WashU weekly Neuroscience publications

Automatic labeling of cortical sulci for the human fetal brain based on spatio-temporal information of gyrification
(2019) NeuroImage, 188, pp. 473-482. 

Yun, H.J.^{a b} , Chung, A.W.^{a b} , Vasung, L.^{a b} , Yang, E.^c , Tarui, T.^{a b d e} , Rollins, C.K.^f , Ortinau, C.M.^g , Grant, P.E.^{a b c} , Im, K.^{a b}

^a Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
^b Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
^c Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
^d Mother Infant Research Institute, Tufts Medical Center, Tufts University School of Medicine, Boston, MA 02111, United States
^e Department of Pediatrics, Tufts Medical Center, Tufts University School of Medicine, Boston, MA 02111, United States
^f Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
^g Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110, United States

Abstract
Accurate parcellation and labeling of primary cortical sulci in the human fetal brain is useful for regional analysis of brain development. However, human fetal brains show large spatio-temporal changes in brain size, cortical folding patterns, and relative position/size of cortical regions, making accurate automatic sulcal labeling challenging. Here, we introduce a novel sulcal labeling method for the fetal brain using spatio-temporal gyrification information from multiple fetal templates. First, spatial probability maps of primary sulci are generated on the templates from 23 to 33 gestational weeks and registered to an individual brain. Second, temporal weights, which determine the level of contribution to the labeling for each template, are defined by similarity of gyrification between the individual and the template brains. We combine the weighted sulcal probability maps from the multiple templates and adopt sulcal basin-wise approach to assign sulcal labels to each basin. Our labeling method was applied to 25 fetuses (22.9–29.6 gestational weeks), and the labeling accuracy was compared to manually assigned sulcal labels using the Dice coefficient. Moreover, our multi-template basin-wise approach was compared to a single-template approach, which does not consider the temporal dynamics of gyrification, and a fully-vertex-wise approach. The mean accuracy of our approach was 0.958 across subjects, significantly higher than the accuracies of the other approaches. This novel approach shows highly accurate sulcal labeling and provides a reliable means to examine characteristics of cortical regions in the fetal brain. © 2018 Elsevier Inc.

Author Keywords
Cortical surface;  Fetal brain;  Multi-template labeling;  Parcellation;  Primary sulci

Document Type: Article
Publication Stage: Final
Source: Scopus

Life engagement is associated with higher GDP among societies
(2019) Journal of Research in Personality, 78, pp. 210-214. 

Hill, P.L.^a , Cheung, F.^b , Kube, A.^a , Burrow, A.L.^c

^a Department of Psychological and Brain Sciences, Washington University in St. Louis, United States
^b School of Public Health, University of Hong Kong, Hong Kong
^c Department of Human Development, Cornell University, United States

Abstract
Research suggests that individuals who report a greater sense of purpose in life tend to fare better economically, which may be expected to extend to the societal-level, in terms of higher GDP. However, previous work has demonstrated a negative association between citizens’ reports of purpose and GDP. The current study reconciles these differences by specifically considering an important component of sense of purpose missing in the past societal work, namely life engagement. Using data representative of 99% of the world’s adult population from 2013 to 2015 Gallup World Poll, results demonstrate a clear positive association between life engagement and countries’ GDP. Moreover, results demonstrate the distinctive importance of assessing life engagement relative to alternative metrics for related constructs. © 2018 Elsevier Inc.

Author Keywords
GDP;  Life engagement;  Purpose in life;  Society-level outcomes

Document Type: Article
Publication Stage: Final
Source: Scopus

Clinical challenges and future therapeutic approaches for neuronal ceroid lipofuscinosis
(2019) The Lancet Neurology, 18 (1), pp. 107-116. 

Mole, S.E.^a , Anderson, G.^d , Band, H.A.^e , Berkovic, S.F.^f , Cooper, J.D.^g , Kleine Holthaus, S.-M.^b , McKay, T.R.^h , Medina, D.L.ⁱ , Rahim, A.A.^c , Schulz, A.^j , Smith, A.J.^b

