in independent datasets. Polygenic risk scores derived from the EA GWAS of AD predicted AD in two EA datasets (P <.01; 0.61%-1.82% of variance). Identified novel variants (ie, rs1912461, rs61826952) were associated with differential central evoked theta power (loss − gain; P =.0037) and reward-related ventral striatum reactivity (P =.008), respectively. This study suggests that studying individual criteria may unveil new insights into the genetic etiology of AD liability. © 2019 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society

Author Keywords
alcohol dependence; DSM-IV alcohol dependence criterion; DSM-IV criterion count; DSM-IV individual criteria; event-related theta oscillations; functional magnetic resonance imaging; genome-wide association study; item response analysis; meta-analysis; polygenic risk score

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

“Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)” (2019) Nature Genetics

Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)
(2019) Nature Genetics, .

Malik, R.^a , Chauhan, G.^b ^c , Traylor, M.^d , Sargurupremraj, M.^c ^e , Okada, Y.^f ^g ^h , Mishra, A.^c ^e , Rutten-Jacobs, L.^d , Giese, A.-K.ⁱ , van der Laan, S.W.^j , Gretarsdottir, S.^k , Anderson, C.D.^l ^m ⁿ , Chong, M.^o , Adams, H.H.H.^p ^q , Ago, T.^r , Almgren, P.^s , Amouyel, P.^t ^u , Ay, H.^m ^v , Bartz, T.M.^w , Benavente, O.R.^x , Bevan, S.^y , Boncoraglio, G.B.^z , Brown, R.D., Jr^aa , Butterworth, A.S.^ab ^ac , Carrera, C.^ad ^ae , Carty, C.L.^af ^ag , Chasman, D.I.^ah ^ai , Chen, W.-M.^aj , Cole, J.W.^ak , Correa, A.^al , Cotlarciuc, I.^am , Cruchaga, C.^an ^ao , Danesh, J.^ab ^ap ^aq ^ar , de Bakker, P.I.W.^as ^at , DeStefano, A.L.^au ^av , den Hoed, M.^aw , Duan, Q.^ax , Engelter, S.T.^ay ^az , Falcone, G.J.^ba ^bb , Gottesman, R.F.^bc , Grewal, R.P.^bd , Gudnason, V.^be ^bf , Gustafsson, S.^bg , Haessler, J.^bh , Harris, T.B.^bi , Hassan, A.^bj , Havulinna, A.S.^bk ^bl , Heckbert, S.R.^bm , Holliday, E.G.^bn ^bo , Howard, G.^bp , Hsu, F.-C.^bq , Hyacinth, H.I.^br , Ikram, M.A.^p , Ingelsson, E.^bs ^bt , Irvin, M.R.^bu , Jian, X.^bv , Jiménez-Conde, J.^bw , Johnson, J.A.^bx ^by , Jukema, J.W.^bz , Kanai, M.^f ^g ^ca , Keene, K.L.^cb ^cc , Kissela, B.M.^cd , Kleindorfer, D.O.^cd , Kooperberg, C.^bh , Kubo, M.^ce , Lange, L.A.^cf , Langefeld, C.D.^cg , Langenberg, C.^ch , Launer, L.J.^ci , Lee, J.-M.^cj , Lemmens, R.^ck ^cl , Leys, D.^cm , Lewis, C.M.^cn ^co , Lin, W.-Y.^ab ^cp , Lindgren, A.G.^cq ^cr , Lorentzen, E.^cs , Magnusson, P.K.^ct , Maguire, J.^cu , Manichaikul, A.^aj , McArdle, P.F.^cv , Meschia, J.F.^cw , Mitchell, B.D.^cv ^cx , Mosley, T.H.^cy ^cz , Nalls, M.A.^da ^db , Ninomiya, T.^dc , O’Donnell, M.J.^o ^dd , Psaty, B.M.^de ^df ^dg ^dh , Pulit, S.L.^as ^di , Rannikmäe, K.^dj ^dk , Reiner, A.P.^bm ^dl , Rexrode, K.M.^dm , Rice, K.^dn , Rich, S.S.^aj , Ridker, P.M.^ah ^ai , Rost, N.S.ⁱ ^m , Rothwell, P.M.^do , Rotter, J.I.^dp ^dq , Rundek, T.^dr , Sacco, R.L.^dr , Sakaue, S.^g ^ds , Sale, M.M.^dt , Salomaa, V.^bk , Sapkota, B.R.^du , Schmidt, R.^dv , Schmidt, C.O.^dw , Schminke, U.^dx , Sharma, P.^am , Slowik, A.^dy , Sudlow, C.L.M.^dj ^dk , Tanislav, C.^dz , Tatlisumak, T.^ea ^eb , Taylor, K.D.^dp ^dq , Thijs, V.N.S.^ec ^ed , Thorleifsson, G.^k , Thorsteinsdottir, U.^k , Tiedt, S.^a , Trompet, S.^ee , Tzourio, C.^c ^ef ^eg , van Duijn, C.M.^eh ^ei , Walters, M.^ej , Wareham, N.J.^ch , Wassertheil-Smoller, S.^ek , Wilson, J.G.^el , Wiggins, K.L.^de , Yang, Q.^au , Yusuf, S.^o , Bis, J.C.^de , Pastinen, T.^en , Ruusalepp, A.^eo ^ep ^eq , Schadt, E.E.^er , Koplev, S.^er , Björkegren, J.L.M.^er ^es ^et ^eu , Codoni, V.^ev ^ew , Civelek, M.^dt ^ex , Smith, N.L.^bm ^ey ^ez , Trégouët, D.A.^ev ^ew , Christophersen, I.E.^bb ^fa ^fb , Roselli, C.^bb , Lubitz, S.A.^bb ^fa , Ellinor, P.T.^bb ^fa , Tai, E.S.^fc , Kooner, J.S.^fd , Kato, N.^fe , He, J.^ff , van der Harst, P.^fg , Elliott, P.^fh , Chambers, J.C.^fi ^fj , Takeuchi, F.^fe , Johnson, A.D.^av ^fk , Malik, R.^a , Chauhan, G.^b , Traylor, M.^c , Sargurupremraj, M.^d ^e , Okada, Y.^f ^g ^h , Mishra, A.^d ^e , Rutten-Jacobs, L.^c , Giese, A.-K.ⁱ , van der Laan, S.W.^j , Gretarsdottir, S.^k , Anderson, C.D.^l ^m ⁿ , Chong, M.^o , Adams, H.H.H.^p ^q , Ago, T.^r , Almgren, P.^s , Amouyel, P.^t ^u , Ay, H.^m ^v , Bartz, T.M.^w , Benavente, O.R.^x , Bevan, S.^y , Boncoraglio, G.B.^z , Brown, R.D., Jr^aa , Butterworth, A.S.^ab ^ac , Carrera, C.^ad ^ae , Carty, C.L.^af ^ag , Chasman, D.I.^ah ^ai , Chen, W.-M.^aj , Cole, J.W.^ak , Correa, A.^al , Cotlarciuc, I.^am , Cruchaga, C.^an ^ao , Danesh, J.^ab ^ap ^aq ^ar , de Bakker, P.I.W.^as ^at , DeStefano, A.L.^au ^av , den Hoed, M.^aw , Duan, Q.^ax , Engelter, S.T.^ay ^az , Falcone, G.J.^ba ^bb , Gottesman, R.F.^bc , Grewal, R.P.^bd , Gudnason, V.^be ^bf , Gustafsson, S.^bg , Haessler, J.^bh , Harris, T.B.^bi , Hassan, A.^bj , Havulinna, A.S.^bk ^bl , Heckbert, S.R.^bm , Holliday, E.G.^bn ^bo , Howard, G.^bp , Hsu, F.-C.^bq , Hyacinth, H.I.^br , Ikram, M.A.^p , Ingelsson, E.^bs ^bt , Irvin, M.R.^bu , Jian, X.^bv , Jiménez-Conde, J.^bw , Johnson, J.A.^bx ^by , Jukema, J.W.^bz , Kanai, M.^f ^g ^ca , Keene, K.L.^cb ^cc , Kissela, B.M.^cd , Kleindorfer, D.O.^cd , Kooperberg, C.^bh , Kubo, M.^ce , Lange, L.A.^cf , Langefeld, C.D.^cg , Langenberg, C.^ch , Launer, L.J.^ci , Lee, J.-M.^cj , Lemmens, R.^ck ^cl , Leys, D.^cm , Lewis, C.M.^cn ^co , Lin, W.-Y.^ab ^cp , Lindgren, A.G.^cq ^cr , Lorentzen, E.^cs , Magnusson, P.K.^ct , Maguire, J.^cu , Manichaikul, A.^aj , McArdle, P.F.^cv , Meschia, J.F.^cw , Mitchell, B.D.^cv ^cx , Mosley, T.H.^cy ^cz , Nalls, M.A.^da ^db , Ninomiya, T.^dc , O’Donnell, M.J.^o ^dd , Psaty, B.M.^de ^df ^dg ^dh , Pulit, S.L.^as ^di , Rannikmäe, K.^dj ^dk , Reiner, A.P.^bm ^dl , Rexrode, K.M.^dm , Rice, K.^dn , Rich, S.S.^aj , Ridker, P.M.^ah ^ai , Rost, N.S.ⁱ ^m , Rothwell, P.M.^do , Rotter, J.I.^dp ^dq , Rundek, T.^dr , Sacco, R.L.^dr , Sakaue, S.^g ^ds , Sale, M.M.^dt , Salomaa, V.^bk , Sapkota, B.R.^du , Schmidt, R.^dv , Schmidt, C.O.^dw , Schminke, U.^dx , Sharma, P.^am , Slowik, A.^dy , Sudlow, C.L.M.^dj ^dk , Tanislav, C.^dz , Tatlisumak, T.^ea ^eb , Taylor, K.D.^dp ^dq , Thijs, V.N.S.^ec ^ed , Thorleifsson, G.^k , Thorsteinsdottir, U.^k , Tiedt, S.^a , Trompet, S.^ee , Tzourio, C.^e ^ef ^eg , van Duijn, C.M.^eh ^ei , Walters, M.^ej , Wareham, N.J.^ch , Wassertheil-Smoller, S.^ek , Wilson, J.G.^el , Wiggins, K.L.^de , Yang, Q.^au , Yusuf, S.^o , Amin, N.^p , Aparicio, H.S.^av ^gc , Arnett, D.K.^gd , Attia, J.^ge , Beiser, A.S.^au ^av , Berr, C.^gf , Buring, J.E.^ah ^ai , Bustamante, M.^gg , Caso, V.^gh , Cheng, Y.-C.^gi , Choi, S.H.^av ^gj , Chowhan, A.^av ^gc , Cullell, N.^ae , Dartigues, J.-F.^gk ^gl , Delavaran, H.^cq ^cr , Delgado, P.^gm , Dörr, M.^gn ^go , Engström, G.^s , Ford, I.^gp , Gurpreet, W.S.^gq , Hamsten, A.^gr ^gs , Heitsch, L.^gt , Hozawa, A.^gu , Ibanez, L.^gv , Ilinca, A.^cq ^cr , Ingelsson, M.^gw , Iwasaki, M.^gx , Jackson, R.D.^gy , Jood, K.^gz , Jousilahti, P.^bk , Kaffashian, S.^c , Kalra, L.^ha , Kamouchi, M.^hb , Kitazono, T.^hc , Kjartansson, O.^hd , Kloss, M.^he , Koudstaal, P.J.^hf , Krupinski, J.^hg , Labovitz, D.L.^hh , Laurie, C.C.^dn , Levi, C.R.^hi , Li, L.^hj , Lind, L.^hk , Lindgren, C.M.^hl ^hm , Lioutas, V.^av ^hn , Liu, Y.M.^ho , Lopez, O.L.^hp , Makoto, H.^hq , Martinez-Majander, N.^fp , Matsuda, K.^hq , Minegishi, N.^gu , Montaner, J.^hr , Morris, A.P.^hs ^ht , Muiño, E.^ae , Müller-Nurasyid, M.^hu ^hv ^hw , Norrving, B.^cq ^cr , Ogishima, S.^gu , Parati, E.A.^hx , Peddareddygari, L.R.^bd , Pedersen, N.L.^ct ^hy , Pera, J.^dy , Perola, M.^bk ^hz , Pezzini, A.^ia , Pileggi, S.^ib , Rabionet, R.^ic , Riba-Llena, I.^ad , Ribasés, M.^id , Romero, J.R.^av ^gc , Roquer, J.^ie ^if , Rudd, A.G.^ig ^ih , Sarin, A.-P.ⁱⁱ ^ij , Sarju, R.^gq , Sarnowski, C.^au ^av , Sasaki, M.^ik , Satizabal, C.L.^av ^gc , Satoh, M.^ik , Sattar, N.^il , Sawada, N.^gx , Sibolt, G.^fp , Sigurdsson, Á.^im , Smith, A.ⁱⁿ , Sobue, K.^ik , Soriano-Tárraga, C.^if , Stanne, T.^io , Stine, O.C.^ip , Stott, D.J.^iq , Strauch, K.^hu ^ir , Takai, T.^gu , Tanaka, H.^is ^it , Tanno, K.^ik , Teumer, A.^iu , Tomppo, L.^fp , Torres-Aguila, N.P.^ae , Touze, E.^iv ^iw , Tsugane, S.^gx , Uitterlinden, A.G.^ix , Valdimarsson, E.M.^iy , van der Lee, S.J.^p , Völzke, H.^iu , Wakai, K.^is , Weir, D.^iz , Williams, S.R.^ja , Wolfe, C.D.A.^ig ^ih , Wong, Q.^dn , Xu, H.^gi , Yamaji, T.^gx , Sanghera, D.K.^du ^fm ^fn , Melander, O.^s , Jern, C.^fo , Strbian, D.^fp ^fq , Fernandez-Cadenas, I.^ad ^ae , Longstreth, W.T., Jr^bm ^fr , Rolfs, A.^fs , Hata, J.^dc , Woo, D.^cd , Rosand, J.^l ^m ⁿ , Pare, G.^o , Hopewell, J.C.^ft , Saleheen, D.^fu , Stefansson, K.^k ^fv , Worrall, B.B.^fw , Kittner, S.J.^ak , Seshadri, S.^av ^fx , Fornage, M.^bv ^fy , Markus, H.S.^c , Howson, J.M.M.^ab , Kamatani, Y.^f ^fz , Debette, S.^d ^e , Dichgans, M.^a ^ga ^gb , Sanghera, D.K.^du ^fm ^fn , Melander, O.^s , Jern, C.^fo , Strbian, D.^fp ^fq , Fernandez-Cadenas, I.^ad ^ae , Longstreth, W.T., Jr^bm ^fr , Rolfs, A.^fs , Hata, J.^dc , Woo, D.^cd , Rosand, J.^l ^m ⁿ , Pare, G.^o , Hopewell, J.C.^ft , Saleheen, D.^fu , Stefansson, K.^k ^fv , Worrall, B.B.^fw , Kittner, S.J.^ak , Seshadri, S.^av ^fx , Fornage, M.^bv ^fy , Markus, H.S.^d , Howson, J.M.M.^ab , Kamatani, Y.^f ^fz , Debette, S.^c ^e , Dichgans, M.^a ^ga ^gb , AFGen Consortium^em , Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium^em , International Genomics of Blood Pressure (iGEN-BP) Consortium^em , INVENT Consortium^em , STARNET^em , BioBank Japan Cooperative Hospital Group^em , COMPASS Consortium^em , EPIC-CVD Consortium^em , EPIC-InterAct Consortium^em , International Stroke Genetics Consortium (ISGC)^em , METASTROKE Consortium^em , Neurology Working Group of the CHARGE Consortium^em , NINDS Stroke Genetics Network (SiGN)^em , UK Young Lacunar DNA Study^em , MEGASTROKE Consortium^fl

