A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Boris A Gutman^{1

2}, Theo G M van Erp^{3

4}, Kathryn Alpert⁵, Christopher R K Ching⁶, Dmitry Isaev⁷, Anjani Ragothaman⁸, Neda Jahanshad⁶, Arvin Saremi⁶, Artemis Zavaliangos-Petropulu⁶, David C Glahn⁹, Li Shen¹⁰, Shan Cong¹⁰, Dag Alnaes¹¹, Ole Andreas Andreassen¹¹, Nhat Trung Doan¹¹, Lars T Westlye^{11

12}, Peter Kochunov¹³, Theodore D Satterthwaite¹⁴, Daniel H Wolf¹⁴, Alexander J Huang³, Charles Kessler³, Andrea Weideman³, Dana Nguyen¹⁵, Bryon A Mueller¹⁶, Lawrence Faziola¹⁷, Steven G Potkin¹⁷, Adrian Preda¹⁷, Daniel H Mathalon^{18

19}, Juan Bustillo²⁰, Vince Calhoun^{21

22}, Judith M Ford^{19

23}, Esther Walton²⁴, Stefan Ehrlich²⁵, Giuseppe Ducci²⁶, Nerisa Banaj²⁷, Fabrizio Piras²⁷, Federica Piras²⁷, Gianfranco Spalletta^{27

28}, Erick J Canales-Rodríguez²⁹, Paola Fuentes-Claramonte²⁹, Edith Pomarol-Clotet²⁹, Joaquim Radua^{29

30}, Raymond Salvador²⁹, Salvador Sarró²⁹, Erin W Dickie³¹, Aristotle Voineskos³¹, Diana Tordesillas-Gutiérrez³², Benedicto Crespo-Facorro³³, Esther Setién-Suero³⁴, Jacqueline Mayoral van Son³³, Stefan Borgwardt^{35

36}, Fabienne Schönborn-Harrisberger³⁵, Derek Morris³⁷, Gary Donohoe³⁸, Laurena Holleran³⁸, Dara Cannon³⁹, Colm McDonald³⁹, Aiden Corvin^{40

41}, Michael Gill^{40

41}, Geraldo Busatto Filho⁴², Pedro G P Rosa⁴², Mauricio H Serpa⁴², Marcus V Zanetti^{42

43}, Irina Lebedeva⁴⁴, Vasily Kaleda⁴⁵, Alexander Tomyshev⁴⁴, Tim Crow⁴⁶, Anthony James⁴⁶, Simon Cervenka⁴⁷, Carl M Sellgren⁴⁸, Helena Fatouros-Bergman⁴⁷, Ingrid Agartz¹¹, Fleur Howells^{49