^a Medical Research Council Laboratory for Molecular Cell Biology and UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
^b UCL Institute of Ophthalmology, University College London, London, United Kingdom
^c UCL School of Pharmacy, University College London, London, United Kingdom
^d Department of Histopathology, Great Ormond Street Hospital, London, United Kingdom
^e Batten Disease Family Association, Farnborough, United Kingdom
^f Epilepsy Research Centre, Department of Medicine, Austin Health & Northern Health, University of Melbourne, Melbourne, VIC, Australia
^g Department of Pediatrics, Washington University School of Medicine, St Louis, MO, United States
^h Centre for Bioscience, Manchester Metropolitan University, Manchester, United Kingdom
ⁱ Telethon Institute of Genetics and Medicine, Naples, Italy
^j Department of Pediatrics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Abstract
Treatment of the neuronal ceroid lipofuscinoses, also known as Batten disease, is at the start of a new era because of diagnostic and therapeutic advances relevant to this group of inherited neurodegenerative and life-limiting disorders that affect children. Diagnosis has improved with the use of comprehensive DNA-based tests that simultaneously screen for many genes. The identification of disease-causing mutations in 13 genes provides a basis for understanding the molecular mechanisms underlying neuronal ceroid lipofuscinoses, and for the development of targeted therapies. These targeted therapies include enzyme replacement therapies, gene therapies targeting the brain and the eye, cell therapies, and pharmacological drugs that could modulate defective molecular pathways. Such therapeutic developments have the potential to enable earlier diagnosis and better targeted therapeutic management. The first approved treatment is an intracerebroventricularly administered enzyme for neuronal ceroid lipofuscinosis type 2 disease that delays symptom progression. Efforts are underway to make similar progress for other forms of the disorder. © 2019 Elsevier Ltd

Document Type: Review
Publication Stage: Final
Source: Scopus

Misdiagnosis of multiple sclerosis: Impact of the 2017 McDonald criteria on clinical practice
(2019) Neurology, 92 (1), pp. 26-33. 

Solomon, A.J., Naismith, R.T., Cross, A.H.

From the Department of Neurological Sciences (A.J.S.), Larner College of Medicine at The University of Vermont, University Health Center, Burlington; and Department of Neurology (R.T.N., A.H.C.), Washington University in St. Louis, MO

Abstract
Misdiagnosis of multiple sclerosis (MS) (the incorrect assignment of a diagnosis of MS) remains a problem in contemporary clinical practice. Studies indicate that misdiagnosed patients are often exposed to prolonged unnecessary health care risks and morbidity. The recently published 2017 revision of the McDonald criteria for the diagnosis of MS provides an opportunity to consider the effect of these revisions on the problem of MS misdiagnosis. The 2017 McDonald criteria include several new recommendations to reduce potential for misdiagnoses. The criteria should be used for the types of patients in which validation studies were performed, specifically those patients who present with typical demyelinating syndromes. MRI lesion characteristics were defined for which McDonald criteria would be expected to perform with accuracy. However, 2017 revisions, which now include assessment for cortical lesions, and the inclusion of symptomatic lesions and positive oligoclonal bands for the fulfillment of diagnostic criteria, may have the potential to lead to misdiagnosis of MS if not applied appropriately. While the 2017 McDonald criteria integrate issues relating to MS misdiagnosis and incorporate specific recommendations for its prevention more prominently than prior criteria, the interpretation of clinical and radiologic assessments upon which these criteria depend will continue to allow misdiagnoses. In patients with atypical clinical presentations, the revised McDonald criteria may not be readily applied. In those situations, further evaluation or monitoring rather than immediate diagnosis of MS is prudent. © 2018 American Academy of Neurology.

Document Type: Review
Publication Stage: Final
Source: Scopus

Epigenetic regulator UHRF1 inactivates REST and growth suppressor gene expression via DNA methylation to promote axon regeneration
(2018) Proceedings of the National Academy of Sciences of the United States of America, 115 (52), pp. E12417-E12426. 

Oh, Y.M.^a , Mahar, M.^a , Ewan, E.E.^a , Leahy, K.M.^a , Zhao, G.^a , Cavalli, V.^{a b c}

^a Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Injured peripheral sensory neurons switch to a regenerative state after axon injury, which requires transcriptional and epigenetic changes. However, the roles and mechanisms of gene inactivation after injury are poorly understood. Here, we show that DNA methylation, which generally leads to gene silencing, is required for robust axon regeneration after peripheral nerve lesion. Ubiquitin-like containing PHD ring finger 1 (UHRF1), a critical epigenetic regulator involved in DNA methylation, increases upon axon injury and is required for robust axon regeneration. The increased level of UHRF1 results from a decrease in miR-9. The level of another target of miR-9, the transcriptional regulator RE1 silencing transcription factor (REST), transiently increases after injury and is required for axon regeneration. Mechanistically, UHRF1 interacts with DNA methyltransferases (DNMTs) and H3K9me3 at the promoter region to repress the expression of the tumor suppressor gene phosphatase and tensin homolog (PTEN) and REST. Our study reveals an epigenetic mechanism that silences tumor suppressor genes and restricts REST expression in time after injury to promote axon regeneration. © 2018 National Academy of Sciences. All Rights Reserved.