^a Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
^b Centre for Brain Research, Indian Institute of Science, Bangalore, India
^c INSERM U1219 Bordeaux Population Health Research Center, University of Bordeaux, Bordeaux, France
^d Stroke Research Group, Division of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
^e Department of Neurology, Institute for Neurodegenerative Disease, Bordeaux University Hospital, Bordeaux, France
^f Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
^g Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
^h Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
ⁱ Department of Neurology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, MA, United States
^j Laboratory of Experimental Cardiology, Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
^k deCODE genetics/AMGEN Inc., Reykjavik, Iceland
^l Center for Genomic Medicine, MGH, Boston, MA, United States
^m J. Philip Kistler Stroke Research Center, Department of Neurology, MGH, Boston, MA, United States
ⁿ Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, United States
^o Population Health Research Institute, McMaster University, Hamilton, ON, Canada
^p Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
^q Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
^r Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^s Department of Clinical Sciences, Lund University, Malmö, Sweden
^t INSERM, Institut Pasteur de Lille, LabEx DISTALZ-UMR1167, Risk Factors and Molecular Determinants of Aging-Related Diseases, Université Lille, Lille, France
^u Centre Hospitalier Université Lille, Epidemiology and Public Health Department, Lille, France
^v AA Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Harvard Medical School, Boston, MA, United States
^w Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, Seattle, WA, United States
^x Division of Neurology, Faculty of Medicine, Brain Research Center, University of British Columbia, Vancouver, BC, Canada
^y School of Life Science, University of Lincoln, Lincoln, United Kingdom
^z Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
^aa Department of Neurology, Mayo Clinic Rochester, Rochester, MN, United States
^ab MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
^ac National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
^ad Neurovascular Research Laboratory, Vall d’Hebron Institut of Research, Neurology and Medicine Departments-Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona, Spain
^ae Stroke Pharmacogenomics and Genetics, Fundacio Docència i Recerca MutuaTerrassa, Terrassa, Spain
^af Children’s Research Institute, Children’s National Medical Center, Washington, DC, United States
^ag Center for Translational Science, George Washington University, Washington, DC, United States
^ah Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, United States
^ai Harvard Medical School, Boston, MA, United States
^aj Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
^ak Department of Neurology, University of Maryland School of Medicine and Baltimore VAMC, Baltimore, MD, United States
^al Departments of Medicine, Pediatrics and Population Health Science, University of Mississippi Medical Center, Jackson, MS, United States
^am Institute of Cardiovascular Research, Royal Holloway University of London, London, UK, and Ashford and St Peters Hospital, Surrey, United Kingdom
^an Department of Psychiatry, Hope Center Program on Protein Aggregation and Neurodegeneration (HPAN), Washington University School of Medicine, St. Louis, MO, United States
^ao Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States
^ap NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
^aq Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
^ar British Heart Foundation, Cambridge Centre of Excellence, Department of Medicine, University of Cambridge, Cambridge, United Kingdom
^as Department of Medical Genetics, University Medical Center Utrecht, Utrecht, Netherlands
^at Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
^au Boston University School of Public Health, Boston, MA, United States
^av Framingham Heart Study, Framingham, MA, United States
^aw Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
^ax Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
^ay Department of Neurology and Stroke Center, Basel University Hospital, Basel, Switzerland
^az Neurorehabilitation Unit, University of Basel and University Center for Medicine of Aging and Rehabilitation Basel, Felix Platter Hospital, Basel, Switzerland
^ba Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
^bb Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, United States
^bc Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^bd Neuroscience Institute, SF Medical Center, Trenton, NJ, United States
^be Icelandic Heart Association Research Institute, Kopavogur, Iceland
^bf Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^bg Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
^bh Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
^bi Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^bj Department of Neurology, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
^bk National Institute for Health and Welfare, Helsinki, Finland
^bl FIMM–Institute for Molecular Medicine Finland, Helsinki, Finland
^bm Department of Epidemiology, University of Washington, Seattle, WA, United States
^bn Public Health Stream, Hunter Medical Research Institute, New Lambton, NSW, Australia
^bo Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia
^bp School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
^bq Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
^br Aflac Cancer and Blood Disorder Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
^bs Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States
^bt Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
^bu Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
^bv Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, United States
^bw Neurovascular Research Group (NEUVAS), Neurology Department, Institut Hospital del Mar d’Investigació Mèdica, Universitat Autònoma de Barcelona, Barcelona, Spain
^bx Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, FL, United States
^by Division of Cardiovascular Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
^bz Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
^ca Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, United States
^cb Department of Biology, East Carolina University, Greenville, NC, United States
^cc Center for Health Disparities, East Carolina University, Greenville, NC, United States
^cd University of Cincinnati College of Medicine, Cincinnati, OH, United States
^ce RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
^cf Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United States
^cg Center for Public Health Genomics and Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States
^ch MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, United Kingdom
^ci Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^cj Department of Neurology, Radiology, and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, United States
^ck Department of Neurosciences, Experimental Neurology, KU Leuven–University of Leuven, Leuven, Belgium
^cl VIB Center for Brain & Disease Research, University Hospitals Leuven, Department of Neurology, Leuven, Belgium
^cm INSERM U 1171, CHU Lille, Université Lille, Lille, France
^cn Department of Medical and Molecular Genetics, King’s College London, London, United Kingdom
^co SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
^cp Northern Institute for Cancer Research, Newcastle University, Newcastle, United Kingdom
^cq Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
^cr Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
^cs Bioinformatics Core Facility, University of Gothenburg, Gothenburg, Sweden
^ct Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^cu University of Technology Sydney, Faculty of Health, Ultimo, NSW, Australia
^cv Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
^cw Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
^cx Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, United States
^cy Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
^cz Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, MS, United States
^da Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^db Data Tecnica International, Glen Echo, MD, United States
^dc Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^dd Clinical Research Facility, Department of Medicine, NUI Galway, Galway, Ireland
^de Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
^df Department of Epidemiology, University of Washington, Seattle, WA, United States
^dg Department of Health Services, University of Washington, Seattle, WA, United States
^dh Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
^di Brain Center Rudolf Magnus, Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
^dj Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
^dk Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
^dl Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, United States
^dm Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
^dn Department of Biostatistics, University of Washington, Seattle, WA, United States
^do Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
^dp Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, CA, United States
^dq Division of Genomic Outcomes, Department of Pediatrics, Harbor–UCLA Medical Center, Torrance, CA, United States
^dr Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, United States
^ds Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
^dt Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
^du Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
^dv Department of Neurology, Medical University of Graz, Graz, Austria
^dw Institute for Community Medicine, SHIP-KEF, University Medicine Greifswald, Greifswald, Germany
^dx Department of Neurology, University Medicine Greifswald, Greifswald, Germany
^dy Department of Neurology, Jagiellonian University, Krakow, Poland
^dz Department of Neurology, Justus Liebig University, Giessen, Germany
^ea Department of Clinical Neurosciences/Neurology, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
^eb Sahlgrenska University Hospital, Gothenburg, Sweden
^ec Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria, Australia
^ed Austin Health, Department of Neurology, Heidelberg, VIC, Australia
^ee Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
^ef INSERM, Bordeaux, U1219, France
^eg Department of Public Health, Bordeaux University Hospital, Bordeaux, France
^eh Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
^ei Center for Medical Systems Biology, Leiden, Netherlands
^ej School of Medicine, Dentistry and Nursing at the University of Glasgow, Glasgow, United Kingdom
^ek Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, United States
^el Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States
^em Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States
^en Department of Human Genetics, McGill University, Montreal, QC, Canada
^eo Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Tartu, Estonia
^ep Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
^eq Clinical Gene Networks AB, Stockholm, Sweden
^er Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, New York, NY, United States
^es Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, Biomeedikum, University of Tartu, Tartu, Estonia
^et Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
^eu Clinical Gene Networks AB, Stockholm, Sweden
^ev UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Sorbonne Universités, Paris, France
^ew ICAN Institute for Cardiometabolism and Nutrition, Paris, France
^ex Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
^ey Group Health Research Institute, Group Health Cooperative, Seattle, WA, United States
^ez Seattle Epidemiologic Research and Information Center, VA Office of Research and Development, Seattle, WA, United States
^fa Cardiovascular Research Center, MGH, Boston, MA, United States
^fb Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
^fc Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
^fd National Heart and Lung Institute, Imperial College London, London, United Kingdom
^fe Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
^ff Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
^fg Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^fh MRC-PHE Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics and the NIHR Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom
^fi Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
^fj Department of Cardiology, Ealing Hospital NHS Trust, Southall, United Kingdom
^fk National Heart, Lung and Blood Research Institute, Division of Intramural Research, Population Sciences Branch, Framingham, MA, United States
^fl National Heart, Lung and Blood Research Institute, Division of Intramural Research, Population Sciences Branch, Framingham, MA, United States
^fm Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
^fn Oklahoma Center for Neuroscience, Oklahoma City, OK, United States
^fo Department of Pathology and Genetics, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
^fp Department of Neurology, Helsinki University Hospital, Helsinki, Finland
^fq Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland
^fr Department of Neurology, University of Washington, Seattle, WA, United States
^fs Albrecht Kossel Institute, University Clinic of Rostock, Rostock, Germany
^ft Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
^fu Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^fv Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^fw Departments of Neurology and Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, United States
^fx Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, San Antonio, TX, United States
^fy Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, United States
^fz Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
^ga Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^gb German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
^gc Boston University School of Medicine, Boston, MA, United States
^gd University of Kentucky College of Public Health, Lexington, KY, United States
^ge University of Newcastle and Hunter Medical Research Institute, New Lambton, NSW, Australia
^gf INSERM, U1061, Université Montpellier, Montpellier, France
^gg Centre for Research in Environmental Epidemiology, Barcelona, Spain
^gh Department of Neurology, Università degli Studi di Perugia, Umbria, Italy
^gi Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
^gj Broad Institute, Cambridge, MA, United States
^gk Bordeaux Population Health Research Center, INSERM, UMR 1219, Université Bordeaux, Bordeaux, France
^gl Department of Neurology, Memory Clinic, Bordeaux University Hospital, Bordeaux, France
^gm Neurovascular Research Laboratory. Vall d’Hebron Institut of Research, Neurology and Medicine Departments- Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona, Spain
^gn Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
^go DZHK, Greifswald, Germany
^gp Robertson Center for Biostatistics, University of Glasgow, Glasgow, United Kingdom
^gq Hero DMC Heart Institute, Dayanand Medical College & Hospital, Ludhiana, India
^gr Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
^gs Karolinska Institutet, Stockholm, Sweden
^gt Division of Emergency Medicine, and Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^gu Tohoku Medical Megabank Organization, Sendai, Japan
^gv Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^gw Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden
^gx Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
^gy Department of Internal Medicine and the Center for Clinical and Translational Science, Ohio State University, Columbus, OH, United States
^gz Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Goteborg, Sweden
^ha Department of Basic and Clinical Neurosciences, King’s College London, London, United Kingdom
^hb Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^hc Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^hd Departments of Neurology & Radiology, Landspitali National University Hospital, Reykjavik, Iceland
^he Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
^hf Department of Neurology, Erasmus University Medical Center, Rotterdam, Netherlands
^hg Hospital Universitari Mutua Terrassa, Terrassa (Barcelona), Spain
^hh Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, United States
^hi John Hunter Hospital, Hunter Medical Research Institute and University of Newcastle, Newcastle, NSW, Australia
^hj Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
^hk Department of Medical Sciences, Uppsala University, Uppsala, Sweden
^hl Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
^hm Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
^hn Beth Israel Deaconess Medical Center, Boston, MA, United States
^ho Wake Forest School of Medicine, Wake Forest, NC, United States
^hp Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
^hq BioBank Japan, Laboratory of Clinical Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
^hr Neurovascular Research Laboratory, Vall d’Hebron Institut of Research, Neurology and Medicine Departments-Universitat Autònoma de Barcelona, Vall d’Hebrón Hospital, Barcelona, Spain
^hs Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
^ht Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
^hu Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
^hv Department of Medicine I, Ludwig-Maximilians-Universität, Munich, Germany
^hw DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
^hx Department of Cerebrovascular Diseases, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan, Italy
^hy MEB, Karolinska Institutet, Stockholm, Sweden
^hz Estonian Genome Center, University of Tartu, Tartu, Estonia
^ia Department of Clinical and Experimental Sciences, Neurology Clinic, University of Brescia, Brescia, Italy
^ib Translational Genomics Unit, Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
^ic Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
^id Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addictions, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain
^ie Department of Neurology, IMIM–Hospital del Mar, and Universitat Autònoma de Barcelona, Barceloina, Spain
^if IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
^ig National Institute for Health Research Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
^ih Division of Health and Social Care Research, King’s College London, London, United Kingdom
ⁱⁱ FIMM–Institute for Molecular Medicine Finland, Helsinki, Finland
^ij THL–National Institute for Health and Welfare, Helsinki, Finland
^ik Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
^il BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom
^im deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
ⁱⁿ Icelandic Heart Association, Reykjavik, Iceland
^io Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Goteborg, Sweden
^ip Department of Epidemiology, University of Maryland School of Medicine, Baltimore, MD, United States
^iq Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, United Kingdom
^ir IBE, Faculty of Medicine, LMU Munich, Munich, Germany
^is Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
^it Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
^iu Institute for Community Medicine, SHIP-KEF, University Medicine Greifswald, Greifswald, Germany
^iv Department of Neurology, Caen University Hospital, Caen, France
^iw University of Caen Normandy, Caen, France
^ix Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
^iy Landspitali University Hospital, Reykjavik, Iceland
^iz Survey Research Center, University of Michigan, Ann Arbor, MI, United States
^ja Department of Neurology, University of Virginia, Charlottesville, VA, United States

Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

“Author Correction: GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability (Nature Neuroscience, (2018), 21, 9, (1161-1170), 10.1038/s41593-018-0206-1)” (2019) Nature Neuroscience

Author Correction: GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability (Nature Neuroscience, (2018), 21, 9, (1161-1170), 10.1038/s41593-018-0206-1)
(2019) Nature Neuroscience, .

Pasman, J.A.^a , Verweij, K.J.H.^a ^b , Gerring, Z.^c , Stringer, S.^d , Sanchez-Roige, S.^e , Treur, J.L.^f , Abdellaoui, A.^b , Nivard, M.G.^g , Baselmans, B.M.L.^g , Ong, J.-S.^c , Ip, H.F.^g , van der Zee, M.D.^g , Bartels, M.^g , Day, F.R.^h , Fontanillas, P.ⁱ , Elson, S.L.ⁱ , de Wit, H.^j , Davis, L.K.^k , MacKillop, J.^l , Derringer, J.L.^m , Branje, S.J.T.ⁿ , Hartman, C.A.^o , Heath, A.C.^p , van Lier, P.A.C.^q , Madden, P.A.F.^p , Mägi, R.^r , Meeus, W.ⁿ , Montgomery, G.W.^s , Oldehinkel, A.J.^o , Pausova, Z.^t , Ramos-Quiroga, J.A.^u ^v ^w ^x , Paus, T.^y ^z , Ribases, M.^u ^v ^w , Kaprio, J.^aa , Boks, M.P.M.^ab , Bell, J.T.^ac , Spector, T.D.^ac , Gelernter, J.^ad , Boomsma, D.I.^g , Martin, N.G.^c , MacGregor, S.^c , Perry, J.R.B.^h , Palmer, A.A.^e ^ae , Posthuma, D.^d , Munafò, M.R.^f ^af , Gillespie, N.A.^c ^ag , Derks, E.M.^c , Vink, J.M.^a , the 23andMe Research Team^ah , The Substance Use Disorders Working Group of the Psychiatric Genomics Consortium^ah , International Cannabis Consortium^ai