50}, Dan J Stein^{49

50

51}, Henk Temmingh⁴⁹, Anne Uhlmann^{49

52}, Greig I de Zubicaray⁵³, Katie L McMahon⁵⁴, Margie Wright⁵⁵, Derin Cobia^{5

56}, John G Csernansky⁵, Paul M Thompson⁶, Jessica A Turner⁵⁷, Lei Wang^{5

58}

Affiliations

¹ Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA.
² Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia.
³ Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA.
⁴ Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, California, USA.
⁵ Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
⁶ Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
⁷ Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
⁸ Department of biomedical engineering, Oregon Health and Science university, Portland, Oregon, USA.
⁹ Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁰ Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
¹¹ NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
¹² Department of Psychology, University of Oslo, Oslo, Norway.
¹³ Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.
¹⁴ Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
¹⁵ Department of Pediatrics, University of California Irvine, Irvine, California, USA.
¹⁶ Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
¹⁷ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA.
¹⁸ Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA.
¹⁹ Judith Ford Mental Health, VA San Francisco Healthcare System, San Francisco, California, USA.
²⁰ Departments of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, New Mexico, USA.
²¹ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology], Emory University, Atlanta, Georgia, USA.
²² Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico, USA.
²³ Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California, USA.
²⁴ Department of Psychology, University of Bath, Bath, UK.
²⁵ Division of Psychological & Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU-Dresden, Dresden, Germany.
²⁶ Mental Health Department, ASL Roma1 DSM, Rome, Italy.
²⁷ Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
²⁸ Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA.
²⁹ FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Spain.
³⁰ Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
³¹ Centre for Addiction and Mental Health (CAMH), Toronto, Canada.
³² Department of Radiology, IDIVAL, Marqués de Valdecilla University Hospital, Santander, Spain.
³³ University Hospital Virgen del Rocio, IBiS, University of Sevilla, CIBERSAM, Sevilla, Spain.
³⁴ University Hospital Marqués de Valdecilla, IDIVAL, CIBERSAM, Santander, Spain.
³⁵ Department of Psychiatry, University of Basel, Basel, Switzerland.
³⁶ Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.
³⁷ Centre for Neuroimaging and Cognitive Genomics, Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland.
³⁸ Centre for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland Galway, Galway, Ireland.
³⁹ Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland.
⁴⁰ Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
⁴¹ Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
⁴² Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil.
⁴³ Hospital Sirio-Libanes, Sao Paulo, SP, Brazil.
⁴⁴ Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia.
⁴⁵ Department of Endogenous Mental Disorders, Mental Health Research Center, Moscow, Russia.
⁴⁶ Department of Psychiatry, University of Oxford, Oxford, UK.
⁴⁷ Centre for Psychiatry Reserach, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
⁴⁸ Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
⁴⁹ Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, WC, South Africa.
⁵⁰ Neuroscience Institute, University of Cape Town, Cape Town, WC, South Africa.
⁵¹ SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, WC, South Africa.
⁵² Department of Child and Adolescent Psychiatry, TU Dresden, Germany.
⁵³ School of Psychology, Faculty of Health, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
⁵⁴ School of Clinical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
⁵⁵ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
⁵⁶ Department of Psychology and Neuroscience Center, Brigham Young University, Provo, Utah, USA.
⁵⁷ Psychology & Neuroscience, Georgia State University, Atlanta, Georgia, USA.
⁵⁸ Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, Ohio, USA.

PMID: 34498337
PMCID: PMC8675416
DOI: 10.1002/hbm.25625

Meta-Analysis

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Boris A Gutman et al. Hum Brain Mapp. 2022 Jan.

. 2022 Jan;43(1):352-372.

doi: 10.1002/hbm.25625. Epub 2021 Sep 8.

Authors

Affiliations

¹ Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA.
² Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia.
³ Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA.
⁴ Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, California, USA.
⁵ Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
⁶ Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
⁷ Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
⁸ Department of biomedical engineering, Oregon Health and Science university, Portland, Oregon, USA.
⁹ Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁰ Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
¹¹ NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
¹² Department of Psychology, University of Oslo, Oslo, Norway.
¹³ Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.
¹⁴ Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
¹⁵ Department of Pediatrics, University of California Irvine, Irvine, California, USA.
¹⁶ Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
¹⁷ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA.
¹⁸ Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA.
¹⁹ Judith Ford Mental Health, VA San Francisco Healthcare System, San Francisco, California, USA.
²⁰ Departments of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, New Mexico, USA.
²¹ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology], Emory University, Atlanta, Georgia, USA.
²² Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico, USA.
²³ Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California, USA.
²⁴ Department of Psychology, University of Bath, Bath, UK.
²⁵ Division of Psychological & Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU-Dresden, Dresden, Germany.
²⁶ Mental Health Department, ASL Roma1 DSM, Rome, Italy.
²⁷ Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy.
²⁸ Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA.
²⁹ FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Spain.
³⁰ Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
³¹ Centre for Addiction and Mental Health (CAMH), Toronto, Canada.
³² Department of Radiology, IDIVAL, Marqués de Valdecilla University Hospital, Santander, Spain.
³³ University Hospital Virgen del Rocio, IBiS, University of Sevilla, CIBERSAM, Sevilla, Spain.
³⁴ University Hospital Marqués de Valdecilla, IDIVAL, CIBERSAM, Santander, Spain.
³⁵ Department of Psychiatry, University of Basel, Basel, Switzerland.
³⁶ Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.
³⁷ Centre for Neuroimaging and Cognitive Genomics, Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland.
³⁸ Centre for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland Galway, Galway, Ireland.
³⁹ Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland.
⁴⁰ Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
⁴¹ Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
⁴² Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil.
⁴³ Hospital Sirio-Libanes, Sao Paulo, SP, Brazil.
⁴⁴ Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia.
⁴⁵ Department of Endogenous Mental Disorders, Mental Health Research Center, Moscow, Russia.
⁴⁶ Department of Psychiatry, University of Oxford, Oxford, UK.
⁴⁷ Centre for Psychiatry Reserach, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
⁴⁸ Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
⁴⁹ Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, WC, South Africa.
⁵⁰ Neuroscience Institute, University of Cape Town, Cape Town, WC, South Africa.
⁵¹ SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, WC, South Africa.
⁵² Department of Child and Adolescent Psychiatry, TU Dresden, Germany.
⁵³ School of Psychology, Faculty of Health, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
⁵⁴ School of Clinical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
⁵⁵ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
⁵⁶ Department of Psychology and Neuroscience Center, Brigham Young University, Provo, Utah, USA.
⁵⁷ Psychology & Neuroscience, Georgia State University, Atlanta, Georgia, USA.
⁵⁸ Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, Ohio, USA.