Author Keywords
Axon regeneration;  DNMT;  Epigenetic;  REST;  UHRF1

Document Type: Article
Publication Stage: Final
Source: Scopus

New insights into the role of TREM2 in Alzheimer’s disease
(2018) Molecular Neurodegeneration, 13 (1), art. no. 66, . 

Gratuze, M.^{a b c} , Leyns, C.E.G.^{a b c} , Holtzman, D.M.^{a b c}

^a Department of Neurology, St. Louis, United States
^b Hope Center for Neurological Disorders, St. Louis, United States
^c Knight Alzheimer’s Disease Research Center, Washington University, School of Medicine, St. Louis, MO 63110, United States

Abstract
Alzheimer’s disease (AD) is the leading cause of dementia. The two histopathological markers of AD are amyloid plaques composed of the amyloid-β (Aβ) peptide, and neurofibrillary tangles of aggregated, abnormally hyperphosphorylated tau protein. The majority of AD cases are late-onset, after the age of 65, where a clear cause is still unknown. However, there are likely different multifactorial contributors including age, enviornment, biology and genetics which can increase risk for the disease. Genetic predisposition is considerable, with heritability estimates of 60-80%. Genetic factors such as rare variants of TREM2 (triggering receptor expressed on myeloid cells-2) strongly increase the risk of developing AD, confirming the role of microglia in AD pathogenesis. In the last 5 years, several studies have dissected the mechanisms by which TREM2, as well as its rare variants affect amyloid and tau pathologies and their consequences in both animal models and in human studies. In this review, we summarize increases in our understanding of the involvement of TREM2 and microglia in AD development that may open new therapeutic strategies targeting the immune system to influence AD pathogenesis. © 2018 The Author(s).

Author Keywords
Alzheimer’s disease;  ApoE;  Gliosis;  Microglia;  Neurodegeneration;  TREM2

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

Genome-wide interaction study of a proxy for stress-sensitivity and its prediction of major depressive disorder
(2018) PLoS ONE, 13 (12), art. no. e0209160, . 