^a Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
^b Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam, Netherlands
^c Genetic Epidemiology, Statistical Genetics, and Translational Neurogenomics Laboratories, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^d Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^e Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
^f MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
^g Department of Biological Psychology/Netherlands Twin Register, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^h MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, United Kingdom
ⁱ 23andMe, Inc, Mountain View, CA, United States
^j Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
^k Vanderbilt Genetics Institute; Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, United States
^l Peter Boris Centre for Addictions Research and Michael G. DeGroote Centre for Medicinal Cannabis Research, McMaster University/St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
^m Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, United States
ⁿ Department of Youth and Family, Utrecht University, Utrecht, Netherlands
^o Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^p Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^q Department of Developmental Psychology and EMGO Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^r Estonian Genome Center, University of Tartu, Tartu, Estonia
^s Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
^t Hospital for Sick Children, Toronto, ON, Canada
^u Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
^v Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Spain
^w Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Barcelona, Spain
^x Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
^y Rotman Research Institute, Baycrest, Toronto, ON, Canada
^z Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
^aa Institute for Molecular Medicine Finland FIMM, HiLIFE Unit, University of Helsinki, Helsinki, Finland
^ab Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
^ac Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
^ad Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
^ae Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, United States
^af UK Centre for Tobacco and Alcohol Studies and School of Experimental Psychology, University of Bristol, Bristol, United Kingdom
^ag Department of Psychiatry, Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, United States

Abstract
Several occurrences of the word ‘schizophrenia’ have been re-worded as ‘liability to schizophrenia’ or ‘schizophrenia risk’, including in the title, which should have been “GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability,” as well as in Supplementary Figures 1–10 and Supplementary Tables 7–10, to more accurately reflect the findings of the work. © 2019, Springer Nature America, Inc.

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

“Publisher Correction: Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection (Nature Genetics, (2018), 50, 3, (381-389), 10.1038/s41588-018-0059-2)” (2019) Nature Genetics

Publisher Correction: Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection (Nature Genetics, (2018), 50, 3, (381-389), 10.1038/s41588-018-0059-2)
(2019) Nature Genetics, .

Pardiñas, A.F.^a , Holmans, P.^a , Pocklington, A.J.^a , Escott-Price, V.^a , Ripke, S.^b ^c , Carrera, N.^a , Legge, S.E.^a , Bishop, S.^a , Cameron, D.^a , Hamshere, M.L.^a , Han, J.^a , Hubbard, L.^a , Lynham, A.^a , Mantripragada, K.^a , Rees, E.^a , MacCabe, J.H.^d , McCarroll, S.A.^e , Baune, B.T.^f , Breen, G.^g ^h , Byrne, E.M.ⁱ ^j , Dannlowski, U.^k , Eley, T.C.^g , Hayward, C.^l , Martin, N.G.^m ⁿ , McIntosh, A.M.^o ^p , Plomin, R.^g , Porteous, D.J.^l , Wray, N.R.ⁱ ^j , Caballero, A.^q , Geschwind, D.H.^r , Huckins, L.M.^s , Ruderfer, D.M.^s , Santiago, E.^t , Sklar, P.^s , Stahl, E.A.^s , Won, H.^r , Agerbo, E.^u ^v , Als, T.D.^u ^w ^x , Andreassen, O.A.^y ^z , Bækvad-Hansen, M.^u ^aa , Mortensen, P.B.^u ^v ^w , Pedersen, C.B.^u ^v , Børglum, A.D.^u ^w ^x , Bybjerg-Grauholm, J.^u ^aa , Djurovic, S.^ab ^ac , Durmishi, N.^ad , Pedersen, M.G.^u ^v , Golimbet, V.^ae , Grove, J.^u ^w ^x ^af , Hougaard, D.M.^u ^aa , Mattheisen, M.^u ^w ^x , Molden, E.^ag , Mors, O.^u ^ah , Nordentoft, M.^u ^ai , Pejovic-Milovancevic, M.^aj , Sigurdsson, E.^ak , Silagadze, T.^al , Hansen, C.S.^u ^aa , Stefansson, K.^am , Stefansson, H.^am , Steinberg, S.^am , Tosato, S.^an , Werge, T.^u ^ao ^ap , Harold, D.^au ^av , Sims, R.^au , Gerrish, A.^au , Chapman, J.^au , Escott-Price, V.^a , Abraham, R.^au , Hollingworth, P.^au , Pahwa, J.^au , Denning, N.^au , Thomas, C.^au , Taylor, S.^au , Powell, J.^aw , Proitsi, P.^aw , Lupton, M.^aw , Lovestone, S.^aw ^ax , Passmore, P.^ay , Craig, D.^ay , McGuinness, B.^ay , Johnston, J.^ay , Todd, S.^ay , Maier, W.^az , Jessen, F.^az , Heun, R.^az , Schurmann, B.^az ^ba , Ramirez, A.^az , Becker, T.^bb , Herold, C.^bb , Lacour, A.^bb , Drichel, D.^bb , Nothen, M.^bc , Goate, A.^bd , Cruchaga, C.^bd , Nowotny, P.^bd , Morris, J.C.^bd , Mayo, K.^bd , Holmans, P.^a , O’Donovan, M.^a , Owen, M.^a , Williams, J.^au , Achilla, E.^be , Agerbo, E.^u ^v , Barr, C.L.^bf , Böttger, T.W.^bg , Breen, G.^g ^h , Cohen, D.^bh , Collier, D.A.^g ^ar , Curran, S.^bi ^bj , Dempster, E.^bk , Dima, D.^g , Sabes-Figuera, R.^be , Flanagan, R.J.^bl , Frangou, S.^bm , Frank, J.^bn , Gasse, C.^bg ^bo , Gaughran, F.^d , Giegling, I.^as , Grove, J.^u ^w ^x ^af , Hannon, E.^bk , Hartmann, A.M.^as , Heißerer, B.^bp , Helthuis, M.^bq , Horsdal, H.T.^bg , Ingimarsson, O.^br , Jollie, K.^bq , Kennedy, J.L.^bs , Köhler, O.^ag , Konte, B.^as , Lang, M.^bn , Legge, S.E.^a , Lewis, C.^g , MacCabe, J.^d , Malhotra, A.K.^bt , McCrone, P.^be , Meier, S.M.^bg , Mill, J.^g ^bk , Mors, O.^u ^ah , Mortensen, P.B.^u ^v ^w , Nöthen, M.M.^bc , O’Donovan, M.C.^a , Owen, M.J.^a , Pardiñas, A.F.^a , Pedersen, C.B.^u ^v , Rietschel, M.^bn , Rujescu, D.^as ^at , Schwalber, A.^bp , Sigurdsson, E.^br , Sørensen, H.J.^ai , Spencer, B.^bu , Stefansson, H.^am , Støvring, H.^bo , Strohmaier, J.^bn , Sullivan, P.^bv ^bw , Vassos, E.^g , Verbelen, M.^g , Walters, J.T.R.^a , Werge, T.^u ^ao ^ap , Collier, D.A.^g ^ar , Rujescu, D.^as ^at , Kirov, G.^a , Owen, M.J.^a , O’Donovan, M.C.^a , Walters, J.T.R.^a , GERAD1 Consortium^aq , CRESTAR Consortium^aq

^a MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
^b Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^c Department of Psychiatry and Psychotherapy, Charité, Campus Mitte, Berlin, Germany
^d Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^e Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^f Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
^g MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^h NIHR Biomedical Research Centre for Mental Health, Maudsley Hospital and Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
ⁱ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
^j Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
^k Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
^l Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^m School of Psychology, University of Queensland, Brisbane, QLD, Australia
ⁿ QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^o Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
^p Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
^q Departamento de Bioquímica, Genética e Inmunología. Facultad de Biología, Universidad de Vigo, Vigo, Spain
^r Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
^s Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^t Departamento de Biología Funcional. Facultad de Biología, Universidad de Oviedo, Oviedo, Spain
^u iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
^v National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
^w iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
^x Department of Biomedicine– Human Genetics, Aarhus University, Aarhus, Denmark
^y Institute of Clinical Medicine, University of Oslo, Oslo, Norway
^z NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
^aa Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
^ab NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
^ac Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
^ad Department of Child and Adolescent Psychiatry, University Clinic of Psychiatry, Skopje, Macedonia
^ae Department of Clinical Genetics, Mental Health Research Center, Moscow, Russian Federation
^af Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
^ag Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
^ah Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
^ai Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
^aj Department of Psychiatry, School of Medicine, University of Belgrade, Belgrade, Serbia
^ak Department of Psychiatry, National University Hospital, Reykjavik, Iceland
^al Department of Psychiatry and Drug Addiction, Tbilisi State Medical University (TSMU), Tbilisi, Georgia
^am deCODE Genetics, Reykjavik, Iceland
^an Section of Psychiatry, Department of Public Health and Community Medicine, University of Verona, Verona, Italy
^ao Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
^ap Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
^aq Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
^ar Discovery Neuroscience Research, Eli Lilly and Company, Lilly Research Laboratories, Windlesham, United Kingdom
^as Department of Psychiatry, University of Halle, Halle, Germany
^at Department of Psychiatry, University of Munich, Munich, Germany
^au MRC Centre for Neuropsychiatric Genetics and Genomics, Neurosciences and Mental Health Research Institute, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, United Kingdom
^av Neuropsychiatric Genetics Group, Department of Psychiatry, Trinity Centre for Health Sciences, St James’s Hospital, Dublin, Ireland
^aw Institute of Psychiatry, Department of Neuroscience, King’s College London, London, United Kingdom
^ax Department of Psychiatry, University of Oxford, Oxford, United Kingdom
^ay Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University, Belfast, United Kingdom
^az Department of Psychiatry, University of Bonn, Bonn, Germany
^ba Institute for Molecular Psychiatry, University of Bonn, Bonn, Germany
^bb Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
^bc Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
^bd Departments of Psychiatry, Neurology and Genetics, Washington University School of Medicine, St. Louis, MO, United States
^be Centre for Economics of Mental and Physical Health, Health Service and Population Research Department, Institute of Psychiatry, King’s College London, London, United Kingdom
^bf Toronto Western Research Institute, University Health Network, Toronto, ON, Canada
^bg National Centre for Register-Based Research, Department of Economics and Business, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
^bh Department of Community Mental Health, Mental Health Organization North–Holland North, Heerhugowaard, Netherlands
^bi Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^bj Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
^bk University of Exeter Medical School, RILD, University of Exeter, Exeter, United Kingdom
^bl Toxicology Unit, Department of Clinical Biochemistry, King’s College Hospital NHS Foundation Trust, London, United Kingdom
^bm Clinical Neurosciences Studies Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^bn Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University, Mannheim, Germany
^bo Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
^bp Concentris Research Management, Fürstenfeldbruck, Germany
^bq Leyden Delta, Nijmegen, Netherlands
^br Department of Psychiatry, Landspitali University Hospital, Reykjavik, Iceland
^bs Centre for Addiction and Mental Health, Toronto, ON, Canada
^bt Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY, United States
^bu Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
^bv Center for Psychiatric Genomics, Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
^bw Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

“2-Hydroxypropyl-β-cyclodextrin is the active component in a triple combination formulation for treatment of Niemann-Pick C1 disease” (2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids

2-Hydroxypropyl-β-cyclodextrin is the active component in a triple combination formulation for treatment of Niemann-Pick C1 disease
(2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids, .