PMID: 34498337
PMCID: PMC8675416
DOI: 10.1002/hbm.25625

Abstract

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Keywords: schizophrenia; structure; subcortical shape.

PubMed Disclaimer

Conflict of interest statement

One of the authors (TGMvE) has had a research contract with Otsuka Pharmaceutical. One of the authors (AP) has served as a consultant for Boehringer Ingelheim. One of the authors (DJS) has received research grants and/or honoraria from Lundbeck and Sun. One of the authors (DHM) has served as a consultant for Boehringer Ingelheim, Aptinyx, and Greenwich Biosciences. One of the authors (SC) has received grant support from AstraZeneca as co‐investigator, and has served as a speaker for Otsuka Pharmaceuticals. Authors PMT, CRKC, and NJ received a research grant from Biogen, Inc. (Boston) for research unrelated to this manuscript. The remaining authors declare no potential conflict of interest.

Figures

**FIGURE 1**
Vertex‐wise effects of diagnosis (i.e., schizophrenia vs. control) on each hemisphere for (a) thickness, (b) surface dilation/contraction (log Jacobian determinant). The effects are tested in models that included diagnosis sex, age, age x sex, age², age² x sex, and ICV. Vertex‐wise effect sizes (Cohen's d, see text) are visualized on subcortical surfaces. The subcortical structures—1. hippocampus, 2. amygdala, 3. putamen, 4. accumbens, 5. pallidum, 6. caudate, and 7. thalamus—are shown as a group situated in template space, from front, back, top, and bottom viewpoints of the brain. L = left hemisphere. R = right hemisphere. Color scale indicates the intensity of effect sizes. Cooler colors (i.e., negative effect sizes) indicate thinning, surface contraction for schizophrenia as compared to controls, and warmer colors (i.e., positive effect sizes) indicate thickening, surface dilation. Gray color indicates nonsignificant surface vertices after multiple comparison correction

**FIGURE 2**
Overall and vertex‐wise effects of diagnosis (i.e., schizophrenia vs. control) across hemispheres for (a) thickness, (b) surface dilation/contraction (log Jacobian determinant). The effects are tested in models that included diagnosis sex, age, age x sex, age², age² x sex, and ICV. In the left column, mean positive effect sizes and mean negative effect sizes across each subcortical structure surface (see text) are shown as bar plots. The middle column shows the vertex‐wise effects of diagnosis on interhemispheric means (see text). The right column shows the vertex‐wise effects of diagnosis on interhemispheric absolute differences (reproduced from Figure 3a,b right columns). The subcortical structures—1. hippocampus, 2. amygdala, 3. putamen, 4. accumbens, 5. pallidum, 6. caudate, and 7. thalamus—are positioned generally from a bottom viewpoint, with some slightly rotated about their own principal axis to be oblique, for better exposure: caudate—pi/7 or about 25°, accumbens—pi/10 or 18°, pallidum—pi/3 or 60°. Color scale indicates the intensity of effect sizes. Cooler colors (i.e., negative effect sizes) indicate reduced asymmetry for schizophrenia as compared to controls, and warmer colors (i.e., positive effect sizes) indicate exaggerated asymmetry. Gray color indicates nonsignificant surface vertices after multiple comparison correction