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

^a Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^b Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom
^c Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^d Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
^e Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
^f Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^g Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany
^h Medical and Population Genetics, Broad Institute, Cambridge, MA, United States
ⁱ Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
^j Department of Biomedicine, Aarhus University, Aarhus, Denmark
^k iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
^l iPSYCH, Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark
^m Dept of Biological Psychology, EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
ⁿ Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
^o Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
^p National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
^q Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
^r Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
^s Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^t Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
^u Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
^v Department of Psychiatry, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, Netherlands
^w Virginia Institute for Psychiatric and Behavior Genetics, Richmond, VA, United States
^x Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
^y Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^z Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark
^aa Human Genetics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
^ab Statistical Genomics and Systems Genetics, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
^ac Department of Psychiatry, University Hospital of Lausanne, Prilly, Vaud, Switzerland
^ad MRC Social Genetic and Developmental Psychiatry Centre, King’s College London, London, United Kingdom
^ae Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
^af Centre for Advanced Imaging, University of Queensland, Saint Lucia, QLD, Australia
^ag Queensland Brain Institute, University of Queensland, Saint Lucia, QLD, Australia
^ah Psychological Medicine, Cardiff University, Cardiff, United Kingdom
^ai Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
^aj Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, United States
^ak Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
^al Institute of Human Genetics, University of Bonn, Bonn, Germany
^am Life and Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
^an Epidemiology, Erasmus MC, Rotterdam, Zuid-Holland, Netherlands
^ao Psychiatry, Dokuz Eylul University School of Medicine, Izmir, Turkey
^ap Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
^aq Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, United States
^ar Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, United States
^as Neuroscience and Mental Health, Cardiff University, Cardiff, United Kingdom
^at Bioinformatics, University of British Columbia, Vancouver, BC, Canada
^au Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^av Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, United States
^aw Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
^ax Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
^ay Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
^az Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
^ba Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
^bb Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^bc Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
^bd Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
^be Danish Headache Centre, Department of Neurology, Rigshospitalet, Glostrup, Denmark
^bf Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Copenhagen, Denmark
^bg iPSYCH, Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, Denmark
^bh Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
^bi Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
^bj Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
^bk Max Planck Institute of Psychiatry, Munich, Germany
^bl Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
^bm Psychiatry and The Behavioral Sciences, University of Southern California, Los Angeles, CA, United States
^bn Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
^bo Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
^bp Informatics Program, Boston Children’s Hospital, Boston, MA, United States
^bq Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
^br Department of Endocrinology, Herlev University Hospital, University of Copenhagen, Copenhagen, Denmark
^bs Institute of Social and Preventive Medicine (IUMSP), University Hospital of Lausanne, Lausanne, VD, Switzerland
^bt Swiss Institute of Bioinformatics, Lausanne, VD, Switzerland
^bu Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
^bv Mental Health, NHS 24, Glasgow, United Kingdom
^bw Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
^bx Statistics, University of Oxford, Oxford, United Kingdom
^by Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, United States
^bz School of Psychology and Counseling, Queensland University of Technology, Brisbane, QLD, Australia
^ca Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, South Brisbane, QLD, Australia
^cb Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
^cc Estonian Genome Center, University of Tartu, Tartu, Estonia
^cd Medical Genetics, University of British Columbia, Vancouver, BC, Canada
^ce Statistics, University of British Columbia, Vancouver, BC, Canada
^cf DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
^cg Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
^ch Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
^ci Humus, Reykjavik, Iceland
^cj MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
^ck Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^cl Clinical Genetics, Vrije Universiteit Medical Center, Amsterdam, Netherlands
^cm Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^cn Solid Biosciences, Boston, MA, United States
^co Department of Psychiatry, Washington University, Saint Louis School of Medicine, Saint Louis, MO, United States
^cp Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain
^cq Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
^cr Department of Psychiatry and Psychotherapy, Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany
^cs Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany
^ct Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States
^cu Behavioral Health Services, Kaiser Permanente Washington, Seattle, WA, United States
^cv Faculty of Medicine, Department of Psychiatry, University of Iceland, Reykjavik, Iceland
^cw School of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia
^cx Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
^cy deCODE Genetics/Amgen, Reykjavik, Iceland
^cz College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom
^da Institute of Epidemiology and Social Medicine, University of Münster, Münster, Nordrhein-Westfalen, Germany
^db Institute for Community Medicine, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
^dc Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
^dd KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
^de Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, United Kingdom
^df Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
^dg Internal Medicine, Erasmus MC, Rotterdam, Zuid-Holland, Netherlands
^dh Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery and Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
^di Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
^dj Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands
^dk Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^dl Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, MA, United States
^dm Institute for Molecular Bioscience, Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
^dn Department of Psychiatry, University of Münster, Münster, Nordrhein-Westfalen, Germany
^do Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
^dp Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany
^dq Amsterdam Public Health Institute, Vrije Universiteit Medical Center, Amsterdam, Netherlands
^dr Centre for Integrative Biology, Università degli Studi di Trento, Trento, Trentino-Alto Adige, Italy
^ds Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
^dt Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, United States
^du Department of Psychiatry, University of Toronto, Toronto, ON, Canada
^dv Centre for Addiction and Mental Health, Toronto, ON, Canada
^dw Division of Psychiatry, University College London, London, United Kingdom
^dx Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, United States
^dy Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
^dz Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, Denmark
^ea University of Liverpool, Liverpool, United Kingdom
^eb Mental Health Center Copenhagen, Copenhagen Universtity Hospital, Copenhagen, Denmark
^ec Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, CT, United States
^ed Psychiatry, Harvard Medical School, Boston, MA, United States
^ee Psychiatry, University of Iowa, Iowa City, IA, United States
^ef Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Niedersachsen, Germany
^eg Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD, United States
^eh Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^ei Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland, Netherlands
^ej Psychiatry, Erasmus MC, Rotterdam, Zuid-Holland, Netherlands
^ek Psychiatry, Dalhousie University, Halifax, NS, Canada
^el Division of Epidemiology, New York State Psychiatric Institute, New York, NY, United States
^em Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
^en Department of Medical and Molecular Genetics, King’s College London, London, United Kingdom
^eo Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
^ep NIHR BRC for Mental Health, King’s College London, London, United Kingdom
^eq Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^er Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^es University Medical School, NHS in Aberdeen, Dundee, Edinburgh and Glasgow, United Kingdom