Davidson, J.^a , Molitor, E.^a , Moores, S.^a , Gale, S.E.^a , Subramanian, K.^a , Jiang, X.^a , Sidhu, R.^a , Kell, P.^a , Zhang, J.^a , Fujiwara, H.^a , Davidson, C.^b , Helquist, P.^c , Melancon, B.J.^c , Grigalunas, M.^c , Liu, G.^c , Salahi, F.^c , Wiest, O.^c , Xu, X.^d , Porter, F.D.^e , Pipalia, N.H.^f , Cruz, D.L.^f , Holson, E.B.^g , Schaffer, J.E.^a , Walkley, S.U.^b , Maxfield, F.R.^f , Ory, D.S.^a

^a Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Dominick P. Purpura Department of Neuroscience, Rose F. Kennedy Intellectual and Developmental Disabilities Research Center, Albert Einstein College of Medicine, 1410 Pelham Parkway South, Bronx, NY 10461, United States
^c Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556 5670, United States
^d Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr., Rockville, MD 20850, United States
^e Section on Molecular Dysmorphology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, DHHS, Bethesda, MD 20892, United States
^f Department of Biochemistry, Weill Cornell Medical College, New York, NY 10065, United States
^g KDac Therapeutics, Cambridge, MA 02139, United States

Abstract
Niemann-Pick type C1 (NPC1) disease is a fatal neurovisceral disease for which there are no FDA approved treatments, though cyclodextrin (HPβCD) slows disease progression in preclinical models and in an early phase clinical trial. Our goal was to evaluate the mechanism of action of a previously described combination-therapy, Triple Combination Formulation (TCF) – comprised of the histone deacetylase inhibitor (HDACi) vorinostat/HPβCD/PEG – shown to prolong survival in Npc1 mice. In these studies, TCF’s benefit was attributed to enhanced vorinostat pharmacokinetics (PK). Here, we show that TCF reduced lipid storage, extended lifespan, and preserved neurological function in Npc1 mice. Unexpectedly, substitution of an inactive analog for vorinostat in TCF revealed similar efficacy. We demonstrate that the efficacy of TCF was attributable to enhanced HPβCD PK and independent of NPC1 protein expression. We conclude that although HDACi effectively reduce cholesterol storage in NPC1-deficient cells, HDACi are ineffective in vivo in Npc1 mice. © 2019 Elsevier B.V.

Author Keywords
Cholesterol; Cyclodextrin; Histone deacetylase inhibitors; Neurodegeneration; Niemann-Pick C; NPC1 protein

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

“Correction to: Standards for Neurologic Critical Care Units: A Statement for Healthcare Professionals from The Neurocritical Care Society (Neurocritical Care, (2018), 29, 2, (145-160), 10.1007/s12028-018-0601-1)” (2019) Neurocritical Care

Correction to: Standards for Neurologic Critical Care Units: A Statement for Healthcare Professionals from The Neurocritical Care Society (Neurocritical Care, (2018), 29, 2, (145-160), 10.1007/s12028-018-0601-1)
(2019) Neurocritical Care, .

Moheet, A.M.^a , Livesay, S.L.^b , Abdelhak, T.^c , Bleck, T.P.^b , Human, T.^d , Karanjia, N.^e , Lamer-Rosen, A.^a , Medow, J.^f , Nyquist, P.A.^g , Rosengart, A.^a , Smith, W.^h , Torbey, M.T.ⁱ , Chang, C.W.J.^j

^a Cedars-Sinai Medical Center, Los Angeles, CA, United States
^b Rush University, Chicago, IL, United States
^c Spectrum Health, Grand Rapids, MI, United States
^d Washington University, St. Louis, MO, United States
^e University of California, San Diego, San Diego, CA, United States
^f School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
^g Johns Hopkins, Baltimore, MD, United States
^h University of California, San Francisco, San Francisco, CA, United States
ⁱ The Ohio State University, Columbus, OH, United States
^j The Queen’s Medical Center, University of Hawaii, Honolulu, HI, United States

Abstract
The authors note that there is a discrepancy between the text of the paper and Table 2 regarding physician subspecialty certification requirements in neurocritical care for Level II centers. The text should read: “Physicians providing neurocritical care to patients in Level I NCCU should have subspecialty training in NCC and either be eligible for or have subspecialty certification in neurocritical care and hold privileges to provide neurocritical care at their organization. Physicians in Level II and III units should demonstrate evidence of subspecialty training in NCC.“ The original Table 2 is correct. © 2019, Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.

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

“Early socioemotional competence, psychopathology, and latent class profiles of reparative prosocial behaviors from preschool through early adolescence” (2019) Development and Psychopathology

Early socioemotional competence, psychopathology, and latent class profiles of reparative prosocial behaviors from preschool through early adolescence
(2019) Development and Psychopathology, .

Donohue, M.R., Tillman, R., Luby, J.

Department of Psychiatry, Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO 63108, United States

Abstract
Children who have difficulty using reparative behaviors following transgressions display a wide range of poorer social and emotional outcomes. Despite the importance of reparative skills, no study has charted the developmental trajectory of these behaviors or pinpointed predictors of poorer reparative abilities. To address these gaps in the literature, this study applied growth mixture modeling to parent reports of children’s reparative behaviors (N = 230) in a 9-year longitudinal data set spanning from preschool to early adolescence. Three distinct trajectories of reparative behaviors were found: a low-stable, moderate-stable, and high-stable latent class. Poorer emotion understanding, social withdrawal, social rejection, and maladaptive guilt in the preschool period predicted membership in a low-stable reparative trajectory. Externalizing diagnoses, particularly conduct disorder and oppositional defiant disorder, also predicted membership in a low-stable reparative trajectory. Preschool-onset depression predicted membership in a low-stable reparative trajectory through high levels of maladaptive guilt. The findings from this study suggest that socioemotional deficits in the preschool period set children on longstanding trajectories of impaired reparative responding. Thus, emotion understanding, social functioning, maladaptive guilt, and early psychiatric symptoms should be targeted in early preventive interventions. © Cambridge University Press 2019.

Author Keywords
emotion understanding; growth mixture modeling; preschool depression; reparative behaviors; social functioning

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

“NMDA Antagonists for Treatment-Resistant Depression” (2019) Handbook of Experimental Pharmacology

NMDA Antagonists for Treatment-Resistant Depression
(2019) Handbook of Experimental Pharmacology, 250, pp. 287-305.

Farber, N.B.

Residency Training, Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Fifteen to thirty percent of patients with major depressive disorder do not respond to antidepressants that target the monoaminergic systems. NMDA antagonists are currently being actively investigated as a treatment for these patients. Ketamine is the most widely studied of the compounds. A brief infusion of a low dose of this agent produces rapid improvement in depressive symptoms that lasts for several days. The improvement occurs after the agent has produced its well characterized psychotomimetic and cognitive side effects. Multiple infusions of the agent (e.g., 2–3× per week for several weeks) provide relief from depressive symptoms, but the symptoms reoccur once the treatment has been stopped. A 96-h infusion of a higher dose using add-on clonidine to mitigate the psychotomimetic effects appears to also provide relief and resulted in about 40% of the subjects still having a good response 8Â weeks after the infusion. As this was a pilot study, additional work is needed to confirm and extend this finding. Nitrous oxide also has had positive results. Of the other investigational agents, CERC-301 and rapastinel remain in clinical development. When careful monitoring of neuropsychiatric symptoms has been conducted, these agents all produce similar side effects in the same dose range, indicating that NMDA receptor blockade produces both the wanted and unwanted effects. Research is still needed to determine the appropriate dose, schedule, and ways to mitigate against unwanted side effects of NMDA receptor blockade. These hurdles need to be overcome before ketamine and similar agents can be prescribed routinely to patients. © 2018, Springer Nature Switzerland AG.

Author Keywords
CERC-301; Ketamine; Major depressive disorder; Memantine; Nitrous oxide; NMDA antagonists; Rapastinel; Treatment-resistant depression

Document Type: Book Chapter
Publication Stage: Final
Source: Scopus

“Vitamin D genes influence MS relapses in children” (2019) Multiple Sclerosis Journal

Vitamin D genes influence MS relapses in children
(2019) Multiple Sclerosis Journal, .