**FIGURE 3**
Effects of diagnosis (i.e., schizophrenia vs. control) for (a) asymmetry index of thickness, (b) asymmetry index of surface dilation/contraction (log Jacobian determinant). Vertex‐wise asymmetry indices for thickness and surface dilation/contraction were calculated as the absolute values of left‐versus‐right differences. The effects are tested in models that included diagnosis sex, age, age x sex, age², age² x sex, and ICV. Effect sizes (Cohen's d, see text) are visualized on subcortical surfaces. In the left two columns, the subcortical structures—1. hippocampus, 2. amygdala, 3. putamen, 4. accumbens, 5. pallidum, 6. caudate, and 7. thalamus—are shown as a group situated in template space, from front left, front right, top and bottom viewpoints of the brain. L = left hemisphere. R = right hemisphere. In the right column, the subcortical structures are positioned generally from a bottom viewpoint, with some slightly rotated about their own principal axis to be oblique, for better exposure: caudate—pi/7 or about 25°, accumbens—pi/10 or 18°, pallidum—pi/3 or 60°. Color scale indicates intensity of effect sizes. Cooler colors (i.e., negative effect sizes) indicate reduced asymmetry for schizophrenia as compared with controls, and warmer colors (i.e., positive effect sizes) indicate exaggerated asymmetry. Gray color indicates nonsignificant surface vertices after multiple comparison correction

**FIGURE 4**
Overall and vertex‐wise effects of chlorpromazine dose equivalents across both hemispheres for (a) thickness, (b) surface dilation/contraction (log Jacobian determinant). The effects are tested in models that included diagnosis sex, age, age x sex, age², age² x sex, and ICV. Small but statistically significant relationships are found between higher chlorpromazine dose equivalents and locally reduced thickness (a) and surface contraction (b) in the hippocampus, amygdala, caudate, accumbens, and thalamus. In the left column, the subcortical structures—1. hippocampus, 2. amygdala, 4. accumbens, 6. caudate, and 7. thalamus—are positioned generally from a bottom viewpoint, with some slightly rotated about their own principal axis to be oblique, for better exposure: caudate—pi/7 or about 25°, accumbens—pi/10 or 18°. In the right column, the subcortical structures are positioned generally from a top viewpoint with the same rotations. Color scale indicates intensity of effect sizes. Cooler colors indicate negative associations, that is, higher chlorpromazine dose equivalents are associated with reduced surface measures. Gray color indicates nonsignificant surface vertices after multiple comparison correction. Putamen and pallidum are not shown as no effects were found for these subcortical structures

**FIGURE 5**
Overall and vertex‐wise effects of SAPS total scores across both hemispheres for (a) thickness, (b) surface dilation/contraction (log Jacobian determinant). The effects are tested in models that included diagnosis sex, age, age x sex, age², age² x sex, and ICV. Small but statistically significant relationships are found between higher SAPS total scores and locally reduced thickness in the amygdala, caudate, and thalamus (a), and surface contraction (b) in the hippocampus, amygdala, caudate, and thalamus. In the left column, the subcortical structures—1. hippocampus (panel b, surface contraction only), 2. amygdala, 6. caudate, and 7. thalamus—are positioned generally from a bottom viewpoint, with some slightly rotated about their own principal axis to be oblique, for better exposure: caudate—pi/7 or about 25°, accumbens—pi/10 or 18°. In the right column, the subcortical structures are positioned generally from a top viewpoint with the same rotations. Color scale indicates the intensity of effect sizes. Cooler colors indicate negative associations, that is, higher chlorpromazine dose equivalents are associated with reduced surface measures. Gray color indicates nonsignificant surface vertices after multiple comparison correction. Accumbens, putamen, and pallidum are not shown as no effects were found for these subcortical structures

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A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Affiliations

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Authors

Affiliations

Abstract

Conflict of interest statement

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