Abstract
Individual response to stress is correlated with neuroticism and is an important predictor of both neuroticism and the onset of major depressive disorder (MDD). Identification of the genetics underpinning individual differences in response to negative events (stress-sensitivity) may improve our understanding of the molecular pathways involved, and its association with stress-related illnesses. We sought to generate a proxy for stress-sensitivity through modelling the interaction between SNP allele and MDD status on neuroticism score in order to identify genetic variants that contribute to the higher neuroticism seen in individuals with a lifetime diagnosis of depression compared to unaffected individuals. Meta-analysis of genome-wide interaction studies (GWIS) in UK Biobank (N = 23,092) and Generation Scotland: Scottish Family Health Study (N = 7,155) identified no genome-wide significance SNP interactions. However, gene-based tests identified a genome-wide significant gene, ZNF366, a negative regulator of glucocorticoid receptor function implicated in alcohol dependence (p = 1.48×10-7; Bonferroni-corrected significance threshold p < 2.79×10-6). Using summary statistics from the stress-sensitivity term of the GWIS, SNP heritability for stress-sensitivity was estimated at 5.0%. In models fitting polygenic risk scores of both MDD and neuroticism derived from independent GWAS, we show that polygenic risk scores derived from the UK Biobank stress-sensitivity GWIS significantly improved the prediction of MDD in Generation Scotland. This study may improve interpretation of larger genome-wide association studies of MDD and other stress-related illnesses, and the understanding of the etiological mechanisms underpinning stress-sensitivity. © 2018 Arnau-Soler et al. This is an open access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

The Itch–Scratch Cycle: A Neuroimmune Perspective
(2018) Trends in Immunology, 39 (12), pp. 980-991. 

Mack, M.R.^{a b} , Kim, B.S.^{a b c d}

^a Center for the Study of Itch, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Division of Dermatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110, United States
^d Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
Relentless, repetitive itching and scratching is a debilitating feature of many chronic inflammatory skin disorders such as atopic dermatitis. While well known clinically, this itch–scratch cycle has historically lacked in-depth mechanistic understanding. However, recent advances at the interface of itch neurobiology and skin immunology have shed new light on this phenomenon. In this review, we highlight recent advances in our understanding of the neuroimmunology of chronic itch centered around three key points of entry into the itch–scratch cycle: the epithelial barrier, the immune system, and the peripheral nervous system. Furthermore, we explore novel neuro-epithelial-immune interactions that may represent promising therapeutic paradigms. © 2018

Author Keywords
atopic dermatitis;  cytokines;  itch;  neuroimmunity;  proteases;  scratch;  skin barrier

Document Type: Review
Publication Stage: Final
Source: Scopus

Selective D2 receptor PET in manganese-exposed workers
(2018) Neurology, 91 (11), pp. e1022-e1030. 

Criswell, S.R., Warden, M.N., Searles Nielsen, S., Perlmutter, J.S., Moerlein, S.M., Sheppard, L., Lenox-Krug, J., Checkoway, H., Racette, B.A.

From the Department of Neurology (S.R.C., M.N.W., S.S.N., J.S.P., J.L.-K., B.A.R.), Department of Radiology (J.S.P., S.M.M.), Department of Neuroscience (J.S.P.), Program in Physical Therapy (J.S.P.), Program in Occupational Therapy (J.S.P.), and Department of Biochemistry and Molecular Biophysics (S.M.M.), Washington University School of Medicine, St. Louis, MO; Department of Environmental and Occupational Health Sciences (L.S.) and Department of Biostatistics (L.S.), University of Washington, School of Public Health, Seattle; Department of Family Medicine and Public Health (H.C.) and Department of Neurosciences (H.C.), University of California, San Diego, School of Medicine, La Jolla; and School of Public Health (B.A.R.), Faculty of Health Sciences, University of the Witwatersrand, Parktown, South Africa