Graves, J.S.^a , Barcellos, L.F.^b , Krupp, L.^c , Belman, A.^d , Shao, X.^b , Quach, H.^b , Hart, J.^a , Chitnis, T.^e , Weinstock-Guttman, B.^f , Aaen, G.^g , Benson, L.^h , Gorman, M.^h , Greenberg, B.ⁱ , Lotze, T.^j , Soe, M.^k , Ness, J.^l , Rodriguez, M.^m , Rose, J.ⁿ , Schreiner, T.^o , Tillema, J.-M.^m , Waldman, A.^p , Casper, T.C.^q , Waubant, E.^a

^a Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
^b School of Public Health, University of California, Berkeley, Berkeley, CA, United States
^c Pediatric Multiple Sclerosis Center, New York University Langone Medical Center, New York, NY, United States
^d Stony Brook University, Stony Brook, NY, United States
^e Partners Pediatric Multiple Sclerosis Center, Massachusetts General Hospital for Children, Boston, MA, United States
^f Jacobs Pediatric Multiple Sclerosis Center, SUNY University at Buffalo, Buffalo, NY, United States
^g Pediatric Multiple Sclerosis Center, Loma Linda University Children’s Hospital, San Bernardino, CA, United States
^h Pediatric Multiple Sclerosis and Related Disorders Program, Boston Children’s Hospital, Boston, MA, United States
ⁱ University of Texas Southwestern Medical Center, Dallas, TX, United States
^j The Blue Bird Circle Clinic for Multiple Sclerosis, Texas Children’s Hospital, Houston, TX, United States
^k Pediatric MS & Demyelinating Disease Center, Washington University, St. Louis, MI, United States
^l Center for Pediatric-Onset Demyelinating Disease, Children’s of Alabama, Birmingham, AL, United States
^m Pediatric Multiple Sclerosis Center, Mayo Clinic, Rochester, MA, United States
ⁿ Department of Neurology, The University of Utah, Salt Lake City, UT, United States
^o Rocky Mountain MS Center, University, of Colorado, Denver, Denver, CO, United States
^p Children’s Hospital of Philadelphia, Philadelphia, PA, United States
^q Department of Pediatrics, The University of Utah, Salt Lake City, UT, United States

Abstract
Objective: The aim of this study was to determine whether a vitamin D genetic risk score (vitDGRS) is associated with 25-hydroxyvitamin D (25(OH)D) level and multiple sclerosis (MS) relapses in children. Methods: DNA samples were typed for single nucleotide polymorphisms (SNPs) from four genes previously identified to be associated with 25(OH)D levels. SNPs with strong associations with 25(OH)D after multiple comparison correction were used to create a genetic risk score (vitDGRS). Cox regression models tested associations of vitDGRS with relapse hazard. Results: Two independent SNPs within or near GC and NADSYN1/DHCR7 genes were strongly associated with 25(OH)D levels in the discovery cohort (n = 182) after Bonferroni correction. The vitDGRS of these SNPs explained 4.5% of the variance of 25(OH)D level after adjustment for genetic ancestry. Having the highest versus lowest vitDGRS was associated with 11 ng/mL lower 25(OH)D level (95% confidence interval (CI) = −17.5, −4.5, p = 0.001) in the discovery cohort. Adjusting for ancestry, sex, disease-modifying therapy (DMT), and HLA-DRB1*15 carrier status, the highest versus lowest vitDGRS was associated with 2.6-fold (95% CI = 1.37, 5.03, p = 0.004) and 2.0-fold (95% CI = 0.75, 5.20, p = 0.16) higher relapse hazard in the discovery and replication cohorts, respectively. Conclusion: The vitDGRS identifies children at greater risk of relapse. These findings support a causal role for vitamin D in MS course. © The Author(s), 2019.

Author Keywords
epidemiology; Genetics; pediatric multiple sclerosis; vitamin D

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

“Combined effect of posttraumatic stress disorder and prescription opioid use on risk of cardiovascular disease” (2019) European Journal of Preventive Cardiology

Combined effect of posttraumatic stress disorder and prescription opioid use on risk of cardiovascular disease
(2019) European Journal of Preventive Cardiology, .

Scherrer, J.F.^a ^b , Salas, J.^a ^b , Lustman, P.^c ^d , Tuerk, P.^e , Gebauer, S.^a ^b , Norman, S.B.^f , Schneider, F.D.^g , Chard, K.M.^h ⁱ , van den Berk-Clark, C.^a , Cohen, B.E.^j ^k , Schnurr, P.P.^l

^a Department of Family and Community Medicine, Saint Louis University School of Medicine, United States
^b Harry S. Truman Veterans Administration Medical Center, Columbia, United States
^c Department of Psychiatry, Washington University School of Medicine, St. Louis, United States
^d The Bell Street Clinic Opioid Addiction Treatment Programs, VA St. Louis Healthcare System, United States
^e Sheila C. Johnson Center for Clinical Services, Department of Human Services, University of Virginia, Charlottesville, United States
^f National Center for PTSD and Department of Psychiatry, University of California San Diego, United States
^g Department of Family and Community Medicine, University of Texas Southwestern Medical Center, Dallas, United States
^h Trauma Recovery Center Cincinnati VAMC, United States
ⁱ Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, United States
^j Department of Medicine, University of California San Francisco School of Medicine, United States
^k San Francisco VAMC, United States
^l National Center for PTSD and Department of Psychiatry, Geisel School of Medicine at Dartmouth, United States

Abstract
Aim: Prescription opioid analgesic use (OAU) is associated with increased risk of cardiovascular disease (CVD). OAU is more common in patients with than without posttraumatic stress disorder (PTSD), and PTSD is associated with higher CVD risk. We determined whether PTSD and OAU have an additive or multiplicative association with incident CVD. Methods and results: Veterans Health Affairs patient medical record data from 2008 to 2015 was used to identify 2861 patients 30–70 years of age, free of cancer, CVD and OAU for 12 months before index date. We defined a four-level exposure variable: 1) no PTSD/no OAU, 2) OAU alone, 3) PTSD alone and 4) PTSD+OAU. Cox proportional hazard models estimated the association between the exposure variable and incident CVD. The mean age was 49.0 (±11.0), 85.7% were male and 58.3% were White, 34.4% had no PTSD/no OAU, 32.9% had PTSD alone, 10.6% had OAU alone, and 22.1% had PTSD+OAU. Compared with patients with no PTSD/no OAU, those with PTSD alone were not at increased risk of incident CVD (hazard ratio = 0.82; 95% confidence interval (CI): 0.63–1.17); however, OAU alone and PTSD+OAU were both significantly associated with incident CVD (hazard ratio = 1.99; 95% CI:1.36–2.92 and hazard ratio = 2.20; 95% CI: 1.61–3.02). There was no significant additive or multiplicative PTSD and OAU association with incident CVD. Conclusion: OAU is associated with nearly a two-fold increased risk of CVD in patients with and without PTSD. Despite no additive or multiplicative interaction effects, the high prevalence of OAU in PTSD may represent a novel contributor to the elevated CVD burden among patients with PTSD. © The European Society of Cardiology 2019.

Author Keywords
cardiovascular disease; cohort; epidemiology; medical records; opioids; PTSD

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

“Body-, Eating-, and Exercise-Related Comparisons During Eating Disorder Recovery and Validation of the BEECOM-R” (2019) Psychology of Women Quarterly

Body-, Eating-, and Exercise-Related Comparisons During Eating Disorder Recovery and Validation of the BEECOM-R
(2019) Psychology of Women Quarterly, .

Saunders, J.F.^a , Eaton, A.A.^b , Fitzsimmons-Craft, E.E.^c

^a Women’s Research Institute of Nevada, University of Nevada Las Vegas, Las Vegas, NV, United States
^b Department of Psychology, Florida International University, Miami, FL, United States
^c Department of Psychiatry, Washington University School of Medicine, Washington University in Saint Louis, Saint Louis, MO, United States

Abstract
Social comparison tendencies are strongly associated with body dissatisfaction and disordered eating. In the current study, we quantitatively examined the structure and predictive value of these constructs during eating disorder recovery. We revised an existing measure of body-, eating-, and exercise-related social comparisons, the Body, Eating, and Exercise Comparison Orientation Measure (BEECOM), to improve psychometric properties. We also assessed the psychometric properties of the shortened Body, Eating, and Exercise Comparison Orientation Measure-Revised (BEECOM-R) in a comparison sample, resulting in an abbreviated measure suitable for recovering, clinical, and non-clinical samples. Finally, we used the revised measure to examine the additive influence of body-, eating-, and exercise-related comparisons on shape and weight dissatisfaction and disordered eating cognitions among 150 women (ages of 18–35 years) in self-identified recovery. Results suggest that body-, eating-, and exercise-related social comparisons all continue to correlate with body dissatisfaction and disordered eating during recovery. A minority of participants reported these comparisons to be helpful during the recovery process. We recommend social comparison as a clinical target for most women seeking support for eating pathology. Additional online materials for this article are available on PWQ’s website at http://journals.sagepub.com/doi/suppl/10.1177/0361684319851718. © The Author(s) 2019.

Author Keywords
body dissatisfaction; eating disorder; eating pathology; recovery; social comparison

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

“Neuroma Management: Capping Nerve Injuries With an Acellular Nerve Allograft Can Limit Axon Regeneration” (2019) Hand

Neuroma Management: Capping Nerve Injuries With an Acellular Nerve Allograft Can Limit Axon Regeneration
(2019) Hand, .

Hong, T., Wood, I., Hunter, D.A., Yan, Y., Mackinnon, S.E., Wood, M.D., Moore, A.M.

Washington University School of Medicine, St. Louis, MO, United States

Abstract
Background: Management of painful neuromas continues to challenge clinicians. Controlling axon growth to prevent neuroma has gained considerable traction. A logical extension of this idea is to therefore develop an approach to control and arrest axon growth. Given the limits in axonal regeneration across acellular nerve allografts (ANAs), these constructs could provide a means to reliably terminate axon regeneration from an injured nerve. The purpose of this study was to determine if attaching an ANA to an injured nerve could provide a means to control and limit axon regeneration in a predictable manner. Methods: Twenty (20) adult rats received a sciatic nerve transection, where only the proximal nerve was repaired using an ANA of variable length (0.5, 2.5, and 5.0 cm) or left unrepaired (control). The nerves were harvested 5 weeks post-operatively for gross and histomorphometric analysis. The extent of myelinated axons in regenerated tissue was quantified. Results: At 5 weeks, limited axon regeneration within the ANAs was observed. All lengths of ANAs lead to reduced myelinated axon numbers in the most terminal tissue region compared to untreated injured nerve (P =.002). Additionally, ANA length 2.5 cm or greater did not contain any axons at the most terminal tissue region. Conclusions: This study demonstrates a proof of concept that ANAs attached to the proximal end of an injured nerve can limit axon growth in a controlled manner. Furthermore, the extent of axon growth from the injured nerve into the ANA is dependent on the ANA length. © The Author(s) 2019.

Author Keywords
acellular nerve allograft; diagnosis; neuroma; pain; peripheral nerve; rat

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

“Sensitivity of the Memory Validity Profile (MVP): Raising the bar” (2019) Child Neuropsychology

Sensitivity of the Memory Validity Profile (MVP): Raising the bar
(2019) Child Neuropsychology, .