Abstract
OBJECTIVE: To investigate the associations between manganese (Mn) exposure, D2 dopamine receptors (D2Rs), and parkinsonism using [11C](N-methyl)benperidol (NMB) PET. METHODS: We used NMB PET to evaluate 50 workers with a range of Mn exposure: 22 Mn-exposed welders, 15 Mn-exposed workers, and 13 nonexposed workers. Cumulative Mn exposure was estimated from work histories, and movement disorder specialists examined all workers. We calculated NMB D2R nondisplaceable binding potential (BPND) for the striatum, globus pallidus, thalamus, and substantia nigra (SN). Multivariate analysis of covariance with post hoc descriptive discriminate analysis identified regional differences by exposure group. We used linear regression to examine the association among Mn exposure, Unified Parkinson’s Disease Rating Scale motor subsection 3 (UPDRS3) score, and regional D2R BPND. RESULTS: D2R BPND in the SN had the greatest discriminant power among exposure groups (p < 0.01). Age-adjusted SN D2R BPND was 0.073 (95% confidence interval [CI] 0.022-0.124) greater in Mn-exposed welders and 0.068 (95% CI 0.013-0.124) greater in Mn-exposed workers compared to nonexposed workers. After adjustment for age, SN D2R BPND was 0.0021 (95% CI 0.0005-0.0042) higher for each year of Mn exposure. Each 0.10 increase in SN D2R BPND was associated with a 2.65 (95% CI 0.56-4.75) increase in UPDRS3 score. CONCLUSIONS AND RELEVANCE: Nigral D2R BPND increased with Mn exposure and clinical parkinsonism, indicating dose-dependent dopaminergic dysfunction of the SN in Mn neurotoxicity. © 2018 American Academy of Neurology.

Document Type: Article
Publication Stage: Final
Source: Scopus

Emerging evidence for cannabis’ role in opioid use disorder
(2018) Cannabis and Cannabinoid Research, 3 (1), pp. 179-189. 

Wiese, B.^{a b} , Wilson-Poe, A.R.^b

^a Department of Psychology, University of Missouri-St. Louis, St. Louis, MO, United States
^b Department of Anesthesiology, Pain Center, Washington University, School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States

Abstract
Introduction: The opioid epidemic has become an immense problem in North America, and despite decades of research on the most effective means to treat opioid use disorder (OUD), overdose deaths are at an all-time high, and relapse remains pervasive. Discussion: Although there are a number of FDA-approved opioid replacement therapies and maintenance medications to help ease the severity of opioid withdrawal symptoms and aid in relapse prevention, these medications are not risk free nor are they successful for all patients. Furthermore, there are legal and logistical bottlenecks to obtaining traditional opioid replacement therapies such as methadone or buprenorphine, and the demand for these services far outweighs the supply and access. To fill the gap between efficacious OUD treatments and the widespread prevalence of misuse, relapse, and overdose, the development of novel, alternative, or adjunct OUD treatment therapies is highly warranted. In this article, we review emerging evidence that suggests that cannabis may play a role in ameliorating the impact of OUD. Herein, we highlight knowledge gaps and discuss cannabis’ potential to prevent opioid misuse (as an analgesic alternative), alleviate opioid withdrawal symptoms, and decrease the likelihood of relapse. Conclusion: The compelling nature of these data and the relative safety profile of cannabis warrant further exploration of cannabis as an adjunct or alternative treatment for OUD. © 2018 Beth Wiese and Adrianne R. Wilson-Poe.

Author Keywords
cannabis;  opioid addiction;  opioid treatment;  relapse prevention

Document Type: Review
Publication Stage: Final
Source: Scopus

Meeting Update-Society for Neuro-Oncology 2017 Annual Meeting
(2018) Neuro-oncology, 20 (2), pp. 156-159. 

Strowd, R.E.^a , Kim, A.H.^b , Wen, P.Y.^c

^a Department of Neurology and Internal Medicine, Section on Hematology and Oncology, Wake Forest School of Medicine, Comprehensive Cancer Center of Wake Forest University, Winston-Salem, NC, United States
^b Department of Neurological Surgery, Washington University School of Medicine, Siteman Cancer Center, St Louis, MO, United States
^c Center for Neuro-Oncology, Dana Farber/Brigham and Women’s Cancer Center and Division of Neurology, Brigham and Women’s Hospital, Boston, MA, United States

Document Type: Article
Publication Stage: Final
Source: Scopus

Improving pediatricians’ knowledge and skills in suicide prevention: Opportunities for social work
(2018) Qualitative Social Work, . Article in Press. 

Behrman, G.U.^a , Secrest, S.^b , Ballew, P.^a , Matthieu, M.M.^c , Glowinski, A.L.^d , Scherrer, J.F.^b

^a CHADS Coalition for Mental Health, Saint Louis University, St. Louis, USA, United States
^b Department of Family and Community Medicine, Saint Louis University School of Medicine, St. Louis, USA, United States
^c School of Social Work, Saint Louis University, St. Louis, USA, United States
^d Department of Psychiatry, Washington University School of Medicine, St. Louis, USA, United States

Abstract
Primary care physicians are key gatekeepers for detecting suicidal intent. However, research indicates training gaps for these providers. Standardized screening for suicide risk in primary care can detect youth with suicidal ideation and prompt a referral to behavioral health care before a suicide attempt. What is needed in adolescent primary care is further training in utilizing standardized mental health screening and employing best practices for suicide prevention. In this study, qualitative research methods were used in surfacing community and medical perspectives regarding skills, knowledge, and values that are needed in pediatric medicine to adequately assess for depression and anxiety. Five focus groups were conducted with pediatric residents, adolescents, parents of adolescents who died by suicide, parents with adolescents in the mental health system, and community mental health professionals. Four themes were identified that illustrate what is needed in pediatric training to lower the risks for adolescent suicide: broken mental health system of care; improving doctor/patient/family communication; alleviating stigma; and early detection and treatment that addresses medications, substance abuse, and recovery resources. The goals of this grant funded study were to analyze the data from these focus groups and compare findings across groups to create modules for Saint Louis University pediatric resident training in suicide prevention. This research project can serve as a springboard for social workers to partner with medical educators in their communities to train primary care physicians for early detection of depression, anxiety, and substance abuse to lower adolescent suicide risks. © 2018, The Author(s) 2018.

Author Keywords
adolescents;  Education;  suicide prevention

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

The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study’s baseline neurocognitive battery
(2018) Developmental Cognitive Neuroscience, . Article in Press. 

Thompson, W.K.^a , Barch, D.M.^b , Bjork, J.M.^c , Gonzalez, R.^d , Nagel, B.J.^e , Nixon, S.J.^f , Luciana, M.^g

^a Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA 92093, United States
^b Departments of Psychological & Brain Sciences, Psychiatry and Radiology, Washington University, St. Louis, MO 63130, United States
^c Institute for Drug and Alcohol Studies, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23219, United States
^d Center for Children and Families, Department of Psychology, Florida International University, Miami, FL 33199, United States
^e Departments of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, United States
^f Department of Psychiatry, University of Florida, Gainesville, FL 32611, United States
^g Department of Psychology, University of Minnesota, Minneapolis, MN 55455, United States

Abstract
The Adolescent Brain Cognitive Development (ABCD) study is poised to be the largest single-cohort long-term longitudinal study of neurodevelopment and child health in the United States. Baseline data on N= 4521 children aged 9–10 were released for public access on November 2, 2018. In this paper we performed principal component analyses of the neurocognitive assessments administered to the baseline sample. The neurocognitive battery included seven measures from the NIH Toolbox as well as five other tasks. We implemented a Bayesian Probabilistic Principal Components Analysis (BPPCA) model that incorporated nesting of subjects within families and within data collection sites. We extracted varimax-rotated component scores from a three-component model and associated these scores with parent-rated Child Behavior Checklist (CBCL) internalizing, externalizing, and stress reactivity. We found evidence for three broad components that encompass general cognitive ability, executive function, and learning/memory. These were significantly associated with CBCL scores in a differential manner but with small effect sizes. These findings set the stage for longitudinal analysis of neurocognitive and psychopathological data from the ABCD cohort as they age into the period of maximal adolescent risk-taking. © 2018

Author Keywords
Adolescence;  Child behavior checklist;  Externalizing;  Internalizing;  Neurocognition;  NIH toolbox;  Principal components analysis;  Stress reactivity

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

Impaired copper transport in schizophrenia results in a copper-deficient brain state: A new side to the dysbindin story
(2018) World Journal of Biological Psychiatry, . Article in Press. 