Dodd, J.N.^a , Murphy, S.^b , Bosworth, C.^c

^a Psychological Services, WellStar Medical Group, Marietta, GA, United States
^b Department of Psychology, Southern Illinois University, Edwardsville, IL, United States
^c Department of Psychology, St. Louis Children’s Hospital/Washington University, St. Louis, MO, United States

Abstract
The Memory Validity Profile (MVP) is a performance validity test (PVT) designed specifically for pediatric populations and utilizes specific cut-points for identifying noncredible performance at different ages. This study aims to evaluate the MVP using a known-groups design to determine optimal cut-off scores for detecting noncredible performance in youths with mild traumatic brain injury (mTBI) across different age-groups. Participants were 114 youths (age 5–17) with mTBI who were referred for neuropsychological evaluation in a hospital-based concussion clinic. All participants were administered the Nonverbal-Medical Symptom Validity Test (NV-MSVT) and the MVP. Participants who failed the NV-MSVT were also administered the TOMM. Participants who failed both the NV-MSVT and the TOMM comprised the criterion group (i.e., the “Failed two PVTs” group). Participants who failed only one PVT were excluded from the analysis. ROC curve analyses revealed good discriminability (AUC =.844: 95%, CI = 676–1.0, p =.001) with acceptable sensitivity (.73) and specificity (.91) for an optimal MVP Total score cut-off ≤31. There were no differences in MVP Total scores across age-groups. In conclusion, adopting stricter cut-points for non-credible performance and applying these consistently across all age groups in a mTBI population increases the clinical utility of the MVP. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
brain injury; memory validity profile; Pediatric; performance validity

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

“Measures of general and abdominal obesity and disability severity in a large population of people with multiple sclerosis” (2019) Multiple Sclerosis Journal

Measures of general and abdominal obesity and disability severity in a large population of people with multiple sclerosis
(2019) Multiple Sclerosis Journal, .

Fitzgerald, K.C.^a , Salter, A.^b , Tyry, T.^c , Fox, R.J.^d , Cutter, G.^e , Marrie, R.A.^f

^a Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
^b Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^c Dignity Health, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
^d Mellen Center for Multiple Sclerosis, Cleveland Clinic Foundation, Cleveland, OH, United States
^e Department of Biostatistics, University of Alabama at Birmingham School of Public Health, Birmingham, AL, United States
^f Departments of Internal Medicine and Community Health Sciences, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada

Abstract
Background: Metabolic comorbidity is overrepresented in people with multiple sclerosis (MS) and is associated with adverse MS outcomes. Excess visceral adiposity, approximated using waist circumference (WC), is a risk factor for metabolic comorbidity and predicts poorer outcomes in other neurologic diseases. Objective: To evaluate the association between WC and clinical and disease characteristics in people with MS. Methods: North American Research Committee on MS (NARCOMS) registry participants reported height and weight (used to calculate body mass index (BMI)) and were mailed a tape measure with instructions to measure WC. We considered WC continuously and used cut-points derived from the abdominal obesity criteria for the metabolic syndrome (men: WC ⩾ 40 in; women: WC ⩾ 35 in). We assessed the association between WC and disability (Patient-Determined Disease Steps) and symptom severity (validated scales) using multivariable-adjusted multinomial models. Results: Of 6367 responders with MS, we included 5832 (92%). Of these, 3181 (55%) reported WC meeting criteria for the abdominal obesity component of metabolic syndrome. In multivariable models adjusting for overall obesity status, WC was associated with 47% increased odds of severe versus mild disability (odds ratio (OR): 1.47; 95% confidence interval (CI): 1.22–1.78). Conclusions: Increased WC is associated with more severe disability, even after adjusting for overall obesity in this large cross-sectional survey. © The Author(s), 2019.

Author Keywords
comorbidity; Epidemiology; obesity

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

“Neurostimulation Therapies” (2019) Handbook of Experimental Pharmacology

Neurostimulation Therapies
(2019) Handbook of Experimental Pharmacology, 250, pp. 181-224.

Trapp, N.T.^a , Xiong, W.^b , Conway, C.R.^b ^c

^a University of Iowa Hospitals and Clinics, Iowa City, IA, United States
^b Washington University School of Medicine, St. Louis, MO, United States
^c John Cochran Division, VA St. Louis Health Care System, St. Louis, MO, United States

Abstract
Depression is one of the most disabling conditions in the world. In many cases patients continue to suffer with depressive disorders despite a series of adequate trials of medication and psychotherapy. Neuromodulation treatments offer a qualitatively different modality of treatment that can frequently prove efficacious in these treatment-refractory patients. The field of neuromodulation focuses on the use of electrical/electromagnetic energy, both invasively and noninvasively, to interface with and ultimately alter activity within the human brain for therapeutic purposes. These treatments provide another set of options to offer patients when clinically indicated, and knowledge of their safety, risks and benefits, and appropriate clinical application is essential for modernÂ psychiatrists and other mental health professionals. Although neuromodulation techniques hold tremendous promise, only three such treatments are currently approved by the United States Food and Drug Administration (FDA) for the treatment of major depressive disorder: electroconvulsive therapy (ECT), vagus nerve stimulation (VNS), and repetitive transcranial magnetic stimulation (rTMS). Additionally, numerous other neurostimulation modalities (deep brain stimulation [DBS], magnetic seizure therapy [MST], transcranial electric stimulation [tES], and trigeminal nerve stimulation [TNS]), though currently experimental, show considerable therapeutic promise. Researchers are actively looking for ways to optimize outcomes and clinical benefits by making neuromodulation treatments safer, more efficacious, and more durable. © 2018, Springer International Publishing AG, part of Springer Nature.

Author Keywords
Brain stimulation; Electroconvulsive therapy; Neuromodulation; Neurostimulation; Repetitive transcranial magnetic stimulation; Treatment-resistant depression; Treatment-resistant mood disorders

Document Type: Book Chapter
Publication Stage: Final
Source: Scopus

“The Wishbone: A Cranial Midline Localizing Device” (2019) World Neurosurgery

The Wishbone: A Cranial Midline Localizing Device
(2019) World Neurosurgery, .

Zanaty, M.^a , Banu, M.^b , Flouty, O.^a , Grady, S.^c , Holland, M.T.^a , Isaacs, A.^d , Kung, D.^c , Limbrick, D.D., Jr.^d , McKhann, G., II^b , Nagahama, Y.^a , Zipfel, G.J.^d , Howard, M.A., III^a

^a Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
^b Department of Neurosurgery, Columbia University, New York, NY, United States
^c Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
^d Department of Neurosurgery, Washington University, Saint Louis, MO, United States

Abstract
Objective: The Wishbone device is designed to enable surgeons to quickly and accurately localize the cranial midline. It is intended to be of particular use when localizing the burr hole site during posterior ventriculoperitoneal shunt (VPS) surgery. Methods: The Wishbone is a simple mechanical device with 2 adjustable caliper arms that reversibly attach to a patient’s left and right external auditory canals. The Wishbone’s laser localizer illuminates the midline scalp. The Wishbone was used to localize the posterior midline in a pilot series of patients undergoing VPS surgery. Midline localization and ventricular catheter placement accuracy were determined using findings from postoperative computed tomography scans. Results: The Wishbone is a mechanically robust device and proved easy for surgeons to use. Forty patients underwent VPS surgery using the Wishbone to localize the posterior midline. The localization procedure took less than 3 minutes. The average distance separating the Wishbone-localized midline scalp location and the computed tomography−defined anatomical midline was 2.9 mm (95% confidence interval 1.6–4.1 mm). In all cases, the ventricular catheter entered the ipsilateral lateral ventricle. The catheter tips were placed in the ipsilateral (n = 34) or contralateral (n = 5) frontal horn in all but 1 patient. In 1 patient, the catheter tip entered the parenchyma due to a burr hole localization error in the rostrocaudal dimension, unrelated to the Wishbone. Conclusions: We describe a simple, efficient, and cost-effective system for accurately localizing the posterior cranial midline. A larger patient series is required to definitively compare its clinical utility relative to frameless stereotaxis-based midline localization methods. © 2019 Elsevier Inc.

Author Keywords
Midline localizing device; Parietal shunt; Posterior shunt; Posterior ventriculoperitoneal shunt

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

“Development of a carbon-11 PET radiotracer for imaging TRPC5 in the brain” (2019) Organic and Biomolecular Chemistry

Development of a carbon-11 PET radiotracer for imaging TRPC5 in the brain
(2019) Organic and Biomolecular Chemistry, 17 (22), pp. 5586-5594.

Yu, Y.^a , Liang, Q.^a , Liu, H.^a , Luo, Z.^a , Hu, H.^b , Perlmutter, J.S.^a ^c , Tu, Z.^a

^a Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Department of Anesthesiology, Center for the Study of Itch, Washington University School of Medicine in, St. Louis, MO 63110, United States
^c Department of Neurology, Neuroscience, Physical Therapy and Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
The transient receptor potential channel subfamily member 5 (TRPC5) is a calcium permeable cation channel widely expressed in the brain. Accumulating evidence indicates that it plays a crucial role in psychiatric disorders including depression and anxiety. Positron emission tomography (PET) combined with a TRPC5 specific radioligand may provide a unique tool to investigate the functions of TRPC5 in animal disease models to guide drug development targeting TRPC5. To develop a TRPC5 PET radiotracer, the potent TRPC5 inhibitor HC608 was chosen for C-11 radiosynthesis through the N-demethyl amide precursor 7 reacting with [11C]methyl iodide. Under optimized conditions, [11C]HC608 was achieved with good radiochemical yield (25 ± 5%), high chemical and radiochemical purity (>99%), and high specific activity (204-377 GBq μmol-1, decay corrected to the end of bombardment, EOB). The in vitro autoradiography study revealed that [11C]HC608 specifically binds to TRPC5. Moreover, initial in vivo evaluation of [11C]HC608 performed in rodents and the microPET study in the brain of non-human primates further demonstrated that [11C]HC608 was able to penetrate the blood brain barrier and sufficiently accumulate in the brain. These results suggest that [11C]HC608 has the potential to be a PET tracer for imaging TRPC5 in vivo. © The Royal Society of Chemistry.

Document Type: Article
Publication Stage: Final
Source: Scopus

“The intergenerational transmission of childhood maltreatment: Nonspecificity of maltreatment type and associations with borderline personality pathology” (2019) Development and Psychopathology

The intergenerational transmission of childhood maltreatment: Nonspecificity of maltreatment type and associations with borderline personality pathology
(2019) Development and Psychopathology, .