Schoonover, K.E.^a , Queern, S.L.^{b c} , Lapi, S.E.^{b c} , Roberts, R.C.^d

^a Department of Psychology and Behavioral Neuroscience, University of Alabama at Birmingham, Birmingham, AL, United States
^b Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
^c Department of Chemistry, Washington University in St. Louis, St. Louis, MO, United States
^d Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States

Abstract
Objectives: Several schizophrenia brain regions exhibit decreased dysbindin. Dysbindin modulates copper transport crucial for myelination, monoamine metabolism and cellular homeostasis. Schizophrenia patients (SZP) exhibit increased plasma copper, while copper-decreasing agents produce schizophrenia-like behavioural and pathological abnormalities. Therefore, we sought to determine dysbindin and copper transporter protein expression and copper content in SZP. Methods: We studied the copper-rich substantia nigra (SN) using Western blot and inductively-coupled plasma mass spectrometry. We characterised specific protein domains of copper transporters ATP7A, CTR1, ATP7B and dysbindin isoforms 1 A and 1B/C in SZP (n = 15) and matched controls (n = 11), and SN copper content in SZP (n = 14) and matched controls (n = 11). As a preliminary investigation, we compared medicated (ON; n = 11) versus unmedicated SZP (OFF; n = 4). Results: SZP exhibited increased C terminus, but not N terminus, ATP7A. SZP expressed less transmembrane CTR1 and dysbindin 1B/C than controls. ON exhibited increased C terminus ATP7A protein versus controls. OFF exhibited less N terminus ATP7A protein than controls and ON, suggesting medication-induced rescue of the ATP7A N terminus. SZP exhibited less SN copper content than controls. Conclusions: These results provide the first evidence of disrupted copper transport in schizophrenia SN that appears to result in a copper-deficient state. Furthermore, copper homeostasis may be modulated by specific dysbindin isoforms and antipsychotic treatment. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
copper;  dysbindin;  post-mortem;  Schizophrenia;  substantia nigra

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

Concurrent analysis of white matter bundles and grey matter networks in the chimpanzee
(2018) Brain Structure and Function, . Article in Press. 

Mars, R.B.^{a b} , O’Muircheartaigh, J.^{c d e f} , Folloni, D.^g , Li, L.^h , Glasser, M.F.ⁱ , Jbabdi, S.^a , Bryant, K.L.^b

^a Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom
^b Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
^c Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, London, United Kingdom
^d Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, Sackler Institute for Translational Neurodevelopment, London, United Kingdom
^e MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
^f Division of Imaging Sciences and Biomedical Engineering, Centre for the Developing Brain, St Thomas’ Hospital, King’s College London, London, United Kingdom
^g Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
^h Marcus Autism Center, Children’s Healthcare of Atlanta, Emory University, Atlanta, GA, United States
ⁱ Departments of Radiology and Neuroscience, Washington University Medical School, Saint Louis, MO, United States

Abstract
Understanding the phylogeny of the human brain requires an appreciation of brain organization of our closest animal relatives. Neuroimaging tools such as magnetic resonance imaging (MRI) allow us to study whole-brain organization in species which can otherwise not be studied. Here, we used diffusion MRI to reconstruct the connections of the cortical hemispheres of the chimpanzee. This allowed us to perform an exploratory analysis of the grey matter structures of the chimpanzee cerebral cortex and their underlying white matter connectivity profiles. We identified a number of networks that strongly resemble those found in other primates, including the corticospinal system, limbic connections through the cingulum bundle and fornix, and occipital–temporal and temporal–frontal systems. Notably, chimpanzee temporal cortex showed a strong resemblance to that of the human brain, providing some insight into the specialization of the two species’ shared lineage. © 2018, The Author(s).

Author Keywords
Brain organization;  Comparative;  Connectivity;  Diffusion MRI;  Frontal cortex;  Great ape;  Limbic system;  Networks;  Temporal cortex;  Tractography

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

Demarcating depression
(2018) Ratio, . Article in Press. 

Tully, I.

Department of Philosophy, Washington University in St. Louis, One Brookings Dr, St. Louis, MO 63130, United States

Abstract
How to draw the line between depression-as-disorder and non-pathological depressive symptoms continues to be a contested issue in psychiatry. Relatively few philosophers have waded into this debate, but the tools of philosophical analysis are quite relevant to it. In this paper, I defend a particular answer to this question, the Contextual approach. On this view, depression is a disorder if and only if it is a disproportionate response to a justifying cause or else is unconnected to any justifying cause. I present four objections to this approach and then defend it from them. Along the way, I explain why it matters whether we get this question right. © 2018 John Wiley & Sons Ltd

Author Keywords
depression;  fittingness;  mental illness;  psychiatry;  sadness

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