Paul, S.E.^a , Boudreaux, M.J.^a , Bondy, E.^a , Tackett, J.L.^b , Oltmanns, T.F.^a , Bogdan, R.^a

^a Department of Psychological and Brain Sciences, Washington University in St. Louis, 1125 One Brookings Drive, St. Louis, MO 63130, United States
^b Department of Psychology, Northwestern University, Evanston, IL, United States

Abstract
One generation’s experience of childhood maltreatment is associated with that of the next. However, whether this intergenerational transmission is specific to distinct forms of maltreatment and what factors may contribute to its continuity remains unclear. Borderline personality pathology is predicted by childhood maltreatment and characterized by features (e.g., dysregulated emotion, relationship instability, impulsivity, and inconsistent appraisals of others) that may contribute to its propagation. Among 364 older adults and 573 of their adult children (total n = 937), self-reported exposure to distinct forms of childhood maltreatment (i.e., emotional, physical, and sexual abuse, and emotional and physical neglect as assessed by the Childhood Trauma Questionnaire) showed homotypic and heterotypic associations across generations with little evidence that latent factors unique to specific forms of maltreatment show generational continuity. General nonspecific indices of childhood maltreatment showed evidence of intergenerational transmission after accounting for demographic factors and parent socioeconomic status (b = 0.126, p = 9.21 × 10-4). This continuity was partially mediated by parental borderline personality pathology (assessed longitudinally through a variety of measures and sources, indirect effect: b = 0.031, 95% confidence interval [0.003, 0.060]). The intergenerational continuity of childhood maltreatment may largely represent general risk for nonspecific maltreatment that may, in part, be propagated by borderline personality pathology and/or shared risk factors. © Cambridge University Press 2019.

Author Keywords
abuse; borderline personality; childhood maltreatment; intergenerational transmission; neglect; stress

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

“Reply to ‘Assembling the brain trust: the multidisciplinary imperative in neuro-oncology’” (2019) Nature Reviews Clinical Oncology

Reply to ‘Assembling the brain trust: the multidisciplinary imperative in neuro-oncology’
(2019) Nature Reviews Clinical Oncology, .

Aldape, K.^a , Brindle, K.M.^b , Chesler, L.^c , Chopra, R.^c , Gajjar, A.^d , Gilbert, M.R.^e , Gottardo, N.^f , Gutmann, D.H.^g , Hargrave, D.^h , Holland, E.C.ⁱ , Jones, D.T.W.^j , Joyce, J.A.^k , Kearns, P.^l , Kieran, M.W.^m , Mellinghoff, I.K.ⁿ , Merchant, M.^o , Pfister, S.M.^p , Pollard, S.M.^q , Ramaswamy, V.^r , Rich, J.N.^s , Robinson, G.W.^d , Rowitch, D.H.^t , Sampson, J.H.^u , Taylor, M.D.^v , Workman, P.^c , Gilbertson, R.J.^b ^w

^a Department of Pathology, University Health Network, Toronto, ON, Canada
^b CRUK Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
^c The Institute of Cancer Research, London, United Kingdom
^d Department of Oncology, St Jude Children’s Research Hospital, Memphis, TN, United States
^e Center for Cancer Research, National Cancer Institute, Bethesda, MD, United States
^f Telethon Kids Institute, Subiaco, WA, Australia
^g Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^h Great Ormond Street Hospital for Children, Great Ormond Street, London, United Kingdom
ⁱ Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
^j Pediatric Glioma Research Group, Hopp Children’s Cancer Center at the NCT Heidelberg, Heidelberg, Germany
^k Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
^l Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
^m Dana-Farber Boston Children’s Cancer and Blood Disorder’s Center and Harvard Medical School, Boston, MA, United States
ⁿ Human Oncology and Pathogenesis Program and Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
^o Oncology, AstraZeneca IMED Biotech Unit, Boston, MA, United States
^p Division of Pediatric Oncology, Hopp Children’s Cancer Center at the NCT Heidelberg, Heidelberg, Germany
^q Cancer Research UK Edinburgh Centre and Medical Research Council Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
^r Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
^s Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, San Diego, CA, United States
^t Department of Pediatrics, University of Cambridge and Wellcome Trust-MRC Stem Cell Institute, Cambridge, United Kingdom
^u The Preston Robert Tisch Brain Tumour Center, Duke Cancer Center, Durham, NC, United States
^v The Arthur and Sonia Labatt Brain Tumour Research Centre, and Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada
^w CRUK Cambridge Institute and Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge, United Kingdom

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

“Eye-hand re-coordination: A pilot investigation of gaze and reach biofeedback in chronic stroke” (2019) Progress in Brain Research

Eye-hand re-coordination: A pilot investigation of gaze and reach biofeedback in chronic stroke
(2019) Progress in Brain Research, .

Rizzo, J.-R.^a ^b ^e , Beheshti, M.^a , Shafieesabet, A.^a , Fung, J.^a , Hosseini, M.^a , Rucker, J.C.^b ^c , Snyder, L.H.^d , Hudson, T.E.^a ^b

^a Department of Physical Medicine & Rehabilitation, NYU School of Medicine, New York, NY, United States
^b Department of Neurology, NYU School of Medicine, New York, NY, United States
^c Department of Ophthalmology, NYU School of Medicine, New York, NY, United States
^d Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
^e Department of Biomedical Engineering, NYU School of Engineering, New York, NY, United States

Abstract
Within the domain of motor performance, eye-hand coordination centers on close relationships between visuo-perceptual, ocular and appendicular motor systems. This coordination is critically dependent on a cycle of feedforward predictions and feedback-based corrective mechanisms. While intrinsic feedback harnesses naturally available movement-dependent sensory channels to modify movement errors, extrinsic feedback may be provided synthetically by a third party for further supplementation. Extrinsic feedback has been robustly explored in hand-focused, motor control studies, such as through computer-based visual displays, highlighting the spatial errors of reaches. Similar attempts have never been tested for spatial errors related to eye movements, despite the potential to alter ocular motor performance. Stroke creates motor planning deficits, resulting in the inability to generate predictions of motor performance. In this study involving visually guided pointing, we use an interactive computer display to provide extrinsic feedback of hand endpoint errors in an initial baseline experiment (pre-) and then feedback of both eye and hand errors in a second experiment (post-) to chronic stroke participants following each reach trial. We tested the hypothesis that extrinsic feedback of eye and hand would improve predictions and therefore feedforward control. We noted this improvement through gains in the spatial and temporal aspects of eye-hand coordination or an improvement in the decoupling noted as incoordination post-stroke in previous studies, returning performance toward healthy, control behavior. More specifically, results show that stroke participants, following the interventional feedback for eye and hand, improved both their accuracy and timing. This was evident through a temporal re-synchronization between eyes and hands, improving correlations between movement timing, as well as reducing the overall time interval (delay) between effectors. These experiments provide a strong indication that an extrinsic feedback intervention at appropriate therapeutic doses may improve eye-hand coordination during stroke rehabilitation. © 2019 Elsevier B.V.

Author Keywords
Biofeedback; Coordination; Eye; Hand; Re-coordination; Stroke

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

“Cognitive oriented strategy training augmented rehabilitation (COSTAR) for ischemic stroke: a pilot exploratory randomized controlled study” (2019) Disability and Rehabilitation

Cognitive oriented strategy training augmented rehabilitation (COSTAR) for ischemic stroke: a pilot exploratory randomized controlled study
(2019) Disability and Rehabilitation, .

Wolf, T.J.^a , Doherty, M.^b , Boone, A.^a , Rios, J.^c , Polatajko, H.^d , Baum, C.^e , McEwen, S.^c ^d

^a Department of Occupational Therapy, University of Missouri, Columbia, MO, United States
^b Department of Occupational Science and Occupational Therapy, Saint Louis University, St. Louis, MO, United States
^c Sunnybrook Research Institute, St. John’s Rehab Program, Toronto, Canada
^d Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada
^e Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Purpose: To investigate the effect of adding cognitive strategy training to task-specific training (TST), called Cognitive Oriented Strategy Training Augmented Rehabilitation (COSTAR), compared with TST on activity and participation for chronic stroke survivors in an outpatient occupational therapy setting Materials and methods: We conducted an exploratory, single-blind, randomized controlled trial. Participants were randomized to TST or COSTAR protocol. Our primary outcomes measured activity and participation after stroke: the Stroke Impact Scale (SIS), Canadian Occupational Performance Measure (COPM), and Performance Quality Rating Scale (PQRS). Results: Forty-four participants were randomized. The COSTAR group had an attrition rate of 50% and an average of 9.8 of 12 sessions were completed; the TST group had an attrition rate of 25% and an average of 10.7 sessions were completed. Generally both groups improved on the majority of primary and secondary outcomes. There is little evidence to support a beneficial effect of COSTAR over TST for improvement of primary measures of activity performance or secondary measures. Conclusion: Negligible findings may be attributed to an inadvertent treatment group equivalency. Further, the research design did not allow for adequate measurement of the effect of each intervention on participants’ ability to generalize learned skills.Implications for rehabilitation Stroke rehabilitation is largely based upon the principles of task-specific training, which is associated with improvements in upper extremity motor performance; however, TST requires a heavy dosage and lacks generalization to untrained activities. Cognitive strategy use has been associated with improved generalization of treatment to untrained activities and novel contexts however, it is often not used in TST protocols. The results of this preliminary study found no clear advantage between task-specific training and strategy-adapted task-specific training on trained and untrained activities when both interventions targeted activity performance. Task-specific training, if focused at the activity performance level rather than the impairment reduction level, may have a stronger effect on improving in individual’s ability to participate in everyday life activities even without the use of cognitive-strategies. Incorporating cognitive strategy-use into TST would likely produce the greatest effect on generalization and transfer of the treatment effects to other activities and contexts rather than solely on activity performance of trained activities. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
activities of daily living; cognition; neurological rehabilitation; occupational therapy; Stroke; stroke rehabilitation

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

“Moralized memory: binding values predict inflated estimates of the group’s historical influence” (2019) Memory

Moralized memory: binding values predict inflated estimates of the group’s historical influence
(2019) Memory, .

Churchill, L., Yamashiro, J.K., Roediger, H.L., III

Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Collective memories are memories or historical knowledge shared by individual group members, which shape their collective identity. Ingroup inflation, which has previously also been referred to as national narcissism or state narcissism, is the finding that group members judge their own group to have been significantly more historically influential than do people from outside the group. We examined the role of moral motivations in this biased remembering. A sample of 2118 participants, on average 42 from each state of the United States, rated their home state’s contribution to U.S. history, as well as that of ten other states randomly selected. We demonstrated an ingroup inflation effect in estimates of the group’s historical influence. Participants’ endorsement of binding values–loyalty, authority, and sanctity, but particularly loyalty–positively predicted the size of this effect. Endorsement of individuating values–care and fairness–did not predict collective narcissism. Moral motives may shape biases in collective remembering. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
binding values; cognitive attractor; Collective memory; collective narcissism; moral foundations theory

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